Back| C- |
| C_i C_i compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{bl'}_i}{{bu'}_i
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
the following method uses more flops than necessary but
compute contribution to diagonal block(s) of reduced system.
{c'}_i = {C_i}-{{b'}_i}{{b'}_i}^
|
| CABS1 CABS1 do 20 k = i, l + 1, -1 tst1 = CABS1( h( k-1, k-1 ) ) + cabs1( h( k, k ) $ tst1 = clanhs( '1', i-l+1, h( l, l ), ldh, work ) xj = CABS1( x( j ) do 20 k = i, l + 1, -1 tst1 = CABS1( h( k-1, k-1 ) ) + cabs1( h( k, k ) $ tst1 = zlanhs( '1', i-l+1, h( l, l ), ldh, work ) xj = CABS1( x( j ) |
| cache cache elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cache d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cache d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cache d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cache d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal |
| calcs calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs move block into place that it will be expected to be for calcs |
| Calculate Calculate Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate pre-Calculate bw^ pre-Calculate bw^ 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate pre-Calculate bw^ pre-Calculate bw^ 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate pre-Calculate bw^ pre-Calculate bw^ 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate pre-Calculate bw^ pre-Calculate bw^ 5. iterative refinement is applied to improve the computed solution matrix and Calculate error bounds and backward error estimate Calculate new ja one while dropping off unused processors Calculate new ja one while dropping off unused processors |
| calculates calculates receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine calculates the size for al entry of the corresponding work array, and no error message if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the optima in the first entry of the correspondingwork array, and no if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding equation via the routine slaed4 (as called by pdlaed3). this routine also calculates the eigenvectors of the curren if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the siz these values is returned in the first entry of the if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the siz each of these values is returned in the first entry of the if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding equation via the routine slaed4 (as called by pslaed3). this routine also calculates the eigenvectors of the curren if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the siz these values is returned in the first entry of the if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the siz each of these values is returned in the first entry of the if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimu as the first element of work and no error message is issued if lwork = -1, then lwork is global input and a workspace query is assumed; the routine calculates the size for al entry of the corresponding work array, and no error message if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the optima in the first entry of the correspondingwork array, and no if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates th values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding receive previously transmitted matrix section, which forms the right-hand-side for the triangular solve that calculates if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimu values is returned in the first entry of the corresponding |
| calculating calculating the second stage consists of calculating the update equation via the routine slaed4 (as called by pdlaed3). the second stage consists of calculating the update equation via the routine slaed4 (as called by pslaed3). |
| calculation calculation an operation involves more than one vector, the processes which re- ceive the result will be the union of the following calculation fo in general, be modified, split and reordered by the calculation on output, intvl contains the converged intervals. an operation involves more than one vector, the processes which re- ceive the result will be the union of the following calculation fo an operation involves more than one vector, the processes which re- ceive the result will be the union of the following calculation fo in general, be modified, split and reordered by the calculation on output, intvl contains the converged intervals. an operation involves more than one vector, the processes which re- ceive the result will be the union of the following calculation fo |
| Call Call complex temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pcdbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri complex temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pcdttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri complex temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pcgbtrf must be Called first ===================================================================== its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single Call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a Call locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pcheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc pcheevx computes selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix a by Calling the recommended sequenc specifying a range of values or a range of indices for the desired its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pchengst Calls pchegst when uplo='u', hence pchengst provide support for uplo='u' is limited to Calling the old, slow, pchetr its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a Call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pcgebrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pchetrd to redistribute d, its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pchetrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), if the scaling needed for a in the dot product is 1, Call pcdotu to perform the dot product this is the unblocked form of the algorithm, Calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, Calling level 3 pblas notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pcpbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pcpttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), process. pcstein decides on the allocation of work among the processes and then Calls sstein2 (modified lapack routine) on eac expected orthogonalization may not be done. its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), double precision temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pddbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri double precision temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pddttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri double precision temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pdgbtrf must be Called first ===================================================================== its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single Call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a Call locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pdgebrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), sort the eigenpairs so that they are in twos for double shifts. only Call if several need sortin this is an auxiliary routine Called by pdsytrd to redistribute d, its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pdsytrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is the unblocked form of the algorithm, Calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, Calling level 3 pblas notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), double precision temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pdpbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), double precision temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pdpttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), process. pdstein decides on the allocation of work among the processes and then Calls dstein2 (modified lapack routine) on eac expected orthogonalization may not be done. pdsyev computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc pdsyevx computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc specifying a range of values or a range of indices for the desired its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdsyngst Calls pdhegst when uplo='u', hence pdhengst provide support for uplo='u' is limited to Calling the old, slow, pdsytr its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a Call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pjlaenv is Called from the scalapack symmetric and hermitia problem-dependent parameters for the local environment. see ispec its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), real temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine psdbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri real temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine psdttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri real temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine psgbtrf must be Called first ===================================================================== its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single Call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a Call locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by psgebrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), sort the eigenpairs so that they are in twos for double shifts. only Call if several need sortin this is an auxiliary routine Called by pssytrd to redistribute d, its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pssytrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is the unblocked form of the algorithm, Calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, Calling level 3 pblas notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), real temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pspbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), real temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pspttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), process. psstein decides on the allocation of work among the processes and then Calls sstein2 (modified lapack routine) on eac expected orthogonalization may not be done. pssyev computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc pssyevx computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc specifying a range of values or a range of indices for the desired its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pssyngst Calls pshegst when uplo='u', hence pshengst provide support for uplo='u' is limited to Calling the old, slow, pssytr its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a Call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex*16 temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pzdbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex*16 temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pzdttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri complex*16 temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pzgbtrf must be Called first ===================================================================== its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single Call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), receive if k were distributed over the c processes of its process row. the values of locr() and locc() may be determined via a Call locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by Calling the recommended sequenc pzheevx computes selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix a by Calling the recommended sequenc specifying a range of values or a range of indices for the desired its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzhengst Calls pzhegst when uplo='u', hence pzhengst provide support for uplo='u' is limited to Calling the old, slow, pzhetr its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), of its process row. the values of locp() and locq() may be determined via a Call t locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pzgebrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pzhetrd to redistribute d, its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), this is an auxiliary routine Called by pzhetrd notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), if the scaling needed for a in the dot product is 1, Call pzdotu to perform the dot product this is the unblocked form of the algorithm, Calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, Calling level 3 pblas notes its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex*16 temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pzpbtrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), complex*16 temporary workspace. this space may be overwritten in between Calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. Call utility routine that forms "standard-form" gri routine pzpttrf must be Called first ===================================================================== Call utility routine that forms "standard-form" gri process. pzstein decides on the allocation of work among the processes and then Calls dstein2 (modified lapack routine) on eac expected orthogonalization may not be done. its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be determined via a Call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), |
| called called that can be sent through. clamsh should only be called when there are multiple shifts/bulge unreduced hessenberg matrix because of two or more consecutive that can be sent through. dlamsh should only be called when there are multiple shifts/bulge unreduced hessenberg matrix because of two or more consecutive small if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pcdbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pcdttrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pcgbtrf must be called first ===================================================================== pchettrd is not intended to be called directly. all users ar appropriate. a must be in cyclic format (i.e. mb = nb = 1), this is an auxiliary routine called by pcgebrd notes where a' is the conjugate transpose of a, and pclacon must be re-called with all the other parameters unchanged ix (global input) integer this routine does a global maximum and must be called by al "congested." as a remedy, when we first hit a border, a 6x6 *local* matrix is generated on one node (called smalla) an passed back and everything stays a lot simpler. this is an auxiliary routine called by pcgehrd. in the followin this is an auxiliary routine called by pchetrd to redistribute d, this routine does a global maximum and must be called by al this is an auxiliary routine called by pchetrd notes if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pcpbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pcpttrf must be called first ===================================================================== the code robust against possible overflow. but scaling has not yet been implemented in pclattrs which is called by this routine to solv if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pddbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pddttrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pdgbtrf must be called first ===================================================================== this is an auxiliary routine called by pdgebrd notes a' * x, if kase=2, pdlacon must be re-called with all the other parameter this routine does a global maximum and must be called by al eigenvalues. this is done by finding the roots of the secular equation via the routine slaed4 (as called by pdlaed3) problem. "congested." as a remedy, when we first hit a border, a 6x6 *local* matrix is generated on one node (called smalla) an passed back and everything stays a lot simpler. this is an auxiliary routine called by pdgehrd. in the followin this is an auxiliary routine called by pdsytrd to redistribute d, this routine does a global maximum and must be called by al this is an auxiliary routine called by pdsytrd notes if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pdpbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pdpttrf must be called first ===================================================================== pdsyttrd is not intended to be called directly. all users ar appropriate. a must be in cyclic format (i.e. mb = nb = 1), pjlaenv is called from the scalapack symmetric and hermitia problem-dependent parameters for the local environment. see ispec if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine psdbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine psdttrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine psgbtrf must be called first ===================================================================== this is an auxiliary routine called by psgebrd notes a' * x, if kase=2, pslacon must be re-called with all the other parameter this routine does a global maximum and must be called by al eigenvalues. this is done by finding the roots of the secular equation via the routine slaed4 (as called by pslaed3) problem. "congested." as a remedy, when we first hit a border, a 6x6 *local* matrix is generated on one node (called smalla) an passed back and everything stays a lot simpler. this is an auxiliary routine called by psgehrd. in the followin this is an auxiliary routine called by pssytrd to redistribute d, this routine does a global maximum and must be called by al this is an auxiliary routine called by pssytrd notes if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pspbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pspttrf must be called first ===================================================================== pssyttrd is not intended to be called directly. all users ar appropriate. a must be in cyclic format (i.e. mb = nb = 1), if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pzdbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pzdttrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pzgbtrf must be called first ===================================================================== pzhettrd is not intended to be called directly. all users ar appropriate. a must be in cyclic format (i.e. mb = nb = 1), this is an auxiliary routine called by pzgebrd notes where a' is the conjugate transpose of a, and pzlacon must be re-called with all the other parameters unchanged ix (global input) integer this routine does a global maximum and must be called by al "congested." as a remedy, when we first hit a border, a 6x6 *local* matrix is generated on one node (called smalla) an passed back and everything stays a lot simpler. this is an auxiliary routine called by pzgehrd. in the followin this is an auxiliary routine called by pzhetrd to redistribute d, this routine does a global maximum and must be called by al this is an auxiliary routine called by pzhetrd notes if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pzpbtrf must be called first ===================================================================== if the factorization routine and the solve routine are to be called coefficient matrix), the auxiliary space af *must not be altered* routine pzpttrf must be called first ===================================================================== the code robust against possible overflow. but scaling has not yet been implemented in pzlattrs which is called by this routine to solv that can be sent through. slamsh should only be called when there are multiple shifts/bulge unreduced hessenberg matrix because of two or more consecutive small that can be sent through. zlamsh should only be called when there are multiple shifts/bulge unreduced hessenberg matrix because of two or more consecutive |
| calling calling this is the unblocked version of the algorithm, calling level 2 blas arguments on entry, lda specifies the first dimension of a as declared in the calling (sub) program. lda must be at leas unchanged on exit. this is the unblocked version of the algorithm, calling level 2 blas arguments on entry, lda specifies the first dimension of a as declared in the calling (sub) program. lda must be at leas unchanged on exit. indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling tool function numroc; nprow and npcol can be determined by calling the subroutine blacs_gridinfo if lwork = -1, then lwork is global input and a workspace indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling of columns of the matrix vt when distributed across 1-dimensional "row" of processes. calling the lapac and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pcheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pcheevx computes selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by calling support for uplo='u' is limited to calling the old, slow, pchetr indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling this is the unblocked form of the algorithm, calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, calling level 3 pblas notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling tool function numroc; nprow and npcol can be determined by calling the subroutine blacs_gridinfo if lwork = -1, then lwork is global input and a workspace indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling of columns of the matrix vt when distributed across 1-dimensional "row" of processes. calling the lapac and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling this is the unblocked form of the algorithm, calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, calling level 3 pblas notes indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pdsyev computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pdsyevd computes all the eigenvalues and eigenvectors of a real symmetric matrix a by calling the recommended sequenc pdsyevx computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired myrow, mycol, nprow and npcol can be determined by calling the subroutine blacs_gridinfo for large n, no extra workspace is needed, however the numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by calling support for uplo='u' is limited to calling the old, slow, pdsytr indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling name (global input) character*(*) the name of the calling subroutine, in either upper case o indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling tool function numroc; nprow and npcol can be determined by calling the subroutine blacs_gridinfo if lwork = -1, then lwork is global input and a workspace indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling of columns of the matrix vt when distributed across 1-dimensional "row" of processes. calling the lapac and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling this is the unblocked form of the algorithm, calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, calling level 3 pblas notes indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pssyev computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pssyevd computes all the eigenvalues and eigenvectors of a real symmetric matrix a by calling the recommended sequenc pssyevx computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired myrow, mycol, nprow and npcol can be determined by calling the subroutine blacs_gridinfo for large n, no extra workspace is needed, however the numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by calling support for uplo='u' is limited to calling the old, slow, pssytr indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling tool function numroc; nprow and npcol can be determined by calling the subroutine blacs_gridinfo if lwork = -1, then lwork is global input and a workspace indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling of columns of the matrix vt when distributed across 1-dimensional "row" of processes. calling the lapac and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pzheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pzheevx computes selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by calling support for uplo='u' is limited to calling the old, slow, pzhetr indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling this is the unblocked form of the algorithm, calling level 2 blas on should be strictly local to one process. this is the blocked form of the algorithm, calling level 3 pblas notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling this is the unblocked version of the algorithm, calling level 2 blas arguments on entry, lda specifies the first dimension of a as declared in the calling (sub) program. lda must be at leas unchanged on exit. this is the unblocked version of the algorithm, calling level 2 blas arguments on entry, lda specifies the first dimension of a as declared in the calling (sub) program. lda must be at leas unchanged on exit. |
| calls calls complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. pchengst calls pchegst when uplo='u', hence pchengst provide the first column of a send data and only processes that own the first column of b receive data. the calls to cgebs2d/cgebr2 complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. process. pcstein decides on the allocation of work among the processes and then calls sstein2 (modified lapack routine) on eac expected orthogonalization may not be done. been implemented in pclattrs which is called by this routine to solve the triangular systems. pclattrs just calls pctrsv each eigenvector is normalized so that the element of largest double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. values in d, w, and rho, between 1 and k. it makes the appropriate calls to slaed this code makes very mild assumptions about floating point the first column of a send data and only processes that own the first column of b receive data. the calls to dgebs2d/dgebr2 double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. double precision temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. process. pdstein decides on the allocation of work among the processes and then calls dstein2 (modified lapack routine) on eac expected orthogonalization may not be done. pdsyngst calls pdhegst when uplo='u', hence pdhengst provide real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. values in d, w, and rho, between 1 and k. it makes the appropriate calls to slaed this code makes very mild assumptions about floating point the first column of a send data and only processes that own the first column of b receive data. the calls to sgebs2d/sgebr2 real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. process. psstein decides on the allocation of work among the processes and then calls sstein2 (modified lapack routine) on eac expected orthogonalization may not be done. pssyngst calls pshegst when uplo='u', hence pshengst provide complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. pzhengst calls pzhegst when uplo='u', hence pzhengst provide the first column of a send data and only processes that own the first column of b receive data. the calls to zgebs2d/zgebr2 complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. complex*16 temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. process. pzstein decides on the allocation of work among the processes and then calls dstein2 (modified lapack routine) on eac expected orthogonalization may not be done. been implemented in pzlattrs which is called by this routine to solve the triangular systems. pzlattrs just calls pztrsv each eigenvector is normalized so that the element of largest |
| can can subsequent shifts in an effort to maximize the number of bulges that can be sent through (nbulge > 1) and the first shift is starting in the middle of an subsequent shifts in an effort to maximize the number of bulges that can be sent through (nbulge > 1) and the first shift is starting in the middle of an dlasorte sorts eigenpairs so that real eigenpairs are together and complex are together. this way one can employ 2x2 shifts easil this routine does no parallel work. blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin locr and locc values can be computed using the scalapac calling the subroutine blacs_gridinfo. indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin in case of a homogeneous process grid this upper limit can processor. and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin of a complex hermitian matrix a by calling the recommended sequence of scalapack routines. eigenvalues/vectors can be selected b eigenvalues. the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pchegvx cannot guarante numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin on entry, the hessenberg matrix whose tridiagonal part is being scanned the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node determine the number of columns we have so we can check workspac indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin a(i,j) is computed without over/underflow if the final result cto * a(i,j) / cfrom can be represented withou pclasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin compute a bound on the computed solution vector to see if the level 2 pblas routine pctrsv can be used
if n = 1, m_x = 1 and incx = 1, then one can't determine if a proces
process of coordinate {rsrc_x, csrc_x} receives the result;
blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1) a m-by-k matrix where y can be a, af, b and x notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, note : to obtain orthogonal vectors, it is best if
eigenvalues are computed to highest accuracy ( this can b
slamch('u') --- abstol is an input parameter
triangular matrix sub( a ) is singular and its inverse can not be computed ==================================================================== and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin locr and locc values can be computed using the scalapac calling the subroutine blacs_gridinfo. indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin in case of a homogeneous process grid this upper limit can processor. and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin on entry, the hessenberg matrix whose tridiagonal part is being scanned the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node least max_j |e(j)^2| *safe_min, and at least safe_min, where safe_min is at least the smallest number that can divide 1. see pdlapdct for the "paranoid" implementation of the sturm sorted set. then it tries to deflate the size of the problem. there are two ways in which deflation can occur: when two or mor z vector. for each such occurrence the order of the related secular determine the number of columns we have so we can check workspac indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. least max_j |e(j)^2| *safe_min, and at least safe_min, where safe_min is at least the smallest number that can divide 1. ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin a(i,j) is computed without over/underflow if the final result cto * a(i,j) / cfrom can be represented withou pdlasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1) a m-by-k matrix where y can be a, af, b and x notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, note : to obtain orthogonal vectors, it is best if
eigenvalues are computed to highest accuracy ( this can b
dlamch('u') --- abstol is an input parameter
of a real symmetric matrix a by calling the recommended sequence of scalapack routines. eigenvalues/vectors can be selected b eigenvalues. the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsygvx cannot guarante numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin triangular matrix sub( a ) is singular and its inverse can not be computed ==================================================================== and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin
if n = 1, m_x = 1 and incx = 1, then one can't determine if a proces
process of coordinate {rsrc_x, csrc_x} receives the result;
value to all procesors (i.e. global output). however some, in particular, the panel blocking factor can be differen values on different processors (i.e. local output).
if n = 1, m_x = 1 and incx = 1, then one can't determine if a proces
process of coordinate {rsrc_x, csrc_x} receives the result;
blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin locr and locc values can be computed using the scalapac calling the subroutine blacs_gridinfo. indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin in case of a homogeneous process grid this upper limit can processor. and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin on entry, the hessenberg matrix whose tridiagonal part is being scanned the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node least max_j |e(j)^2| *safe_min, and at least safe_min, where safe_min is at least the smallest number that can divide 1. see pslapdct for the "paranoid" implementation of the sturm sorted set. then it tries to deflate the size of the problem. there are two ways in which deflation can occur: when two or mor z vector. for each such occurrence the order of the related secular determine the number of columns we have so we can check workspac indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. least max_j |e(j)^2| *safe_min, and at least safe_min, where safe_min is at least the smallest number that can divide 1. ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin a(i,j) is computed without over/underflow if the final result cto * a(i,j) / cfrom can be represented withou pslasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1) a m-by-k matrix where y can be a, af, b and x notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, note : to obtain orthogonal vectors, it is best if
eigenvalues are computed to highest accuracy ( this can b
slamch('u') --- abstol is an input parameter
of a real symmetric matrix a by calling the recommended sequence of scalapack routines. eigenvalues/vectors can be selected b eigenvalues. the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssygvx cannot guarante numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin triangular matrix sub( a ) is singular and its inverse can not be computed ==================================================================== and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin locr and locc values can be computed using the scalapac calling the subroutine blacs_gridinfo. indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and b( ib:ib+n-1, jb:jb+nrhs-1 ) otherwise. several right hand side vectors b and solution vectors x can be handled in a single call the n-by-nrhs right hand side matrix sub( b ) and the m-by-nrhs and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin in case of a homogeneous process grid this upper limit can processor. and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin of a complex hermitian matrix a by calling the recommended sequence of scalapack routines. eigenvalues/vectors can be selected b eigenvalues. the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzhegvx cannot guarante numroc ia a scalapack tool functions myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin pjlaenv is a scalapack envionmental inquiry function myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin on entry, the hessenberg matrix whose tridiagonal part is being scanned the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node determine the number of columns we have so we can check workspac indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by calling th of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. of the matrix. a sum of row (column) i of the complete matrix can be obtained by adding along row i and column i of the th the point of reflection. the pictures below demonstrate this. ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin a(i,j) is computed without over/underflow if the final result cto * a(i,j) / cfrom can be represented withou pzlasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin compute a bound on the computed solution vector to see if the level 2 pblas routine pztrsv can be used
if n = 1, m_x = 1 and incx = 1, then one can't determine if a proces
process of coordinate {rsrc_x, csrc_x} receives the result;
blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1) a m-by-k matrix where y can be a, af, b and x notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, note : to obtain orthogonal vectors, it is best if
eigenvalues are computed to highest accuracy ( this can b
dlamch('u') --- abstol is an input parameter
triangular matrix sub( a ) is singular and its inverse can not be computed ==================================================================== and numroc, indxg2p are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin ilcm, indxg2p and numroc are scalapack tool functions; myrow, mycol, nprow and npcol can be determined by callin subsequent shifts in an effort to maximize the number of bulges that can be sent through (nbulge > 1) and the first shift is starting in the middle of an slasorte sorts eigenpairs so that real eigenpairs are together and complex are together. this way one can employ 2x2 shifts easil this routine does no parallel work. subsequent shifts in an effort to maximize the number of bulges that can be sent through (nbulge > 1) and the first shift is starting in the middle of an |
| cannot cannot blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pcheev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pcheevd cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pcheevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pchegvx cannot guarante blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, means before entering this routine. pctrrfs does not do iterative refinement because doing so cannot improve the backward error notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, (only the first nsplit elements will actually be used, but since the user cannot know a priori what value nsplit wil the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsyev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsyevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsygvx cannot guarante means before entering this routine. pdtrrfs does not do iterative refinement because doing so cannot improve the backward error notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, (only the first nsplit elements will actually be used, but since the user cannot know a priori what value nsplit wil the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssyev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssyevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssygvx cannot guarante means before entering this routine. pstrrfs does not do iterative refinement because doing so cannot improve the backward error notes blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzheev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzheev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzheevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzhegvx cannot guarante blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, blocksize cannot be too small restriction on nb, the size of each block on each processor, means before entering this routine. pztrrfs does not do iterative refinement because doing so cannot improve the backward error notes |
| canonical canonical jpvt(j) = i then the jth column of p is the ith canonical unit vector ===================================================================== jpvt(j) = i then the jth column of p is the ith canonical unit vector ===================================================================== jpvt(j) = i then the jth column of p is the ith canonical unit vector ===================================================================== jpvt(j) = i then the jth column of p is the ith canonical unit vector ===================================================================== |
| case case depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. zero out space in case result is smaller than storage bloc depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. in this case the loop over the levels will not b depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating by finding that eigenvalues were not identical across the process grid. in this case, the accuracy o --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- ----------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' --------------- -------------- -------------------------------------- dt_a (global) desca[ dt_ ] the descriptor type. in this case ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. zero out space in case result is smaller than storage bloc depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. in this case the loop over the levels will not b depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dt_a (global) desca[ dt_ ] the descriptor type. in this case ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating by finding that eigenvalues were not identical across the process grid. in this case, the accuracy o --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- ----------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating this routine will not function correctly if it is converted to all lower case. converting it to all upper case is allowed arguments --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. zero out space in case result is smaller than storage bloc depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. in this case the loop over the levels will not b depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dt_a (global) desca[ dt_ ] the descriptor type. in this case ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating by finding that eigenvalues were not identical across the process grid. in this case, the accuracy o --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- ----------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. zero out space in case result is smaller than storage bloc depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dt_a (global) desca[ dt_ ] the descriptor type. in this case ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. in this case the loop over the levels will not b depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating by finding that eigenvalues were not identical across the process grid. in this case, the accuracy o --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- ----------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. ************************************************************** case uplo = 'u' --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating --------------- -------------- -------------------------------------- dtype_a(global) desca( dtype_ )the descriptor type. in this case ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicating |
| cases cases or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or 'c' and pivroc='r' or 'r', the last piece of this array of size mb_a (resp. nb_a) is used as workspace. in those cases or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or 'c' and pivroc='r' or 'r', the last piece of this array of size mb_a (resp. nb_a) is used as workspace. in those cases or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or 'c' and pivroc='r' or 'r', the last piece of this array of size mb_a (resp. nb_a) is used as workspace. in those cases or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or 'c' and pivroc='r' or 'r', the last piece of this array of size mb_a (resp. nb_a) is used as workspace. in those cases or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description or new one-dimensional descriptors, though the processor grid in both cases *must be one-dimensional*. we describe both types below each global data object is described by an associated description |
| cause cause enough space to compute all the eigenvectors orthogonally will cause serious degradation i pcstein will perform no better than cstein on 1 enough space to compute all the eigenvectors orthogonally will cause serious degradation i pcstein will perform no better than cstein on 1 processor. be as accurate as the absolute and relative tolerances. this is generally caused by arithmeti = 2 : there is a mismatch between the number of enough space to compute all the eigenvectors orthogonally will cause serious degradation i pdstein will perform no better than dstein on 1 enough space to compute all the eigenvectors orthogonally will cause serious degradation i pdstein will perform no better than dstein on 1 processor. be as accurate as the absolute and relative tolerances. this is generally caused by arithmeti = 2 : there is a mismatch between the number of enough space to compute all the eigenvectors orthogonally will cause serious degradation i psstein will perform no better than sstein on 1 enough space to compute all the eigenvectors orthogonally will cause serious degradation i psstein will perform no better than sstein on 1 processor. enough space to compute all the eigenvectors orthogonally will cause serious degradation i pzstein will perform no better than zstein on 1 enough space to compute all the eigenvectors orthogonally will cause serious degradation i pzstein will perform no better than zstein on 1 processor. |
| caused caused be as accurate as the absolute and relative tolerances. this is generally caused by arithmeti = 2 : there is a mismatch between the number of be as accurate as the absolute and relative tolerances. this is generally caused by arithmeti = 2 : there is a mismatch between the number of |
| causes causes move entry that causes spike to auxiliary storag move entry that causes spike to auxiliary storag move entry that causes spike to auxiliary storag move entry that causes spike to auxiliary storag |
| CBDSQR CBDSQR wbdtosvd = size*(wantu*nru + wantvt*ncvt) + max(wCBDSQR |
| CCOMBAMAX1 CCOMBAMAX1 CCOMBAMAX1 finds the element having maximum real part absolut |
| Cdbtrf Cdbtrf Cdbtrf computes an lu factorization of a real m-by-n band matrix |
| CDTTRF CDTTRF CDTTRF computes an lu factorization of a complex tridiagonal matrix with factors of the tridiagonal matrix a from the lu factorization computed by CDTTRF arguments |
| CDTTRSV CDTTRSV CDTTRSV solves one of the systems of equation u * x = b, u**t * x = b, or u**h * x = b, |
| ceil ceil an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( 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ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ an upper bound for these quantities may be computed by: locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_ |
| ceiling ceiling iceil( x, y ) is a scalapack function returning ceiling(x/y when lrwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lrwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lrwork is too small: iceil( x, y ) is a scalapack function returning ceiling(x/y when lrwork is too small: |
| ceive ceive locr( k ) denotes the number of elements of k that a process would receive if k were distributed over the p processes of it similarly, locc( k ) denotes the number of elements of k that a locr( k ) denotes the number of elements of k that a process would receive if k were distributed over the p processes of it similarly, locc( k ) denotes the number of elements of k that a locr( k ) denotes the number of elements of k that a process would receive if k were distributed over the p processes of it similarly, locc( k ) denotes the number of elements of k that a locr( k ) denotes the number of elements of k that a process would receive if k were distributed over the p processes of it similarly, locc( k ) denotes the number of elements of k that a |
| CFROM CFROM pclascl multiplies the m-by-n complex distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar cto/CFROM. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pdlascl multiplies the m-by-n real distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar cto/CFROM. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pslascl multiplies the m-by-n real distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar cto/CFROM. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pzlascl multiplies the m-by-n complex distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar cto/CFROM. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that |
| CGBTRS CGBTRS update the last bw columns of a_i (code modified from CGBTRS only the eliminations of unknowns > ln-bw have an effect on |
| CGEBR2D CGEBR2D the first column of a send data and only processes that own the first column of b receive data. the calls to cgebs2d/CGEBR2D |
| CGEBS2D CGEBS2D the first column of a send data and only processes that own the first column of b receive data. the calls to CGEBS2D/cgebr2 |
| change change processors was not stably factorable wo/interchanges tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution). pclaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of based on pcamax from level 1 pblas. the change is to use th error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. processors was not stably factorable wo/interchanges tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution). pdlaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. based on pdzasum from the level 1 pblas. the change i based on pscasum from the level 1 pblas. the change i processors was not stably factorable wo/interchanges tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution). pslaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. processors was not stably factorable wo/interchanges tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution). pzlaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of based on pzamax from level 1 pblas. the change is to use th error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. the componentwise relative backward error of each solution vector x(j) (i.e., the smallest relative change i tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a tridiagonal matrices. thus, for tridiagonal matrices, dtype_a = 501 or 502 can be used interchangeabl we require that the distributed vectors storing the diagonals of a error of each solution vector (i.e., the smallest re- lative change in any entry of sub( a ) or sub( b this array is tied to the distributed matrix x. |
| changed changed otherwise: apply reflectors to the columns of the matrix unchanged on exit a (global input/output) complex array, (lda,*) otherwise: apply reflectors to the columns of the matrix unchanged on exit a (global input/output) double precision array, (lda,*) orfac, icluster() and gap() parameters added meaning of info is changed functional differences: = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; orfac, icluster() and gap() parameters added meaning of info is changed functional differences: = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; orfac, icluster() and gap() parameters added meaning of info is changed functional differences: orfac, icluster() and gap() parameters added meaning of info is changed functional differences: = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; = 'u': upper triangular part is set; the strictly lower triangular part of sub( a ) is not changed triangular part of sub( a ) is not changed; otherwise: apply reflectors to the columns of the matrix unchanged on exit a (global input/output) real array, (lda,*) otherwise: apply reflectors to the columns of the matrix unchanged on exit a (global input/output) complex*16 array, (lda,*) |
| changes changes use rev <> 0 to send locally replicated b from node (ii,jj) to its owner (which changes depending on its location i use rev <> 0 to send locally replicated b from node (ii,jj) to its owner (which changes depending on its location i use rev <> 0 to send locally replicated b from node (ii,jj) to its owner (which changes depending on its location i use rev <> 0 to send locally replicated b from node (ii,jj) to its owner (which changes depending on its location i |
| character character uplo (input) character* type (global input) character* (apply from left) uplo (input) character* of the tridiagonal matrix a is stored and the form of the uplo - character*1 lower triangular matrix as follows: uplo (input) character* type (global input) character* (apply from left) trans (input) character = 'n': l * x = b (no transpose) uplo - character*1 lower triangular matrix as follows: such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: jobz (input) character* = 'v': compute eigenvalues and eigenvectors. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a cmach (global input) character* = 'e' or 'e', pdlamch := eps such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a id (global input) character* = 'd': sort d in decreasing order. (not implemented yet) such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a
range (global input) character
= 'a': ("all") all eigenvalues will be found.
compz (input) character* = 'i': compute eigenvectors of tridiagonal matrix also. such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: jobz (input) character* = 'v': compute eigenvalues and eigenvectors. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a name (global input) character*(* lower case. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 't' or 'c': solve with a(1:n, ja:ja+n-1)^t; such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a cmach (global input) character* = 'e' or 'e', pslamch := eps such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a id (global input) character* = 'd': sort d in decreasing order. (not implemented yet) such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a
range (global input) character
= 'a': ("all") all eigenvalues will be found.
compz (input) character* = 'i': compute eigenvectors of tridiagonal matrix also. such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: jobz (input) character* = 'v': compute eigenvalues and eigenvectors. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a trans (global input) character = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ); such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: jobz (input) character* = 'v': compute eigenvalues and eigenvectors. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. uplo (global input) character = 'l': lower triangle of a(1:n, ja:ja+n-1) is stored. such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a uplo (input) character* type (global input) character* (apply from left) trans (input) character = 'n': l * x = b (no transpose) uplo - character*1 lower triangular matrix as follows: uplo (input) character* type (global input) character* (apply from left) uplo (input) character* of the tridiagonal matrix a is stored and the form of the uplo - character*1 lower triangular matrix as follows: |
| chart chart note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, |
| chases chases restore the hessenberg form in the (k-1)th column, and thus chases the bulge one step toward the bottom of the activ restore the hessenberg form in the (k-1)th column, and thus chases the bulge one step toward the bottom of the activ |
| check check check the infinity norm of the iterate convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape determine the number of columns we have so we can check workspac convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is an n-by-nrhs distributed matrix denoted by sub( b ). a check is mad convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape determine the number of columns we have so we can check workspac convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is an n-by-nrhs distributed matrix denoted by sub( b ). a check is mad convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape determine the number of columns we have so we can check workspac convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is an n-by-nrhs distributed matrix denoted by sub( b ). a check is mad convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape determine the number of columns we have so we can check workspac convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape convert descriptor into standard form for easy access to parameters, check that grid is of right shape distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is an n-by-nrhs distributed matrix denoted by sub( b ). a check is mad check the infinity norm of the iterate |
| checked checked this is a scalapack internal subroutine and arguments are not checked for unreasonable values arguments this is a scalapack internal procedure and arguments are not checked this is a scalapack internal procedure and arguments are not checked passed a value of -1. 3) the parameter value returned by pjlaenv is checked for validit retrieve the optimal blocksize for strtri as follows: this is a scalapack internal subroutine and arguments are not checked for unreasonable values arguments this is a scalapack internal procedure and arguments are not checked this is a scalapack internal procedure and arguments are not checked |
| checking checking argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin argument checking that is specific to divide & conquer routin |
| checks checks consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same in its present form, pcheev assumes a homogeneous system and makes only spot checks of the consistency of the eigenvalues across th heterogeneous system may return incorrect results without any error consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same pdlaecv checks if the input intervals [ intvl(2*i-1), intvl(2*i) ] pdlaecv modifies kf to be the index of the last converged interval, consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same in its present form, pdsyev assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error in its present form, pdsyevd assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same pslaecv checks if the input intervals [ intvl(2*i-1), intvl(2*i) ] pslaecv modifies kf to be the index of the last converged interval, consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same in its present form, pssyev assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error in its present form, pssyevd assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same in its present form, pzheev assumes a homogeneous system and makes only spot checks of the consistency of the eigenvalues across th heterogeneous system may return incorrect results without any error consistency checks for desca and descb context must be the same consistency checks for desca and descb context must be the same |
| CHEEVX CHEEVX pCHEEVX computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by |
| CHETRD CHETRD support for uplo='u' is limited to calling the old, slow, pCHETRD |
| choice choice o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on the diagonal. this choice of sr and sc puts the condition numbe over all possible diagonal scalings. o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on the diagonal. this choice of sr and sc puts the condition numbe over all possible diagonal scalings. o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on the diagonal. this choice of sr and sc puts the condition numbe over all possible diagonal scalings. o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on the diagonal. this choice of sr and sc puts the condition numbe over all possible diagonal scalings. o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: o(nb) on each processor. if this is too small, divide and conquer is a poor choice of algorithm submatrix reference: |
| Cholesky Cholesky u * x = b, or u**h * x = b, where l or u is the Cholesky factor of a hermitian positiv a = u**h*d*u or a = l*d*l**h (computed by cpttrf). l**t* x = b, or l * x = b, where l is the Cholesky factor of a hermitian positiv a = l*d*l**h (computed by dpttrf). on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b matrix is overwritten by the triangular factor u or l from the Cholesky factorization sub( b ) = u**h*u o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b Cholesky factorization is used to factor a reordering o on entry, this array contains the local pieces of the n-by-n symmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format 1-norm) of a complex hermitian positive definite distributed matrix using the Cholesky factorization a = u**h*u or a = l*l**h computed b on entry, this array contains the factors l or u from the Cholesky factorization sub( a ) = l*l**h or u**h*u, a the Cholesky decomposition is used to factor sub( a ) a sub( a ) = u**h * u, if uplo = 'u', or pcposvx uses the Cholesky factorization a = u**h*u or a = l*l**h t pcpotf2 computes the Cholesky factorization of a complex hermitia pcpotrf computes the Cholesky factorization of an n-by-n comple a(ia:ia+n-1, ja:ja+n-1). distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the Cholesky factorization sub( a ) = u**h*u or l*l**h computed b where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a n-by-n hermitian positive definite distributed matrix using the Cholesky sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). Cholesky factorization is used to factor a reordering o on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format Cholesky factorization is used to factor a reordering o on entry, this array contains the local pieces of the n-by-n symmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format 1-norm) of a real symmetric positive definite distributed matrix using the Cholesky factorization a = u**t*u or a = l*l**t computed b on entry, this array contains the factors l or u from the Cholesky factorization sub( a ) = l*l**t or u**t*u, a the Cholesky decomposition is used to factor sub( a ) a sub( a ) = u**t * u, if uplo = 'u', or pdposvx uses the Cholesky factorization a = u**t*u or a = l*l**t t pdpotf2 computes the Cholesky factorization of a real symmetri pdpotrf computes the Cholesky factorization of an n-by-n rea a(ia:ia+n-1, ja:ja+n-1). distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the Cholesky factorization sub( a ) = u**t*u or l*l**t computed b where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a n-by-n symmetric positive definite distributed matrix using the Cholesky sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). Cholesky factorization is used to factor a reordering o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b matrix is overwritten by the triangular factor u or l from the Cholesky factorization sub( b ) = u**t*u o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format Cholesky factorization is used to factor a reordering o on entry, this array contains the local pieces of the n-by-n symmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format 1-norm) of a real symmetric positive definite distributed matrix using the Cholesky factorization a = u**t*u or a = l*l**t computed b on entry, this array contains the factors l or u from the Cholesky factorization sub( a ) = l*l**t or u**t*u, a the Cholesky decomposition is used to factor sub( a ) a sub( a ) = u**t * u, if uplo = 'u', or psposvx uses the Cholesky factorization a = u**t*u or a = l*l**t t pspotf2 computes the Cholesky factorization of a real symmetri pspotrf computes the Cholesky factorization of an n-by-n rea a(ia:ia+n-1, ja:ja+n-1). distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the Cholesky factorization sub( a ) = u**t*u or l*l**t computed b where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a n-by-n symmetric positive definite distributed matrix using the Cholesky sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). Cholesky factorization is used to factor a reordering o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b matrix is overwritten by the triangular factor u or l from the Cholesky factorization sub( b ) = u**t*u o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format on entry, this array contains the local pieces of the n-by-n unsymmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b matrix is overwritten by the triangular factor u or l from the Cholesky factorization sub( b ) = u**h*u o this array contains the local pieces of the triangular factor from the Cholesky factorization of sub( b ), as returned b Cholesky factorization is used to factor a reordering o on entry, this array contains the local pieces of the n-by-n symmetric banded distributed Cholesky factor l o this local portion is stored in the packed banded format 1-norm) of a complex hermitian positive definite distributed matrix using the Cholesky factorization a = u**h*u or a = l*l**h computed b on entry, this array contains the factors l or u from the Cholesky factorization sub( a ) = l*l**h or u**h*u, a the Cholesky decomposition is used to factor sub( a ) a sub( a ) = u**h * u, if uplo = 'u', or pzposvx uses the Cholesky factorization a = u**h*u or a = l*l**h t pzpotf2 computes the Cholesky factorization of a complex hermitia pzpotrf computes the Cholesky factorization of an n-by-n comple a(ia:ia+n-1, ja:ja+n-1). distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the Cholesky factorization sub( a ) = u**h*u or l*l**h computed b where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a n-by-n hermitian positive definite distributed matrix using the Cholesky sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). Cholesky factorization is used to factor a reordering o l**t* x = b, or l * x = b, where l is the Cholesky factor of a hermitian positiv a = l*d*l**h (computed by spttrf). u * x = b, or u**h * x = b, where l or u is the Cholesky factor of a hermitian positiv a = u**h*d*u or a = l*d*l**h (computed by zpttrf). |
| choose choose choose partition entry as median of choose partition entry as median of determine where the matrix splits and choose ql or qr iteratio element is smaller. pjlaenv is called from the scalapack symmetric and hermitian tailored eigen-routines to choose for a description of the parameters. choose partition entry as median of choose partition entry as median of determine where the matrix splits and choose ql or qr iteratio element is smaller. |
| chosen chosen reduce its condition number. r returns the row scale factors and c the column scale factors, chosen to try to make the largest entry i b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1. tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro (with respect to the two-norm). sr and sc contain the scale factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri the diagonal. this choice of sr and sc puts the condition number tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro reduce its condition number. r returns the row scale factors and c the column scale factors, chosen to try to make the largest entry i b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1. tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro (with respect to the two-norm). sr and sc contain the scale factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri the diagonal. this choice of sr and sc puts the condition number tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro reduce its condition number. r returns the row scale factors and c the column scale factors, chosen to try to make the largest entry i b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1. tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro (with respect to the two-norm). sr and sc contain the scale factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri the diagonal. this choice of sr and sc puts the condition number tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro reduce its condition number. r returns the row scale factors and c the column scale factors, chosen to try to make the largest entry i b(i,j) = r(i) * a(i,j) * c(j) have absolute value 1. tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro (with respect to the two-norm). sr and sc contain the scale factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri the diagonal. this choice of sr and sc puts the condition number tau is a scalar and z( k ) is an ( n - m ) element vector. tau and z( k ) are chosen to annihilate the elements of the kth ro |
| CHSEQR CHSEQR contain an n-by-n matrix q (usually the unitary matrix q of schur vectors returned by CHSEQR) if howmny = 'a', the matrix y of left eigenvectors of t; |
| chunk chunk this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: copy a chunk of elements from global a(m-1:,m-1: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: copy a chunk of elements from global a(m-1:,m-1: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: this formula in words is: no processor may have more than one chunk of the matrix blocksize cannot be too small: |
| ciated ciated each global data object is described by an associated descriptio the mapping between an object element and its corresponding process each global data object is described by an associated descriptio the mapping between an object element and its corresponding process each global data object is described by an associated descriptio the mapping between an object element and its corresponding process each global data object is described by an associated descriptio the mapping between an object element and its corresponding process |
| circumstances circumstances block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix block to send to neighboring processor. depending on circumstances, may need to transpose the matrix |
| CLACON CLACON pCLACON estimates the 1-norm of a square, complex distributed matri products. x and v are aligned with the distributed matrix a, this the serial version was contributed to lapack by nick higham for use with CLACON arguments the serial version of this routine was originally contributed by nick higham for use with CLACON notes |
| CLADIV CLADIV x( j ) = CLADIV( x( j ), tjjs |
| CLAHQR CLAHQR CLAHQR used to have a single row application and a singl more clever. we break each transformation down into 3 this code is basically a parallelization of the following snip of lapack code from CLAHQR look for a single small subdiagonal element. |
| CLAMSH CLAMSH CLAMSH sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges |
| CLANHS CLANHS if( tst1.eq.zero ) $ tst1 = CLANHS( '1', i-l+1, h( l, l ), ldh, work $ go to 30 |
| CLANV2 CLANV2 CLANV2 computes the schur factorization of a complex 2-by- |
| CLAREF CLAREF CLAREF applies one or several householder reflectors of size rows or columns. |
| Clear Clear these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same |
| Cleary Cleary code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. implemented for scalapack by: andrew j. Cleary, livermore national lab and university of tenn. based on code written by : peter arbenz, eth zurich, 1996. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. implemented for scalapack by: andrew j. Cleary, livermore national lab and university of tenn. based on code written by : peter arbenz, eth zurich, 1996. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. implemented for scalapack by: andrew j. Cleary, livermore national lab and university of tenn. based on code written by : peter arbenz, eth zurich, 1996. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. implemented for scalapack by: andrew j. Cleary, livermore national lab and university of tenn. based on code written by : peter arbenz, eth zurich, 1996. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. code developer: andrew j. Cleary, university of tennessee this version released: august, 2001. these are alignment restrictions that may or may not be removed in future releases. -andy Cleary, april 14, 1996 block sizes must be the same |
| clever clever column application to h. here we do something a little more clever. we break each transformation down into 1.) the minimum amount of work it takes to determine column application to h. here we do something a little more clever. we break each transformation down into 1.) the minimum amount of work it takes to determine column application to h. here we do something a little more clever. we break each transformation down into 1.) the minimum amount of work it takes to determine column application to h. here we do something a little more clever. we break each transformation down into 1.) the minimum amount of work it takes to determine |
| close close if eigenvalues j and j-1 are too close, add a relativel absolute value of largest distributed matrix element. if amax is very close to overflow or very close to underflow largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
absolute value of largest matrix element. if amax is very close to overflow or very close to underflow, the matri absolute value of largest distributed matrix element. if amax is very close to overflow or very close to underflow there are two ways in which deflation can occur: when two or more eigenvalues are close together or if there is a tiny entry in th equation problem is reduced by one. absolute value of largest matrix element. if amax is very close to overflow or very close to underflow, the matri largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
absolute value of largest distributed matrix element. if amax is very close to overflow or very close to underflow there are two ways in which deflation can occur: when two or more eigenvalues are close together or if there is a tiny entry in th equation problem is reduced by one. absolute value of largest matrix element. if amax is very close to overflow or very close to underflow, the matri largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
absolute value of largest distributed matrix element. if amax is very close to overflow or very close to underflow largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
largest cluster, where a cluster is defined as a set of
close eigenvalues: { w(k),...,w(k+clustersize-1)
variable definitions:
absolute value of largest matrix element. if amax is very close to overflow or very close to underflow, the matri if eigenvalues j and j-1 are too close, add a relativel |
| cluster cluster the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from cstein2). the absolute tolerance for the eigenvalues. an eigenvalue (or cluster) is considered to be located if it has bee less. if abstol is less than or equal to zero, then ulp*|t| nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from dstein2). the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the absolute tolerance for the eigenvalues. an eigenvalue (or cluster) is considered to be located if it has bee less. if abstol is less than or equal to zero, then ulp*|t| nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from sstein2). the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from zstein2). |
| clustered clustered pcheevx does not promise orthogonality for eigenvectors associated with tighly clustered eigenvalues that are on different processes. the extent of reorthogonalization pdsyevx does not promise orthogonality for eigenvectors associated with tighly clustered eigenvalues that are on different processes. the extent of reorthogonalization pssyevx does not promise orthogonality for eigenvectors associated with tighly clustered eigenvalues that are on different processes. the extent of reorthogonalization pzheevx does not promise orthogonality for eigenvectors associated with tighly clustered eigenvalues that are on different processes. the extent of reorthogonalization |
| clusters clusters the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from cstein2). nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from dstein2). the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from sstein2). the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (clustersize-1)* largest cluster, where a cluster is defined as a set of nvec = floor(( lwork- max(5*n,np00*mq00) )/n). eigenvectors corresponding to eigenvalue clusters of siz orthogonality is similar to that obtained from zstein2). |
| CLUSTERSIZE CLUSTERSIZE the following to lrwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of the following to lrwork: (CLUSTERSIZE-1)* largest cluster, where a cluster is defined as a set of |
| CMACH CMACH CMACH (global input) character* = 'e' or 'e', pdlamch := eps CMACH (global input) character* = 'e' or 'e', pslamch := eps |
| CNORM CNORM scale the column norms by tscal if the maximum element in CNORM i scale the column norms by tscal if the maximum element in CNORM i |
| code code use unblocked code use unblocked code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((max(bwl,bwu)*nrhs), max(bwl,bwu)*max(bwl,bwu)) get values out of descriptor for use in code nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(10*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code 2*(nb+2) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(nrhs*(nb+2*bwl+4*bwu), 1) get values out of descriptor for use in code (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) if laf is not large enough, an error code will be returne rcond = 0), the matrix is singular to working precision. this condition is indicated by a return code of info > 0 ferr (local output) real array, dimension locc(n_b) support for uplo='u' is limited to calling the old, slow, pchetrd code the matrix a does not hold the same values that it would in an unblocked code nor the values that it would hold i when we hit a border, there are row and column transforms that overlap over several processors and the code gets ver *local* matrix is generated on one node (called smalla) and pclamr1d has not been tested except withint the contect of pcheptrd, the prototype reduction to tridiagonal form code purpose the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. this code is basically a parallelization of the following sni if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((bw*nrhs), bw*bw) get values out of descriptor for use in code (nb+2*bw)*bw if laf is not large enough, an error code will be returne get values out of descriptor for use in code is singular to working precision. this condition is indicated by a return code of info > 0, and the solution an if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code all of b or a submatrix of b). important note: the current version of this code support get values out of descriptor for use in code note : if the eigenvectors obtained are not orthogonal, increase lwork and run the code again notes substitution. it is the hope that scaling would be used to make the the code robust against possible overflow. but scaling has not ye the triangular systems. pclattrs just calls pctrsv. if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((max(bwl,bwu)*nrhs), max(bwl,bwu)*max(bwl,bwu)) get values out of descriptor for use in code nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(10*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code 2*(nb+2) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(nrhs*(nb+2*bwl+4*bwu), 1) get values out of descriptor for use in code (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) if laf is not large enough, an error code will be returne rcond = 0), the matrix is singular to working precision. this condition is indicated by a return code of info > 0 ferr (local output) double precision array, dimension locc(n_b) this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits when we hit a border, there are row and column transforms that overlap over several processors and the code gets ver *local* matrix is generated on one node (called smalla) and pdlamr1d has not been tested except withint the contect of pdsyptrd, the prototype reduction to tridiagonal form code purpose the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. this code is basically a parallelization of the following sni if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((bw*nrhs), bw*bw) get values out of descriptor for use in code (nb+2*bw)*bw if laf is not large enough, an error code will be returne get values out of descriptor for use in code is singular to working precision. this condition is indicated by a return code of info > 0, and the solution an if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code all of b or a submatrix of b). important note: the current version of this code support get values out of descriptor for use in code this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits note : if the eigenvectors obtained are not orthogonal, increase lwork and run the code again notes support for uplo='u' is limited to calling the old, slow, pdsytrd code the matrix a does not hold the same values that it would in an unblocked code nor the values that it would hold i if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((max(bwl,bwu)*nrhs), max(bwl,bwu)*max(bwl,bwu)) get values out of descriptor for use in code nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(10*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code 2*(nb+2) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(nrhs*(nb+2*bwl+4*bwu), 1) get values out of descriptor for use in code (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) if laf is not large enough, an error code will be returne rcond = 0), the matrix is singular to working precision. this condition is indicated by a return code of info > 0 ferr (local output) real array, dimension locc(n_b) this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits when we hit a border, there are row and column transforms that overlap over several processors and the code gets ver *local* matrix is generated on one node (called smalla) and pslamr1d has not been tested except withint the contect of pssyptrd, the prototype reduction to tridiagonal form code purpose the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. this code is basically a parallelization of the following sni if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((bw*nrhs), bw*bw) get values out of descriptor for use in code (nb+2*bw)*bw if laf is not large enough, an error code will be returne get values out of descriptor for use in code is singular to working precision. this condition is indicated by a return code of info > 0, and the solution an if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code all of b or a submatrix of b). important note: the current version of this code support get values out of descriptor for use in code this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits note : if the eigenvectors obtained are not orthogonal, increase lwork and run the code again notes support for uplo='u' is limited to calling the old, slow, pssytrd code the matrix a does not hold the same values that it would in an unblocked code nor the values that it would hold i if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((max(bwl,bwu)*nrhs), max(bwl,bwu)*max(bwl,bwu)) get values out of descriptor for use in code nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(10*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code 2*(nb+2) if laf is not large enough, an error code will be returne get values out of descriptor for use in code if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max(nrhs*(nb+2*bwl+4*bwu), 1) get values out of descriptor for use in code (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) if laf is not large enough, an error code will be returne rcond = 0), the matrix is singular to working precision. this condition is indicated by a return code of info > 0 ferr (local output) double precision array, dimension locc(n_b) support for uplo='u' is limited to calling the old, slow, pzhetrd code the matrix a does not hold the same values that it would in an unblocked code nor the values that it would hold i when we hit a border, there are row and column transforms that overlap over several processors and the code gets ver *local* matrix is generated on one node (called smalla) and pzlamr1d has not been tested except withint the contect of pzheptrd, the prototype reduction to tridiagonal form code purpose the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. this code is basically a parallelization of the following sni if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((bw*nrhs), bw*bw) get values out of descriptor for use in code (nb+2*bw)*bw if laf is not large enough, an error code will be returne get values out of descriptor for use in code is singular to working precision. this condition is indicated by a return code of info > 0, and the solution an if lwork is too small, the minimal acceptable size will be returned in work(1) and an error code is returned. lwork> +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol) get values out of descriptor for use in code all of b or a submatrix of b). important note: the current version of this code support get values out of descriptor for use in code note : if the eigenvectors obtained are not orthogonal, increase lwork and run the code again notes substitution. it is the hope that scaling would be used to make the the code robust against possible overflow. but scaling has not ye the triangular systems. pzlattrs just calls pztrsv. use unblocked code use unblocked code |
| codes codes note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. pchentrd is a prototype version of pchetrd which uses tailored codes (either the serial, chetrd, or the parallel code, pchettrd reference: n.j. higham, "fortran codes for estimating the one-norm o acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. reference: n.j. higham, "fortran codes for estimating the one-norm o acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. pdsyntrd is a prototype version of pdsytrd which uses tailored codes (either the serial, dsytrd, or the parallel code, pdsyttrd anticipation of future needs, even though pjlaenv is only sparsely used at present in scalapack. most scalapack codes use the inpu hence there is no need or opportunity to set the algorithmic or note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. reference: n.j. higham, "fortran codes for estimating the one-norm o acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. pssyntrd is a prototype version of pssytrd which uses tailored codes (either the serial, ssytrd, or the parallel code, pssyttrd note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. pzhentrd is a prototype version of pzhetrd which uses tailored codes (either the serial, zhetrd, or the parallel code, pzhettrd reference: n.j. higham, "fortran codes for estimating the one-norm o acm trans. math. soft., vol. 14, no. 4, pp. 381-396, december 1988. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: banded codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. note: tridiagonal codes can use either the old two dimensiona both cases *must be one-dimensional*. we describe both types below. |
| coefficient coefficient separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered pcporfs improves the computed solution to a system of linear equations when the coefficient matrix is hermitian positive definit solutions. separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pctrtrs or some other separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered pdporfs improves the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definit solutions. separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pdtrtrs or some other separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered psporfs improves the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definit solutions. separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pstrtrs or some other separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered pzporfs improves the computed solution to a system of linear equations when the coefficient matrix is hermitian positive definit solutions. separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered separately (to solve various sets of righthand sides using the same coefficient matrix), the auxiliary space af *must not be altered solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pztrtrs or some other |
| coefficients coefficients lptr is the pointer to the beginning of the coefficients of lptr is the pointer to the beginning of the coefficients of lptr is the pointer to the beginning of the coefficients of lptr is the pointer to the beginning of the coefficients of |
| col col array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to determine the number of columns we have so we can check workspac if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' id (global input) integer q's global row/col index, which points to the beginnin determine the number of columns we have so we can check workspac if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to id (global input) integer q's global row/col index, which points to the beginnin determine the number of columns we have so we can check workspac if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to determine the number of columns we have so we can check workspac if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' |
| COLCND COLCND COLCND (global output) rea to the largest c(j) (ja <= j <= ja+n-1). if colcnd >= 0.1, it COLCND (global input) rea ja <= j <= ja+n-1. COLCND (global output) double precisio to the largest c(j) (ja <= j <= ja+n-1). if colcnd >= 0.1, it COLCND (global input) double precisio ja <= j <= ja+n-1. COLCND (global output) rea to the largest c(j) (ja <= j <= ja+n-1). if colcnd >= 0.1, it COLCND (global input) rea ja <= j <= ja+n-1. COLCND (global output) double precisio to the largest c(j) (ja <= j <= ja+n-1). if colcnd >= 0.1, it COLCND (global input) double precisio ja <= j <= ja+n-1. |
| COLMAXS COLMAXS find COLMAXS find COLMAXS find COLMAXS find COLMAXS find COLMAXS find COLMAXS |
| cols cols data if ii>=0,jj=-1, then all cols in row ii receive the dat if rev<>0, then ii is the source row index for the node(s) copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^c stored, conjugate transpose data if ii>=0,jj=-1, then all cols in row ii receive the dat if rev<>0, then ii is the source row index for the node(s) copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^t stored, transpose data if ii>=0,jj=-1, then all cols in row ii receive the dat if rev<>0, then ii is the source row index for the node(s) copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^t stored, transpose data if ii>=0,jj=-1, then all cols in row ii receive the dat if rev<>0, then ii is the source row index for the node(s) copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^c stored, conjugate transpose |
| COLSUMS COLSUMS refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' refered to as rowsums, and the column sums shown by | are refered to as COLSUMS. infinity-norm = 1-norm = rowsums+colsums uplo = 'u' uplo = 'l' |
| COLTYP COLTYP COLTYP (workspace/output) integer array, dimension (n following types a column in the q2 matrix is: COLTYP (workspace/output) integer array, dimension (n following types a column in the q2 matrix is: |
| column column n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer ju is the index of the last column affected by the curren i1 and i2 are the indices of the first row and last column of being computed, i1 and i2 are set inside the main loop. claref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer ju is the index of the last column affected by the curren dlaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcgeqpf computes a qr factorization with column pivoting of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to pclabrd reduces the first nb rows and columns of a complex genera or lower bidiagonal form by an unitary transformation q' * a * p, and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute more. the receiving node can be specified precisely, or all nodes can receive, or just one row or column of nodes notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pclahrd reduces the first nb columns of a complex genera elements below the k-th subdiagonal are zero. the reduction is although all processes call pcgemr2d, only the processes that own the first column of a send data and only processes that own th spread the data down. where norm1 denotes the one norm of a matrix (maximum column sum) normf denotes the frobenius norm of a matrix (square root of sum of if the matrix is hermitian, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is handle first block of columns separatel if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is loop over remaining block of column or inv( p ) to a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column or a column. the pivot vector should be aligned with the distributed or inv( p ) to a m-by-n distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. th pivoting the rows of sub( a ), ipiv should be distributed along a array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the array v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pclaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pclatrd reduces nb rows and columns of a complex hermitia tridiagonal form by an unitary similarity transformation array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute compute the 1-norm of each column, not including the diagonal array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcung2l generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pcung2r generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcungql generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pcungqr generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdgeqpf computes a qr factorization with column pivoting of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdlabrd reduces the first nb rows and columns of a real genera or lower bidiagonal form by an orthogonal transformation q' * a * p, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute more. the receiving node can be specified precisely, or all nodes can receive, or just one row or column of nodes notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute jq (global input) integer q's global column index, which points to the beginning o jq (global input) integer q's global column index, which points to the beginning o nb (global input) integer the blocking factor used to distribute the columns of th nb (global input) integer the blocking factor used to distribute the columns of th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pdlahrd reduces the first nb columns of a real general n-by-(n-k+1 k-th subdiagonal are zero. the reduction is performed by an orthogo- although all processes call pdgemr2d, only the processes that own the first column of a send data and only processes that own th spread the data down. where norm1 denotes the one norm of a matrix (maximum column sum) normf denotes the frobenius norm of a matrix (square root of sum of handle first block of columns separatel if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is loop over remaining block of column or inv( p ) to a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column or a column. the pivot vector should be aligned with the distributed or inv( p ) to a m-by-n distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. th pivoting the rows of sub( a ), ipiv should be distributed along a array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the array v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of columns to be operated on i.e the number o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdlaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdlatrd reduces nb rows and columns of a real symmetric distribute form by an orthogonal similarity transformation q' * sub( a ) * q, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdorg2l generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pdorg2r generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdorgql generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pdorgqr generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- iblock (global output) integer array, dimension (n) at each row/column j where e(j) is zero or small, th matrix. on exit iblock(i) specifies which block (from 1 jq (global input) integer q's global column index, which points to the beginning o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psgeqpf computes a qr factorization with column pivoting of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pslabrd reduces the first nb rows and columns of a real genera or lower bidiagonal form by an orthogonal transformation q' * a * p, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute more. the receiving node can be specified precisely, or all nodes can receive, or just one row or column of nodes notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute jq (global input) integer q's global column index, which points to the beginning o jq (global input) integer q's global column index, which points to the beginning o nb (global input) integer the blocking factor used to distribute the columns of th nb (global input) integer the blocking factor used to distribute the columns of th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pslahrd reduces the first nb columns of a real general n-by-(n-k+1 k-th subdiagonal are zero. the reduction is performed by an orthogo- although all processes call psgemr2d, only the processes that own the first column of a send data and only processes that own th spread the data down. where norm1 denotes the one norm of a matrix (maximum column sum) normf denotes the frobenius norm of a matrix (square root of sum of handle first block of columns separatel if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is loop over remaining block of column or inv( p ) to a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column or a column. the pivot vector should be aligned with the distributed or inv( p ) to a m-by-n distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. th pivoting the rows of sub( a ), ipiv should be distributed along a array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the array v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of columns to be operated on i.e the number o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pslaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pslatrd reduces nb rows and columns of a real symmetric distribute form by an orthogonal similarity transformation q' * sub( a ) * q, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psorg2l generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a psorg2r generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psorgql generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a psorgqr generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pspoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- iblock (global output) integer array, dimension (n) at each row/column j where e(j) is zero or small, th matrix. on exit iblock(i) specifies which block (from 1 jq (global input) integer q's global column index, which points to the beginning o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzgeqpf computes a qr factorization with column pivoting of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to pzlabrd reduces the first nb rows and columns of a complex genera or lower bidiagonal form by an unitary transformation q' * a * p, and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute more. the receiving node can be specified precisely, or all nodes can receive, or just one row or column of nodes notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pzlahrd reduces the first nb columns of a complex genera elements below the k-th subdiagonal are zero. the reduction is although all processes call pzgemr2d, only the processes that own the first column of a send data and only processes that own th spread the data down. where norm1 denotes the one norm of a matrix (maximum column sum) normf denotes the frobenius norm of a matrix (square root of sum of if the matrix is hermitian, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is handle first block of columns separatel if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is loop over remaining block of column or inv( p ) to a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column or a column. the pivot vector should be aligned with the distributed or inv( p ) to a m-by-n distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. th pivoting the rows of sub( a ), ipiv should be distributed along a array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the distributed matrix v, an h = i - v * t * v' is sub( c ) only distributed over a process column array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute is sub( c ) only distributed over a process column if storev = 'c', the vector which defines the elementary reflector h(i) is stored in the i-th column of the array v, an h = i - v * t * v' array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzlaswp performs a series of row or column interchanges o interchange is initiated for each of rows or columns k1 trough k2 of array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzlatrd reduces nb rows and columns of a complex hermitia tridiagonal form by an unitary similarity transformation array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute compute the 1-norm of each column, not including the diagonal array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzung2l generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pzung2r generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzungql generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pzungqr generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer ju is the index of the last column affected by the curren slaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer ju is the index of the last column affected by the curren i1 and i2 are the indices of the first row and last column of being computed, i1 and i2 are set inside the main loop. zlaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei |
| columns columns n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer here a11, a21 and a31 denote the current block of jb columns partitioning are jb, i2, i3 respectively, and the numbers nrhs (input) integer the number of right hand sides, i.e., the number of columns ihi to ilo in steps of 1 or 2. each iteration of the loop works with the active submatrix in rows and columns l to i h(l,l-1) is negligible so that the matrix splits. to one or two matrices (if column is specified) on either their rows or columns arguments nrhs (input) integer the number of right hand sides, i.e., the number of columns n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer here a11, a21 and a31 denote the current block of jb columns partitioning are jb, i2, i3 respectively, and the numbers nrhs (input) integer the number of right hand sides, i.e., the number of columns to one or two matrices (if column is specified) on either their rows or columns arguments nrhs (input) integer the number of right hand sides, i.e., the number of columns scale submatrix in rows and columns l to len n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to pclabrd reduces the first nb rows and columns of a complex genera or lower bidiagonal form by an unitary transformation q' * a * p, and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pclahrd reduces the first nb columns of a complex genera elements below the k-th subdiagonal are zero. the reduction is array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute loop over the remaining rows/columns of the matrix handle first block of columns separatel loop over the remaining rows/columns of the matrix loop over remaining block of columns matrix a. this routine will transpose the pivot vector if necessary. for example if the row pivots should be applied to the columns o ipiv should be distributed along a process row and replicated over all process columns for column pivoting notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). one interchange is initiated for each of rows or columns k1 trough k2 o already been broadcast along the process row or column. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pclatrd reduces nb rows and columns of a complex hermitia tridiagonal form by an unitary similarity transformation array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the scaling factor are stored along process rows in sr and along process columns in sc. the duplication of information simplifie array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcung2l generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pcung2r generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pcungql generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pcungqr generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdlabrd reduces the first nb rows and columns of a real genera or lower bidiagonal form by an orthogonal transformation q' * a * p, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute eigenvalue while working on the submatrix lying in global rows and columns mod(info,n+1) ===================================================================== nb (global input) integer the blocking factor used to distribute the columns of th nb (global input) integer the blocking factor used to distribute the columns of th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pdlahrd reduces the first nb columns of a real general n-by-(n-k+1 k-th subdiagonal are zero. the reduction is performed by an orthogo- array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute handle first block of columns separatel loop over the remaining rows/columns of the matrix loop over remaining block of columns matrix a. this routine will transpose the pivot vector if necessary. for example if the row pivots should be applied to the columns o ipiv should be distributed along a process row and replicated over all process columns for column pivoting notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of columns to be operated on i.e the number o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). one interchange is initiated for each of rows or columns k1 trough k2 o already been broadcast along the process row or column. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdlatrd reduces nb rows and columns of a real symmetric distribute form by an orthogonal similarity transformation q' * sub( a ) * q, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdorg2l generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pdorg2r generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pdorgql generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pdorgqr generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the scaling factor are stored along process rows in sr and along process columns in sc. the duplication of information simplifie array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns eigenvalue while working on the submatrix lying in global rows and columns mod(info,n+1) further details array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pslabrd reduces the first nb rows and columns of a real genera or lower bidiagonal form by an orthogonal transformation q' * a * p, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute eigenvalue while working on the submatrix lying in global rows and columns mod(info,n+1) ===================================================================== nb (global input) integer the blocking factor used to distribute the columns of th nb (global input) integer the blocking factor used to distribute the columns of th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pslahrd reduces the first nb columns of a real general n-by-(n-k+1 k-th subdiagonal are zero. the reduction is performed by an orthogo- array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute handle first block of columns separatel loop over the remaining rows/columns of the matrix loop over remaining block of columns matrix a. this routine will transpose the pivot vector if necessary. for example if the row pivots should be applied to the columns o ipiv should be distributed along a process row and replicated over all process columns for column pivoting notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of columns to be operated on i.e the number o array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). one interchange is initiated for each of rows or columns k1 trough k2 o already been broadcast along the process row or column. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pslatrd reduces nb rows and columns of a real symmetric distribute form by an orthogonal similarity transformation q' * sub( a ) * q, array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psorg2l generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a psorg2r generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute psorgql generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a psorgqr generates an m-by-n real distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the scaling factor are stored along process rows in sr and along process columns in sc. the duplication of information simplifie array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns eigenvalue while working on the submatrix lying in global rows and columns mod(info,n+1) further details array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca[ n_ ] the number of columns in the globa mb_a (global) desca[ mb_ ] the blocking factor used to distribu- n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute vectors b and solution vectors x can be handled in a single call; when trans = 'n', the solution vectors are stored as the columns o right hand side matrix sub( b ) otherwise. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute v is an n-by-n orthogonal matrix. the diagonal elements of sigma are the singular values of a and the columns of u and v are th singular values are returned in array s in decreasing order and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute matrix a. n_a (global) desca( n_ ) the number of columns in the distri mb_a (global) desca( mb_ ) the blocking factor used to distribute np = the number of rows local to a given process. nq = the number of columns local to a given process jobz (input) character*1 array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to pzlabrd reduces the first nb rows and columns of a complex genera or lower bidiagonal form by an unitary transformation q' * a * p, and array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute determine the number of columns we have so we can check workspac pzlahrd reduces the first nb columns of a complex genera elements below the k-th subdiagonal are zero. the reduction is array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute loop over the remaining rows/columns of the matrix handle first block of columns separatel loop over the remaining rows/columns of the matrix loop over remaining block of columns matrix a. this routine will transpose the pivot vector if necessary. for example if the row pivots should be applied to the columns o ipiv should be distributed along a process row and replicated over all process columns for column pivoting notes array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). one interchange is initiated for each of rows or columns k1 trough k2 o already been broadcast along the process row or column. array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzlatrd reduces nb rows and columns of a complex hermitia tridiagonal form by an unitary similarity transformation array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute the scaling factor are stored along process rows in sr and along process columns in sc. the duplication of information simplifie array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo n (global input) integer the number of rows and columns to be operated on, i.e. th number of columns in each processo array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzung2l generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pzung2r generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute pzungql generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a pzungqr generates an m-by-n complex distributed matrix q denoting a(ia:ia+m-1,ja:ja+n-1) with orthonormal columns, which is defined a m array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute array a. n_a (global) desca( n_ ) the number of columns in the globa mb_a (global) desca( mb_ ) the blocking factor used to distribute n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer here a11, a21 and a31 denote the current block of jb columns partitioning are jb, i2, i3 respectively, and the numbers nrhs (input) integer the number of right hand sides, i.e., the number of columns to one or two matrices (if column is specified) on either their rows or columns arguments nrhs (input) integer the number of right hand sides, i.e., the number of columns scale submatrix in rows and columns l to len n (input) integer the number of columns of the matrix a. n >= 0 kl (input) integer here a11, a21 and a31 denote the current block of jb columns partitioning are jb, i2, i3 respectively, and the numbers nrhs (input) integer the number of right hand sides, i.e., the number of columns ihi to ilo in steps of 1 or 2. each iteration of the loop works with the active submatrix in rows and columns l to i h(l,l-1) is negligible so that the matrix splits. to one or two matrices (if column is specified) on either their rows or columns arguments nrhs (input) integer the number of right hand sides, i.e., the number of columns |
| Columnwise Columnwise local pieces of the distributed matrix b of right hand side vectors, stored Columnwise on exit, sub( b ) is overwritten by the solution vectors, reflectors are stored: = 'c': Columnwise reflectors are stored (see also further details): = 'c': Columnwise reflectors are stored: = 'c': Columnwise (not supported yet reflectors are stored (see also further details): = 'c': Columnwise (not supported yet local pieces of the distributed matrix b of right hand side vectors, stored Columnwise on exit, sub( b ) is overwritten by the solution vectors, reflectors are stored: = 'c': Columnwise reflectors are stored (see also further details): = 'c': Columnwise reflectors are stored: = 'c': Columnwise (not supported yet reflectors are stored (see also further details): = 'c': Columnwise (not supported yet local pieces of the distributed matrix b of right hand side vectors, stored Columnwise on exit, sub( b ) is overwritten by the solution vectors, reflectors are stored: = 'c': Columnwise reflectors are stored (see also further details): = 'c': Columnwise reflectors are stored: = 'c': Columnwise (not supported yet reflectors are stored (see also further details): = 'c': Columnwise (not supported yet local pieces of the distributed matrix b of right hand side vectors, stored Columnwise on exit, sub( b ) is overwritten by the solution vectors, reflectors are stored: = 'c': Columnwise reflectors are stored (see also further details): = 'c': Columnwise reflectors are stored: = 'c': Columnwise (not supported yet reflectors are stored (see also further details): = 'c': Columnwise (not supported yet |
| Combine Combine Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side would naturally happen at the bottom of the loop) in order to Combine the spread of v( : , i-1 ) with the spread of h( : , i in order to compute h( :, i ), we must update a( :, i ) Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side would naturally happen at the bottom of the loop) in order to Combine the spread of v( : , i-1 ) with the spread of h( : , i in order to compute h( :, i ), we must update a( :, i ) Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side would naturally happen at the bottom of the loop) in order to Combine the spread of v( : , i-1 ) with the spread of h( : , i in order to compute h( :, i ), we must update a( :, i ) Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side would naturally happen at the bottom of the loop) in order to Combine the spread of v( : , i-1 ) with the spread of h( : , i in order to compute h( :, i ), we must update a( :, i ) Combine contribution to locally stored right hand side Combine contribution to locally stored right hand side |
| combined combined on entry, d contains the eigenvalues of the two submatrices to be combined (those which were deflated) sorted into increasing order. on entry, d contains the eigenvalues of the two submatrices to be combined (those which were deflated) sorted into increasing order. on entry, d contains the eigenvalues of the two submatrices to be combined (those which were deflated) sorted into increasing order. on entry, d contains the eigenvalues of the two submatrices to be combined (those which were deflated) sorted into increasing order. |
| comes comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes for large n, no extra workspace is needed, however the biggest boost in performance comes for small n, so i than a megabyte per process). for large n, no extra workspace is needed, however the biggest boost in performance comes for small n, so i than a megabyte per process). receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes for large n, no extra workspace is needed, however the biggest boost in performance comes for small n, so i than a megabyte per process). for large n, no extra workspace is needed, however the biggest boost in performance comes for small n, so i than a megabyte per process). receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes receive offdiagonal block from processor to right. if this is the first group of processors, the receive comes |
| comments comments such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a this is an auxiliary routine called by pcgehrd. in the following comments sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) arguments such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a error bounds on the solution and a condition estimate are also provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1 such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a will, in general, be reordered on output. see the comments in pdlaebz for more on the function n(w) nval (input/output) integer array, dimension (2*(kl-kf)) such a global array has an associated description vector desca. in the following comments, the character _ should be read a this is an auxiliary routine called by pdgehrd. in the following comments sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) arguments such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a error bounds on the solution and a condition estimate are also provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1 such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a will, in general, be reordered on output. see the comments in pslaebz for more on the function n(w) nval (input/output) integer array, dimension (2*(kl-kf)) such a global array has an associated description vector desca. in the following comments, the character _ should be read a this is an auxiliary routine called by psgehrd. in the following comments sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) arguments such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a error bounds on the solution and a condition estimate are also provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1 such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a in the following comments, the character _ should be read a block cyclicly distributed matrix. its description vector is desca: such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a this is an auxiliary routine called by pzgehrd. in the following comments sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) arguments such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a error bounds on the solution and a condition estimate are also provided. in the following comments y denotes y(iy:iy+m-1,jy:jy+k-1 such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a such a global array has an associated description vector desca. in the following comments, the character _ should be read a |
| common common nb_a ) where lcm is the least common multiple of proces end if lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th let lcm be the least common multiple of nprow and npcol if( nprow.eq.npcol ) then lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th nb_a ) where lcm is the least common multiple of proces end if lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th let lcm be the least common multiple of nprow and npcol if( nprow.eq.npcol ) then lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th nb_a ) where lcm is the least common multiple of proces end if lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th let lcm be the least common multiple of nprow and npcol if( nprow.eq.npcol ) then lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th nb_a ) where lcm is the least common multiple of proces end if lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th let lcm be the least common multiple of nprow and npcol if( nprow.eq.npcol ) then lcm(nprow,npcol) ) here lcm is least common multiple, and nprowxnpcol is th |
| communicate communicate ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares ****************************** reduced system has been solved, communicate solutions to neares |
| communicated communicated sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor sizes of the extra triangles communicated bewtween processor |
| communication communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication pclacon estimates the 1-norm of a square, complex distributed matrix a. reverse communication is used for evaluating matrix-vecto information is implicitly contained within iv, ix, descv, and descx. pclacp2 copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pclacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). pclacpy copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pclacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). finishing up. even if rotn=1, in order to minimize border communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so bot this is the unblocked form of the algorithm, calling level 2 blas. no communication is performed by this routine, the matrix to operat ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ===================================================================== pdlacon estimates the 1-norm of a square, real distributed matrix a. reverse communication is used for evaluating matrix-vector products is implicitly contained within iv, ix, descv, and descx. pdlacp2 copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pdlacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). pdlacpy copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pdlacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). this is the unblocked form of the algorithm, calling level 2 blas. no communication is performed by this routine, the matrix to operat ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ===================================================================== pslacon estimates the 1-norm of a square, real distributed matrix a. reverse communication is used for evaluating matrix-vector products is implicitly contained within iv, ix, descv, and descx. pslacp2 copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pslacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). pslacpy copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pslacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). this is the unblocked form of the algorithm, calling level 2 blas. no communication is performed by this routine, the matrix to operat ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication pzlacon estimates the 1-norm of a square, complex distributed matrix a. reverse communication is used for evaluating matrix-vecto information is implicitly contained within iv, ix, descv, and descx. pzlacp2 copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pzlacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). pzlacpy copies all or part of a distributed matrix a to another distributed matrix b. no communication is performed, pzlacp a(ia:ia+m-1,ja:ja+n-1) and sub( b ) denotes b(ib:ib+m-1,jb:jb+n-1). finishing up. even if rotn=1, in order to minimize border communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so bot this is the unblocked form of the algorithm, calling level 2 blas. no communication is performed by this routine, the matrix to operat ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication ja = ib alignment restriction that prevents unnecessary communication |
| compared compared the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. the divide and conqer algorithm assumes the matrix is narrowly banded compared with the number of equations. in this situation with columns atomic and rows divided amongst the processes. |
| comparison comparison following differs in comparison to pslahqr following differs in comparison to pdlahqr |
| compensate compensate amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. of the values computed by pdlamch. this subroutine is needed because pdlamch does not compensate for poor arithmetic in the upper half o amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. of the values computed by pslamch. this subroutine is needed because pslamch does not compensate for poor arithmetic in the upper half o amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. amount by which the eigenvalues should be scaled to compensate for the scaling performed in this routine returned here to allow for future enhancement. |
| compilation compilation the appropriate slmake.inc file to include the compiler switch -dno_ieee. this switch only affects the compilation of pslaiect.c arguments the appropriate slmake.inc file to include the compiler switch -dno_ieee. this switch only affects the compilation of pdlaiect.c arguments the appropriate slmake.inc file to include the compiler switch -dno_ieee. this switch only affects the compilation of pslaiect.c arguments the appropriate slmake.inc file to include the compiler switch -dno_ieee. this switch only affects the compilation of pdlaiect.c arguments |
| compile compile note : it is assumed that the user is on an ieee machine. if the user is not on an ieee mchine, set the compile time flag no_iee are needed for the "fast" sturm count are : (a) infinity note : it is assumed that the user is on an ieee machine. if the user is not on an ieee mchine, set the compile time flag no_iee are needed for the "fast" sturm count are : (a) infinity |
| compiler compiler to a system which does not have ieee 754 arithmetic, modify the appropriate slmake.inc file to include the compiler switc to a system which does not have ieee 754 arithmetic, modify the appropriate slmake.inc file to include the compiler switc to a system which does not have ieee 754 arithmetic, modify the appropriate slmake.inc file to include the compiler switc to a system which does not have ieee 754 arithmetic, modify the appropriate slmake.inc file to include the compiler switc |
| complete complete and columns is at the same place. for example, all rotn row transforms are all complete 3.) the majority of the row and column transforms if the matrix is hermitian, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is and columns is at the same place. for example, all rotn row transforms are all complete 3.) the majority of the row and column transforms if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is and columns is at the same place. for example, all rotn row transforms are all complete 3.) the majority of the row and column transforms if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is and columns is at the same place. for example, all rotn row transforms are all complete 3.) the majority of the row and column transforms if the matrix is hermitian, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is if the matrix is symmetric, we address only a triangular portion of the matrix. a sum of row (column) i of the complete matri triangular matrix, stopping/starting at the diagonal, which is |
| completed completed > 0: if info = +i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = +i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other nonsingular, and the factorization was not completed info-nprocs representing interactions with other > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor computed. <= n: u(ia+i-1,ia+i-1) is exactly zero. the factorization has been completed, but th and error bounds could not be computed. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. positive definite, and the factorization was not completed info-nprocs representing interactions with other a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== positive definite, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other nonsingular, and the factorization was not completed info-nprocs representing interactions with other > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor computed. <= n: u(ia+i-1,ia+i-1) is exactly zero. the factorization has been completed, but th and error bounds could not be computed. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. positive definite, and the factorization was not completed info-nprocs representing interactions with other a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== positive definite, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other nonsingular, and the factorization was not completed info-nprocs representing interactions with other > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor computed. <= n: u(ia+i-1,ia+i-1) is exactly zero. the factorization has been completed, but th and error bounds could not be computed. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. positive definite, and the factorization was not completed info-nprocs representing interactions with other a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== positive definite, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other diagonally dominant-like, and the factorization was not completed info-nprocs representing interactions with other nonsingular, and the factorization was not completed info-nprocs representing interactions with other > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor computed. <= n: u(ia+i-1,ia+i-1) is exactly zero. the factorization has been completed, but th and error bounds could not be computed. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. > 0: if info = k, u(ia+k-1,ja+k-1) is exactly zero. the factorization has been completed, but the factor it is used to solve a system of equations. positive definite, and the factorization was not completed info-nprocs representing interactions with other a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== positive definite, and the factorization was not completed info-nprocs representing interactions with other > 0: if info = +i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = +i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. > 0: if info = i, u(i,i) is exactly zero. the factorization has been completed, but the factor u is exactl to solve a system of equations. |
| completion completion matrix s was not originally in schur form. 0 indicates successful completion implemented by: g. henry, november 17, 1996 on output, work(1) returns the workspace needed to guarantee completion. if the input parameters are incorrect, work(1 on output rwork(1) returns the real workspace needed to guarantee completion. if the input parameters are incorrect version 1.0: on output, work(1) returns the workspace needed to guarantee completion incorrect. version 1.0: on output, work(1) returns the workspace needed to guarantee completion incorrect. on output, work(1) returns the workspace needed to guarantee completion. if the input parameters are incorrect, work(1 on output rwork(1) returns the real workspace needed to guarantee completion. if the input parameters are incorrect matrix s was not originally in schur form. 0 indicates successful completion implemented by: g. henry, november 17, 1996 |
| COMPLEX COMPLEX v1 (local input/local output) COMPLEX array o its global index. v1(1) = amax, v1(2) = indx. ab (input/output) COMPLEX array, dimension (ldab,n 2*kl+ku+1; rows 1 to kl of the array need not be set. cdttrf computes an lu factorization of a COMPLEX tridiagonal matrix dl (input) COMPLEX array, dimension (n-1 lu factorization of a. s (local input/output) COMPLEX array, ( lds,* is referenced. it is assumed that s has jblk double shifts clanv2 computes the schur factorization of a COMPLEX 2-by- a (global input/output) COMPLEX array, (lda,* the updated matrix on exit. e (input) COMPLEX array, dimension (n-1 factor u or l from the factorization computed by cpttrf t - COMPLEX array of dimension ( ldt, n ) upper triangular part of the array t must contain the upper ddttrf computes an lu factorization of a COMPLEX tridiagonal matrix dl (input) COMPLEX array, dimension (n-1 lu factorization of a. dlasorte sorts eigenpairs so that real eigenpairs are together and COMPLEX are together. this way one can employ 2x2 shifts easil this routine does no parallel work. e (input) COMPLEX array, dimension (n-1 factor u or l from the factorization computed by dpttrf where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. stored in a(1:n,ja:ja+n-1) and af by pcdbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. stored in a(1:n,ja:ja+n-1) and af by pcdttrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. stored in a(1:n,ja:ja+n-1) and af by pcgbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. pcgebd2 reduces a COMPLEX general m-by-n distributed matri form b by an unitary transformation: q' * sub( a ) * p = b. pcgebrd reduces a COMPLEX general m-by-n distributed matri form b by an unitary transformation: q' * sub( a ) * p = b. pcgecon estimates the reciprocal of the condition number of a general distributed COMPLEX matrix a(ia:ia+n-1,ja:ja+n-1), in either th pcgetrf. a (local input) COMPLEX pointer into the local memor local pieces of the m-by-n distributed matrix whose pcgehd2 reduces a COMPLEX general distributed matrix sub( a q' * sub( a ) * q = h, where pcgehrd reduces a COMPLEX general distributed matrix sub( a q' * sub( a ) * q = h, where pcgelq2 computes a lq factorization of a COMPLEX distributed m-by- pcgelqf computes a lq factorization of a COMPLEX distributed m-by- pcgels solves overdetermined or underdetermined COMPLEX linea or its conjugate-transpose, using a qr or lq factorization of pcgeql2 computes a ql factorization of a COMPLEX distributed m-by- pcgeqlf computes a ql factorization of a COMPLEX distributed m-by- a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the m-by-n distributed matrix pcgeqr2 computes a qr factorization of a COMPLEX distributed m-by- pcgeqrf computes a qr factorization of a COMPLEX distributed m-by- a (local input) COMPLEX pointer into the loca this array contains the local pieces of the distributed pcgerq2 computes a rq factorization of a COMPLEX distributed m-by- pcgerqf computes a rq factorization of a COMPLEX distributed m-by- pcgesv computes the solution to a COMPLEX system of linear equation sub( a ) * x = sub( b ), a (local input/workspace) block cyclic COMPLEX global dimension (m, n), local dimension (mp, nq) pcgesvx uses the lu factorization to compute the solution to a COMPLEX system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), a (local input/local output) COMPLEX pointer into th on entry, this array contains the local pieces of the m-by-n a (local input/local output) COMPLEX pointer into th on entry, this array contains the local pieces of the m-by-n a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the l and u obtained by the a (local input) COMPLEX pointer into the loca on entry, this array contains the local pieces of the factors a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the n-by-m distributed matrix a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the m-by-n distributed matrix a (local input/workspace) block cyclic COMPLEX array locc(ja+n-1) ) a (local input/workspace) block cyclic COMPLEX array locc(ja+n-1) ) pcheevx computes selected eigenvalues and, optionally, eigenvectors of a COMPLEX hermitian matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired pchegs2 reduces a COMPLEX hermitian-definite generalized eigenproble pchegst reduces a COMPLEX hermitian-definite generalized eigenproble the eigenvectors of a COMPLEX generalized hermitian-definite eigenproblem, of the for sub( b )*sub( a )*x=(lambda)*x. pchengst reduces a COMPLEX hermitian-definite generalize pchentrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pchetd2 reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pchetrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pchettrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pclabrd reduces the first nb rows and columns of a COMPLEX genera or lower bidiagonal form by an unitary transformation q' * a * p, and pclacgv conjugates a COMPLEX vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and pclacon estimates the 1-norm of a square, COMPLEX distributed matri products. x and v are aligned with the distributed matrix a, this a (global input) COMPLEX array, dimensio on entry, the hessenberg matrix whose tridiagonal part is a (local input) COMPLEX pointer into the local memor contains the local pieces of the distributed matrix sub( a ) a (global input/output) COMPLEX array, dimensio on entry, the parallel matrix to be copied into or from. a (local input) COMPLEX pointer into the local memor contains the local pieces of the distributed matrix sub( a ) z (local output) COMPLEX arra the eigenvectors on output. the eigenvectors are distributed pclahrd reduces the first nb columns of a COMPLEX genera elements below the k-th subdiagonal are zero. the reduction is a (local output) COMPLEX*16 pointer into th on output, a is replicated across all processes in a (local input) COMPLEX pointer into the local memor local pieces of the distributed matrix sub( a ). a (local input/local output) COMPLEX pointer into th on entry, this array contains the local pieces of the a (local input/local output) COMPLEX pointer into th on entry, this local array contains the local pieces of the a (local input/local output) COMPLEX pointer into th containing on entry the m-by-n matrix sub( a ). on exit, a (input/output) COMPLEX pointer into the loca on entry, the local pieces of the distributed symmetric pclarfb applies a COMPLEX block reflector q or its conjugat denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pclarfg generates a COMPLEX elementary reflector h of order n, suc pclarft forms the triangular factor t of a COMPLEX block reflector pclarzb applies a COMPLEX block reflector q or its conjugat denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pclarzt forms the triangular factor t of a COMPLEX block reflecto reflectors as returned by pctzrzf. pclascl multiplies the m-by-n COMPLEX distributed matrix sub( a is done without over/underflow as long as the final result alpha (global input) COMPLEX set. alpha (global input) COMPLEX set. a (global input) COMPLEX array, dimension (desca(lld_),* being scanned. x (input) COMPLEX x( i ) = x(ix+(jx-1)*m_x +(i-1)*incx ), 1 <= i <= n. a (local input/local output) COMPLEX pointer into th on entry, this array contains the local pieces of the distri- a (local input) COMPLEX pointer into the local memor contains the local pieces of the distributed matrix the trace pclatrd reduces nb rows and columns of a COMPLEX hermitia tridiagonal form by an unitary similarity transformation pclatrz reduces the m-by-n ( m<=n ) COMPLEX upper trapezoida to upper triangular form by means of unitary transformations. a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the triangular factor l or u. a (local input/local output) COMPLEX pointer into th on entry, the local pieces of the triangular factor l or u. a (global input) COMPLEX array, dimensio on entry, the hessenberg matrix. x (local input) COMPLEX array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bw. stored in a(1:n,ja:ja+n-1) and af by pcpbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bw. pcpocon estimates the reciprocal of the condition number (in the 1-norm) of a COMPLEX hermitian positive definite distributed matri pcpotrf. a (local input) COMPLEX pointer into the local memory to a n-by-n hermitian positive definite distributed matrix a (local input) COMPLEX pointer into the loca this array contains the local pieces of the n-by-n hermitian pcposv computes the solution to a COMPLEX system of linear equation sub( a ) * x = sub( b ), pcposvx uses the cholesky factorization a = u**h*u or a = l*l**h to compute the solution to a COMPLEX system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), pcpotf2 computes the cholesky factorization of a COMPLEX hermitia pcpotrf computes the cholesky factorization of an n-by-n COMPLEX a(ia:ia+n-1, ja:ja+n-1). pcpotri computes the inverse of a COMPLEX hermitian positive definit cholesky factorization sub( a ) = u**h*u or l*l**h computed by a (local input) COMPLEX pointer into local memory t array contains the factors l or u from the cholesky facto- where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. stored in a(1:n,ja:ja+n-1) and af by pcpttrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. pcsrscl multiplies an n-element COMPLEX distributed vecto underflow as long as the final sub( x )/a does not overflow or z (local output) COMPLEX array z contains the computed eigenvectors associated with the a (local input) COMPLEX pointer into the local memor contains the local pieces of the triangular distributed pctrevc computes some or all of the right and/or left eigenvectors of a COMPLEX upper triangular matrix t in parallel the right eigenvector x and the left eigenvector y of t corresponding a (local input) COMPLEX pointer into the local memor array contains the local pieces of the original triangular pctrti2 computes the inverse of a COMPLEX upper or lower triangula contained in one and only one process memory space (local operation). a (local input/local output) COMPLEX pointer into th on entry, this array contains the local pieces of the a (local input) COMPLEX pointer into the local memor contains the local pieces of the distributed triangular pctzrzf reduces the m-by-n ( m<=n ) COMPLEX upper trapezoidal matri of unitary transformations. pcung2l generates an m-by-n COMPLEX distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pcung2r generates an m-by-n COMPLEX distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pcungl2 generates an m-by-n COMPLEX distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pcunglq generates an m-by-n COMPLEX distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pcungql generates an m-by-n COMPLEX distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pcungqr generates an m-by-n COMPLEX distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pcungr2 generates an m-by-n COMPLEX distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pcungrq generates an m-by-n COMPLEX distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pcunm2l overwrites the general COMPLEX m-by-n distributed matri pcunm2r overwrites the general COMPLEX m-by-n distributed matri if vect = 'q', pcunmbr overwrites the general COMPLEX distribute pcunmhr overwrites the general COMPLEX m-by-n distributed matri pcunml2 overwrites the general COMPLEX m-by-n distributed matri pcunmlq overwrites the general COMPLEX m-by-n distributed matri pcunmql overwrites the general COMPLEX m-by-n distributed matri pcunmqr overwrites the general COMPLEX m-by-n distributed matri pcunmr2 overwrites the general COMPLEX m-by-n distributed matri pcunmr3 overwrites the general COMPLEX m-by-n distributed matri pcunmrq overwrites the general COMPLEX m-by-n distributed matri pcunmrz overwrites the general COMPLEX m-by-n distributed matri pcunmtr overwrites the general COMPLEX m-by-n distributed matri reference: n.j. higham, "fortran codes for estimating the one-norm of a real or COMPLEX matrix, with applications to condition estimation" a (local output) COMPLEX*16 pointer into th on output, a is replicated across all processes in pdsyngst reduces a COMPLEX hermitian-definite generalize pdsyttrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pdzsum1 returns the sum of absolute values of a COMPLEX pscsum1 returns the sum of absolute values of a COMPLEX reference: n.j. higham, "fortran codes for estimating the one-norm of a real or COMPLEX matrix, with applications to condition estimation" a (local output) COMPLEX*16 pointer into th on output, a is replicated across all processes in pssyngst reduces a COMPLEX hermitian-definite generalize pssyttrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. stored in a(1:n,ja:ja+n-1) and af by pzdbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. pzdrscl multiplies an n-element COMPLEX distributed vecto underflow as long as the final sub( x )/a does not overflow or where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. stored in a(1:n,ja:ja+n-1) and af by pzdttrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. stored in a(1:n,ja:ja+n-1) and af by pzgbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bwl, bwu. pzgebd2 reduces a COMPLEX general m-by-n distributed matri form b by an unitary transformation: q' * sub( a ) * p = b. pzgebrd reduces a COMPLEX general m-by-n distributed matri form b by an unitary transformation: q' * sub( a ) * p = b. pzgecon estimates the reciprocal of the condition number of a general distributed COMPLEX matrix a(ia:ia+n-1,ja:ja+n-1), in either th pzgetrf. a (local input) COMPLEX*16 pointer into the local memor local pieces of the m-by-n distributed matrix whose pzgehd2 reduces a COMPLEX general distributed matrix sub( a q' * sub( a ) * q = h, where pzgehrd reduces a COMPLEX general distributed matrix sub( a q' * sub( a ) * q = h, where pzgelq2 computes a lq factorization of a COMPLEX distributed m-by- pzgelqf computes a lq factorization of a COMPLEX distributed m-by- pzgels solves overdetermined or underdetermined COMPLEX linea or its conjugate-transpose, using a qr or lq factorization of pzgeql2 computes a ql factorization of a COMPLEX distributed m-by- pzgeqlf computes a ql factorization of a COMPLEX distributed m-by- a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the m-by-n distributed matrix pzgeqr2 computes a qr factorization of a COMPLEX distributed m-by- pzgeqrf computes a qr factorization of a COMPLEX distributed m-by- a (local input) COMPLEX*16 pointer into the loca this array contains the local pieces of the distributed pzgerq2 computes a rq factorization of a COMPLEX distributed m-by- pzgerqf computes a rq factorization of a COMPLEX distributed m-by- pzgesv computes the solution to a COMPLEX system of linear equation sub( a ) * x = sub( b ), a (local input/workspace) block cyclic COMPLEX*1 global dimension (m, n), local dimension (mp, nq) pzgesvx uses the lu factorization to compute the solution to a COMPLEX system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), a (local input/local output) COMPLEX*16 pointer into th on entry, this array contains the local pieces of the m-by-n a (local input/local output) COMPLEX*16 pointer into th on entry, this array contains the local pieces of the m-by-n a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the l and u obtained by the a (local input) COMPLEX*16 pointer into the loca on entry, this array contains the local pieces of the factors a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the n-by-m distributed matrix a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the m-by-n distributed matrix a (local input/workspace) block cyclic COMPLEX*16 array locc(ja+n-1) ) a (local input/workspace) block cyclic COMPLEX*16 array locc(ja+n-1) ) pzheevx computes selected eigenvalues and, optionally, eigenvectors of a COMPLEX hermitian matrix a by calling the recommended sequenc specifying a range of values or a range of indices for the desired pzhegs2 reduces a COMPLEX hermitian-definite generalized eigenproble pzhegst reduces a COMPLEX hermitian-definite generalized eigenproble the eigenvectors of a COMPLEX generalized hermitian-definite eigenproblem, of the for sub( b )*sub( a )*x=(lambda)*x. pzhengst reduces a COMPLEX hermitian-definite generalize pzhentrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzhetd2 reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzhetrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzhettrd reduces a COMPLEX hermitian matrix sub( a ) to hermitia q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzlabrd reduces the first nb rows and columns of a COMPLEX genera or lower bidiagonal form by an unitary transformation q' * a * p, and pzlacgv conjugates a COMPLEX vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and pzlacon estimates the 1-norm of a square, COMPLEX distributed matri products. x and v are aligned with the distributed matrix a, this a (global input) COMPLEX*16 array, dimensio on entry, the hessenberg matrix whose tridiagonal part is a (local input) COMPLEX*16 pointer into the local memor contains the local pieces of the distributed matrix sub( a ) a (global input/output) COMPLEX*16 array, dimensio on entry, the parallel matrix to be copied into or from. a (local input) COMPLEX*16 pointer into the local memor contains the local pieces of the distributed matrix sub( a ) z (local output) COMPLEX*16 arra the eigenvectors on output. the eigenvectors are distributed pzlahrd reduces the first nb columns of a COMPLEX genera elements below the k-th subdiagonal are zero. the reduction is a (local output) COMPLEX*16 pointer into th on output, a is replicated across all processes in a (local input) COMPLEX*16 pointer into the local memor local pieces of the distributed matrix sub( a ). a (local input/local output) COMPLEX*16 pointer into th on entry, this array contains the local pieces of the a (local input/local output) COMPLEX*16 pointer into th on entry, this local array contains the local pieces of the a (local input/local output) COMPLEX*16 pointer into th containing on entry the m-by-n matrix sub( a ). on exit, a (input/output) COMPLEX*16 pointer into the loca on entry, the local pieces of the distributed symmetric pzlarfb applies a COMPLEX block reflector q or its conjugat denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pzlarfg generates a COMPLEX elementary reflector h of order n, suc pzlarft forms the triangular factor t of a COMPLEX block reflector pzlarzb applies a COMPLEX block reflector q or its conjugat denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pzlarzt forms the triangular factor t of a COMPLEX block reflecto reflectors as returned by pztzrzf. pzlascl multiplies the m-by-n COMPLEX distributed matrix sub( a is done without over/underflow as long as the final result alpha (global input) COMPLEX*1 set. alpha (global input) COMPLEX*1 set. a (global input) COMPLEX*16 array, dimension (desca(lld_),* being scanned. x (input) COMPLEX*1 x( i ) = x(ix+(jx-1)*m_x +(i-1)*incx ), 1 <= i <= n. a (local input/local output) COMPLEX*16 pointer into th on entry, this array contains the local pieces of the distri- a (local input) COMPLEX*16 pointer into the local memor contains the local pieces of the distributed matrix the trace pzlatrd reduces nb rows and columns of a COMPLEX hermitia tridiagonal form by an unitary similarity transformation pzlatrz reduces the m-by-n ( m<=n ) COMPLEX upper trapezoida to upper triangular form by means of unitary transformations. a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the triangular factor l or u. a (local input/local output) COMPLEX*16 pointer into th on entry, the local pieces of the triangular factor l or u. a (global input) COMPLEX*16 array, dimensio on entry, the hessenberg matrix. x (local input) COMPLEX*16 array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bw. stored in a(1:n,ja:ja+n-1) and af by pzpbtrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix with bandwidth bw. pzpocon estimates the reciprocal of the condition number (in the 1-norm) of a COMPLEX hermitian positive definite distributed matri pzpotrf. a (local input) COMPLEX*16 pointer into the local memory to a n-by-n hermitian positive definite distributed matrix a (local input) COMPLEX*16 pointer into the loca this array contains the local pieces of the n-by-n hermitian pzposv computes the solution to a COMPLEX system of linear equation sub( a ) * x = sub( b ), pzposvx uses the cholesky factorization a = u**h*u or a = l*l**h to compute the solution to a COMPLEX system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), pzpotf2 computes the cholesky factorization of a COMPLEX hermitia pzpotrf computes the cholesky factorization of an n-by-n COMPLEX a(ia:ia+n-1, ja:ja+n-1). pzpotri computes the inverse of a COMPLEX hermitian positive definit cholesky factorization sub( a ) = u**h*u or l*l**h computed by a (local input) COMPLEX*16 pointer into local memory t array contains the factors l or u from the cholesky facto- where a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. stored in a(1:n,ja:ja+n-1) and af by pzpttrf. a(1:n, ja:ja+n-1) is an n-by-n COMPLEX matrix. z (local output) COMPLEX*16 array z contains the computed eigenvectors associated with the a (local input) COMPLEX*16 pointer into the local memor contains the local pieces of the triangular distributed pztrevc computes some or all of the right and/or left eigenvectors of a COMPLEX upper triangular matrix t in parallel the right eigenvector x and the left eigenvector y of t corresponding a (local input) COMPLEX*16 pointer into the local memor array contains the local pieces of the original triangular pztrti2 computes the inverse of a COMPLEX upper or lower triangula contained in one and only one process memory space (local operation). a (local input/local output) COMPLEX*16 pointer into th on entry, this array contains the local pieces of the a (local input) COMPLEX*16 pointer into the local memor contains the local pieces of the distributed triangular pztzrzf reduces the m-by-n ( m<=n ) COMPLEX upper trapezoidal matri of unitary transformations. pzung2l generates an m-by-n COMPLEX distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pzung2r generates an m-by-n COMPLEX distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pzungl2 generates an m-by-n COMPLEX distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pzunglq generates an m-by-n COMPLEX distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pzungql generates an m-by-n COMPLEX distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pzungqr generates an m-by-n COMPLEX distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pzungr2 generates an m-by-n COMPLEX distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pzungrq generates an m-by-n COMPLEX distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pzunm2l overwrites the general COMPLEX m-by-n distributed matri pzunm2r overwrites the general COMPLEX m-by-n distributed matri if vect = 'q', pzunmbr overwrites the general COMPLEX distribute pzunmhr overwrites the general COMPLEX m-by-n distributed matri pzunml2 overwrites the general COMPLEX m-by-n distributed matri pzunmlq overwrites the general COMPLEX m-by-n distributed matri pzunmql overwrites the general COMPLEX m-by-n distributed matri pzunmqr overwrites the general COMPLEX m-by-n distributed matri pzunmr2 overwrites the general COMPLEX m-by-n distributed matri pzunmr3 overwrites the general COMPLEX m-by-n distributed matri pzunmrq overwrites the general COMPLEX m-by-n distributed matri pzunmrz overwrites the general COMPLEX m-by-n distributed matri pzunmtr overwrites the general COMPLEX m-by-n distributed matri sdttrf computes an lu factorization of a COMPLEX tridiagonal matrix dl (input) COMPLEX array, dimension (n-1 lu factorization of a. slasorte sorts eigenpairs so that real eigenpairs are together and COMPLEX are together. this way one can employ 2x2 shifts easil this routine does no parallel work. e (input) COMPLEX array, dimension (n-1 factor u or l from the factorization computed by spttrf v1 (local input/local output) COMPLEX*16 array o its global index. v1(1) = amax, v1(2) = indx. ab (input/output) COMPLEX*16 array, dimension (ldab,n 2*kl+ku+1; rows 1 to kl of the array need not be set. zdttrf computes an lu factorization of a COMPLEX tridiagonal matrix dl (input) COMPLEX array, dimension (n-1 lu factorization of a. s (local input/output) COMPLEX*16 array, ( lds,* is referenced. it is assumed that s has jblk double shifts zlanv2 computes the schur factorization of a COMPLEX 2-by- a (global input/output) COMPLEX*16 array, (lda,* the updated matrix on exit. e (input) COMPLEX array, dimension (n-1 factor u or l from the factorization computed by zpttrf t - COMPLEX*16 array of dimension ( ldt, n ) upper triangular part of the array t must contain the upper |
| compliant compliant pcheevx assumes ieee 754 standard compliant arithmetic. to por the appropriate slmake.inc file to include the compiler switch pdsyevx assumes ieee 754 standard compliant arithmetic. to por the appropriate slmake.inc file to include the compiler switch pssyevx assumes ieee 754 standard compliant arithmetic. to por the appropriate slmake.inc file to include the compiler switch pzheevx assumes ieee 754 standard compliant arithmetic. to por the appropriate slmake.inc file to include the compiler switch |
| complicated complicated furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande |
| component component n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) |
| components components scale x so that its components are less than or equal t n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) n (global input) pointer to integer the number of components of the distributed vector sub( x ) scale x so that its components are less than or equal t n (global input) pointer to integer the number of components of the distributed vector sub( x ) |
| componentwise componentwise berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) real array, dimension locc(n_b). the componentwise relative backward error of each solutio any entry of a(ia:ia+n-1,ja:ja+n-1) or berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) real array, dimension (loc(n_b)) the componentwise relative backward error of each solutio any entry of a or b that makes x(j) an exact solution). berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array, dimension locc(n_b). the componentwise relative backward error of each solutio any entry of a(ia:ia+n-1,ja:ja+n-1) or berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array, dimension (loc(n_b)) the componentwise relative backward error of each solutio any entry of a or b that makes x(j) an exact solution). berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) real array, dimension locc(n_b). the componentwise relative backward error of each solutio any entry of a(ia:ia+n-1,ja:ja+n-1) or berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) real array, dimension (loc(n_b)) the componentwise relative backward error of each solutio any entry of a or b that makes x(j) an exact solution). berr (local output) real array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array, dimension locc(n_b). the componentwise relative backward error of each solutio any entry of a(ia:ia+n-1,ja:ja+n-1) or berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) berr (local output) double precision array, dimension (loc(n_b)) the componentwise relative backward error of each solutio any entry of a or b that makes x(j) an exact solution). berr (local output) double precision array of local dimension locc(jb+nrhs-1). the componentwise relative backwar lative change in any entry of sub( a ) or sub( b ) |
| Comput Comput pcheevd Computes all the eigenvalues and eigenvectors of a hermitia ======= pdstedc Computes all eigenvalues and eigenvectors of conquer algorithm. pdsyevd Computes all the eigenvalues and eigenvector of scalapack routines. ======= psstedc Computes all eigenvalues and eigenvectors of conquer algorithm. pssyevd Computes all the eigenvalues and eigenvector of scalapack routines. pzheevd Computes all the eigenvalues and eigenvectors of a hermitia |
| computation computation nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. on output, work(1) returns the workspace needed for the computation lwork (local input) integer insufficient space and pcheevx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) insufficient space and pchegvx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) mvr2) followed by a transpose and a sum across the columns. in the local computation, work( invt ) is used to comput v^t * tril(a,-1) perform the local computation within a process colum perform the local computation within a process colum perform the local computation within a process colum perform the local computation within a process colum nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ictxt (global input) integer the blacs context handle in which the computation take ijob (input) integer specifies the computation done by pdlaeb endpoints of the interval. ictxt (global input) integer the blacs context handle in which the computation take perform the local computation within a process colum perform the local computation within a process colum nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas insufficient space and pdsyevx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) insufficient space and pdsygvx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) mvr2) followed by a transpose and a sum across the columns. in the local computation, work( invt ) is used to comput v^t * tril(a,-1) nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ictxt (global input) integer the blacs context handle in which the computation take ijob (input) integer specifies the computation done by pslaeb endpoints of the interval. ictxt (global input) integer the blacs context handle in which the computation take perform the local computation within a process colum perform the local computation within a process colum nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas insufficient space and pssyevx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) insufficient space and pssygvx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) mvr2) followed by a transpose and a sum across the columns. in the local computation, work( invt ) is used to comput v^t * tril(a,-1) nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*max(bwl,bwu) the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= (bwl+bwu)+1 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. on output, work(1) returns the workspace needed for the computation lwork (local input) integer insufficient space and pzheevx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) insufficient space and pzhegvx is not able to detect this before beginning computation. to get all the eigenvector space to hold the eigenvectors in z (m .le. descz(n_)) mvr2) followed by a transpose and a sum across the columns. in the local computation, work( invt ) is used to comput v^t * tril(a,-1) perform the local computation within a process colum perform the local computation within a process colum perform the local computation within a process colum perform the local computation within a process colum nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2*bw the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ******************************************************************* phase 1: local computation phase nb >= 2 the bulk of parallel computation is done on the matrix of siz is a poor choice of algorithm. ***************************************** local computation phas |
| compute compute j2 and j3 are computed after ju has been updated factorize the current block of jb columns if remaining matrix is 2-by-2, use slae2 or slaev2 to compute its eigensystem j2 and j3 are computed after ju has been updated factorize the current block of jb columns compute eigenvectors of matrix blocks compute the eigenvalues and eigenvectors of the tridiagona save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n pcgesvx uses the lu factorization to compute the solution to pcheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pcheevd computes all the eigenvalues and eigenvectors of a hermitia pcheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pchegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a compute the 1-norm of each column, not including the diagonal save and compute new value of n save and compute new value of n pcposvx uses the cholesky factorization a = u**h*u or a = l*l**h to compute the solution to a complex system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), save and compute new value of n save and compute new value of n pctrevc computes some or all of the right and/or left eigenvectors o save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n pdgesvx uses the lu factorization to compute the solution to a rea pdlaed0 computes all eigenvalues and corresponding eigenvectors of pdlaed1 computes the updated eigensystem of a diagona in parallel. < 0: if info = -i, the i-th argument had an illegal value. > 0: the algorithm failed to compute the ith eigenvalue ===================================================================== save and compute new value of n save and compute new value of n pdposvx uses the cholesky factorization a = u**t*u or a = l*l**t to compute the solution to a real system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), save and compute new value of n save and compute new value of n ======= pdstedc computes all eigenvalues and eigenvectors of conquer algorithm. pdsyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pdsyevd computes all the eigenvalues and eigenvector of scalapack routines. pdsyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdsygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n psgesvx uses the lu factorization to compute the solution to a rea pslaed0 computes all eigenvalues and corresponding eigenvectors of pslaed1 computes the updated eigensystem of a diagona in parallel. < 0: if info = -i, the i-th argument had an illegal value. > 0: the algorithm failed to compute the ith eigenvalue ===================================================================== save and compute new value of n save and compute new value of n psposvx uses the cholesky factorization a = u**t*u or a = l*l**t to compute the solution to a real system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), save and compute new value of n save and compute new value of n ======= psstedc computes all eigenvalues and eigenvectors of conquer algorithm. pssyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pssyevd computes all the eigenvalues and eigenvector of scalapack routines. pssyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pssygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n save and compute new value of n pzgesvx uses the lu factorization to compute the solution to pzheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pzheevd computes all the eigenvalues and eigenvectors of a hermitia pzheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pzhegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a compute the 1-norm of each column, not including the diagonal save and compute new value of n save and compute new value of n pzposvx uses the cholesky factorization a = u**h*u or a = l*l**h to compute the solution to a complex system of linear equation a(ia:ia+n-1,ja:ja+n-1) * x = b(ib:ib+n-1,jb:jb+nrhs-1), save and compute new value of n save and compute new value of n pztrevc computes some or all of the right and/or left eigenvectors o j2 and j3 are computed after ju has been updated factorize the current block of jb columns compute eigenvectors of matrix blocks compute the eigenvalues and eigenvectors of the tridiagona j2 and j3 are computed after ju has been updated factorize the current block of jb columns if remaining matrix is 2-by-2, use dlae2 or dlaev2 to compute its eigensystem |
| computed computed j2 and j3 are computed after ju has been updated factorize the current block of jb columns with factors of the tridiagonal matrix a from the lu factorization computed by cdttrf arguments to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop definite tridiagonal matrix a such that a = u**h*d*u or a = l*d*l**h (computed by cpttrf) arguments j2 and j3 are computed after ju has been updated factorize the current block of jb columns with factors of the tridiagonal matrix a from the lu factorization computed by ddttrf arguments definite tridiagonal matrix a such that a = l*d*l**h (computed by dpttrf) arguments locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed complex matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-norm or the infinity-norm, using the lu factorization computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pcgerfs improves the computed solution to a system of linea the solutions. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a only the first min(m,n) columns of u and rows of vt = v**t are computed notes locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pcgetri computes the inverse of a distributed matrix using the lu factorization computed by pcgetrf. this method inverts u and the inva by solving the system inva*l = inv(u) for inva. with a general n-by-n distributed matrix sub( a ) using the lu factorization computed by pcgetrf and sub( b ) denotes b(ib:ib+n-1,jb:jb+nrhs-1). locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pclaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cycli to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a compute a bound on the computed solution vector to see if th locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'*u or l*l' as computed by pcpbtrf routine pcpbtrf must be called first. 1-norm) of a complex hermitian positive definite distributed matrix using the cholesky factorization a = u**h*u or a = l*l**h computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pcporfs improves the computed solution to a system of linea and provides error bounds and backward error estimates for the locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the cholesky factorization sub( a ) = u**h*u or l*l**h computed b hermitian positive definite distributed matrix using the cholesky factorization sub( a ) = u**h*u or l*l**h computed by pcpotrf locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'd *u or l*d l' as computed by pcpttrf routine pcpttrf must be called first. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a of orthogonalization is controlled by the input parameter lwork. eigenvectors that are to be orthogonalized are computed by the sam processes and then calls sstein2 (modified lapack routine) on each the norm of a(ia:ia+n-1,ja:ja+n-1) is computed and an estimate i of the condition number is computed as locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a the solution matrix x must be computed by pctrtrs or some othe refinement because doing so cannot improve the backward error. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed real matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-norm or the infinity-norm, using the lu factorization computed by pdgetrf an estimate is obtained for norm(inv(a(ia:ia+n-1,ja:ja+n-1))), and locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdgerfs improves the computed solution to a system of linea the solutions. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a only the first min(m,n) columns of u and rows of vt = v**t are computed notes locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdgetri computes the inverse of a distributed matrix using the lu factorization computed by pdgetrf. this method inverts u and the inva by solving the system inva*l = inv(u) for inva. with a general n-by-n distributed matrix sub( a ) using the lu factorization computed by pdgetrf sub( b ) denotes b(ib:ib+n-1,jb:jb+nrhs-1). locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdlabad takes as input the values computed by pdlamch for underflo the log of large is sufficiently large. this subroutine is intended locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdlaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cycli to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'*u or l*l' as computed by pdpbtrf routine pdpbtrf must be called first. 1-norm) of a real symmetric positive definite distributed matrix using the cholesky factorization a = u**t*u or a = l*l**t computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pdporfs improves the computed solution to a system of linea and provides error bounds and backward error estimates for the locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the cholesky factorization sub( a ) = u**t*u or l*l**t computed b symmetric positive definite distributed matrix using the cholesky factorization sub( a ) = u**t*u or l*l**t computed by pdpotrf locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a will be used, where |t| means the 1-norm of t. eigenvalues will be computed most accurately when abstol i note : if eigenvectors are desired later by inverse iteration of orthogonalization is controlled by the input parameter lwork. eigenvectors that are to be orthogonalized are computed by the sam processes and then calls dstein2 (modified lapack routine) on each the amount of workspace required to guarantee that all eigenvectors are computed is qrmem = 2*n-2 locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a the norm of a(ia:ia+n-1,ja:ja+n-1) is computed and an estimate i of the condition number is computed as the solution matrix x must be computed by pdtrtrs or some othe refinement because doing so cannot improve the backward error. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed real matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-norm or the infinity-norm, using the lu factorization computed by psgetrf an estimate is obtained for norm(inv(a(ia:ia+n-1,ja:ja+n-1))), and locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a psgerfs improves the computed solution to a system of linea the solutions. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a only the first min(m,n) columns of u and rows of vt = v**t are computed notes locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a psgetri computes the inverse of a distributed matrix using the lu factorization computed by psgetrf. this method inverts u and the inva by solving the system inva*l = inv(u) for inva. with a general n-by-n distributed matrix sub( a ) using the lu factorization computed by psgetrf sub( b ) denotes b(ib:ib+n-1,jb:jb+nrhs-1). locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pslabad takes as input the values computed by pslamch for underflo the log of large is sufficiently large. this subroutine is intended locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pslaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cycli to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'*u or l*l' as computed by pspbtrf routine pspbtrf must be called first. 1-norm) of a real symmetric positive definite distributed matrix using the cholesky factorization a = u**t*u or a = l*l**t computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a psporfs improves the computed solution to a system of linea and provides error bounds and backward error estimates for the locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the cholesky factorization sub( a ) = u**t*u or l*l**t computed b symmetric positive definite distributed matrix using the cholesky factorization sub( a ) = u**t*u or l*l**t computed by pspotrf locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a will be used, where |t| means the 1-norm of t. eigenvalues will be computed most accurately when abstol i note : if eigenvectors are desired later by inverse iteration of orthogonalization is controlled by the input parameter lwork. eigenvectors that are to be orthogonalized are computed by the sam processes and then calls sstein2 (modified lapack routine) on each the amount of workspace required to guarantee that all eigenvectors are computed is qrmem = 2*n-2 locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a the norm of a(ia:ia+n-1,ja:ja+n-1) is computed and an estimate i of the condition number is computed as the solution matrix x must be computed by pstrtrs or some othe refinement because doing so cannot improve the backward error. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed complex matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-norm or the infinity-norm, using the lu factorization computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pzgerfs improves the computed solution to a system of linea the solutions. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a only the first min(m,n) columns of u and rows of vt = v**t are computed notes locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pzgetri computes the inverse of a distributed matrix using the lu factorization computed by pzgetrf. this method inverts u and the inva by solving the system inva*l = inv(u) for inva. with a general n-by-n distributed matrix sub( a ) using the lu factorization computed by pzgetrf and sub( b ) denotes b(ib:ib+n-1,jb:jb+nrhs-1). locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locq( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locq( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pzlaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cycli to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a compute a bound on the computed solution vector to see if th locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'*u or l*l' as computed by pzpbtrf routine pzpbtrf must be called first. 1-norm) of a complex hermitian positive definite distributed matrix using the cholesky factorization a = u**h*u or a = l*l**h computed b locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a pzporfs improves the computed solution to a system of linea and provides error bounds and backward error estimates for the locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using the cholesky factorization sub( a ) = u**h*u or l*l**h computed b hermitian positive definite distributed matrix using the cholesky factorization sub( a ) = u**h*u or l*l**h computed by pzpotrf locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a depending on the value of uplo, a stores either u or l in the equn a(1:n, ja:ja+n-1) = u'd *u or l*d l' as computed by pzpttrf routine pzpttrf must be called first. of orthogonalization is controlled by the input parameter lwork. eigenvectors that are to be orthogonalized are computed by the sam processes and then calls dstein2 (modified lapack routine) on each the norm of a(ia:ia+n-1,ja:ja+n-1) is computed and an estimate i of the condition number is computed as locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a the solution matrix x must be computed by pztrtrs or some othe refinement because doing so cannot improve the backward error. locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a locc( n ) = numroc( n, nb_a, mycol, csrc_a, npcol ). an upper bound for these quantities may be computed by locc( n ) <= ceil( ceil(n/nb_a)/npcol )*nb_a j2 and j3 are computed after ju has been updated factorize the current block of jb columns with factors of the tridiagonal matrix a from the lu factorization computed by sdttrf arguments definite tridiagonal matrix a such that a = l*d*l**h (computed by spttrf) arguments j2 and j3 are computed after ju has been updated factorize the current block of jb columns with factors of the tridiagonal matrix a from the lu factorization computed by zdttrf arguments to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop definite tridiagonal matrix a such that a = u**h*d*u or a = l*d*l**h (computed by zpttrf) arguments |
| Computer Computer see w. kahan "accurate eigenvalues of a symmetric tridiagonal matrix", report cs41, Computer science dept., stanfor see w. kahan "accurate eigenvalues of a symmetric tridiagonal matrix", report cs41, Computer science dept., stanfor |
| computers computers but not optimal, performance on many of the currently available computers. users are encouraged to modify this subroutine to se and problem size information in the arguments. |
| computes computes cdbtrf computes an lu factorization of a real m-by-n band matrix cdttrf computes an lu factorization of a complex tridiagonal matrix clanv2 computes the schur factorization of a complex 2-by- ddbtrf computes an lu factorization of a real m-by-n band matrix ddttrf computes an lu factorization of a complex tridiagonal matrix pcgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c pcgelq2 computes a lq factorization of a complex distributed m-by- pcgelqf computes a lq factorization of a complex distributed m-by- pcgeql2 computes a ql factorization of a complex distributed m-by- pcgeqlf computes a ql factorization of a complex distributed m-by- pcgeqpf computes a qr factorization with column pivoting of pcgeqr2 computes a qr factorization of a complex distributed m-by- pcgeqrf computes a qr factorization of a complex distributed m-by- pcgerq2 computes a rq factorization of a complex distributed m-by- pcgerqf computes a rq factorization of a complex distributed m-by- pcgesv computes the solution to a complex system of linear equation sub( a ) * x = sub( b ), pcgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as pcgetf2 computes an lu factorization of a general m-by- partial pivoting with row interchanges. pcgetrf computes an lu factorization of a general m-by-n distribute row interchanges. pcgetri computes the inverse of a distributed matrix using the l computes the inverse of sub( a ) = a(ia:ia+n-1,ja:ja+n-1) denoted pcggqrf computes a generalized qr factorization o an n-by-p matrix sub( b ) = b(ib:ib+n-1,jb:jb+p-1): pcggrqf computes a generalized rq factorization o and a p-by-n matrix sub( b ) = b(ib:ib+p-1,jb:jb+n-1): pcheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pcheevd computes all the eigenvalues and eigenvectors of a hermitia pcheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by pchegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form = 'f' (forward) applies pivots forward from top of matrix. computes p*sub( a ) matrix. computes inv( p )*sub( a ). = 'f' (forward) applies pivots forward from top of matrix. computes p * sub( a ) matrix. computes inv( p ) * sub( a ). pclatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pclauu2 computes the product u * u' or l' * l, where the triangula the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pclauum computes the product u * u' or l' * l, where the triangula the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pcmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, pcpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number pcposv computes the solution to a complex system of linear equation sub( a ) * x = sub( b ), pcpotf2 computes the cholesky factorization of a complex hermitia pcpotrf computes the cholesky factorization of an n-by-n comple a(ia:ia+n-1, ja:ja+n-1). pcpotri computes the inverse of a complex hermitian positive definit cholesky factorization sub( a ) = u**h*u or l*l**h computed by pcstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pcstein does not pctrevc computes some or all of the right and/or left eigenvectors o pctrti2 computes the inverse of a complex upper or lower triangula contained in one and only one process memory space (local operation). pctrtri computes the inverse of a upper or lower triangula pdgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c pdgelq2 computes a lq factorization of a real distributed m-by- pdgelqf computes a lq factorization of a real distributed m-by- pdgeql2 computes a ql factorization of a real distributed m-by- pdgeqlf computes a ql factorization of a real distributed m-by- pdgeqpf computes a qr factorization with column pivoting of pdgeqr2 computes a qr factorization of a real distributed m-by- pdgeqrf computes a qr factorization of a real distributed m-by- pdgerq2 computes a rq factorization of a real distributed m-by- pdgerqf computes a rq factorization of a real distributed m-by- pdgesv computes the solution to a real system of linear equation sub( a ) * x = sub( b ), pdgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as pdgetf2 computes an lu factorization of a general m-by- partial pivoting with row interchanges. pdgetrf computes an lu factorization of a general m-by-n distribute row interchanges. pdgetri computes the inverse of a distributed matrix using the l computes the inverse of sub( a ) = a(ia:ia+n-1,ja:ja+n-1) denoted pdggqrf computes a generalized qr factorization o an n-by-p matrix sub( b ) = b(ib:ib+n-1,jb:jb+p-1): pdggrqf computes a generalized rq factorization o and a p-by-n matrix sub( b ) = b(ib:ib+p-1,jb:jb+n-1): pdlaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is pdlaed0 computes all eigenvalues and corresponding eigenvectors of pdlaed1 computes the updated eigensystem of a diagona in parallel. = 'f' (forward) applies pivots forward from top of matrix. computes p*sub( a ) matrix. computes inv( p )*sub( a ). = 'f' (forward) applies pivots forward from top of matrix. computes p * sub( a ) matrix. computes inv( p ) * sub( a ). pdlatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pdlauu2 computes the product u * u' or l' * l, where the triangula the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pdlauum computes the product u * u' or l' * l, where the triangula the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pdpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number pdposv computes the solution to a real system of linear equation sub( a ) * x = sub( b ), pdpotf2 computes the cholesky factorization of a real symmetri pdpotrf computes the cholesky factorization of an n-by-n rea a(ia:ia+n-1, ja:ja+n-1). pdpotri computes the inverse of a real symmetric positive definit cholesky factorization sub( a ) = u**t*u or l*l**t computed by pdstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a ======= pdstedc computes all eigenvalues and eigenvectors of conquer algorithm. pdstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pdstein does not pdsyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pdsyevd computes all the eigenvalues and eigenvector of scalapack routines. pdsyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by pdsygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form pdtrti2 computes the inverse of a real upper or lower triangula contained in one and only one process memory space (local operation). pdtrtri computes the inverse of a upper or lower triangula psgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c psgelq2 computes a lq factorization of a real distributed m-by- psgelqf computes a lq factorization of a real distributed m-by- psgeql2 computes a ql factorization of a real distributed m-by- psgeqlf computes a ql factorization of a real distributed m-by- psgeqpf computes a qr factorization with column pivoting of psgeqr2 computes a qr factorization of a real distributed m-by- psgeqrf computes a qr factorization of a real distributed m-by- psgerq2 computes a rq factorization of a real distributed m-by- psgerqf computes a rq factorization of a real distributed m-by- psgesv computes the solution to a real system of linear equation sub( a ) * x = sub( b ), psgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as psgetf2 computes an lu factorization of a general m-by- partial pivoting with row interchanges. psgetrf computes an lu factorization of a general m-by-n distribute row interchanges. psgetri computes the inverse of a distributed matrix using the l computes the inverse of sub( a ) = a(ia:ia+n-1,ja:ja+n-1) denoted psggqrf computes a generalized qr factorization o an n-by-p matrix sub( b ) = b(ib:ib+n-1,jb:jb+p-1): psggrqf computes a generalized rq factorization o and a p-by-n matrix sub( b ) = b(ib:ib+p-1,jb:jb+n-1): pslaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is pslaed0 computes all eigenvalues and corresponding eigenvectors of pslaed1 computes the updated eigensystem of a diagona in parallel. = 'f' (forward) applies pivots forward from top of matrix. computes p*sub( a ) matrix. computes inv( p )*sub( a ). = 'f' (forward) applies pivots forward from top of matrix. computes p * sub( a ) matrix. computes inv( p ) * sub( a ). pslatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pslauu2 computes the product u * u' or l' * l, where the triangula the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pslauum computes the product u * u' or l' * l, where the triangula the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pspoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number psposv computes the solution to a real system of linear equation sub( a ) * x = sub( b ), pspotf2 computes the cholesky factorization of a real symmetri pspotrf computes the cholesky factorization of an n-by-n rea a(ia:ia+n-1, ja:ja+n-1). pspotri computes the inverse of a real symmetric positive definit cholesky factorization sub( a ) = u**t*u or l*l**t computed by psstebz computes the eigenvalues of a symmetric tridiagonal matrix i the interval [vl, vu], or the eigenvalues indexed il through iu. a ======= psstedc computes all eigenvalues and eigenvectors of conquer algorithm. psstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. psstein does not pssyev computes all eigenvalues and, optionally, eigenvector of scalapack routines. pssyevd computes all the eigenvalues and eigenvector of scalapack routines. pssyevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by pssygvx computes all the eigenvalues, and optionally of a real generalized sy-definite eigenproblem, of the form pstrti2 computes the inverse of a real upper or lower triangula contained in one and only one process memory space (local operation). pstrtri computes the inverse of a upper or lower triangula pzgeequ computes row and column scalings intended to equilibrate a reduce its condition number. r returns the row scale factors and c pzgelq2 computes a lq factorization of a complex distributed m-by- pzgelqf computes a lq factorization of a complex distributed m-by- pzgeql2 computes a ql factorization of a complex distributed m-by- pzgeqlf computes a ql factorization of a complex distributed m-by- pzgeqpf computes a qr factorization with column pivoting of pzgeqr2 computes a qr factorization of a complex distributed m-by- pzgeqrf computes a qr factorization of a complex distributed m-by- pzgerq2 computes a rq factorization of a complex distributed m-by- pzgerqf computes a rq factorization of a complex distributed m-by- pzgesv computes the solution to a complex system of linear equation sub( a ) * x = sub( b ), pzgesvd computes the singular value decomposition (svd) of a singular vectors. the svd is written as pzgetf2 computes an lu factorization of a general m-by- partial pivoting with row interchanges. pzgetrf computes an lu factorization of a general m-by-n distribute row interchanges. pzgetri computes the inverse of a distributed matrix using the l computes the inverse of sub( a ) = a(ia:ia+n-1,ja:ja+n-1) denoted pzggqrf computes a generalized qr factorization o an n-by-p matrix sub( b ) = b(ib:ib+n-1,jb:jb+p-1): pzggrqf computes a generalized rq factorization o and a p-by-n matrix sub( b ) = b(ib:ib+p-1,jb:jb+n-1): pzheev computes selected eigenvalues and, optionally, eigenvector of scalapack routines. pzheevd computes all the eigenvalues and eigenvectors of a hermitia pzheevx computes selected eigenvalues and, optionally, eigenvector of scalapack routines. eigenvalues/vectors can be selected by pzhegvx computes all the eigenvalues, and optionally of a complex generalized hermitian-definite eigenproblem, of the form = 'f' (forward) applies pivots forward from top of matrix. computes p*sub( a ) matrix. computes inv( p )*sub( a ). = 'f' (forward) applies pivots forward from top of matrix. computes p * sub( a ) matrix. computes inv( p ) * sub( a ). pzlatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pzlauu2 computes the product u * u' or l' * l, where the triangula the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzlauum computes the product u * u' or l' * l, where the triangula the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pzmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, pzpoequ computes row and column scalings intended t sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number pzposv computes the solution to a complex system of linear equation sub( a ) * x = sub( b ), pzpotf2 computes the cholesky factorization of a complex hermitia pzpotrf computes the cholesky factorization of an n-by-n comple a(ia:ia+n-1, ja:ja+n-1). pzpotri computes the inverse of a complex hermitian positive definit cholesky factorization sub( a ) = u**h*u or l*l**h computed by pzstein computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pzstein does not pztrevc computes some or all of the right and/or left eigenvectors o pztrti2 computes the inverse of a complex upper or lower triangula contained in one and only one process memory space (local operation). pztrtri computes the inverse of a upper or lower triangula sdbtrf computes an lu factorization of a real m-by-n band matrix sdttrf computes an lu factorization of a complex tridiagonal matrix zdbtrf computes an lu factorization of a real m-by-n band matrix zdttrf computes an lu factorization of a complex tridiagonal matrix zlanv2 computes the schur factorization of a complex 2-by- |
| Computing Computing pcgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally Computing the left and/or righ see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. the traditional way of Computing v (and the one used in pzlatrd.f an v = tau * v based on code written by : peter arbenz, eth zurich, 1996. last modified by: peter arbenz, institute of scientific Computing pdgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally Computing the left and/or righ in addition, this routine performs a global minimization and maximi- zation on these values, to support heterogeneous Computing networks arguments the final stage consists of Computing the updated eigenvector the current problem are multiplied with the eigenvectors from see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. the traditional way of Computing v (and the one used in pzlatrd.f an v = tau * v based on code written by : peter arbenz, eth zurich, 1996. last modified by: peter arbenz, institute of scientific Computing psgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally Computing the left and/or righ in addition, this routine performs a global minimization and maximi- zation on these values, to support heterogeneous Computing networks arguments the final stage consists of Computing the updated eigenvector the current problem are multiplied with the eigenvectors from see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. the traditional way of Computing v (and the one used in pzlatrd.f an v = tau * v pzgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally Computing the left and/or righ see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. see "Computing small singular values of bidiagonal matrice kahan, lapack working note #3. the traditional way of Computing v (and the one used in pzlatrd.f an v = tau * v |
| COMPZ COMPZ COMPZ (input) character* = 'i': compute eigenvectors of tridiagonal matrix also. COMPZ (input) character* = 'i': compute eigenvectors of tridiagonal matrix also. |
| con con error bounds on the solution and a condition estimate are als pctrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. error bounds on the solution and a condition estimate are als pdtrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. error bounds on the solution and a condition estimate are als pstrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. error bounds on the solution and a condition estimate are als pztrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. |
| concatenated concatenated opts (global input) character*(*) the character options to the subroutine name, concatenated trans = 't', and diag = 'n' for a triangular routine would |
| concatenation concatenation the scalapack routines: 1) opts is a concatenation of all of the character options t argument list for name, even if they are not used in determining |
| conceivably conceivably which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine |
| condition condition pcgecon estimates the reciprocal of the condition number of a genera 1-norm or the infinity-norm, using the lu factorization computed by m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) and reduce its condition number. r returns the row scale factors and each row and column of the distributed matrix b with elements error bounds on the solution and a condition estimate are als reference: n.j. higham, "fortran codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation" pcpocon estimates the reciprocal of the condition number (in th using the cholesky factorization a = u**h*u or a = l*l**h computed by equilibrate a distributed hermitian positive definite matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition numbe factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri- error bounds on the solution and a condition estimate are als a m-by-k matrix where y can be a, af, b and x. pctrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. pdgecon estimates the reciprocal of the condition number of a genera or the infinity-norm, using the lu factorization computed by pdgetrf. m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) and reduce its condition number. r returns the row scale factors and each row and column of the distributed matrix b with elements error bounds on the solution and a condition estimate are als reference: n.j. higham, "fortran codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation" pdpocon estimates the reciprocal of the condition number (in th using the cholesky factorization a = u**t*u or a = l*l**t computed by equilibrate a distributed symmetric positive definite matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition numbe factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri- error bounds on the solution and a condition estimate are als a m-by-k matrix where y can be a, af, b and x. pdtrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. psgecon estimates the reciprocal of the condition number of a genera or the infinity-norm, using the lu factorization computed by psgetrf. m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) and reduce its condition number. r returns the row scale factors and each row and column of the distributed matrix b with elements error bounds on the solution and a condition estimate are als reference: n.j. higham, "fortran codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation" pspocon estimates the reciprocal of the condition number (in th using the cholesky factorization a = u**t*u or a = l*l**t computed by equilibrate a distributed symmetric positive definite matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition numbe factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri- error bounds on the solution and a condition estimate are als a m-by-k matrix where y can be a, af, b and x. pstrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. pzgecon estimates the reciprocal of the condition number of a genera 1-norm or the infinity-norm, using the lu factorization computed by m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) and reduce its condition number. r returns the row scale factors and each row and column of the distributed matrix b with elements error bounds on the solution and a condition estimate are als reference: n.j. higham, "fortran codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation" pzpocon estimates the reciprocal of the condition number (in th using the cholesky factorization a = u**h*u or a = l*l**h computed by equilibrate a distributed hermitian positive definite matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition numbe factors, s(i) = 1/sqrt(a(i,i)), chosen so that the scaled distri- error bounds on the solution and a condition estimate are als a m-by-k matrix where y can be a, af, b and x. pztrcon estimates the reciprocal of the condition number of 1-norm or the infinity-norm. |
| conditionals conditionals pdlapdct counts the number of negative eigenvalues of (t - sigma i). this implementation of the sturm sequence loop has conditionals i floating point number. pdlapdct will be referred to as the "paranoid" pslapdct counts the number of negative eigenvalues of (t - sigma i). this implementation of the sturm sequence loop has conditionals i floating point number. pslapdct will be referred to as the "paranoid" |
| conditions conditions sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af if neither of the above error conditions hold and jobz = 'v' sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af the distributed submatrices sub( x ) and sub( b ) should be distributed the same way on the same processes. these conditions sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af if neither of the above error conditions hold and jobz = 'v' the distributed submatrices sub( x ) and sub( b ) should be distributed the same way on the same processes. these conditions sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af if neither of the above error conditions hold and jobz = 'v' the distributed submatrices sub( x ) and sub( b ) should be distributed the same way on the same processes. these conditions sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af if neither of the above error conditions hold and jobz = 'v' sub( x ) and sub( b ) ) should be distributed the same way on the same processes. these conditions ensure that sub( a ) and sub( af the distributed submatrices sub( x ) and sub( b ) should be distributed the same way on the same processes. these conditions |
| confusing confusing the value of a is confusing. it is easiest to state th so we will start there. the value of a is confusing. it is easiest to state th so we will start there. the value of a is confusing. it is easiest to state th so we will start there. the value of a is confusing. it is easiest to state th so we will start there. |
| congested congested overlap over several processors and the code gets very "congested." as a remedy, when we first hit a border, a 6x work is done on that. at the end of the border, the data is overlap over several processors and the code gets very "congested." as a remedy, when we first hit a border, a 6x work is done on that. at the end of the border, the data is overlap over several processors and the code gets very "congested." as a remedy, when we first hit a border, a 6x work is done on that. at the end of the border, the data is overlap over several processors and the code gets very "congested." as a remedy, when we first hit a border, a 6x work is done on that. at the end of the border, the data is |
| conjg conjg x := conjg( t' ) *y, and w := t *z where x is an n element vector and t is an n by n where tau is a complex scalar, and v is a complex vector with v(1:i-1) = 0 and v(i) = 1; conjg(v(i+1:n)) is stored on exit i where tau is a complex scalar, and v is a complex vector with v(1:i-1) = 0 and v(i) = 1; conjg(v(i+1:n)) is stored on exit i where tau is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o where tau is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o where taub is a complex scalar, and v is a complex vector with v(p-k+i+1:p) = 0 and v(p-k+i) = 1; conjg(v(1:p-k+i-1)) is stored o to form z explicitly, use scalapack subroutine pcungrq. where taua is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o to form q explicitly, use scalapack subroutine pcungrq. where tau is a complex scalar, and v is a complex vector with v(1:i-1) = 0 and v(i) = 1; conjg(v(i+1:n)) is stored on exit i where tau is a complex scalar, and v is a complex vector with v(1:i-1) = 0 and v(i) = 1; conjg(v(i+1:n)) is stored on exit i where tau is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o where tau is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o where taub is a complex scalar, and v is a complex vector with v(p-k+i+1:p) = 0 and v(p-k+i) = 1; conjg(v(1:p-k+i-1)) is stored o to form z explicitly, use scalapack subroutine pzungrq. where taua is a complex scalar, and v is a complex vector with v(n-k+i+1:n) = 0 and v(n-k+i) = 1; conjg(v(1:n-k+i-1)) is stored o to form q explicitly, use scalapack subroutine pzungrq. x := conjg( t' ) *y, and w := t *z where x is an n element vector and t is an n by n |
| conjugate conjugate = 't': a**t * x = b (transpose) = 'c': a**h * x = b (conjugate transpose n (input) integer = 'n': l * x = b (no transpose) = 'c': u**h * x = b (conjugate transpose = 't': a**t * x = b (transpose) = 'c': a**h * x = b (conjugate transpose n (input) integer apply factorization to lower connection block bl_i conjugate transpose the connection block in preparation move the connection block in preparation. note: for ease of use in solution of reduced system, store l's off-diagonal block in conjugate transpose form systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), or its conjugate-transpose, using a qr or lq factorization o = 'c': sub( a )**h * sub( x ) = sub( b ) (conjugate transpose n (global input) integer = 'c': a(ia:ia+n-1,ja:ja+n-1)**h * x(ix:ix+n-1,jx:jx+nrhs-1) = b(ib:ib+n-1,jb:jb+nrhs-1) (conjugate transpose n (global input) integer = 't': sub( a )**t * x = sub( b ) (transpose) = 'c': sub( a )**h * x = sub( b ) (conjugate transpose n (global input) integer where inv( sub( b ) ) denotes the inverse of the matrix sub( b ), and z' denotes the conjugate transpose of matrix z notes where inv( sub( b ) ) denotes the inverse of the matrix sub( b ), and z' denotes the conjugate transpose of matrix z notes a' * x, if kase=2, where a' is the conjugate transpose of a, and pclacon mus pclarfb applies a complex block reflector q or its conjugate denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pclarzb applies a complex block reflector q or its conjugate denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. the factorization is obtained by householder's method. the kth transformation matrix, z( k ), whose conjugate transpose is used t the form conjugate transpose the connection block in preparation note: for ease of use in solution of reduced system, store l's off-diagonal block in conjugate transpose form where y' denotes the conjugate transpose of the vector y if all eigenvectors are requested, the routine may either return the = 'c': sub( a )**h * sub( x ) = sub( b ) (conjugate transpose diag (global input) character*1 = 't': solve sub( a )**t * x = sub( b ) (transpose) = 'c': solve sub( a )**h * x = sub( b ) (conjugate transpose diag (global input) character the factorization is obtained by householder's method. the kth transformation matrix, z( k ), whose conjugate transpose is used t the form = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q or p; = 'c': conjugate transpose, apply q**h or p**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'c': sub( a )**t * sub( x ) = sub( b ) (conjugate transpose = transpose = 'c': sub( a )**t * sub( x ) = sub( b ) (conjugate transpose = transpose = 'c': sub( a )**t * sub( x ) = sub( b ) (conjugate transpose = transpose = 'c': sub( a )**t * sub( x ) = sub( b ) (conjugate transpose = transpose apply factorization to lower connection block bl_i conjugate transpose the connection block in preparation move the connection block in preparation. note: for ease of use in solution of reduced system, store l's off-diagonal block in conjugate transpose form systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1), or its conjugate-transpose, using a qr or lq factorization o = 'c': sub( a )**h * sub( x ) = sub( b ) (conjugate transpose n (global input) integer = 'c': a(ia:ia+n-1,ja:ja+n-1)**h * x(ix:ix+n-1,jx:jx+nrhs-1) = b(ib:ib+n-1,jb:jb+nrhs-1) (conjugate transpose n (global input) integer = 't': sub( a )**t * x = sub( b ) (transpose) = 'c': sub( a )**h * x = sub( b ) (conjugate transpose n (global input) integer where inv( sub( b ) ) denotes the inverse of the matrix sub( b ), and z' denotes the conjugate transpose of matrix z notes where inv( sub( b ) ) denotes the inverse of the matrix sub( b ), and z' denotes the conjugate transpose of matrix z notes a' * x, if kase=2, where a' is the conjugate transpose of a, and pzlacon mus pzlarfb applies a complex block reflector q or its conjugate denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. pzlarzb applies a complex block reflector q or its conjugate denoting c(ic:ic+m-1,jc:jc+n-1), from the left or the right. the factorization is obtained by householder's method. the kth transformation matrix, z( k ), whose conjugate transpose is used t the form conjugate transpose the connection block in preparation note: for ease of use in solution of reduced system, store l's off-diagonal block in conjugate transpose form where y' denotes the conjugate transpose of the vector y if all eigenvectors are requested, the routine may either return the = 'c': sub( a )**h * sub( x ) = sub( b ) (conjugate transpose diag (global input) character*1 = 't': solve sub( a )**t * x = sub( b ) (transpose) = 'c': solve sub( a )**h * x = sub( b ) (conjugate transpose diag (global input) character the factorization is obtained by householder's method. the kth transformation matrix, z( k ), whose conjugate transpose is used t the form = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q or p; = 'c': conjugate transpose, apply q**h or p**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 'n': no transpose, apply q; = 'c': conjugate transpose, apply q**h m (global input) integer = 't': a**t * x = b (transpose) = 'c': a**h * x = b (conjugate transpose n (input) integer = 't': a**t * x = b (transpose) = 'c': a**h * x = b (conjugate transpose n (input) integer = 'n': l * x = b (no transpose) = 'c': u**h * x = b (conjugate transpose |
| conjugate_transpose conjugate_transpose = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer = 'n': solve with a(1:n, ja:ja+n-1); = 'c': solve with conjugate_transpose( a(1:n, ja:ja+n-1) ) n (global input) integer |
| conjugated conjugated local memory to an array of dimension (lld_x,*). on entry the vector to be conjugated on exit the conjugated vector. local memory to an array of dimension (lld_x,*). on entry the vector to be conjugated on exit the conjugated vector. |
| conjugates conjugates pclacgv conjugates a complex vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and pzlacgv conjugates a complex vector of length n, sub( x ), wher x(ix:ix+n-1,jx) if incx = 1, and |
| connection connection apply factorization to lower connection block bl_ apply factorization to upper connection block bu_i use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ conjugate transpose the connection block in preparation. use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ apply factorization to upper connection block bu_i use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ transpose the connection block in preparation. use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ apply factorization to upper connection block bu_i use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ transpose the connection block in preparation. use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ apply factorization to upper connection block bu_i use factorization of odd-even connection block to modif apply factorization to lower connection block bl_ use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ conjugate transpose the connection block in preparation. use factorization of odd-even connection block to modif apply factorization to odd-even connection block b_ use factorization of odd-even connection block to modif |
| Conqer Conqer the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, the divide and Conqer algorithm assumes the matrix is narrowl it is best to distribute the input matrix a one-dimensionally, |
| Conquer Conquer the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. pcheevd computes all the eigenvalues and eigenvectors of a hermitian matrix a by using a divide and Conquer algorithm arguments the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. pdlaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and Conquer method the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin symmetric tridiagonal matrix in parallel, using the divide and Conquer algorithm this code makes very mild assumptions about floating point reference: f. tisseur and j. dongarra, "a parallel divide and Conquer algorithm for the symmetric eigenvalue proble siam j. sci. comput., 6:20 (1999), pp. 2223--2236. the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. pslaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and Conquer method the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin symmetric tridiagonal matrix in parallel, using the divide and Conquer algorithm this code makes very mild assumptions about floating point reference: f. tisseur and j. dongarra, "a parallel divide and Conquer algorithm for the symmetric eigenvalue proble siam j. sci. comput., 6:20 (1999), pp. 2223--2236. the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. pzheevd computes all the eigenvalues and eigenvectors of a hermitian matrix a by using a divide and Conquer algorithm arguments the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin the mapping for matrices must be blocked, reflecting the nature of the divide and Conquer algorithm as a task-parallel algorithm chunk of the matrix. argument checking that is specific to divide & Conquer routin |
| consecutive consecutive look for two consecutive small subdiagonal elements clamsh sends multiple shifts through a small (single node) matrix to see how consecutive small subdiagonal elements are modified b that can be sent through. dlamsh sends multiple shifts through a small (single node) matrix to see how consecutive small subdiagonal elements are modified b that can be sent through. pclaconsb looks for two consecutive small subdiagonal elements b given by h44, h33, & h43h34 and see if this would make a look for two consecutive small subdiagonal elements pdlaconsb looks for two consecutive small subdiagonal elements b given by h44, h33, & h43h34 and see if this would make a look for two consecutive small subdiagonal elements pslaconsb looks for two consecutive small subdiagonal elements b given by h44, h33, & h43h34 and see if this would make a look for two consecutive small subdiagonal elements pzlaconsb looks for two consecutive small subdiagonal elements b given by h44, h33, & h43h34 and see if this would make a look for two consecutive small subdiagonal elements slamsh sends multiple shifts through a small (single node) matrix to see how consecutive small subdiagonal elements are modified b that can be sent through. look for two consecutive small subdiagonal elements zlamsh sends multiple shifts through a small (single node) matrix to see how consecutive small subdiagonal elements are modified b that can be sent through. |
| consecutively consecutively if howmny = 's', the left eigenvectors of t specified by select, stored consecutively in the column eigenvalues. if howmny = 's', the left eigenvectors of t specified by select, stored consecutively in the column eigenvalues. |
| consequence consequence note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, note that a consequence of this chart is that it is not possibl to opposite requirements for the orientation of the blacs grid, |
| considered considered because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix the result are only available in the scope of sub( x ), i.e if because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix when the result of a vector-oriented pblas call is a scalar, it will because vectors may be seen as particular matrices, a distributed vector is considered to be a distributed matrix arguments is narrower than abstol, or than reltol times the larger (in magnitude) endpoint, then it is considered to be sufficientl this must be at least zero. reltol times the larger (in magnitude) endpoint, then it is considered to have "converged" reltol times the larger (in magnitude) endpoint, or if because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix the result are only available in the scope of sub( x ), i.e if because vectors may be seen as particular matrices, a distributed vector is considered to be a distributed matrix arguments the absolute tolerance for the eigenvalues. an eigenvalue (or cluster) is considered to be located if it has bee less. if abstol is less than or equal to zero, then ulp*|t| because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix when the result of a vector-oriented pblas call is a scalar, it will because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix when the result of a vector-oriented pblas call is a scalar, it will is narrower than abstol, or than reltol times the larger (in magnitude) endpoint, then it is considered to be sufficientl this must be at least zero. reltol times the larger (in magnitude) endpoint, then it is considered to have "converged" reltol times the larger (in magnitude) endpoint, or if because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix the result are only available in the scope of sub( x ), i.e if because vectors may be seen as particular matrices, a distributed vector is considered to be a distributed matrix arguments the absolute tolerance for the eigenvalues. an eigenvalue (or cluster) is considered to be located if it has bee less. if abstol is less than or equal to zero, then ulp*|t| because vectors may be seen as particular matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix arguments because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix the result are only available in the scope of sub( x ), i.e if because vectors may be viewed as a subclass of matrices, a distributed vector is considered to be a distributed matrix when the result of a vector-oriented pblas call is a scalar, it will |
| consist consist form z1 which consist of the last row of q form z1 which consist of the last row of q |
| consistency consistency pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec in its present form, pcheev assumes a homogeneous system and makes only spot checks of the consistency of the eigenvalues across th heterogeneous system may return incorrect results without any error pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same in its present form, pdsyev assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error in its present form, pdsyevd assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same in its present form, pssyev assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error in its present form, pssyevd assumes a homogeneous system and makes no checks for consistency of the eigenvalues or eigenvectors acros heterogeneous system may return incorrect results without any error pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec in its present form, pzheev assumes a homogeneous system and makes only spot checks of the consistency of the eigenvalues across th heterogeneous system may return incorrect results without any error pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same pack params and positions into arrays for global consistency chec consistency checks for desca and descb context must be the same |
| consists consists the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns the eigenvectors of the original matrix are stored in q, and the eigenvalues are in d. the algorithm consists of three stages the first stage consists of deflating the size of the problem the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns the eigenvectors of the original matrix are stored in q, and the eigenvalues are in d. the algorithm consists of three stages the first stage consists of deflating the size of the problem the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns the splitting points, at which t breaks up into submatrices. the first submatrix consists of rows/columns 1 to isplit(1) etc., and the nsplit-th consists of rows/columns |
| constant constant eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer alpha (global input) complex the constant to which the offdiagonal elements are to b alpha (global input) complex the constant to which the offdiagonal elements are to b alpha (global input) double precision the constant to which the offdiagonal elements are to b alpha (global input) double precision the constant to which the offdiagonal elements are to b eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer alpha (global input) real the constant to which the offdiagonal elements are to b alpha (global input) real the constant to which the offdiagonal elements are to b eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer eigenvectors correspoding to the i^th cluster may be as high as ( c * n ) / gap(i) where c is a small constant info (global output) integer alpha (global input) complex*16 the constant to which the offdiagonal elements are to b alpha (global input) complex*16 the constant to which the offdiagonal elements are to b |
| constants constants set machine-dependent constants for the stopping criterion get machine constants set machine-dependent constants for the stopping criterion set machine-dependent constants for the stopping criterion set machine-dependent constants for the stopping criterion set machine-dependent constants for the stopping criterion get machine constants set machine-dependent constants for the stopping criterion |
| cont cont use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context use new context from standard grid as context |
| contain contain dl (input/output) complex array, dimension (n-1) on entry, dl must contain the (n-1) subdiagonal elements o on exit, dl is overwritten by the (n-1) multipliers that before entry with uplo = 'u' or 'u', the leading n by n upper triangular part of the array t must contain the uppe t is not referenced. dl (input/output) complex array, dimension (n-1) on entry, dl must contain the (n-1) subdiagonal elements o on exit, dl is overwritten by the (n-1) multipliers that before entry with uplo = 'u' or 'u', the leading n by n upper triangular part of the array t must contain the uppe t is not referenced. sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(n,m) by the elements below the diagonal, with the array taua, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper w (global output) real array, dimension (n) on normal exit, the first m entries contain the selecte w (global output) real array, dimension (n) on normal exit, the first m entries contain the selecte local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number (with respect to the two-norm). sr and sc contain the scal buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not w (global input/global output) real array, dim (m) on input, the first m elements of w contain all th eigenvalues should be grouped by split-off block and ordered on entry, if side = 'l' or 'b' and howmny = 'b', vl must contain an n-by-n matrix q (usually the unitary matrix q o on exit, if side = 'l' or 'b', vl contains: local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pcgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pcgeqrf in the k columns of its array local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pcgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pcgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pcgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pcgeqrf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pcgerqf in the k rows of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pcgerqf in the k rows of its distributed to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pcgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pcgeqrf in the k columns of its distributed matrix locc(ja+min(nq,k)-1) if vect = 'q', locr(ia+min(nq,k)-1) if vect = 'p', tau(i) must contain the scalar factor of th as returned by pdgebrd in its array argument tauq or taup. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pcgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pcgeqrf in the k columns of its distributed matrix lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(n,m) by the elements below the diagonal, with the array taua, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pdgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pdgeqrf in the k columns of its array local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pdgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pdgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pdgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pdgeqrf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pdgerqf in the k rows of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pdgerqf in the k rows of its distributed to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pdgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pdgeqrf in the k columns of its distributed matrix locc(ja+min(nq,k)-1) if vect = 'q', locr(ia+min(nq,k)-1) if vect = 'p', tau(i) must contain the scalar factor of th as returned by pdgebrd in its array argument tauq or taup. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pdgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pdgeqrf in the k columns of its distributed matrix lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number (with respect to the two-norm). sr and sc contain the scal buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not w (global output) double precision array, dimension (n) on exit, the first m elements of w contain the eigenvalue w (global input/global output) double precision array, dim (m) on input, the first m elements of w contain all th eigenvalues should be grouped by split-off block and ordered w (global output) double precision array, dimension (n) on normal exit, the first m entries contain the selecte w (global output) double precision array, dimension (n) on normal exit, the first m entries contain the selecte local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(n,m) by the elements below the diagonal, with the array taua, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same copy o and will contain the entire array. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by psgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by psgeqrf in the k columns of its array local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by psgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by psgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by psgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by psgeqrf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by psgerqf in the k rows of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by psgerqf in the k rows of its distributed to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen psgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen psgeqrf in the k columns of its distributed matrix locc(ja+min(nq,k)-1) if vect = 'q', locr(ia+min(nq,k)-1) if vect = 'p', tau(i) must contain the scalar factor of th as returned by pdgebrd in its array argument tauq or taup. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen psgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen psgeqrf in the k columns of its distributed matrix lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number (with respect to the two-norm). sr and sc contain the scal buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not w (global output) real array, dimension (n) on exit, the first m elements of w contain the eigenvalue w (global input/global output) real array, dim (m) on input, the first m elements of w contain all th eigenvalues should be grouped by split-off block and ordered w (global output) real array, dimension (n) on normal exit, the first m entries contain the selecte w (global output) real array, dimension (n) on normal exit, the first m entries contain the selecte local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and below the diagonal of sub( a ) contain the m by min(m,n the elements above the diagonal, with the array tau, repre- local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, repre- sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(m,n) by the elements below the diagonal, with the array tau, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, the elements on and above the diagonal of sub( a ) contain the min(n,m) by the elements below the diagonal, with the array taua, sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper w (global output) double precision array, dimension (n) on normal exit, the first m entries contain the selecte w (global output) double precision array, dimension (n) on normal exit, the first m entries contain the selecte local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. sub( a ) = a(ia:ia+n-1,ja:ja+n-1) and reduce its condition number (with respect to the two-norm). sr and sc contain the scal buted matrix b with elements b(i,j) = s(i)*a(i,j)*s(j) has ones on equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not w (global input/global output) double precision array, dim (m) on input, the first m elements of w contain all th eigenvalues should be grouped by split-off block and ordered on entry, if side = 'l' or 'b' and howmny = 'b', vl must contain an n-by-n matrix q (usually the unitary matrix q o on exit, if side = 'l' or 'b', vl contains: local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pzgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pzgeqrf in the k columns of its array local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pzgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pzgelqf in the k rows of its distributed matrix local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic as returned by pzgeqlf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the j-th column must contain the vector whic returned by pzgeqrf in the k columns of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pzgerqf in the k rows of its distributed local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, the i-th row must contain the vector which define returned by pzgerqf in the k rows of its distributed to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pzgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pzgeqrf in the k columns of its distributed matrix locc(ja+min(nq,k)-1) if vect = 'q', locr(ia+min(nq,k)-1) if vect = 'p', tau(i) must contain the scalar factor of th as returned by pdgebrd in its array argument tauq or taup. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pzgeqlf in the k columns of its distributed matrix to an array of dimension (lld_a,locc(ja+k-1)). on entry, the j-th column must contain the vector which defines the elemen pzgeqrf in the k columns of its distributed matrix lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must contain the vector which defines the elementary reflecto k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). l (global input) integer the columns of the distributed submatrix sub( a ) containin if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary dl (input/output) complex array, dimension (n-1) on entry, dl must contain the (n-1) subdiagonal elements o on exit, dl is overwritten by the (n-1) multipliers that before entry with uplo = 'u' or 'u', the leading n by n upper triangular part of the array t must contain the uppe t is not referenced. dl (input/output) complex array, dimension (n-1) on entry, dl must contain the (n-1) subdiagonal elements o on exit, dl is overwritten by the (n-1) multipliers that before entry with uplo = 'u' or 'u', the leading n by n upper triangular part of the array t must contain the uppe t is not referenced. |
| contained contained products. x and v are aligned with the distributed matrix a, this information is implicitly contained within iv, ix, descv, and descx notes block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should be contained in one and only one process memory space (local operation) notes x and v are aligned with the distributed matrix a, this information is implicitly contained within iv, ix, descv, and descx notes pdlaebz contains the iteration loop which computes the eigenvalues contained in the input intervals [ intvl(2*j-1), intvl(2*j) ] wher the count of eigenvalues of a symmetric tridiagonal matrix less than block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should be contained in one and only one process memory space (local operation) notes x and v are aligned with the distributed matrix a, this information is implicitly contained within iv, ix, descv, and descx notes pslaebz contains the iteration loop which computes the eigenvalues contained in the input intervals [ intvl(2*j-1), intvl(2*j) ] wher the count of eigenvalues of a symmetric tridiagonal matrix less than block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should be contained in one and only one process memory space (local operation) notes products. x and v are aligned with the distributed matrix a, this information is implicitly contained within iv, ix, descv, and descx notes block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should be contained in one and only one process memory space (local operation) notes |
| containing containing distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that on exit, if info <= n, the part of sub( b ) containing th the cholesky factorization sub( b ) = u**h*u or a (local input) complex pointer into the local memory to an array of dimension (lld_a, locc(ja+n-1)) containing th ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while local memory to an array of dimension (lld_a,locc(ja+n-1)) containing on entry the m-by-n matrix sub( a ). on exit form of the equilibrated distributed submatrix. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing x (local input) complex array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). sx (local input/local output) complex array containing the local pieces of a distributed matrix o ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that a (local input) double precision pointer into the local memory to an array of dimension (lld_a, locc(ja+n-1)) containing th ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while local memory to an array of dimension (lld_a,locc(ja+n-1)) containing on entry the m-by-n matrix sub( a ). on exit form of the equilibrated distributed submatrix. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). sx (local input/local output) double precision array containing the local pieces of a distributed matrix o ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) on exit, if info <= n, the part of sub( b ) containing th the cholesky factorization sub( b ) = u**t*u or x (local input) complex*16 array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) x (local input) complex array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that a (local input) real pointer into the local memory to an array of dimension (lld_a, locc(ja+n-1)) containing th ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while local memory to an array of dimension (lld_a,locc(ja+n-1)) containing on entry the m-by-n matrix sub( a ). on exit form of the equilibrated distributed submatrix. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). sx (local input/local output) real array containing the local pieces of a distributed matrix o ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) on exit, if info <= n, the part of sub( b ) containing th the cholesky factorization sub( b ) = u**t*u or distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that sx (local input/local output) complex*16 array containing the local pieces of a distributed matrix o ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that on exit, if info <= n, the part of sub( b ) containing th the cholesky factorization sub( b ) = u**h*u or a (local input) complex*16 pointer into the local memory to an array of dimension (lld_a, locc(ja+n-1)) containing th ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while ii, jj : local indices into array a icurrow : process row containing diagonal bloc irsc0 : pointer to part of work used to store the rowsums while local memory to an array of dimension (lld_a,locc(ja+n-1)) containing on entry the m-by-n matrix sub( a ). on exit form of the equilibrated distributed submatrix. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing x (local input) complex*16 array containing the loca ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) distributed matrices. on exit, this array contains information containing detail note that permutations are performed on the matrix, so that matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. l (global input) integer the columns of the distributed submatrix sub( a ) containing if side = 'l', m >= l >= 0, if side = 'r', n >= l >= 0. |
| contains contains lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are to an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l and unit diagonal elements of l are not stored. r (local output) real array, dimension locr(m_a) if info = 0 or info > ia+m-1, r(ia:ia+m-1) contains the ro matrix a, and replicated across every process column. r is local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are tau (local output) complex, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. tau (local output) complex, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower tau (local output) complex, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) complex, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) complex, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. memory to an array of local dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper on entry, the local pieces of the n-by-n distributed matrix sub( a ) to be factored. on exit, this array contains th sub( a ) = p*l*u; the unit diagonal elements of l are not (mp, sizeq), global dimension (m, size) if jobu = 'v', u contains the first min(m,n) columns of af(iaf:iaf+n-1,jaf:jaf+n-1) is an input argument and on entry contains the factors l and u from the factorizatio if equed .ne. 'n', then af is the factored form of the local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- the local pieces of the factors l and u from the factoriza- local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- array contains the local pieces of the factors l and u from factorization sub( a ) = p*l*u computed by pcgetrf. on exit, if info = 0, sub( a ) contains the inverse of th memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the factor diagonal elements of l are not stored. taua (local output) complex, array, dimension locc(ja+min(n,m)-1). this array contains the scalar factor matrix q. taua is tied to the distributed matrix a. (see sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper local dimension ( lld_z, locc(jz+n-1) ) z contains the orthonormal eigenvectors of the matrix a iz (global input) integer corresponding to the selected eigenvalues. if an eigenvector fails to converge, then that column of z contains the lates eigenvector is returned in ifail. local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locq(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the first nb rows and columns of the matrix are overwritten; to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a the local memory to an array of dimension (lld_a, locc(ja+n-k)). on entry, this array contains the the loca a(ia:ia+n-1,ja:ja+n-k). on exit, the elements on and above local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th interchanges will be applied. on exit, the local pieces local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this local array contains the local pieces of th interchanges will be applied. on exit, this array contains matrix sub( a ). if uplo = 'u', the leading n-by-n upper triangular part of sub( a ) contains the upper triangula of sub( a ) is not referenced. if uplo = 'l', the leading side = 'l', ( lld_v, locc(jv+n-1) ) if storev = 'r' and side = 'r'. it contains the local pieces of the distribute see further details. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. if storev = 'c', and (locr(iv+k-1),locc(jv+n-1)) if storev = 'r'. the distributed matrix v contains th to an array of dimension (lld_v, locc(jv+m-1)) if side = 'l', (lld_v, locc(jv+n-1)) if side = 'r'. it contains the loca householder transformation as returned by pctzrzf. to an array of local dimension (locr(iv+k-1),locc(jv+n-1)). the distributed matrix v contains the householder vectors local memory to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute pieces of the distributed matrix multiplied by cto/cfrom. to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: local memory to an array of dimension (lld_a, * ). on entry, this array contains the local pieces of the distri applied. on exit the permuted distributed matrix. to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix the trac local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian of sub( a ), with the array tau, represent the unitary matrix v (global output) complex array of size 3. contains the transform on ouput further details ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l or u fro l*l', as computed by pcpotrf. sr (local output) real array, dimension locr(m_a) if info = 0, sr(ia:ia+n-1) contains the row scale factor and replicated across every process column. sr is tied to the memory to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the n-by-n hermitia if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the factors l or u from the cholesky facto matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto dimension (descz(dlen_), n/npcol + nb) z contains the computed eigenvectors associated with th set to its current iterate after maxits iterations ( see to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the triangular distribute n-by-n upper triangular part of this distributed matrix con- schur vectors returned by chseqr). on exit, if side = 'l' or 'b', vl contains if howmny = 'b', the matrix q*y; to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the original triangula if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a,locc(ja+n-1)), this array contains the local pieces of the triangular matri part of the matrix sub( a ) contains the upper triangular local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th n-by-n upper triangular part of the matrix sub( a ) contains to an array of dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the distributed triangula triangular part of sub( a ) contains the upper triangular sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian sub( a ), with the array tau, represent the unitary matrix z matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer returned by pcgeqrf in the k columns of its array argument a(ia:*,ja:ja+k-1). on exit, this array contains returned by pcgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th returned by pcgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia:*,ja:ja+k-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer tau (local input) complex, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. if side = 'l', and locc(ja+n-2) if side = 'r'. this array contains the scalar factors tau(j) of the elementar the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are to an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l and unit diagonal elements of l are not stored. r (local output) double precision array, dimension locr(m_a) if info = 0 or info > ia+m-1, r(ia:ia+m-1) contains the ro matrix a, and replicated across every process column. r is local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are tau (local output) double precision array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. tau (local output) double precision array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower tau (local output) double precision array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) double precision array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) double precision array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. memory to an array of local dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper on entry, the local pieces of the n-by-n distributed matrix sub( a ) to be factored. on exit, this array contains th sub( a ) = p*l*u; the unit diagonal elements of l are not (mp, sizeq), global dimension (m, size) if jobu = 'v', u contains the first min(m,n) columns of af(iaf:iaf+n-1,jaf:jaf+n-1) is an input argument and on entry contains the factors l and u from the factorizatio if equed .ne. 'n', then af is the factored form of the local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- the local pieces of the factors l and u from the factoriza- local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- array contains the local pieces of the factors l and u from factorization sub( a ) = p*l*u computed by pdgetrf. on exit, if info = 0, sub( a ) contains the inverse of th memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the factor diagonal elements of l are not stored. taua (local output) double precision array, dimension locc(ja+min(n,m)-1). this array contains the scalar factor orthogonal matrix q. taua is tied to the distributed matrix sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the first nb rows and columns of the matrix are overwritten; to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a pdlaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is in general, be reordered on output. on input, intvl contains the kl-kf input intervals kf-1, and the unconverged intervals, kf thru' kl-1. local dimension ( lld_q, locc(jq+n-1)) q contains the orthonormal eigenvectors of the symmetri on output, q is distributed across the p processes in block local dimension ( lld_q, locc(jq+n-1)) q contains the orthonormal eigenvectors of the symmetri d (input/output) double precision array, dimension (n) on entry, d contains the eigenvalues of the two submatrices t on exit, d contains the trailing (n-k) updated eigenvalues d (input/output) double precision array, dimension (n) on entry, d contains the eigenvalues of the two submatrices t on exit, d contains the trailing (n-k) updated eigenvalues the local memory to an array of dimension (lld_a, locc(ja+n-k)). on entry, this array contains the the loca a(ia:ia+n-1,ja:ja+n-k). on exit, the elements on and above d (input) double precision array, dimension (2*n - 1) contains the diagonals and the squares of the off-diagona assumed to be interleaved in memory for better cache local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th interchanges will be applied. on exit, the local pieces local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this local array contains the local pieces of th interchanges will be applied. on exit, this array contains matrix sub( a ). if uplo = 'u', the leading n-by-n upper triangular part of sub( a ) contains the upper triangula of sub( a ) is not referenced. if uplo = 'l', the leading byall is exactly duplicated on all processes it contains the same values as bycol, but it is replicate byall is exactly duplicated on all processes it contains the same values as byrow, but it is replicate side = 'l', ( lld_v, locc(jv+n-1) ) if storev = 'r' and side = 'r'. it contains the local pieces of the distribute see further details. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. if storev = 'c', and (locr(iv+k-1),locc(jv+n-1)) if storev = 'r'. the distributed matrix v contains th to an array of dimension (lld_v, locc(jv+m-1)) if side = 'l', (lld_v, locc(jv+n-1)) if side = 'r'. it contains the loca householder transformation as returned by pdtzrzf. to an array of local dimension (locr(iv+k-1),locc(jv+n-1)). the distributed matrix v contains the householder vectors local memory to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute pieces of the distributed matrix multiplied by cto/cfrom. to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_q, locc(jq+n-1) ). this array contains the local pieces of the distributed matrix sub( a local memory to an array of dimension (lld_a, * ). on entry, this array contains the local pieces of the distri applied. on exit the permuted distributed matrix. to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix the trac local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian of sub( a ), with the array tau, represent the orthogonal v (global output) double precision array of size 3. contains the transform on ouput implemented by: g. henry, november 17, 1996 matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer returned by pdgeqrf in the k columns of its array argument a(ia:*,ja:ja+k-1). on exit, this array contains returned by pdgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th returned by pdgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia:*,ja:ja+k-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer tau (local input) double precision array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. if side = 'l', and locc(ja+n-2) if side = 'r'. this array contains the scalar factors tau(j) of the elementar the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) double precision array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). to an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l or or l*l', as computed by pdpotrf. sr (local output) double precision array, dimension locr(m_a) if info = 0, sr(ia:ia+n-1) contains the row scale factor and replicated across every process column. sr is tied to the memory to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the n-by-n symmetri if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the factors l or u from the cholesky facto matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto = 'v': compute eigenvectors of original dense symmetric matrix also. on entry, z contains the orthogona tridiagonal form. (not implemented yet) dimension (descz(dlen_), n/npcol + nb) z contains the computed eigenvectors associated with th set to its current iterate after maxits iterations ( see local dimension ( lld_z, locc(jz+n-1) ) z contains the orthonormal eigenvector corresponding to the selected eigenvalues. if an eigenvector fails to converge, then that column of z contains the lates eigenvector is returned in ifail. local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locq(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the triangular distribute n-by-n upper triangular part of this distributed matrix con- to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the original triangula if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a,locc(ja+n-1)), this array contains the local pieces of the triangular matri part of the matrix sub( a ) contains the upper triangular local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th n-by-n upper triangular part of the matrix sub( a ) contains to an array of dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the distributed triangula triangular part of sub( a ) contains the upper triangular sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian sub( a ), with the array tau, represent the orthogonal matrix ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are to an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l and unit diagonal elements of l are not stored. r (local output) real array, dimension locr(m_a) if info = 0 or info > ia+m-1, r(ia:ia+m-1) contains the ro matrix a, and replicated across every process column. r is local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are tau (local output) real, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. tau (local output) real, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower tau (local output) real, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) real, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) real, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. memory to an array of local dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper on entry, the local pieces of the n-by-n distributed matrix sub( a ) to be factored. on exit, this array contains th sub( a ) = p*l*u; the unit diagonal elements of l are not (mp, sizeq), global dimension (m, size) if jobu = 'v', u contains the first min(m,n) columns of af(iaf:iaf+n-1,jaf:jaf+n-1) is an input argument and on entry contains the factors l and u from the factorizatio if equed .ne. 'n', then af is the factored form of the local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- the local pieces of the factors l and u from the factoriza- local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- array contains the local pieces of the factors l and u from factorization sub( a ) = p*l*u computed by psgetrf. on exit, if info = 0, sub( a ) contains the inverse of th memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the factor diagonal elements of l are not stored. taua (local output) real, array, dimension locc(ja+min(n,m)-1). this array contains the scalar factor orthogonal matrix q. taua is tied to the distributed matrix sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the first nb rows and columns of the matrix are overwritten; to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a pslaebz contains the iteration loop which computes the eigenvalue j = 1,...,minp. it uses and computes the function n(w), which is in general, be reordered on output. on input, intvl contains the kl-kf input intervals kf-1, and the unconverged intervals, kf thru' kl-1. local dimension ( lld_q, locc(jq+n-1)) q contains the orthonormal eigenvectors of the symmetri on output, q is distributed across the p processes in block local dimension ( lld_q, locc(jq+n-1)) q contains the orthonormal eigenvectors of the symmetri d (input/output) real array, dimension (n) on entry, d contains the eigenvalues of the two submatrices t on exit, d contains the trailing (n-k) updated eigenvalues d (input/output) real array, dimension (n) on entry, d contains the eigenvalues of the two submatrices t on exit, d contains the trailing (n-k) updated eigenvalues the local memory to an array of dimension (lld_a, locc(ja+n-k)). on entry, this array contains the the loca a(ia:ia+n-1,ja:ja+n-k). on exit, the elements on and above d (input) real array, dimension (2*n - 1) contains the diagonals and the squares of the off-diagona assumed to be interleaved in memory for better cache local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th interchanges will be applied. on exit, the local pieces local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this local array contains the local pieces of th interchanges will be applied. on exit, this array contains matrix sub( a ). if uplo = 'u', the leading n-by-n upper triangular part of sub( a ) contains the upper triangula of sub( a ) is not referenced. if uplo = 'l', the leading byall is exactly duplicated on all processes it contains the same values as bycol, but it is replicate byall is exactly duplicated on all processes it contains the same values as byrow, but it is replicate side = 'l', ( lld_v, locc(jv+n-1) ) if storev = 'r' and side = 'r'. it contains the local pieces of the distribute see further details. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. if storev = 'c', and (locr(iv+k-1),locc(jv+n-1)) if storev = 'r'. the distributed matrix v contains th to an array of dimension (lld_v, locc(jv+m-1)) if side = 'l', (lld_v, locc(jv+n-1)) if side = 'r'. it contains the loca householder transformation as returned by pstzrzf. to an array of local dimension (locr(iv+k-1),locc(jv+n-1)). the distributed matrix v contains the householder vectors local memory to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute pieces of the distributed matrix multiplied by cto/cfrom. to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_q, locc(jq+n-1) ). this array contains the local pieces of the distributed matrix sub( a local memory to an array of dimension (lld_a, * ). on entry, this array contains the local pieces of the distri applied. on exit the permuted distributed matrix. to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix the trac local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian of sub( a ), with the array tau, represent the orthogonal v (global output) real array of size 3. contains the transform on output implemented by: g. henry, november 17, 1996 matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer returned by psgeqrf in the k columns of its array argument a(ia:*,ja:ja+k-1). on exit, this array contains returned by psgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th returned by psgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia:*,ja:ja+k-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer tau (local input) real, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. if side = 'l', and locc(ja+n-2) if side = 'r'. this array contains the scalar factors tau(j) of the elementar the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) real, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l or u fro l*l', as computed by pspotrf. sr (local output) real array, dimension locr(m_a) if info = 0, sr(ia:ia+n-1) contains the row scale factor and replicated across every process column. sr is tied to the memory to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the n-by-n symmetri if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the factors l or u from the cholesky facto matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto = 'v': compute eigenvectors of original dense symmetric matrix also. on entry, z contains the orthogona tridiagonal form. (not implemented yet) dimension (descz(dlen_), n/npcol + nb) z contains the computed eigenvectors associated with th set to its current iterate after maxits iterations ( see local dimension ( lld_z, locc(jz+n-1) ) z contains the orthonormal eigenvector corresponding to the selected eigenvalues. if an eigenvector fails to converge, then that column of z contains the lates eigenvector is returned in ifail. local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locq(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the triangular distribute n-by-n upper triangular part of this distributed matrix con- to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the original triangula if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a,locc(ja+n-1)), this array contains the local pieces of the triangular matri part of the matrix sub( a ) contains the upper triangular local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th n-by-n upper triangular part of the matrix sub( a ) contains to an array of dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the distributed triangula triangular part of sub( a ) contains the upper triangular sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian sub( a ), with the array tau, represent the orthogonal matrix lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bwl+bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto must be of size >= desca( nb_ ). on exit, this array contains information containing th must be of size >= desca( nb_ ). on exit, this array contains information containing th lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(2*bwl+2*bwu+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the diagonal and the first superdiagonal of sub( a ) are to an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l and unit diagonal elements of l are not stored. r (local output) double precision array, dimension locr(m_a) if info = 0 or info > ia+m-1, r(ia:ia+m-1) contains the ro matrix a, and replicated across every process column. r is local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of the n-by- the upper triangle and the first subdiagonal of sub( a ) are tau (local output) complex*16, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. tau (local output) complex*16, array, dimension locr(ia+min(m,n)-1). this array contains the scalar factor matrix a. local memory to an array of local dimension (lld_b, locc(jb+nrhs-1)). on entry, this array contains th vectors, stored columnwise; lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower lower triangle of the distributed submatrix a( ia+m-n:ia+m-1, ja:ja+n-1 ) contains the n-by-n lowe the (n-m)-th superdiagonal contain the m by n lower tau (local output) complex*16, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) complex*16, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. tau (local output) complex*16, array, dimension locc(ja+min(m,n)-1). this array contains the scalar factor distributed matrix a. memory to an array of local dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper on entry, the local pieces of the n-by-n distributed matrix sub( a ) to be factored. on exit, this array contains th sub( a ) = p*l*u; the unit diagonal elements of l are not (mp, sizeq), global dimension (m, size) if jobu = 'v', u contains the first min(m,n) columns of af(iaf:iaf+n-1,jaf:jaf+n-1) is an input argument and on entry contains the factors l and u from the factorizatio if equed .ne. 'n', then af is the factored form of the local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- the local pieces of the factors l and u from the factoriza- local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the m-by- array contains the local pieces of the factors l and u from factorization sub( a ) = p*l*u computed by pzgetrf. on exit, if info = 0, sub( a ) contains the inverse of th memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of the factor diagonal elements of l are not stored. taua (local output) complex*16, array, dimension locc(ja+min(n,m)-1). this array contains the scalar factor matrix q. taua is tied to the distributed matrix a. (see sub( a ) which is to be factored. on exit, if m <= n, the upper triangle of a( ia:ia+m-1, ja+n-m:ja+n-1 ) contains th and above the (m-n)-th subdiagonal contain the m by n upper local dimension ( lld_z, locc(jz+n-1) ) z contains the orthonormal eigenvectors of the matrix a iz (global input) integer corresponding to the selected eigenvalues. if an eigenvector fails to converge, then that column of z contains the lates eigenvector is returned in ifail. local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th the leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locq(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th the first nb rows and columns of the matrix are overwritten; to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a to an array of dimension (lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix sub( a the local memory to an array of dimension (lld_a, locc(ja+n-k)). on entry, this array contains the the loca a(ia:ia+n-1,ja:ja+n-k). on exit, the elements on and above local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th interchanges will be applied. on exit, the local pieces local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this local array contains the local pieces of th interchanges will be applied. on exit, this array contains matrix sub( a ). if uplo = 'u', the leading n-by-n upper triangular part of sub( a ) contains the upper triangula of sub( a ) is not referenced. if uplo = 'l', the leading side = 'l', ( lld_v, locc(jv+n-1) ) if storev = 'r' and side = 'r'. it contains the local pieces of the distribute see further details. local memory to an array of dimension (lld_x,*). this array contains the local pieces of the distributed vector sub( x ) the vector x. on exit, it is overwritten with the vector v. if storev = 'c', and (locr(iv+k-1),locc(jv+n-1)) if storev = 'r'. the distributed matrix v contains th to an array of dimension (lld_v, locc(jv+m-1)) if side = 'l', (lld_v, locc(jv+n-1)) if side = 'r'. it contains the loca householder transformation as returned by pztzrzf. to an array of local dimension (locr(iv+k-1),locc(jv+n-1)). the distributed matrix v contains the householder vectors local memory to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distribute pieces of the distributed matrix multiplied by cto/cfrom. to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: to an array of dimension (lld_a,locc(ja+n-1)). this array contains the local pieces of the distributed matrix sub( a is set as follows: local memory to an array of dimension (lld_a, * ). on entry, this array contains the local pieces of the distri applied. on exit the permuted distributed matrix. to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the distributed matrix the trac local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th leading n-by-n upper triangular part of sub( a ) contains sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian of sub( a ), with the array tau, represent the unitary matrix v (global output) complex*16 array of size 3. contains the transform on ouput further details ( (jx-1)*m_x + ix + ( n - 1 )*abs( incx ) ) this array contains the entries of the distributed vecto lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th used in lapack. please see the notes below and the lld_a >=(bw+1) (stored in desca). on entry, this array contains the local pieces of th l^t a(1:n, ja:ja+n-1). an array of dimension ( lld_a, locc(ja+n-1) ). on entry, this array contains the local pieces of the factors l or u fro l*l', as computed by pzpotrf. sr (local output) double precision array, dimension locr(m_a) if info = 0, sr(ia:ia+n-1) contains the row scale factor and replicated across every process column. sr is tied to the memory to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the n-by-n hermitia if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of equilibrated before it is factored. = 'f': on entry, af contains the factored form of a with scaling factors given by s. a and af will not local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the local pieces of th if uplo = 'u', the leading n-by-n upper triangular part of an array of dimension (lld_a, locc(ja+n-1)). on entry, this array contains the factors l or u from the cholesky facto matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). matrix. on exit, this array contains information containing th must be of size >= desca( nb_ ). dimension (descz(dlen_), n/npcol + nb) z contains the computed eigenvectors associated with th set to its current iterate after maxits iterations ( see to an array of dimension ( lld_a, locc(ja+n-1) ). this array contains the local pieces of the triangular distribute n-by-n upper triangular part of this distributed matrix con- schur vectors returned by zhseqr). on exit, if side = 'l' or 'b', vl contains if howmny = 'b', the matrix q*y; to an array of local dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the original triangula if uplo = 'u', the leading n-by-n upper triangular part of local memory to an array of dimension (lld_a,locc(ja+n-1)), this array contains the local pieces of the triangular matri part of the matrix sub( a ) contains the upper triangular local memory to an array of dimension (lld_a,locc(ja+n-1)). on entry, this array contains the local pieces of th n-by-n upper triangular part of the matrix sub( a ) contains to an array of dimension (lld_a,locc(ja+n-1) ). this array contains the local pieces of the distributed triangula triangular part of sub( a ) contains the upper triangular sub( a ) which is to be factored. on exit, the leading m-by-m upper triangular part of sub( a ) contains the upper trian sub( a ), with the array tau, represent the unitary matrix z matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer returned by pzgeqrf in the k columns of its array argument a(ia:*,ja:ja+k-1). on exit, this array contains returned by pzgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th returned by pzgelqf in the k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). on exit, this array contains th matrix argument a(ia:*,ja+n-k:ja+n-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia:*,ja:ja+k-1). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer matrix argument a(ia+m-k:ia+m-1,ja:*). on exit, this array contains the local pieces of the m-by-n distributed matrix q ia (global input) integer tau (local input) complex*16, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. if side = 'l', and locc(ja+n-2) if side = 'r'. this array contains the scalar factors tau(j) of the elementar the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ja+n-1) this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ja+k-1). this array contains the scalar factors tau(j) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. tau (local input) complex*16, array, dimension locc(ia+k-1). this array contains the scalar factors tau(i) of th tau is tied to the distributed matrix a. uplo (global input) character = 'u': upper triangle of a(ia:*,ja:*) contains elementar = 'l': lower triangle of a(ia:*,ja:*) contains elementary |
| contect contect pclamr1d has not been tested except withint the contect o pdlamr1d has not been tested except withint the contect o pslamr1d has not been tested except withint the contect o pzlamr1d has not been tested except withint the contect o |
| contents contents pivot indices for local factorizations. users *should not* alter the contents betwee pivot indices for local factorizations. users *should not* alter the contents betwee the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follo the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follow global dimension (m, n), local dimension (mp, nq) on exit, the contents of a are destroyed ia (global input) integer the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-k) on exit are illustrated by th the contents of a on exit are illustrated by the following example pivot indices for local factorizations. users *should not* alter the contents betwee pivot indices for local factorizations. users *should not* alter the contents betwee the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follo the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follow global dimension (m, n), local dimension (mp, nq) on exit, the contents of a are destroyed ia (global input) integer the contents of sub( a ) on exit are illustrated by the followin the second sub-eigenvector matrix). on exit, the contents of z have been destroyed by the updatin the second sub-eigenvector matrix). on exit, the contents of z have been destroyed by the updatin the contents of a(ia:ia+n-1,ja:ja+n-k) on exit are illustrated by th the contents of a on exit are illustrated by the following example the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin pivot indices for local factorizations. users *should not* alter the contents betwee pivot indices for local factorizations. users *should not* alter the contents betwee the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follo the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follow global dimension (m, n), local dimension (mp, nq) on exit, the contents of a are destroyed ia (global input) integer the contents of sub( a ) on exit are illustrated by the followin the second sub-eigenvector matrix). on exit, the contents of z have been destroyed by the updatin the second sub-eigenvector matrix). on exit, the contents of z have been destroyed by the updatin the contents of a(ia:ia+n-1,ja:ja+n-k) on exit are illustrated by th the contents of a on exit are illustrated by the following example the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin pivot indices for local factorizations. users *should not* alter the contents betwee pivot indices for local factorizations. users *should not* alter the contents betwee the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follo the contents of a(ia:ia+n-1,ja:ja+n-1) are illustrated by the follow global dimension (m, n), local dimension (mp, nq) on exit, the contents of a are destroyed ia (global input) integer the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of sub( a ) on exit are illustrated by the followin the contents of a(ia:ia+n-1,ja:ja+n-k) on exit are illustrated by th the contents of a on exit are illustrated by the following example |
| context context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dt_a = 1. ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle in which the computation take dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle, indicating the global context o ictxt (global input) integer the blacs context handle, indicating the global context o dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle in which the computation take dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dt_a = 1. ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle range (global input) character dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle in which the computation take dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle, indicating the global context o ictxt (global input) integer the blacs context handle, indicating the global context o dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle in which the computation take dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dt_a = 1. ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- ictxt (global input) integer the blacs context handle range (global input) character dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dt_a = 1. ctxt_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- use new context from standard grid as context dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- context must be the sam these are alignment restrictions that may or may not be removed dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. ctxt_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- |
| CONTEXTC CONTEXTC with myprowc defined when a new context is created as: call blacs_get( desca( ctxt_ ), 0, CONTEXTC call blacs_gridinfo( contextc, nprowc, npcolc, myprowc, with myprowc defined when a new context is created as: call blacs_get( desca( ctxt_ ), 0, CONTEXTC call blacs_gridinfo( contextc, nprowc, npcolc, myprowc, |
| contiguous contiguous the eigenvectors on input. each eigenvector resides entirely in one process. each process holds a contiguous set o process holds is: sum for i=[0,iam-1) of nvs(i) the eigenvectors on input. each eigenvector resides entirely in one process. each process holds a contiguous set o process holds is: sum for i=[0,iam-1) of nvs(i) the eigenvectors on input. each eigenvector resides entirely in one process. each process holds a contiguous set o process holds is: sum for i=[0,iam-1) of nvs(i) the eigenvectors on input. each eigenvector resides entirely in one process. each process holds a contiguous set o process holds is: sum for i=[0,iam-1) of nvs(i) |
| continue continue continue for additional iterations after norm reache no errors found, continue no errors found, continue $ go to 30 20 continue csumj = csumj + ( a( i, j )*uscal )*x( i ) 130 continue csumj = csumj + ( a( i, j )*uscal )*x( i ) no errors found, continue no errors found, continue no errors found, continue no errors found, continue $ go to 30 20 continue no errors found, continue no errors found, continue no errors found, continue no errors found, continue $ go to 30 20 continue no errors found, continue no errors found, continue no errors found, continue no errors found, continue $ go to 30 20 continue csumj = csumj + ( a( i, j )*uscal )*x( i ) 130 continue csumj = csumj + ( a( i, j )*uscal )*x( i ) no errors found, continue no errors found, continue continue for additional iterations after norm reache |
| Contributed Contributed Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and the serial version clacon has been Contributed by nick higham march 16, 1988. the serial version was Contributed to lapack by nick higham for us the serial version dlacon has been Contributed by nick higham march 16, 1988. Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and the serial version of this routine was originally Contributed b the serial version of this routine was originally Contributed b the serial version slacon has been Contributed by nick higham march 16, 1988. Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and Contributed by francoise tisseur, university of manchester reference: f. tisseur and j. dongarra, "a parallel divide and the serial version zlacon has been Contributed by nick higham march 16, 1988. the serial version was Contributed to lapack by nick higham for us |
| contribution contribution compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou compute contribution to diagonal block(s) of reduced system use the "spike" fillin to calculate contribution to previou |
| control control the two integers npact (nu. of active processors) and npstr (stride between active processors) are used to control th determine machine dependent parameters to control overflow the two integers npact (nu. of active processors) and npstr (stride between active processors) are used to control th = 3: the algorithmic blocking factor; = 4: execution path control the two integers npact (nu. of active processors) and npstr (stride between active processors) are used to control th the two integers npact (nu. of active processors) and npstr (stride between active processors) are used to control th determine machine dependent parameters to control overflow |
| controlled controlled that are on different processes. the extent of reorthogonalization is controlled by the input parameter lwork version 1.4 limitations: orthogonalize vectors that are on different processes. the extent of orthogonalization is controlled by the input parameter lwork process. pcstein decides on the allocation of work among the orthogonalize vectors that are on different processes. the extent of orthogonalization is controlled by the input parameter lwork process. pdstein decides on the allocation of work among the that are on different processes. the extent of reorthogonalization is controlled by the input parameter lwork version 1.4 limitations: orthogonalize vectors that are on different processes. the extent of orthogonalization is controlled by the input parameter lwork process. psstein decides on the allocation of work among the that are on different processes. the extent of reorthogonalization is controlled by the input parameter lwork version 1.4 limitations: that are on different processes. the extent of reorthogonalization is controlled by the input parameter lwork version 1.4 limitations: orthogonalize vectors that are on different processes. the extent of orthogonalization is controlled by the input parameter lwork process. pzstein decides on the allocation of work among the |
| controls controls lwork (local input) integer lwork controls the extent of orthogonalization which can b allocated on each process is lwork (local input) integer lwork controls the extent of orthogonalization which can b allocated on each process is lwork (local input) integer lwork controls the extent of orthogonalization which can b allocated on each process is lwork (local input) integer lwork controls the extent of orthogonalization which can b allocated on each process is |
| convention convention distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, distributed are the individual vectors storing the diagonals, we have adopted the convention that both the p-by-1 descriptor an tridiagonal matrices. thus, for tridiagonal matrices, |
| conventions conventions the following conventions have been used when calling pjlaenv fro 1) opts is a concatenation of all of the character options to |
| converge converge > 0: if info = 1 through n, the i(th) eigenvalue did not converge in csteqr2 after a total of 30*n iterations by finding that eigenvalues were not identical across > 0: if info = 1 through n, the i(th) eigenvalue did not converge in pslaed3 alignment requirements the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to z contains the computed eigenvectors associated with the specified eigenvalues. any vector which fails to converge i sstein2 ). magnitude) endpoint, then it is considered to be sufficiently small, i.e., converged note: in the (theoretically impossible) event that bisection does not converge for some or all eigenvalues, info is se negative block number. z contains the computed eigenvectors associated with the specified eigenvalues. any vector which fails to converge i dstein2 ). > 0: if info = 1 through n, the i(th) eigenvalue did not converge in dsteqr2 after a total of 30*n iterations by finding that eigenvalues were not identical across the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to magnitude) endpoint, then it is considered to be sufficiently small, i.e., converged note: in the (theoretically impossible) event that bisection does not converge for some or all eigenvalues, info is se negative block number. z contains the computed eigenvectors associated with the specified eigenvalues. any vector which fails to converge i sstein2 ). > 0: if info = 1 through n, the i(th) eigenvalue did not converge in ssteqr2 after a total of 30*n iterations by finding that eigenvalues were not identical across the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to > 0: if info = 1 through n, the i(th) eigenvalue did not converge in zsteqr2 after a total of 30*n iterations by finding that eigenvalues were not identical across > 0: if info = 1 through n, the i(th) eigenvalue did not converge in pdlaed3 alignment requirements the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converge of width less than or equal to z contains the computed eigenvectors associated with the specified eigenvalues. any vector which fails to converge i dstein2 ). |
| converged converged with the active submatrix in rows and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo, o the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo or the global a(l,l-1) is negligibl magnitude) endpoint, then it is considered to be sufficiently small, i.e., converged pdlaecv checks if the input intervals [ intvl(2*i-1), intvl(2*i) ], i = kf, ... , kl-1, have "converged" i.e., on output, all intervals [ intvl(2*i-1), intvl(2*i) ], i < kf, and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo or the global a(l,l-1) is negligibl the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to magnitude) endpoint, then it is considered to be sufficiently small, i.e., converged pslaecv checks if the input intervals [ intvl(2*i-1), intvl(2*i) ], i = kf, ... , kl-1, have "converged" i.e., on output, all intervals [ intvl(2*i-1), intvl(2*i) ], i < kf, and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo or the global a(l,l-1) is negligibl the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to the absolute error tolerance for the eigenvalues. an approximate eigenvalue is accepted as converged of width less than or equal to and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo or the global a(l,l-1) is negligibl with the active submatrix in rows and columns l to i. eigenvalues i+1 to ihi have already converged. either l = ilo, o |
| convergence convergence this is the lookahead loop, going until we have convergence or too many steps have been taken two consecutive small subdiagonal elements will stall convergence of a double shift if their product is smal necessary to scan the "tridiagonal portion of the matrix." in two consecutive small subdiagonal elements will stall convergence of a double shift if their product is smal necessary to scan the "tridiagonal portion of the matrix." in ijob (input) integer specifies the criterion for "convergence" of an interval reltol times the larger (in magnitude) endpoint, then two consecutive small subdiagonal elements will stall convergence of a double shift if their product is smal necessary to scan the "tridiagonal portion of the matrix." in ijob (input) integer specifies the criterion for "convergence" of an interval reltol times the larger (in magnitude) endpoint, then two consecutive small subdiagonal elements will stall convergence of a double shift if their product is smal necessary to scan the "tridiagonal portion of the matrix." in this is the lookahead loop, going until we have convergence or too many steps have been taken |
| Convert Convert Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t Convert descriptor into standard form for easy access t |
| converted converted this routine will not function correctly if it is converted to al |
| Converting Converting this routine will not function correctly if it is converted to all lower case. Converting it to all upper case is allowed arguments |
| coordinate coordinate row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, csrc_x} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, csrc_x} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, csrc_x} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, csrc_x} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
|
| coordinates coordinates (irow1,icol1) is (i,j)-coordinates of h(istart,istart (irow1,icol1) is (i,j)-coordinates of h(istart,istart (irow1,icol1) is (i,j)-coordinates of h(istart,istart (irow1,icol1) is (i,j)-coordinates of h(istart,istart |
| cope cope furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande furthermore, the elements in the same row are ldb=llda-1 apart the complicated formulas are to cope with the bande |
| copied copied zero out any junk entries that were copied and ipiv are not modified. = 'n': the matrix a(ia:ia+n-1,ja:ja+n-1) will be copied t = 'e': the matrix a(ia:ia+n-1,ja:ja+n-1) will be equili- specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; array into a local replicated array or vise versa. notice that the entire submatrix that is copied gets placed on one node o can receive, or just one row or column of nodes. specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; be modified. = 'n': the matrix a will be copied to af and factored copied to af and factored. zero out any junk entries that were copied and ipiv are not modified. = 'n': the matrix a(ia:ia+n-1,ja:ja+n-1) will be copied t = 'e': the matrix a(ia:ia+n-1,ja:ja+n-1) will be equili- specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; array into a local replicated array or vise versa. notice that the entire submatrix that is copied gets placed on one node o can receive, or just one row or column of nodes. specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; contains the local pieces of the distributed matrix sub( a ) to be copied from iq (global input) integer be modified. = 'n': the matrix a will be copied to af and factored copied to af and factored. zero out any junk entries that were copied and ipiv are not modified. = 'n': the matrix a(ia:ia+n-1,ja:ja+n-1) will be copied t = 'e': the matrix a(ia:ia+n-1,ja:ja+n-1) will be equili- specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; array into a local replicated array or vise versa. notice that the entire submatrix that is copied gets placed on one node o can receive, or just one row or column of nodes. specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; contains the local pieces of the distributed matrix sub( a ) to be copied from iq (global input) integer be modified. = 'n': the matrix a will be copied to af and factored copied to af and factored. zero out any junk entries that were copied and ipiv are not modified. = 'n': the matrix a(ia:ia+n-1,ja:ja+n-1) will be copied t = 'e': the matrix a(ia:ia+n-1,ja:ja+n-1) will be equili- specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; array into a local replicated array or vise versa. notice that the entire submatrix that is copied gets placed on one node o can receive, or just one row or column of nodes. specifies the part of the distributed matrix sub( a ) to be copied lower triangular part of sub( a ) is not referenced; be modified. = 'n': the matrix a will be copied to af and factored copied to af and factored. |
| copies copies pclacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pclacp3 is an auxiliary routine that copies from a global paralle the entire submatrix that is copied gets placed on one node or pclacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pdlacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pdlacp3 is an auxiliary routine that copies from a global paralle the entire submatrix that is copied gets placed on one node or pdlacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pslacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pslacp3 is an auxiliary routine that copies from a global paralle the entire submatrix that is copied gets placed on one node or pslacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pzlacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes pzlacp3 is an auxiliary routine that copies from a global paralle the entire submatrix that is copied gets placed on one node or pzlacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes |
| Copy Copy Copy the matrix t so it won't be destroyed in factorization Copy d block into af storage for solve first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy from a and af into block bidiagonal matrix (tail of af dbptr = pointer to diagonal blocks in a lwork = locr(n+mod(ia-1,mb_a))*nb_a. work is used to keep a Copy of at most an entire column block of sub( a ) if lwork = -1, then lwork is global input and a workspace distributed matrix b. no communication is performed, pclacp2 performs a local Copy sub( a ) := sub( b ), where sub( a ) denote pclacp2 requires that only dimension of the matrix operands is distributed matrix b. no communication is performed, pclacpy performs a local Copy sub( a ) := sub( b ), where sub( a ) denote Copy submatrix of size 2*jblk and prepare to do generalize Copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^c stored, conjugate transpose first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy d block into af storage for solve first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy from a and af into block bidiagonal matrix (tail of af dbptr = pointer to diagonal blocks in a lwork = locr(n+mod(ia-1,mb_a))*nb_a. work is used to keep a Copy of at most an entire column block of sub( a ) if lwork = -1, then lwork is global input and a workspace distributed matrix b. no communication is performed, pdlacp2 performs a local Copy sub( a ) := sub( b ), where sub( a ) denote pdlacp2 requires that only dimension of the matrix operands is distributed matrix b. no communication is performed, pdlacpy performs a local Copy sub( a ) := sub( b ), where sub( a ) denote dlamda (global output) double precision array, dimension (n) a Copy of the first k eigenvalues which will be used b dlamda (global output) double precision array, dimension (n) a Copy of the first k eigenvalues which will be used b Copy submatrix of size 2*jblk and prepare to do generalize it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same Copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same Copy o and will contain the entire array. Copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^t stored, transpose first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy d block into af storage for solve first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy from a and af into block bidiagonal matrix (tail of af dbptr = pointer to diagonal blocks in a lwork = locr(n+mod(ia-1,mb_a))*nb_a. work is used to keep a Copy of at most an entire column block of sub( a ) if lwork = -1, then lwork is global input and a workspace distributed matrix b. no communication is performed, pslacp2 performs a local Copy sub( a ) := sub( b ), where sub( a ) denote pslacp2 requires that only dimension of the matrix operands is distributed matrix b. no communication is performed, pslacpy performs a local Copy sub( a ) := sub( b ), where sub( a ) denote dlamda (global output) real array, dimension (n) a Copy of the first k eigenvalues which will be used b dlamda (global output) real array, dimension (n) a Copy of the first k eigenvalues which will be used b Copy submatrix of size 2*jblk and prepare to do generalize it assumes that the input array, bycol, is distributed across rows and that all process columns contain the same Copy o and will contain the entire array. it assumes that the input array, byrow, is distributed across columns and that all process rows contain the same Copy o and will contain the entire array. Copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^t stored, transpose first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy d block into af storage for solve first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy from a and af into block bidiagonal matrix (tail of af dbptr = pointer to diagonal blocks in a lwork = locr(n+mod(ia-1,mb_a))*nb_a. work is used to keep a Copy of at most an entire column block of sub( a ) if lwork = -1, then lwork is global input and a workspace distributed matrix b. no communication is performed, pzlacp2 performs a local Copy sub( a ) := sub( b ), where sub( a ) denote pzlacp2 requires that only dimension of the matrix operands is distributed matrix b. no communication is performed, pzlacpy performs a local Copy sub( a ) := sub( b ), where sub( a ) denote Copy submatrix of size 2*jblk and prepare to do generalize Copy matrix h_i (the last bw cols of g_i) to af storag since we have g_i^c stored, conjugate transpose first Copy and multiply it into temporary storage Copy last diagonal block into af storage for subsequen Copy the matrix t so it won't be destroyed in factorization |
| copying copying local copying in the block bidiagonal are i (global input) integer a(i,i) is the global location that the copying starts from local copying in the block bidiagonal are i (global input) integer a(i,i) is the global location that the copying starts from local copying in the block bidiagonal are i (global input) integer a(i,i) is the global location that the copying starts from local copying in the block bidiagonal are i (global input) integer a(i,i) is the global location that the copying starts from |
| corners corners on entry, q contains the eigenvectors of two submatrices in the two square blocks with corners at (1,1), (n1,n1 on exit, q contains the trailing (n-k) updated eigenvectors on entry, q contains the eigenvectors of two submatrices in the two square blocks with corners at (1,1), (n1,n1 on exit, q contains the trailing (n-k) updated eigenvectors on entry, q contains the eigenvectors of two submatrices in the two square blocks with corners at (1,1), (n1,n1 on exit, q contains the trailing (n-k) updated eigenvectors on entry, q contains the eigenvectors of two submatrices in the two square blocks with corners at (1,1), (n1,n1 on exit, q contains the trailing (n-k) updated eigenvectors |
| correct correct different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pcheevd cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pcheevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pchegvx cannot guarante the result are only available in the scope of sub( x ), i.e if sub( x ) is distributed along a process row, the correct results ar is distributed along a process column, the correct results are only the result are only available in the scope of sub( x ), i.e if sub( x ) is distributed along a process row, the correct results ar is distributed along a process column, the correct results are only the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsyevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pdsygvx cannot guarante the result are only available in the scope of sub( x ), i.e if sub( x ) is distributed along a process row, the correct results ar is distributed along a process column, the correct results are only the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssyevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pssygvx cannot guarante different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzheev cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzheevx cannot guarante the array descriptor for the distributed matrix a. if desca( ctxt_ ) is incorrect, pzhegvx cannot guarante the result are only available in the scope of sub( x ), i.e if sub( x ) is distributed along a process row, the correct results ar is distributed along a process column, the correct results are only |
| correctly correctly i am not sure that this works correctly when ib and jb are not equa with 1 used in its place. i am not sure that this works correctly when ib and jb are not equa with 1 used in its place. this routine will not function correctly if it is converted to al i am not sure that this works correctly when ib and jb are not equa with 1 used in its place. i am not sure that this works correctly when ib and jb are not equa with 1 used in its place. |
| correctness correctness see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin see "on the correctness of parallel bisection in floatin |
| correspoding correspoding by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig by the array iclustr. as a result, the dot product between eigenvectors correspoding to the i^th cluster may be as hig |
| correspond correspond vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pcstein does no of orthogonalization is controlled by the input parameter lwork. in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pdstein does no of orthogonalization is controlled by the input parameter lwork. vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. psstein does no of orthogonalization is controlled by the input parameter lwork. vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces in parallel, using inverse iteration. the eigenvectors found correspond to user specified eigenvalues. pzstein does no of orthogonalization is controlled by the input parameter lwork. |
| corresponding corresponding ccombamax1 finds the element having maximum real part absolute value as well as its corresponding globl index arguments the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces are the singular values of a and the columns of u and v are the corresponding right and left singular vectors, respectively. th only the first min(m,n) columns of u and rows of vt = v**t are vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces tributed matrix. this vector stores the information required to establish the mapping between a matrix entry and its corresponding work arrays. each of these values is returned in the first entry of the corresponding work array, and no error messag vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes the k-th subdiagonal in the first nb columns are overwritten with the corresponding elements of the reduced distribute array tau, represent the matrix q as a product of elementary vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the right eigenvector x and the left eigenvector y of t corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces are the singular values of a and the columns of u and v are the corresponding right and left singular vectors, respectively. th only the first min(m,n) columns of u and rows of vt = v**t are vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the desired counts, n(w), at the endpoints of the corresponding intervals. this array will, in general pdlaed0 computes all eigenvalues and corresponding eigenvectors of where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes the k-th subdiagonal in the first nb columns are overwritten with the corresponding elements of the reduced distribute array tau, represent the matrix q as a product of elementary vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces pdlasrt sort the numbers in d in increasing order and the corresponding vectors in q arguments vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces and optimal size for all work arrays. each of these values is returned in the first entry of the corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces tributed matrix. this vector stores the information required to establish the mapping between a matrix entry and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces are the singular values of a and the columns of u and v are the corresponding right and left singular vectors, respectively. th only the first min(m,n) columns of u and rows of vt = v**t are vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the desired counts, n(w), at the endpoints of the corresponding intervals. this array will, in general pslaed0 computes all eigenvalues and corresponding eigenvectors of where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes the k-th subdiagonal in the first nb columns are overwritten with the corresponding elements of the reduced distribute array tau, represent the matrix q as a product of elementary vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces pslasrt sort the numbers in d in increasing order and the corresponding vectors in q arguments vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces and optimal size for all work arrays. each of these values is returned in the first entry of the corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces tributed matrix. this vector stores the information required to establish the mapping between a matrix entry and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces are the singular values of a and the columns of u and v are the corresponding right and left singular vectors, respectively. th only the first min(m,n) columns of u and rows of vt = v**t are vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces tributed matrix. this vector stores the information required to establish the mapping between a matrix entry and its corresponding work arrays. each of these values is returned in the first entry of the corresponding work array, and no error messag vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces where they are computed, to a scalapack standard block cyclic array, sorted so that the corresponding eigenvalues are sorted notes the k-th subdiagonal in the first nb columns are overwritten with the corresponding elements of the reduced distribute array tau, represent the matrix q as a product of elementary vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: the matrices factored on each processor. the factors of these submatrices overwrite the corresponding parts of 2) reduced system phase: vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the right eigenvector x and the left eigenvector y of t corresponding vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces zcombamax1 finds the element having maximum real part absolute value as well as its corresponding globl index arguments |
| correspondingwork correspondingwork size for all work arrays. each of these values is returned in the first entry of the correspondingwork array, and n size for all work arrays. each of these values is returned in the first entry of the correspondingwork array, and n |
| cost cost minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lrwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lrwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lrwork: minimal workspace is supplied and orfac is too small. if you want to guarantee orthogonality (at the cost the following to lrwork: |
| could could if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in if the processor could not locally factor, it jumps here the factorization has been completed, but the factor u is exactly singular, so the solution could not b factor u is exactly singular, so the solution and error bounds could not be computed factorization has been completed, but the > 0: if info = k, u(ia+k-1,ia+k-1) is exactly zero; the matrix is singular and its inverse could not b this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed the computation of v, which could be performed in any processo processor column that owns a( :, i+1 ) so that a( :, i+1 ) m(j) could overflow, set xbnd to 0 if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== > 0: if info = i, the (i,i) element of the factor u or l is zero, and the inverse could not be computed ===================================================================== if the processor could not locally factor, it jumps here this output array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not b orfac and info). eigenvectors corresponding to clusters of if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in if the processor could not locally factor, it jumps here the factorization has been completed, but the factor u is exactly singular, so the solution could not b factor u is exactly singular, so the solution and error bounds could not be computed factorization has been completed, but the > 0: if info = k, u(ia+k-1,ia+k-1) is exactly zero; the matrix is singular and its inverse could not b which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== > 0: if info = i, the (i,i) element of the factor u or l is zero, and the inverse could not be computed ===================================================================== if the processor could not locally factor, it jumps here which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine this output array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not b orfac and info). eigenvectors corresponding to clusters of this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed the computation of v, which could be performed in any processo processor column that owns a( :, i+1 ) so that a( :, i+1 ) if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in if the processor could not locally factor, it jumps here the factorization has been completed, but the factor u is exactly singular, so the solution could not b factor u is exactly singular, so the solution and error bounds could not be computed factorization has been completed, but the > 0: if info = k, u(ia+k-1,ia+k-1) is exactly zero; the matrix is singular and its inverse could not b which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== > 0: if info = i, the (i,i) element of the factor u or l is zero, and the inverse could not be computed ===================================================================== if the processor could not locally factor, it jumps here which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. it could conceivably fail on hexadecimal or decimal machine this output array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not b orfac and info). eigenvectors corresponding to clusters of this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed the computation of v, which could be performed in any processo processor column that owns a( :, i+1 ) so that a( :, i+1 ) if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in if the processor could not locally factor, it jumps here the factorization has been completed, but the factor u is exactly singular, so the solution could not b factor u is exactly singular, so the solution and error bounds could not be computed factorization has been completed, but the > 0: if info = k, u(ia+k-1,ia+k-1) is exactly zero; the matrix is singular and its inverse could not b this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed this array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not be reorthogonalize eigenvectors corresponding to clusters of eigenvalues indexed the computation of v, which could be performed in any processo processor column that owns a( :, i+1 ) so that a( :, i+1 ) m(j) could overflow, set xbnd to 0 if the processor could not locally factor, it jumps here discard temporary matrix stored beginning in a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed, and th is not positive definite, so the factorization could not be completed, and the solution and erro = n+1: rcond is less than machine precision. the a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== a(ia:ia+k-1,ja:ja+k-1) is not positive definite, and the factorization could not be completed ===================================================================== > 0: if info = i, the (i,i) element of the factor u or l is zero, and the inverse could not be computed ===================================================================== if the processor could not locally factor, it jumps here this output array contains indices of eigenvectors corresponding to a cluster of eigenvalues that could not b orfac and info). eigenvectors corresponding to clusters of |
| couldn couldn this is set if the input matrix had an odd number of real eigenvalues and things couldn't be paired or if the inpu 0 indicates successful completion. this is set if the input matrix had an odd number of real eigenvalues and things couldn't be paired or if the inpu 0 indicates successful completion. |
| Count Count update iteration Count j = 1,...,minp. it uses and computes the function n(w), which is the Count of eigenvalues of a symmetric tridiagonal matrix less tha reltol times the larger (in magnitude) endpoint, or if the Counts at the endpoints are identical to the count considered to have "converged". pdlapdct Counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a to 1 (in slmake.inc). the features of ieee arithmetic that are needed for the "fast" sturm Count are : (a) infinit point number is assumed be in the 32nd bit position j = 1,...,minp. it uses and computes the function n(w), which is the Count of eigenvalues of a symmetric tridiagonal matrix less tha reltol times the larger (in magnitude) endpoint, or if the Counts at the endpoints are identical to the count considered to have "converged". pslapdct Counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a to 1 (in slmake.inc). the features of ieee arithmetic that are needed for the "fast" sturm Count are : (a) infinit point number is assumed be in the 32nd or 64th bit position update iteration Count |
| counts counts intvlct (input/output) integer array, dimension (2*mmax) the counts at the endpoints of the intervals. intvlct(2*j-1 the function value n(intvl(2*j-1)), and intvlct(2*j) is the reltol times the larger (in magnitude) endpoint, or if the counts at the endpoints are identical to the count considered to have "converged". pdlapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a intvlct (input/output) integer array, dimension (2*mmax) the counts at the endpoints of the intervals. intvlct(2*j-1 the function value n(intvl(2*j-1)), and intvlct(2*j) is the reltol times the larger (in magnitude) endpoint, or if the counts at the endpoints are identical to the count considered to have "converged". pslapdct counts the number of negative eigenvalues of (t - sigma i) the innermost loop to avoid overflow and determine the sign of a |
| CPTTRF CPTTRF definite tridiagonal matrix a such that a = u**h*d*u or a = l*d*l**h (computed by CPTTRF) arguments |
| CPTTRSV CPTTRSV CPTTRSV solves one of the triangular system u * x = b, or u**h * x = b, |
| Cray Cray the log of large is sufficiently large. this subroutine is intended to identify machines with a large exponent range, such as the Crays of the values computed by pdlamch. this subroutine is needed because add/subtract, or on those binary machines without guard digits which subtract like the Cray x-mp, cray y-mp, cray c-90, or cray-2 without guard digits, but we know of none. add/subtract, or on those binary machines without guard digits which subtract like the Cray x-mp, cray y-mp, cray c-90, or cray-2 without guard digits, but we know of none. see dlaed3 for details. the log of large is sufficiently large. this subroutine is intended to identify machines with a large exponent range, such as the Crays of the values computed by pslamch. this subroutine is needed because add/subtract, or on those binary machines without guard digits which subtract like the Cray x-mp, cray y-mp, cray c-90, or cray-2 without guard digits, but we know of none. add/subtract, or on those binary machines without guard digits which subtract like the Cray x-mp, cray y-mp, cray c-90, or cray-2 without guard digits, but we know of none. see slaed3 for details. |
| Crays Crays the log of large is sufficiently large. this subroutine is intended to identify machines with a large exponent range, such as the Crays of the values computed by pdlamch. this subroutine is needed because the log of large is sufficiently large. this subroutine is intended to identify machines with a large exponent range, such as the Crays of the values computed by pslamch. this subroutine is needed because |
| Create Create rho (input) double precision the subdiagonal entry used to Create the rank-1 modification work (local workspace/output) double precision array, Create and do these transform rho (input) real the subdiagonal entry used to Create the rank-1 modification work (local workspace/output) real array, Create and do these transform |
| created created auxiliary fillin space. fillin is created during the factorization routin is to be solved using pcdbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pcdttrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pcgbtrs after the factorization the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. auxiliary fillin space. fillin is created during the factorization routin is to be solved using pcpbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pcpttrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pddbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pddttrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pdgbtrs after the factorization the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. auxiliary fillin space. fillin is created during the factorization routin is to be solved using pdpbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pdpttrs after the factorization with myprowc defined when a new context is created as call blacs_gridinit( contextc, 'r', nprocs, 1 ) auxiliary fillin space. fillin is created during the factorization routin is to be solved using psdbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using psdttrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using psgbtrs after the factorization the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. auxiliary fillin space. fillin is created during the factorization routin is to be solved using pspbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pspttrs after the factorization with myprowc defined when a new context is created as call blacs_gridinit( contextc, 'r', nprocs, 1 ) auxiliary fillin space. fillin is created during the factorization routin is to be solved using pzdbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pzdttrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pzgbtrs after the factorization the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. the point of reflection. the pictures below demonstrate this. in the following code, the row sums created by --- rows below ar to as colsums. infinity-norm = 1-norm = rowsums+colsums. auxiliary fillin space. fillin is created during the factorization routin is to be solved using pzpbtrs after the factorization auxiliary fillin space. fillin is created during the factorization routin is to be solved using pzpttrs after the factorization |
| creating creating from the vector v and applies it from left and right to h, thus creating a nonzero bulge below the subdiagonal each subsequent iteration determines a reflection g to the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering the individual pieces are factored independently and in parallel. these factors are applied to the matrix creating space af. mathematically, this is equivalent to reordering from the vector v and applies it from left and right to h, thus creating a nonzero bulge below the subdiagonal each subsequent iteration determines a reflection g to |
| criterion criterion set machine-dependent constants for the stopping criterion compute reorthogonalization criterion and stopping criterion set machine-dependent constants for the stopping criterion ijob (input) integer specifies the criterion for "convergence" of an interval reltol times the larger (in magnitude) endpoint, then set machine-dependent constants for the stopping criterion ijob (input) integer specifies the criterion for "convergence" of an interval reltol times the larger (in magnitude) endpoint, then set machine-dependent constants for the stopping criterion set machine-dependent constants for the stopping criterion compute reorthogonalization criterion and stopping criterion set machine-dependent constants for the stopping criterion |
| critical critical a group of rotn transformations (this is on the critical path.) (loops 50-120 2.) the small work it takes so that each of the rows a group of rotn transformations (this is on the critical path.) (loops 130-180 and columns is at the same place. for example, a group of rotn transformations (this is on the critical path.) (loops 130-180 and columns is at the same place. for example, a group of rotn transformations (this is on the critical path.) (loops 50-120 2.) the small work it takes so that each of the rows |
| CS41 CS41 see w. kahan "accurate eigenvalues of a symmetric tridiagonal matrix", report CS41, computer science dept., stanfor see w. kahan "accurate eigenvalues of a symmetric tridiagonal matrix", report CS41, computer science dept., stanfor |
| csrc csrc holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: holding part of the matrix, of size 1xnp where np is adjusted, starting at csrc=0, with ja modified to reflect dropped procs first processor to hold part of the matrix: |
| CSRC_ CSRC_ row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. np = numroc( n, nb, myrow, iarow, nprow ), nq = numroc( n, nb, mycol, descq( CSRC_ ), npcol iwork (local workspace/local output) integer array, row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca[ csrc_ ] the process column over which th distributed. np = numroc( n, nb, myrow, descq( rsrc_ ), nprow ) nq = numroc( n, nb, mycol, descq( CSRC_ ), npcol if lwork = -1, the lwork is global input and a workspace row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. np = numroc( n, nb, myrow, iarow, nprow ), nq = numroc( n, nb, mycol, descq( CSRC_ ), npcol iwork (local workspace/local output) integer array, row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca[ csrc_ ] the process column over which th distributed. np = numroc( n, nb, myrow, descq( rsrc_ ), nprow ) nq = numroc( n, nb, mycol, descq( CSRC_ ), npcol if lwork = -1, the lwork is global input and a workspace row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_a (global) desca( csrc_ ) the process column over which th distributed. |
| CSRC_A CSRC_A row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca[ csrc_ ] the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the matrix a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th lld_a (local) desca( lld_ ) the leading dimension of the local row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. first row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. row of the array a is distributed. CSRC_A (global) desca( csrc_ ) the process column over which th distributed. |
| CSRC_B CSRC_B ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) npb0 = numroc( n+iroffb, mb_b, myrow, ibrow, nprow ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) pqb0 = numroc( p+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) nqb0 = numroc( n+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) npb0 = numroc( n+iroffb, mb_b, myrow, ibrow, nprow ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) pqb0 = numroc( p+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) nqb0 = numroc( n+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) npb0 = numroc( n+iroffb, mb_b, myrow, ibrow, nprow ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) pqb0 = numroc( p+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) nqb0 = numroc( n+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) npb0 = numroc( n+iroffb, mb_b, myrow, ibrow, nprow ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) pqb0 = numroc( p+icoffb, nb_b, mycol, ibcol, npcol ), ibrow = indxg2p( ib, mb_b, myrow, rsrc_b, nprow ), ibcol = indxg2p( jb, nb_b, mycol, CSRC_B, npcol ) nqb0 = numroc( n+icoffb, nb_b, mycol, ibcol, npcol ), |
| CSRC_C CSRC_C icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) npc0 = numroc( n+icoffc, mb_c, myrow, icrow, nprow ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( ic, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( n+icoffc, nb_c, mycol, iccol, npcol ), icrow = indxg2p( icc, mb_c, myrow, rsrc_c, nprow ), iccol = indxg2p( jcc, nb_c, mycol, CSRC_C, npcol ) nqc0 = numroc( ni+icoffc, nb_c, mycol, iccol, npcol ), |
| CSRC_Q CSRC_Q iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) iqcol = indxg2p( jq, mb_q, mycol, CSRC_Q, npcol iwork (local workspace/output) integer array, dimension (liwork) iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) iqcol = indxg2p( jq, mb_q, mycol, CSRC_Q, npcol iwork (local workspace/output) integer array, dimension (liwork) |
| CSRC_V CSRC_V ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), ivrow = indxg2p( iv, mb_v, myrow, rsrc_v, nprow ), ivcol = indxg2p( jv, nb_v, mycol, CSRC_V, npcol ) npv0 = numroc( n+iroffv, mb_v, myrow, ivrow, nprow ), |
| CSRC_X CSRC_X row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, CSRC_X} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, CSRC_X} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, CSRC_X} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
row or process column owns the vector operand, therefore only the
process of coordinate {rsrc_x, CSRC_X} receives the result
if incx = m_x, then sub( x ) is a vector distributed over a process
|
| CSTEIN CSTEIN performance. in the limit (i.e. clustersize = n-1) pCSTEIN will perform no better than cstein on for clustersize = n/sqrt(nprow*npcol) reorthogonalizing performance. in the limit (i.e. clustersize = n-1) pCSTEIN will perform no better than cstein on 1 processor all eigenvectors will increase the total execution time pCSTEIN computes the eigenvectors of a symmetric tridiagonal matri correspond to user specified eigenvalues. pcstein does not |
| CSTEIN2 CSTEIN2 nvec - ceil(m/p) + 1 are guaranteed to be orthogonal ( the orthogonality is similar to that obtained from CSTEIN2) max(5*n,np00*mq00) + ceil(m/p)*n, |
| CSTEQR2 CSTEQR2 > 0: if info = 1 through n, the i(th) eigenvalue did not converge in CSTEQR2 after a total of 30*n iterations by finding that eigenvalues were not identical across |
| CSUMJ CSUMJ do 130 i = 1, j - 1 CSUMJ = csumj + ( a( i, j )*uscal )*x( i do 140 i = j + 1, n do 130 i = 1, j - 1 CSUMJ = csumj + ( a( i, j )*uscal )*x( i do 140 i = j + 1, n |
| CTO CTO pclascl multiplies the m-by-n complex distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar CTO/cfrom. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pdlascl multiplies the m-by-n real distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar CTO/cfrom. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pslascl multiplies the m-by-n real distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar CTO/cfrom. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that pzlascl multiplies the m-by-n complex distributed matrix sub( a ) denoting a(ia:ia+m-1,ja:ja+n-1) by the real scalar CTO/cfrom. thi cto * a(i,j) / cfrom does not over/underflow. type specifies that |
| CTOT CTOT CTOT (workspace) integer array, dimension( npcol, 4 psm (workspace) integer array, dimension( npcol, 4) CTOT (workspace) integer array, dimension( npcol, 4 npcol (global input) integer CTOT (workspace) integer array, dimension( npcol, 4 psm (workspace) integer array, dimension( npcol, 4) CTOT (workspace) integer array, dimension( npcol, 4 npcol (global input) integer |
| CTRMM CTRMM copy matrix hu_i (the last bwl rows of gu_i) to afl storage as per requirements of blas routine CTRMM conjugate transpose hu_i to hu_i^c. copy matrix h_i (the last bw cols of g_i) to af storage as per requirements of blas routine CTRMM h_i^c to h_i. |
| CTRMVT CTRMVT CTRMVT performs the matrix-vector operation x := conjg( t' ) *y, and w := t *z, |
| CTXT CTXT dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- |
| CTXT_ CTXT_ dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- the array descriptor for the distributed matrix a. if desca( CTXT_ ) is incorrect, pcheevd cannot guarante dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- the array descriptor for the distributed matrix z. descz( CTXT_ ) must equal desca( ctxt_ work (local workspace/output) double precision array, dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- the array descriptor for the distributed matrix z. descz( CTXT_ ) must equal desca( ctxt_ work (local workspace/output) real array, dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_a (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- the array descriptor for the distributed matrix a. if desca( CTXT_ ) is incorrect, pzheev cannot guarante dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_a (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- |
| CTXT_A CTXT_A dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_A (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_A (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_A (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dt_a = 1. CTXT_A (global) desca[ ctxt_ ] the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a(global) desca( dtype_) the descriptor type. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- dtype_a = 1. CTXT_A (global) desca( ctxt_ ) the blacs context handle, indicatin ted over. the context itself is glo- |
| CURCOL CURCOL liip1 and ltlip1) is subtle. within the current processor column (i.e. mycol .eq. CURCOL) they are the same. however above the diagonal, on these processors, ltli = lii+1. liip1 and ltlip1) is subtle. within the current processor column (i.e. mycol .eq. CURCOL) they are the same. however above the diagonal, on these processors, ltli = lii+1. liip1 and ltlip1) is subtle. within the current processor column (i.e. mycol .eq. CURCOL) they are the same. however above the diagonal, on these processors, ltli = lii+1. liip1 and ltlip1) is subtle. within the current processor column (i.e. mycol .eq. CURCOL) they are the same. however above the diagonal, on these processors, ltli = lii+1. |
| Cure Cure point arithmetic. Cure: increase the parameter "fudge", recompile point arithmetic. Cure: increase the parameter "fudge", recompile |
| current current ju is the index of the last column affected by the current skip the current step: the subdiagonal info is just noise ju is the index of the last column affected by the current depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. the following variables are global indices into a: index: the current global row and column number column in the trailing block of a. set all values for bulges. all bulges are stored in intermediate steps as loops over ki. their current "task however, because there are many bulges, k1(ki) & k2(ki) might depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. all of b or a submatrix of b). important note: the current version of this code support current alignment restrictio specified eigenvalues. any vector which fails to converge is set to its current iterate after maxits iterations ( se on output, z is distributed across the p processes in block depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. equation via the routine slaed4 (as called by pdlaed3). this routine also calculates the eigenvectors of the current set all values for bulges. all bulges are stored in intermediate steps as loops over ki. their current "task however, because there are many bulges, k1(ki) & k2(ki) might depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. all of b or a submatrix of b). important note: the current version of this code support current alignment restrictio specified eigenvalues. any vector which fails to converge is set to its current iterate after maxits iterations ( se on output, z is distributed across the p processes in block the following variables are global indices into a: index: the current global row and column number column in the trailing block of a. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. equation via the routine slaed4 (as called by pslaed3). this routine also calculates the eigenvectors of the current set all values for bulges. all bulges are stored in intermediate steps as loops over ki. their current "task however, because there are many bulges, k1(ki) & k2(ki) might depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. all of b or a submatrix of b). important note: the current version of this code support current alignment restrictio specified eigenvalues. any vector which fails to converge is set to its current iterate after maxits iterations ( se on output, z is distributed across the p processes in block the following variables are global indices into a: index: the current global row and column number column in the trailing block of a. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. the following variables are global indices into a: index: the current global row and column number column in the trailing block of a. set all values for bulges. all bulges are stored in intermediate steps as loops over ki. their current "task however, because there are many bulges, k1(ki) & k2(ki) might depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. current alignment restrictio depends on a variety of parameters, especially the bandwidth. currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. all of b or a submatrix of b). important note: the current version of this code support current alignment restrictio specified eigenvalues. any vector which fails to converge is set to its current iterate after maxits iterations ( se on output, z is distributed across the p processes in block ju is the index of the last column affected by the current ju is the index of the last column affected by the current skip the current step: the subdiagonal info is just noise |
| Currently Currently depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. Currently, only storev = 'r' and direct = 'b' are supported notes Currently, only storev = 'r' and direct = 'b' are supported notes depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. Currently, only storev = 'r' and direct = 'b' are supported notes Currently, only storev = 'r' and direct = 'b' are supported notes depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. this version provides a set of parameters which should give good, but not optimal, performance on many of the Currently availabl the tuning parameters for their particular machine using the option depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. Currently, only storev = 'r' and direct = 'b' are supported notes Currently, only storev = 'r' and direct = 'b' are supported notes depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. Currently, only storev = 'r' and direct = 'b' are supported notes Currently, only storev = 'r' and direct = 'b' are supported notes depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. depends on a variety of parameters, especially the bandwidth. Currently, only algorithms designed for the case n/p >> bw ar partitioning, domain decomposition-type, etc. |
| cut cut on entry, the off-diagonal element associated with the rank-1 cut which originally split the two submatrices which are no on exit, rho has been modified to the value required by on entry, the off-diagonal element associated with the rank-1 cut which originally split the two submatrices which are no on exit, rho has been modified to the value required by on entry, the off-diagonal element associated with the rank-1 cut which originally split the two submatrices which are no on exit, rho has been modified to the value required by on entry, the off-diagonal element associated with the rank-1 cut which originally split the two submatrices which are no on exit, rho has been modified to the value required by |
| cyclic cyclic non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding a (local input/workspace) block cyclic complex array locc(ja+n-1) ) let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. pclaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cyclic non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as on output, q is distributed across the p processes in block cyclic format iq (global input) integer pdlaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cyclic let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature on output, q is distributed across the p processes in block cyclic format iq (global input) integer let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding a (local input/workspace) block cyclic double precision array locc(ja+n-1) ) let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as on output, q is distributed across the p processes in block cyclic format iq (global input) integer pslaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cyclic let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature on output, q is distributed across the p processes in block cyclic format iq (global input) integer let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding a (local input/workspace) block cyclic real array locc(ja+n-1) ) let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding a (local input/workspace) block cyclic complex*16 array locc(ja+n-1) ) let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. pzlaevswp moves the eigenvectors (potentially unsorted) from where they are computed, to a scalapack standard block cyclic non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature non-cyclic restriction: very important the mapping for matrices must be blocked, reflecting the nature let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as |
| cyclicly cyclicly let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as ===== a description vector is associated with each 2d block-cyclicly dis establish the mapping between a matrix entry and its corresponding let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distribute such a global array has an associated description vector desca. let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as let a be a generic term for any 2d block cyclicly distributed array in the following comments, the character _ should be read as |