Back| M- |
| M_A M_A value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the distribute n_a (global) desca( n_ ) the number of columns in the distri- value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca[ m_ ] the number of rows in the globa n_a (global) desca[ n_ ] the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca[ m_ ] the number of rows in the globa n_a (global) desca[ n_ ] the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the distribute n_a (global) desca( n_ ) the number of columns in the distri- value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca[ m_ ] the number of rows in the globa n_a (global) desca[ n_ ] the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the distribute n_a (global) desca( n_ ) the number of columns in the distri- value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca[ m_ ] the number of rows in the globa n_a (global) desca[ n_ ] the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the distribute n_a (global) desca( n_ ) the number of columns in the distri- value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global value) may vary. M_A (global) desca( m_ ) the number of rows in the globa n_a (global) desca( n_ ) the number of columns in the global |
| M_AF M_AF ipiv (local input) integer array of dimension locr(M_AF)+mb_af by pcgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(M_AF)+mb_af by pdgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(M_AF)+mb_af by psgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(M_AF)+mb_af by pzgetrf. ipiv(i) -> the global row local row i |
| M_P M_P if( nprow.eq.npcol ) then ldw = locc( M_P + mod(ip-1, mb_p) ) + mb_ ldw = locc( m_p + mod(ip-1, mb_p) ) + if( nprow.eq.npcol ) then ldw = locc( M_P + mod(ip-1, mb_p) ) + mb_ ldw = locc( m_p + mod(ip-1, mb_p) ) + if( nprow.eq.npcol ) then ldw = locc( M_P + mod(ip-1, mb_p) ) + mb_ ldw = locc( m_p + mod(ip-1, mb_p) ) + if( nprow.eq.npcol ) then ldw = locc( M_P + mod(ip-1, mb_p) ) + mb_ ldw = locc( m_p + mod(ip-1, mb_p) ) + |
| M_V M_V tau (local input) complex, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) complex, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) double precision array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) double precision array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) real, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) real, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) complex*16, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. tau (local input) complex*16, array, dimension locr(iv+k-1) if incv = M_V, and locc(jv+k-1) otherwise. this arra vectors. tau is tied to the distributed matrix v. |
| M_X M_X on entry the vector to be conjugated x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and M_X the vector for which a scaled sum of squares is computed. x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n ix (global input) integer where sub( x ) denotes x(ix:ix+n-1,jx) if incx = 1, x(ix,jx:jx+n-1) if incx = M_X notes where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X notes the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and M_X the vector for which a scaled sum of squares is computed. x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n ix (global input) integer where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X notes where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X based on pdzasum from the level 1 pblas. the change is where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X based on pscasum from the level 1 pblas. the change is the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and M_X the vector for which a scaled sum of squares is computed. x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n ix (global input) integer where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X notes where sub( x ) denotes x(ix:ix+n-1,jx:jx), if incx = 1, x(ix:ix,jx:jx+n-1), if incx = M_X notes on entry the vector to be conjugated x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n the global increment for the elements of x. only two values of incx are supported in this version, namely 1 and M_X the vector for which a scaled sum of squares is computed. x( i ) = x(ix+(jx-1)*M_X +(i-1)*incx ), 1 <= i <= n ix (global input) integer where sub( x ) denotes x(ix:ix+n-1,jx) if incx = 1, x(ix,jx:jx+n-1) if incx = M_X notes |
| m21 m21 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 |
| m31 m31 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked |
| m32 m32 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 |
| m42 m42 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked |
| m43 m43 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 |
| m53 m53 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked |
| m54 m54 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 |
| m64 m64 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 * a31 a42 a53 a64 * * m31 m42 m53 m64 * array elements marked * are not used by the routine; elements marked |
| m65 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 a11 a22 a33 a44 a55 a66 u11 u22 u33 u44 u55 u66 a21 a32 a43 a54 a65 * m21 m32 m43 m54 m65 |
| macheps macheps between eigenvectors corresponding to the i^th cluster may be as high as ( o(n)*macheps ) / gap(i) info (global output) integer between eigenvectors corresponding to the i^th cluster may be as high as ( o(n)*macheps ) / gap(i) info (global output) integer between eigenvectors corresponding to the i^th cluster may be as high as ( o(n)*macheps ) / gap(i) info (global output) integer between eigenvectors corresponding to the i^th cluster may be as high as ( o(n)*macheps ) / gap(i) info (global output) integer |
| machine machine set machine-dependent constants for the stopping criterion ulp (local input) real on entry, machine precisio ulp (local input) double precision on entry, machine precisio get machine constants of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix set machine-dependent constants for the stopping criterion determine machine dependent parameters to control overflow of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form small, i.e., converged. note : this should be at least radix*machine epsilon pivmin (input) double precision small, i.e., converged. note : this should be at least radix*machine epsilon ===================================================================== set machine-dependent constants for the stopping criterion pdlamch determines double precision machine parameters arguments of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form note : it is assumed that the user is on an ieee machine. if the use to 1 (in slmake.inc). the features of ieee arithmetic that where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix computers. users are encouraged to modify this subroutine to set the tuning parameters for their particular machine using the optio of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form small, i.e., converged. note : this should be at least radix*machine epsilon pivmin (input) real small, i.e., converged. note : this should be at least radix*machine epsilon ===================================================================== set machine-dependent constants for the stopping criterion pslamch determines single precision machine parameters arguments of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form note : it is assumed that the user is on an ieee machine. if the use to 1 (in slmake.inc). the features of ieee arithmetic that where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix where eps is the machine precision. if abstol is less tha where norm(t) is the 1-norm of the tridiagonal matrix set machine-dependent constants for the stopping criterion determine machine dependent parameters to control overflow of the matrix a. if the reciprocal of the condition number is less than machine precision, steps 4-6 are skipped 4. the system of equations is solved for x using the factored form ulp (local input) real on entry, machine precisio get machine constants set machine-dependent constants for the stopping criterion ulp (local input) double precision on entry, machine precisio |
| machines machines 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 this code makes very mild assumptions about floating point arithmetic. it will work on machines with a guard digit i which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. a "fudge factor" to widen the gershgorin intervals. ideally, a value of 1 should work, but on machines with slopp publicly released versions should be large enough to handle this code makes very mild assumptions about floating point arithmetic. it will work on machines with a guard digit i which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. 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 this code makes very mild assumptions about floating point arithmetic. it will work on machines with a guard digit i which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. a "fudge factor" to widen the gershgorin intervals. ideally, a value of 1 should work, but on machines with slopp publicly released versions should be large enough to handle this code makes very mild assumptions about floating point arithmetic. it will work on machines with a guard digit i which subtract like the cray x-mp, cray y-mp, cray c-90, or cray-2. |
| made made when the result of a vector-oriented pblas call is a scalar, it will be made available only within the scope which owns the vector(s then, the processes which receive the answer will be (note that if 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 made 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 made when the result of a vector-oriented pblas call is a scalar, it will be made available only within the scope which owns the vector(s then, the processes which receive the answer will be (note that if when the result of a vector-oriented pblas call is a scalar, it will be made available only within the scope which owns the vector(s then, the processes which receive the answer will be (note that if 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 made when the result of a vector-oriented pblas call is a scalar, it will be made available only within the scope which owns the vector(s then, the processes which receive the answer will be (note that if 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 made |
| magnitude magnitude to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(ix:ix+n-1,jx:jx+nrhs-1). if xtrue is the true solution, ferr(j) bounds the magnitude of the largest entry i in x(j). the estimate is as reliable as the estimate for to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(j) (the j-th column of the solution matrix x). if xtrue is the true solution, ferr(j) bounds the magnitude the magnitude of the largest entry in x(j). the quality of each eigenvector is normalized so that the element of largest magnitude has magnitude 1; here the magnitude of a complex numbe each solution vector of sub( x ). if xtrue is the true solution, ferr bounds the magnitude of the largest entr largest entry in sub( x ). the estimate is as reliable as to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(ix:ix+n-1,jx:jx+nrhs-1). if xtrue is the true solution, ferr(j) bounds the magnitude of the largest entry i in x(j). the estimate is as reliable as the estimate for 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. = 0 : when an interval is narrower than abstol, or than reltol times the larger (in magnitude) endpoint, the = 1 : when an interval is narrower than abstol, or than to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(j) (the j-th column of the solution matrix x). if xtrue is the true solution, ferr(j) bounds the magnitude the magnitude of the largest entry in x(j). the quality of each solution vector of sub( x ). if xtrue is the true solution, ferr bounds the magnitude of the largest entr largest entry in sub( x ). the estimate is as reliable as to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(ix:ix+n-1,jx:jx+nrhs-1). if xtrue is the true solution, ferr(j) bounds the magnitude of the largest entry i in x(j). the estimate is as reliable as the estimate for 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. = 0 : when an interval is narrower than abstol, or than reltol times the larger (in magnitude) endpoint, the = 1 : when an interval is narrower than abstol, or than to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(j) (the j-th column of the solution matrix x). if xtrue is the true solution, ferr(j) bounds the magnitude the magnitude of the largest entry in x(j). the quality of each solution vector of sub( x ). if xtrue is the true solution, ferr bounds the magnitude of the largest entr largest entry in sub( x ). the estimate is as reliable as to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(ix:ix+n-1,jx:jx+nrhs-1). if xtrue is the true solution, ferr(j) bounds the magnitude of the largest entry i in x(j). the estimate is as reliable as the estimate for to sub( x ), ferr is an estimated upper bound for the magnitude of the largest element in (sub( x ) - xtrue the estimate is as reliable as the estimate for rcond, and x(j) (the j-th column of the solution matrix x). if xtrue is the true solution, ferr(j) bounds the magnitude the magnitude of the largest entry in x(j). the quality of each eigenvector is normalized so that the element of largest magnitude has magnitude 1; here the magnitude of a complex numbe each solution vector of sub( x ). if xtrue is the true solution, ferr bounds the magnitude of the largest entr largest entry in sub( x ). the estimate is as reliable as |
mailin ifail. ensure abstol=2.0*pslamch( 'u' ) send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be failed to converge. their indices are stored in ifail. send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be in ifail. ensure abstol=2.0*pdlamch( 'u' ) send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be failed to converge. their indices are stored in ifail. send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be in ifail. ensure abstol=2.0*pslamch( 'u' ) send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be failed to converge. their indices are stored in ifail. send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be in ifail. ensure abstol=2.0*pdlamch( 'u' ) send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be failed to converge. their indices are stored in ifail. send e-mail to scalapack@cs.utk.ed to one or more clusters of eigenvalues could not be |
| main main where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the main to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the main offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) complex pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) double precision pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) real pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop offset in columns to beginning of main partition in each pro offset in columns to beginning of main partition in each pro d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro d (local input/local output) complex*16 pointer to local part of global vector storing the main diagonal of th on exit, this array contains information containing the offset in columns to beginning of main partition in each pro where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the main where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the main to which transformations must be applied. if eigenvalues only are being computed, i1 and i2 are set inside the main loop |
| maintain maintain if lrwork is too small to guarantee orthogonality, pcheevx attempts to maintain orthogonality i spacing between the eigenvalues. if lrwork is too small to guarantee orthogonality, pchegvx attempts to maintain orthogonality i spacing between the eigenvalues. if lwork is too small to guarantee orthogonality, pdsyevx attempts to maintain orthogonality i spacing between the eigenvalues. if lwork is too small to guarantee orthogonality, pdsygvx attempts to maintain orthogonality i spacing between the eigenvalues. if lwork is too small to guarantee orthogonality, pssyevx attempts to maintain orthogonality i spacing between the eigenvalues. if lwork is too small to guarantee orthogonality, pssygvx attempts to maintain orthogonality i spacing between the eigenvalues. if lrwork is too small to guarantee orthogonality, pzheevx attempts to maintain orthogonality i spacing between the eigenvalues. if lrwork is too small to guarantee orthogonality, pzhegvx attempts to maintain orthogonality i spacing between the eigenvalues. |
| major major h(m+2,m-1). since these elements may be on separate processors, the first major loop (10) goes over the tridiagona the node owning h(m,m) does not. this will occur on a border h(m+2,m-1). since these elements may be on separate processors, the first major loop (10) goes over the tridiagona the node owning h(m,m) does not. this will occur on a border h(m+2,m-1). since these elements may be on separate processors, the first major loop (10) goes over the tridiagona the node owning h(m,m) does not. this will occur on a border h(m+2,m-1). since these elements may be on separate processors, the first major loop (10) goes over the tridiagona the node owning h(m,m) does not. this will occur on a border |
| majority majority through some column tmp. (loops 250-260) 3.) the majority of the row and column transform (row transforms are in loops 280-380) through some column tmp. (loops within 190) 3.) the majority of the row and column transform (loops 290 on.) through some column tmp. (loops within 190) 3.) the majority of the row and column transform (loops 290 on.) through some column tmp. (loops 250-260) 3.) the majority of the row and column transform (row transforms are in loops 280-380) |
| make make determine the effect of starting the double-shift qr iteration at row m, and see if this would make h(m,m-1 $ ( two*( c*oldrp-b )+safmin ) make sure that we are making progres skip the current step: the subdiagonal info is just noise. check to make sure no processors have found error check to make sure no processors have found error this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrice 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. seeing the effect of starting a double shift qr iteration given by h44, h33, & h43h34 and see if this would make make sure it's divisible by lcm (we want even workloads! check to make sure no processors have found error check to make sure no processors have found error the algorithm used in this program is basically backward (forward) substitution. it is the hope that scaling would be used to make th been implemented in pclattrs which is called by this routine to solve check to make sure no processors have found error check to make sure no processors have found error this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrice 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. seeing the effect of starting a double shift qr iteration given by h44, h33, & h43h34 and see if this would make make sure it's divisible by lcm (we want even workloads! check to make sure no processors have found error check to make sure no processors have found error check to make sure no processors have found error check to make sure no processors have found error this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrice 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. seeing the effect of starting a double shift qr iteration given by h44, h33, & h43h34 and see if this would make make sure it's divisible by lcm (we want even workloads! check to make sure no processors have found error check to make sure no processors have found error check to make sure no processors have found error check to make sure no processors have found error this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrice 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. seeing the effect of starting a double shift qr iteration given by h44, h33, & h43h34 and see if this would make make sure it's divisible by lcm (we want even workloads! check to make sure no processors have found error check to make sure no processors have found error the algorithm used in this program is basically backward (forward) substitution. it is the hope that scaling would be used to make th been implemented in pzlattrs which is called by this routine to solve determine the effect of starting the double-shift qr iteration at row m, and see if this would make h(m,m-1 $ ( two*( c*oldrp-b )+safmin ) make sure that we are making progres skip the current step: the subdiagonal info is just noise. |
| makes makes lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) any entry of a(ia:ia+n-1,ja:ja+n-1) or b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution) with the matrices b and x. in its present form, pcheev assumes a homogeneous system and makes different processes. because of this, it is possible that a the routine makes only one pass through the vector sub( x ) notes lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) vector x(j) (i.e., the smallest relative change in any entry of a or b that makes x(j) an exact solution) work (local workspace/local output) complex array, lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) any entry of a(ia:ia+n-1,ja:ja+n-1) or b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution) with the matrices b and x. pdlaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th the routine makes only one pass through the vector sub( x ) notes lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) vector x(j) (i.e., the smallest relative change in any entry of a or b that makes x(j) an exact solution) work (local workspace/local output) double precision array, this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits in its present form, pdsyev assumes a homogeneous system and makes the different processes. because of this, it is possible that a in its present form, pdsyevd assumes a homogeneous system and makes the different processes. because of this, it is possible that a lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) any entry of a(ia:ia+n-1,ja:ja+n-1) or b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution) with the matrices b and x. pslaed3 finds the roots of the secular equation, as defined by the values in d, w, and rho, between 1 and k. it makes th the routine makes only one pass through the vector sub( x ) notes lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) vector x(j) (i.e., the smallest relative change in any entry of a or b that makes x(j) an exact solution) work (local workspace/local output) real array, this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits in its present form, pssyev assumes a homogeneous system and makes the different processes. because of this, it is possible that a in its present form, pssyevd assumes a homogeneous system and makes the different processes. because of this, it is possible that a lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) any entry of a(ia:ia+n-1,ja:ja+n-1) or b(ib:ib+n-1,jb:jb+nrhs-1) that makes x(j) an exact solution) with the matrices b and x. in its present form, pzheev assumes a homogeneous system and makes different processes. because of this, it is possible that a the routine makes only one pass through the vector sub( x ) notes lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) vector x(j) (i.e., the smallest relative change in any entry of a or b that makes x(j) an exact solution) work (local workspace/local output) complex*16 array, lative change in any entry of sub( a ) or sub( b ) that makes sub( x ) an exact solution) |
| making making $ ( two*( c*oldrp-b )+safmin ) make sure that we are making progres skip the current step: the subdiagonal info is just noise. $ ( two*( c*oldrp-b )+safmin ) make sure that we are making progres skip the current step: the subdiagonal info is just noise. |
| Manchester Manchester 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, university of Manchester. it was originally named sonest, date the serial version dlacon has been contributed by nick higham, university of Manchester. it was originally named sonest, date 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 slacon has been contributed by nick higham, university of Manchester. it was originally named sonest, date 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, university of Manchester. it was originally named sonest, date |
| manner manner the eigenvectors on output. the eigenvectors are distributed in a block cyclic manner in both dimensions, with the eigenvectors on output. the eigenvectors are distributed in a block cyclic manner in both dimensions, with the eigenvectors on output. the eigenvectors are distributed in a block cyclic manner in both dimensions, with the eigenvectors on output. the eigenvectors are distributed in a block cyclic manner in both dimensions, with |
| mantissa mantissa prec = eps*base t = number of (base) digits in the mantissa emin = minimum exponent before (gradual) underflow prec = eps*base t = number of (base) digits in the mantissa emin = minimum exponent before (gradual) underflow |
| manual manual used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details used in lapack. please see the notes below and the scalapack manual for more detail on the format o |
| many many this is the lookahead loop, going until we have convergence or too many steps have been taken currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer is returned. note that when range='v', pcheevx does not know how many eigenvectors are requested unti and as long as lrwork is large enough to allow pcheevx to is returned. note that when range='v', pchegvx does not know how many eigenvectors are requested unti and as long as lrwork is large enough to allow pchegvx to if we are starting in the middle because of consecutive small subdiagonal elements, we need to see how many bulges w subdiagonal property. currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) migh finishing up. currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c is returned. note that when range='v', pdsyevx does not know how many eigenvectors are requested unti and as long as lwork is large enough to allow pdsyevx to is returned. note that when range='v', pdsygvx does not know how many eigenvectors are requested unti and as long as lwork is large enough to allow pdsygvx to 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 currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) migh finishing up. currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c is returned. note that when range='v', pssyevx does not know how many eigenvectors are requested unti and as long as lwork is large enough to allow pssyevx to is returned. note that when range='v', pssygvx does not know how many eigenvectors are requested unti and as long as lwork is large enough to allow pssygvx to currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer is returned. note that when range='v', pzheevx does not know how many eigenvectors are requested unti and as long as lrwork is large enough to allow pzheevx to is returned. note that when range='v', pzhegvx does not know how many eigenvectors are requested unti and as long as lrwork is large enough to allow pzhegvx to if we are starting in the middle because of consecutive small subdiagonal elements, we need to see how many bulges w subdiagonal property. currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c currently, only algorithms designed for the case n/p >> bw are implemented. these go by many names, including divide and conquer for tridiagonal matrices, it is obvious: n/p >> bw(=1), and so d&c this is the lookahead loop, going until we have convergence or too many steps have been taken |
| mapping mapping the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object 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 correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object 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 correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its correspondin vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object 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 array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces vector. this vector stores the information required to establish the mapping between an object element and its corresponding proces |
| Marbwus Marbwus andrew j. cleary, livermore national lab and university of tenn., and Marbwus hegland, australian natonal university. feb., 1997 andrew j. cleary, livermore national lab and university of tenn., and Marbwus hegland, australian natonal university. feb., 1997 |
| March March university of manchester. it was originally named sonest, dated March 16, 1988 reference: n.j. higham, "fortran codes for estimating the one-norm of university of manchester. it was originally named sonest, dated March 16, 1988 reference: n.j. higham, "fortran codes for estimating the one-norm of university of manchester. it was originally named sonest, dated March 16, 1988 reference: n.j. higham, "fortran codes for estimating the one-norm of university of manchester. it was originally named sonest, dated March 16, 1988 reference: n.j. higham, "fortran codes for estimating the one-norm of |
| margin margin in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) |
| Mark Mark implemented by Mark r. fahey, may 28, 199 ===================================================================== implemented by Mark r. fahey, june, 200 ===================================================================== implemented by Mark r. fahey, june, 200 ===================================================================== implemented by Mark r. fahey, may 28, 199 ===================================================================== |
| marked marked array elements marked * are not used by the routine; elements marke elements of u, because of fill-in resulting from the row array elements marked * are not used by the routine; elements marke elements of u, because of fill-in resulting from the row array elements marked * are not used by the routine; elements marke elements of u, because of fill-in resulting from the row array elements marked * are not used by the routine; elements marke elements of u, because of fill-in resulting from the row |
| Markus Markus andrew j. cleary, livermore national lab and university of tenn., and Markus hegland, australian national university. feb., 1997 last modified by: peter arbenz, institute of scientific computing, andrew j. cleary, livermore national lab and university of tenn., and Markus hegland, australian national university. feb., 1997 last modified by: peter arbenz, institute of scientific computing, |
| Math Math a real or complex matrix, with applications to condition estimation", acm trans. Math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. Math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. Math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== a real or complex matrix, with applications to condition estimation", acm trans. Math. soft., vol. 14, no. 4, pp. 381-396, december 1988 ===================================================================== |
| Mathematically Mathematically fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. Mathematically, this is equivalent to reorderin leading submatrix of size equal to the sum of the sizes of fillin, which is stored in a non-inspectable way in auxiliary space af. 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| mation mation m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper or lower bidiagonal form by an unitary transformation q' * a * p, an mation to the unreduced part of sub( a ). m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper or lower bidiagonal form by an unitary transformation q' * a * p, an mation to the unreduced part of sub( a ). |
| matrices matrices where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the mai claref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the mai dlaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one scalapack manual for more detail on the format of distributed matrices of the factorization. this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrices scalapack manual for more detail on the format of distributed matrices ja (global input) integer the matrices q and p are represented as products of elementar the matrices q and p are represented as products of elementar the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex pointer into the local matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the lu decomposition with partial pivoting and row interchanges is where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also see "computing small singular values of bidiagonal matrices kahan, lapack working note #3. n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex pointer into the pchentrd is faster than pchetrd on almost all matrices enough workspace is available to use the tailored codes. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) or lower bidiagonal form by an unitary transformation q' * a * p, and returns the matrices x and y which are needed to apply the transfor because vectors may be viewed as a subclass of matrices, performed by an unitary similarity transformation q' * a * q. the routine returns the matrices v and t which determine q as a bloc because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, tridiagonal form by an unitary similarity transformation q' * sub( a ) * q, and returns the matrices v and w which ar because vectors may be viewed as a subclass of matrices, scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex pointer into the local denoting b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the cholesky decomposition is used to factor sub( a ) as where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one because vectors may be seen as particular matrices, a distribute if all eigenvectors are requested, the routine may either return the matrices x and/or y of right or left eigenvectors of t, or th matrix. if t was obtained from the schur factorization of an the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex pointer into the local memory here q and p**h are the unitary distributed matrices determined b bidiagonal form: a(ia:*,ja:*) = q * b * p**h. q and p**h are defined scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one scalapack manual for more detail on the format of distributed matrices of the factorization. this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrices scalapack manual for more detail on the format of distributed matrices ja (global input) integer the matrices q and p are represented as products of elementar the matrices q and p are represented as products of elementar the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) double precision pointer into the local matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the lu decomposition with partial pivoting and row interchanges is where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also or lower bidiagonal form by an orthogonal transformation q' * a * p, and returns the matrices x and y which are needed to apply th nal similarity transformation q' * a * q. the routine returns the matrices v and t which determine q as a block reflector i - v*t*v' because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, form by an orthogonal similarity transformation q' * sub( a ) * q, and returns the matrices v and w which are needed to apply th here q and p**t are the orthogonal distributed matrices determined b bidiagonal form: a(ia:*,ja:*) = q * b * p**t. q and p**t are defined scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) double precision pointer into the local denoting b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the cholesky decomposition is used to factor sub( a ) as where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one because vectors may be seen as particular matrices, a distribute see "computing small singular values of bidiagonal matrices kahan, lapack working note #3. n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) double precision pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) double precision pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) double precision pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) double precision pointer into the pdsyntrd is faster than pdsytrd on almost all matrices enough workspace is available to use the tailored codes. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) double precision pointer into the local memory because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one scalapack manual for more detail on the format of distributed matrices of the factorization. this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrices scalapack manual for more detail on the format of distributed matrices ja (global input) integer the matrices q and p are represented as products of elementar the matrices q and p are represented as products of elementar the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) real pointer into the local matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the lu decomposition with partial pivoting and row interchanges is where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also or lower bidiagonal form by an orthogonal transformation q' * a * p, and returns the matrices x and y which are needed to apply th nal similarity transformation q' * a * q. the routine returns the matrices v and t which determine q as a block reflector i - v*t*v' because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, form by an orthogonal similarity transformation q' * sub( a ) * q, and returns the matrices v and w which are needed to apply th here q and p**t are the orthogonal distributed matrices determined b bidiagonal form: a(ia:*,ja:*) = q * b * p**t. q and p**t are defined scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) real pointer into the local denoting b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the cholesky decomposition is used to factor sub( a ) as where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one because vectors may be seen as particular matrices, a distribute see "computing small singular values of bidiagonal matrices kahan, lapack working note #3. n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) real pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) real pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) real pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) real pointer into the pssyntrd is faster than pssytrd on almost all matrices enough workspace is available to use the tailored codes. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) real pointer into the local memory scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer because vectors may be seen as particular matrices, a distribute p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one scalapack manual for more detail on the format of distributed matrices of the factorization. this node stops work after this stage -- an extra copy is required to make the odd and even frontal matrices scalapack manual for more detail on the format of distributed matrices ja (global input) integer the matrices q and p are represented as products of elementar the matrices q and p are represented as products of elementar the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex*16 pointer into the local matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the lu decomposition with partial pivoting and row interchanges is where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also see "computing small singular values of bidiagonal matrices kahan, lapack working note #3. n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex*16 pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex*16 pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex*16 pointer into the n (global input) integer the order of the matrices sub( a ) and sub( b ). n >= 0 a (local input/local output) complex*16 pointer into the pzhentrd is faster than pzhetrd on almost all matrices enough workspace is available to use the tailored codes. the following variables give the number of rows and/or columns in various matrices nq: the number of local columns in a( 1:n, 1:n ) or lower bidiagonal form by an unitary transformation q' * a * p, and returns the matrices x and y which are needed to apply the transfor because vectors may be viewed as a subclass of matrices, performed by an unitary similarity transformation q' * a * q. the routine returns the matrices v and t which determine q as a bloc because vectors may be viewed as a subclass of matrices, because vectors may be viewed as a subclass of matrices, tridiagonal form by an unitary similarity transformation q' * sub( a ) * q, and returns the matrices v and w which ar because vectors may be viewed as a subclass of matrices, scalapack manual for more detail on the format of distributed matrices of the factorization. scalapack manual for more detail on the format of distributed matrices ja (global input) integer the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex*16 pointer into the local denoting b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs distributed matrices the cholesky decomposition is used to factor sub( a ) as where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x and b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrhs matrices error bounds on the solution and a condition estimate are also p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one p*nb>= mod(ja-1,nb)+n. the mapping for matrices must be blocked, reflecting the natur this formula in words is: no processor may have more than one if all eigenvectors are requested, the routine may either return the matrices x and/or y of right or left eigenvectors of t, or th matrix. if t was obtained from the schur factorization of an the number of right hand sides, i.e., the number of columns of the matrices sub( b ) and sub( x ). nrhs >= 0 a (local input) complex*16 pointer into the local memory here q and p**h are the unitary distributed matrices determined b bidiagonal form: a(ia:*,ja:*) = q * b * p**h. q and p**h are defined where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the mai slaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei where l is a product of unit lower bidiagonal matrices and u is upper triangular with nonzeros in only the mai zlaref applies one or several householder reflectors of size 3 to one or two matrices (if column is specified) on either thei |
| matrix matrix cdbtrf computes an lu factorization of a real m-by-n band matrix the active part of the matrix is partitione a11 a12 a13 cdttrf computes an lu factorization of a complex tridiagonal matrix u * x = b, u**t * x = b, or u**h * x = b, with factors of the tridiagonal matrix a from the lu factorizatio 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. clamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges clanv2 computes the schur factorization of a complex 2-by-2 nonhermitian matrix in standard form [ a b ] = [ cs -sn ] [ aa bb ] [ cs sn ] type (global input) character*1 if 'r': apply reflectors to the rows of the matrix otherwise: apply reflectors to the columns of the matrix where l or u is the cholesky factor of a hermitian positive definite tridiagonal matrix a such tha if remaining matrix is 2-by-2, use slae2 or slaev ctrmvt performs the matrix-vector operation x := conjg( t' ) *y, and w := t *z, ddbtrf computes an lu factorization of a real m-by-n band matrix the active part of the matrix is partitione a11 a12 a13 ddttrf computes an lu factorization of a complex tridiagonal matrix u * x = b, u**t * x = b, or u**h * x = b, with factors of the tridiagonal matrix a from the lu factorizatio dlamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges type (global input) character*1 if 'r': apply reflectors to the rows of the matrix otherwise: apply reflectors to the columns of the matrix s (local input/output) double precision array, dimension lds on entry, a matrix already in schur form the eigenvalues. the resulting matrix is no longer where l is the cholesky factor of a hermitian positive definite tridiagonal matrix a such tha compute eigenvectors of matrix blocks compute the eigenvalues and eigenvectors of the tridiagonal matrix dtrmvt performs the matrix-vector operation x := t' *y, and w := t *z, banded diagonally dominant-like distributed matrix with bandwidth bwl, bwu gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int tridiagonal diagonally dominant-like distributed matrix gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int banded distributed matrix with bandwidth bwl, bwu gaussian elimination with pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex pcgebd2 reduces a complex general m-by-n distributed matrix form b by an unitary transformation: q' * sub( a ) * p = b. pcgebrd reduces a complex general m-by-n distributed matrix 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. pcgeequ computes row and column scalings intended to equilibrate an m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) an the column scale factors, chosen to try to make the largest entry in 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-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pcgelqf computes a lq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pcgels solves overdetermined or underdetermined complex linear systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ). it is assumed that sub( a ) has full rank. pcgeql2 computes a ql factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pcgeqlf computes a ql factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pcgeqpf computes a qr factorization with column pivoting of a m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ) * p = q * r. pcgeqr2 computes a qr factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes pcgeqrf computes a qr factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pcgerq2 computes a rq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes pcgerqf computes a rq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes where sub( a ) = a(ia:ia+n-1,ja:ja+n-1) is an n-by-n distributed matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrh pcgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally computing the left and/or righ where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pcgetf2 computes an lu factorization of a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) usin pcgetrf computes an lu factorization of a general m-by-n distributed matrix sub( a ) = (ia:ia+m-1,ja:ja+n-1) using partial pivoting wit 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 with a general n-by-n distributed matrix sub( a ) using the l sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1), op( a ) = a, a**t or a**h pcggqrf computes a generalized qr factorization of an n-by-m matrix sub( a ) = a(ia:ia+n-1,ja:ja+m-1) an pcggrqf computes a generalized rq factorization of an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1 pcheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pcheevd computes all the eigenvalues and eigenvectors of a hermitian matrix a by using a divide and conquer algorithm arguments 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 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process 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 general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to uppe returns the matrices x and y which are needed to apply the transfor- let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pclacon estimates the 1-norm of a square, complex distributed matrix products. x and v are aligned with the distributed matrix a, this let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pclacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes 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. pclacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process ihi to ilo in steps of our schur block size (<=2*iblk). each iteration of the loop works with the active submatrix in row converged. either l = ilo or the global a(l,l-1) is negligible pclahrd reduces the first nb columns of a complex general n-by-(n-k+1) distributed matrix a(ia:ia+n-1,ja:ja+n-k) so tha performed by an unitary similarity transformation q' * a * q. the n (global input) integer the size of the matrix to be transposed a (local output) complex*16 pointer into the or the infinity norm, or the element of largest absolute value of a distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1) pclange returns the value if the matrix is hermitian, we address only a triangular portio can be obtained by adding along row i and column i of the the find sum of global matrix columns and store on row 0 o if the matrix is symmetric, we address only a triangular portio can be obtained by adding along row i and column i of the the upper triangular matrix pclapiv applies either p (permutation matrix indicated by ipiv sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pclapv2 applies either p (permutation matrix indicated by ipiv a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pclaqge equilibrates a general m-by-n distributed matrix factors in the vectors r and c. pclaqsy equilibrates a symmetric distributed matrix vectors sr and sc. sub( c ) is a proper distributed matrix pclarfb applies a complex block reflector q or its conjugate transpose q**h to a complex m-by-n distributed matrix sub( c sub( c ) is a proper distributed matrix if the elements of sub( x ) are all zero and x(iax,jax) is real, then tau = 0 and h is taken to be the unit matrix otherwise 1 <= real(tau) <= 2 and abs(tau-1) <= 1. 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' sub( c ) is a proper distributed matrix pclarzb applies a complex block reflector q or its conjugate transpose q**h to a complex m-by-n distributed matrix sub( c sub( c ) is a proper distributed matrix let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pclascl multiplies the m-by-n complex distributed matrix sub( a is done without over/underflow as long as the final result pclase2 initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pclase2 requires that only dimension of the matrix pclaset initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pclasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pclaswp performs a series of row or column interchanges on the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). on sub( a ). this routine assumes that the pivoting information has pclatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pclatrd reduces nb rows and columns of a complex hermitian distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) to comple q' * sub( a ) * q, and returns the matrices v and w which are pclatrz reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix sub( a ) = [a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1) factor u or l is stored in the upper or lower triangular part of the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, factor u or l is stored in the upper or lower triangular part of the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process banded symmetric positive definite distributed matrix with bandwidth bw cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int pcpocon estimates the reciprocal of the condition number (in the 1-norm) of a complex hermitian positive definite distributed matrix pcpotrf. pcpoequ computes row and column scalings intended to equilibrate a distributed hermitian positive definite matrix (with respect to the two-norm). sr and sc contain the scale pcporfs improves the computed solution to a system of linear equations when the coefficient matrix is hermitian positive definit solutions. where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is an n-by-n hermitian distributed positive definite matrix and x and sub( b matrices. where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pcpotf2 computes the cholesky factorization of a complex hermitian positive definite distributed matrix sub( a )=a(ia:ia+n-1,ja:ja+n-1) the factorization has the form pcpotrf computes the cholesky factorization of an n-by-n complex hermitian positive definite distributed matrix sub( a ) denotin pcpotri computes the inverse of a complex hermitian positive definite distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using th pcpotrf. 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 cholesk sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). tridiagonal symmetric positive definite distributed matrix cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pcstein computes the eigenvectors of a symmetric tridiagonal matrix correspond to user specified eigenvalues. pcstein does not pctrcon estimates the reciprocal of the condition number of a triangular distributed matrix a(ia:ia+n-1,ja:ja+n-1), in either th 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 solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pctrtrs or some other pctrti2 computes the inverse of a complex upper or lower triangular block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should b pctrtri computes the inverse of a upper or lower triangular distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) notes where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a triangular distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is a to verify that sub( a ) is nonsingular. pctzrzf reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix 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 matrix pcunm2r overwrites the general complex m-by-n distributed matrix if vect = 'q', pcunmbr overwrites the general complex distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1) wit side = 'l' side = 'r' pcunmhr overwrites the general complex m-by-n distributed matrix pcunml2 overwrites the general complex m-by-n distributed matrix pcunmlq overwrites the general complex m-by-n distributed matrix pcunmql overwrites the general complex m-by-n distributed matrix pcunmqr overwrites the general complex m-by-n distributed matrix pcunmr2 overwrites the general complex m-by-n distributed matrix pcunmr3 overwrites the general complex m-by-n distributed matrix pcunmrq overwrites the general complex m-by-n distributed matrix pcunmrz overwrites the general complex m-by-n distributed matrix pcunmtr overwrites the general complex m-by-n distributed matrix banded diagonally dominant-like distributed matrix with bandwidth bwl, bwu gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int tridiagonal diagonally dominant-like distributed matrix gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int banded distributed matrix with bandwidth bwl, bwu gaussian elimination with pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real pdgebd2 reduces a real general m-by-n distributed matrix form b by an orthogonal transformation: q' * sub( a ) * p = b. pdgebrd reduces a real general m-by-n distributed matrix form b by an orthogonal transformation: q' * sub( a ) * p = b. pdgecon estimates the reciprocal of the condition number of a general distributed real matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-nor pdgeequ computes row and column scalings intended to equilibrate an m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) an the column scale factors, chosen to try to make the largest entry in pdgehd2 reduces a real general distributed matrix sub( a tion: q' * sub( a ) * q = h, where pdgehrd reduces a real general distributed matrix sub( a tion: q' * sub( a ) * q = h, where pdgelq2 computes a lq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pdgelqf computes a lq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pdgels solves overdetermined or underdetermined real linear systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) assumed that sub( a ) has full rank. pdgeql2 computes a ql factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pdgeqlf computes a ql factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pdgeqpf computes a qr factorization with column pivoting of a m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ) * p = q * r. pdgeqr2 computes a qr factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes pdgeqrf computes a qr factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdgerq2 computes a rq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes pdgerqf computes a rq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes where sub( a ) = a(ia:ia+n-1,ja:ja+n-1) is an n-by-n distributed matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrh pdgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally computing the left and/or righ where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pdgetf2 computes an lu factorization of a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) usin pdgetrf computes an lu factorization of a general m-by-n distributed matrix sub( a ) = (ia:ia+m-1,ja:ja+n-1) using partial pivoting wit 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 with a general n-by-n distributed matrix sub( a ) using the l sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1), op( a ) = a or a**t and pdggqrf computes a generalized qr factorization of an n-by-m matrix sub( a ) = a(ia:ia+n-1,ja:ja+m-1) an pdggrqf computes a generalized rq factorization of an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1 pdlabrd reduces the first nb rows and columns of a real general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to uppe and returns the matrices x and y which are needed to apply the pdlacon estimates the 1-norm of a square, real distributed matrix a x and v are aligned with the distributed matrix a, this information let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdlacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes 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. pdlacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes j = 1,...,minp. it uses and computes the function n(w), which is the count of eigenvalues of a symmetric tridiagonal matrix less tha pdlaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method pdlaed1 computes the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix the blacs context handle, indicating the global context of the operation on the matrix. the context itself is global k (output) integer the blacs context handle, indicating the global context of the operation on the matrix. the context itself is global k (output) integer let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process ihi to ilo in steps of our schur block size (<=2*iblk). each iteration of the loop works with the active submatrix in row converged. either l = ilo or the global a(l,l-1) is negligible pdlahrd reduces the first nb columns of a real general n-by-(n-k+1) distributed matrix a(ia:ia+n-1,ja:ja+n-k) so that elements below th nal similarity transformation q' * a * q. the routine returns the n (global input) integer the size of the matrix to be transposed a (local output) complex*16 pointer into the or the infinity norm, or the element of largest absolute value of a distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1) pdlange returns the value find sum of global matrix columns and store on row 0 o if the matrix is symmetric, we address only a triangular portio can be obtained by adding along row i and column i of the the upper triangular matrix n (input) integer the order of the tridiagonal matrix t. n >= 1 d (input) double precision array, dimension (2*n - 1) pdlapiv applies either p (permutation matrix indicated by ipiv sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pdlapv2 applies either p (permutation matrix indicated by ipiv a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pdlaqge equilibrates a general m-by-n distributed matrix factors in the vectors r and c. pdlaqsy equilibrates a symmetric distributed matrix vectors sr and sc. let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process sub( c ) is a proper distributed matrix pdlarfb applies a real block reflector q or its transpose q**t to a real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1 if the elements of sub( x ) are all zero, then tau = 0 and h is taken to be the unit matrix otherwise 1 <= tau <= 2. 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' sub( c ) is a proper distributed matrix pdlarzb applies a real block reflector q or its transpose q**t to a real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdlascl multiplies the m-by-n real distributed matrix sub( a is done without over/underflow as long as the final result pdlase2 initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pdlase2 requires that only dimension of the matrix pdlaset initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pdlasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes the number of columns to be operated on i.e the number of columns of the distributed submatrix sub( q ). n >= 0 d (global input/output) double precision array, dimmension (n) let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdlaswp performs a series of row or column interchanges on the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). on sub( a ). this routine assumes that the pivoting information has pdlatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pdlatrd reduces nb rows and columns of a real symmetric distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) to symmetric tridiagona and returns the matrices v and w which are needed to apply the pdlatrz reduces the m-by-n ( m<=n ) real upper trapezoidal matrix upper triangular form by means of orthogonal transformations. factor u or l is stored in the upper or lower triangular part of the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, factor u or l is stored in the upper or lower triangular part of the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdorg2l generates an m-by-n real distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pdorg2r generates an m-by-n real distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pdorgl2 generates an m-by-n real distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pdorglq generates an m-by-n real distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n pdorgql generates an m-by-n real distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m pdorgqr generates an m-by-n real distributed matrix q denotin the first n columns of a product of k elementary reflectors of order pdorgr2 generates an m-by-n real distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pdorgrq generates an m-by-n real distributed matrix q denotin last m rows of a product of k elementary reflectors of order n pdorm2l overwrites the general real m-by-n distributed matrix pdorm2r overwrites the general real m-by-n distributed matrix if vect = 'q', pdormbr overwrites the general real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1) wit side = 'l' side = 'r' pdormhr overwrites the general real m-by-n distributed matrix pdorml2 overwrites the general real m-by-n distributed matrix pdormlq overwrites the general real m-by-n distributed matrix pdormql overwrites the general real m-by-n distributed matrix pdormqr overwrites the general real m-by-n distributed matrix pdormr2 overwrites the general real m-by-n distributed matrix pdormr3 overwrites the general real m-by-n distributed matrix pdormrq overwrites the general real m-by-n distributed matrix pdormrz overwrites the general real m-by-n distributed matrix pdormtr overwrites the general real m-by-n distributed matrix banded symmetric positive definite distributed matrix with bandwidth bw cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int pdpocon estimates the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite distributed matrix pdpotrf. pdpoequ computes row and column scalings intended to equilibrate a distributed symmetric positive definite matrix (with respect to the two-norm). sr and sc contain the scale pdporfs improves the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definit solutions. where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is an n-by-n symmetric distributed positive definite matrix and x and sub( b matrices. where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pdpotf2 computes the cholesky factorization of a real symmetric positive definite distributed matrix sub( a )=a(ia:ia+n-1,ja:ja+n-1) the factorization has the form pdpotrf computes the cholesky factorization of an n-by-n real symmetric positive definite distributed matrix sub( a ) denotin pdpotri computes the inverse of a real symmetric positive definite distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using th pdpotrf. 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 cholesk sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). tridiagonal symmetric positive definite distributed matrix cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process 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 a symmetric tridiagonal matrix in parallel, using the divide an pdstein computes the eigenvectors of a symmetric tridiagonal matrix correspond to user specified eigenvalues. pdstein does not 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 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pdsyntrd reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pdsytd2 reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pdsytrd reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). 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). pdtrcon estimates the reciprocal of the condition number of a triangular distributed matrix a(ia:ia+n-1,ja:ja+n-1), in either th solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pdtrtrs or some other pdtrti2 computes the inverse of a real upper or lower triangular block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should b pdtrtri computes the inverse of a upper or lower triangular distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) notes where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a triangular distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is a to verify that sub( a ) is nonsingular. pdtzrzf reduces the m-by-n ( m<=n ) real upper trapezoidal matrix of orthogonal transformations. let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process banded diagonally dominant-like distributed matrix with bandwidth bwl, bwu gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int tridiagonal diagonally dominant-like distributed matrix gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int banded distributed matrix with bandwidth bwl, bwu gaussian elimination with pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real psgebd2 reduces a real general m-by-n distributed matrix form b by an orthogonal transformation: q' * sub( a ) * p = b. psgebrd reduces a real general m-by-n distributed matrix form b by an orthogonal transformation: q' * sub( a ) * p = b. psgecon estimates the reciprocal of the condition number of a general distributed real matrix a(ia:ia+n-1,ja:ja+n-1), in either the 1-nor psgeequ computes row and column scalings intended to equilibrate an m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) an the column scale factors, chosen to try to make the largest entry in psgehd2 reduces a real general distributed matrix sub( a tion: q' * sub( a ) * q = h, where psgehrd reduces a real general distributed matrix sub( a tion: q' * sub( a ) * q = h, where psgelq2 computes a lq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes psgelqf computes a lq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes psgels solves overdetermined or underdetermined real linear systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) assumed that sub( a ) has full rank. psgeql2 computes a ql factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes psgeqlf computes a ql factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes psgeqpf computes a qr factorization with column pivoting of a m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ) * p = q * r. psgeqr2 computes a qr factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes psgeqrf computes a qr factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process psgerq2 computes a rq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes psgerqf computes a rq factorization of a real distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes where sub( a ) = a(ia:ia+n-1,ja:ja+n-1) is an n-by-n distributed matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrh psgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally computing the left and/or righ where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an psgetf2 computes an lu factorization of a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) usin psgetrf computes an lu factorization of a general m-by-n distributed matrix sub( a ) = (ia:ia+m-1,ja:ja+n-1) using partial pivoting wit 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 with a general n-by-n distributed matrix sub( a ) using the l sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1), op( a ) = a or a**t and psggqrf computes a generalized qr factorization of an n-by-m matrix sub( a ) = a(ia:ia+n-1,ja:ja+m-1) an psggrqf computes a generalized rq factorization of an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1 pslabrd reduces the first nb rows and columns of a real general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to uppe and returns the matrices x and y which are needed to apply the pslacon estimates the 1-norm of a square, real distributed matrix a x and v are aligned with the distributed matrix a, this information let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pslacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes 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. pslacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes j = 1,...,minp. it uses and computes the function n(w), which is the count of eigenvalues of a symmetric tridiagonal matrix less tha pslaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method pslaed1 computes the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix the blacs context handle, indicating the global context of the operation on the matrix. the context itself is global k (output) integer the blacs context handle, indicating the global context of the operation on the matrix. the context itself is global k (output) integer let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process ihi to ilo in steps of our schur block size (<=2*iblk). each iteration of the loop works with the active submatrix in row converged. either l = ilo or the global a(l,l-1) is negligible pslahrd reduces the first nb columns of a real general n-by-(n-k+1) distributed matrix a(ia:ia+n-1,ja:ja+n-k) so that elements below th nal similarity transformation q' * a * q. the routine returns the n (global input) integer the size of the matrix to be transposed a (local output) complex*16 pointer into the or the infinity norm, or the element of largest absolute value of a distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1) pslange returns the value find sum of global matrix columns and store on row 0 o if the matrix is symmetric, we address only a triangular portio can be obtained by adding along row i and column i of the the upper triangular matrix n (input) integer the order of the tridiagonal matrix t. n >= 1 d (input) real array, dimension (2*n - 1) pslapiv applies either p (permutation matrix indicated by ipiv sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pslapv2 applies either p (permutation matrix indicated by ipiv a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pslaqge equilibrates a general m-by-n distributed matrix factors in the vectors r and c. pslaqsy equilibrates a symmetric distributed matrix vectors sr and sc. let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process sub( c ) is a proper distributed matrix pslarfb applies a real block reflector q or its transpose q**t to a real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1 if the elements of sub( x ) are all zero, then tau = 0 and h is taken to be the unit matrix otherwise 1 <= tau <= 2. 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' sub( c ) is a proper distributed matrix pslarzb applies a real block reflector q or its transpose q**t to a real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pslascl multiplies the m-by-n real distributed matrix sub( a is done without over/underflow as long as the final result pslase2 initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pslase2 requires that only dimension of the matrix pslaset initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pslasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes the number of columns to be operated on i.e the number of columns of the distributed submatrix sub( q ). n >= 0 d (global input/output) real array, dimmension (n) let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pslaswp performs a series of row or column interchanges on the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). on sub( a ). this routine assumes that the pivoting information has pslatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pslatrd reduces nb rows and columns of a real symmetric distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) to symmetric tridiagona and returns the matrices v and w which are needed to apply the pslatrz reduces the m-by-n ( m<=n ) real upper trapezoidal matrix upper triangular form by means of orthogonal transformations. factor u or l is stored in the upper or lower triangular part of the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, factor u or l is stored in the upper or lower triangular part of the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process psorg2l generates an m-by-n real distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m psorg2r generates an m-by-n real distributed matrix q denotin the first n columns of a product of k elementary reflectors of order psorgl2 generates an m-by-n real distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n psorglq generates an m-by-n real distributed matrix q denotin the first m rows of a product of k elementary reflectors of order n psorgql generates an m-by-n real distributed matrix q denotin the last n columns of a product of k elementary reflectors of order m psorgqr generates an m-by-n real distributed matrix q denotin the first n columns of a product of k elementary reflectors of order psorgr2 generates an m-by-n real distributed matrix q denotin last m rows of a product of k elementary reflectors of order n psorgrq generates an m-by-n real distributed matrix q denotin last m rows of a product of k elementary reflectors of order n psorm2l overwrites the general real m-by-n distributed matrix psorm2r overwrites the general real m-by-n distributed matrix if vect = 'q', psormbr overwrites the general real distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1) wit side = 'l' side = 'r' psormhr overwrites the general real m-by-n distributed matrix psorml2 overwrites the general real m-by-n distributed matrix psormlq overwrites the general real m-by-n distributed matrix psormql overwrites the general real m-by-n distributed matrix psormqr overwrites the general real m-by-n distributed matrix psormr2 overwrites the general real m-by-n distributed matrix psormr3 overwrites the general real m-by-n distributed matrix psormrq overwrites the general real m-by-n distributed matrix psormrz overwrites the general real m-by-n distributed matrix psormtr overwrites the general real m-by-n distributed matrix banded symmetric positive definite distributed matrix with bandwidth bw cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int pspocon estimates the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite distributed matrix pspotrf. pspoequ computes row and column scalings intended to equilibrate a distributed symmetric positive definite matrix (with respect to the two-norm). sr and sc contain the scale psporfs improves the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definit solutions. where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is an n-by-n symmetric distributed positive definite matrix and x and sub( b matrices. where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pspotf2 computes the cholesky factorization of a real symmetric positive definite distributed matrix sub( a )=a(ia:ia+n-1,ja:ja+n-1) the factorization has the form pspotrf computes the cholesky factorization of an n-by-n real symmetric positive definite distributed matrix sub( a ) denotin pspotri computes the inverse of a real symmetric positive definite distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using th pspotrf. 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 cholesk sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). tridiagonal symmetric positive definite distributed matrix cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n real adjust addressing into matrix space to properly get int let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process 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 a symmetric tridiagonal matrix in parallel, using the divide an psstein computes the eigenvectors of a symmetric tridiagonal matrix correspond to user specified eigenvalues. psstein does not 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 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pssyntrd reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pssytd2 reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). pssytrd reduces a real symmetric matrix sub( a ) to symmetri q' * sub( a ) * q = t, where sub( a ) = a(ia:ia+n-1,ja:ja+n-1). 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). pstrcon estimates the reciprocal of the condition number of a triangular distributed matrix a(ia:ia+n-1,ja:ja+n-1), in either th solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pstrtrs or some other pstrti2 computes the inverse of a real upper or lower triangular block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should b pstrtri computes the inverse of a upper or lower triangular distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) notes where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a triangular distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is a to verify that sub( a ) is nonsingular. pstzrzf reduces the m-by-n ( m<=n ) real upper trapezoidal matrix of orthogonal transformations. banded diagonally dominant-like distributed matrix with bandwidth bwl, bwu gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process tridiagonal diagonally dominant-like distributed matrix gaussian elimination without pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int banded distributed matrix with bandwidth bwl, bwu gaussian elimination with pivoting adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex pzgebd2 reduces a complex general m-by-n distributed matrix form b by an unitary transformation: q' * sub( a ) * p = b. pzgebrd reduces a complex general m-by-n distributed matrix 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. pzgeequ computes row and column scalings intended to equilibrate an m-by-n distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja:ja+n-1) an the column scale factors, chosen to try to make the largest entry in 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-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pzgelqf computes a lq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = l * q notes pzgels solves overdetermined or underdetermined complex linear systems involving an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ). it is assumed that sub( a ) has full rank. pzgeql2 computes a ql factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pzgeqlf computes a ql factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * l notes pzgeqpf computes a qr factorization with column pivoting of a m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) sub( a ) * p = q * r. pzgeqr2 computes a qr factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes pzgeqrf computes a qr factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = q * r notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pzgerq2 computes a rq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes pzgerqf computes a rq factorization of a complex distributed m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) = r * q notes where sub( a ) = a(ia:ia+n-1,ja:ja+n-1) is an n-by-n distributed matrix and x and sub( b ) = b(ib:ib+n-1,jb:jb+nrhs-1) are n-by-nrh pzgesvd computes the singular value decomposition (svd) of an m-by-n matrix a, optionally computing the left and/or righ where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pzgetf2 computes an lu factorization of a general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) usin pzgetrf computes an lu factorization of a general m-by-n distributed matrix sub( a ) = (ia:ia+m-1,ja:ja+n-1) using partial pivoting wit 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 with a general n-by-n distributed matrix sub( a ) using the l sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1), op( a ) = a, a**t or a**h pzggqrf computes a generalized qr factorization of an n-by-m matrix sub( a ) = a(ia:ia+n-1,ja:ja+m-1) an pzggrqf computes a generalized rq factorization of an m-by-n matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1 pzheev computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a by calling the recommended sequenc pzheevd computes all the eigenvalues and eigenvectors of a hermitian matrix a by using a divide and conquer algorithm arguments 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 let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process 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 general m-by-n distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to uppe returns the matrices x and y which are needed to apply the transfor- let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pzlacon estimates the 1-norm of a square, complex distributed matrix products. x and v are aligned with the distributed matrix a, this let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pzlacp2 copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes 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. pzlacpy copies all or part of a distributed matrix a to anothe performs a local copy sub( a ) := sub( b ), where sub( a ) denotes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process ihi to ilo in steps of our schur block size (<=2*iblk). each iteration of the loop works with the active submatrix in row converged. either l = ilo or the global a(l,l-1) is negligible pzlahrd reduces the first nb columns of a complex general n-by-(n-k+1) distributed matrix a(ia:ia+n-1,ja:ja+n-k) so tha performed by an unitary similarity transformation q' * a * q. the n (global input) integer the size of the matrix to be transposed a (local output) complex*16 pointer into the or the infinity norm, or the element of largest absolute value of a distributed matrix sub( a ) = a(ia:ia+m-1, ja:ja+n-1) pzlange returns the value if the matrix is hermitian, we address only a triangular portio can be obtained by adding along row i and column i of the the find sum of global matrix columns and store on row 0 o if the matrix is symmetric, we address only a triangular portio can be obtained by adding along row i and column i of the the upper triangular matrix pzlapiv applies either p (permutation matrix indicated by ipiv sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pzlapv2 applies either p (permutation matrix indicated by ipiv a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pzlaqge equilibrates a general m-by-n distributed matrix factors in the vectors r and c. pzlaqsy equilibrates a symmetric distributed matrix vectors sr and sc. sub( c ) is a proper distributed matrix pzlarfb applies a complex block reflector q or its conjugate transpose q**h to a complex m-by-n distributed matrix sub( c sub( c ) is a proper distributed matrix if the elements of sub( x ) are all zero and x(iax,jax) is real, then tau = 0 and h is taken to be the unit matrix otherwise 1 <= real(tau) <= 2 and abs(tau-1) <= 1. 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' sub( c ) is a proper distributed matrix pzlarzb applies a complex block reflector q or its conjugate transpose q**h to a complex m-by-n distributed matrix sub( c sub( c ) is a proper distributed matrix let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pzlascl multiplies the m-by-n complex distributed matrix sub( a is done without over/underflow as long as the final result pzlase2 initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pzlase2 requires that only dimension of the matrix pzlaset initializes an m-by-n distributed matrix sub( a ) denotin offdiagonals. pzlasmsub looks for a small subdiagonal element from the bottom of the matrix that it can safely set to zero notes let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process pzlaswp performs a series of row or column interchanges on the distributed matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1). on sub( a ). this routine assumes that the pivoting information has pzlatra computes the trace of an n-by-n distributed matrix sub( a process of the grid. pzlatrd reduces nb rows and columns of a complex hermitian distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) to comple q' * sub( a ) * q, and returns the matrices v and w which are pzlatrz reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix sub( a ) = [a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1) factor u or l is stored in the upper or lower triangular part of the matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, factor u or l is stored in the upper or lower triangular part of the distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) if uplo = 'u' or 'u' then the upper triangle of the result is stored, let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process let k be the number of rows or columns of a distributed matrix locr( k ) denotes the number of elements of k that a process banded symmetric positive definite distributed matrix with bandwidth bw cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int pzpocon estimates the reciprocal of the condition number (in the 1-norm) of a complex hermitian positive definite distributed matrix pzpotrf. pzpoequ computes row and column scalings intended to equilibrate a distributed hermitian positive definite matrix (with respect to the two-norm). sr and sc contain the scale pzporfs improves the computed solution to a system of linear equations when the coefficient matrix is hermitian positive definit solutions. where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is an n-by-n hermitian distributed positive definite matrix and x and sub( b matrices. where a(ia:ia+n-1,ja:ja+n-1) is an n-by-n matrix and x an pzpotf2 computes the cholesky factorization of a complex hermitian positive definite distributed matrix sub( a )=a(ia:ia+n-1,ja:ja+n-1) the factorization has the form pzpotrf computes the cholesky factorization of an n-by-n complex hermitian positive definite distributed matrix sub( a ) denotin pzpotri computes the inverse of a complex hermitian positive definite distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) using th pzpotrf. 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 cholesk sub( b ) denotes the distributed matrix b(ib:ib+n-1,jb:jb+nrhs-1). tridiagonal symmetric positive definite distributed matrix cholesky factorization is used to factor a reordering of adjust addressing into matrix space to properly get int where a(1:n, ja:ja+n-1) is the matrix used to produce the factor a(1:n, ja:ja+n-1) is an n-by-n complex adjust addressing into matrix space to properly get int pzstein computes the eigenvectors of a symmetric tridiagonal matrix correspond to user specified eigenvalues. pzstein does not pztrcon estimates the reciprocal of the condition number of a triangular distributed matrix a(ia:ia+n-1,ja:ja+n-1), in either th 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 solution to a system of linear equations with a triangular coefficient matrix the solution matrix x must be computed by pztrtrs or some other pztrti2 computes the inverse of a complex upper or lower triangular block matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1). this matrix should b pztrtri computes the inverse of a upper or lower triangular distributed matrix sub( a ) = a(ia:ia+n-1,ja:ja+n-1) notes where sub( a ) denotes a(ia:ia+n-1,ja:ja+n-1) and is a triangular distributed matrix of order n, and b(ib:ib+n-1,jb:jb+nrhs-1) is a to verify that sub( a ) is nonsingular. pztzrzf reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix 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 matrix pzunm2r overwrites the general complex m-by-n distributed matrix if vect = 'q', pzunmbr overwrites the general complex distributed m-by-n matrix sub( c ) = c(ic:ic+m-1,jc:jc+n-1) wit side = 'l' side = 'r' pzunmhr overwrites the general complex m-by-n distributed matrix pzunml2 overwrites the general complex m-by-n distributed matrix pzunmlq overwrites the general complex m-by-n distributed matrix pzunmql overwrites the general complex m-by-n distributed matrix pzunmqr overwrites the general complex m-by-n distributed matrix pzunmr2 overwrites the general complex m-by-n distributed matrix pzunmr3 overwrites the general complex m-by-n distributed matrix pzunmrq overwrites the general complex m-by-n distributed matrix pzunmrz overwrites the general complex m-by-n distributed matrix pzunmtr overwrites the general complex m-by-n distributed matrix sdbtrf computes an lu factorization of a real m-by-n band matrix the active part of the matrix is partitione a11 a12 a13 sdttrf computes an lu factorization of a complex tridiagonal matrix u * x = b, u**t * x = b, or u**h * x = b, with factors of the tridiagonal matrix a from the lu factorizatio slamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges type (global input) character*1 if 'r': apply reflectors to the rows of the matrix otherwise: apply reflectors to the columns of the matrix s (local input/output) real array, dimension lds on entry, a matrix already in schur form the eigenvalues. the resulting matrix is no longer where l is the cholesky factor of a hermitian positive definite tridiagonal matrix a such tha compute eigenvectors of matrix blocks compute the eigenvalues and eigenvectors of the tridiagonal matrix strmvt performs the matrix-vector operation x := t' *y, and w := t *z, zdbtrf computes an lu factorization of a real m-by-n band matrix the active part of the matrix is partitione a11 a12 a13 zdttrf computes an lu factorization of a complex tridiagonal matrix u * x = b, u**t * x = b, or u**h * x = b, with factors of the tridiagonal matrix a from the lu factorizatio 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. zlamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges zlanv2 computes the schur factorization of a complex 2-by-2 nonhermitian matrix in standard form [ a b ] = [ cs -sn ] [ aa bb ] [ cs sn ] type (global input) character*1 if 'r': apply reflectors to the rows of the matrix otherwise: apply reflectors to the columns of the matrix where l or u is the cholesky factor of a hermitian positive definite tridiagonal matrix a such tha if remaining matrix is 2-by-2, use dlae2 or dlaev ztrmvt performs the matrix-vector operation x := conjg( t' ) *y, and w := t *z, |
| max max array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=min(m,j+kl on exit, details of the factorization: u is stored as an ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer in the calling (sub) program. lda must be at least max( 1, n ) array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=min(m,j+kl on exit, details of the factorization: u is stored as an ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer in the calling (sub) program. lda must be at least max( 1, n ) returned in work(1) and an error code is returned. lwork>= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu size of user-input auxiliary fillin space af. must be >= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu and the minimum acceptable size will be returned in af( 1 ) (12*npcol+3*nb) +max(10*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) +max(nrhs*(nb+2*bwl+4*bwu), 1 info (global output) integer 2) reduced system phase: a small (max(bwl,bwu)* (p-1)) system is formed representin factors) in the space af. a parallel block cyclic reduction lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, and lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, distributed matrix a. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lwork = n + ( np0 + mq0 + nb ) * nb, with np0 = numroc( max( n, nb, 2 ), nb, 0, 0, nprow lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locp(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, ( max(abs(a(i,j))), norm = 'm' or 'm' with ia <= i <= ia+m-1 ( find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, scl = max( scale, abs( real( x( i ) ) ), abs( aimag( x( i ) ) ) ) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, scale the column norms by tscal if the maximum element in cnorm i lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, pcmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, (nb+2*bw)*bw +max((bw*nrhs), bw*bw info (global output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (12*npcol + 3*nb) +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca[ lld_ ] the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, returned in work(1) and an error code is returned. lwork>= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu size of user-input auxiliary fillin space af. must be >= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu and the minimum acceptable size will be returned in af( 1 ) (12*npcol+3*nb) +max(10*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) +max(nrhs*(nb+2*bwl+4*bwu), 1 info (global output) integer 2) reduced system phase: a small (max(bwl,bwu)* (p-1)) system is formed representin factors) in the space af. a parallel block cyclic reduction lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, and lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, ldq (input) integer the leading dimension of the array q. ldq >= max(1,nq) rho (global input/output) double precision ldq (input) integer the leading dimension of the array q. ldq >= max(1,nq) rho (global input/output) double precision lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, itermax = node (iafirst,jafirst) owns a(1,1) ( max(abs(a(i,j))), norm = 'm' or 'm' with ia <= i <= ia+m-1 ( find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, the dimension of the array work. lwork = max( n, np * ( nb + nq ) np = numroc( n, nb, myrow, iarow, nprow ), scl = max( scale, abs( x( i ) ) ) scale and sumsq must be supplied in scale and sumsq respectively. lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+2*bw)*bw +max((bw*nrhs), bw*bw info (global output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (12*npcol + 3*nb) +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca[ lld_ ] the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, work (local workspace) double precision array, dimension ( max( 5*n, 7 ) lwork (local input) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, distributed matrix a. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lwork (local input) integer lwork >= max( 1+6*n+2*np*nq, trilwmin ) + 2* np = numroc( n, nb, myrow, iarow, nprow ) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locp(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, = 4: execution path control; = 5: maximum size for direct call to the lapack routin name (global input) character*(*) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, returned in work(1) and an error code is returned. lwork>= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu size of user-input auxiliary fillin space af. must be >= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu and the minimum acceptable size will be returned in af( 1 ) (12*npcol+3*nb) +max(10*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) +max(nrhs*(nb+2*bwl+4*bwu), 1 info (global output) integer 2) reduced system phase: a small (max(bwl,bwu)* (p-1)) system is formed representin factors) in the space af. a parallel block cyclic reduction lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, and lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, ldq (input) integer the leading dimension of the array q. ldq >= max(1,nq) rho (global input/output) real ldq (input) integer the leading dimension of the array q. ldq >= max(1,nq) rho (global input/output) real lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, itermax = node (iafirst,jafirst) owns a(1,1) ( max(abs(a(i,j))), norm = 'm' or 'm' with ia <= i <= ia+m-1 ( find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, the dimension of the array work. lwork = max( n, np * ( nb + nq ) np = numroc( n, nb, myrow, iarow, nprow ), scl = max( scale, abs( x( i ) ) ) scale and sumsq must be supplied in scale and sumsq respectively. lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+2*bw)*bw +max((bw*nrhs), bw*bw info (global output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (12*npcol + 3*nb) +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca[ lld_ ] the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, work (local workspace) real array, dimension ( max( 5*n, 7 ) lwork (local input) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, distributed matrix a. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lwork (local input) integer lwork >= max( 1+6*n+2*np*nq, trilwmin ) + 2* np = numroc( n, nb, myrow, iarow, nprow ) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locp(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, returned in work(1) and an error code is returned. lwork>= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu size of user-input auxiliary fillin space af. must be >= nb*(bwl+bwu)+6*max(bwl,bwu)*max(bwl,bwu and the minimum acceptable size will be returned in af( 1 ) lld_a (local) desca[ lld_ ] the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (12*npcol+3*nb) +max(10*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (nb+bwu)*(bwl+bwu)+6*(bwl+bwu)*(bwl+2*bwu) +max(nrhs*(nb+2*bwl+4*bwu), 1 info (global output) integer 2) reduced system phase: a small (max(bwl,bwu)* (p-1)) system is formed representin factors) in the space af. a parallel block cyclic reduction lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, and lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, distributed matrix a. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lwork = n + ( np0 + mq0 + nb ) * nb, with np0 = numroc( max( n, nb, 2 ), nb, 0, 0, nprow lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locp(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, ( max(abs(a(i,j))), norm = 'm' or 'm' with ia <= i <= ia+m-1 ( find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) find max(abs(a(i,j))) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, scl = max( scale, abs( real( x( i ) ) ), abs( aimag( x( i ) ) ) ) lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, scale the column norms by tscal if the maximum element in cnorm i lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, pzmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, (nb+2*bw)*bw +max((bw*nrhs), bw*bw info (global output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, (12*npcol + 3*nb) +max((10+2*min(100,nrhs))*npcol+4*nrhs, 8*npcol info (local output) integer lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, lld_a (local) desca( lld_ ) the leading dimension of the local array. lld_a >= max(1,locr(m_a)) let k be the number of rows or columns of a distributed matrix, array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=min(m,j+kl on exit, details of the factorization: u is stored as an ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer in the calling (sub) program. lda must be at least max( 1, n ) array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=min(m,j+kl on exit, details of the factorization: u is stored as an ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer ldb (input) integer the leading dimension of the array b. ldb >= max(1,n) info (output) integer in the calling (sub) program. lda must be at least max( 1, n ) |
| max_j max_j implementation of the sturm sequence loop. this must be at least max_j |e(j)^2| *safe_min, and at least safe_min, wher without overflow. implementation of the sturm sequence loop. this must be at least max_j |e(j)^2| *safe_min, and at least safe_min, wher without overflow. implementation of the sturm sequence loop. this must be at least max_j |e(j)^2| *safe_min, and at least safe_min, wher without overflow. implementation of the sturm sequence loop. this must be at least max_j |e(j)^2| *safe_min, and at least safe_min, wher without overflow. |
| maximi maximi in addition, this routine performs a global minimization and maximi in addition, this routine performs a global minimization and maximi |
| maximize maximize see how consecutive small subdiagonal elements are modified by subsequent shifts in an effort to maximize the number of bulge clamsh should only be called when there are multiple shifts/bulges see how consecutive small subdiagonal elements are modified by subsequent shifts in an effort to maximize the number of bulge dlamsh should only be called when there are multiple shifts/bulges see how consecutive small subdiagonal elements are modified by subsequent shifts in an effort to maximize the number of bulge slamsh should only be called when there are multiple shifts/bulges see how consecutive small subdiagonal elements are modified by subsequent shifts in an effort to maximize the number of bulge zlamsh should only be called when there are multiple shifts/bulges |
| maximum maximum ccombamax1 finds the element having maximum real part absolut on entry, the number of bulges to send through h ( >1 ). nbulge should be less than the maximum determined (jblk) on exit, the maximum number of bulges that can be sent on entry, the number of bulges to send through h ( >1 ). nbulge should be less than the maximum determined (jblk) on exit, the maximum number of bulges that can be sent size of separator blocks is maximum of bandwidth size of separator blocks is maximum of bandwidth itmax is the maximum number of steps of iterative refinement notes this routine does a global maximum and must be called by al do not exceed maximum determined 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 find maximum sum of columns for 1-nor find maximum sum of columns for 1-nor this routine does a global maximum and must be called by al scale the column norms by tscal if the maximum element in cnorm i pcmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, itmax is the maximum number of steps of iterative refinement notes size of separator blocks is maximum of bandwidth size of separator blocks is maximum of bandwidth itmax is the maximum number of steps of iterative refinement notes this routine does a global maximum and must be called by al mmax (input) integer the maximum number of intervals that may be generated. i quit with info = mmax+1. do not exceed maximum determined 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 find maximum sum of columns for 1-nor find maximum sum of columns for 1-nor this routine does a global maximum and must be called by al itmax is the maximum number of steps of iterative refinement notes = 4: execution path control; = 5: maximum size for direct call to the lapack routin name (global input) character*(*) size of separator blocks is maximum of bandwidth size of separator blocks is maximum of bandwidth itmax is the maximum number of steps of iterative refinement notes this routine does a global maximum and must be called by al mmax (input) integer the maximum number of intervals that may be generated. i quit with info = mmax+1. do not exceed maximum determined 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 find maximum sum of columns for 1-nor find maximum sum of columns for 1-nor this routine does a global maximum and must be called by al itmax is the maximum number of steps of iterative refinement notes size of separator blocks is maximum of bandwidth size of separator blocks is maximum of bandwidth itmax is the maximum number of steps of iterative refinement notes this routine does a global maximum and must be called by al do not exceed maximum determined 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 find maximum sum of columns for 1-nor find maximum sum of columns for 1-nor this routine does a global maximum and must be called by al scale the column norms by tscal if the maximum element in cnorm i pzmax1 computes the global index of the maximum element in absolut in indx and the value is returned in amax, itmax is the maximum number of steps of iterative refinement notes on entry, the number of bulges to send through h ( >1 ). nbulge should be less than the maximum determined (jblk) on exit, the maximum number of bulges that can be sent zcombamax1 finds the element having maximum real part absolut on entry, the number of bulges to send through h ( >1 ). nbulge should be less than the maximum determined (jblk) on exit, the maximum number of bulges that can be sent |
| MAXINDEX MAXINDEX index: the current global row and column number. MAXINDEX: the global row and column for the first row an liib, lijb: the first row, column in index: the current global row and column number. MAXINDEX: the global row and column for the first row an liib, lijb: the first row, column in index: the current global row and column number. MAXINDEX: the global row and column for the first row an liib, lijb: the first row, column in index: the current global row and column number. MAXINDEX: the global row and column for the first row an liib, lijb: the first row, column in |
| MAXITS MAXITS 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 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 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 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 |
| may may go through, n should be at least 4*nbulge+2. otherwise, nbulge may be reduced by this routine ulp (local input) real implemented by mark r. fahey, may 28, 199 ===================================================================== implemented by: m. fahey, may 28, 199 ===================================================================== go through, n should be at least 4*nbulge+2. otherwise, nbulge may be reduced by this routine ulp (local input) double precision the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro guarantee completion. if the input parameters are incorrect, rwork(1) may also be incorrect lrwork (local input) integer bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. cto * a(i,j) / cfrom does not over/underflow. type specifies that sub( a ) may be full, upper triangular, lower triangular or uppe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. individual process. if insufficient workspace is allocated, the expected orthogonalization may not be done note : if the eigenvectors obtained are not orthogonal, increase bal, but the handle (the integer value) may vary array a. if all eigenvectors are requested, the routine may either return th products q*x and/or q*y, where q is an input unitary bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. mmax (input) integer the maximum number of intervals that may be generated. i quit with info = mmax+1. i.e., on output, all intervals [ intvl(2*i-1), intvl(2*i) ], i < kf, have converged. note that the input intervals may be reordered b bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. cto * a(i,j) / cfrom does not over/underflow. type specifies that sub( a ) may be full, upper triangular, lower triangular or uppe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. pdstebz computes the eigenvalues of a symmetric tridiagonal matrix in parallel. the user may ask for all eigenvalues, all eigenvalues i static partitioning of work is done at the beginning of pdstebz which individual process. if insufficient workspace is allocated, the expected orthogonalization may not be done note : if the eigenvectors obtained are not orthogonal, increase the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. n4 (global input) integer problem dimensions for the subroutine name; these may not al bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. mmax (input) integer the maximum number of intervals that may be generated. i quit with info = mmax+1. i.e., on output, all intervals [ intvl(2*i-1), intvl(2*i) ], i < kf, have converged. note that the input intervals may be reordered b bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. cto * a(i,j) / cfrom does not over/underflow. type specifies that sub( a ) may be full, upper triangular, lower triangular or uppe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. psstebz computes the eigenvalues of a symmetric tridiagonal matrix in parallel. the user may ask for all eigenvalues, all eigenvalues i static partitioning of work is done at the beginning of psstebz which individual process. if insufficient workspace is allocated, the expected orthogonalization may not be done note : if the eigenvectors obtained are not orthogonal, increase the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro the different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. different processes. because of this, it is possible that a heterogeneous system may return incorrect results without any erro guarantee completion. if the input parameters are incorrect, rwork(1) may also be incorrect lrwork (local input) integer bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. sub( a ) = a(ia:ia+m-1,ja:ja+n-1), resulting in row or column pivoting. the pivot vector may be distributed across a process ro matrix a. this routine will transpose the pivot vector if necessary. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. cto * a(i,j) / cfrom does not over/underflow. type specifies that sub( a ) may be full, upper triangular, lower triangular or uppe bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. the index in the global array a that points to the start of the matrix to be operated on (which may be either all of block to send to neighboring processor. depending on circumstances, may need to transpose the matrix the index in the global array a that points to the start of the matrix to be operated on (which may be either all of these are alignment restrictions that may or may not be remove individual process. if insufficient workspace is allocated, the expected orthogonalization may not be done note : if the eigenvectors obtained are not orthogonal, increase bal, but the handle (the integer value) may vary array a. if all eigenvectors are requested, the routine may either return th products q*x and/or q*y, where q is an input unitary bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. bal, but the handle (the integer value) may vary array a. go through, n should be at least 4*nbulge+2. otherwise, nbulge may be reduced by this routine ulp (local input) real go through, n should be at least 4*nbulge+2. otherwise, nbulge may be reduced by this routine ulp (local input) double precision implemented by mark r. fahey, may 28, 199 ===================================================================== implemented by: m. fahey, may 28, 199 ===================================================================== |
| MB_ MB_ array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. 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MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. 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MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_a (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute |
| MB_A MB_A array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute should be true: ( MB_A.eq.nb_a.eq.mb_z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute should be true: ( MB_A.eq.nb_a.eq.mb_z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute should be true: ( MB_A.eq.nb_a.eq.mb_z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca[ mb_ ] the blocking factor used to distribu nb_a (global) desca[ nb_ ] the blocking factor used to distribu- array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute buted matrix a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute should be true: ( MB_A.eq.nb_a.eq.mb_z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used t nb_a (global) desca( nb_ ) the blocking factor used to array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute array a. MB_A (global) desca( mb_ ) the blocking factor used to distribut nb_a (global) desca( nb_ ) the blocking factor used to distribute |
| MB_AF MB_AF ipiv (local input) integer array of dimension locr(m_af)+MB_AF by pcgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(m_af)+MB_AF by pdgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(m_af)+MB_AF by psgetrf. ipiv(i) -> the global row local row i ipiv (local input) integer array of dimension locr(m_af)+MB_AF by pzgetrf. ipiv(i) -> the global row local row i |
| MB_B MB_B iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), lrwork is local input and must be at least lrwork >= locr( n + mod(ib-1,MB_B) ) if lrwork = -1, then lrwork is global input and a workspace nb_a * nb_a, MB_B * ( npb0 + pqb0 + mb_b ) ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), lrwork is local input and must be at least lrwork >= locr( n + mod( ib-1, MB_B ) ) if lrwork = -1, then lrwork is global input and a workspace lrwork is local input and must be at least lrwork >= locr( n + mod( ib-1, MB_B ) ) if lrwork = -1, then lrwork is global input and a workspace iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), liwork is local input and must be at least liwork >= locr( n + mod(ib-1,MB_B) ) if liwork = -1, then liwork is global input and a workspace nb_a * nb_a, MB_B * ( npb0 + pqb0 + mb_b ) ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), liwork is local input and must be at least liwork >= locr( n + mod( ib-1, MB_B ) ) if liwork = -1, then liwork is global input and a workspace liwork is local input and must be at least liwork >= locr( n + mod( ib-1, MB_B ) ) if liwork = -1, then liwork is global input and a workspace iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), liwork is local input and must be at least liwork >= locr( n + mod(ib-1,MB_B) ) if liwork = -1, then liwork is global input and a workspace nb_a * nb_a, MB_B * ( npb0 + pqb0 + mb_b ) ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), liwork is local input and must be at least liwork >= locr( n + mod( ib-1, MB_B ) ) if liwork = -1, then liwork is global input and a workspace liwork is local input and must be at least liwork >= locr( n + mod( ib-1, MB_B ) ) if liwork = -1, then liwork is global input and a workspace iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), lrwork is local input and must be at least lrwork >= locr( n + mod(ib-1,MB_B) ) if lrwork = -1, then lrwork is global input and a workspace nb_a * nb_a, MB_B * ( npb0 + pqb0 + mb_b ) ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), iroffb = mod( ib-1, MB_B ), icoffb = mod( jb-1, nb_b ) ibcol = indxg2p( jb, nb_b, mycol, csrc_b, npcol ), lrwork is local input and must be at least lrwork >= locr( n + mod( ib-1, MB_B ) ) if lrwork = -1, then lrwork is global input and a workspace lrwork is local input and must be at least lrwork >= locr( n + mod( ib-1, MB_B ) ) if lrwork = -1, then lrwork is global input and a workspace |
| MB_C MB_C iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( ic-1, MB_C ), icoffc = mod( jc-1, nb_c ) iccol = indxg2p( jc, nb_c, mycol, csrc_c, npcol ), iroffc = mod( icc-1, MB_C ), icoffc = mod( jcc-1, nb_c ) iccol = indxg2p( jcc, nb_c, mycol, csrc_c, npcol ), |
| MB_P MB_P when rowcol='c' or 'c': >= locr( n + mod(ip-1,MB_P) ) if pivroc='c' or 'c' this array contains the pivoting information. ipiv(i) is the when rowcol='c' or 'c': >= locr( n + mod(ip-1,MB_P) ) if pivroc='c' or 'c' this array contains the pivoting information. ipiv(i) is the when rowcol='c' or 'c': >= locr( n + mod(ip-1,MB_P) ) if pivroc='c' or 'c' this array contains the pivoting information. ipiv(i) is the when rowcol='c' or 'c': >= locr( n + mod(ip-1,MB_P) ) if pivroc='c' or 'c' this array contains the pivoting information. ipiv(i) is the |
| MB_Q MB_Q lwork = 6*n + 2*np*nq, with np = numroc( n, MB_Q, myrow, iqrow, nprow iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) lwork = 6*n + 2*np*nq, with np = numroc( n, MB_Q, myrow, iqrow, nprow iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) |
| MB_V MB_V v (local workspace) complex pointer into the local memory to an array of dimension locr(n+mod(iv-1,MB_V)). o (w is not returned). t (local input) complex array, dimension MB_V by mb_ gular matrix t in the representation of the block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) complex pointer into the local memory t (local input) complex array, dimension MB_V by mb_ block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) complex pointer into the local memory v (local workspace) double precision pointer into the local memory to an array of dimension locr(n+mod(iv-1,MB_V)). o (w is not returned). t (local input) double precision array, dimension MB_V by mb_ gular matrix t in the representation of the block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) double precision pointer into the local memory t (local input) double precision array, dimension MB_V by mb_ block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) double precision pointer into the local memory v (local workspace) real pointer into the local memory to an array of dimension locr(n+mod(iv-1,MB_V)). o (w is not returned). t (local input) real array, dimension MB_V by mb_ gular matrix t in the representation of the block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) real pointer into the local memory t (local input) real array, dimension MB_V by mb_ block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) real pointer into the local memory v (local workspace) complex*16 pointer into the local memory to an array of dimension locr(n+mod(iv-1,MB_V)). o (w is not returned). t (local input) complex*16 array, dimension MB_V by mb_ gular matrix t in the representation of the block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) complex*16 pointer into the local memory t (local input) complex*16 array, dimension MB_V by mb_ block reflector. the order of the triangular factor t (= the number of elementary reflectors). 1 <= k <= MB_V (= nb_v) v (input/output) complex*16 pointer into the local memory |
| MB_X MB_X local memory to an array of dimension locr(n+mod(ix-1,MB_X)). on an intermediate return, a * x, if kase=1, local memory to an array of dimension locr(n+mod(ix-1,MB_X)). on an intermediate return, a * x, if kase=1, local memory to an array of dimension locr(n+mod(ix-1,MB_X)). on an intermediate return, a * x, if kase=1, local memory to an array of dimension locr(n+mod(ix-1,MB_X)). on an intermediate return, a * x, if kase=1, |
| MB_Z MB_Z ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where should be true: ( mb_a.eq.nb_a.eq.MB_Z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where should be true: ( mb_a.eq.nb_a.eq.MB_Z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where should be true: ( mb_a.eq.nb_a.eq.MB_Z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where should be true: ( mb_a.eq.nb_a.eq.MB_Z.eq.nb_z .and. iroffa.eq.icoffa .and with iroffa = mod( ia-1, mb_a ) ( mb_a.eq.nb_a.eq.MB_Z .and. iroffa.eq.iroffz .and. iroffa.eq.0 .and where |
| mchine mchine 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 |
| meaning meaning orfac, icluster() and gap() parameters added meaning of info is change functional differences: orfac, icluster() and gap() parameters added meaning of info is change functional differences: orfac, icluster() and gap() parameters added meaning of info is change functional differences: orfac, icluster() and gap() parameters added meaning of info is change functional differences: |
| meaningful meaningful the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors n (global input) integer the number of meaningful entries of the block reflector h the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors. l > 0 a (local input/local output) complex pointer into the the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors n (global input) integer the number of meaningful entries of the block reflector h the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors. l > 0 a (local input/local output) double precision pointer into the the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors n (global input) integer the number of meaningful entries of the block reflector h the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors. l > 0 a (local input/local output) real pointer into the the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors n (global input) integer the number of meaningful entries of the block reflector h the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors. l > 0 a (local input/local output) complex*16 pointer into the the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors the columns of the distributed submatrix sub( a ) containing the meaningful part of the householder reflectors |
| means means remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste in order to compute h( :, i ), we must update a( :, i ) which means that the processor column owning a( :, i ) mus matrix sub( a ) = [a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1)] to upper triangular form by means of unitary transformations the upper trapezoidal matrix sub( a ) is factored as the solution matrix x must be computed by pctrtrs or some other means before entering this routine. pctrrfs does not do iterativ pctzrzf reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper triangular form by means sub( a ) = [ a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1) ] to upper triangular form by means of orthogonal transformations the upper trapezoidal matrix sub( a ) is factored as less. if abstol is less than or equal to zero, then ulp*|t|
will be used, where |t| means the 1-norm of t
set to the underflow threshold dlamch('u'), not zero.
remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste in order to compute h( :, i ), we must update a( :, i ) which means that the processor column owning a( :, i ) mus the solution matrix x must be computed by pdtrtrs or some other means before entering this routine. pdtrrfs does not do iterativ pdtzrzf reduces the m-by-n ( m<=n ) real upper trapezoidal matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper triangular form by means sub( a ) = [ a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1) ] to upper triangular form by means of orthogonal transformations the upper trapezoidal matrix sub( a ) is factored as less. if abstol is less than or equal to zero, then ulp*|t|
will be used, where |t| means the 1-norm of t
set to the underflow threshold slamch('u'), not zero.
remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste in order to compute h( :, i ), we must update a( :, i ) which means that the processor column owning a( :, i ) mus the solution matrix x must be computed by pstrtrs or some other means before entering this routine. pstrrfs does not do iterativ pstzrzf reduces the m-by-n ( m<=n ) real upper trapezoidal matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper triangular form by means remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste remaining equal and assuming enough workspace. less workspace means less reorthogonalization but faste in order to compute h( :, i ), we must update a( :, i ) which means that the processor column owning a( :, i ) mus matrix sub( a ) = [a(ia:ia+m-1,ja:ja+m-1) a(ia:ia+m-1,ja+n-l:ja+n-1)] to upper triangular form by means of unitary transformations the upper trapezoidal matrix sub( a ) is factored as the solution matrix x must be computed by pztrtrs or some other means before entering this routine. pztrrfs does not do iterativ pztzrzf reduces the m-by-n ( m<=n ) complex upper trapezoidal matrix sub( a ) = a(ia:ia+m-1,ja:ja+n-1) to upper triangular form by means |
| median median choose partition entry as median of choose partition entry as median of choose partition entry as median of choose partition entry as median of |
| Megabyte Megabyte is wise to provide the extra workspace (typically less than a Megabyte per process) if clustersize >= n/sqrt(nprow*npcol), then providing is wise to provide the extra workspace (typically less than a Megabyte per process) if clustersize >= n/sqrt(nprow*npcol), then providing is wise to provide the extra workspace (typically less than a Megabyte per process) if clustersize >= n/sqrt(nprow*npcol), then providing is wise to provide the extra workspace (typically less than a Megabyte per process) if clustersize >= n/sqrt(nprow*npcol), then providing |
| memory memory a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the last processor does not need to send anything. biptr = location of triangle b_i in memory a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. establish the mapping between a matrix entry and its corresponding process and memory location in the following comments, the character _ should be read as conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) complex pointer into the local memory to an array of dimension (lld_a pieces of the n-by-(n-k+1) general distributed matrix a (local output) complex*16 pointer into the local memory to an array of dimension (locc(ja+n-1)) this processor column. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) complex pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. 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 the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the last processor does not need to send anything. biptr = location of triangle b_i in memory a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cach d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) double precision pointer into the local memory to an array of dimension (lld_a pieces of the n-by-(n-k+1) general distributed matrix a (local output) complex*16 pointer into the local memory to an array of dimension (locc(ja+n-1)) this processor column. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cach d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. q (local input) double precision pointer into the local memory contains the local pieces of the distributed matrix sub( a ) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) double precision pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. establish the mapping between a matrix entry and its corresponding process and memory location in the following comments, the character _ should be read as conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. 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 the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the last processor does not need to send anything. biptr = location of triangle b_i in memory a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cach d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) real pointer into the local memory to an array of dimension (lld_a pieces of the n-by-(n-k+1) general distributed matrix a (local output) complex*16 pointer into the local memory to an array of dimension (locc(ja+n-1)) this processor column. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. elements of the tridiagonal matrix t. these elements are assumed to be interleaved in memory for better cach d(1),d(3),...,d(2*n-1), while the squares of the off-diagonal the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. q (local input) real pointer into the local memory contains the local pieces of the distributed matrix sub( a ) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) real pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. establish the mapping between a matrix entry and its corresponding process and memory location in the following comments, the character _ should be read as conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. 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 the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the last processor does not need to send anything. biptr = location of triangle b_i in memory a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. establish the mapping between a matrix entry and its corresponding process and memory location in the following comments, the character _ should be read as conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures" (see also lapack working note 132) the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) complex*16 pointer into the local memory to an array of dimension (lld_a pieces of the n-by-(n-k+1) general distributed matrix a (local output) complex*16 pointer into the local memory to an array of dimension (locc(ja+n-1)) this processor column. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the a (local input/local output) complex*16 pointer into local memory to an array with first dimensio on entry, this array contains the local pieces of the the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the array descriptor for the distributed matrix a. contains information of mapping of a to memory. pleas the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. 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 the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. the mapping between an object element and its corresponding process and memory location let a be a generic term for any 2d block cyclicly distributed array. |
| ment ment .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . |
| ments ments .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . .. .. array arguments . |
| message message values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer heterogeneous system may return incorrect results without any error messages notes work arrays. each of these values is returned in the first entry of the corresponding work array, and no error message values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/output) real array, in the first entry of the correspondingwork array, and no error message is issued by pxerbla rwork (local workspace/output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/global output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) real array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace) integer array, dimension ( max( 4*n, 14 ) ) size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/global output) integer array, heterogeneous system may return incorrect results without any error messages notes heterogeneous system may return incorrect results without any error messages arguments these values is returned in the first entry of the corresponding work arrays, and no error message is issued b each of these values is returned in the first entry of the corresponding work array, and no error message is issued b values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace) integer array, dimension ( max( 4*n, 14 ) ) size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/global output) integer array, heterogeneous system may return incorrect results without any error messages notes heterogeneous system may return incorrect results without any error messages arguments these values is returned in the first entry of the corresponding work arrays, and no error message is issued b each of these values is returned in the first entry of the corresponding work array, and no error message is issued b values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer size for the work array. the required workspace is returned as the first element of work and no error message is issue values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/local output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer heterogeneous system may return incorrect results without any error messages notes work arrays. each of these values is returned in the first entry of the corresponding work array, and no error message values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/output) double precision array, in the first entry of the correspondingwork array, and no error message is issued by pxerbla rwork (local workspace/output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (local output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer in the following overview of the steps performed, m in the margin indicates message traffic and c indicates o(n^2 nb/sqrt(p) values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla iwork (local workspace/global output) integer array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla rwork (local workspace/local output) double precision array, values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla info (global output) integer values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla values is returned in the first entry of the corresponding work array, and no error message is issued by pxerbla |
| messages messages heterogeneous system may return incorrect results without any error messages notes communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so both border messages can be handled at once rules: heterogeneous system may return incorrect results without any error messages notes heterogeneous system may return incorrect results without any error messages arguments heterogeneous system may return incorrect results without any error messages notes heterogeneous system may return incorrect results without any error messages arguments heterogeneous system may return incorrect results without any error messages notes communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so both border messages can be handled at once rules: |
| met met finishing up. even if rotn=1, in order to minimize border communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so bot finishing up. even if rotn=1, in order to minimize border communication sometimes k1(ki)=hbl-2 & k2(ki)=hbl-1 so bot |
| method method the following method uses more flops than necessary bu 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. the factorization is obtained by householder's method. the kt introduce zeros into the (m - k + 1)th row of sub( a ), is given in the following method uses more flops than necessary bu the factorization is obtained by householder's method. the kt introduce zeros into the (m - k + 1)th row of sub( a ), is given in the following method uses more flops than necessary bu 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. pdlaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method the factorization is obtained by householder's method. the kt the (m - k + 1)th row of sub( a ), is given in the form the following method uses more flops than necessary bu the factorization is obtained by householder's method. the kt the (m - k + 1)th row of sub( a ), is given in the form the following method uses more flops than necessary bu 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. pslaed0 computes all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method the factorization is obtained by householder's method. the kt the (m - k + 1)th row of sub( a ), is given in the form the following method uses more flops than necessary bu the factorization is obtained by householder's method. the kt the (m - k + 1)th row of sub( a ), is given in the form the following method uses more flops than necessary bu 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. the factorization is obtained by householder's method. the kt introduce zeros into the (m - k + 1)th row of sub( a ), is given in the following method uses more flops than necessary bu the factorization is obtained by householder's method. the kt introduce zeros into the (m - k + 1)th row of sub( a ), is given in |
| middle middle clamsh should only be called when there are multiple shifts/bulges (nbulge > 1) and the first shift is starting in the middle of a small subdiagonal elements. dlamsh should only be called when there are multiple shifts/bulges (nbulge > 1) and the first shift is starting in the middle of a subdiagonal elements. if we are starting in the middle because of consecutive smal can send through without breaking the consecutive small mod(istart-1,hbl) = hbl-1 c.) work in the middle of the block whe mod(istart-1,hbl) = hbl-1 c.) work in the middle of the block whe if we are starting in the middle because of consecutive smal can send through without breaking the consecutive small slamsh should only be called when there are multiple shifts/bulges (nbulge > 1) and the first shift is starting in the middle of a subdiagonal elements. zlamsh should only be called when there are multiple shifts/bulges (nbulge > 1) and the first shift is starting in the middle of a small subdiagonal elements. |
| might might over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) might finishing up. even if rotn=1, in order to minimize border over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) might finishing up. over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) might finishing up. over the global m to i-1 values is always k1(ki) to k2(ki). however, because there are many bulges, k1(ki) & k2(ki) might finishing up. even if rotn=1, in order to minimize border |
| mild mild this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits this code makes very mild assumptions about floating poin add/subtract, or on those binary machines without guard digits |
| MIN MIN array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=MIN(m,j+kl on exit, details of the factorization: u is stored as an array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=MIN(m,j+kl on exit, details of the factorization: u is stored as an put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pcgels solves overdeterMINed or underdetermined complex linea or its conjugate-transpose, using a qr or lq factorization of its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where sigma is an m-by-n matrix which is zero except for its MIN(m,n) diagonal elements, u is an m-by-m orthogonal matrix, an are the singular values of a and the columns of u and v are the its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. the size given in lwork. on exit, work( 1 ) contains the MINimal lwork lwork (local input or global input) integer put MINimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the MINimum acceptable size will be returned in af( 1 work (local workspace/local output) want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. here q and p**h are the unitary distributed matrices deterMINed b bidiagonal form: a(ia:*,ja:*) = q * b * p**h. q and p**h are defined put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pdgels solves overdeterMINed or underdetermined real linea or its transpose, using a qr or lq factorization of sub( a ). it is its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where sigma is an m-by-n matrix which is zero except for its MIN(m,n) diagonal elements, u is an m-by-m orthogonal matrix, an are the singular values of a and the columns of u and v are the its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), sub-matrix. MIN(1,n) <= n1 <= n d (global input/output) double precision array, dimension (n) the location of the last eigenvalue in the leading sub-matrix. MIN(1,n) < n1 < n nb (global input) integer its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), here q and p**t are the orthogonal distributed matrices deterMINed b bidiagonal form: a(ia:*,ja:*) = q * b * p**t. q and p**t are defined put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. the size given in lwork. on exit, work( 1 ) contains the MINimal lwork lwork (local input or global input) integer put MINimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the MINimum acceptable size will be returned in af( 1 work (local workspace/local output) want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), psgels solves overdeterMINed or underdetermined real linea or its transpose, using a qr or lq factorization of sub( a ). it is its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where sigma is an m-by-n matrix which is zero except for its MIN(m,n) diagonal elements, u is an m-by-m orthogonal matrix, an are the singular values of a and the columns of u and v are the its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), sub-matrix. MIN(1,n) <= n1 <= n d (global input/output) real array, dimension (n) the location of the last eigenvalue in the leading sub-matrix. MIN(1,n) < n1 < n nb (global input) integer its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), here q and p**t are the orthogonal distributed matrices deterMINed b bidiagonal form: a(ia:*,ja:*) = q * b * p**t. q and p**t are defined put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. the size given in lwork. on exit, work( 1 ) contains the MINimal lwork lwork (local input or global input) integer put MINimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the MINimum acceptable size will be returned in af( 1 work (local workspace/local output) want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. put MINimum value of laf into af( 1 check worksize its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), pzgels solves overdeterMINed or underdetermined complex linea or its conjugate-transpose, using a qr or lq factorization of its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), where sigma is an m-by-n matrix which is zero except for its MIN(m,n) diagonal elements, u is an m-by-m orthogonal matrix, an are the singular values of a and the columns of u and v are the its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), its process row. the values of locr() and locc() may be deterMINed via a call to th locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), put MINimum value of laf into af( 1 check worksize want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. the size given in lwork. on exit, work( 1 ) contains the MINimal lwork lwork (local input or global input) integer put MINimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the MINimum acceptable size will be returned in af( 1 work (local workspace/local output) want to find errors with MIN( ), so if no error, set it to a bi descriptor multiplier. here q and p**h are the unitary distributed matrices deterMINed b bidiagonal form: a(ia:*,ja:*) = q * b * p**h. q and p**h are defined array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=MIN(m,j+kl on exit, details of the factorization: u is stored as an array ab as follows: ab(kl+ku+1+i-j,j) = a(i,j) for max(1,j-ku)<=i<=MIN(m,j+kl on exit, details of the factorization: u is stored as an |
| minimal minimal the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer if jobz='n' work(1) = minimal workspace for eigenvalues only generate all the eigenvectors. the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer work (local workspace) complex array, dimension (lwork) on exit, work( 1 ) returns the minimal and optimal workspac lwork (local input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer if jobz='n' work(1) = minimal=optimal amount of workspac generate all the eigenvectors. the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer work (local workspace) double precision array, dimension (lwork) on exit, work( 1 ) returns the minimal and optimal workspac lwork (local input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer if jobz='n' work(1) = minimal=optimal amount of workspac generate all the eigenvectors. the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer work (local workspace) real array, dimension (lwork) on exit, work( 1 ) returns the minimal and optimal workspac lwork (local input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer if jobz='n' work(1) = minimal workspace for eigenvalues only generate all the eigenvectors. the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add the computed eigenvectors may not be orthogonal if the minimal workspace is supplied and orfac is too small of potentially poor performance) you should add dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work( 1 ) returns the minimal and optimal lwork lwork (local or global input) integer work (local workspace) complex*16 array, dimension (lwork) on exit, work( 1 ) returns the minimal and optimal workspac lwork (local input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer the size given in lwork. on exit, work( 1 ) contains the minimal lwork lwork (local input or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer dimension (lwork) on exit, work(1) returns the minimal and optimal lwork lwork (local or global input) integer |
| minimization minimization in addition, this routine performs a global minimization and maximi in addition, this routine performs a global minimization and maximi |
| minimize minimize an overdetermined system, i.e., solve the least squares problem minimize || sub( b ) - sub( a )*x || 2. if trans = 'n' and m < n: find the minimum norm solution of we keep the block column of a up-to-date to minimize th the block column of a is reasonably load balanced whereas go past that range while later bulges (ki+1,ki+2,etc..) are finishing up. even if rotn=1, in order to minimize borde border messages can be handled at once. an overdetermined system, i.e., solve the least squares problem minimize || sub( b ) - sub( a )*x || 2. if trans = 'n' and m < n: find the minimum norm solution of we keep the block column of a up-to-date to minimize th the block column of a is reasonably load balanced whereas an overdetermined system, i.e., solve the least squares problem minimize || sub( b ) - sub( a )*x || 2. if trans = 'n' and m < n: find the minimum norm solution of we keep the block column of a up-to-date to minimize th the block column of a is reasonably load balanced whereas an overdetermined system, i.e., solve the least squares problem minimize || sub( b ) - sub( a )*x || 2. if trans = 'n' and m < n: find the minimum norm solution of we keep the block column of a up-to-date to minimize th the block column of a is reasonably load balanced whereas go past that range while later bulges (ki+1,ki+2,etc..) are finishing up. even if rotn=1, in order to minimize borde border messages can be handled at once. |
| minimum minimum storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding 2. if trans = 'n' and m < n: find the minimum norm solution o if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding in case of a homogeneous process grid this upper limit can be used as an estimate of the minimum workspace for ever if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum as the first element of work and no error message is issued if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum values is returned in the first entry of the corresponding if lrwork = -1, then lrwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding parts: 1.) the minimum amount of work it takes to determin the critical path.) (loops 50-120) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) orthogonalization (see orfac). note that this may overestimate the minimum workspace needed lwork (local input) integer if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding 2. if trans = 'n' and m < n: find the minimum norm solution o if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding in case of a homogeneous process grid this upper limit can be used as an estimate of the minimum workspace for ever if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding abstol (input) double precision the minimum (absolute) width of an interval. when an interva magnitude) endpoint, then it is considered to be sufficiently abstol (input) double precision the minimum (absolute) width of an interval. when an interva magnitude) endpoint, then it is considered to be sufficiently parts: 1.) the minimum amount of work it takes to determin the critical path.) (loops 130-180) eps = relative machine precision sfmin = safe minimum, such that 1/sfmin does not overflo prec = eps*base pivmin (input) double precision the minimum absolute of a "pivot" in this "paranoid least max_j |e(j)^2| *safe_min, and at least safe_min, where if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) output minimum worksiz if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum as the first element of work and no error message is issued orthogonalization (see orfac). note that this may overestimate the minimum workspace needed lwork (local input) integer if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum as the first element of work and no error message is issued if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum values is returned in the first entry of the corresponding if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding 2. if trans = 'n' and m < n: find the minimum norm solution o if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding in case of a homogeneous process grid this upper limit can be used as an estimate of the minimum workspace for ever if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding abstol (input) real the minimum (absolute) width of an interval. when an interva magnitude) endpoint, then it is considered to be sufficiently abstol (input) real the minimum (absolute) width of an interval. when an interva magnitude) endpoint, then it is considered to be sufficiently parts: 1.) the minimum amount of work it takes to determin the critical path.) (loops 130-180) eps = relative machine precision sfmin = safe minimum, such that 1/sfmin does not overflo prec = eps*base pivmin (input) real the minimum absolute of a "pivot" in this "paranoid least max_j |e(j)^2| *safe_min, and at least safe_min, where if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) output minimum worksiz if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum as the first element of work and no error message is issued orthogonalization (see orfac). note that this may overestimate the minimum workspace needed lwork (local input) integer if lwork = -1, the lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum as the first element of work and no error message is issued if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum values is returned in the first entry of the corresponding if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding 2. if trans = 'n' and m < n: find the minimum norm solution o if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding in case of a homogeneous process grid this upper limit can be used as an estimate of the minimum workspace for ever if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum as the first element of work and no error message is issued if liwork = -1, then liwork is global input and a workspace query is assumed; the routine only calculates the minimum values is returned in the first entry of the corresponding if lrwork = -1, then lrwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding parts: 1.) the minimum amount of work it takes to determin the critical path.) (loops 50-120) storing the local blocks of the distri- buted array a. minimum value of lld_ type_a = 501: lld_a >= put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding put minimum value of laf into af( 1 check worksize if laf is not large enough, an error code will be returned and the minimum acceptable size will be returned in af( 1 work (local workspace/local output) orthogonalization (see orfac). note that this may overestimate the minimum workspace needed lwork (local input) integer if lwork = -1, then lwork is global input and a workspace query is assumed; the routine only calculates the minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum 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 minimum values is returned in the first entry of the corresponding |
| MININDEX MININDEX the following variables point into the arrays a, v, h, v^t, h^t: bindex =index-MININDEX: the column index in v, h, v^t, h^t lij: local index j: the local column number for column index the following variables point into the arrays a, v, h, v^t, h^t: bindex =index-MININDEX: the column index in v, h, v^t, h^t lij: local index j: the local column number for column index the following variables point into the arrays a, v, h, v^t, h^t: bindex =index-MININDEX: the column index in v, h, v^t, h^t lij: local index j: the local column number for column index the following variables point into the arrays a, v, h, v^t, h^t: bindex =index-MININDEX: the column index in v, h, v^t, h^t lij: local index j: the local column number for column index |
| minor minor if (mod(info/16,2).ne.0) then ifail(1) indicates the order of the smallest minor which is not positive definite indices of the eigenvectors that failed to converge. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed, and the > 0: if info = i, and i is <= n: if info = i, the leading minor of order i of could not be completed, and the solution and error info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed, and the > 0: if info = i, and i is <= n: if info = i, the leading minor of order i of could not be completed, and the solution and error info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. if (mod(info/16,2).ne.0) then ifail(1) indicates the order of the smallest minor which is not positive definite indices of the eigenvectors that failed to converge. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed, and the > 0: if info = i, and i is <= n: if info = i, the leading minor of order i of could not be completed, and the solution and error info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. if (mod(info/16,2).ne.0) then ifail(1) indicates the order of the smallest minor which is not positive definite indices of the eigenvectors that failed to converge. if (mod(info/16,2).ne.0) then ifail(1) indicates the order of the smallest minor which is not positive definite indices of the eigenvectors that failed to converge. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed, and the > 0: if info = i, and i is <= n: if info = i, the leading minor of order i of could not be completed, and the solution and error info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. info = -i. > 0: if info = k, the leading minor of order k the factorization could not be completed. |
| MINP MINP contained in the input intervals [ intvl(2*j-1), intvl(2*j) ] where j = 1,...,MINP. it uses and computes the function n(w), which i or equal to its argument w. contained in the input intervals [ intvl(2*j-1), intvl(2*j) ] where j = 1,...,MINP. it uses and computes the function n(w), which i or equal to its argument w. |
| mismatch mismatch which is less accurate than pdlamch says. = 2 : there is a mismatch between the number o = 3 : range='i', and the gershgorin interval initially which is less accurate than pslamch says. = 2 : there is a mismatch between the number o = 3 : range='i', and the gershgorin interval initially |
| MMAX MMAX MMAX (input) intege more than mmax intervals are generated, then pdlaebz will MMAX (input) intege more than mmax intervals are generated, then pslaebz will |
| MOD MOD non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ), icoffa = mod( ja-1, nb ) iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*ceil(npcol-1,nprow)) ). where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) npa0 = numroc( ihi+iroffa, nb, myrow, iarow, nprow ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) iarow = indxg2p( ia, nb, myrow, rsrc_a, nprow ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr( n + MOD(ia-1,mb_a) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc lwork is local input and must be at least lwork = locr(n+MOD(ia-1,mb_a))*nb_a. work is used to keep iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), where iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== iroffa.eq.0 .and.iroffa.eq.iroffz. and. iarow.eq.izrow) with iroffa = MOD( ia-1, mb_a pcheevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, MODif -dno_ieee. this switch only affects the compilation of pslaiect.c. set to twice the underflow threshold 2*pslamch('s') not zero.
if this routine returns with ((MOD(info,2).ne.0) .or
eigenvectors did not converge, try setting abstol to
( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== nq = numroc( n+MOD( ia-1, nb_y ), nb_y, mycol, iacol, npcol v (local workspace) complex pointer into the local memory to an array of dimension locr(n+MOD(iv-1,mb_v)). o (w is not returned). rules: if MOD(k1(ki)-1,hbl) < hbl-2 then mod(k2(ki)-1,hbl)<hbl- k2(ki)-k1(ki) <= rotn iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), >= locr( ia+m-1 ) + mb_a if pivroc='c' or 'c', >= locc( m + MOD(jp-1,nb_p) ) if pivroc='r' or 'r', and >= locr( n + mod(ip-1,mb_p) ) if pivroc='c' or 'c', iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*max(1,ceil(q-1,p))) ). lwork is local input and must be at least lwork >= 2*locr( n + MOD( ia-1, mb_a ) if lwork = -1, then lwork is global input and a workspace this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. 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. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*ceil(q-1,p)) ). lwork is local input and must be at least lwork >= 2*locr( n + MOD( ia-1, mb_a ) ) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), pcgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pcgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) iacol = indxg2p( jaa, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o pcgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pcgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ), icoffa = mod( ja-1, nb ) iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + 2*locc(n+mod(ja-1,nb_a) locc(n+mod(ja-1,nb_a)) + where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) npa0 = numroc( ihi+iroffa, nb, myrow, iarow, nprow ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) iarow = indxg2p( ia, nb, myrow, rsrc_a, nprow ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 3*locr( n + MOD(ia-1,mb_a) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc lwork is local input and must be at least lwork = locr(n+MOD(ia-1,mb_a))*nb_a. work is used to keep iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), nq = numroc( n+MOD( ia-1, nb_y ), nb_y, mycol, iacol, npcol v (local workspace) double precision pointer into the local memory to an array of dimension locr(n+MOD(iv-1,mb_v)). o (w is not returned). eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) ===================================================================== rules: if MOD(k1(ki)-1,hbl) < hbl-2 then mod(k2(ki)-1,hbl)<hbl- if mod(k1(ki)-1,hbl) = hbl-1 then mod(k2(ki)-1,hbl)=hbl-1 iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), >= locr( ia+m-1 ) + mb_a if pivroc='c' or 'c', >= locc( m + MOD(jp-1,nb_p) ) if pivroc='r' or 'r', and >= locr( n + mod(ip-1,mb_p) ) if pivroc='c' or 'c', byall(i) = bycol( numroc(i,desc( nb_ ),myrow,0,nprow ) on the procs whose myrow == MOD((i-1)/desc( nb_ ),nprow work (local workspace) double precision dimension (lwork) byall(i) = byrow( numroc(i,desc( mb_ ),mycol,0,npcol ) on the procs whose mycol == MOD((i-1)/desc( mb_ ),npcol work (local workspace) double precision dimension (lwork) iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), pdgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pdgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) iacol = indxg2p( jaa, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o pdgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pdgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + 2*locc(n+mod(ja-1,nb_a)) nb_a*ceil(npcol-1,nprow)) ). lwork is local input and must be at least lwork >= 3*locr( n + MOD( ia-1, mb_a ) if lwork = -1, then lwork is global input and a workspace this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) further details 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. where iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) alignment requirements pdsyevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, MODif -dno_ieee. this switch only affects the compilation of pdlaiect.c. set to twice the underflow threshold 2*pdlamch('s') not zero.
if this routine returns with ((MOD(info,2).ne.0) .or
eigenvectors did not converge, try setting abstol to
( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + locc(n+mod(ja-1,nb_a) locc(n+mod(ja-1,nb_a)) + lwork is local input and must be at least lwork >= 3*locr( n + MOD( ia-1, mb_a ) ) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ), icoffa = mod( ja-1, nb ) iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + 2*locc(n+mod(ja-1,nb_a) locc(n+mod(ja-1,nb_a)) + where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) npa0 = numroc( ihi+iroffa, nb, myrow, iarow, nprow ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) iarow = indxg2p( ia, nb, myrow, rsrc_a, nprow ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 3*locr( n + MOD(ia-1,mb_a) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc lwork is local input and must be at least lwork = locr(n+MOD(ia-1,mb_a))*nb_a. work is used to keep iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), nq = numroc( n+MOD( ia-1, nb_y ), nb_y, mycol, iacol, npcol v (local workspace) real pointer into the local memory to an array of dimension locr(n+MOD(iv-1,mb_v)). o (w is not returned). eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) ===================================================================== rules: if MOD(k1(ki)-1,hbl) < hbl-2 then mod(k2(ki)-1,hbl)<hbl- if mod(k1(ki)-1,hbl) = hbl-1 then mod(k2(ki)-1,hbl)=hbl-1 iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), >= locr( ia+m-1 ) + mb_a if pivroc='c' or 'c', >= locc( m + MOD(jp-1,nb_p) ) if pivroc='r' or 'r', and >= locr( n + mod(ip-1,mb_p) ) if pivroc='c' or 'c', byall(i) = bycol( numroc(i,desc( nb_ ),myrow,0,nprow ) on the procs whose myrow == MOD((i-1)/desc( nb_ ),nprow work (local workspace) real dimension (lwork) byall(i) = byrow( numroc(i,desc( mb_ ),mycol,0,npcol ) on the procs whose mycol == MOD((i-1)/desc( mb_ ),npcol work (local workspace) real dimension (lwork) iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), psgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), psgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) iacol = indxg2p( jaa, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o psgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), psgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + 2*locc(n+mod(ja-1,nb_a)) nb_a*ceil(npcol-1,nprow)) ). lwork is local input and must be at least lwork >= 3*locr( n + MOD( ia-1, mb_a ) if lwork = -1, then lwork is global input and a workspace this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) further details 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. where iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== eigenvalue while working on the submatrix lying in global rows and columns MOD(info,n+1) alignment requirements pssyevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, MODif -dno_ieee. this switch only affects the compilation of pslaiect.c. set to twice the underflow threshold 2*pslamch('s') not zero.
if this routine returns with ((MOD(info,2).ne.0) .or
eigenvectors did not converge, try setting abstol to
( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) + locc(n+mod(ja-1,nb_a) locc(n+mod(ja-1,nb_a)) + lwork is local input and must be at least lwork >= 3*locr( n + MOD( ia-1, mb_a ) ) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ), icoffa = mod( ja-1, nb ) iacol = indxg2p( ja, nb, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*ceil(npcol-1,nprow)) ). where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) npa0 = numroc( ihi+iroffa, nb, myrow, iarow, nprow ), where nb = mb_a = nb_a, iroffa = MOD( ia-1, nb ) iarow = indxg2p( ia, nb, myrow, rsrc_a, nprow ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), lwork is local input and must be at least lwork >= 2*locr( n + MOD(ia-1,mb_a) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc lwork is local input and must be at least lwork = locr(n+MOD(ia-1,mb_a))*nb_a. work is used to keep iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), where iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== iroffa.eq.0 .and.iroffa.eq.iroffz. and. iarow.eq.izrow) with iroffa = MOD( ia-1, mb_a pzheevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, MODif -dno_ieee. this switch only affects the compilation of pdlaiect.c. set to twice the underflow threshold 2*pdlamch('s') not zero.
if this routine returns with ((MOD(info,2).ne.0) .or
eigenvectors did not converge, try setting abstol to
( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== ( mb_a.eq.nb_a .and. iroffa.eq.icoffa .and. iroffa.eq.0 ) with iroffa = MOD( ia-1, mb_a ) and icoffa = mod( ja-1, nb_a ) ===================================================================== nq = numroc( n+MOD( ia-1, nb_y ), nb_y, mycol, iacol, npcol v (local workspace) complex*16 pointer into the local memory to an array of dimension locr(n+MOD(iv-1,mb_v)). o (w is not returned). rules: if MOD(k1(ki)-1,hbl) < hbl-2 then mod(k2(ki)-1,hbl)<hbl- k2(ki)-k1(ki) <= rotn iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), >= locr( ia+m-1 ) + mb_a if pivroc='c' or 'c', >= locc( m + MOD(jp-1,nb_p) ) if pivroc='r' or 'r', and >= locr( n + mod(ip-1,mb_p) ) if pivroc='c' or 'c', iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroffv = MOD( iv-1, mb_v ), icoffv = mod( jv-1, nb_v ) ivcol = indxg2p( jv, nb_v, mycol, csrc_v, npcol ), iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*max(1,ceil(q-1,p))) ). lwork is local input and must be at least lwork >= 2*locr( n + MOD( ia-1, mb_a ) if lwork = -1, then lwork is global input and a workspace this routine requires n <= nb_a-MOD(ja-1, nb_a) and square bloc non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. non-cyclic restriction: very important! p*nb>= MOD(ja-1,nb)+n of the divide and conquer algorithm as a task-parallel algorithm. 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. lwork is local input and must be at least lwork >= 2*locr(n+MOD(ia-1,mb_a)) nb_a*ceil(q-1,p)) ). lwork is local input and must be at least lwork >= 2*locr( n + MOD( ia-1, mb_a ) ) if lwork = -1, then lwork is global input and a workspace iroff = MOD( ia-1, mb_a ), icoff = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ) iacol = indxg2p( ja, nb_a, mycol, csrc_a, npcol ), pzgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pzgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) iacol = indxg2p( jaa, nb_a, mycol, csrc_a, npcol ), iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o pzgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pzgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is MODified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is MODified by the routine but restored o iroffa = MOD( iaa-1, mb_a ), icoffa = mod( jaa-1, nb_a ) npa0 = numroc( ni+iroffa, mb_a, myrow, iarow, nprow ), |
| Modification Modification ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side pdlaed1 computes the updated eigensystem of a diagonal matrix after Modification by a rank-one symmetric matrix ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side pslaed1 computes the updated eigensystem of a diagonal matrix after Modification by a rank-one symmetric matrix ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side ************************************* Modification loo the distance for sending and receiving for each level starts send Modifications to prior processor's right hand side |
| modifications modifications send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side send modifications to prior processor's right hand side |
| modified modified 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. reorthogonalize by modified gram-schmidt if eigenvalues ar 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: where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote a(ia:ia+n-1,ja:ja+n-1), af(iaf:iaf+n-1,jaf:jaf+n-1), and ipiv are not modified af(iaf:iaf+n-1,jaf:jaf+n-1) and factored. where a denotes an element of the original matrix a(ia:ia+n-1,ja:ja+n-k), h denotes a modified element of the uppe defining h(i). k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th 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: with scaling factors given by s. a and af will not be modified = 'e': the matrix a will be equilibrated if necessary, then 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: 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. (desct(lld_),*) the upper triangular matrix t. t is modified, but restore pcgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pcgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o pcgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pcgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o 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: based on code written by : peter arbenz, eth zurich, 1996. last modified by: peter arbenz, institute of scientific computing where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote a(ia:ia+n-1,ja:ja+n-1), af(iaf:iaf+n-1,jaf:jaf+n-1), and ipiv are not modified af(iaf:iaf+n-1,jaf:jaf+n-1) and factored. endpoint of the j-th interval. the input intervals will, in general, be modified, split and reordered by th on input, intvl contains the minp input intervals. being recombined. on exit, rho has been modified to the value required b being recombined. on exit, rho has been modified to the value required b where a denotes an element of the original matrix a(ia:ia+n-1,ja:ja+n-k), h denotes a modified element of the uppe defining h(i). k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th pdgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pdgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o pdgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pdgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o 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: with scaling factors given by s. a and af will not be modified = 'e': the matrix a will be equilibrated if necessary, then 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: 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. 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: based on code written by : peter arbenz, eth zurich, 1996. last modified by: peter arbenz, institute of scientific computing where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote a(ia:ia+n-1,ja:ja+n-1), af(iaf:iaf+n-1,jaf:jaf+n-1), and ipiv are not modified af(iaf:iaf+n-1,jaf:jaf+n-1) and factored. endpoint of the j-th interval. the input intervals will, in general, be modified, split and reordered by th on input, intvl contains the minp input intervals. being recombined. on exit, rho has been modified to the value required b being recombined. on exit, rho has been modified to the value required b where a denotes an element of the original matrix a(ia:ia+n-1,ja:ja+n-k), h denotes a modified element of the uppe defining h(i). k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th psgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), psgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o psgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), psgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o 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: with scaling factors given by s. a and af will not be modified = 'e': the matrix a will be equilibrated if necessary, then 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: 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. 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: where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote where a denotes an element of the original matrix sub( a ), h denotes a modified element of the upper hessenberg matrix h, and vi denote a(ia:ia+n-1,ja:ja+n-1), af(iaf:iaf+n-1,jaf:jaf+n-1), and ipiv are not modified af(iaf:iaf+n-1,jaf:jaf+n-1) and factored. where a denotes an element of the original matrix a(ia:ia+n-1,ja:ja+n-k), h denotes a modified element of the uppe defining h(i). k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th k = 3. the elements equal to 1 are not stored; the corresponding array elements are modified but restored on exit. the rest of th 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: with scaling factors given by s. a and af will not be modified = 'e': the matrix a will be equilibrated if necessary, then 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: 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. (desct(lld_),*) the upper triangular matrix t. t is modified, but restore pzgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pzgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o pzgeqlf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ), pzgeqrf in the k columns of its distributed matrix argument a(ia:*,ja:ja+k-1). a(ia:*,ja:ja+k-1) is modified b if side = 'l', lld_a >= max( 1, locr(ia+m-1) ); k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o k rows of its distributed matrix argument a(ia:ia+k-1,ja:*). a(ia:ia+k-1,ja:*) is modified by the routine but restored o 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. reorthogonalize by modified gram-schmidt if eigenvalues ar 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. |
| modifies modifies i = kf, ... , kl-1, have "converged". pdlaecv modifies kf to be the index of the last converged interval have converged. note that the input intervals may be reordered by i = kf, ... , kl-1, have "converged". pslaecv modifies kf to be the index of the last converged interval have converged. note that the input intervals may be reordered by |
| modify modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify pcheevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, modify -dno_ieee. this switch only affects the compilation of pslaiect.c. do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify pdsyevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, modify -dno_ieee. this switch only affects the compilation of pdlaiect.c. 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. do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify pssyevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, modify -dno_ieee. this switch only affects the compilation of pslaiect.c. do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify pzheevx assumes ieee 754 standard compliant arithmetic. to port to a system which does not have ieee 754 arithmetic, modify -dno_ieee. this switch only affects the compilation of pdlaiect.c. do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify do until this proc is needed to modify other procs' equation use factorization of odd-even connection block to modify |
| MorC MorC c v = tril(a) * h; vt = ht * tril(a,-1) MorC v = v - h*v*h - v*h* m c = v' * h c v = tril(a) * h; vt = ht * tril(a,-1) MorC v = v - h*v*h - v*h* m c = v' * h c v = tril(a) * h; vt = ht * tril(a,-1) MorC v = v - h*v*h - v*h* m c = v' * h c v = tril(a) * h; vt = ht * tril(a,-1) MorC v = v - h*v*h - v*h* m c = v' * h |
| more more (nbulge > 1) and the first shift is starting in the middle of an unreduced hessenberg matrix because of two or more consecutiv (nbulge > 1) and the first shift is starting in the middle of an unreduced hessenberg matrix because of two or more consecutive smal used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details element (l,ln+1) is swapped with element (j,ln+1) etc furthermore, the elements in the same row are ldb=llda-1 apar data format: used in lapack. please see the notes below and the scalapack manual for more detail on the format o all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors pchengst performs the same function as pchegst, but is based on rank 2k updates, which are faster and more scalable tha margin indicates message traffic and c indicates o(n^2 nb/sqrt(p)) or more flops per processor inner loop: the node owning h(m,m) does not. this will occur on a border and can happen in no more than 3 locations per block assumin values: a buffer to send diagonally down and right, a buffer the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node 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 then, the processes which receive the answer will be (note that if an operation involves more than one vector, the processes which re each vector): used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on on normal exit, all elements of ifail are zero. if one or more eigenvectors fail to converge after maxit if mod(info,m+1)>0, then used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details element (l,ln+1) is swapped with element (j,ln+1) etc furthermore, the elements in the same row are ldb=llda-1 apar data format: used in lapack. please see the notes below and the scalapack manual for more detail on the format o the node owning h(m,m) does not. this will occur on a border and can happen in no more than 3 locations per block assumin values: a buffer to send diagonally down and right, a buffer the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node the maximum number of intervals that may be generated. if more than mmax intervals are generated, then pdlaebz wil 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)) sorted set. then it tries to deflate the size of the problem. there are two ways in which deflation can occur: when two or more z vector. for each such occurrence the order of the related secular 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 used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on on normal exit, all elements of ifail are zero. if one or more eigenvectors fail to converge after maxit if mod(info,m+1)>0, then all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors pdsyngst performs the same function as pdhegst, but is based on rank 2k updates, which are faster and more scalable tha margin indicates message traffic and c indicates o(n^2 nb/sqrt(p)) or more flops per processor inner loop: then, the processes which receive the answer will be (note that if an operation involves more than one vector, the processes which re each vector): then, the processes which receive the answer will be (note that if an operation involves more than one vector, the processes which re each vector): used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details element (l,ln+1) is swapped with element (j,ln+1) etc furthermore, the elements in the same row are ldb=llda-1 apar data format: used in lapack. please see the notes below and the scalapack manual for more detail on the format o the node owning h(m,m) does not. this will occur on a border and can happen in no more than 3 locations per block assumin values: a buffer to send diagonally down and right, a buffer the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node the maximum number of intervals that may be generated. if more than mmax intervals are generated, then pslaebz wil 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)) sorted set. then it tries to deflate the size of the problem. there are two ways in which deflation can occur: when two or more z vector. for each such occurrence the order of the related secular 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 used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on on normal exit, all elements of ifail are zero. if one or more eigenvectors fail to converge after maxit if mod(info,m+1)>0, then all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors pssyngst performs the same function as pshegst, but is based on rank 2k updates, which are faster and more scalable tha margin indicates message traffic and c indicates o(n^2 nb/sqrt(p)) or more flops per processor inner loop: used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details element (l,ln+1) is swapped with element (j,ln+1) etc furthermore, the elements in the same row are ldb=llda-1 apar data format: used in lapack. please see the notes below and the scalapack manual for more detail on the format o all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors all eigenvectors will increase the total execution time by a factor of 2 or more grow as the square of the cluster size, all other factors pzhengst performs the same function as pzhegst, but is based on rank 2k updates, which are faster and more scalable tha margin indicates message traffic and c indicates o(n^2 nb/sqrt(p)) or more flops per processor inner loop: the node owning h(m,m) does not. this will occur on a border and can happen in no more than 3 locations per block assumin values: a buffer to send diagonally down and right, a buffer the entire submatrix that is copied gets placed on one node or more. the receiving node can be specified precisely, or all node 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 then, the processes which receive the answer will be (note that if an operation involves more than one vector, the processes which re each vector): used in lapack. please see the notes below and the scalapack manual for more detail on the format o on exit, this array contains information containing details the following method uses more flops than necessary bu used in lapack. please see the notes below and the scalapack manual for more detail on the format o of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on of the divide and conquer algorithm as a task-parallel algorithm. this formula in words is: no processor may have more than on on normal exit, all elements of ifail are zero. if one or more eigenvectors fail to converge after maxit if mod(info,m+1)>0, then (nbulge > 1) and the first shift is starting in the middle of an unreduced hessenberg matrix because of two or more consecutive smal (nbulge > 1) and the first shift is starting in the middle of an unreduced hessenberg matrix because of two or more consecutiv |
| Moreover Moreover Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): Moreover, this routine requires the distributed submatrices sub( a ) i.e., if f(x,y) = mod( x-1, y ): |
| most most 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 if jobz='v', setting abstol to pslamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. if jobz='v', setting abstol to pslamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. 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 static partitioning of work is done at the beginning of pdstebz which results in all processes finding an (almost) equal number o if jobz='v', setting abstol to pdlamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. if jobz='v', setting abstol to pdlamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. most parameters set via a call to pjlaenv must be identica value to all procesors (i.e. global output). however some, 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 static partitioning of work is done at the beginning of psstebz which results in all processes finding an (almost) equal number o if jobz='v', setting abstol to pslamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. if jobz='v', setting abstol to pslamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. 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 if jobz='v', setting abstol to pdlamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. if jobz='v', setting abstol to pdlamch( context, 'u') yields the most orthogonal eigenvectors the absolute error tolerance for the eigenvalues. |
| MOUT MOUT MOUT (output) intege MOUT (output) intege |
| Move Move apply factorization to upper connection block bu_i Move the connection block in preparation Move entry that causes spike to auxiliary storag Move block into place that it will be expected to be fo Move block into place that it will be expected to be fo apply factorization to upper connection block bu_i Move the connection block in preparation Move entry that causes spike to auxiliary storag Move block into place that it will be expected to be fo Move block into place that it will be expected to be fo apply factorization to upper connection block bu_i Move the connection block in preparation Move entry that causes spike to auxiliary storag Move block into place that it will be expected to be fo Move block into place that it will be expected to be fo apply factorization to upper connection block bu_i Move the connection block in preparation Move entry that causes spike to auxiliary storag Move block into place that it will be expected to be fo Move block into place that it will be expected to be fo |
| moves moves pclaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. pdlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. pslaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. pzlaevswp moves the eigenvectors (potentially unsorted) fro array, sorted so that the corresponding eigenvalues are sorted. |
| Mp0 Mp0 lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max(3,Mp0 + nq0) if lwork = -1, then lwork is global input and a workspace lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), wpslared1d = nq0, wpslared2d = Mp0 nq0 if norm = '1', 'o' or 'o', Mp0 if norm = 'i' or 'i' where work (local workspace) complex array, dimension (lwork) lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max(3,Mp0 + nq0) + locc(ja+n-1)+nq0 iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), wpdlared1d = nq0, wpdlared2d = Mp0 nq0 if norm = '1', 'o' or 'o', Mp0 if norm = 'i' or 'i' where work (local workspace) double precision array, dimension (lwork) lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max(3,Mp0 + nq0) + locc(ja+n-1)+nq0 iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), wpslared1d = nq0, wpslared2d = Mp0 nq0 if norm = '1', 'o' or 'o', Mp0 if norm = 'i' or 'i' where work (local workspace) real array, dimension (lwork) lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max(3,Mp0 + nq0) if lwork = -1, then lwork is global input and a workspace lwork is local input and must be at least lwork >= Mp0 + max( 1, nq0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( Mp0 + nq0 + nb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), wpdlared1d = nq0, wpdlared2d = Mp0 nq0 if norm = '1', 'o' or 'o', Mp0 if norm = 'i' or 'i' where work (local workspace) complex*16 array, dimension (lwork) lwork >= nq0 + max( 1, Mp0 ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( Mp0 + nq0 + mb_a ), wher iroff = mod( ia-1, mb_a ), icoff = mod( ja-1, nb_a ), |
| MpA0 MpA0 lwork is local input and must be at least lwork >= max( MpA0, nqa0 where nb = mb_a = nb_a, iroffa = mod( ia-1, nb ) lwork is local input and must be at least lwork >= nb*( MpA0 + nqa0 + 1 ) + nqa where nb = mb_a = nb_a, ltau = numroc( ja+min(m,n)-1, nb_a, mycol, csrc_a, npcol ), lwf = nb_a * ( MpA0 + nqa0 + nb_a nb_a * nb_a lwork is local input and must be at least lwork >= max( mb_a * ( MpA0 + nqa0 + mb_a ) mb_a * mb_a, lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max( MpA0, nqa0 where nb = mb_a = nb_a, iroffa = mod( ia-1, nb ) lwork is local input and must be at least lwork >= nb*( MpA0 + nqa0 + 1 ) + nqa where nb = mb_a = nb_a, ltau = numroc( ja+min(m,n)-1, nb_a, mycol, csrc_a, npcol ), lwf = nb_a * ( MpA0 + nqa0 + nb_a nb_a * nb_a lwork is local input and must be at least lwork >= max( mb_a * ( MpA0 + nqa0 + mb_a ) mb_a * mb_a, lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max( MpA0, nqa0 where nb = mb_a = nb_a, iroffa = mod( ia-1, nb ) lwork is local input and must be at least lwork >= nb*( MpA0 + nqa0 + 1 ) + nqa where nb = mb_a = nb_a, ltau = numroc( ja+min(m,n)-1, nb_a, mycol, csrc_a, npcol ), lwf = nb_a * ( MpA0 + nqa0 + nb_a nb_a * nb_a lwork is local input and must be at least lwork >= max( mb_a * ( MpA0 + nqa0 + mb_a ) mb_a * mb_a, lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= max( MpA0, nqa0 where nb = mb_a = nb_a, iroffa = mod( ia-1, nb ) lwork is local input and must be at least lwork >= nb*( MpA0 + nqa0 + 1 ) + nqa where nb = mb_a = nb_a, ltau = numroc( ja+min(m,n)-1, nb_a, mycol, csrc_a, npcol ), lwf = nb_a * ( MpA0 + nqa0 + nb_a nb_a * nb_a lwork is local input and must be at least lwork >= max( mb_a * ( MpA0 + nqa0 + mb_a ) mb_a * mb_a, lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= MpA0 + max( 1, nqa0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nb_a * ( nqa0 + MpA0 + nb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= nqa0 + max( 1, MpA0 ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), lwork is local input and must be at least lwork >= mb_a * ( MpA0 + nqa0 + mb_a ), wher iroffa = mod( ia-1, mb_a ), icoffa = mod( ja-1, nb_a ), |
| MpB0 MpB0 lwf = nb_a * ( mpa0 + nqa0 + nb_a ) lws = max( (nb_a*(nb_a-1))/2, (nrhsqb0 + MpB0)*nb_a ) else lwf = nb_a * ( mpa0 + nqa0 + nb_a ) lws = max( (nb_a*(nb_a-1))/2, (nrhsqb0 + MpB0)*nb_a ) else lwf = nb_a * ( mpa0 + nqa0 + nb_a ) lws = max( (nb_a*(nb_a-1))/2, (nrhsqb0 + MpB0)*nb_a ) else lwf = nb_a * ( mpa0 + nqa0 + nb_a ) lws = max( (nb_a*(nb_a-1))/2, (nrhsqb0 + MpB0)*nb_a ) else |
| MpC0 MpC0 if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', mi = ihi-ilo; ni = n; icc = ic + ilo; jcc = jc; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + mi = m-1; ni = n; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', mi = ihi-ilo; ni = n; icc = ic + ilo; jcc = jc; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + mi = m-1; ni = n; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', mi = ihi-ilo; ni = n; icc = ic + ilo; jcc = jc; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + mi = m-1; ni = n; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, if side = 'l', lwork >= ( nqc0 + MpC0 ) * lwork >= ( nqc0 + max( npv0 + numroc( numroc( n+icoffc, lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( 1, nqc0 ) numroc( n+icoffc,nb_a,0,0,npcol ),nb_a,0,0,lcmq ) ); if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', mi = ihi-ilo; ni = n; icc = ic + ilo; jcc = jc; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', if side = 'l', lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); lwork is local input and must be at least if side = 'l', lwork >= MpC0 + max( max( 1, nqc0 ), numroc if side = 'r', lwork >= nqc0 + max( 1, mpc0 ); if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( MpC0 + max( mqa0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + mi = m-1; ni = n; lwork >= max( (nb_a*(nb_a-1))/2, (nqc0 + MpC0)*nb_a ) else if side = 'r', |
| MQ0 MQ0 if eigenvectors are requested: lwork = n + ( np0 + MQ0 + nb ) * nb mq0 = numroc( max( n, nb, 2 ), nb, 0, 0, npcol ) if eigenvectors are requested: lwork >= n + ( np0 + MQ0 + nb ) * n if eigenvectors are requested: lwork >= n + ( np0 + MQ0 + nb ) * n eigenvectors are computed is: lwork >= 5*n + max( 5*nn, np0 * MQ0 + 2 * nb * nb ) eigenvectors are computed is: lwork >= 5 * n + max( 5*nn, np0 * MQ0 + 2 * nb * nb ) eigenvectors are computed is: lwork >= 5*n + max( 5*nn, np0 * MQ0 + 2 * nb * nb ) eigenvectors are computed is: lwork >= 5 * n + max( 5*nn, np0 * MQ0 + 2 * nb * nb ) if eigenvectors are requested: lwork = n + ( np0 + MQ0 + nb ) * nb mq0 = numroc( max( n, nb, 2 ), nb, 0, 0, npcol ) if eigenvectors are requested: lwork >= n + ( np0 + MQ0 + nb ) * n if eigenvectors are requested: lwork >= n + ( np0 + MQ0 + nb ) * n |
| MQ00 MQ00 allocated on each process is nvec = floor(( lwork- max(5*n,np00*MQ00) )/n) nvec - ceil(m/p) + 1 are guaranteed to be orthogonal ( the allocated on each process is nvec = floor(( lwork- max(5*n,np00*MQ00) )/n) nvec - ceil(m/p) + 1 are guaranteed to be orthogonal ( the allocated on each process is nvec = floor(( lwork- max(5*n,np00*MQ00) )/n) nvec - ceil(m/p) + 1 are guaranteed to be orthogonal ( the allocated on each process is nvec = floor(( lwork- max(5*n,np00*MQ00) )/n) nvec - ceil(m/p) + 1 are guaranteed to be orthogonal ( the |
| MqA0 MqA0 lwork is local input and must be at least lwork >= max( nb_a * ( npa0 + MqA0 + nb_a ) nb_a * nb_a, if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + lwork is local input and must be at least lwork >= max( nb_a * ( npa0 + MqA0 + nb_a ) nb_a * nb_a, if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + lwork is local input and must be at least lwork >= max( nb_a * ( npa0 + MqA0 + nb_a ) nb_a * nb_a, if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + lwork is local input and must be at least lwork >= max( nb_a * ( npa0 + MqA0 + nb_a ) nb_a * nb_a, if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + if side = 'l', lwork >= max( (mb_a*(mb_a-1))/2, ( mpc0 + max( MqA0 mb_a, 0, 0, lcmp ), nqc0 ) )*mb_a ) + |
| MqV0 MqV0 if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k if side = 'l', lwork >= ( mpc0 + max( MqV0 + numroc( numroc( m+iroffc nqc0 ) ) * k |
| much much than overflow**(1/2) * underflow**(1/4) in absolute value, and for greatest accuracy, it should not be much smalle than overflow**(1/2) * underflow**(1/4) in absolute value, and for greatest accuracy, it should not be much smalle in absolute value, and for greatest accuracy, it should not be much smaller than that e (global input) double precision array, dimension (n-1) than overflow**(1/2) * underflow**(1/4) in absolute value, and for greatest accuracy, it should not be much smalle than overflow**(1/2) * underflow**(1/4) in absolute value, and for greatest accuracy, it should not be much smalle in absolute value, and for greatest accuracy, it should not be much smaller than that e (global input) real array, dimension (n-1) |
| multiple multiple clamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges dlamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges 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 scale x if necessary to avoid overflow when adding a multiple of column j of a 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 the first stage consists of deflating the size of the problem when there are multiple eigenvalues or if there is a zero i secular equation problem is reduced by one. this stage is 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 the first stage consists of deflating the size of the problem when there are multiple eigenvalues or if there is a zero i secular equation problem is reduced by one. this stage is 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 scale x if necessary to avoid overflow when adding a multiple of column j of a slamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges zlamsh sends multiple shifts through a small (single node) matrix t subsequent shifts in an effort to maximize the number of bulges |
| multiplication multiplication since there is no element-by-element vector multiplication i since there is no element-by-element vector multiplication i since there is no element-by-element vector multiplication i since there is no element-by-element vector multiplication i |
| multiplications multiplications tril(a) * v + v^t * tril(a,-1). this is performed as two local triangular matrix-vector multiplications (both i in the local computation, work( invt ) is used to compute tril(a) * v + v^t * tril(a,-1). this is performed as two local triangular matrix-vector multiplications (both i in the local computation, work( invt ) is used to compute tril(a) * v + v^t * tril(a,-1). this is performed as two local triangular matrix-vector multiplications (both i in the local computation, work( invt ) is used to compute tril(a) * v + v^t * tril(a,-1). this is performed as two local triangular matrix-vector multiplications (both i in the local computation, work( invt ) is used to compute |
| multiplied multiplied 6. if fact = 'e' and equilibration was used, the matrix x is premultiplied by diag(c) (if trans = 'n') or diag(r) (i before equilibration. = 'r': row equilibration, i.e., sub( a ) has been pre- multiplied by diag(r(ia:ia+m-1)) multiplied by diag(c(ja:ja+n-1)), specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) cto (global input) real the distributed matrix sub( a ) is multiplied by cto/cfrom result cto * a(i,j) / cfrom can be represented without 6. if fact = 'e' and equilibration was used, the matrix x is premultiplied by diag(c) (if trans = 'n') or diag(r) (i before equilibration. directly using the updated eigenvalues. the eigenvectors for the current problem are multiplied with the eigenvectors fro = 'r': row equilibration, i.e., sub( a ) has been pre- multiplied by diag(r(ia:ia+m-1)) multiplied by diag(c(ja:ja+n-1)), specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) cto (global input) double precision the distributed matrix sub( a ) is multiplied by cto/cfrom result cto * a(i,j) / cfrom can be represented without 6. if fact = 'e' and equilibration was used, the matrix x is premultiplied by diag(c) (if trans = 'n') or diag(r) (i before equilibration. directly using the updated eigenvalues. the eigenvectors for the current problem are multiplied with the eigenvectors fro = 'r': row equilibration, i.e., sub( a ) has been pre- multiplied by diag(r(ia:ia+m-1)) multiplied by diag(c(ja:ja+n-1)), specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) cto (global input) real the distributed matrix sub( a ) is multiplied by cto/cfrom result cto * a(i,j) / cfrom can be represented without 6. if fact = 'e' and equilibration was used, the matrix x is premultiplied by diag(c) (if trans = 'n') or diag(r) (i before equilibration. = 'r': row equilibration, i.e., sub( a ) has been pre- multiplied by diag(r(ia:ia+m-1)) multiplied by diag(c(ja:ja+n-1)), specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) specifies the order in which the elementary reflectors are multiplied to form the block reflector = 'b': h = h(k) . . . h(2) h(1) (backward) cto (global input) double precision the distributed matrix sub( a ) is multiplied by cto/cfrom result cto * a(i,j) / cfrom can be represented without |
| multiplier multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier number. if there already is an error, multiply by the the descriptor multiplier |
| multipliers multipliers upper triangular band matrix with kl+ku superdiagonals in rows 1 to kl+ku+1, and the multipliers used during th see below for further details. compute multipliers a. on exit, dl is overwritten by the (n-1) multipliers tha dl (input) complex array, dimension (n-1) the (n-1) multipliers that define the matrix l from th upper triangular band matrix with kl+ku superdiagonals in rows 1 to kl+ku+1, and the multipliers used during th see below for further details. compute multipliers a. on exit, dl is overwritten by the (n-1) multipliers tha dl (input) complex array, dimension (n-1) the (n-1) multipliers that define the matrix l from th upper triangular band matrix with kl+ku superdiagonals in rows 1 to kl+ku+1, and the multipliers used during th see below for further details. compute multipliers a. on exit, dl is overwritten by the (n-1) multipliers tha dl (input) complex array, dimension (n-1) the (n-1) multipliers that define the matrix l from th upper triangular band matrix with kl+ku superdiagonals in rows 1 to kl+ku+1, and the multipliers used during th see below for further details. compute multipliers a. on exit, dl is overwritten by the (n-1) multipliers tha dl (input) complex array, dimension (n-1) the (n-1) multipliers that define the matrix l from th |
| multiplies multiplies pclascl multiplies the m-by-n complex distributed matrix sub( a is done without over/underflow as long as the final result pcsrscl multiplies an n-element complex distributed vecto underflow as long as the final sub( x )/a does not overflow or pdlascl multiplies the m-by-n real distributed matrix sub( a is done without over/underflow as long as the final result pdrscl multiplies an n-element real distributed vector sub( x ) b long as the final result sub( x )/a does not overflow or underflow. pslascl multiplies the m-by-n real distributed matrix sub( a is done without over/underflow as long as the final result psrscl multiplies an n-element real distributed vector sub( x ) b long as the final result sub( x )/a does not overflow or underflow. pzdrscl multiplies an n-element complex distributed vecto underflow as long as the final sub( x )/a does not overflow or pzlascl multiplies the m-by-n complex distributed matrix sub( a is done without over/underflow as long as the final result |
| multiply multiply want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th want to find errors with min( ), so if no error, set it to a big number. if there already is an error, multiply by the th |
| multiplying multiplying scale by 1/cnorm(j) to avoid overflow when multiplying x(j) times column j x( j ) = cladiv( x( j ), tjjs ) scale by 1/cnorm(j) to avoid overflow when multiplying x(j) times column j x( j ) = zladiv( x( j ), tjjs ) |
| must must the block size must not exceed the limit set by the size of th 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 i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar on entry, n specifies the order of the matrix a. n must be at least zero the block size must not exceed the limit set by the size of th 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 on entry, n specifies the order of the matrix a. n must be at least zero complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine pcdbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed part of global vector storing the lower diagonal of the matrix. globally, dl(1) is not referenced, and dl must b must be of size >= desca( nb_ ). routine pcdttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine pcgbtrf must be called first ===================================================================== the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(nprow-1,npcol),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas if m >= n, then the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas a(ia:ia+n-1,ja:ja+n-1). if fact = 'f' and equed is not 'n', then a(ia:ia+n-1,ja:ja+n-1) must have been equilibrated b not modified if fact = 'f' or 'n', or if fact = 'e' and the dimension of the array work. lwork is local input and must be at leas copy of at most an entire column block of sub( a ). the dimension of the array work. lwork is local input and must be at leas max( (nb_a*(nb_a-1))/2, (pqb0 + npb0)*nb_a ) + the dimension of the array work. lwork is local input and must be at leas max( (mb_a*(mb_a-1))/2, (ppb0 + nqb0)*mb_a ) + the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) complex array, the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) complex array, before beginning computation. to get all the eigenvectors requested, the user must supply both sufficien and sufficient workspace to compute them. (see lwork below.) sub( b ) must have been previously factorized as u**h*u or l*l**h b sub( b ) must have been previously factorized as u**h*u or l*l**h b the array descriptor for the distributed matrix b. descb( ctxt_ ) must equal desca( ctxt_ vl (global input) real sub( b ) must have been previously factorized as u**h*u or l*l**h b the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas encourage to call pchetrd which will then call pchettrd if appropriate. a must be in cyclic format (i.e. mb = nb = 1) only lower triangular storage is supported. of incx are supported in this version, namely 1 and m_x. incx must not be zero ===================================================================== a' * x, if kase=2, where a' is the conjugate transpose of a, and pclacon must on exit, lwork is the size of the work buffer. this must be at least 7*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar the dimension of the array work. lwork is local input and must be at leas ipiv must always be a distributed vector (not a matrix). thus jp must be 1 the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin contains the local pieces of the distributed vector sub( x ). before entry, the incremented array sub( x ) must contai the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin result cto * a(i,j) / cfrom can be represented without over/underflow. cfrom must be nonzero m (global input) integer on exit, lwork is the size of the work buffer. this must be at least 2*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the scale and sumsq must be supplied in scale and sumsq respectively of incx are supported in this version, namely 1 and m_x. incx must not be zero ===================================================================== complex temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine pcpbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*max(1,ceil(p-1,q)),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas on entry, the hermitian matrix a, except if fact = 'f' and equed = 'y', then a must contain the equilibrated matri n-by-n upper triangular part of a contains the upper factors of the matrix. must be of size >= desca( nb_ ) e (local input/local output) complex pointer to local since there is no element-by-element vector multiplication in the blas, this loop must be hardwired in without a blas cal routine pcpttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the scalar a which is used to divide each component of sub( x ). sa must be >= 0, or the subroutine will divide b orthogonality is similar to that obtained from cstein2). note : lwork must be no smaller than and should have the same input value on all processes. the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(p-1,q),locc(n+mod(ja-1,nb_a)) + to select the eigenvector corresponding to the j-th eigenvalue, select(j) must be set to .true. n (global input) integer the solution matrix x must be computed by pctrtrs or some othe refinement because doing so cannot improve the backward error. the dimension of the array work. lwork is local input and must be at leas 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. ihi (global input) integer ilo and ihi must have the same values as in the previous cal distributed submatrix q(ia+ilo:ia+ihi-1,ia+ilo:ja+ihi-1). and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pcgelqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pcgelqf in the 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 and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pcgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pctzrzf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pcgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pctzrzf in the if side = 'r' and uplo = 'l', ltau = locc(ja+n-2). tau(i) must contain the scalar factor of the elementar distributed matrix a. 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. routine pddbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed part of global vector storing the lower diagonal of the matrix. globally, dl(1) is not referenced, and dl must b must be of size >= desca( nb_ ). routine pddttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed 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. routine pdgbtrf must be called first ===================================================================== the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas + max( 2, max( nb_a*max( 1, ceil(nprow-1,npcol) ), the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas if m >= n, then the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas a(ia:ia+n-1,ja:ja+n-1). if fact = 'f' and equed is not 'n', then a(ia:ia+n-1,ja:ja+n-1) must have been equilibrated b not modified if fact = 'f' or 'n', or if fact = 'e' and the dimension of the array work. lwork is local input and must be at leas copy of at most an entire column block of sub( a ). the dimension of the array work. lwork is local input and must be at leas max( (nb_a*(nb_a-1))/2, (pqb0 + npb0)*nb_a ) + the dimension of the array work. lwork is local input and must be at leas max( (mb_a*(mb_a-1))/2, (ppb0 + nqb0)*mb_a ) + a' * x, if kase=2, pdlacon must be re-called with all the other parameter on exit, lwork is the size of the work buffer. this must be at least 7*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the small, i.e., converged. this must be at least zero reltol (input) double precision small, i.e., converged. note : this must be at least zero reltol (input) double precision i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar the dimension of the array work. lwork is local input and must be at leas entries are d(2),d(4),...,d(2*n-2). to avoid overflow, the matrix must be scaled so that its largest entry is no greate and for greatest accuracy, it should not be much smaller ipiv must always be a distributed vector (not a matrix). thus jp must be 1 ia (global input) integer ia must be equal to ja (global input) integer ia (global input) integer ia must be equal to ja (global input) integer the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin contains the local pieces of the distributed vector sub( x ). before entry, the incremented array sub( x ) must contai the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin result cto * a(i,j) / cfrom can be represented without over/underflow. cfrom must be nonzero m (global input) integer on exit, lwork is the size of the work buffer. this must be at least 2*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the scale and sumsq must be supplied in scale and sumsq respectively 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. ihi (global input) integer ilo and ihi must have the same values as in the previous cal distributed submatrix q(ia+ilo:ia+ihi-1,ia+ilo:ja+ihi-1). and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdgelqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdgelqf in the 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 and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdtzrzf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pdtzrzf in the if side = 'r' and uplo = 'l', ltau = locc(ja+n-2). tau(i) must contain the scalar factor of the elementar distributed matrix a. 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. routine pdpbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(nprow-1,npcol),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas on entry, the symmetric matrix a, except if fact = 'f' and equed = 'y', then a must contain the equilibrated matri n-by-n upper triangular part of a contains the upper factors of the matrix. must be of size >= desca( nb_ ) e (local input/local output) double precision pointer to local since there is no element-by-element vector multiplication in the blas, this loop must be hardwired in without a blas cal routine pdpttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the scalar a which is used to divide each component of sub( x ). sa must be >= 0, or the subroutine will divide b for eigenvalues. eigenvalues greater than vu will not be returned. vu must be greater than vl. not referenced i orthogonality is similar to that obtained from dstein2). note : lwork must be no smaller than and should have the same input value on all processes. the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) double precision array, the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) double precision array, before beginning computation. to get all the eigenvectors requested, the user must supply both sufficien and sufficient workspace to compute them. (see lwork below.) sub( b ) must have been previously factorized as u**t*u or l*l**t b sub( b ) must have been previously factorized as u**t*u or l*l**t b the array descriptor for the distributed matrix b. descb( ctxt_ ) must equal desca( ctxt_ vl (global input) double precision sub( b ) must have been previously factorized as u**h*u or l*l**h b the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas encourage to call pdsytrd which will then call pdhettrd if appropriate. a must be in cyclic format (i.e. mb = nb = 1) only lower triangular storage is supported. the dimension of the array work. lwork is local input and must be at leas + max( 2, max( nb_a*max( 1, ceil(nprow-1,npcol) ), the solution matrix x must be computed by pdtrtrs or some othe refinement because doing so cannot improve the backward error. the dimension of the array work. lwork is local input and must be at leas most parameters set via a call to pjlaenv must be identica value to all procesors (i.e. global output). however some, real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine psdbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed part of global vector storing the lower diagonal of the matrix. globally, dl(1) is not referenced, and dl must b must be of size >= desca( nb_ ). routine psdttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine psgbtrf must be called first ===================================================================== the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas + max( 2, max( nb_a*max( 1, ceil(nprow-1,npcol) ), the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas if m >= n, then the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas a(ia:ia+n-1,ja:ja+n-1). if fact = 'f' and equed is not 'n', then a(ia:ia+n-1,ja:ja+n-1) must have been equilibrated b not modified if fact = 'f' or 'n', or if fact = 'e' and the dimension of the array work. lwork is local input and must be at leas copy of at most an entire column block of sub( a ). the dimension of the array work. lwork is local input and must be at leas max( (nb_a*(nb_a-1))/2, (pqb0 + npb0)*nb_a ) + the dimension of the array work. lwork is local input and must be at leas max( (mb_a*(mb_a-1))/2, (ppb0 + nqb0)*mb_a ) + a' * x, if kase=2, pslacon must be re-called with all the other parameter on exit, lwork is the size of the work buffer. this must be at least 7*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the small, i.e., converged. this must be at least zero reltol (input) real small, i.e., converged. note : this must be at least zero reltol (input) real i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar the dimension of the array work. lwork is local input and must be at leas entries are d(2),d(4),...,d(2*n-2). to avoid overflow, the matrix must be scaled so that its largest entry is no greate and for greatest accuracy, it should not be much smaller ipiv must always be a distributed vector (not a matrix). thus jp must be 1 ia (global input) integer ia must be equal to ja (global input) integer ia (global input) integer ia must be equal to ja (global input) integer the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin contains the local pieces of the distributed vector sub( x ). before entry, the incremented array sub( x ) must contai the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin result cto * a(i,j) / cfrom can be represented without over/underflow. cfrom must be nonzero m (global input) integer on exit, lwork is the size of the work buffer. this must be at least 2*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the scale and sumsq must be supplied in scale and sumsq respectively 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. ihi (global input) integer ilo and ihi must have the same values as in the previous cal distributed submatrix q(ia+ilo:ia+ihi-1,ia+ilo:ja+ihi-1). and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by psgelqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by psgelqf in the 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 and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by psgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pstzrzf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by psgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pstzrzf in the if side = 'r' and uplo = 'l', ltau = locc(ja+n-2). tau(i) must contain the scalar factor of the elementar distributed matrix a. real temporary workspace. this space may be overwritten in between calls to routines. work must b on exit, work( 1 ) contains the minimal lwork. routine pspbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(nprow-1,npcol),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas on entry, the symmetric matrix a, except if fact = 'f' and equed = 'y', then a must contain the equilibrated matri n-by-n upper triangular part of a contains the upper factors of the matrix. must be of size >= desca( nb_ ) e (local input/local output) real pointer to local since there is no element-by-element vector multiplication in the blas, this loop must be hardwired in without a blas cal routine pspttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the scalar a which is used to divide each component of sub( x ). sa must be >= 0, or the subroutine will divide b for eigenvalues. eigenvalues greater than vu will not be returned. vu must be greater than vl. not referenced i orthogonality is similar to that obtained from sstein2). note : lwork must be no smaller than and should have the same input value on all processes. the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) real array, the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) real array, before beginning computation. to get all the eigenvectors requested, the user must supply both sufficien and sufficient workspace to compute them. (see lwork below.) sub( b ) must have been previously factorized as u**t*u or l*l**t b sub( b ) must have been previously factorized as u**t*u or l*l**t b the array descriptor for the distributed matrix b. descb( ctxt_ ) must equal desca( ctxt_ vl (global input) real sub( b ) must have been previously factorized as u**h*u or l*l**h b the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas encourage to call pssytrd which will then call pshettrd if appropriate. a must be in cyclic format (i.e. mb = nb = 1) only lower triangular storage is supported. the dimension of the array work. lwork is local input and must be at leas + max( 2, max( nb_a*max( 1, ceil(nprow-1,npcol) ), the solution matrix x must be computed by pstrtrs or some othe refinement because doing so cannot improve the backward error. the dimension of the array work. lwork is local input and must be at leas 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. routine pzdbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the scalar a which is used to divide each component of sub( x ). sa must be >= 0, or the subroutine will divide b part of global vector storing the lower diagonal of the matrix. globally, dl(1) is not referenced, and dl must b must be of size >= desca( nb_ ). routine pzdttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed 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. routine pzgbtrf must be called first ===================================================================== the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(nprow-1,npcol),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas if m >= n, then the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas a(ia:ia+n-1,ja:ja+n-1). if fact = 'f' and equed is not 'n', then a(ia:ia+n-1,ja:ja+n-1) must have been equilibrated b not modified if fact = 'f' or 'n', or if fact = 'e' and the dimension of the array work. lwork is local input and must be at leas copy of at most an entire column block of sub( a ). the dimension of the array work. lwork is local input and must be at leas max( (nb_a*(nb_a-1))/2, (pqb0 + npb0)*nb_a ) + the dimension of the array work. lwork is local input and must be at leas max( (mb_a*(mb_a-1))/2, (ppb0 + nqb0)*mb_a ) + the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) complex*16 array, the array descriptor for the distributed matrix z. descz( ctxt_ ) must equal desca( ctxt_ work (local workspace/output) complex*16 array, before beginning computation. to get all the eigenvectors requested, the user must supply both sufficien and sufficient workspace to compute them. (see lwork below.) sub( b ) must have been previously factorized as u**h*u or l*l**h b sub( b ) must have been previously factorized as u**h*u or l*l**h b the array descriptor for the distributed matrix b. descb( ctxt_ ) must equal desca( ctxt_ vl (global input) double precision sub( b ) must have been previously factorized as u**h*u or l*l**h b the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas the dimension of the array work. lwork is local input and must be at leas encourage to call pzhetrd which will then call pzhettrd if appropriate. a must be in cyclic format (i.e. mb = nb = 1) only lower triangular storage is supported. of incx are supported in this version, namely 1 and m_x. incx must not be zero ===================================================================== a' * x, if kase=2, where a' is the conjugate transpose of a, and pzlacon must on exit, lwork is the size of the work buffer. this must be at least 7*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar the dimension of the array work. lwork is local input and must be at leas ipiv must always be a distributed vector (not a matrix). thus jp must be 1 the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin contains the local pieces of the distributed vector sub( x ). before entry, the incremented array sub( x ) must contai the distributed submatrices v(iv:*, jv:*) and c(ic:ic+m-1,jc:jc+n-1) must verify some alignment properties, namely the followin result cto * a(i,j) / cfrom can be represented without over/underflow. cfrom must be nonzero m (global input) integer on exit, lwork is the size of the work buffer. this must be at least 2*ceil( ceil( (i-l)/hbl ) here lcm is least common multiple, and nprowxnpcol is the scale and sumsq must be supplied in scale and sumsq respectively of incx are supported in this version, namely 1 and m_x. incx must not be zero ===================================================================== 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. routine pzpbtrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*max(1,ceil(p-1,q)),locc(n+mod(ja-1,nb_a)) + the dimension of the array work. lwork is local input and must be at leas on entry, the hermitian matrix a, except if fact = 'f' and equed = 'y', then a must contain the equilibrated matri n-by-n upper triangular part of a contains the upper factors of the matrix. must be of size >= desca( nb_ ) e (local input/local output) complex*16 pointer to local since there is no element-by-element vector multiplication in the blas, this loop must be hardwired in without a blas cal routine pzpttrf must be called first ===================================================================== context must be the sam these are alignment restrictions that may or may not be removed orthogonality is similar to that obtained from zstein2). note : lwork must be no smaller than and should have the same input value on all processes. the dimension of the array work. lwork is local input and must be at leas max( 2, max(nb_a*ceil(p-1,q),locc(n+mod(ja-1,nb_a)) + to select the eigenvector corresponding to the j-th eigenvalue, select(j) must be set to .true. n (global input) integer the solution matrix x must be computed by pztrtrs or some othe refinement because doing so cannot improve the backward error. the dimension of the array work. lwork is local input and must be at leas 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. ihi (global input) integer ilo and ihi must have the same values as in the previous cal distributed submatrix q(ia+ilo:ia+ihi-1,ia+ilo:ja+ihi-1). and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pzgelqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pzgelqf in the 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 and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pzgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pztzrzf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pzgerqf in the and (lld_a,locc(ja+n-1)) if side='r', where lld_a >= max(1,locr(ia+k-1)); on entry, the i-th row must h(i), ia <= i <= ia+k-1, as returned by pztzrzf in the if side = 'r' and uplo = 'l', ltau = locc(ja+n-2). tau(i) must contain the scalar factor of the elementar distributed matrix a. the block size must not exceed the limit set by the size of th 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 on entry, n specifies the order of the matrix a. n must be at least zero the block size must not exceed the limit set by the size of th 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 i1 and i2 are the indices of the first row and last column of h to which transformations must be applied. if eigenvalues only ar on entry, n specifies the order of the matrix a. n must be at least zero |
| MVR2 MVR2 two local triangular matrix-vector multiplications (both in MVR2) followed by a transpose and a sum across the columns tril(a) * v and work( inv ) is used to compute two local triangular matrix-vector multiplications (both in MVR2) followed by a transpose and a sum across the columns tril(a) * v and work( inv ) is used to compute two local triangular matrix-vector multiplications (both in MVR2) followed by a transpose and a sum across the columns tril(a) * v and work( inv ) is used to compute two local triangular matrix-vector multiplications (both in MVR2) followed by a transpose and a sum across the columns tril(a) * v and work( inv ) is used to compute |
| MYCOL MYCOL locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a lbwl, lbwu: lower and upper bandwidth of local solver note that for MYCOL > 0 one has lower triangular blocks mycol = 0 where it is bwu less and mycol=npcol-1 where it locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) arguments np = numroc( n, nb, myrow, iarow, nprow ) nq = numroc( n, nb, MYCOL, iacol, npcol iwork (local workspace/output) integer array, dimension (liwork) locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locq( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a xj = cabs1( x( j ) ) if( ( myrow.eq.itmp1x ) .and. ( MYCOL.eq.itmp2x ) tjjs = a( j, j )*tscal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a lbwl, lbwu: lower and upper bandwidth of local solver note that for MYCOL > 0 one has lower triangular blocks mycol = 0 where it is bwu less and mycol=npcol-1 where it locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a np = numroc( n, mb_q, myrow, iqrow, nprow ) nq = numroc( n, nb_q, MYCOL, iqcol, npcol iqcol = indxg2p( jq, mb_q, mycol, csrc_q, npcol ) locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a np = numroc( n, nb, myrow, iarow, nprow ), nq = numroc( n, nb, MYCOL, descq( csrc_ ), npcol iwork (local workspace/local output) integer array, locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a 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 locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) arguments np = numroc( n, nb, myrow, iarow, nprow ) nq = numroc( n, nb, MYCOL, iacol, npcol if lwork = -1, the lwork is global input and a workspace locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locq( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a lbwl, lbwu: lower and upper bandwidth of local solver note that for MYCOL > 0 one has lower triangular blocks mycol = 0 where it is bwu less and mycol=npcol-1 where it locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a np = numroc( n, mb_q, myrow, iqrow, nprow ) nq = numroc( n, nb_q, MYCOL, iqcol, npcol iqcol = indxg2p( jq, mb_q, mycol, csrc_q, npcol ) locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a np = numroc( n, nb, myrow, iarow, nprow ), nq = numroc( n, nb, MYCOL, descq( csrc_ ), npcol iwork (local workspace/local output) integer array, locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a 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 locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) arguments np = numroc( n, nb, myrow, iarow, nprow ) nq = numroc( n, nb, MYCOL, iacol, npcol if lwork = -1, the lwork is global input and a workspace locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locq( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a lbwl, lbwu: lower and upper bandwidth of local solver note that for MYCOL > 0 one has lower triangular blocks mycol = 0 where it is bwu less and mycol=npcol-1 where it locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) arguments np = numroc( n, nb, myrow, iarow, nprow ) nq = numroc( n, nb, MYCOL, iacol, npcol iwork (local workspace/output) integer array, dimension (liwork) locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locp( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locq( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locp( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a xj = cabs1( x( j ) ) if( ( myrow.eq.itmp1x ) .and. ( MYCOL.eq.itmp2x ) tjjs = a( j, j )*tscal locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if ( MYCOL .lt. np-1 )..." loo locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a end of "if( MYCOL/level_dist .le. (npcol-1)/level_dist-2 )... ************ locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a locr( m ) = numroc( m, mb_a, myrow, rsrc_a, nprow ), locc( n ) = numroc( n, nb_a, MYCOL, csrc_a, npcol ) locr( m ) <= ceil( ceil(m/mb_a)/nprow )*mb_a |
| MYPCOLC MYPCOLC call blacs_gridinfo( contextc, nprowc, npcolc, myprowc, MYPCOLC if lwork = -1, the lwork is global input and a workspace call blacs_gridinfo( contextc, nprowc, npcolc, myprowc, MYPCOLC if lwork = -1, the lwork is global input and a workspace |
| MYPROWC MYPROWC nq = numroc( max( n, nb, 2 ), nb, 0, 0, npcol ) nrc = numroc( n, nb, MYPROWC, 0, nprocs sizemqrleft = the workspace requirement for pdormtr nq = numroc( max( n, nb, 2 ), nb, 0, 0, npcol ) nrc = numroc( n, nb, MYPROWC, 0, nprocs sizemqrleft = the workspace requirement for psormtr |
| MYROW MYROW scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: to the scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) lrwork >= 1 + 9*n + 3*np*nq, np = numroc( n, nb, MYROW, iarow, nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: the scalapack tool function, numroc: locp( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: xj = cabs1( x( j ) ) if( ( MYROW.eq.itmp1x ) .and. ( mycol.eq.itmp2x ) tjjs = a( j, j )*tscal scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: to the scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: lwork = 6*n + 2*np*nq, with np = numroc( n, mb_q, MYROW, iqrow, nprow iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: where np = numroc( n, nb, MYROW, iarow, nprow ) scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: lwork = 6*n + 2*np*nq np = numroc( n, nb, MYROW, descq( rsrc_ ), nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) trilwmin = 3*n + max( nb*( np+1 ), 3*nb ) np = numroc( n, nb, MYROW, iarow, nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: the scalapack tool function, numroc: locp( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: to the scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: lwork = 6*n + 2*np*nq, with np = numroc( n, mb_q, MYROW, iqrow, nprow iqrow = indxg2p( iq, nb_q, myrow, rsrc_q, nprow ) scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: where np = numroc( n, nb, MYROW, iarow, nprow ) scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: lwork = 6*n + 2*np*nq np = numroc( n, nb, MYROW, descq( rsrc_ ), nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) trilwmin = 3*n + max( nb*( np+1 ), 3*nb ) np = numroc( n, nb, MYROW, iarow, nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: the scalapack tool function, numroc: locp( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: to the scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) lrwork >= 1 + 9*n + 3*np*nq, np = numroc( n, nb, MYROW, iarow, nprow scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: the scalapack tool function, numroc: locp( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: xj = cabs1( x( j ) ) if( ( MYROW.eq.itmp1x ) .and. ( mycol.eq.itmp2x ) tjjs = a( j, j )*tscal scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: scalapack tool function, numroc: locr( m ) = numroc( m, mb_a, MYROW, rsrc_a, nprow ) an upper bound for these quantities may be computed by: |