SuperLU_DIST
4.0
superlu_dist on CPU and GPU clusters
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Redistribute the symbolic structure of L and U from the distribution. More...
Functions | |
float | zdist_psymbtonum (fact_t fact, int_t n, SuperMatrix *A, ScalePermstruct_t *ScalePermstruct, Pslu_freeable_t *Pslu_freeable, LUstruct_t *LUstruct, gridinfo_t *grid) |
Redistribute the symbolic structure of L and U from the distribution.
– Parallel symbolic factorization auxialiary routine (version 2.3) – – Distributes the data from parallel symbolic factorization – to numeric factorization INRIA France - July 1, 2004 Laura Grigori
November 1, 2007 Feburary 20, 2008 October 15, 2008
float zdist_psymbtonum | ( | fact_t | fact, |
int_t | n, | ||
SuperMatrix * | A, | ||
ScalePermstruct_t * | ScalePermstruct, | ||
Pslu_freeable_t * | Pslu_freeable, | ||
LUstruct_t * | LUstruct, | ||
gridinfo_t * | grid | ||
) |
Purpose
Distribute the input matrix onto the 2D process mesh.
Arguments
fact (input) fact_t Specifies whether or not the L and U structures will be re-used. = SamePattern_SameRowPerm: L and U structures are input, and unchanged on exit. This routine should not be called for this case, an error is generated. Instead, pddistribute routine should be called. = DOFACT or SamePattern: L and U structures are computed and output.
n (Input) int Dimension of the matrix.
A (Input) SuperMatrix* The distributed input matrix A of dimension (A->nrow, A->ncol). A may be overwritten by diag(R)*A*diag(C)*Pc^T. The type of A can be: Stype = NR; Dtype = SLU_D; Mtype = GE.
ScalePermstruct (Input) ScalePermstruct_t* The data structure to store the scaling and permutation vectors describing the transformations performed to the original matrix A.
Glu_freeable (Input) *Glu_freeable_t The global structure describing the graph of L and U.
LUstruct (Input) LUstruct_t* Data structures for L and U factors.
grid (Input) gridinfo_t* The 2D process mesh.
Return value
< 0, number of bytes allocated on return from the dist_symbLU0, number of bytes allocated for performing the distribution
of the data, when out of memory. (an approximation).