MatCreateAIJCUSPARSE#

Creates a sparse matrix in MATAIJCUSPARSE (compressed row) format. This matrix will ultimately be pushed down to NVIDIA GPUs and use the CuSPARSE library for calculations.

Synopsis#

#include "petscmat.h" 
PetscErrorCode MatCreateAIJCUSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)

Collective

Input Parameters#

comm - MPI communicator, set to PETSC_COMM_SELF
m - number of local rows (or PETSC_DECIDE to have calculated if M is given) This value should be the same as the local size used in creating the \(y\) vector for the matrix-vector product \(y = Ax\).
n - This value should be the same as the local size used in creating the \(x\) vector for the matrix-vector product \(y = Ax\). (or PETSC_DECIDE to have calculated if N is given) For square matrices n is almost always m.
M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix (same value is used for all local rows)
d_nnz - array containing the number of nonzeros in the various rows of the DIAGONAL portion of the local submatrix (possibly different for each row) or NULL, if d_nz is used to specify the nonzero structure. The size of this array is equal to the number of local rows, i.e m. For matrices you plan to factor you must leave room for the diagonal entry and put in the entry even if it is zero.
o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local submatrix (same value is used for all local rows).
o_nnz - array containing the number of nonzeros in the various rows of the OFF-DIAGONAL portion of the local submatrix (possibly different for each row) or NULL, if o_nz is used to specify the nonzero structure. The size of this array is equal to the number of local rows, i.e m.

Output Parameter#

A - the matrix

Notes#

It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), MatXXXXSetPreallocation() paradigm instead of this routine directly. [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation()]

The AIJ format, also called the compressed row storage), is fully compatible with standard Fortran storage. That is, the stored row and column indices can begin at either one (as in Fortran) or zero.

When working with matrices for GPUs, it is often better to use the MatSetPreallocationCOO() and MatSetValuesCOO() paradigm rather than using this routine and MatSetValues()

Level#

intermediate

Location#

src/mat/impls/aij/mpi/mpicusparse/mpiaijcusparse.cu

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