#include "petscmat.h" #undef __FUNCT__ #define __FUNCT__ "MatCreateAIJCUSPARSE" 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 on MPI_Comm
comm | - MPI communicator, set to PETSC_COMM_SELF | |
m | - number of rows | |
n | - number of columns | |
nz | - number of nonzeros per row (same for all rows) | |
nnz | - array containing the number of nonzeros in the various rows (possibly different for each row) or NULL |
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 Yale sparse matrix format or compressed row storage), is fully compatible with standard Fortran 77 storage. That is, the stored row and column indices can begin at either one (as in Fortran) or zero. See the users' manual for details.
Specify the preallocated storage with either nz or nnz (not both). Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory allocation. For large problems you MUST preallocate memory or you will get TERRIBLE performance, see the users' manual chapter on matrices.
By default, this format uses inodes (identical nodes) when possible, to improve numerical efficiency of matrix-vector products and solves. We search for consecutive rows with the same nonzero structure, thereby reusing matrix information to achieve increased efficiency.
Level:intermediate
Location:src/mat/impls/aij/mpi/mpicusparse/mpiaijcusparse.cu
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