Creates a sparse matrix in
MATAIJCUSPARSE (compressed row) format (the default parallel PETSc format). This matrix will ultimately pushed down to NVIDIA GPUs and use the CuSPARSE library for calculations. For good matrix assembly performance the user should preallocate the matrix storage by setting the parameter nz (or the array nnz). By setting these parameters accurately, performance during matrix assembly can be increased by more than a factor of 50.
comm - MPI communicator, set to
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
A - the matrix
It is recommended that one use the
MatXXXXSetPreallocation() paradgm instead of this routine directly.
[MatXXXXSetPreallocation() is, for example,
If nnz is given then nz is ignored
The AIJ format, also called 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.