# MatCreateMPIAIJPERM#

Creates a sparse parallel matrix whose local portions are stored as `MATSEQAIJPERM`

matrices (a matrix class that inherits from SEQAIJ but includes some optimizations to allow more effective vectorization).

## Synopsis#

Collective

## Input Parameters#

MPI communicator**comm -**number of local rows (or**m -**`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.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 -**`N`

is given) For square matrices`n`

is almost always`m`

.number of global rows (or**M -**`PETSC_DETERMINE`

to have calculated if`m`

is given)number of global columns (or**N -**`PETSC_DETERMINE`

to have calculated if`n`

is given)number of nonzeros per row in DIAGONAL portion of local submatrix (same value is used for all local rows)**d_nz -**array containing the number of nonzeros in the various rows of the DIAGONAL portion of the local submatrix (possibly different for each row) or**d_nnz -**`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.number of nonzeros per row in the OFF-DIAGONAL portion of local submatrix (same value is used for all local rows).**o_nz -**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**o_nnz -**`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#

the matrix**A -**

## Options Database Keys#

Do not use inodes**-mat_no_inode -**Sets inode limit (max limit=5)**-mat_inode_limit**-

## Notes#

If the *_nnz parameter is given then the *_nz parameter is ignored

`m`

,`n`

,`M`

,`N`

parameters specify the size of the matrix, and its partitioning across
processors, while `d_nz`

,`d_nnz`

,`o_nz`

,`o_nnz`

parameters specify the approximate
storage requirements for this matrix.

If `PETSC_DECIDE`

or `PETSC_DETERMINE`

is used for a particular argument on one
processor than it must be used on all processors that share the object for
that argument.

The user MUST specify either the local or global matrix dimensions (possibly both).

The parallel matrix is partitioned such that the first m0 rows belong to
process 0, the next m1 rows belong to process 1, the next m2 rows belong
to process 2 etc.. where m0,m1,m2… are the input parameter `m`

.

The DIAGONAL portion of the local submatrix of a processor can be defined as the submatrix which is obtained by extraction the part corresponding to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the first row that belongs to the processor, and r2 is the last row belonging to the this processor. This is a square mxm matrix. The remaining portion of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.

If `o_nnz`

, `d_nnz`

are specified, then `o_nz`

, and `d_nz`

are ignored.

When calling this routine with a single process communicator, a matrix of
type `MATSEQAIJPERM`

is returned. If a matrix of type `MATMPIAIJPERM`

is desired
for this type of communicator, use the construction mechanism

```
MatCreate(...,&A);
MatSetType(A,MPIAIJ);
MatMPIAIJSetPreallocation(A,...);
```

By default, this format uses inodes (identical nodes) when possible. We search for consecutive rows with the same nonzero structure, thereby reusing matrix information to achieve increased efficiency.

## See Also#

Matrices, `Mat`

, Sparse Matrix Creation, `MATMPIAIJPERM`

, `MatCreate()`

, `MatCreateSeqAIJPERM()`

, `MatSetValues()`

## Level#

intermediate

## Location#

src/mat/impls/aij/mpi/aijperm/mpiaijperm.c

Index of all Mat routines

Table of Contents for all manual pages

Index of all manual pages