Actual source code: mpiaijperm.c

      1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
      2: /*@C
      3:   MatCreateMPIAIJPERM - Creates a sparse parallel matrix whose local
      4:   portions are stored as `MATSEQAIJPERM` matrices (a matrix class that inherits
      5:   from SEQAIJ but includes some optimizations to allow more effective
      6:   vectorization).

      8:   Collective

     10:   Input Parameters:
     11: + comm  - MPI communicator
     12: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
     13:            This value should be the same as the local size used in creating the
     14:            y vector for the matrix-vector product y = Ax.
     15: . n     - This value should be the same as the local size used in creating the
     16:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
     17:        calculated if `N` is given) For square matrices `n` is almost always `m`.
     18: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
     19: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
     20: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
     21:            (same value is used for all local rows)
     22: . d_nnz - array containing the number of nonzeros in the various rows of the
     23:            DIAGONAL portion of the local submatrix (possibly different for each row)
     24:            or `NULL`, if `d_nz` is used to specify the nonzero structure.
     25:            The size of this array is equal to the number of local rows, i.e `m`.
     26:            For matrices you plan to factor you must leave room for the diagonal entry and
     27:            put in the entry even if it is zero.
     28: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
     29:            submatrix (same value is used for all local rows).
     30: - o_nnz - array containing the number of nonzeros in the various rows of the
     31:            OFF-DIAGONAL portion of the local submatrix (possibly different for
     32:            each row) or `NULL`, if `o_nz` is used to specify the nonzero
     33:            structure. The size of this array is equal to the number
     34:            of local rows, i.e `m`.

     36:   Output Parameter:
     37: . A - the matrix

     39:   Options Database Keys:
     40: + -mat_no_inode          - Do not use inodes
     41: - -mat_inode_limit limit - Sets inode limit (max limit=5)

     43:   Level: intermediate

     45:   Notes:
     46:   If the *_nnz parameter is given then the *_nz parameter is ignored

     48:   `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
     49:   processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
     50:   storage requirements for this matrix.

     52:   If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one
     53:   processor than it must be used on all processors that share the object for
     54:   that argument.

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

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

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

     70:   If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.

     72:   When calling this routine with a single process communicator, a matrix of
     73:   type `MATSEQAIJPERM` is returned.  If a matrix of type `MATMPIAIJPERM` is desired
     74:   for this type of communicator, use the construction mechanism
     75: .vb
     76:    MatCreate(...,&A);
     77:    MatSetType(A,MPIAIJ);
     78:    MatMPIAIJSetPreallocation(A,...);
     79: .ve

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

     85: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJPERM`, `MatCreate()`, `MatCreateSeqAIJPERM()`, `MatSetValues()`
     86: @*/
     87: PetscErrorCode MatCreateMPIAIJPERM(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)
     88: {
     89:   PetscMPIInt size;

     91:   PetscFunctionBegin;
     92:   PetscCall(MatCreate(comm, A));
     93:   PetscCall(MatSetSizes(*A, m, n, M, N));
     94:   PetscCallMPI(MPI_Comm_size(comm, &size));
     95:   if (size > 1) {
     96:     PetscCall(MatSetType(*A, MATMPIAIJPERM));
     97:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
     98:   } else {
     99:     PetscCall(MatSetType(*A, MATSEQAIJPERM));
    100:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
    101:   }
    102:   PetscFunctionReturn(PETSC_SUCCESS);
    103: }

    105: static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJPERM(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
    106: {
    107:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

    109:   PetscFunctionBegin;
    110:   PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz));
    111:   PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->A));
    112:   PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->B, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->B));
    113:   PetscFunctionReturn(PETSC_SUCCESS);
    114: }

    116: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat A, MatType type, MatReuse reuse, Mat *newmat)
    117: {
    118:   Mat B = *newmat;

    120:   PetscFunctionBegin;
    121:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));

    123:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJPERM));
    124:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJPERM));
    125:   *newmat = B;
    126:   PetscFunctionReturn(PETSC_SUCCESS);
    127: }

    129: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJPERM(Mat A)
    130: {
    131:   PetscFunctionBegin;
    132:   PetscCall(MatSetType(A, MATMPIAIJ));
    133:   PetscCall(MatConvert_MPIAIJ_MPIAIJPERM(A, MATMPIAIJPERM, MAT_INPLACE_MATRIX, &A));
    134:   PetscFunctionReturn(PETSC_SUCCESS);
    135: }

    137: /*MC
    138:    MATAIJPERM - "AIJPERM" - A matrix type to be used for sparse matrices.

    140:    This matrix type is identical to `MATSEQAIJPERM` when constructed with a single process communicator,
    141:    and `MATMPIAIJPERM` otherwise.  As a result, for single process communicators,
    142:   `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
    143:   for communicators controlling multiple processes.  It is recommended that you call both of
    144:   the above preallocation routines for simplicity.

    146:    Options Database Key:
    147: . -mat_type aijperm - sets the matrix type to `MATAIJPERM`

    149:   Level: beginner

    151: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJPERM()`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL`
    152: M*/