Actual source code: mpiaijperm.c


  2: #include <../src/mat/impls/aij/mpi/mpiaij.h>
  3: /*@C
  4:    MatCreateMPIAIJPERM - Creates a sparse parallel matrix whose local
  5:    portions are stored as `MATSEQAIJPERM` matrices (a matrix class that inherits
  6:    from SEQAIJ but includes some optimizations to allow more effective
  7:    vectorization).  The same guidelines that apply to `MATMPIAIJ` matrices for
  8:    preallocating the matrix storage apply here as well.

 10:       Collective

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

 38:    Output Parameter:
 39: .  A - the matrix

 41:    Notes:
 42:    If the *_nnz parameter is given then the *_nz parameter is ignored

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

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

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

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

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

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

 68:    When calling this routine with a single process communicator, a matrix of
 69:    type `MATSEQAIJPERM` is returned.  If a matrix of type `MATMPIAIJPERM` is desired
 70:    for this type of communicator, use the construction mechanism:
 71:     `MatCreate`(...,&A); `MatSetType`(A,MPIAIJ); `MatMPIAIJSetPreallocation`(A,...);

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

 77:    Options Database Keys:
 78: +  -mat_no_inode  - Do not use inodes
 79: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

 81:    Level: intermediate

 83: .seealso: [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJPERM`, `MatCreate()`, `MatCreateSeqAIJPERM()`, `MatSetValues()`
 84: @*/
 85: 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)
 86: {
 87:   PetscMPIInt size;

 89:   MatCreate(comm, A);
 90:   MatSetSizes(*A, m, n, M, N);
 91:   MPI_Comm_size(comm, &size);
 92:   if (size > 1) {
 93:     MatSetType(*A, MATMPIAIJPERM);
 94:     MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz);
 95:   } else {
 96:     MatSetType(*A, MATSEQAIJPERM);
 97:     MatSeqAIJSetPreallocation(*A, d_nz, d_nnz);
 98:   }
 99:   return 0;
100: }

102: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJPERM(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
103: {
104:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

106:   MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz);
107:   MatConvert_SeqAIJ_SeqAIJPERM(b->A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->A);
108:   MatConvert_SeqAIJ_SeqAIJPERM(b->B, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->B);
109:   return 0;
110: }

112: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat A, MatType type, MatReuse reuse, Mat *newmat)
113: {
114:   Mat B = *newmat;

116:   if (reuse == MAT_INITIAL_MATRIX) MatDuplicate(A, MAT_COPY_VALUES, &B);

118:   PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJPERM);
119:   PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJPERM);
120:   *newmat = B;
121:   return 0;
122: }

124: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJPERM(Mat A)
125: {
126:   MatSetType(A, MATMPIAIJ);
127:   MatConvert_MPIAIJ_MPIAIJPERM(A, MATMPIAIJPERM, MAT_INPLACE_MATRIX, &A);
128:   return 0;
129: }

131: /*MC
132:    MATAIJPERM - MATAIJPERM = "AIJPERM" - A matrix type to be used for sparse matrices.

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

140:    Options Database Keys:
141: . -mat_type aijperm - sets the matrix type to `MATAIJPERM` during a call to `MatSetFromOptions()`

143:   Level: beginner

145: .seealso: `MatCreateMPIAIJPERM()`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL`
146: M*/