Date: 2022-03-04T17:37:51+00:00

Actual source code: mpibaijmkl.c

      1: #include <../src/mat/impls/baij/mpi/mpibaij.h>

      3: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat,MatType,MatReuse,Mat*);

      5: static PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJMKL(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
      6: {
      7:   Mat_MPIBAIJ     *b = (Mat_MPIBAIJ*)B->data;

     11:   MatMPIBAIJSetPreallocation_MPIBAIJ(B,bs,d_nz,d_nnz,o_nz,o_nnz);
     12:   MatConvert_SeqBAIJ_SeqBAIJMKL(b->A,MATSEQBAIJMKL,MAT_INPLACE_MATRIX,&b->A);
     13:   MatConvert_SeqBAIJ_SeqBAIJMKL(b->B,MATSEQBAIJMKL,MAT_INPLACE_MATRIX,&b->B);
     14:   return(0);
     15: }

     17: static PetscErrorCode MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A,MatType type,MatReuse reuse,Mat *newmat)
     18: {
     20:   Mat            B = *newmat;

     23:   if (reuse == MAT_INITIAL_MATRIX) {
     24:     MatDuplicate(A,MAT_COPY_VALUES,&B);
     25:   }

     27:   PetscObjectChangeTypeName((PetscObject) B, MATMPIBAIJMKL);
     28:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJMKL);
     29:   *newmat = B;
     30:   return(0);
     31: }

     33: /*@C
     34:    MatCreateBAIJMKL - Creates a sparse parallel matrix in block AIJ format
     35:    (block compressed row).
     36:    This type inherits from BAIJ and is largely identical, but uses sparse BLAS
     37:    routines from Intel MKL whenever possible.
     38:    MatMult, MatMultAdd, MatMultTranspose, and MatMultTransposeAdd
     39:    operations are currently supported.
     40:    If the installed version of MKL supports the "SpMV2" sparse
     41:    inspector-executor routines, then those are used by default.
     42:    Default PETSc kernels are used otherwise.
     43:    For good matrix assembly performance the user should preallocate the matrix
     44:    storage by setting the parameters d_nz (or d_nnz) and o_nz (or o_nnz).
     45:    By setting these parameters accurately, performance can be increased by more
     46:    than a factor of 50.

     48:    Collective

     50:    Input Parameters:
     51: +  comm - MPI communicator
     52: .  bs   - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
     53:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
     54: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
     55:            This value should be the same as the local size used in creating the
     56:            y vector for the matrix-vector product y = Ax.
     57: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
     58:            This value should be the same as the local size used in creating the
     59:            x vector for the matrix-vector product y = Ax.
     60: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
     61: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
     62: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local
     63:            submatrix  (same for all local rows)
     64: .  d_nnz - array containing the number of nonzero blocks in the various block rows
     65:            of the in diagonal portion of the local (possibly different for each block
     66:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry
     67:            and set it even if it is zero.
     68: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
     69:            submatrix (same for all local rows).
     70: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
     71:            off-diagonal portion of the local submatrix (possibly different for
     72:            each block row) or NULL.

     74:    Output Parameter:
     75: .  A - the matrix

     77:    Options Database Keys:
     78: +   -mat_block_size - size of the blocks to use
     79: -   -mat_use_hash_table <fact>

     81:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
     82:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
     83:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

     85:    Notes:
     86:    If the *_nnz parameter is given then the *_nz parameter is ignored

     88:    A nonzero block is any block that as 1 or more nonzeros in it

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

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

     96:    Storage Information:
     97:    For a square global matrix we define each processor's diagonal portion
     98:    to be its local rows and the corresponding columns (a square submatrix);
     99:    each processor's off-diagonal portion encompasses the remainder of the
    100:    local matrix (a rectangular submatrix).

    102:    The user can specify preallocated storage for the diagonal part of
    103:    the local submatrix with either d_nz or d_nnz (not both).  Set
    104:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
    105:    memory allocation.  Likewise, specify preallocated storage for the
    106:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

    108:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
    109:    the figure below we depict these three local rows and all columns (0-11).

    111: .vb
    112:            0 1 2 3 4 5 6 7 8 9 10 11
    113:           --------------------------
    114:    row 3  |o o o d d d o o o o  o  o
    115:    row 4  |o o o d d d o o o o  o  o
    116:    row 5  |o o o d d d o o o o  o  o
    117:           --------------------------
    118: .ve

    120:    Thus, any entries in the d locations are stored in the d (diagonal)
    121:    submatrix, and any entries in the o locations are stored in the
    122:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
    123:    stored simply in the MATSEQBAIJMKL format for compressed row storage.

    125:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
    126:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
    127:    In general, for PDE problems in which most nonzeros are near the diagonal,
    128:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
    129:    or you will get TERRIBLE performance; see the users' manual chapter on
    130:    matrices.

    132:    Level: intermediate

    134: .seealso: MatCreate(), MatCreateSeqBAIJMKL(), MatSetValues(), MatCreateBAIJMKL(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
    135: @*/

    137: PetscErrorCode  MatCreateBAIJMKL(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
    138: {
    140:   PetscMPIInt    size;

    143:   MatCreate(comm,A);
    144:   MatSetSizes(*A,m,n,M,N);
    145:   MPI_Comm_size(comm,&size);
    146:   if (size > 1) {
    147:     MatSetType(*A,MATMPIBAIJMKL);
    148:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
    149:   } else {
    150:     MatSetType(*A,MATSEQBAIJMKL);
    151:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
    152:   }
    153:   return(0);
    154: }

    156: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJMKL(Mat A)
    157: {

    161:   MatSetType(A,MATMPIBAIJ);
    162:   MatConvert_MPIBAIJ_MPIBAIJMKL(A,MATMPIBAIJMKL,MAT_INPLACE_MATRIX,&A);
    163:   return(0);
    164: }

    166: /*MC
    167:    MATBAIJMKL - MATBAIJMKL = "BAIJMKL" - A matrix type to be used for sparse matrices.

    169:    This matrix type is identical to MATSEQBAIJMKL when constructed with a single process communicator,
    170:    and MATMPIBAIJMKL otherwise.  As a result, for single process communicators,
    171:   MatSeqBAIJSetPreallocation() is supported, and similarly MatMPIBAIJSetPreallocation() is supported
    172:   for communicators controlling multiple processes.  It is recommended that you call both of
    173:   the above preallocation routines for simplicity.

    175:    Options Database Keys:
    176: . -mat_type baijmkl - sets the matrix type to "BAIJMKL" during a call to MatSetFromOptions()

    178:   Level: beginner

    180: .seealso: MatCreateBAIJMKL(), MATSEQBAIJMKL, MATMPIBAIJMKL
    181: M*/