Actual source code: mpibaijmkl.c

petsc-3.14.6 2021-03-30
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  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);
 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);
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

181: M*/