1 #include <../src/mat/impls/baij/mpi/mpibaij.h> 2 3 #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE) 4 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat,MatType,MatReuse,Mat*); 5 6 PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJMKL(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz) 7 { 8 Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)B->data; 9 PetscErrorCode ierr; 10 11 PetscFunctionBegin; 12 ierr = MatMPIBAIJSetPreallocation_MPIBAIJ(B,bs,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 13 ierr = MatConvert_SeqBAIJ_SeqBAIJMKL(b->A,MATSEQBAIJMKL,MAT_INPLACE_MATRIX,&b->A);CHKERRQ(ierr); 14 ierr = MatConvert_SeqBAIJ_SeqBAIJMKL(b->B,MATSEQBAIJMKL,MAT_INPLACE_MATRIX,&b->B);CHKERRQ(ierr); 15 PetscFunctionReturn(0); 16 } 17 18 PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A,MatType type,MatReuse reuse,Mat *newmat) 19 { 20 PetscErrorCode ierr; 21 Mat B = *newmat; 22 23 PetscFunctionBegin; 24 if (reuse == MAT_INITIAL_MATRIX) { 25 ierr = MatDuplicate(A,MAT_COPY_VALUES,&B);CHKERRQ(ierr); 26 } 27 28 ierr = PetscObjectChangeTypeName((PetscObject) B, MATMPIBAIJMKL);CHKERRQ(ierr); 29 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJMKL);CHKERRQ(ierr); 30 *newmat = B; 31 PetscFunctionReturn(0); 32 } 33 #endif 34 /*@C 35 MatCreateBAIJMKL - Creates a sparse parallel matrix in block AIJ format 36 (block compressed row). 37 This type inherits from BAIJ and is largely identical, but uses sparse BLAS 38 routines from Intel MKL whenever possible. 39 MatMult, MatMultAdd, MatMultTranspose, and MatMultTransposeAdd 40 operations are currently supported. 41 If the installed version of MKL supports the "SpMV2" sparse 42 inspector-executor routines, then those are used by default. 43 Default PETSc kernels are used otherwise. 44 For good matrix assembly performance the user should preallocate the matrix 45 storage by setting the parameters d_nz (or d_nnz) and o_nz (or o_nnz). 46 By setting these parameters accurately, performance can be increased by more 47 than a factor of 50. 48 49 Collective on MPI_Comm 50 51 Input Parameters: 52 + comm - MPI communicator 53 . bs - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row 54 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs() 55 . m - number of local rows (or PETSC_DECIDE to have calculated if M is given) 56 This value should be the same as the local size used in creating the 57 y vector for the matrix-vector product y = Ax. 58 . n - number of local columns (or PETSC_DECIDE to have calculated if N is given) 59 This value should be the same as the local size used in creating the 60 x vector for the matrix-vector product y = Ax. 61 . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given) 62 . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given) 63 . d_nz - number of nonzero blocks per block row in diagonal portion of local 64 submatrix (same for all local rows) 65 . d_nnz - array containing the number of nonzero blocks in the various block rows 66 of the in diagonal portion of the local (possibly different for each block 67 row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry 68 and set it even if it is zero. 69 . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local 70 submatrix (same for all local rows). 71 - o_nnz - array containing the number of nonzero blocks in the various block rows of the 72 off-diagonal portion of the local submatrix (possibly different for 73 each block row) or NULL. 74 75 Output Parameter: 76 . A - the matrix 77 78 Options Database Keys: 79 + -mat_block_size - size of the blocks to use 80 - -mat_use_hash_table <fact> 81 82 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 83 MatXXXXSetPreallocation() paradgm instead of this routine directly. 84 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 85 86 Notes: 87 If the *_nnz parameter is given then the *_nz parameter is ignored 88 89 A nonzero block is any block that as 1 or more nonzeros in it 90 91 The user MUST specify either the local or global matrix dimensions 92 (possibly both). 93 94 If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor 95 than it must be used on all processors that share the object for that argument. 96 97 Storage Information: 98 For a square global matrix we define each processor's diagonal portion 99 to be its local rows and the corresponding columns (a square submatrix); 100 each processor's off-diagonal portion encompasses the remainder of the 101 local matrix (a rectangular submatrix). 102 103 The user can specify preallocated storage for the diagonal part of 104 the local submatrix with either d_nz or d_nnz (not both). Set 105 d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic 106 memory allocation. Likewise, specify preallocated storage for the 107 off-diagonal part of the local submatrix with o_nz or o_nnz (not both). 108 109 Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In 110 the figure below we depict these three local rows and all columns (0-11). 111 112 .vb 113 0 1 2 3 4 5 6 7 8 9 10 11 114 -------------------------- 115 row 3 |o o o d d d o o o o o o 116 row 4 |o o o d d d o o o o o o 117 row 5 |o o o d d d o o o o o o 118 -------------------------- 119 .ve 120 121 Thus, any entries in the d locations are stored in the d (diagonal) 122 submatrix, and any entries in the o locations are stored in the 123 o (off-diagonal) submatrix. Note that the d and the o submatrices are 124 stored simply in the MATSEQBAIJMKL format for compressed row storage. 125 126 Now d_nz should indicate the number of block nonzeros per row in the d matrix, 127 and o_nz should indicate the number of block nonzeros per row in the o matrix. 128 In general, for PDE problems in which most nonzeros are near the diagonal, 129 one expects d_nz >> o_nz. For large problems you MUST preallocate memory 130 or you will get TERRIBLE performance; see the users' manual chapter on 131 matrices. 132 133 Level: intermediate 134 135 .keywords: matrix, block, aij, compressed row, sparse, parallel 136 137 .seealso: MatCreate(), MatCreateSeqBAIJMKL(), MatSetValues(), MatCreateBAIJMKL(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR() 138 @*/ 139 140 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) 141 { 142 PetscErrorCode ierr; 143 PetscMPIInt size; 144 145 PetscFunctionBegin; 146 ierr = MatCreate(comm,A);CHKERRQ(ierr); 147 ierr = MatSetSizes(*A,m,n,M,N);CHKERRQ(ierr); 148 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 149 if (size > 1) { 150 #ifdef PETSC_HAVE_MKL_SPARSE_OPTIMIZE 151 ierr = MatSetType(*A,MATMPIBAIJMKL);CHKERRQ(ierr); 152 #else 153 ierr = PetscInfo(A,"MKL baij routines are not supported for used version of MKL. Using PETSc default routines. \n Please use version of MKL 11.3 and higher. \n"); 154 ierr = MatSetType(*A,MATMPIBAIJ);CHKERRQ(ierr); 155 #endif 156 ierr = MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr); 157 } else { 158 #ifdef PETSC_HAVE_MKL_SPARSE_OPTIMIZE 159 ierr = MatSetType(*A,MATSEQBAIJMKL);CHKERRQ(ierr); 160 #else 161 ierr = PetscInfo(A,"MKL baij routines are not supported for used version of MKL. Using PETSc default routines. \n Please use version of MKL 11.3 and higher. \n"); 162 ierr = MatSetType(*A,MATSEQBAIJ);CHKERRQ(ierr); 163 #endif 164 ierr = MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);CHKERRQ(ierr); 165 } 166 PetscFunctionReturn(0); 167 } 168 169 PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJMKL(Mat A) 170 { 171 PetscErrorCode ierr; 172 173 PetscFunctionBegin; 174 ierr = MatSetType(A,MATMPIBAIJ);CHKERRQ(ierr); 175 #ifdef PETSC_HAVE_MKL_SPARSE_OPTIMIZE 176 ierr = MatConvert_MPIBAIJ_MPIBAIJMKL(A,MATMPIBAIJMKL,MAT_INPLACE_MATRIX,&A);CHKERRQ(ierr); 177 #else 178 ierr = PetscInfo(A,"MKL baij routines are not supported for used version of MKL. Using PETSc default routines. \n Please use version of MKL 11.3 and higher. \n"); 179 #endif 180 PetscFunctionReturn(0); 181 } 182 183 /*MC 184 MATBAIJMKL - MATBAIJMKL = "BAIJMKL" - A matrix type to be used for sparse matrices. 185 186 This matrix type is identical to MATSEQBAIJMKL when constructed with a single process communicator, 187 and MATMPIBAIJMKL otherwise. As a result, for single process communicators, 188 MatSeqBAIJSetPreallocation() is supported, and similarly MatMPIBAIJSetPreallocation() is supported 189 for communicators controlling multiple processes. It is recommended that you call both of 190 the above preallocation routines for simplicity. 191 192 Options Database Keys: 193 . -mat_type baijmkl - sets the matrix type to "BAIJMKL" during a call to MatSetFromOptions() 194 195 Level: beginner 196 197 .seealso: MatCreateMPIBAIJMKL(), MATSEQBAIJMKL, MATMPIBAIJMKL 198 M*/ 199 200