1 #include <../src/mat/impls/aij/mpi/mpiaij.h> 2 /*@C 3 MatCreateMPIAIJMKL - Creates a sparse parallel matrix whose local 4 portions are stored as `MATSEQAIJMKL` matrices (a matrix class that inherits 5 from `MATSEQAIJ` but uses some operations provided by Intel MKL). 6 7 Collective 8 9 Input Parameters: 10 + comm - MPI communicator 11 . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given) 12 This value should be the same as the local size used in creating the 13 y vector for the matrix-vector product y = Ax. 14 . n - This value should be the same as the local size used in creating the 15 x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have 16 calculated if N is given) For square matrices n is almost always `m`. 17 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given) 18 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given) 19 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 20 (same value is used for all local rows) 21 . d_nnz - array containing the number of nonzeros in the various rows of the 22 DIAGONAL portion of the local submatrix (possibly different for each row) 23 or `NULL`, if `d_nz` is used to specify the nonzero structure. 24 The size of this array is equal to the number of local rows, i.e `m`. 25 For matrices you plan to factor you must leave room for the diagonal entry and 26 put in the entry even if it is zero. 27 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 28 submatrix (same value is used for all local rows). 29 - o_nnz - array containing the number of nonzeros in the various rows of the 30 OFF-DIAGONAL portion of the local submatrix (possibly different for 31 each row) or `NULL`, if `o_nz` is used to specify the nonzero 32 structure. The size of this array is equal to the number 33 of local rows, i.e `m`. 34 35 Output Parameter: 36 . A - the matrix 37 38 Options Database Key: 39 . -mat_aijmkl_no_spmv2 - disables use of the SpMV2 inspector-executor routines 40 41 Level: intermediate 42 43 Notes: 44 If the *_nnz parameter is given then the *_nz parameter is ignored 45 46 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across 47 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate 48 storage requirements for this matrix. 49 50 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 51 processor than it must be used on all processors that share the object for 52 that argument. 53 54 The user MUST specify either the local or global matrix dimensions 55 (possibly both). 56 57 If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by 58 `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`. 59 60 The parallel matrix is partitioned such that the first `m0` rows belong to 61 process 0, the next `m1` rows belong to process 1, the next `m2` rows belong 62 to process 2, etc., where `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. 63 64 The DIAGONAL portion of the local submatrix of a processor can be defined 65 as the submatrix which is obtained by extraction the part corresponding 66 to the rows `r1` - `r2` and columns `r1` - `r2` of the global matrix, where `r1` is the 67 first row that belongs to the processor, and `r2` is the last row belonging 68 to the this processor. This is a square mxm matrix. The remaining portion 69 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 70 71 If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored. 72 73 When calling this routine with a single process communicator, a matrix of 74 type `MATSEQAIJMKL` is returned. If a matrix of type `MATMPIAIJMKL` is desired 75 for this type of communicator, use the construction mechanism 76 .vb 77 MatCreate(...,&A); 78 MatSetType(A,MPIAIJMKL); 79 MatMPIAIJSetPreallocation(A,...); 80 .ve 81 82 .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJMKL`, `MatCreate()`, `MatCreateSeqAIJMKL()`, 83 `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, 84 `MatGetOwnershipRangesColumn()`, `PetscLayout` 85 @*/ 86 PetscErrorCode MatCreateMPIAIJMKL(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) 87 { 88 PetscMPIInt size; 89 90 PetscFunctionBegin; 91 PetscCall(MatCreate(comm, A)); 92 PetscCall(MatSetSizes(*A, m, n, M, N)); 93 PetscCallMPI(MPI_Comm_size(comm, &size)); 94 if (size > 1) { 95 PetscCall(MatSetType(*A, MATMPIAIJMKL)); 96 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 97 } else { 98 PetscCall(MatSetType(*A, MATSEQAIJMKL)); 99 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 100 } 101 PetscFunctionReturn(PETSC_SUCCESS); 102 } 103 104 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *); 105 106 static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJMKL(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[]) 107 { 108 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 109 110 PetscFunctionBegin; 111 PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz)); 112 PetscCall(MatConvert_SeqAIJ_SeqAIJMKL(b->A, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->A)); 113 PetscCall(MatConvert_SeqAIJ_SeqAIJMKL(b->B, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->B)); 114 PetscFunctionReturn(PETSC_SUCCESS); 115 } 116 117 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat) 118 { 119 Mat B = *newmat; 120 121 PetscFunctionBegin; 122 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B)); 123 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJMKL)); 124 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJMKL)); 125 *newmat = B; 126 PetscFunctionReturn(PETSC_SUCCESS); 127 } 128 129 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJMKL(Mat A) 130 { 131 PetscFunctionBegin; 132 PetscCall(MatSetType(A, MATMPIAIJ)); 133 PetscCall(MatConvert_MPIAIJ_MPIAIJMKL(A, MATMPIAIJMKL, MAT_INPLACE_MATRIX, &A)); 134 PetscFunctionReturn(PETSC_SUCCESS); 135 } 136 137 /*MC 138 MATAIJMKL - MATAIJMKL = "AIJMKL" - A matrix type to be used for sparse matrices. 139 140 This matrix type is identical to `MATSEQAIJMKL` when constructed with a single process communicator, 141 and `MATMPIAIJMKL` 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. 145 146 Options Database Key: 147 . -mat_type aijmkl - sets the matrix type to `MATAIJMKL` during a call to `MatSetFromOptions()` 148 149 Level: beginner 150 151 .seealso: [](ch_matrices), `Mat`, `MATMPIAIJMKL`, `MATSEQAIJMKL`, `MatCreateMPIAIJMKL()`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATSEQAIJPERM`, `MATMPIAIJPERM` 152 M*/ 153