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). The same 6 guidelines that apply to `MATMPIAIJ` matrices for preallocating the matrix 7 storage apply here as well. 8 9 Collective 10 11 Input Parameters: 12 + comm - MPI communicator 13 . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given) 14 This value should be the same as the local size used in creating the 15 y vector for the matrix-vector product y = Ax. 16 . n - This value should be the same as the local size used in creating the 17 x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have 18 calculated if N is given) For square matrices n is almost always `m`. 19 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given) 20 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given) 21 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 22 (same value is used for all local rows) 23 . d_nnz - array containing the number of nonzeros in the various rows of the 24 DIAGONAL portion of the local submatrix (possibly different for each row) 25 or `NULL`, if `d_nz` is used to specify the nonzero structure. 26 The size of this array is equal to the number of local rows, i.e `m`. 27 For matrices you plan to factor you must leave room for the diagonal entry and 28 put in the entry even if it is zero. 29 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 30 submatrix (same value is used for all local rows). 31 - o_nnz - array containing the number of nonzeros in the various rows of the 32 OFF-DIAGONAL portion of the local submatrix (possibly different for 33 each row) or `NULL`, if `o_nz` is used to specify the nonzero 34 structure. The size of this array is equal to the number 35 of local rows, i.e `m`. 36 37 Output Parameter: 38 . A - the matrix 39 40 Options Database Key: 41 . -mat_aijmkl_no_spmv2 - disables use of the SpMV2 inspector-executor routines 42 43 Level: intermediate 44 45 Notes: 46 If the *_nnz parameter is given then the *_nz parameter is ignored 47 48 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across 49 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate 50 storage requirements for this matrix. 51 52 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 53 processor than it must be used on all processors that share the object for 54 that argument. 55 56 The user MUST specify either the local or global matrix dimensions 57 (possibly both). 58 59 The parallel matrix is partitioned such that the first m0 rows belong to 60 process 0, the next m1 rows belong to process 1, the next m2 rows belong 61 to process 2 etc.. where m0,m1,m2... are the input parameter `m`. 62 63 The DIAGONAL portion of the local submatrix of a processor can be defined 64 as the submatrix which is obtained by extraction the part corresponding 65 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 66 first row that belongs to the processor, and r2 is the last row belonging 67 to the this processor. This is a square mxm matrix. The remaining portion 68 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 69 70 If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored. 71 72 When calling this routine with a single process communicator, a matrix of 73 type `MATSEQAIJMKL` is returned. If a matrix of type `MATMPIAIJMKL` is desired 74 for this type of communicator, use the construction mechanism: 75 `MatCreate`(...,&A); `MatSetType`(A,MPIAIJMKL); `MatMPIAIJSetPreallocation`(A,...); 76 77 .seealso: [](chapter_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJMKL`, `MatCreate()`, `MatCreateSeqAIJMKL()`, `MatSetValues()` 78 @*/ 79 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) 80 { 81 PetscMPIInt size; 82 83 PetscFunctionBegin; 84 PetscCall(MatCreate(comm, A)); 85 PetscCall(MatSetSizes(*A, m, n, M, N)); 86 PetscCallMPI(MPI_Comm_size(comm, &size)); 87 if (size > 1) { 88 PetscCall(MatSetType(*A, MATMPIAIJMKL)); 89 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 90 } else { 91 PetscCall(MatSetType(*A, MATSEQAIJMKL)); 92 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 93 } 94 PetscFunctionReturn(PETSC_SUCCESS); 95 } 96 97 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *); 98 99 PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJMKL(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[]) 100 { 101 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 102 103 PetscFunctionBegin; 104 PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz)); 105 PetscCall(MatConvert_SeqAIJ_SeqAIJMKL(b->A, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->A)); 106 PetscCall(MatConvert_SeqAIJ_SeqAIJMKL(b->B, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->B)); 107 PetscFunctionReturn(PETSC_SUCCESS); 108 } 109 110 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat) 111 { 112 Mat B = *newmat; 113 114 PetscFunctionBegin; 115 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B)); 116 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJMKL)); 117 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJMKL)); 118 *newmat = B; 119 PetscFunctionReturn(PETSC_SUCCESS); 120 } 121 122 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJMKL(Mat A) 123 { 124 PetscFunctionBegin; 125 PetscCall(MatSetType(A, MATMPIAIJ)); 126 PetscCall(MatConvert_MPIAIJ_MPIAIJMKL(A, MATMPIAIJMKL, MAT_INPLACE_MATRIX, &A)); 127 PetscFunctionReturn(PETSC_SUCCESS); 128 } 129 130 /*MC 131 MATAIJMKL - MATAIJMKL = "AIJMKL" - A matrix type to be used for sparse matrices. 132 133 This matrix type is identical to `MATSEQAIJMKL` when constructed with a single process communicator, 134 and `MATMPIAIJMKL` otherwise. As a result, for single process communicators, 135 MatSeqAIJSetPreallocation() is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 136 for communicators controlling multiple processes. It is recommended that you call both of 137 the above preallocation routines for simplicity. 138 139 Options Database Key: 140 . -mat_type aijmkl - sets the matrix type to `MATAIJMKL` during a call to `MatSetFromOptions()` 141 142 Level: beginner 143 144 .seealso: [](chapter_matrices), `Mat`, `MATMPIAIJMKL`, `MATSEQAIJMKL`, `MatCreateMPIAIJMKL()`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATSEQAIJPERM`, `MATMPIAIJPERM` 145 M*/ 146