1 2 #include <../src/mat/impls/aij/mpi/mpiaij.h> 3 /*@C 4 MatCreateMPIAIJPERM - Creates a sparse parallel matrix whose local 5 portions are stored as `MATSEQAIJPERM` matrices (a matrix class that inherits 6 from SEQAIJ but includes some optimizations to allow more effective 7 vectorization). The same guidelines that apply to `MATMPIAIJ` matrices for 8 preallocating the matrix storage apply here as well. 9 10 Collective 11 12 Input Parameters: 13 + comm - MPI communicator 14 . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given) 15 This value should be the same as the local size used in creating the 16 y vector for the matrix-vector product y = Ax. 17 . n - This value should be the same as the local size used in creating the 18 x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have 19 calculated if `N` is given) For square matrices `n` is almost always `m`. 20 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given) 21 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given) 22 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix 23 (same value is used for all local rows) 24 . d_nnz - array containing the number of nonzeros in the various rows of the 25 DIAGONAL portion of the local submatrix (possibly different for each row) 26 or `NULL`, if `d_nz` is used to specify the nonzero structure. 27 The size of this array is equal to the number of local rows, i.e `m`. 28 For matrices you plan to factor you must leave room for the diagonal entry and 29 put in the entry even if it is zero. 30 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local 31 submatrix (same value is used for all local rows). 32 - o_nnz - array containing the number of nonzeros in the various rows of the 33 OFF-DIAGONAL portion of the local submatrix (possibly different for 34 each row) or `NULL`, if `o_nz` is used to specify the nonzero 35 structure. The size of this array is equal to the number 36 of local rows, i.e `m`. 37 38 Output Parameter: 39 . A - the matrix 40 41 Options Database Keys: 42 + -mat_no_inode - Do not use inodes 43 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 44 45 Level: intermediate 46 47 Notes: 48 If the *_nnz parameter is given then the *_nz parameter is ignored 49 50 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across 51 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate 52 storage requirements for this matrix. 53 54 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 55 processor than it must be used on all processors that share the object for 56 that argument. 57 58 The user MUST specify either the local or global matrix dimensions 59 (possibly both). 60 61 The parallel matrix is partitioned such that the first m0 rows belong to 62 process 0, the next m1 rows belong to process 1, the next m2 rows belong 63 to process 2 etc.. where m0,m1,m2... are the input parameter `m`. 64 65 The DIAGONAL portion of the local submatrix of a processor can be defined 66 as the submatrix which is obtained by extraction the part corresponding 67 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 68 first row that belongs to the processor, and r2 is the last row belonging 69 to the this processor. This is a square mxm matrix. The remaining portion 70 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 71 72 If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored. 73 74 When calling this routine with a single process communicator, a matrix of 75 type `MATSEQAIJPERM` is returned. If a matrix of type `MATMPIAIJPERM` is desired 76 for this type of communicator, use the construction mechanism 77 .vb 78 MatCreate(...,&A); 79 MatSetType(A,MPIAIJ); 80 MatMPIAIJSetPreallocation(A,...); 81 .ve 82 83 By default, this format uses inodes (identical nodes) when possible. 84 We search for consecutive rows with the same nonzero structure, thereby 85 reusing matrix information to achieve increased efficiency. 86 87 .seealso: [](chapter_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJPERM`, `MatCreate()`, `MatCreateSeqAIJPERM()`, `MatSetValues()` 88 @*/ 89 PetscErrorCode MatCreateMPIAIJPERM(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) 90 { 91 PetscMPIInt size; 92 93 PetscFunctionBegin; 94 PetscCall(MatCreate(comm, A)); 95 PetscCall(MatSetSizes(*A, m, n, M, N)); 96 PetscCallMPI(MPI_Comm_size(comm, &size)); 97 if (size > 1) { 98 PetscCall(MatSetType(*A, MATMPIAIJPERM)); 99 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 100 } else { 101 PetscCall(MatSetType(*A, MATSEQAIJPERM)); 102 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 103 } 104 PetscFunctionReturn(PETSC_SUCCESS); 105 } 106 107 PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJPERM(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[]) 108 { 109 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 110 111 PetscFunctionBegin; 112 PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz)); 113 PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->A)); 114 PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->B, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->B)); 115 PetscFunctionReturn(PETSC_SUCCESS); 116 } 117 118 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat A, MatType type, MatReuse reuse, Mat *newmat) 119 { 120 Mat B = *newmat; 121 122 PetscFunctionBegin; 123 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B)); 124 125 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJPERM)); 126 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJPERM)); 127 *newmat = B; 128 PetscFunctionReturn(PETSC_SUCCESS); 129 } 130 131 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJPERM(Mat A) 132 { 133 PetscFunctionBegin; 134 PetscCall(MatSetType(A, MATMPIAIJ)); 135 PetscCall(MatConvert_MPIAIJ_MPIAIJPERM(A, MATMPIAIJPERM, MAT_INPLACE_MATRIX, &A)); 136 PetscFunctionReturn(PETSC_SUCCESS); 137 } 138 139 /*MC 140 MATAIJPERM - "AIJPERM" - A matrix type to be used for sparse matrices. 141 142 This matrix type is identical to `MATSEQAIJPERM` when constructed with a single process communicator, 143 and `MATMPIAIJPERM` otherwise. As a result, for single process communicators, 144 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 145 for communicators controlling multiple processes. It is recommended that you call both of 146 the above preallocation routines for simplicity. 147 148 Options Database Key: 149 . -mat_type aijperm - sets the matrix type to `MATAIJPERM` 150 151 Level: beginner 152 153 .seealso: [](chapter_matrices), `Mat`, `MatCreateMPIAIJPERM()`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL` 154 M*/ 155