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). 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 Keys: 41 + -mat_no_inode - Do not use inodes 42 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 43 44 Level: intermediate 45 46 Notes: 47 If the *_nnz parameter is given then the *_nz parameter is ignored 48 49 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across 50 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate 51 storage requirements for this matrix. 52 53 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 54 processor than it must be used on all processors that share the object for 55 that argument. 56 57 The user MUST specify either the local or global matrix dimensions 58 (possibly both). 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`. 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 `MATSEQAIJPERM` is returned. If a matrix of type `MATMPIAIJPERM` is desired 75 for this type of communicator, use the construction mechanism 76 .vb 77 MatCreate(...,&A); 78 MatSetType(A,MPIAIJ); 79 MatMPIAIJSetPreallocation(A,...); 80 .ve 81 82 By default, this format uses inodes (identical nodes) when possible. 83 We search for consecutive rows with the same nonzero structure, thereby 84 reusing matrix information to achieve increased efficiency. 85 86 .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJPERM`, `MatCreate()`, `MatCreateSeqAIJPERM()`, `MatSetValues()` 87 @*/ 88 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) 89 { 90 PetscMPIInt size; 91 92 PetscFunctionBegin; 93 PetscCall(MatCreate(comm, A)); 94 PetscCall(MatSetSizes(*A, m, n, M, N)); 95 PetscCallMPI(MPI_Comm_size(comm, &size)); 96 if (size > 1) { 97 PetscCall(MatSetType(*A, MATMPIAIJPERM)); 98 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 99 } else { 100 PetscCall(MatSetType(*A, MATSEQAIJPERM)); 101 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 102 } 103 PetscFunctionReturn(PETSC_SUCCESS); 104 } 105 106 PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJPERM(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_SeqAIJPERM(b->A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->A)); 113 PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->B, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->B)); 114 PetscFunctionReturn(PETSC_SUCCESS); 115 } 116 117 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(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)); 123 124 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJPERM)); 125 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJPERM)); 126 *newmat = B; 127 PetscFunctionReturn(PETSC_SUCCESS); 128 } 129 130 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJPERM(Mat A) 131 { 132 PetscFunctionBegin; 133 PetscCall(MatSetType(A, MATMPIAIJ)); 134 PetscCall(MatConvert_MPIAIJ_MPIAIJPERM(A, MATMPIAIJPERM, MAT_INPLACE_MATRIX, &A)); 135 PetscFunctionReturn(PETSC_SUCCESS); 136 } 137 138 /*MC 139 MATAIJPERM - "AIJPERM" - A matrix type to be used for sparse matrices. 140 141 This matrix type is identical to `MATSEQAIJPERM` when constructed with a single process communicator, 142 and `MATMPIAIJPERM` otherwise. As a result, for single process communicators, 143 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 144 for communicators controlling multiple processes. It is recommended that you call both of 145 the above preallocation routines for simplicity. 146 147 Options Database Key: 148 . -mat_type aijperm - sets the matrix type to `MATAIJPERM` 149 150 Level: beginner 151 152 .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJPERM()`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL` 153 M*/ 154