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 Notes: 42 If the *_nnz parameter is given then the *_nz parameter is ignored 43 44 m,n,M,N parameters specify the size of the matrix, and its partitioning across 45 processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate 46 storage requirements for this matrix. 47 48 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 49 processor than it must be used on all processors that share the object for 50 that argument. 51 52 The user MUST specify either the local or global matrix dimensions 53 (possibly both). 54 55 The parallel matrix is partitioned such that the first m0 rows belong to 56 process 0, the next m1 rows belong to process 1, the next m2 rows belong 57 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 58 59 The DIAGONAL portion of the local submatrix of a processor can be defined 60 as the submatrix which is obtained by extraction the part corresponding 61 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 62 first row that belongs to the processor, and r2 is the last row belonging 63 to the this processor. This is a square mxm matrix. The remaining portion 64 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 65 66 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 67 68 When calling this routine with a single process communicator, a matrix of 69 type `MATSEQAIJPERM` is returned. If a matrix of type `MATMPIAIJPERM` is desired 70 for this type of communicator, use the construction mechanism: 71 `MatCreate`(...,&A); `MatSetType`(A,MPIAIJ); `MatMPIAIJSetPreallocation`(A,...); 72 73 By default, this format uses inodes (identical nodes) when possible. 74 We search for consecutive rows with the same nonzero structure, thereby 75 reusing matrix information to achieve increased efficiency. 76 77 Options Database Keys: 78 + -mat_no_inode - Do not use inodes 79 - -mat_inode_limit <limit> - Sets inode limit (max limit=5) 80 81 Level: intermediate 82 83 .seealso: `MATMPIAIJPERM`, `MatCreate()`, `MatCreateSeqAIJPERM()`, `MatSetValues()` 84 @*/ 85 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) { 86 PetscMPIInt size; 87 88 PetscFunctionBegin; 89 PetscCall(MatCreate(comm, A)); 90 PetscCall(MatSetSizes(*A, m, n, M, N)); 91 PetscCallMPI(MPI_Comm_size(comm, &size)); 92 if (size > 1) { 93 PetscCall(MatSetType(*A, MATMPIAIJPERM)); 94 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 95 } else { 96 PetscCall(MatSetType(*A, MATSEQAIJPERM)); 97 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 98 } 99 PetscFunctionReturn(0); 100 } 101 102 PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJPERM(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[]) { 103 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data; 104 105 PetscFunctionBegin; 106 PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz)); 107 PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->A)); 108 PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(b->B, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &b->B)); 109 PetscFunctionReturn(0); 110 } 111 112 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat A, MatType type, MatReuse reuse, Mat *newmat) { 113 Mat B = *newmat; 114 115 PetscFunctionBegin; 116 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B)); 117 118 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJPERM)); 119 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJPERM)); 120 *newmat = B; 121 PetscFunctionReturn(0); 122 } 123 124 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJPERM(Mat A) { 125 PetscFunctionBegin; 126 PetscCall(MatSetType(A, MATMPIAIJ)); 127 PetscCall(MatConvert_MPIAIJ_MPIAIJPERM(A, MATMPIAIJPERM, MAT_INPLACE_MATRIX, &A)); 128 PetscFunctionReturn(0); 129 } 130 131 /*MC 132 MATAIJPERM - MATAIJPERM = "AIJPERM" - A matrix type to be used for sparse matrices. 133 134 This matrix type is identical to `MATSEQAIJPERM` when constructed with a single process communicator, 135 and `MATMPIAIJPERM` otherwise. As a result, for single process communicators, 136 `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported 137 for communicators controlling multiple processes. It is recommended that you call both of 138 the above preallocation routines for simplicity. 139 140 Options Database Keys: 141 . -mat_type aijperm - sets the matrix type to `MATAIJPERM` during a call to `MatSetFromOptions()` 142 143 Level: beginner 144 145 .seealso: `MatCreateMPIAIJPERM()`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL` 146 M*/ 147