1 #include <../src/mat/impls/aij/mpi/mpiaij.h> 2 /*@C 3 MatCreateMPIAIJSELL - Creates a sparse parallel matrix whose local 4 portions are stored as `MATSEQAIJSELL` matrices (a matrix class that inherits 5 from SEQAIJ but performs some operations in SELL format). 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 Notes: 41 If the *_nnz parameter is given then the *_nz parameter is ignored 42 43 m,n,M,N parameters specify the size of the matrix, and its partitioning across 44 processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate 45 storage requirements for this matrix. 46 47 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one 48 processor than it must be used on all processors that share the object for 49 that argument. 50 51 The user MUST specify either the local or global matrix dimensions 52 (possibly both). 53 54 The parallel matrix is partitioned such that the first m0 rows belong to 55 process 0, the next m1 rows belong to process 1, the next m2 rows belong 56 to process 2 etc.. where m0,m1,m2... are the input parameter 'm'. 57 58 The DIAGONAL portion of the local submatrix of a processor can be defined 59 as the submatrix which is obtained by extraction the part corresponding 60 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the 61 first row that belongs to the processor, and r2 is the last row belonging 62 to the this processor. This is a square mxm matrix. The remaining portion 63 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion. 64 65 If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored. 66 67 When calling this routine with a single process communicator, a matrix of 68 type `MATSEQAIJSELL` is returned. If a matrix of type `MATMPIAIJSELL` is desired 69 for this type of communicator, use the construction mechanism: 70 `MatCreate`(...,&A); `MatSetType`(A,MPIAIJSELL); `MatMPIAIJSetPreallocation`(A,...); 71 72 Options Database Keys: 73 . -mat_aijsell_eager_shadow - Construct shadow matrix upon matrix assembly; default is to take a "lazy" approach, performing this step the first time the matrix is applied 74 75 Level: intermediate 76 77 .seealso: [Sparse Matrix Creation](sec_matsparse), `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATAIJSELL`, `MatCreate()`, `MatCreateSeqAIJSELL()`, `MatSetValues()` 78 @*/ 79 PetscErrorCode MatCreateMPIAIJSELL(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, MATMPIAIJSELL)); 89 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz)); 90 } else { 91 PetscCall(MatSetType(*A, MATSEQAIJSELL)); 92 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz)); 93 } 94 PetscFunctionReturn(0); 95 } 96 97 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJSELL(Mat, MatType, MatReuse, Mat *); 98 99 PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJSELL(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_SeqAIJSELL(b->A, MATSEQAIJSELL, MAT_INPLACE_MATRIX, &b->A)); 106 PetscCall(MatConvert_SeqAIJ_SeqAIJSELL(b->B, MATSEQAIJSELL, MAT_INPLACE_MATRIX, &b->B)); 107 PetscFunctionReturn(0); 108 } 109 110 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(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)); 116 117 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJSELL)); 118 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJSELL)); 119 *newmat = B; 120 PetscFunctionReturn(0); 121 } 122 123 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJSELL(Mat A) 124 { 125 PetscFunctionBegin; 126 PetscCall(MatSetType(A, MATMPIAIJ)); 127 PetscCall(MatConvert_MPIAIJ_MPIAIJSELL(A, MATMPIAIJSELL, MAT_INPLACE_MATRIX, &A)); 128 PetscFunctionReturn(0); 129 } 130 131 /*MC 132 MATAIJSELL - MATAIJSELL = "AIJSELL" - A matrix type to be used for sparse matrices. 133 134 This matrix type is identical to `MATSEQAIJSELL` when constructed with a single process communicator, 135 and `MATMPIAIJSELL` 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 aijsell - sets the matrix type to `MATAIJSELL` during a call to `MatSetFromOptions()` 142 143 Level: beginner 144 145 .seealso: `MatCreateMPIAIJSELL()`, `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJMKL`, `MATMPIAIJMKL` 146 M*/ 147