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).
6
7 Collective
8
9 Input Parameters:
10 + comm - MPI communicator
11 . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
12 This value should be the same as the local size used in creating the
13 y vector for the matrix-vector product y = Ax.
14 . n - This value should be the same as the local size used in creating the
15 x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
16 calculated if `N` is given) For square matrices `n` is almost always `m`.
17 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
18 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
19 . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
20 (same value is used for all local rows)
21 . d_nnz - array containing the number of nonzeros in the various rows of the
22 DIAGONAL portion of the local submatrix (possibly different for each row)
23 or `NULL`, if `d_nz` is used to specify the nonzero structure.
24 The size of this array is equal to the number of local rows, i.e `m`.
25 For matrices you plan to factor you must leave room for the diagonal entry and
26 put in the entry even if it is zero.
27 . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
28 submatrix (same value is used for all local rows).
29 - o_nnz - array containing the number of nonzeros in the various rows of the
30 OFF-DIAGONAL portion of the local submatrix (possibly different for
31 each row) or `NULL`, if `o_nz` is used to specify the nonzero
32 structure. The size of this array is equal to the number
33 of local rows, i.e `m`.
34
35 Output Parameter:
36 . A - the matrix
37
38 Options Database Key:
39 . -mat_aijsell_eager_shadow - Construct shadow matrix upon matrix assembly; default is to take a "lazy" approach, performing this step the first
40 time the matrix is applied
41
42 Level: intermediate
43
44 Notes:
45 If the *_nnz parameter is given then the *_nz parameter is ignored
46
47 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
48 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
49 storage requirements for this matrix.
50
51 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
52 processor than it must be used on all processors that share the object for
53 that argument.
54
55 The user MUST specify either the local or global matrix dimensions
56 (possibly both).
57
58 The parallel matrix is partitioned such that the first m0 rows belong to
59 process 0, the next m1 rows belong to process 1, the next m2 rows belong
60 to process 2 etc.. where m0,m1,m2... are the input parameter `m`.
61
62 The DIAGONAL portion of the local submatrix of a processor can be defined
63 as the submatrix which is obtained by extraction the part corresponding
64 to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the
65 first row that belongs to the processor, and r2 is the last row belonging
66 to the this processor. This is a square mxm matrix. The remaining portion
67 of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.
68
69 If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
70
71 When calling this routine with a single process communicator, a matrix of
72 type `MATSEQAIJSELL` is returned. If a matrix of type `MATMPIAIJSELL` is desired
73 for this type of communicator, use the construction mechanism
74 .vb
75 MatCreate(...,&A);
76 MatSetType(A,MPIAIJSELL);
77 MatMPIAIJSetPreallocation(A,...);
78 .ve
79
80 .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATAIJSELL`, `MatCreate()`, `MatCreateSeqAIJSELL()`, `MatSetValues()`
81 @*/
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)82 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)
83 {
84 PetscMPIInt size;
85
86 PetscFunctionBegin;
87 PetscCall(MatCreate(comm, A));
88 PetscCall(MatSetSizes(*A, m, n, M, N));
89 PetscCallMPI(MPI_Comm_size(comm, &size));
90 if (size > 1) {
91 PetscCall(MatSetType(*A, MATMPIAIJSELL));
92 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
93 } else {
94 PetscCall(MatSetType(*A, MATSEQAIJSELL));
95 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
96 }
97 PetscFunctionReturn(PETSC_SUCCESS);
98 }
99
100 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJSELL(Mat, MatType, MatReuse, Mat *);
101
MatMPIAIJSetPreallocation_MPIAIJSELL(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])102 static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJSELL(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
103 {
104 Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
105
106 PetscFunctionBegin;
107 PetscCall(MatMPIAIJSetPreallocation_MPIAIJ(B, d_nz, d_nnz, o_nz, o_nnz));
108 PetscCall(MatConvert_SeqAIJ_SeqAIJSELL(b->A, MATSEQAIJSELL, MAT_INPLACE_MATRIX, &b->A));
109 PetscCall(MatConvert_SeqAIJ_SeqAIJSELL(b->B, MATSEQAIJSELL, MAT_INPLACE_MATRIX, &b->B));
110 PetscFunctionReturn(PETSC_SUCCESS);
111 }
112
MatConvert_MPIAIJ_MPIAIJSELL(Mat A,MatType type,MatReuse reuse,Mat * newmat)113 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
114 {
115 Mat B = *newmat;
116
117 PetscFunctionBegin;
118 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
119
120 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJSELL));
121 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJSELL));
122 *newmat = B;
123 PetscFunctionReturn(PETSC_SUCCESS);
124 }
125
MatCreate_MPIAIJSELL(Mat A)126 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJSELL(Mat A)
127 {
128 PetscFunctionBegin;
129 PetscCall(MatSetType(A, MATMPIAIJ));
130 PetscCall(MatConvert_MPIAIJ_MPIAIJSELL(A, MATMPIAIJSELL, MAT_INPLACE_MATRIX, &A));
131 PetscFunctionReturn(PETSC_SUCCESS);
132 }
133
134 /*MC
135 MATAIJSELL - "AIJSELL" - A matrix type to be used for sparse matrices.
136
137 This matrix type is identical to `MATSEQAIJSELL` when constructed with a single process communicator,
138 and `MATMPIAIJSELL` otherwise. As a result, for single process communicators,
139 MatSeqAIJSetPreallocation() is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
140 for communicators controlling multiple processes. It is recommended that you call both of
141 the above preallocation routines for simplicity.
142
143 Options Database Key:
144 . -mat_type aijsell - sets the matrix type to `MATAIJSELL`
145
146 Level: beginner
147
148 .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJSELL()`, `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQAIJPERM`, `MATMPIAIJPERM`, `MATSEQAIJMKL`, `MATMPIAIJMKL`
149 M*/
150