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