1 #include <../src/mat/impls/aij/mpi/mpiaij.h>
2 /*@C
3 MatCreateMPIAIJMKL - Creates a sparse parallel matrix whose local
4 portions are stored as `MATSEQAIJMKL` matrices (a matrix class that inherits
5 from `MATSEQAIJ` but uses some operations provided by Intel MKL).
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_aijmkl_no_spmv2 - disables use of the SpMV2 inspector-executor routines
40
41 Level: intermediate
42
43 Notes:
44 If the *_nnz parameter is given then the *_nz parameter is ignored
45
46 `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
47 processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
48 storage requirements for this matrix.
49
50 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
51 processor than it must be used on all processors that share the object for
52 that argument.
53
54 The user MUST specify either the local or global matrix dimensions
55 (possibly both).
56
57 If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
58 `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.
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` on each MPI process.
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 `MATSEQAIJMKL` is returned. If a matrix of type `MATMPIAIJMKL` is desired
75 for this type of communicator, use the construction mechanism
76 .vb
77 MatCreate(...,&A);
78 MatSetType(A,MPIAIJMKL);
79 MatMPIAIJSetPreallocation(A,...);
80 .ve
81
82 .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MATMPIAIJMKL`, `MatCreate()`, `MatCreateSeqAIJMKL()`,
83 `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
84 `MatGetOwnershipRangesColumn()`, `PetscLayout`
85 @*/
MatCreateMPIAIJMKL(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 PetscErrorCode MatCreateMPIAIJMKL(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 {
88 PetscMPIInt size;
89
90 PetscFunctionBegin;
91 PetscCall(MatCreate(comm, A));
92 PetscCall(MatSetSizes(*A, m, n, M, N));
93 PetscCallMPI(MPI_Comm_size(comm, &size));
94 if (size > 1) {
95 PetscCall(MatSetType(*A, MATMPIAIJMKL));
96 PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
97 } else {
98 PetscCall(MatSetType(*A, MATSEQAIJMKL));
99 PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
100 }
101 PetscFunctionReturn(PETSC_SUCCESS);
102 }
103
104 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *);
105
MatMPIAIJSetPreallocation_MPIAIJMKL(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])106 static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJMKL(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_SeqAIJMKL(b->A, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->A));
113 PetscCall(MatConvert_SeqAIJ_SeqAIJMKL(b->B, MATSEQAIJMKL, MAT_INPLACE_MATRIX, &b->B));
114 PetscFunctionReturn(PETSC_SUCCESS);
115 }
116
MatConvert_MPIAIJ_MPIAIJMKL(Mat A,MatType type,MatReuse reuse,Mat * newmat)117 PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(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 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJMKL));
124 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJMKL));
125 *newmat = B;
126 PetscFunctionReturn(PETSC_SUCCESS);
127 }
128
MatCreate_MPIAIJMKL(Mat A)129 PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJMKL(Mat A)
130 {
131 PetscFunctionBegin;
132 PetscCall(MatSetType(A, MATMPIAIJ));
133 PetscCall(MatConvert_MPIAIJ_MPIAIJMKL(A, MATMPIAIJMKL, MAT_INPLACE_MATRIX, &A));
134 PetscFunctionReturn(PETSC_SUCCESS);
135 }
136
137 /*MC
138 MATAIJMKL - MATAIJMKL = "AIJMKL" - A matrix type to be used for sparse matrices.
139
140 This matrix type is identical to `MATSEQAIJMKL` when constructed with a single process communicator,
141 and `MATMPIAIJMKL` 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 aijmkl - sets the matrix type to `MATAIJMKL` during a call to `MatSetFromOptions()`
148
149 Level: beginner
150
151 .seealso: [](ch_matrices), `Mat`, `MATMPIAIJMKL`, `MATSEQAIJMKL`, `MatCreateMPIAIJMKL()`, `MATSEQAIJMKL`, `MATMPIAIJMKL`, `MATSEQAIJSELL`, `MATMPIAIJSELL`, `MATSEQAIJPERM`, `MATMPIAIJPERM`
152 M*/
153