1 #include <../src/mat/impls/baij/mpi/mpibaij.h> /*I "petscmat.h" I*/
2
3 PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);
4
MatMPIBAIJSetPreallocation_MPIBAIJMKL(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt * d_nnz,PetscInt o_nz,const PetscInt * o_nnz)5 static PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJMKL(Mat B, PetscInt bs, PetscInt d_nz, const PetscInt *d_nnz, PetscInt o_nz, const PetscInt *o_nnz)
6 {
7 Mat_MPIBAIJ *b = (Mat_MPIBAIJ *)B->data;
8
9 PetscFunctionBegin;
10 PetscCall(MatMPIBAIJSetPreallocation_MPIBAIJ(B, bs, d_nz, d_nnz, o_nz, o_nnz));
11 PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(b->A, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &b->A));
12 PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(b->B, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &b->B));
13 PetscFunctionReturn(PETSC_SUCCESS);
14 }
15
MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A,MatType type,MatReuse reuse,Mat * newmat)16 static PetscErrorCode MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
17 {
18 Mat B = *newmat;
19
20 PetscFunctionBegin;
21 if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
22
23 PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJMKL));
24 PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJMKL));
25 *newmat = B;
26 PetscFunctionReturn(PETSC_SUCCESS);
27 }
28
29 // PetscClangLinter pragma disable: -fdoc-section-header-unknown
30 /*@
31 MatCreateBAIJMKL - Creates a sparse parallel matrix in `MATBAIJMKL` format (block compressed row).
32
33 Collective
34
35 Input Parameters:
36 + comm - MPI communicator
37 . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
38 blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
39 . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
40 This value should be the same as the local size used in creating the
41 y vector for the matrix-vector product y = Ax.
42 . n - number of local columns (or `PETSC_DECIDE` to have calculated if `N` is given)
43 This value should be the same as the local size used in creating the
44 x vector for the matrix-vector product y = Ax.
45 . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
46 . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
47 . d_nz - number of nonzero blocks per block row in diagonal portion of local
48 submatrix (same for all local rows)
49 . d_nnz - array containing the number of nonzero blocks in the various block rows
50 of the in diagonal portion of the local (possibly different for each block
51 row) or `NULL`. If you plan to factor the matrix you must leave room for the diagonal entry
52 and set it even if it is zero.
53 . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
54 submatrix (same for all local rows).
55 - o_nnz - array containing the number of nonzero blocks in the various block rows of the
56 off-diagonal portion of the local submatrix (possibly different for
57 each block row) or `NULL`.
58
59 Output Parameter:
60 . A - the matrix
61
62 Options Database Keys:
63 + -mat_block_size - size of the blocks to use
64 - -mat_use_hash_table <fact> - set hash table factor
65
66 Level: intermediate
67
68 Notes:
69 It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
70 MatXXXXSetPreallocation() paradigm instead of this routine directly.
71 [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
72
73 This type inherits from `MATBAIJ` and is largely identical, but uses sparse BLAS
74 routines from Intel MKL whenever possible.
75 `MatMult()`, `MatMultAdd()`, `MatMultTranspose()`, and `MatMultTransposeAdd()`
76 operations are currently supported.
77 If the installed version of MKL supports the "SpMV2" sparse
78 inspector-executor routines, then those are used by default.
79 Default PETSc kernels are used otherwise.
80 For good matrix assembly performance the user should preallocate the matrix
81 storage by setting the parameters `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
82 By setting these parameters accurately, performance can be increased by more
83 than a factor of 50.
84
85 If the *_nnz parameter is given then the *_nz parameter is ignored
86
87 A nonzero block is any block that as 1 or more nonzeros in it
88
89 The user MUST specify either the local or global matrix dimensions
90 (possibly both).
91
92 If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one processor
93 than it must be used on all processors that share the object for that argument.
94
95 Storage Information:
96 For a square global matrix we define each processor's diagonal portion
97 to be its local rows and the corresponding columns (a square submatrix);
98 each processor's off-diagonal portion encompasses the remainder of the
99 local matrix (a rectangular submatrix).
100
101 The user can specify preallocated storage for the diagonal part of
102 the local submatrix with either `d_nz` or `d_nnz` (not both). Set
103 `d_nz` = `PETSC_DEFAULT` and `d_nnz` = `NULL` for PETSc to control dynamic
104 memory allocation. Likewise, specify preallocated storage for the
105 off-diagonal part of the local submatrix with `o_nz` or `o_nnz` (not both).
106
107 Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
108 the figure below we depict these three local rows and all columns (0-11).
109
110 .vb
111 0 1 2 3 4 5 6 7 8 9 10 11
112 --------------------------
113 row 3 |o o o d d d o o o o o o
114 row 4 |o o o d d d o o o o o o
115 row 5 |o o o d d d o o o o o o
116 --------------------------
117 .ve
118
119 Thus, any entries in the d locations are stored in the d (diagonal)
120 submatrix, and any entries in the o locations are stored in the
121 o (off-diagonal) submatrix. Note that the d and the o submatrices are
122 stored simply in the `MATSEQBAIJMKL` format for compressed row storage.
123
124 Now `d_nz` should indicate the number of block nonzeros per row in the d matrix,
125 and `o_nz` should indicate the number of block nonzeros per row in the o matrix.
126 In general, for PDE problems in which most nonzeros are near the diagonal,
127 one expects `d_nz` >> `o_nz`.
128
129 .seealso: [](ch_matrices), `Mat`, `MATBAIJMKL`, `MATBAIJ`, `MatCreate()`, `MatCreateSeqBAIJMKL()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
130 @*/
MatCreateBAIJMKL(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat * A)131 PetscErrorCode MatCreateBAIJMKL(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
132 {
133 PetscMPIInt size;
134
135 PetscFunctionBegin;
136 PetscCall(MatCreate(comm, A));
137 PetscCall(MatSetSizes(*A, m, n, M, N));
138 PetscCallMPI(MPI_Comm_size(comm, &size));
139 if (size > 1) {
140 PetscCall(MatSetType(*A, MATMPIBAIJMKL));
141 PetscCall(MatMPIBAIJSetPreallocation(*A, bs, d_nz, d_nnz, o_nz, o_nnz));
142 } else {
143 PetscCall(MatSetType(*A, MATSEQBAIJMKL));
144 PetscCall(MatSeqBAIJSetPreallocation(*A, bs, d_nz, d_nnz));
145 }
146 PetscFunctionReturn(PETSC_SUCCESS);
147 }
148
MatCreate_MPIBAIJMKL(Mat A)149 PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJMKL(Mat A)
150 {
151 PetscFunctionBegin;
152 PetscCall(MatSetType(A, MATMPIBAIJ));
153 PetscCall(MatConvert_MPIBAIJ_MPIBAIJMKL(A, MATMPIBAIJMKL, MAT_INPLACE_MATRIX, &A));
154 PetscFunctionReturn(PETSC_SUCCESS);
155 }
156
157 /*MC
158 MATBAIJMKL - MATBAIJMKL = "BAIJMKL" - A matrix type to be used for sparse matrices.
159
160 This matrix type is identical to `MATSEQBAIJMKL` when constructed with a single process communicator,
161 and `MATMPIBAIJMKL` otherwise. As a result, for single process communicators,
162 `MatSeqBAIJSetPreallocation()` is supported, and similarly `MatMPIBAIJSetPreallocation()` is supported
163 for communicators controlling multiple processes. It is recommended that you call both of
164 the above preallocation routines for simplicity.
165
166 Options Database Key:
167 . -mat_type baijmkl - sets the matrix type to `MATBAIJMKL` during a call to `MatSetFromOptions()`
168
169 Level: beginner
170
171 .seealso: [](ch_matrices), `Mat`, `MatCreateBAIJMKL()`, `MATSEQBAIJMKL`, `MATMPIBAIJMKL`
172 M*/
173