Actual source code: mpibaijmkl.c
1: #include <../src/mat/impls/baij/mpi/mpibaij.h>
3: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);
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;
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: }
16: static PetscErrorCode MatConvert_MPIBAIJ_MPIBAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
17: {
18: Mat B = *newmat;
20: PetscFunctionBegin;
21: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
23: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIBAIJMKL));
24: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIBAIJSetPreallocation_C", MatMPIBAIJSetPreallocation_MPIBAIJMKL));
25: *newmat = B;
26: PetscFunctionReturn(PETSC_SUCCESS);
27: }
29: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
30: /*@C
31: MatCreateBAIJMKL - Creates a sparse parallel matrix in `MATBAIJMKL` format (block compressed row).
33: Collective
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`.
59: Output Parameter:
60: . A - the matrix
62: Options Database Keys:
63: + -mat_block_size - size of the blocks to use
64: - -mat_use_hash_table <fact> - set hash table factor
66: Level: intermediate
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()`]
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.
85: If the *_nnz parameter is given then the *_nz parameter is ignored
87: A nonzero block is any block that as 1 or more nonzeros in it
89: The user MUST specify either the local or global matrix dimensions
90: (possibly both).
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.
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).
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).
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).
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
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.
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`.
129: .seealso: [](ch_matrices), `Mat`, `MATBAIJMKL`, `MATBAIJ`, `MatCreate()`, `MatCreateSeqBAIJMKL()`, `MatSetValues()`, `MatMPIBAIJSetPreallocation()`, `MatMPIBAIJSetPreallocationCSR()`
130: @*/
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;
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: }
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: }
157: /*MC
158: MATBAIJMKL - MATBAIJMKL = "BAIJMKL" - A matrix type to be used for sparse matrices.
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.
166: Options Database Key:
167: . -mat_type baijmkl - sets the matrix type to `MATBAIJMKL` during a call to `MatSetFromOptions()`
169: Level: beginner
171: .seealso: [](ch_matrices), `Mat`, `MatCreateBAIJMKL()`, `MATSEQBAIJMKL`, `MATMPIBAIJMKL`
172: M*/