Actual source code: mpiaijviennacl.cxx
petsc-3.10.5 2019-03-28
1: #include <petscconf.h>
2: #include <../src/mat/impls/aij/mpi/mpiaij.h>
3: #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>
5: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJViennaCL(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
6: {
7: Mat_MPIAIJ *b = (Mat_MPIAIJ*)B->data;
11: PetscLayoutSetUp(B->rmap);
12: PetscLayoutSetUp(B->cmap);
13: if (!B->preallocated) {
14: /* Explicitly create the two MATSEQAIJVIENNACL matrices. */
15: MatCreate(PETSC_COMM_SELF,&b->A);
16: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
17: MatSetType(b->A,MATSEQAIJVIENNACL);
18: PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
19: MatCreate(PETSC_COMM_SELF,&b->B);
20: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
21: MatSetType(b->B,MATSEQAIJVIENNACL);
22: PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
23: }
24: MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
25: MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
26: B->preallocated = PETSC_TRUE;
27: return(0);
28: }
30: PetscErrorCode MatAssemblyEnd_MPIAIJViennaCL(Mat A,MatAssemblyType mode)
31: {
32: Mat_MPIAIJ *b = (Mat_MPIAIJ*)A->data;
34: PetscBool v;
37: MatAssemblyEnd_MPIAIJ(A,mode);
38: PetscObjectTypeCompare((PetscObject)b->lvec,VECSEQVIENNACL,&v);
39: if (!v) {
40: PetscInt m;
41: VecGetSize(b->lvec,&m);
42: VecDestroy(&b->lvec);
43: VecCreateSeqViennaCL(PETSC_COMM_SELF,m,&b->lvec);
44: }
45: return(0);
46: }
48: PetscErrorCode MatDestroy_MPIAIJViennaCL(Mat A)
49: {
53: MatDestroy_MPIAIJ(A);
54: return(0);
55: }
57: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJViennaCL(Mat A)
58: {
62: MatCreate_MPIAIJ(A);
63: PetscFree(A->defaultvectype);
64: PetscStrallocpy(VECVIENNACL,&A->defaultvectype);
65: PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJViennaCL);
66: A->ops->assemblyend = MatAssemblyEnd_MPIAIJViennaCL;
67: PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJVIENNACL);
68: return(0);
69: }
72: /*@
73: MatCreateAIJViennaCL - Creates a sparse matrix in AIJ (compressed row) format
74: (the default parallel PETSc format). This matrix will ultimately be pushed down
75: to GPUs and use the ViennaCL library for calculations. For good matrix
76: assembly performance the user should preallocate the matrix storage by setting
77: the parameter nz (or the array nnz). By setting these parameters accurately,
78: performance during matrix assembly can be increased substantially.
81: Collective on MPI_Comm
83: Input Parameters:
84: + comm - MPI communicator, set to PETSC_COMM_SELF
85: . m - number of rows
86: . n - number of columns
87: . nz - number of nonzeros per row (same for all rows)
88: - nnz - array containing the number of nonzeros in the various rows
89: (possibly different for each row) or NULL
91: Output Parameter:
92: . A - the matrix
94: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
95: MatXXXXSetPreallocation() paradigm instead of this routine directly.
96: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
98: Notes:
99: If nnz is given then nz is ignored
101: The AIJ format (also called the Yale sparse matrix format or
102: compressed row storage), is fully compatible with standard Fortran 77
103: storage. That is, the stored row and column indices can begin at
104: either one (as in Fortran) or zero. See the users' manual for details.
106: Specify the preallocated storage with either nz or nnz (not both).
107: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
108: allocation. For large problems you MUST preallocate memory or you
109: will get TERRIBLE performance, see the users' manual chapter on matrices.
111: Level: intermediate
113: .seealso: MatCreate(), MatCreateAIJ(), MatCreateAIJCUSPARSE(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJVIENNACL, MATAIJVIENNACL
114: @*/
115: PetscErrorCode MatCreateAIJViennaCL(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)
116: {
118: PetscMPIInt size;
121: MatCreate(comm,A);
122: MatSetSizes(*A,m,n,M,N);
123: MPI_Comm_size(comm,&size);
124: if (size > 1) {
125: MatSetType(*A,MATMPIAIJVIENNACL);
126: MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
127: } else {
128: MatSetType(*A,MATSEQAIJVIENNACL);
129: MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
130: }
131: return(0);
132: }
134: /*MC
135: MATAIJVIENNACL - MATMPIAIJVIENNACL= "aijviennacl" = "mpiaijviennacl" - A matrix type to be used for sparse matrices.
137: A matrix type (CSR format) whose data resides on GPUs.
138: All matrix calculations are performed using the ViennaCL library.
140: This matrix type is identical to MATSEQAIJVIENNACL when constructed with a single process communicator,
141: and MATMPIAIJVIENNACL 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.
146: Options Database Keys:
147: + -mat_type mpiaijviennacl - sets the matrix type to "mpiaijviennacl" during a call to MatSetFromOptions()
149: Level: beginner
151: .seealso: MatCreateAIJViennaCL(), MATSEQAIJVIENNACL, MatCreateSeqAIJVIENNACL()
152: M*/