Actual source code: rowscalingviennacl.cxx
1: /*
2: Include files needed for the ViennaCL row-scaling preconditioner:
3: pcimpl.h - private include file intended for use by all preconditioners
4: */
5: #define PETSC_SKIP_IMMINTRIN_H_CUDAWORKAROUND 1
7: #include <petsc/private/pcimpl.h>
8: #include <../src/mat/impls/aij/seq/aij.h>
9: #include <../src/vec/vec/impls/dvecimpl.h>
10: #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>
11: #include <../src/vec/vec/impls/seq/seqviennacl/viennaclvecimpl.h>
12: #include <viennacl/linalg/row_scaling.hpp>
14: /*
15: Private context (data structure) for the ROWSCALINGVIENNACL preconditioner.
16: */
17: typedef struct {
18: viennacl::linalg::row_scaling<viennacl::compressed_matrix<PetscScalar>> *ROWSCALINGVIENNACL;
19: } PC_ROWSCALINGVIENNACL;
21: /*
22: PCSetUp_ROWSCALINGVIENNACL - Prepares for the use of the ROWSCALINGVIENNACL preconditioner
23: by setting data structures and options.
25: Input Parameter:
26: . pc - the preconditioner context
28: Application Interface Routine: PCSetUp()
30: Note:
31: The interface routine PCSetUp() is not usually called directly by
32: the user, but instead is called by PCApply() if necessary.
33: */
34: static PetscErrorCode PCSetUp_ROWSCALINGVIENNACL(PC pc)
35: {
36: PC_ROWSCALINGVIENNACL *rowscaling = (PC_ROWSCALINGVIENNACL *)pc->data;
37: PetscBool flg = PETSC_FALSE;
38: Mat_SeqAIJViennaCL *gpustruct;
40: PetscFunctionBegin;
41: PetscCall(PetscObjectTypeCompare((PetscObject)pc->pmat, MATSEQAIJVIENNACL, &flg));
42: PetscCheck(flg, PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Currently only handles ViennaCL matrices");
43: if (pc->setupcalled != 0) {
44: try {
45: delete rowscaling->ROWSCALINGVIENNACL;
46: } catch (char *ex) {
47: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex);
48: }
49: }
50: try {
51: #if defined(PETSC_USE_COMPLEX)
52: gpustruct = NULL;
53: SETERRQ(PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "No support for complex arithmetic in ROWSCALINGVIENNACL preconditioner");
54: #else
55: PetscCall(MatViennaCLCopyToGPU(pc->pmat));
56: gpustruct = (Mat_SeqAIJViennaCL *)(pc->pmat->spptr);
58: viennacl::linalg::row_scaling_tag pc_tag(1);
59: ViennaCLAIJMatrix *mat = (ViennaCLAIJMatrix *)gpustruct->mat;
60: rowscaling->ROWSCALINGVIENNACL = new viennacl::linalg::row_scaling<viennacl::compressed_matrix<PetscScalar>>(*mat, pc_tag);
61: #endif
62: } catch (char *ex) {
63: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex);
64: }
65: PetscFunctionReturn(PETSC_SUCCESS);
66: }
68: /*
69: PCApply_ROWSCALINGVIENNACL - Applies the ROWSCALINGVIENNACL preconditioner to a vector.
71: Input Parameters:
72: . pc - the preconditioner context
73: . x - input vector
75: Output Parameter:
76: . y - output vector
78: Application Interface Routine: PCApply()
79: */
80: static PetscErrorCode PCApply_ROWSCALINGVIENNACL(PC pc, Vec x, Vec y)
81: {
82: PC_ROWSCALINGVIENNACL *ilu = (PC_ROWSCALINGVIENNACL *)pc->data;
83: PetscBool flg1, flg2;
84: viennacl::vector<PetscScalar> const *xarray = NULL;
85: viennacl::vector<PetscScalar> *yarray = NULL;
87: PetscFunctionBegin;
88: /*how to apply a certain fixed number of iterations?*/
89: PetscCall(PetscObjectTypeCompare((PetscObject)x, VECSEQVIENNACL, &flg1));
90: PetscCall(PetscObjectTypeCompare((PetscObject)y, VECSEQVIENNACL, &flg2));
91: PetscCheck((flg1 && flg2), PetscObjectComm((PetscObject)pc), PETSC_ERR_SUP, "Currently only handles ViennaCL vectors");
92: if (!ilu->ROWSCALINGVIENNACL) PetscCall(PCSetUp_ROWSCALINGVIENNACL(pc));
93: PetscCall(VecSet(y, 0.0));
94: PetscCall(VecViennaCLGetArrayRead(x, &xarray));
95: PetscCall(VecViennaCLGetArrayWrite(y, &yarray));
96: try {
97: #if defined(PETSC_USE_COMPLEX)
99: #else
100: *yarray = *xarray;
101: ilu->ROWSCALINGVIENNACL->apply(*yarray);
102: #endif
103: } catch (char *ex) {
104: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex);
105: }
106: PetscCall(VecViennaCLRestoreArrayRead(x, &xarray));
107: PetscCall(VecViennaCLRestoreArrayWrite(y, &yarray));
108: PetscCall(PetscObjectStateIncrease((PetscObject)y));
109: PetscFunctionReturn(PETSC_SUCCESS);
110: }
112: /*
113: PCDestroy_ROWSCALINGVIENNACL - Destroys the private context for the ROWSCALINGVIENNACL preconditioner
114: that was created with PCCreate_ROWSCALINGVIENNACL().
116: Input Parameter:
117: . pc - the preconditioner context
119: Application Interface Routine: PCDestroy()
120: */
121: static PetscErrorCode PCDestroy_ROWSCALINGVIENNACL(PC pc)
122: {
123: PC_ROWSCALINGVIENNACL *rowscaling = (PC_ROWSCALINGVIENNACL *)pc->data;
125: PetscFunctionBegin;
126: if (rowscaling->ROWSCALINGVIENNACL) {
127: try {
128: delete rowscaling->ROWSCALINGVIENNACL;
129: } catch (char *ex) {
130: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex);
131: }
132: }
134: /*
135: Free the private data structure that was hanging off the PC
136: */
137: PetscCall(PetscFree(pc->data));
138: PetscFunctionReturn(PETSC_SUCCESS);
139: }
141: static PetscErrorCode PCSetFromOptions_ROWSCALINGVIENNACL(PC pc, PetscOptionItems *PetscOptionsObject)
142: {
143: PetscFunctionBegin;
144: PetscOptionsHeadBegin(PetscOptionsObject, "ROWSCALINGVIENNACL options");
145: PetscOptionsHeadEnd();
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
149: /*MC
150: PCROWSCALINGVIENNACLL - A diagonal preconditioner (scaling rows of matrices by their norm) that can be used via the CUDA,
151: OpenCL, and OpenMP backends of ViennaCL
153: Level: advanced
155: .seealso: [](ch_ksp), `PCCreate()`, `PCSetType()`, `PCType`, `PC`
156: M*/
157: PETSC_EXTERN PetscErrorCode PCCreate_ROWSCALINGVIENNACL(PC pc)
158: {
159: PC_ROWSCALINGVIENNACL *rowscaling;
161: PetscFunctionBegin;
162: /*
163: Creates the private data structure for this preconditioner and
164: attach it to the PC object.
165: */
166: PetscCall(PetscNew(&rowscaling));
167: pc->data = (void *)rowscaling;
169: /*
170: Initialize the pointer to zero
171: Initialize number of v-cycles to default (1)
172: */
173: rowscaling->ROWSCALINGVIENNACL = 0;
175: /*
176: Set the pointers for the functions that are provided above.
177: Now when the user-level routines (such as PCApply(), PCDestroy(), etc.)
178: are called, they will automatically call these functions. Note we
179: choose not to provide a couple of these functions since they are
180: not needed.
181: */
182: pc->ops->apply = PCApply_ROWSCALINGVIENNACL;
183: pc->ops->applytranspose = 0;
184: pc->ops->setup = PCSetUp_ROWSCALINGVIENNACL;
185: pc->ops->destroy = PCDestroy_ROWSCALINGVIENNACL;
186: pc->ops->setfromoptions = PCSetFromOptions_ROWSCALINGVIENNACL;
187: pc->ops->view = 0;
188: pc->ops->applyrichardson = 0;
189: pc->ops->applysymmetricleft = 0;
190: pc->ops->applysymmetricright = 0;
191: PetscFunctionReturn(PETSC_SUCCESS);
192: }