Actual source code: aijviennacl.cxx
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
6: #include <petscconf.h>
7: #define PETSC_SKIP_IMMINTRIN_H_CUDAWORKAROUND 1
8: #include <../src/mat/impls/aij/seq/aij.h>
9: #include <petscbt.h>
10: #include <../src/vec/vec/impls/dvecimpl.h>
11: #include <petsc/private/vecimpl.h>
13: #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>
15: #include <algorithm>
16: #include <vector>
17: #include <string>
19: #include "viennacl/linalg/prod.hpp"
21: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat A, MatType type, MatReuse reuse, Mat *newmat);
22: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat, MatFactorType, Mat *);
23: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat);
25: PetscErrorCode MatViennaCLCopyToGPU(Mat A)
26: {
27: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
28: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
30: PetscFunctionBegin;
31: if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) { //some OpenCL SDKs have issues with buffers of size 0
32: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
33: PetscCall(PetscLogEventBegin(MAT_ViennaCLCopyToGPU, A, 0, 0, 0));
35: try {
36: if (a->compressedrow.use) {
37: if (!viennaclstruct->compressed_mat) viennaclstruct->compressed_mat = new ViennaCLCompressedAIJMatrix();
39: // Since PetscInt is different from cl_uint, we have to convert:
40: viennacl::backend::mem_handle dummy;
42: viennacl::backend::typesafe_host_array<unsigned int> row_buffer;
43: row_buffer.raw_resize(dummy, a->compressedrow.nrows + 1);
44: for (PetscInt i = 0; i <= a->compressedrow.nrows; ++i) row_buffer.set(i, (a->compressedrow.i)[i]);
46: viennacl::backend::typesafe_host_array<unsigned int> row_indices;
47: row_indices.raw_resize(dummy, a->compressedrow.nrows);
48: for (PetscInt i = 0; i < a->compressedrow.nrows; ++i) row_indices.set(i, (a->compressedrow.rindex)[i]);
50: viennacl::backend::typesafe_host_array<unsigned int> col_buffer;
51: col_buffer.raw_resize(dummy, a->nz);
52: for (PetscInt i = 0; i < a->nz; ++i) col_buffer.set(i, (a->j)[i]);
54: viennaclstruct->compressed_mat->set(row_buffer.get(), row_indices.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->compressedrow.nrows, a->nz);
55: PetscCall(PetscLogCpuToGpu(((2 * a->compressedrow.nrows) + 1 + a->nz) * sizeof(PetscInt) + (a->nz) * sizeof(PetscScalar)));
56: } else {
57: if (!viennaclstruct->mat) viennaclstruct->mat = new ViennaCLAIJMatrix();
59: // Since PetscInt is in general different from cl_uint, we have to convert:
60: viennacl::backend::mem_handle dummy;
62: viennacl::backend::typesafe_host_array<unsigned int> row_buffer;
63: row_buffer.raw_resize(dummy, A->rmap->n + 1);
64: for (PetscInt i = 0; i <= A->rmap->n; ++i) row_buffer.set(i, (a->i)[i]);
66: viennacl::backend::typesafe_host_array<unsigned int> col_buffer;
67: col_buffer.raw_resize(dummy, a->nz);
68: for (PetscInt i = 0; i < a->nz; ++i) col_buffer.set(i, (a->j)[i]);
70: viennaclstruct->mat->set(row_buffer.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->nz);
71: PetscCall(PetscLogCpuToGpu(((A->rmap->n + 1) + a->nz) * sizeof(PetscInt) + (a->nz) * sizeof(PetscScalar)));
72: }
73: ViennaCLWaitForGPU();
74: } catch (std::exception const &ex) {
75: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
76: }
78: // Create temporary vector for v += A*x:
79: if (viennaclstruct->tempvec) {
80: if (viennaclstruct->tempvec->size() != static_cast<std::size_t>(A->rmap->n)) {
81: delete (ViennaCLVector *)viennaclstruct->tempvec;
82: viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
83: } else {
84: viennaclstruct->tempvec->clear();
85: }
86: } else {
87: viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
88: }
90: A->offloadmask = PETSC_OFFLOAD_BOTH;
92: PetscCall(PetscLogEventEnd(MAT_ViennaCLCopyToGPU, A, 0, 0, 0));
93: }
94: }
95: PetscFunctionReturn(PETSC_SUCCESS);
96: }
98: PetscErrorCode MatViennaCLCopyFromGPU(Mat A, const ViennaCLAIJMatrix *Agpu)
99: {
100: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
101: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
102: PetscInt m = A->rmap->n;
104: PetscFunctionBegin;
105: if (A->offloadmask == PETSC_OFFLOAD_BOTH) PetscFunctionReturn(PETSC_SUCCESS);
106: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED && Agpu) {
107: try {
108: PetscCheck(!a->compressedrow.use, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "ViennaCL: Cannot handle row compression for GPU matrices");
109: PetscCheck((PetscInt)Agpu->size1() == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "GPU matrix has %lu rows, should be %" PetscInt_FMT, Agpu->size1(), m);
110: a->nz = Agpu->nnz();
111: a->maxnz = a->nz; /* Since we allocate exactly the right amount */
112: A->preallocated = PETSC_TRUE;
113: if (a->free_a) PetscCall(PetscShmgetDeallocateArray((void **)a->a));
114: if (a->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)a->j));
115: if (a->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)a->i));
116: PetscCall(PetscShmgetAllocateArray(a->nz, sizeof(PetscScalar), (void **)&a->a));
117: PetscCall(PetscShmgetAllocateArray(a->nz, sizeof(PetscInt), (void **)&a->j));
118: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&a->i));
119: a->free_a = PETSC_TRUE;
120: a->free_ij = PETSC_TRUE;
122: /* Setup row lengths */
123: PetscCall(PetscFree(a->imax));
124: PetscCall(PetscFree(a->ilen));
125: PetscCall(PetscMalloc1(m, &a->imax));
126: PetscCall(PetscMalloc1(m, &a->ilen));
128: /* Copy data back from GPU */
129: viennacl::backend::typesafe_host_array<unsigned int> row_buffer;
130: row_buffer.raw_resize(Agpu->handle1(), Agpu->size1() + 1);
132: // copy row array
133: viennacl::backend::memory_read(Agpu->handle1(), 0, row_buffer.raw_size(), row_buffer.get());
134: (a->i)[0] = row_buffer[0];
135: for (PetscInt i = 0; i < (PetscInt)Agpu->size1(); ++i) {
136: (a->i)[i + 1] = row_buffer[i + 1];
137: a->imax[i] = a->ilen[i] = a->i[i + 1] - a->i[i]; //Set imax[] and ilen[] arrays at the same time as i[] for better cache reuse
138: }
140: // copy column indices
141: viennacl::backend::typesafe_host_array<unsigned int> col_buffer;
142: col_buffer.raw_resize(Agpu->handle2(), Agpu->nnz());
143: viennacl::backend::memory_read(Agpu->handle2(), 0, col_buffer.raw_size(), col_buffer.get());
144: for (PetscInt i = 0; i < (PetscInt)Agpu->nnz(); ++i) (a->j)[i] = col_buffer[i];
146: // copy nonzero entries directly to destination (no conversion required)
147: viennacl::backend::memory_read(Agpu->handle(), 0, sizeof(PetscScalar) * Agpu->nnz(), a->a);
149: PetscCall(PetscLogGpuToCpu(row_buffer.raw_size() + col_buffer.raw_size() + (Agpu->nnz() * sizeof(PetscScalar))));
150: ViennaCLWaitForGPU();
151: /* TODO: Once a->diag is moved out of MatAssemblyEnd(), invalidate it here. */
152: } catch (std::exception const &ex) {
153: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
154: }
155: } else if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED) {
156: PetscFunctionReturn(PETSC_SUCCESS);
157: } else {
158: if (!Agpu && A->offloadmask != PETSC_OFFLOAD_GPU) PetscFunctionReturn(PETSC_SUCCESS);
160: PetscCheck(!a->compressedrow.use, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "ViennaCL: Cannot handle row compression for GPU matrices");
161: if (!Agpu) {
162: viennacl::backend::memory_read(viennaclstruct->mat->handle(), 0, sizeof(PetscScalar) * viennaclstruct->mat->nnz(), a->a);
163: } else {
164: viennacl::backend::memory_read(Agpu->handle(), 0, sizeof(PetscScalar) * Agpu->nnz(), a->a);
165: }
166: }
167: A->offloadmask = PETSC_OFFLOAD_BOTH;
168: /* This assembly prevents resetting the flag to PETSC_OFFLOAD_CPU and recopying */
169: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
170: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
171: PetscFunctionReturn(PETSC_SUCCESS);
172: }
174: static PetscErrorCode MatMult_SeqAIJViennaCL(Mat A, Vec xx, Vec yy)
175: {
176: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
177: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
178: const ViennaCLVector *xgpu = NULL;
179: ViennaCLVector *ygpu = NULL;
181: PetscFunctionBegin;
182: /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
183: PetscCall(MatViennaCLCopyToGPU(A));
184: if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) {
185: PetscCall(VecViennaCLGetArrayRead(xx, &xgpu));
186: PetscCall(VecViennaCLGetArrayWrite(yy, &ygpu));
187: PetscCall(PetscLogGpuTimeBegin());
188: try {
189: if (a->compressedrow.use) {
190: *ygpu = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu);
191: } else {
192: *ygpu = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
193: }
194: ViennaCLWaitForGPU();
195: } catch (std::exception const &ex) {
196: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
197: }
198: PetscCall(PetscLogGpuTimeEnd());
199: PetscCall(VecViennaCLRestoreArrayRead(xx, &xgpu));
200: PetscCall(VecViennaCLRestoreArrayWrite(yy, &ygpu));
201: PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
202: } else {
203: PetscCall(VecSet_SeqViennaCL(yy, 0));
204: }
205: PetscFunctionReturn(PETSC_SUCCESS);
206: }
208: static PetscErrorCode MatMultAdd_SeqAIJViennaCL(Mat A, Vec xx, Vec yy, Vec zz)
209: {
210: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
211: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
212: const ViennaCLVector *xgpu = NULL, *ygpu = NULL;
213: ViennaCLVector *zgpu = NULL;
215: PetscFunctionBegin;
216: /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
217: PetscCall(MatViennaCLCopyToGPU(A));
218: if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) {
219: try {
220: PetscCall(VecViennaCLGetArrayRead(xx, &xgpu));
221: PetscCall(VecViennaCLGetArrayRead(yy, &ygpu));
222: PetscCall(VecViennaCLGetArrayWrite(zz, &zgpu));
223: PetscCall(PetscLogGpuTimeBegin());
224: if (a->compressedrow.use) *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu);
225: else *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
226: if (zz != yy) *zgpu = *ygpu + *viennaclstruct->tempvec;
227: else *zgpu += *viennaclstruct->tempvec;
228: ViennaCLWaitForGPU();
229: PetscCall(PetscLogGpuTimeEnd());
230: PetscCall(VecViennaCLRestoreArrayRead(xx, &xgpu));
231: PetscCall(VecViennaCLRestoreArrayRead(yy, &ygpu));
232: PetscCall(VecViennaCLRestoreArrayWrite(zz, &zgpu));
234: } catch (std::exception const &ex) {
235: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
236: }
237: PetscCall(PetscLogGpuFlops(2.0 * a->nz));
238: } else {
239: PetscCall(VecCopy_SeqViennaCL(yy, zz));
240: }
241: PetscFunctionReturn(PETSC_SUCCESS);
242: }
244: static PetscErrorCode MatAssemblyEnd_SeqAIJViennaCL(Mat A, MatAssemblyType mode)
245: {
246: PetscFunctionBegin;
247: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
248: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
249: if (!A->boundtocpu) PetscCall(MatViennaCLCopyToGPU(A));
250: PetscFunctionReturn(PETSC_SUCCESS);
251: }
253: /* --------------------------------------------------------------------------------*/
254: /*@C
255: MatCreateSeqAIJViennaCL - Creates a sparse matrix in `MATSEQAIJVIENNACL` (compressed row) format
256: (the default parallel PETSc format). This matrix will ultimately be pushed down
257: to GPUs and use the ViennaCL library for calculations.
259: Collective
261: Input Parameters:
262: + comm - MPI communicator, set to `PETSC_COMM_SELF`
263: . m - number of rows
264: . n - number of columns
265: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is set
266: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
268: Output Parameter:
269: . A - the matrix
271: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
272: MatXXXXSetPreallocation() paradigm instead of this routine directly.
273: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
275: Notes:
276: The AIJ format, also called
277: compressed row storage, is fully compatible with standard Fortran
278: storage. That is, the stored row and column indices can begin at
279: either one (as in Fortran) or zero.
281: Specify the preallocated storage with either `nz` or `nnz` (not both).
282: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
283: allocation.
285: Level: intermediate
287: .seealso: `MATSEQAIJVIENNACL`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateAIJCUSPARSE()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
288: @*/
289: PetscErrorCode MatCreateSeqAIJViennaCL(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
290: {
291: PetscFunctionBegin;
292: PetscCall(MatCreate(comm, A));
293: PetscCall(MatSetSizes(*A, m, n, m, n));
294: PetscCall(MatSetType(*A, MATSEQAIJVIENNACL));
295: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
296: PetscFunctionReturn(PETSC_SUCCESS);
297: }
299: static PetscErrorCode MatDestroy_SeqAIJViennaCL(Mat A)
300: {
301: Mat_SeqAIJViennaCL *viennaclcontainer = (Mat_SeqAIJViennaCL *)A->spptr;
303: PetscFunctionBegin;
304: try {
305: if (viennaclcontainer) {
306: delete viennaclcontainer->tempvec;
307: delete viennaclcontainer->mat;
308: delete viennaclcontainer->compressed_mat;
309: delete viennaclcontainer;
310: }
311: A->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
312: } catch (std::exception const &ex) {
313: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
314: }
316: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
317: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
318: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
320: /* this next line is because MatDestroy tries to PetscFree spptr if it is not zero, and PetscFree only works if the memory was allocated with PetscNew or PetscMalloc, which don't call the constructor */
321: A->spptr = 0;
322: PetscCall(MatDestroy_SeqAIJ(A));
323: PetscFunctionReturn(PETSC_SUCCESS);
324: }
326: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJViennaCL(Mat B)
327: {
328: PetscFunctionBegin;
329: PetscCall(MatCreate_SeqAIJ(B));
330: PetscCall(MatConvert_SeqAIJ_SeqAIJViennaCL(B, MATSEQAIJVIENNACL, MAT_INPLACE_MATRIX, &B));
331: PetscFunctionReturn(PETSC_SUCCESS);
332: }
334: static PetscErrorCode MatBindToCPU_SeqAIJViennaCL(Mat, PetscBool);
335: static PetscErrorCode MatDuplicate_SeqAIJViennaCL(Mat A, MatDuplicateOption cpvalues, Mat *B)
336: {
337: Mat C;
339: PetscFunctionBegin;
340: PetscCall(MatDuplicate_SeqAIJ(A, cpvalues, B));
341: C = *B;
343: PetscCall(MatBindToCPU_SeqAIJViennaCL(A, PETSC_FALSE));
344: C->ops->bindtocpu = MatBindToCPU_SeqAIJViennaCL;
346: C->spptr = new Mat_SeqAIJViennaCL();
347: ((Mat_SeqAIJViennaCL *)C->spptr)->tempvec = NULL;
348: ((Mat_SeqAIJViennaCL *)C->spptr)->mat = NULL;
349: ((Mat_SeqAIJViennaCL *)C->spptr)->compressed_mat = NULL;
351: PetscCall(PetscObjectChangeTypeName((PetscObject)C, MATSEQAIJVIENNACL));
353: C->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
355: /* If the source matrix is already assembled, copy the destination matrix to the GPU */
356: if (C->assembled) PetscCall(MatViennaCLCopyToGPU(C));
357: PetscFunctionReturn(PETSC_SUCCESS);
358: }
360: static PetscErrorCode MatSeqAIJGetArray_SeqAIJViennaCL(Mat A, PetscScalar *array[])
361: {
362: PetscFunctionBegin;
363: PetscCall(MatViennaCLCopyFromGPU(A, (const ViennaCLAIJMatrix *)NULL));
364: *array = ((Mat_SeqAIJ *)A->data)->a;
365: PetscFunctionReturn(PETSC_SUCCESS);
366: }
368: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJViennaCL(Mat A, PetscScalar *array[])
369: {
370: PetscFunctionBegin;
371: A->offloadmask = PETSC_OFFLOAD_CPU;
372: *array = NULL;
373: PetscFunctionReturn(PETSC_SUCCESS);
374: }
376: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJViennaCL(Mat A, const PetscScalar *array[])
377: {
378: PetscFunctionBegin;
379: PetscCall(MatViennaCLCopyFromGPU(A, (const ViennaCLAIJMatrix *)NULL));
380: *array = ((Mat_SeqAIJ *)A->data)->a;
381: PetscFunctionReturn(PETSC_SUCCESS);
382: }
384: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJViennaCL(Mat A, const PetscScalar *array[])
385: {
386: PetscFunctionBegin;
387: *array = NULL;
388: /* No A->offloadmask = PETSC_OFFLOAD_CPU since if A->offloadmask was PETSC_OFFLOAD_BOTH, it is still BOTH */
389: PetscFunctionReturn(PETSC_SUCCESS);
390: }
392: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJViennaCL(Mat A, PetscScalar *array[])
393: {
394: PetscFunctionBegin;
395: *array = ((Mat_SeqAIJ *)A->data)->a;
396: PetscFunctionReturn(PETSC_SUCCESS);
397: }
399: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJViennaCL(Mat A, PetscScalar *array[])
400: {
401: PetscFunctionBegin;
402: A->offloadmask = PETSC_OFFLOAD_CPU;
403: *array = NULL;
404: PetscFunctionReturn(PETSC_SUCCESS);
405: }
407: static PetscErrorCode MatBindToCPU_SeqAIJViennaCL(Mat A, PetscBool flg)
408: {
409: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
411: PetscFunctionBegin;
412: A->boundtocpu = flg;
413: if (flg && a->inode.size) {
414: a->inode.use = PETSC_TRUE;
415: } else {
416: a->inode.use = PETSC_FALSE;
417: }
418: if (flg) {
419: /* make sure we have an up-to-date copy on the CPU */
420: PetscCall(MatViennaCLCopyFromGPU(A, (const ViennaCLAIJMatrix *)NULL));
421: A->ops->mult = MatMult_SeqAIJ;
422: A->ops->multadd = MatMultAdd_SeqAIJ;
423: A->ops->assemblyend = MatAssemblyEnd_SeqAIJ;
424: A->ops->duplicate = MatDuplicate_SeqAIJ;
425: PetscCall(PetscMemzero(a->ops, sizeof(Mat_SeqAIJOps)));
426: } else {
427: A->ops->mult = MatMult_SeqAIJViennaCL;
428: A->ops->multadd = MatMultAdd_SeqAIJViennaCL;
429: A->ops->assemblyend = MatAssemblyEnd_SeqAIJViennaCL;
430: A->ops->destroy = MatDestroy_SeqAIJViennaCL;
431: A->ops->duplicate = MatDuplicate_SeqAIJViennaCL;
433: a->ops->getarray = MatSeqAIJGetArray_SeqAIJViennaCL;
434: a->ops->restorearray = MatSeqAIJRestoreArray_SeqAIJViennaCL;
435: a->ops->getarrayread = MatSeqAIJGetArrayRead_SeqAIJViennaCL;
436: a->ops->restorearrayread = MatSeqAIJRestoreArrayRead_SeqAIJViennaCL;
437: a->ops->getarraywrite = MatSeqAIJGetArrayWrite_SeqAIJViennaCL;
438: a->ops->restorearraywrite = MatSeqAIJRestoreArrayWrite_SeqAIJViennaCL;
439: }
440: PetscFunctionReturn(PETSC_SUCCESS);
441: }
443: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
444: {
445: Mat B;
447: PetscFunctionBegin;
448: PetscCheck(reuse != MAT_REUSE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_REUSE_MATRIX is not supported. Consider using MAT_INPLACE_MATRIX instead");
450: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
452: B = *newmat;
454: B->spptr = new Mat_SeqAIJViennaCL();
456: ((Mat_SeqAIJViennaCL *)B->spptr)->tempvec = NULL;
457: ((Mat_SeqAIJViennaCL *)B->spptr)->mat = NULL;
458: ((Mat_SeqAIJViennaCL *)B->spptr)->compressed_mat = NULL;
460: PetscCall(MatBindToCPU_SeqAIJViennaCL(A, PETSC_FALSE));
461: A->ops->bindtocpu = MatBindToCPU_SeqAIJViennaCL;
463: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJVIENNACL));
464: PetscCall(PetscFree(B->defaultvectype));
465: PetscCall(PetscStrallocpy(VECVIENNACL, &B->defaultvectype));
467: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", MatConvert_SeqAIJ_SeqAIJViennaCL));
468: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", MatProductSetFromOptions_SeqAIJ_SeqDense));
469: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", MatProductSetFromOptions_SeqAIJ));
471: B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
473: /* If the source matrix is already assembled, copy the destination matrix to the GPU */
474: if (B->assembled) PetscCall(MatViennaCLCopyToGPU(B));
475: PetscFunctionReturn(PETSC_SUCCESS);
476: }
478: /*MC
479: MATSEQAIJVIENNACL - MATAIJVIENNACL = "aijviennacl" = "seqaijviennacl" - A matrix type to be used for sparse matrices.
481: A matrix type whose data resides on GPUs. These matrices are in CSR format by
482: default. All matrix calculations are performed using the ViennaCL library.
484: Options Database Keys:
485: + -mat_type aijviennacl - sets the matrix type to `MATSEQAIJVIENNACL` during a call to `MatSetFromOptions()
486: . -mat_viennacl_storage_format csr - sets the storage format of matrices for `MatMult()` during a call to `MatSetFromOptions()`.
487: - -mat_viennacl_mult_storage_format csr - sets the storage format of matrices for `MatMult()` during a call to `MatSetFromOptions()`.
489: Level: beginner
491: .seealso: `MatCreateSeqAIJViennaCL()`, `MATAIJVIENNACL`, `MatCreateAIJViennaCL()`
492: M*/
494: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_ViennaCL(void)
495: {
496: PetscFunctionBegin;
497: PetscCall(MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_LU, MatGetFactor_seqaij_petsc));
498: PetscCall(MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_CHOLESKY, MatGetFactor_seqaij_petsc));
499: PetscCall(MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_ILU, MatGetFactor_seqaij_petsc));
500: PetscCall(MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_ICC, MatGetFactor_seqaij_petsc));
501: PetscFunctionReturn(PETSC_SUCCESS);
502: }