1 2 3 /* 4 Defines the basic matrix operations for the AIJ (compressed row) 5 matrix storage format. 6 */ 7 8 #include <petscconf.h> 9 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/ 10 #include <petscbt.h> 11 #include <../src/vec/vec/impls/dvecimpl.h> 12 #include <petsc/private/vecimpl.h> 13 14 #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h> 15 16 17 #include <algorithm> 18 #include <vector> 19 #include <string> 20 21 #include "viennacl/linalg/prod.hpp" 22 23 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat A, MatType type, MatReuse reuse, Mat *newmat); 24 PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat,MatFactorType,Mat*); 25 26 27 PetscErrorCode MatViennaCLCopyToGPU(Mat A) 28 { 29 30 Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr; 31 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 32 PetscErrorCode ierr; 33 34 PetscFunctionBegin; 35 if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) { //some OpenCL SDKs have issues with buffers of size 0 36 if (A->valid_GPU_matrix == PETSC_OFFLOAD_UNALLOCATED || A->valid_GPU_matrix == PETSC_OFFLOAD_CPU) { 37 ierr = PetscLogEventBegin(MAT_ViennaCLCopyToGPU,A,0,0,0);CHKERRQ(ierr); 38 39 try { 40 if (a->compressedrow.use) { 41 if (!viennaclstruct->compressed_mat) viennaclstruct->compressed_mat = new ViennaCLCompressedAIJMatrix(); 42 43 // Since PetscInt is different from cl_uint, we have to convert: 44 viennacl::backend::mem_handle dummy; 45 46 viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(dummy, a->compressedrow.nrows+1); 47 for (PetscInt i=0; i<=a->compressedrow.nrows; ++i) 48 row_buffer.set(i, (a->compressedrow.i)[i]); 49 50 viennacl::backend::typesafe_host_array<unsigned int> row_indices; row_indices.raw_resize(dummy, a->compressedrow.nrows); 51 for (PetscInt i=0; i<a->compressedrow.nrows; ++i) 52 row_indices.set(i, (a->compressedrow.rindex)[i]); 53 54 viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(dummy, a->nz); 55 for (PetscInt i=0; i<a->nz; ++i) 56 col_buffer.set(i, (a->j)[i]); 57 58 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); 59 ierr = PetscLogCpuToGpu(((2*a->compressedrow.nrows)+1+a->nz)*sizeof(PetscInt) + (a->nz)*sizeof(PetscScalar));CHKERRQ(ierr); 60 } else { 61 if (!viennaclstruct->mat) viennaclstruct->mat = new ViennaCLAIJMatrix(); 62 63 // Since PetscInt is in general different from cl_uint, we have to convert: 64 viennacl::backend::mem_handle dummy; 65 66 viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(dummy, A->rmap->n+1); 67 for (PetscInt i=0; i<=A->rmap->n; ++i) 68 row_buffer.set(i, (a->i)[i]); 69 70 viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(dummy, a->nz); 71 for (PetscInt i=0; i<a->nz; ++i) 72 col_buffer.set(i, (a->j)[i]); 73 74 viennaclstruct->mat->set(row_buffer.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->nz); 75 ierr = PetscLogCpuToGpu(((A->rmap->n+1)+a->nz)*sizeof(PetscInt)+(a->nz)*sizeof(PetscScalar));CHKERRQ(ierr); 76 } 77 ViennaCLWaitForGPU(); 78 } catch(std::exception const & ex) { 79 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what()); 80 } 81 82 // Create temporary vector for v += A*x: 83 if (viennaclstruct->tempvec) { 84 if (viennaclstruct->tempvec->size() != static_cast<std::size_t>(A->rmap->n)) { 85 delete (ViennaCLVector*)viennaclstruct->tempvec; 86 viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n); 87 } else { 88 viennaclstruct->tempvec->clear(); 89 } 90 } else { 91 viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n); 92 } 93 94 A->valid_GPU_matrix = PETSC_OFFLOAD_BOTH; 95 96 ierr = PetscLogEventEnd(MAT_ViennaCLCopyToGPU,A,0,0,0);CHKERRQ(ierr); 97 } 98 } 99 PetscFunctionReturn(0); 100 } 101 102 PetscErrorCode MatViennaCLCopyFromGPU(Mat A, const ViennaCLAIJMatrix *Agpu) 103 { 104 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 105 PetscInt m = A->rmap->n; 106 PetscErrorCode ierr; 107 108 109 PetscFunctionBegin; 110 if (A->valid_GPU_matrix == PETSC_OFFLOAD_UNALLOCATED) { 111 try { 112 if (a->compressedrow.use) SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_ARG_WRONG, "ViennaCL: Cannot handle row compression for GPU matrices"); 113 else { 114 115 if ((PetscInt)Agpu->size1() != m) SETERRQ2(PETSC_COMM_WORLD, PETSC_ERR_ARG_SIZ, "GPU matrix has %d rows, should be %d", Agpu->size1(), m); 116 a->nz = Agpu->nnz(); 117 a->maxnz = a->nz; /* Since we allocate exactly the right amount */ 118 A->preallocated = PETSC_TRUE; 119 if (a->singlemalloc) { 120 if (a->a) {ierr = PetscFree3(a->a,a->j,a->i);CHKERRQ(ierr);} 121 } else { 122 if (a->i) {ierr = PetscFree(a->i);CHKERRQ(ierr);} 123 if (a->j) {ierr = PetscFree(a->j);CHKERRQ(ierr);} 124 if (a->a) {ierr = PetscFree(a->a);CHKERRQ(ierr);} 125 } 126 ierr = PetscMalloc3(a->nz,&a->a,a->nz,&a->j,m+1,&a->i);CHKERRQ(ierr); 127 ierr = PetscLogObjectMemory((PetscObject)A, a->nz*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 128 129 a->singlemalloc = PETSC_TRUE; 130 131 /* Setup row lengths */ 132 ierr = PetscFree(a->imax);CHKERRQ(ierr); 133 ierr = PetscFree(a->ilen);CHKERRQ(ierr); 134 ierr = PetscMalloc1(m,&a->imax);CHKERRQ(ierr); 135 ierr = PetscMalloc1(m,&a->ilen);CHKERRQ(ierr); 136 ierr = PetscLogObjectMemory((PetscObject)A, 2*m*sizeof(PetscInt));CHKERRQ(ierr); 137 138 /* Copy data back from GPU */ 139 viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(Agpu->handle1(), Agpu->size1() + 1); 140 141 // copy row array 142 viennacl::backend::memory_read(Agpu->handle1(), 0, row_buffer.raw_size(), row_buffer.get()); 143 (a->i)[0] = row_buffer[0]; 144 for (PetscInt i = 0; i < (PetscInt)Agpu->size1(); ++i) { 145 (a->i)[i+1] = row_buffer[i+1]; 146 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 147 } 148 149 // copy column indices 150 viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(Agpu->handle2(), Agpu->nnz()); 151 viennacl::backend::memory_read(Agpu->handle2(), 0, col_buffer.raw_size(), col_buffer.get()); 152 for (PetscInt i=0; i < (PetscInt)Agpu->nnz(); ++i) 153 (a->j)[i] = col_buffer[i]; 154 155 // copy nonzero entries directly to destination (no conversion required) 156 viennacl::backend::memory_read(Agpu->handle(), 0, sizeof(PetscScalar)*Agpu->nnz(), a->a); 157 158 ierr = PetscLogGpuToCpu(row_buffer.raw_size()+col_buffer.raw_size()+(Agpu->nnz()*sizeof(PetscScalar)));CHKERRQ(ierr); 159 ViennaCLWaitForGPU(); 160 /* TODO: Once a->diag is moved out of MatAssemblyEnd(), invalidate it here. */ 161 } 162 } catch(std::exception const & ex) { 163 SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what()); 164 } 165 166 /* This assembly prevents resetting the flag to PETSC_OFFLOAD_CPU and recopying */ 167 ierr = MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 168 ierr = MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 169 170 A->valid_GPU_matrix = PETSC_OFFLOAD_BOTH; 171 } else SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_ARG_WRONG, "ViennaCL error: Only valid for unallocated GPU matrices"); 172 PetscFunctionReturn(0); 173 } 174 175 PetscErrorCode MatMult_SeqAIJViennaCL(Mat A,Vec xx,Vec yy) 176 { 177 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 178 PetscErrorCode ierr; 179 Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr; 180 const ViennaCLVector *xgpu=NULL; 181 ViennaCLVector *ygpu=NULL; 182 183 PetscFunctionBegin; 184 if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) { 185 ierr = VecViennaCLGetArrayRead(xx,&xgpu);CHKERRQ(ierr); 186 ierr = VecViennaCLGetArrayWrite(yy,&ygpu);CHKERRQ(ierr); 187 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 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 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what()); 197 } 198 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 199 ierr = VecViennaCLRestoreArrayRead(xx,&xgpu);CHKERRQ(ierr); 200 ierr = VecViennaCLRestoreArrayWrite(yy,&ygpu);CHKERRQ(ierr); 201 ierr = PetscLogGpuFlops(2.0*a->nz - a->nonzerorowcnt);CHKERRQ(ierr); 202 } else { 203 ierr = VecSet(yy,0);CHKERRQ(ierr); 204 } 205 PetscFunctionReturn(0); 206 } 207 208 PetscErrorCode MatMultAdd_SeqAIJViennaCL(Mat A,Vec xx,Vec yy,Vec zz) 209 { 210 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 211 PetscErrorCode ierr; 212 Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr; 213 const ViennaCLVector *xgpu=NULL,*ygpu=NULL; 214 ViennaCLVector *zgpu=NULL; 215 216 PetscFunctionBegin; 217 if (A->rmap->n > 0 && A->cmap->n > 0 && a->nz) { 218 try { 219 ierr = VecViennaCLGetArrayRead(xx,&xgpu);CHKERRQ(ierr); 220 ierr = VecViennaCLGetArrayRead(yy,&ygpu);CHKERRQ(ierr); 221 ierr = VecViennaCLGetArrayWrite(zz,&zgpu);CHKERRQ(ierr); 222 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 223 if (a->compressedrow.use) { 224 ViennaCLVector temp = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu); 225 *zgpu = *ygpu + temp; 226 ViennaCLWaitForGPU(); 227 } else { 228 if (zz == xx || zz == yy) { //temporary required 229 ViennaCLVector temp = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu); 230 *zgpu = *ygpu; 231 *zgpu += temp; 232 ViennaCLWaitForGPU(); 233 } else { 234 *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu); 235 *zgpu = *ygpu + *viennaclstruct->tempvec; 236 ViennaCLWaitForGPU(); 237 } 238 } 239 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 240 ierr = VecViennaCLRestoreArrayRead(xx,&xgpu);CHKERRQ(ierr); 241 ierr = VecViennaCLRestoreArrayRead(yy,&ygpu);CHKERRQ(ierr); 242 ierr = VecViennaCLRestoreArrayWrite(zz,&zgpu);CHKERRQ(ierr); 243 244 } catch(std::exception const & ex) { 245 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what()); 246 } 247 ierr = PetscLogGpuFlops(2.0*a->nz);CHKERRQ(ierr); 248 } else { 249 ierr = VecCopy(yy,zz);CHKERRQ(ierr); 250 } 251 PetscFunctionReturn(0); 252 } 253 254 PetscErrorCode MatAssemblyEnd_SeqAIJViennaCL(Mat A,MatAssemblyType mode) 255 { 256 PetscErrorCode ierr; 257 258 PetscFunctionBegin; 259 ierr = MatAssemblyEnd_SeqAIJ(A,mode);CHKERRQ(ierr); 260 if (!A->pinnedtocpu) { 261 ierr = MatViennaCLCopyToGPU(A);CHKERRQ(ierr); 262 } 263 PetscFunctionReturn(0); 264 } 265 266 /* --------------------------------------------------------------------------------*/ 267 /*@ 268 MatCreateSeqAIJViennaCL - Creates a sparse matrix in AIJ (compressed row) format 269 (the default parallel PETSc format). This matrix will ultimately be pushed down 270 to GPUs and use the ViennaCL library for calculations. For good matrix 271 assembly performance the user should preallocate the matrix storage by setting 272 the parameter nz (or the array nnz). By setting these parameters accurately, 273 performance during matrix assembly can be increased substantially. 274 275 276 Collective 277 278 Input Parameters: 279 + comm - MPI communicator, set to PETSC_COMM_SELF 280 . m - number of rows 281 . n - number of columns 282 . nz - number of nonzeros per row (same for all rows) 283 - nnz - array containing the number of nonzeros in the various rows 284 (possibly different for each row) or NULL 285 286 Output Parameter: 287 . A - the matrix 288 289 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 290 MatXXXXSetPreallocation() paradigm instead of this routine directly. 291 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 292 293 Notes: 294 If nnz is given then nz is ignored 295 296 The AIJ format (also called the Yale sparse matrix format or 297 compressed row storage), is fully compatible with standard Fortran 77 298 storage. That is, the stored row and column indices can begin at 299 either one (as in Fortran) or zero. See the users' manual for details. 300 301 Specify the preallocated storage with either nz or nnz (not both). 302 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 303 allocation. For large problems you MUST preallocate memory or you 304 will get TERRIBLE performance, see the users' manual chapter on matrices. 305 306 Level: intermediate 307 308 .seealso: MatCreate(), MatCreateAIJ(), MatCreateAIJCUSPARSE(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ() 309 310 @*/ 311 PetscErrorCode MatCreateSeqAIJViennaCL(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 312 { 313 PetscErrorCode ierr; 314 315 PetscFunctionBegin; 316 ierr = MatCreate(comm,A);CHKERRQ(ierr); 317 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 318 ierr = MatSetType(*A,MATSEQAIJVIENNACL);CHKERRQ(ierr); 319 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);CHKERRQ(ierr); 320 PetscFunctionReturn(0); 321 } 322 323 324 PetscErrorCode MatDestroy_SeqAIJViennaCL(Mat A) 325 { 326 PetscErrorCode ierr; 327 Mat_SeqAIJViennaCL *viennaclcontainer = (Mat_SeqAIJViennaCL*)A->spptr; 328 329 PetscFunctionBegin; 330 try { 331 if (viennaclcontainer) { 332 delete viennaclcontainer->tempvec; 333 delete viennaclcontainer->mat; 334 delete viennaclcontainer->compressed_mat; 335 delete viennaclcontainer; 336 } 337 A->valid_GPU_matrix = PETSC_OFFLOAD_UNALLOCATED; 338 } catch(std::exception const & ex) { 339 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what()); 340 } 341 342 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijviennacl_C",NULL);CHKERRQ(ierr); 343 344 /* 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 */ 345 A->spptr = 0; 346 ierr = MatDestroy_SeqAIJ(A);CHKERRQ(ierr); 347 PetscFunctionReturn(0); 348 } 349 350 351 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJViennaCL(Mat B) 352 { 353 PetscErrorCode ierr; 354 355 PetscFunctionBegin; 356 ierr = MatCreate_SeqAIJ(B);CHKERRQ(ierr); 357 ierr = MatConvert_SeqAIJ_SeqAIJViennaCL(B,MATSEQAIJVIENNACL,MAT_INPLACE_MATRIX,&B); 358 PetscFunctionReturn(0); 359 } 360 361 static PetscErrorCode MatPinToCPU_SeqAIJViennaCL(Mat,PetscBool); 362 static PetscErrorCode MatDuplicate_SeqAIJViennaCL(Mat A,MatDuplicateOption cpvalues,Mat *B) 363 { 364 PetscErrorCode ierr; 365 Mat C; 366 367 PetscFunctionBegin; 368 ierr = MatDuplicate_SeqAIJ(A,cpvalues,B);CHKERRQ(ierr); 369 C = *B; 370 371 ierr = MatPinToCPU_SeqAIJViennaCL(A,PETSC_FALSE);CHKERRQ(ierr); 372 C->ops->pintocpu = MatPinToCPU_SeqAIJViennaCL; 373 374 C->spptr = new Mat_SeqAIJViennaCL(); 375 ((Mat_SeqAIJViennaCL*)C->spptr)->tempvec = NULL; 376 ((Mat_SeqAIJViennaCL*)C->spptr)->mat = NULL; 377 ((Mat_SeqAIJViennaCL*)C->spptr)->compressed_mat = NULL; 378 379 ierr = PetscObjectChangeTypeName((PetscObject)C,MATSEQAIJVIENNACL);CHKERRQ(ierr); 380 381 C->valid_GPU_matrix = PETSC_OFFLOAD_UNALLOCATED; 382 383 /* If the source matrix is already assembled, copy the destination matrix to the GPU */ 384 if (C->assembled) { 385 ierr = MatViennaCLCopyToGPU(C);CHKERRQ(ierr); 386 } 387 388 PetscFunctionReturn(0); 389 } 390 391 static PetscErrorCode MatPinToCPU_SeqAIJViennaCL(Mat A,PetscBool flg) 392 { 393 PetscFunctionBegin; 394 A->pinnedtocpu = flg; 395 if (flg) { 396 A->ops->mult = MatMult_SeqAIJ; 397 A->ops->multadd = MatMultAdd_SeqAIJ; 398 A->ops->assemblyend = MatAssemblyEnd_SeqAIJ; 399 A->ops->duplicate = MatDuplicate_SeqAIJ; 400 } else { 401 A->ops->mult = MatMult_SeqAIJViennaCL; 402 A->ops->multadd = MatMultAdd_SeqAIJViennaCL; 403 A->ops->assemblyend = MatAssemblyEnd_SeqAIJViennaCL; 404 A->ops->destroy = MatDestroy_SeqAIJViennaCL; 405 A->ops->duplicate = MatDuplicate_SeqAIJViennaCL; 406 } 407 PetscFunctionReturn(0); 408 } 409 410 PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat A,MatType type,MatReuse reuse,Mat *newmat) 411 { 412 PetscErrorCode ierr; 413 Mat B; 414 Mat_SeqAIJ *aij; 415 416 PetscFunctionBegin; 417 418 if (reuse == MAT_REUSE_MATRIX) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_SUP,"MAT_REUSE_MATRIX is not supported. Consider using MAT_INPLACE_MATRIX instead"); 419 420 if (reuse == MAT_INITIAL_MATRIX) { 421 ierr = MatDuplicate(A,MAT_COPY_VALUES,newmat);CHKERRQ(ierr); 422 } 423 424 B = *newmat; 425 426 aij = (Mat_SeqAIJ*)B->data; 427 aij->inode.use = PETSC_FALSE; 428 429 B->spptr = new Mat_SeqAIJViennaCL(); 430 431 ((Mat_SeqAIJViennaCL*)B->spptr)->tempvec = NULL; 432 ((Mat_SeqAIJViennaCL*)B->spptr)->mat = NULL; 433 ((Mat_SeqAIJViennaCL*)B->spptr)->compressed_mat = NULL; 434 435 ierr = MatPinToCPU_SeqAIJViennaCL(A,PETSC_FALSE);CHKERRQ(ierr); 436 A->ops->pintocpu = MatPinToCPU_SeqAIJViennaCL; 437 438 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJVIENNACL);CHKERRQ(ierr); 439 ierr = PetscFree(B->defaultvectype);CHKERRQ(ierr); 440 ierr = PetscStrallocpy(VECVIENNACL,&B->defaultvectype);CHKERRQ(ierr); 441 442 ierr = PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijviennacl_C",MatConvert_SeqAIJ_SeqAIJViennaCL);CHKERRQ(ierr); 443 444 B->valid_GPU_matrix = PETSC_OFFLOAD_UNALLOCATED; 445 446 /* If the source matrix is already assembled, copy the destination matrix to the GPU */ 447 if (B->assembled) { 448 ierr = MatViennaCLCopyToGPU(B);CHKERRQ(ierr); 449 } 450 451 PetscFunctionReturn(0); 452 } 453 454 455 /*MC 456 MATSEQAIJVIENNACL - MATAIJVIENNACL = "aijviennacl" = "seqaijviennacl" - A matrix type to be used for sparse matrices. 457 458 A matrix type type whose data resides on GPUs. These matrices are in CSR format by 459 default. All matrix calculations are performed using the ViennaCL library. 460 461 Options Database Keys: 462 + -mat_type aijviennacl - sets the matrix type to "seqaijviennacl" during a call to MatSetFromOptions() 463 . -mat_viennacl_storage_format csr - sets the storage format of matrices for MatMult during a call to MatSetFromOptions(). 464 - -mat_viennacl_mult_storage_format csr - sets the storage format of matrices for MatMult during a call to MatSetFromOptions(). 465 466 Level: beginner 467 468 .seealso: MatCreateSeqAIJViennaCL(), MATAIJVIENNACL, MatCreateAIJViennaCL() 469 M*/ 470 471 472 PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_ViennaCL(void) 473 { 474 PetscErrorCode ierr; 475 476 PetscFunctionBegin; 477 ierr = MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_LU,MatGetFactor_seqaij_petsc);CHKERRQ(ierr); 478 ierr = MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_CHOLESKY,MatGetFactor_seqaij_petsc);CHKERRQ(ierr); 479 ierr = MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_ILU,MatGetFactor_seqaij_petsc);CHKERRQ(ierr); 480 ierr = MatSolverTypeRegister(MATSOLVERPETSC, MATSEQAIJVIENNACL, MAT_FACTOR_ICC,MatGetFactor_seqaij_petsc);CHKERRQ(ierr); 481 PetscFunctionReturn(0); 482 } 483