1 /* 2 Defines the basic matrix operations for the AIJ (compressed row) 3 matrix storage format using the CUSPARSE library, 4 */ 5 #define PETSC_SKIP_SPINLOCK 6 7 #include <petscconf.h> 8 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/ 9 #include <../src/mat/impls/sbaij/seq/sbaij.h> 10 #include <../src/vec/vec/impls/dvecimpl.h> 11 #include <petsc/private/vecimpl.h> 12 #undef VecType 13 #include <../src/mat/impls/aij/seq/seqcusparse/cusparsematimpl.h> 14 15 const char *const MatCUSPARSEStorageFormats[] = {"CSR","ELL","HYB","MatCUSPARSEStorageFormat","MAT_CUSPARSE_",0}; 16 17 static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat,Mat,IS,const MatFactorInfo*); 18 static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat,Mat,IS,const MatFactorInfo*); 19 static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat,Mat,const MatFactorInfo*); 20 21 static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat,Mat,IS,IS,const MatFactorInfo*); 22 static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat,Mat,IS,IS,const MatFactorInfo*); 23 static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat,Mat,const MatFactorInfo*); 24 25 static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat,Vec,Vec); 26 static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat,Vec,Vec); 27 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat,Vec,Vec); 28 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat,Vec,Vec); 29 static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(PetscOptionItems *PetscOptionsObject,Mat); 30 static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat,Vec,Vec); 31 static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat,Vec,Vec,Vec); 32 static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat,Vec,Vec); 33 static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat,Vec,Vec,Vec); 34 35 static PetscErrorCode CsrMatrix_Destroy(CsrMatrix**); 36 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct**); 37 static PetscErrorCode Mat_SeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct**,MatCUSPARSEStorageFormat); 38 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors**); 39 static PetscErrorCode Mat_SeqAIJCUSPARSE_Destroy(Mat_SeqAIJCUSPARSE**); 40 41 PetscErrorCode MatCUSPARSESetStream(Mat A,const cudaStream_t stream) 42 { 43 cusparseStatus_t stat; 44 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 45 46 PetscFunctionBegin; 47 cusparsestruct->stream = stream; 48 stat = cusparseSetStream(cusparsestruct->handle,cusparsestruct->stream);CHKERRCUDA(stat); 49 PetscFunctionReturn(0); 50 } 51 52 PetscErrorCode MatCUSPARSESetHandle(Mat A,const cusparseHandle_t handle) 53 { 54 cusparseStatus_t stat; 55 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 56 57 PetscFunctionBegin; 58 if (cusparsestruct->handle != handle) { 59 if (cusparsestruct->handle) { 60 stat = cusparseDestroy(cusparsestruct->handle);CHKERRCUDA(stat); 61 } 62 cusparsestruct->handle = handle; 63 } 64 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 65 PetscFunctionReturn(0); 66 } 67 68 PetscErrorCode MatCUSPARSEClearHandle(Mat A) 69 { 70 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 71 PetscFunctionBegin; 72 if (cusparsestruct->handle) 73 cusparsestruct->handle = 0; 74 PetscFunctionReturn(0); 75 } 76 77 PetscErrorCode MatFactorGetSolverPackage_seqaij_cusparse(Mat A,const MatSolverPackage *type) 78 { 79 PetscFunctionBegin; 80 *type = MATSOLVERCUSPARSE; 81 PetscFunctionReturn(0); 82 } 83 84 /*MC 85 MATSOLVERCUSPARSE = "cusparse" - A matrix type providing triangular solvers for seq matrices 86 on a single GPU of type, seqaijcusparse, aijcusparse, or seqaijcusp, aijcusp. Currently supported 87 algorithms are ILU(k) and ICC(k). Typically, deeper factorizations (larger k) results in poorer 88 performance in the triangular solves. Full LU, and Cholesky decompositions can be solved through the 89 CUSPARSE triangular solve algorithm. However, the performance can be quite poor and thus these 90 algorithms are not recommended. This class does NOT support direct solver operations. 91 92 Level: beginner 93 94 .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage, MatCreateSeqAIJCUSPARSE(), MATAIJCUSPARSE, MatCreateAIJCUSPARSE(), MatCUSPARSESetFormat(), MatCUSPARSEStorageFormat, MatCUSPARSEFormatOperation 95 M*/ 96 97 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat A,MatFactorType ftype,Mat *B) 98 { 99 PetscErrorCode ierr; 100 PetscInt n = A->rmap->n; 101 102 PetscFunctionBegin; 103 ierr = MatCreate(PetscObjectComm((PetscObject)A),B);CHKERRQ(ierr); 104 (*B)->factortype = ftype; 105 ierr = MatSetSizes(*B,n,n,n,n);CHKERRQ(ierr); 106 ierr = MatSetType(*B,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 107 108 if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) { 109 ierr = MatSetBlockSizesFromMats(*B,A,A);CHKERRQ(ierr); 110 (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJCUSPARSE; 111 (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJCUSPARSE; 112 } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) { 113 (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJCUSPARSE; 114 (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJCUSPARSE; 115 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Factor type not supported for CUSPARSE Matrix Types"); 116 117 ierr = MatSeqAIJSetPreallocation(*B,MAT_SKIP_ALLOCATION,NULL);CHKERRQ(ierr); 118 ierr = PetscObjectComposeFunction((PetscObject)(*B),"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_seqaij_cusparse);CHKERRQ(ierr); 119 PetscFunctionReturn(0); 120 } 121 122 PETSC_INTERN PetscErrorCode MatCUSPARSESetFormat_SeqAIJCUSPARSE(Mat A,MatCUSPARSEFormatOperation op,MatCUSPARSEStorageFormat format) 123 { 124 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 125 126 PetscFunctionBegin; 127 #if CUDA_VERSION>=4020 128 switch (op) { 129 case MAT_CUSPARSE_MULT: 130 cusparsestruct->format = format; 131 break; 132 case MAT_CUSPARSE_ALL: 133 cusparsestruct->format = format; 134 break; 135 default: 136 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unsupported operation %d for MatCUSPARSEFormatOperation. MAT_CUSPARSE_MULT and MAT_CUSPARSE_ALL are currently supported.",op); 137 } 138 #else 139 if (format==MAT_CUSPARSE_ELL || format==MAT_CUSPARSE_HYB) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"ELL (Ellpack) and HYB (Hybrid) storage format require CUDA 4.2 or later."); 140 #endif 141 PetscFunctionReturn(0); 142 } 143 144 /*@ 145 MatCUSPARSESetFormat - Sets the storage format of CUSPARSE matrices for a particular 146 operation. Only the MatMult operation can use different GPU storage formats 147 for MPIAIJCUSPARSE matrices. 148 Not Collective 149 150 Input Parameters: 151 + A - Matrix of type SEQAIJCUSPARSE 152 . op - MatCUSPARSEFormatOperation. SEQAIJCUSPARSE matrices support MAT_CUSPARSE_MULT and MAT_CUSPARSE_ALL. MPIAIJCUSPARSE matrices support MAT_CUSPARSE_MULT_DIAG, MAT_CUSPARSE_MULT_OFFDIAG, and MAT_CUSPARSE_ALL. 153 - format - MatCUSPARSEStorageFormat (one of MAT_CUSPARSE_CSR, MAT_CUSPARSE_ELL, MAT_CUSPARSE_HYB. The latter two require CUDA 4.2) 154 155 Output Parameter: 156 157 Level: intermediate 158 159 .seealso: MatCUSPARSEStorageFormat, MatCUSPARSEFormatOperation 160 @*/ 161 PetscErrorCode MatCUSPARSESetFormat(Mat A,MatCUSPARSEFormatOperation op,MatCUSPARSEStorageFormat format) 162 { 163 PetscErrorCode ierr; 164 165 PetscFunctionBegin; 166 PetscValidHeaderSpecific(A, MAT_CLASSID,1); 167 ierr = PetscTryMethod(A, "MatCUSPARSESetFormat_C",(Mat,MatCUSPARSEFormatOperation,MatCUSPARSEStorageFormat),(A,op,format));CHKERRQ(ierr); 168 PetscFunctionReturn(0); 169 } 170 171 static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(PetscOptionItems *PetscOptionsObject,Mat A) 172 { 173 PetscErrorCode ierr; 174 MatCUSPARSEStorageFormat format; 175 PetscBool flg; 176 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 177 178 PetscFunctionBegin; 179 ierr = PetscOptionsHead(PetscOptionsObject,"SeqAIJCUSPARSE options");CHKERRQ(ierr); 180 ierr = PetscObjectOptionsBegin((PetscObject)A); 181 if (A->factortype==MAT_FACTOR_NONE) { 182 ierr = PetscOptionsEnum("-mat_cusparse_mult_storage_format","sets storage format of (seq)aijcusparse gpu matrices for SpMV", 183 "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)cusparsestruct->format,(PetscEnum*)&format,&flg);CHKERRQ(ierr); 184 if (flg) { 185 ierr = MatCUSPARSESetFormat(A,MAT_CUSPARSE_MULT,format);CHKERRQ(ierr); 186 } 187 } 188 ierr = PetscOptionsEnum("-mat_cusparse_storage_format","sets storage format of (seq)aijcusparse gpu matrices for SpMV and TriSolve", 189 "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)cusparsestruct->format,(PetscEnum*)&format,&flg);CHKERRQ(ierr); 190 if (flg) { 191 ierr = MatCUSPARSESetFormat(A,MAT_CUSPARSE_ALL,format);CHKERRQ(ierr); 192 } 193 ierr = PetscOptionsEnd();CHKERRQ(ierr); 194 PetscFunctionReturn(0); 195 196 } 197 198 static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS isrow,IS iscol,const MatFactorInfo *info) 199 { 200 PetscErrorCode ierr; 201 202 PetscFunctionBegin; 203 ierr = MatILUFactorSymbolic_SeqAIJ(B,A,isrow,iscol,info);CHKERRQ(ierr); 204 B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE; 205 PetscFunctionReturn(0); 206 } 207 208 static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS isrow,IS iscol,const MatFactorInfo *info) 209 { 210 PetscErrorCode ierr; 211 212 PetscFunctionBegin; 213 ierr = MatLUFactorSymbolic_SeqAIJ(B,A,isrow,iscol,info);CHKERRQ(ierr); 214 B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE; 215 PetscFunctionReturn(0); 216 } 217 218 static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS perm,const MatFactorInfo *info) 219 { 220 PetscErrorCode ierr; 221 222 PetscFunctionBegin; 223 ierr = MatICCFactorSymbolic_SeqAIJ(B,A,perm,info);CHKERRQ(ierr); 224 B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE; 225 PetscFunctionReturn(0); 226 } 227 228 static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS perm,const MatFactorInfo *info) 229 { 230 PetscErrorCode ierr; 231 232 PetscFunctionBegin; 233 ierr = MatCholeskyFactorSymbolic_SeqAIJ(B,A,perm,info);CHKERRQ(ierr); 234 B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE; 235 PetscFunctionReturn(0); 236 } 237 238 static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A) 239 { 240 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 241 PetscInt n = A->rmap->n; 242 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 243 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 244 cusparseStatus_t stat; 245 const PetscInt *ai = a->i,*aj = a->j,*vi; 246 const MatScalar *aa = a->a,*v; 247 PetscInt *AiLo, *AjLo; 248 PetscScalar *AALo; 249 PetscInt i,nz, nzLower, offset, rowOffset; 250 PetscErrorCode ierr; 251 252 PetscFunctionBegin; 253 if (A->valid_GPU_matrix == PETSC_CUDA_UNALLOCATED || A->valid_GPU_matrix == PETSC_CUDA_CPU) { 254 try { 255 /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */ 256 nzLower=n+ai[n]-ai[1]; 257 258 /* Allocate Space for the lower triangular matrix */ 259 ierr = cudaMallocHost((void**) &AiLo, (n+1)*sizeof(PetscInt));CHKERRCUDA(ierr); 260 ierr = cudaMallocHost((void**) &AjLo, nzLower*sizeof(PetscInt));CHKERRCUDA(ierr); 261 ierr = cudaMallocHost((void**) &AALo, nzLower*sizeof(PetscScalar));CHKERRCUDA(ierr); 262 263 /* Fill the lower triangular matrix */ 264 AiLo[0] = (PetscInt) 0; 265 AiLo[n] = nzLower; 266 AjLo[0] = (PetscInt) 0; 267 AALo[0] = (MatScalar) 1.0; 268 v = aa; 269 vi = aj; 270 offset = 1; 271 rowOffset= 1; 272 for (i=1; i<n; i++) { 273 nz = ai[i+1] - ai[i]; 274 /* additional 1 for the term on the diagonal */ 275 AiLo[i] = rowOffset; 276 rowOffset += nz+1; 277 278 ierr = PetscMemcpy(&(AjLo[offset]), vi, nz*sizeof(PetscInt));CHKERRQ(ierr); 279 ierr = PetscMemcpy(&(AALo[offset]), v, nz*sizeof(PetscScalar));CHKERRQ(ierr); 280 281 offset += nz; 282 AjLo[offset] = (PetscInt) i; 283 AALo[offset] = (MatScalar) 1.0; 284 offset += 1; 285 286 v += nz; 287 vi += nz; 288 } 289 290 /* allocate space for the triangular factor information */ 291 loTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 292 293 /* Create the matrix description */ 294 stat = cusparseCreateMatDescr(&loTriFactor->descr);CHKERRCUDA(stat); 295 stat = cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 296 stat = cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 297 stat = cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER);CHKERRCUDA(stat); 298 stat = cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT);CHKERRCUDA(stat); 299 300 /* Create the solve analysis information */ 301 stat = cusparseCreateSolveAnalysisInfo(&loTriFactor->solveInfo);CHKERRCUDA(stat); 302 303 /* set the operation */ 304 loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 305 306 /* set the matrix */ 307 loTriFactor->csrMat = new CsrMatrix; 308 loTriFactor->csrMat->num_rows = n; 309 loTriFactor->csrMat->num_cols = n; 310 loTriFactor->csrMat->num_entries = nzLower; 311 312 loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n+1); 313 loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo+n+1); 314 315 loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower); 316 loTriFactor->csrMat->column_indices->assign(AjLo, AjLo+nzLower); 317 318 loTriFactor->csrMat->values = new THRUSTARRAY(nzLower); 319 loTriFactor->csrMat->values->assign(AALo, AALo+nzLower); 320 321 /* perform the solve analysis */ 322 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, 323 loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, 324 loTriFactor->csrMat->values->data().get(), loTriFactor->csrMat->row_offsets->data().get(), 325 loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo);CHKERRCUDA(stat); 326 327 /* assign the pointer. Is this really necessary? */ 328 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtr = loTriFactor; 329 330 ierr = cudaFreeHost(AiLo);CHKERRCUDA(ierr); 331 ierr = cudaFreeHost(AjLo);CHKERRCUDA(ierr); 332 ierr = cudaFreeHost(AALo);CHKERRCUDA(ierr); 333 } catch(char *ex) { 334 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 335 } 336 } 337 PetscFunctionReturn(0); 338 } 339 340 static PetscErrorCode MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(Mat A) 341 { 342 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 343 PetscInt n = A->rmap->n; 344 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 345 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 346 cusparseStatus_t stat; 347 const PetscInt *aj = a->j,*adiag = a->diag,*vi; 348 const MatScalar *aa = a->a,*v; 349 PetscInt *AiUp, *AjUp; 350 PetscScalar *AAUp; 351 PetscInt i,nz, nzUpper, offset; 352 PetscErrorCode ierr; 353 354 PetscFunctionBegin; 355 if (A->valid_GPU_matrix == PETSC_CUDA_UNALLOCATED || A->valid_GPU_matrix == PETSC_CUDA_CPU) { 356 try { 357 /* next, figure out the number of nonzeros in the upper triangular matrix. */ 358 nzUpper = adiag[0]-adiag[n]; 359 360 /* Allocate Space for the upper triangular matrix */ 361 ierr = cudaMallocHost((void**) &AiUp, (n+1)*sizeof(PetscInt));CHKERRCUDA(ierr); 362 ierr = cudaMallocHost((void**) &AjUp, nzUpper*sizeof(PetscInt));CHKERRCUDA(ierr); 363 ierr = cudaMallocHost((void**) &AAUp, nzUpper*sizeof(PetscScalar));CHKERRCUDA(ierr); 364 365 /* Fill the upper triangular matrix */ 366 AiUp[0]=(PetscInt) 0; 367 AiUp[n]=nzUpper; 368 offset = nzUpper; 369 for (i=n-1; i>=0; i--) { 370 v = aa + adiag[i+1] + 1; 371 vi = aj + adiag[i+1] + 1; 372 373 /* number of elements NOT on the diagonal */ 374 nz = adiag[i] - adiag[i+1]-1; 375 376 /* decrement the offset */ 377 offset -= (nz+1); 378 379 /* first, set the diagonal elements */ 380 AjUp[offset] = (PetscInt) i; 381 AAUp[offset] = (MatScalar)1./v[nz]; 382 AiUp[i] = AiUp[i+1] - (nz+1); 383 384 ierr = PetscMemcpy(&(AjUp[offset+1]), vi, nz*sizeof(PetscInt));CHKERRQ(ierr); 385 ierr = PetscMemcpy(&(AAUp[offset+1]), v, nz*sizeof(PetscScalar));CHKERRQ(ierr); 386 } 387 388 /* allocate space for the triangular factor information */ 389 upTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 390 391 /* Create the matrix description */ 392 stat = cusparseCreateMatDescr(&upTriFactor->descr);CHKERRCUDA(stat); 393 stat = cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 394 stat = cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 395 stat = cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER);CHKERRCUDA(stat); 396 stat = cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT);CHKERRCUDA(stat); 397 398 /* Create the solve analysis information */ 399 stat = cusparseCreateSolveAnalysisInfo(&upTriFactor->solveInfo);CHKERRCUDA(stat); 400 401 /* set the operation */ 402 upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 403 404 /* set the matrix */ 405 upTriFactor->csrMat = new CsrMatrix; 406 upTriFactor->csrMat->num_rows = n; 407 upTriFactor->csrMat->num_cols = n; 408 upTriFactor->csrMat->num_entries = nzUpper; 409 410 upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n+1); 411 upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp+n+1); 412 413 upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzUpper); 414 upTriFactor->csrMat->column_indices->assign(AjUp, AjUp+nzUpper); 415 416 upTriFactor->csrMat->values = new THRUSTARRAY(nzUpper); 417 upTriFactor->csrMat->values->assign(AAUp, AAUp+nzUpper); 418 419 /* perform the solve analysis */ 420 stat = cusparse_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, 421 upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, 422 upTriFactor->csrMat->values->data().get(), upTriFactor->csrMat->row_offsets->data().get(), 423 upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo);CHKERRCUDA(stat); 424 425 /* assign the pointer. Is this really necessary? */ 426 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->upTriFactorPtr = upTriFactor; 427 428 ierr = cudaFreeHost(AiUp);CHKERRCUDA(ierr); 429 ierr = cudaFreeHost(AjUp);CHKERRCUDA(ierr); 430 ierr = cudaFreeHost(AAUp);CHKERRCUDA(ierr); 431 } catch(char *ex) { 432 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 433 } 434 } 435 PetscFunctionReturn(0); 436 } 437 438 static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A) 439 { 440 PetscErrorCode ierr; 441 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 442 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 443 IS isrow = a->row,iscol = a->icol; 444 PetscBool row_identity,col_identity; 445 const PetscInt *r,*c; 446 PetscInt n = A->rmap->n; 447 448 PetscFunctionBegin; 449 ierr = MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A);CHKERRQ(ierr); 450 ierr = MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A);CHKERRQ(ierr); 451 452 cusparseTriFactors->workVector = new THRUSTARRAY; 453 cusparseTriFactors->workVector->resize(n); 454 cusparseTriFactors->nnz=a->nz; 455 456 A->valid_GPU_matrix = PETSC_CUDA_BOTH; 457 /*lower triangular indices */ 458 ierr = ISGetIndices(isrow,&r);CHKERRQ(ierr); 459 ierr = ISIdentity(isrow,&row_identity);CHKERRQ(ierr); 460 if (!row_identity) { 461 cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n); 462 cusparseTriFactors->rpermIndices->assign(r, r+n); 463 } 464 ierr = ISRestoreIndices(isrow,&r);CHKERRQ(ierr); 465 466 /*upper triangular indices */ 467 ierr = ISGetIndices(iscol,&c);CHKERRQ(ierr); 468 ierr = ISIdentity(iscol,&col_identity);CHKERRQ(ierr); 469 if (!col_identity) { 470 cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n); 471 cusparseTriFactors->cpermIndices->assign(c, c+n); 472 } 473 ierr = ISRestoreIndices(iscol,&c);CHKERRQ(ierr); 474 PetscFunctionReturn(0); 475 } 476 477 static PetscErrorCode MatSeqAIJCUSPARSEBuildICCTriMatrices(Mat A) 478 { 479 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 480 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 481 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 482 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 483 cusparseStatus_t stat; 484 PetscErrorCode ierr; 485 PetscInt *AiUp, *AjUp; 486 PetscScalar *AAUp; 487 PetscScalar *AALo; 488 PetscInt nzUpper = a->nz,n = A->rmap->n,i,offset,nz,j; 489 Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ*)A->data; 490 const PetscInt *ai = b->i,*aj = b->j,*vj; 491 const MatScalar *aa = b->a,*v; 492 493 PetscFunctionBegin; 494 if (A->valid_GPU_matrix == PETSC_CUDA_UNALLOCATED || A->valid_GPU_matrix == PETSC_CUDA_CPU) { 495 try { 496 /* Allocate Space for the upper triangular matrix */ 497 ierr = cudaMallocHost((void**) &AiUp, (n+1)*sizeof(PetscInt));CHKERRCUDA(ierr); 498 ierr = cudaMallocHost((void**) &AjUp, nzUpper*sizeof(PetscInt));CHKERRCUDA(ierr); 499 ierr = cudaMallocHost((void**) &AAUp, nzUpper*sizeof(PetscScalar));CHKERRCUDA(ierr); 500 ierr = cudaMallocHost((void**) &AALo, nzUpper*sizeof(PetscScalar));CHKERRCUDA(ierr); 501 502 /* Fill the upper triangular matrix */ 503 AiUp[0]=(PetscInt) 0; 504 AiUp[n]=nzUpper; 505 offset = 0; 506 for (i=0; i<n; i++) { 507 /* set the pointers */ 508 v = aa + ai[i]; 509 vj = aj + ai[i]; 510 nz = ai[i+1] - ai[i] - 1; /* exclude diag[i] */ 511 512 /* first, set the diagonal elements */ 513 AjUp[offset] = (PetscInt) i; 514 AAUp[offset] = (MatScalar)1.0/v[nz]; 515 AiUp[i] = offset; 516 AALo[offset] = (MatScalar)1.0/v[nz]; 517 518 offset+=1; 519 if (nz>0) { 520 ierr = PetscMemcpy(&(AjUp[offset]), vj, nz*sizeof(PetscInt));CHKERRQ(ierr); 521 ierr = PetscMemcpy(&(AAUp[offset]), v, nz*sizeof(PetscScalar));CHKERRQ(ierr); 522 for (j=offset; j<offset+nz; j++) { 523 AAUp[j] = -AAUp[j]; 524 AALo[j] = AAUp[j]/v[nz]; 525 } 526 offset+=nz; 527 } 528 } 529 530 /* allocate space for the triangular factor information */ 531 upTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 532 533 /* Create the matrix description */ 534 stat = cusparseCreateMatDescr(&upTriFactor->descr);CHKERRCUDA(stat); 535 stat = cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 536 stat = cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 537 stat = cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER);CHKERRCUDA(stat); 538 stat = cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT);CHKERRCUDA(stat); 539 540 /* Create the solve analysis information */ 541 stat = cusparseCreateSolveAnalysisInfo(&upTriFactor->solveInfo);CHKERRCUDA(stat); 542 543 /* set the operation */ 544 upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 545 546 /* set the matrix */ 547 upTriFactor->csrMat = new CsrMatrix; 548 upTriFactor->csrMat->num_rows = A->rmap->n; 549 upTriFactor->csrMat->num_cols = A->cmap->n; 550 upTriFactor->csrMat->num_entries = a->nz; 551 552 upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 553 upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp+A->rmap->n+1); 554 555 upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz); 556 upTriFactor->csrMat->column_indices->assign(AjUp, AjUp+a->nz); 557 558 upTriFactor->csrMat->values = new THRUSTARRAY(a->nz); 559 upTriFactor->csrMat->values->assign(AAUp, AAUp+a->nz); 560 561 /* perform the solve analysis */ 562 stat = cusparse_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, 563 upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, 564 upTriFactor->csrMat->values->data().get(), upTriFactor->csrMat->row_offsets->data().get(), 565 upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo);CHKERRCUDA(stat); 566 567 /* assign the pointer. Is this really necessary? */ 568 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->upTriFactorPtr = upTriFactor; 569 570 /* allocate space for the triangular factor information */ 571 loTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 572 573 /* Create the matrix description */ 574 stat = cusparseCreateMatDescr(&loTriFactor->descr);CHKERRCUDA(stat); 575 stat = cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 576 stat = cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 577 stat = cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_UPPER);CHKERRCUDA(stat); 578 stat = cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT);CHKERRCUDA(stat); 579 580 /* Create the solve analysis information */ 581 stat = cusparseCreateSolveAnalysisInfo(&loTriFactor->solveInfo);CHKERRCUDA(stat); 582 583 /* set the operation */ 584 loTriFactor->solveOp = CUSPARSE_OPERATION_TRANSPOSE; 585 586 /* set the matrix */ 587 loTriFactor->csrMat = new CsrMatrix; 588 loTriFactor->csrMat->num_rows = A->rmap->n; 589 loTriFactor->csrMat->num_cols = A->cmap->n; 590 loTriFactor->csrMat->num_entries = a->nz; 591 592 loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 593 loTriFactor->csrMat->row_offsets->assign(AiUp, AiUp+A->rmap->n+1); 594 595 loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz); 596 loTriFactor->csrMat->column_indices->assign(AjUp, AjUp+a->nz); 597 598 loTriFactor->csrMat->values = new THRUSTARRAY(a->nz); 599 loTriFactor->csrMat->values->assign(AALo, AALo+a->nz); 600 601 /* perform the solve analysis */ 602 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, 603 loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, 604 loTriFactor->csrMat->values->data().get(), loTriFactor->csrMat->row_offsets->data().get(), 605 loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo);CHKERRCUDA(stat); 606 607 /* assign the pointer. Is this really necessary? */ 608 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtr = loTriFactor; 609 610 A->valid_GPU_matrix = PETSC_CUDA_BOTH; 611 ierr = cudaFreeHost(AiUp);CHKERRCUDA(ierr); 612 ierr = cudaFreeHost(AjUp);CHKERRCUDA(ierr); 613 ierr = cudaFreeHost(AAUp);CHKERRCUDA(ierr); 614 ierr = cudaFreeHost(AALo);CHKERRCUDA(ierr); 615 } catch(char *ex) { 616 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 617 } 618 } 619 PetscFunctionReturn(0); 620 } 621 622 static PetscErrorCode MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(Mat A) 623 { 624 PetscErrorCode ierr; 625 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 626 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 627 IS ip = a->row; 628 const PetscInt *rip; 629 PetscBool perm_identity; 630 PetscInt n = A->rmap->n; 631 632 PetscFunctionBegin; 633 ierr = MatSeqAIJCUSPARSEBuildICCTriMatrices(A);CHKERRQ(ierr); 634 cusparseTriFactors->workVector = new THRUSTARRAY; 635 cusparseTriFactors->workVector->resize(n); 636 cusparseTriFactors->nnz=(a->nz-n)*2 + n; 637 638 /*lower triangular indices */ 639 ierr = ISGetIndices(ip,&rip);CHKERRQ(ierr); 640 ierr = ISIdentity(ip,&perm_identity);CHKERRQ(ierr); 641 if (!perm_identity) { 642 cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n); 643 cusparseTriFactors->rpermIndices->assign(rip, rip+n); 644 cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n); 645 cusparseTriFactors->cpermIndices->assign(rip, rip+n); 646 } 647 ierr = ISRestoreIndices(ip,&rip);CHKERRQ(ierr); 648 PetscFunctionReturn(0); 649 } 650 651 static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B,Mat A,const MatFactorInfo *info) 652 { 653 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 654 IS isrow = b->row,iscol = b->col; 655 PetscBool row_identity,col_identity; 656 PetscErrorCode ierr; 657 658 PetscFunctionBegin; 659 ierr = MatLUFactorNumeric_SeqAIJ(B,A,info);CHKERRQ(ierr); 660 /* determine which version of MatSolve needs to be used. */ 661 ierr = ISIdentity(isrow,&row_identity);CHKERRQ(ierr); 662 ierr = ISIdentity(iscol,&col_identity);CHKERRQ(ierr); 663 if (row_identity && col_identity) { 664 B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering; 665 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering; 666 } else { 667 B->ops->solve = MatSolve_SeqAIJCUSPARSE; 668 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE; 669 } 670 671 /* get the triangular factors */ 672 ierr = MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B);CHKERRQ(ierr); 673 PetscFunctionReturn(0); 674 } 675 676 static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat B,Mat A,const MatFactorInfo *info) 677 { 678 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 679 IS ip = b->row; 680 PetscBool perm_identity; 681 PetscErrorCode ierr; 682 683 PetscFunctionBegin; 684 ierr = MatCholeskyFactorNumeric_SeqAIJ(B,A,info);CHKERRQ(ierr); 685 686 /* determine which version of MatSolve needs to be used. */ 687 ierr = ISIdentity(ip,&perm_identity);CHKERRQ(ierr); 688 if (perm_identity) { 689 B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering; 690 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering; 691 } else { 692 B->ops->solve = MatSolve_SeqAIJCUSPARSE; 693 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE; 694 } 695 696 /* get the triangular factors */ 697 ierr = MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(B);CHKERRQ(ierr); 698 PetscFunctionReturn(0); 699 } 700 701 static PetscErrorCode MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(Mat A) 702 { 703 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 704 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 705 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 706 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 707 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 708 cusparseStatus_t stat; 709 cusparseIndexBase_t indexBase; 710 cusparseMatrixType_t matrixType; 711 cusparseFillMode_t fillMode; 712 cusparseDiagType_t diagType; 713 714 PetscFunctionBegin; 715 716 /*********************************************/ 717 /* Now the Transpose of the Lower Tri Factor */ 718 /*********************************************/ 719 720 /* allocate space for the transpose of the lower triangular factor */ 721 loTriFactorT = new Mat_SeqAIJCUSPARSETriFactorStruct; 722 723 /* set the matrix descriptors of the lower triangular factor */ 724 matrixType = cusparseGetMatType(loTriFactor->descr); 725 indexBase = cusparseGetMatIndexBase(loTriFactor->descr); 726 fillMode = cusparseGetMatFillMode(loTriFactor->descr)==CUSPARSE_FILL_MODE_UPPER ? 727 CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER; 728 diagType = cusparseGetMatDiagType(loTriFactor->descr); 729 730 /* Create the matrix description */ 731 stat = cusparseCreateMatDescr(&loTriFactorT->descr);CHKERRCUDA(stat); 732 stat = cusparseSetMatIndexBase(loTriFactorT->descr, indexBase);CHKERRCUDA(stat); 733 stat = cusparseSetMatType(loTriFactorT->descr, matrixType);CHKERRCUDA(stat); 734 stat = cusparseSetMatFillMode(loTriFactorT->descr, fillMode);CHKERRCUDA(stat); 735 stat = cusparseSetMatDiagType(loTriFactorT->descr, diagType);CHKERRCUDA(stat); 736 737 /* Create the solve analysis information */ 738 stat = cusparseCreateSolveAnalysisInfo(&loTriFactorT->solveInfo);CHKERRCUDA(stat); 739 740 /* set the operation */ 741 loTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 742 743 /* allocate GPU space for the CSC of the lower triangular factor*/ 744 loTriFactorT->csrMat = new CsrMatrix; 745 loTriFactorT->csrMat->num_rows = loTriFactor->csrMat->num_rows; 746 loTriFactorT->csrMat->num_cols = loTriFactor->csrMat->num_cols; 747 loTriFactorT->csrMat->num_entries = loTriFactor->csrMat->num_entries; 748 loTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(loTriFactor->csrMat->num_rows+1); 749 loTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(loTriFactor->csrMat->num_entries); 750 loTriFactorT->csrMat->values = new THRUSTARRAY(loTriFactor->csrMat->num_entries); 751 752 /* compute the transpose of the lower triangular factor, i.e. the CSC */ 753 stat = cusparse_csr2csc(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, 754 loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, 755 loTriFactor->csrMat->values->data().get(), 756 loTriFactor->csrMat->row_offsets->data().get(), 757 loTriFactor->csrMat->column_indices->data().get(), 758 loTriFactorT->csrMat->values->data().get(), 759 loTriFactorT->csrMat->column_indices->data().get(), 760 loTriFactorT->csrMat->row_offsets->data().get(), 761 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 762 763 /* perform the solve analysis on the transposed matrix */ 764 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactorT->solveOp, 765 loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, 766 loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(), 767 loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), 768 loTriFactorT->solveInfo);CHKERRCUDA(stat); 769 770 /* assign the pointer. Is this really necessary? */ 771 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtrTranspose = loTriFactorT; 772 773 /*********************************************/ 774 /* Now the Transpose of the Upper Tri Factor */ 775 /*********************************************/ 776 777 /* allocate space for the transpose of the upper triangular factor */ 778 upTriFactorT = new Mat_SeqAIJCUSPARSETriFactorStruct; 779 780 /* set the matrix descriptors of the upper triangular factor */ 781 matrixType = cusparseGetMatType(upTriFactor->descr); 782 indexBase = cusparseGetMatIndexBase(upTriFactor->descr); 783 fillMode = cusparseGetMatFillMode(upTriFactor->descr)==CUSPARSE_FILL_MODE_UPPER ? 784 CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER; 785 diagType = cusparseGetMatDiagType(upTriFactor->descr); 786 787 /* Create the matrix description */ 788 stat = cusparseCreateMatDescr(&upTriFactorT->descr);CHKERRCUDA(stat); 789 stat = cusparseSetMatIndexBase(upTriFactorT->descr, indexBase);CHKERRCUDA(stat); 790 stat = cusparseSetMatType(upTriFactorT->descr, matrixType);CHKERRCUDA(stat); 791 stat = cusparseSetMatFillMode(upTriFactorT->descr, fillMode);CHKERRCUDA(stat); 792 stat = cusparseSetMatDiagType(upTriFactorT->descr, diagType);CHKERRCUDA(stat); 793 794 /* Create the solve analysis information */ 795 stat = cusparseCreateSolveAnalysisInfo(&upTriFactorT->solveInfo);CHKERRCUDA(stat); 796 797 /* set the operation */ 798 upTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 799 800 /* allocate GPU space for the CSC of the upper triangular factor*/ 801 upTriFactorT->csrMat = new CsrMatrix; 802 upTriFactorT->csrMat->num_rows = upTriFactor->csrMat->num_rows; 803 upTriFactorT->csrMat->num_cols = upTriFactor->csrMat->num_cols; 804 upTriFactorT->csrMat->num_entries = upTriFactor->csrMat->num_entries; 805 upTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(upTriFactor->csrMat->num_rows+1); 806 upTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(upTriFactor->csrMat->num_entries); 807 upTriFactorT->csrMat->values = new THRUSTARRAY(upTriFactor->csrMat->num_entries); 808 809 /* compute the transpose of the upper triangular factor, i.e. the CSC */ 810 stat = cusparse_csr2csc(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, 811 upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, 812 upTriFactor->csrMat->values->data().get(), 813 upTriFactor->csrMat->row_offsets->data().get(), 814 upTriFactor->csrMat->column_indices->data().get(), 815 upTriFactorT->csrMat->values->data().get(), 816 upTriFactorT->csrMat->column_indices->data().get(), 817 upTriFactorT->csrMat->row_offsets->data().get(), 818 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 819 820 /* perform the solve analysis on the transposed matrix */ 821 stat = cusparse_analysis(cusparseTriFactors->handle, upTriFactorT->solveOp, 822 upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, 823 upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(), 824 upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), 825 upTriFactorT->solveInfo);CHKERRCUDA(stat); 826 827 /* assign the pointer. Is this really necessary? */ 828 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->upTriFactorPtrTranspose = upTriFactorT; 829 PetscFunctionReturn(0); 830 } 831 832 static PetscErrorCode MatSeqAIJCUSPARSEGenerateTransposeForMult(Mat A) 833 { 834 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 835 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 836 Mat_SeqAIJCUSPARSEMultStruct *matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 837 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 838 cusparseStatus_t stat; 839 cusparseIndexBase_t indexBase; 840 cudaError_t err; 841 842 PetscFunctionBegin; 843 844 /* allocate space for the triangular factor information */ 845 matstructT = new Mat_SeqAIJCUSPARSEMultStruct; 846 stat = cusparseCreateMatDescr(&matstructT->descr);CHKERRCUDA(stat); 847 indexBase = cusparseGetMatIndexBase(matstruct->descr); 848 stat = cusparseSetMatIndexBase(matstructT->descr, indexBase);CHKERRCUDA(stat); 849 stat = cusparseSetMatType(matstructT->descr, CUSPARSE_MATRIX_TYPE_GENERAL);CHKERRCUDA(stat); 850 851 /* set alpha and beta */ 852 err = cudaMalloc((void **)&(matstructT->alpha),sizeof(PetscScalar));CHKERRCUDA(err); 853 err = cudaMemcpy(matstructT->alpha,&ALPHA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 854 err = cudaMalloc((void **)&(matstructT->beta),sizeof(PetscScalar));CHKERRCUDA(err); 855 err = cudaMemcpy(matstructT->beta,&BETA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 856 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 857 858 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 859 CsrMatrix *matrix = (CsrMatrix*)matstruct->mat; 860 CsrMatrix *matrixT= new CsrMatrix; 861 matrixT->num_rows = A->rmap->n; 862 matrixT->num_cols = A->cmap->n; 863 matrixT->num_entries = a->nz; 864 matrixT->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 865 matrixT->column_indices = new THRUSTINTARRAY32(a->nz); 866 matrixT->values = new THRUSTARRAY(a->nz); 867 868 /* compute the transpose of the upper triangular factor, i.e. the CSC */ 869 indexBase = cusparseGetMatIndexBase(matstruct->descr); 870 stat = cusparse_csr2csc(cusparsestruct->handle, matrix->num_rows, 871 matrix->num_cols, matrix->num_entries, 872 matrix->values->data().get(), 873 matrix->row_offsets->data().get(), 874 matrix->column_indices->data().get(), 875 matrixT->values->data().get(), 876 matrixT->column_indices->data().get(), 877 matrixT->row_offsets->data().get(), 878 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 879 880 /* assign the pointer */ 881 matstructT->mat = matrixT; 882 883 } else if (cusparsestruct->format==MAT_CUSPARSE_ELL || cusparsestruct->format==MAT_CUSPARSE_HYB) { 884 #if CUDA_VERSION>=5000 885 /* First convert HYB to CSR */ 886 CsrMatrix *temp= new CsrMatrix; 887 temp->num_rows = A->rmap->n; 888 temp->num_cols = A->cmap->n; 889 temp->num_entries = a->nz; 890 temp->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 891 temp->column_indices = new THRUSTINTARRAY32(a->nz); 892 temp->values = new THRUSTARRAY(a->nz); 893 894 895 stat = cusparse_hyb2csr(cusparsestruct->handle, 896 matstruct->descr, (cusparseHybMat_t)matstruct->mat, 897 temp->values->data().get(), 898 temp->row_offsets->data().get(), 899 temp->column_indices->data().get());CHKERRCUDA(stat); 900 901 /* Next, convert CSR to CSC (i.e. the matrix transpose) */ 902 CsrMatrix *tempT= new CsrMatrix; 903 tempT->num_rows = A->rmap->n; 904 tempT->num_cols = A->cmap->n; 905 tempT->num_entries = a->nz; 906 tempT->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 907 tempT->column_indices = new THRUSTINTARRAY32(a->nz); 908 tempT->values = new THRUSTARRAY(a->nz); 909 910 stat = cusparse_csr2csc(cusparsestruct->handle, temp->num_rows, 911 temp->num_cols, temp->num_entries, 912 temp->values->data().get(), 913 temp->row_offsets->data().get(), 914 temp->column_indices->data().get(), 915 tempT->values->data().get(), 916 tempT->column_indices->data().get(), 917 tempT->row_offsets->data().get(), 918 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 919 920 /* Last, convert CSC to HYB */ 921 cusparseHybMat_t hybMat; 922 stat = cusparseCreateHybMat(&hybMat);CHKERRCUDA(stat); 923 cusparseHybPartition_t partition = cusparsestruct->format==MAT_CUSPARSE_ELL ? 924 CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO; 925 stat = cusparse_csr2hyb(cusparsestruct->handle, A->rmap->n, A->cmap->n, 926 matstructT->descr, tempT->values->data().get(), 927 tempT->row_offsets->data().get(), 928 tempT->column_indices->data().get(), 929 hybMat, 0, partition);CHKERRCUDA(stat); 930 931 /* assign the pointer */ 932 matstructT->mat = hybMat; 933 934 /* delete temporaries */ 935 if (tempT) { 936 if (tempT->values) delete (THRUSTARRAY*) tempT->values; 937 if (tempT->column_indices) delete (THRUSTINTARRAY32*) tempT->column_indices; 938 if (tempT->row_offsets) delete (THRUSTINTARRAY32*) tempT->row_offsets; 939 delete (CsrMatrix*) tempT; 940 } 941 if (temp) { 942 if (temp->values) delete (THRUSTARRAY*) temp->values; 943 if (temp->column_indices) delete (THRUSTINTARRAY32*) temp->column_indices; 944 if (temp->row_offsets) delete (THRUSTINTARRAY32*) temp->row_offsets; 945 delete (CsrMatrix*) temp; 946 } 947 #else 948 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"ELL (Ellpack) and HYB (Hybrid) storage format for the Matrix Transpose (in MatMultTranspose) require CUDA 5.0 or later."); 949 #endif 950 } 951 /* assign the compressed row indices */ 952 matstructT->cprowIndices = new THRUSTINTARRAY; 953 954 /* assign the pointer */ 955 ((Mat_SeqAIJCUSPARSE*)A->spptr)->matTranspose = matstructT; 956 PetscFunctionReturn(0); 957 } 958 959 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat A,Vec bb,Vec xx) 960 { 961 PetscInt n = xx->map->n; 962 const PetscScalar *barray; 963 PetscScalar *xarray; 964 thrust::device_ptr<const PetscScalar> bGPU; 965 thrust::device_ptr<PetscScalar> xGPU; 966 cusparseStatus_t stat; 967 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 968 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 969 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 970 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 971 PetscErrorCode ierr; 972 973 PetscFunctionBegin; 974 /* Analyze the matrix and create the transpose ... on the fly */ 975 if (!loTriFactorT && !upTriFactorT) { 976 ierr = MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A);CHKERRQ(ierr); 977 loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 978 upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 979 } 980 981 /* Get the GPU pointers */ 982 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 983 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 984 xGPU = thrust::device_pointer_cast(xarray); 985 bGPU = thrust::device_pointer_cast(barray); 986 987 /* First, reorder with the row permutation */ 988 thrust::copy(thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), 989 thrust::make_permutation_iterator(bGPU+n, cusparseTriFactors->rpermIndices->end()), 990 xGPU); 991 992 /* First, solve U */ 993 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, 994 upTriFactorT->csrMat->num_rows, &ALPHA, upTriFactorT->descr, 995 upTriFactorT->csrMat->values->data().get(), 996 upTriFactorT->csrMat->row_offsets->data().get(), 997 upTriFactorT->csrMat->column_indices->data().get(), 998 upTriFactorT->solveInfo, 999 xarray, tempGPU->data().get());CHKERRCUDA(stat); 1000 1001 /* Then, solve L */ 1002 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, 1003 loTriFactorT->csrMat->num_rows, &ALPHA, loTriFactorT->descr, 1004 loTriFactorT->csrMat->values->data().get(), 1005 loTriFactorT->csrMat->row_offsets->data().get(), 1006 loTriFactorT->csrMat->column_indices->data().get(), 1007 loTriFactorT->solveInfo, 1008 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1009 1010 /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */ 1011 thrust::copy(thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), 1012 thrust::make_permutation_iterator(xGPU+n, cusparseTriFactors->cpermIndices->end()), 1013 tempGPU->begin()); 1014 1015 /* Copy the temporary to the full solution. */ 1016 thrust::copy(tempGPU->begin(), tempGPU->end(), xGPU); 1017 1018 /* restore */ 1019 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1020 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1021 ierr = WaitForGPU();CHKERRCUDA(ierr); 1022 1023 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1024 PetscFunctionReturn(0); 1025 } 1026 1027 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat A,Vec bb,Vec xx) 1028 { 1029 const PetscScalar *barray; 1030 PetscScalar *xarray; 1031 cusparseStatus_t stat; 1032 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1033 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 1034 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 1035 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1036 PetscErrorCode ierr; 1037 1038 PetscFunctionBegin; 1039 /* Analyze the matrix and create the transpose ... on the fly */ 1040 if (!loTriFactorT && !upTriFactorT) { 1041 ierr = MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A);CHKERRQ(ierr); 1042 loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 1043 upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 1044 } 1045 1046 /* Get the GPU pointers */ 1047 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1048 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1049 1050 /* First, solve U */ 1051 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, 1052 upTriFactorT->csrMat->num_rows, &ALPHA, upTriFactorT->descr, 1053 upTriFactorT->csrMat->values->data().get(), 1054 upTriFactorT->csrMat->row_offsets->data().get(), 1055 upTriFactorT->csrMat->column_indices->data().get(), 1056 upTriFactorT->solveInfo, 1057 barray, tempGPU->data().get());CHKERRCUDA(stat); 1058 1059 /* Then, solve L */ 1060 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, 1061 loTriFactorT->csrMat->num_rows, &ALPHA, loTriFactorT->descr, 1062 loTriFactorT->csrMat->values->data().get(), 1063 loTriFactorT->csrMat->row_offsets->data().get(), 1064 loTriFactorT->csrMat->column_indices->data().get(), 1065 loTriFactorT->solveInfo, 1066 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1067 1068 /* restore */ 1069 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1070 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1071 ierr = WaitForGPU();CHKERRCUDA(ierr); 1072 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1073 PetscFunctionReturn(0); 1074 } 1075 1076 static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat A,Vec bb,Vec xx) 1077 { 1078 const PetscScalar *barray; 1079 PetscScalar *xarray; 1080 thrust::device_ptr<const PetscScalar> bGPU; 1081 thrust::device_ptr<PetscScalar> xGPU; 1082 cusparseStatus_t stat; 1083 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1084 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 1085 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 1086 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1087 PetscErrorCode ierr; 1088 1089 PetscFunctionBegin; 1090 PetscCheckTypeName(bb,VECSEQCUDA); 1091 PetscCheckTypeName(xx,VECSEQCUDA); 1092 1093 /* Get the GPU pointers */ 1094 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1095 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1096 xGPU = thrust::device_pointer_cast(xarray); 1097 bGPU = thrust::device_pointer_cast(barray); 1098 1099 /* First, reorder with the row permutation */ 1100 thrust::copy(thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), 1101 thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->end()), 1102 xGPU); 1103 1104 /* Next, solve L */ 1105 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1106 loTriFactor->csrMat->num_rows, &ALPHA, loTriFactor->descr, 1107 loTriFactor->csrMat->values->data().get(), 1108 loTriFactor->csrMat->row_offsets->data().get(), 1109 loTriFactor->csrMat->column_indices->data().get(), 1110 loTriFactor->solveInfo, 1111 xarray, tempGPU->data().get());CHKERRCUDA(stat); 1112 1113 /* Then, solve U */ 1114 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1115 upTriFactor->csrMat->num_rows, &ALPHA, upTriFactor->descr, 1116 upTriFactor->csrMat->values->data().get(), 1117 upTriFactor->csrMat->row_offsets->data().get(), 1118 upTriFactor->csrMat->column_indices->data().get(), 1119 upTriFactor->solveInfo, 1120 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1121 1122 /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */ 1123 thrust::copy(thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), 1124 thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->end()), 1125 tempGPU->begin()); 1126 1127 /* Copy the temporary to the full solution. */ 1128 thrust::copy(tempGPU->begin(), tempGPU->end(), xGPU); 1129 1130 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1131 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1132 ierr = WaitForGPU();CHKERRCUDA(ierr); 1133 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1134 PetscFunctionReturn(0); 1135 } 1136 1137 static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A,Vec bb,Vec xx) 1138 { 1139 const PetscScalar *barray; 1140 PetscScalar *xarray; 1141 cusparseStatus_t stat; 1142 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1143 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 1144 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 1145 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1146 PetscErrorCode ierr; 1147 1148 PetscFunctionBegin; 1149 /* Get the GPU pointers */ 1150 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1151 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1152 1153 /* First, solve L */ 1154 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1155 loTriFactor->csrMat->num_rows, &ALPHA, loTriFactor->descr, 1156 loTriFactor->csrMat->values->data().get(), 1157 loTriFactor->csrMat->row_offsets->data().get(), 1158 loTriFactor->csrMat->column_indices->data().get(), 1159 loTriFactor->solveInfo, 1160 barray, tempGPU->data().get());CHKERRCUDA(stat); 1161 1162 /* Next, solve U */ 1163 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1164 upTriFactor->csrMat->num_rows, &ALPHA, upTriFactor->descr, 1165 upTriFactor->csrMat->values->data().get(), 1166 upTriFactor->csrMat->row_offsets->data().get(), 1167 upTriFactor->csrMat->column_indices->data().get(), 1168 upTriFactor->solveInfo, 1169 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1170 1171 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1172 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1173 ierr = WaitForGPU();CHKERRCUDA(ierr); 1174 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1175 PetscFunctionReturn(0); 1176 } 1177 1178 static PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A) 1179 { 1180 1181 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1182 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1183 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1184 PetscInt m = A->rmap->n,*ii,*ridx; 1185 PetscErrorCode ierr; 1186 cusparseStatus_t stat; 1187 cudaError_t err; 1188 1189 PetscFunctionBegin; 1190 if (A->valid_GPU_matrix == PETSC_CUDA_UNALLOCATED || A->valid_GPU_matrix == PETSC_CUDA_CPU) { 1191 ierr = PetscLogEventBegin(MAT_CUSPARSECopyToGPU,A,0,0,0);CHKERRQ(ierr); 1192 if (A->assembled && A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { 1193 CsrMatrix *matrix = (CsrMatrix*)matstruct->mat; 1194 /* copy values only */ 1195 matrix->values->assign(a->a, a->a+a->nz); 1196 } else { 1197 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&matstruct,cusparsestruct->format); 1198 try { 1199 cusparsestruct->nonzerorow=0; 1200 for (int j = 0; j<m; j++) cusparsestruct->nonzerorow += ((a->i[j+1]-a->i[j])>0); 1201 1202 if (a->compressedrow.use) { 1203 m = a->compressedrow.nrows; 1204 ii = a->compressedrow.i; 1205 ridx = a->compressedrow.rindex; 1206 } else { 1207 /* Forcing compressed row on the GPU */ 1208 int k=0; 1209 ierr = PetscMalloc1(cusparsestruct->nonzerorow+1, &ii);CHKERRQ(ierr); 1210 ierr = PetscMalloc1(cusparsestruct->nonzerorow, &ridx);CHKERRQ(ierr); 1211 ii[0]=0; 1212 for (int j = 0; j<m; j++) { 1213 if ((a->i[j+1]-a->i[j])>0) { 1214 ii[k] = a->i[j]; 1215 ridx[k]= j; 1216 k++; 1217 } 1218 } 1219 ii[cusparsestruct->nonzerorow] = a->nz; 1220 m = cusparsestruct->nonzerorow; 1221 } 1222 1223 /* allocate space for the triangular factor information */ 1224 matstruct = new Mat_SeqAIJCUSPARSEMultStruct; 1225 stat = cusparseCreateMatDescr(&matstruct->descr);CHKERRCUDA(stat); 1226 stat = cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 1227 stat = cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL);CHKERRCUDA(stat); 1228 1229 err = cudaMalloc((void **)&(matstruct->alpha),sizeof(PetscScalar));CHKERRCUDA(err); 1230 err = cudaMemcpy(matstruct->alpha,&ALPHA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1231 err = cudaMalloc((void **)&(matstruct->beta),sizeof(PetscScalar));CHKERRCUDA(err); 1232 err = cudaMemcpy(matstruct->beta,&BETA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1233 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 1234 1235 /* Build a hybrid/ellpack matrix if this option is chosen for the storage */ 1236 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1237 /* set the matrix */ 1238 CsrMatrix *matrix= new CsrMatrix; 1239 matrix->num_rows = m; 1240 matrix->num_cols = A->cmap->n; 1241 matrix->num_entries = a->nz; 1242 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1243 matrix->row_offsets->assign(ii, ii + m+1); 1244 1245 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1246 matrix->column_indices->assign(a->j, a->j+a->nz); 1247 1248 matrix->values = new THRUSTARRAY(a->nz); 1249 matrix->values->assign(a->a, a->a+a->nz); 1250 1251 /* assign the pointer */ 1252 matstruct->mat = matrix; 1253 1254 } else if (cusparsestruct->format==MAT_CUSPARSE_ELL || cusparsestruct->format==MAT_CUSPARSE_HYB) { 1255 #if CUDA_VERSION>=4020 1256 CsrMatrix *matrix= new CsrMatrix; 1257 matrix->num_rows = m; 1258 matrix->num_cols = A->cmap->n; 1259 matrix->num_entries = a->nz; 1260 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1261 matrix->row_offsets->assign(ii, ii + m+1); 1262 1263 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1264 matrix->column_indices->assign(a->j, a->j+a->nz); 1265 1266 matrix->values = new THRUSTARRAY(a->nz); 1267 matrix->values->assign(a->a, a->a+a->nz); 1268 1269 cusparseHybMat_t hybMat; 1270 stat = cusparseCreateHybMat(&hybMat);CHKERRCUDA(stat); 1271 cusparseHybPartition_t partition = cusparsestruct->format==MAT_CUSPARSE_ELL ? 1272 CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO; 1273 stat = cusparse_csr2hyb(cusparsestruct->handle, matrix->num_rows, matrix->num_cols, 1274 matstruct->descr, matrix->values->data().get(), 1275 matrix->row_offsets->data().get(), 1276 matrix->column_indices->data().get(), 1277 hybMat, 0, partition);CHKERRCUDA(stat); 1278 /* assign the pointer */ 1279 matstruct->mat = hybMat; 1280 1281 if (matrix) { 1282 if (matrix->values) delete (THRUSTARRAY*)matrix->values; 1283 if (matrix->column_indices) delete (THRUSTINTARRAY32*)matrix->column_indices; 1284 if (matrix->row_offsets) delete (THRUSTINTARRAY32*)matrix->row_offsets; 1285 delete (CsrMatrix*)matrix; 1286 } 1287 #endif 1288 } 1289 1290 /* assign the compressed row indices */ 1291 matstruct->cprowIndices = new THRUSTINTARRAY(m); 1292 matstruct->cprowIndices->assign(ridx,ridx+m); 1293 1294 /* assign the pointer */ 1295 cusparsestruct->mat = matstruct; 1296 1297 if (!a->compressedrow.use) { 1298 ierr = PetscFree(ii);CHKERRQ(ierr); 1299 ierr = PetscFree(ridx);CHKERRQ(ierr); 1300 } 1301 cusparsestruct->workVector = new THRUSTARRAY; 1302 cusparsestruct->workVector->resize(m); 1303 } catch(char *ex) { 1304 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1305 } 1306 cusparsestruct->nonzerostate = A->nonzerostate; 1307 } 1308 ierr = WaitForGPU();CHKERRCUDA(ierr); 1309 A->valid_GPU_matrix = PETSC_CUDA_BOTH; 1310 ierr = PetscLogEventEnd(MAT_CUSPARSECopyToGPU,A,0,0,0);CHKERRQ(ierr); 1311 } 1312 PetscFunctionReturn(0); 1313 } 1314 1315 static PetscErrorCode MatCreateVecs_SeqAIJCUSPARSE(Mat mat, Vec *right, Vec *left) 1316 { 1317 PetscErrorCode ierr; 1318 PetscInt rbs,cbs; 1319 1320 PetscFunctionBegin; 1321 ierr = MatGetBlockSizes(mat,&rbs,&cbs);CHKERRQ(ierr); 1322 if (right) { 1323 ierr = VecCreate(PetscObjectComm((PetscObject)mat),right);CHKERRQ(ierr); 1324 ierr = VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 1325 ierr = VecSetBlockSize(*right,cbs);CHKERRQ(ierr); 1326 ierr = VecSetType(*right,VECSEQCUDA);CHKERRQ(ierr); 1327 ierr = PetscLayoutReference(mat->cmap,&(*right)->map);CHKERRQ(ierr); 1328 } 1329 if (left) { 1330 ierr = VecCreate(PetscObjectComm((PetscObject)mat),left);CHKERRQ(ierr); 1331 ierr = VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 1332 ierr = VecSetBlockSize(*left,rbs);CHKERRQ(ierr); 1333 ierr = VecSetType(*left,VECSEQCUDA);CHKERRQ(ierr); 1334 ierr = PetscLayoutReference(mat->rmap,&(*left)->map);CHKERRQ(ierr); 1335 } 1336 PetscFunctionReturn(0); 1337 } 1338 1339 struct VecCUDAPlusEquals 1340 { 1341 template <typename Tuple> 1342 __host__ __device__ 1343 void operator()(Tuple t) 1344 { 1345 thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t); 1346 } 1347 }; 1348 1349 static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy) 1350 { 1351 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1352 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1353 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1354 const PetscScalar *xarray; 1355 PetscScalar *yarray; 1356 PetscErrorCode ierr; 1357 cusparseStatus_t stat; 1358 1359 PetscFunctionBegin; 1360 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1361 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1362 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1363 ierr = VecSet(yy,0);CHKERRQ(ierr); 1364 ierr = VecCUDAGetArrayWrite(yy,&yarray);CHKERRQ(ierr); 1365 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1366 CsrMatrix *mat = (CsrMatrix*)matstruct->mat; 1367 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1368 mat->num_rows, mat->num_cols, mat->num_entries, 1369 matstruct->alpha, matstruct->descr, mat->values->data().get(), mat->row_offsets->data().get(), 1370 mat->column_indices->data().get(), xarray, matstruct->beta, 1371 yarray);CHKERRCUDA(stat); 1372 } else { 1373 #if CUDA_VERSION>=4020 1374 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat; 1375 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1376 matstruct->alpha, matstruct->descr, hybMat, 1377 xarray, matstruct->beta, 1378 yarray);CHKERRCUDA(stat); 1379 #endif 1380 } 1381 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1382 ierr = VecCUDARestoreArrayWrite(yy,&yarray);CHKERRQ(ierr); 1383 if (!cusparsestruct->stream) { 1384 ierr = WaitForGPU();CHKERRCUDA(ierr); 1385 } 1386 ierr = PetscLogFlops(2.0*a->nz - cusparsestruct->nonzerorow);CHKERRQ(ierr); 1387 PetscFunctionReturn(0); 1388 } 1389 1390 static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy) 1391 { 1392 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1393 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1394 Mat_SeqAIJCUSPARSEMultStruct *matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1395 const PetscScalar *xarray; 1396 PetscScalar *yarray; 1397 PetscErrorCode ierr; 1398 cusparseStatus_t stat; 1399 1400 PetscFunctionBegin; 1401 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1402 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1403 if (!matstructT) { 1404 ierr = MatSeqAIJCUSPARSEGenerateTransposeForMult(A);CHKERRQ(ierr); 1405 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1406 } 1407 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1408 ierr = VecSet(yy,0);CHKERRQ(ierr); 1409 ierr = VecCUDAGetArrayWrite(yy,&yarray);CHKERRQ(ierr); 1410 1411 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1412 CsrMatrix *mat = (CsrMatrix*)matstructT->mat; 1413 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1414 mat->num_rows, mat->num_cols, 1415 mat->num_entries, matstructT->alpha, matstructT->descr, 1416 mat->values->data().get(), mat->row_offsets->data().get(), 1417 mat->column_indices->data().get(), xarray, matstructT->beta, 1418 yarray);CHKERRCUDA(stat); 1419 } else { 1420 #if CUDA_VERSION>=4020 1421 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstructT->mat; 1422 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1423 matstructT->alpha, matstructT->descr, hybMat, 1424 xarray, matstructT->beta, 1425 yarray);CHKERRCUDA(stat); 1426 #endif 1427 } 1428 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1429 ierr = VecCUDARestoreArrayWrite(yy,&yarray);CHKERRQ(ierr); 1430 if (!cusparsestruct->stream) { 1431 ierr = WaitForGPU();CHKERRCUDA(ierr); 1432 } 1433 ierr = PetscLogFlops(2.0*a->nz - cusparsestruct->nonzerorow);CHKERRQ(ierr); 1434 PetscFunctionReturn(0); 1435 } 1436 1437 1438 static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy,Vec zz) 1439 { 1440 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1441 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1442 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1443 thrust::device_ptr<PetscScalar> zptr; 1444 const PetscScalar *xarray; 1445 PetscScalar *zarray; 1446 PetscErrorCode ierr; 1447 cusparseStatus_t stat; 1448 1449 PetscFunctionBegin; 1450 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1451 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1452 try { 1453 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1454 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1455 ierr = VecCUDAGetArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1456 zptr = thrust::device_pointer_cast(zarray); 1457 1458 /* multiply add */ 1459 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1460 CsrMatrix *mat = (CsrMatrix*)matstruct->mat; 1461 /* here we need to be careful to set the number of rows in the multiply to the 1462 number of compressed rows in the matrix ... which is equivalent to the 1463 size of the workVector */ 1464 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1465 mat->num_rows, mat->num_cols, 1466 mat->num_entries, matstruct->alpha, matstruct->descr, 1467 mat->values->data().get(), mat->row_offsets->data().get(), 1468 mat->column_indices->data().get(), xarray, matstruct->beta, 1469 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1470 } else { 1471 #if CUDA_VERSION>=4020 1472 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat; 1473 if (cusparsestruct->workVector->size()) { 1474 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1475 matstruct->alpha, matstruct->descr, hybMat, 1476 xarray, matstruct->beta, 1477 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1478 } 1479 #endif 1480 } 1481 1482 /* scatter the data from the temporary into the full vector with a += operation */ 1483 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))), 1484 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))) + cusparsestruct->workVector->size(), 1485 VecCUDAPlusEquals()); 1486 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1487 ierr = VecCUDARestoreArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1488 1489 } catch(char *ex) { 1490 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1491 } 1492 ierr = WaitForGPU();CHKERRCUDA(ierr); 1493 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1494 PetscFunctionReturn(0); 1495 } 1496 1497 static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy,Vec zz) 1498 { 1499 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1500 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1501 Mat_SeqAIJCUSPARSEMultStruct *matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1502 thrust::device_ptr<PetscScalar> zptr; 1503 const PetscScalar *xarray; 1504 PetscScalar *zarray; 1505 PetscErrorCode ierr; 1506 cusparseStatus_t stat; 1507 1508 PetscFunctionBegin; 1509 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1510 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1511 if (!matstructT) { 1512 ierr = MatSeqAIJCUSPARSEGenerateTransposeForMult(A);CHKERRQ(ierr); 1513 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1514 } 1515 1516 try { 1517 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1518 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1519 ierr = VecCUDAGetArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1520 zptr = thrust::device_pointer_cast(zarray); 1521 1522 /* multiply add with matrix transpose */ 1523 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1524 CsrMatrix *mat = (CsrMatrix*)matstructT->mat; 1525 /* here we need to be careful to set the number of rows in the multiply to the 1526 number of compressed rows in the matrix ... which is equivalent to the 1527 size of the workVector */ 1528 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1529 mat->num_rows, mat->num_cols, 1530 mat->num_entries, matstructT->alpha, matstructT->descr, 1531 mat->values->data().get(), mat->row_offsets->data().get(), 1532 mat->column_indices->data().get(), xarray, matstructT->beta, 1533 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1534 } else { 1535 #if CUDA_VERSION>=4020 1536 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstructT->mat; 1537 if (cusparsestruct->workVector->size()) { 1538 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1539 matstructT->alpha, matstructT->descr, hybMat, 1540 xarray, matstructT->beta, 1541 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1542 } 1543 #endif 1544 } 1545 1546 /* scatter the data from the temporary into the full vector with a += operation */ 1547 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))), 1548 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))) + cusparsestruct->workVector->size(), 1549 VecCUDAPlusEquals()); 1550 1551 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1552 ierr = VecCUDARestoreArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1553 1554 } catch(char *ex) { 1555 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1556 } 1557 ierr = WaitForGPU();CHKERRCUDA(ierr); 1558 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1559 PetscFunctionReturn(0); 1560 } 1561 1562 static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A,MatAssemblyType mode) 1563 { 1564 PetscErrorCode ierr; 1565 1566 PetscFunctionBegin; 1567 ierr = MatAssemblyEnd_SeqAIJ(A,mode);CHKERRQ(ierr); 1568 if (A->factortype==MAT_FACTOR_NONE) { 1569 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1570 } 1571 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 1572 A->ops->mult = MatMult_SeqAIJCUSPARSE; 1573 A->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1574 A->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1575 A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1576 PetscFunctionReturn(0); 1577 } 1578 1579 /* --------------------------------------------------------------------------------*/ 1580 /*@ 1581 MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in AIJ (compressed row) format 1582 (the default parallel PETSc format). This matrix will ultimately pushed down 1583 to NVidia GPUs and use the CUSPARSE library for calculations. For good matrix 1584 assembly performance the user should preallocate the matrix storage by setting 1585 the parameter nz (or the array nnz). By setting these parameters accurately, 1586 performance during matrix assembly can be increased by more than a factor of 50. 1587 1588 Collective on MPI_Comm 1589 1590 Input Parameters: 1591 + comm - MPI communicator, set to PETSC_COMM_SELF 1592 . m - number of rows 1593 . n - number of columns 1594 . nz - number of nonzeros per row (same for all rows) 1595 - nnz - array containing the number of nonzeros in the various rows 1596 (possibly different for each row) or NULL 1597 1598 Output Parameter: 1599 . A - the matrix 1600 1601 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 1602 MatXXXXSetPreallocation() paradgm instead of this routine directly. 1603 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 1604 1605 Notes: 1606 If nnz is given then nz is ignored 1607 1608 The AIJ format (also called the Yale sparse matrix format or 1609 compressed row storage), is fully compatible with standard Fortran 77 1610 storage. That is, the stored row and column indices can begin at 1611 either one (as in Fortran) or zero. See the users' manual for details. 1612 1613 Specify the preallocated storage with either nz or nnz (not both). 1614 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 1615 allocation. For large problems you MUST preallocate memory or you 1616 will get TERRIBLE performance, see the users' manual chapter on matrices. 1617 1618 By default, this format uses inodes (identical nodes) when possible, to 1619 improve numerical efficiency of matrix-vector products and solves. We 1620 search for consecutive rows with the same nonzero structure, thereby 1621 reusing matrix information to achieve increased efficiency. 1622 1623 Level: intermediate 1624 1625 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATSEQAIJCUSPARSE, MATAIJCUSPARSE 1626 @*/ 1627 PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 1628 { 1629 PetscErrorCode ierr; 1630 1631 PetscFunctionBegin; 1632 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1633 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 1634 ierr = MatSetType(*A,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1635 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);CHKERRQ(ierr); 1636 PetscFunctionReturn(0); 1637 } 1638 1639 static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A) 1640 { 1641 PetscErrorCode ierr; 1642 1643 PetscFunctionBegin; 1644 if (A->factortype==MAT_FACTOR_NONE) { 1645 if (A->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) { 1646 ierr = Mat_SeqAIJCUSPARSE_Destroy((Mat_SeqAIJCUSPARSE**)&A->spptr);CHKERRQ(ierr); 1647 } 1648 } else { 1649 ierr = Mat_SeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors**)&A->spptr);CHKERRQ(ierr); 1650 } 1651 ierr = MatDestroy_SeqAIJ(A);CHKERRQ(ierr); 1652 PetscFunctionReturn(0); 1653 } 1654 1655 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B) 1656 { 1657 PetscErrorCode ierr; 1658 cusparseStatus_t stat; 1659 cusparseHandle_t handle=0; 1660 1661 PetscFunctionBegin; 1662 ierr = MatCreate_SeqAIJ(B);CHKERRQ(ierr); 1663 if (B->factortype==MAT_FACTOR_NONE) { 1664 /* you cannot check the inode.use flag here since the matrix was just created. 1665 now build a GPU matrix data structure */ 1666 B->spptr = new Mat_SeqAIJCUSPARSE; 1667 ((Mat_SeqAIJCUSPARSE*)B->spptr)->mat = 0; 1668 ((Mat_SeqAIJCUSPARSE*)B->spptr)->matTranspose = 0; 1669 ((Mat_SeqAIJCUSPARSE*)B->spptr)->workVector = 0; 1670 ((Mat_SeqAIJCUSPARSE*)B->spptr)->format = MAT_CUSPARSE_CSR; 1671 ((Mat_SeqAIJCUSPARSE*)B->spptr)->stream = 0; 1672 ((Mat_SeqAIJCUSPARSE*)B->spptr)->handle = 0; 1673 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1674 ((Mat_SeqAIJCUSPARSE*)B->spptr)->handle = handle; 1675 ((Mat_SeqAIJCUSPARSE*)B->spptr)->stream = 0; 1676 } else { 1677 /* NEXT, set the pointers to the triangular factors */ 1678 B->spptr = new Mat_SeqAIJCUSPARSETriFactors; 1679 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtr = 0; 1680 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtr = 0; 1681 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtrTranspose = 0; 1682 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtrTranspose = 0; 1683 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->rpermIndices = 0; 1684 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->cpermIndices = 0; 1685 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->workVector = 0; 1686 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->handle = 0; 1687 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1688 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->handle = handle; 1689 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->nnz = 0; 1690 } 1691 1692 B->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE; 1693 B->ops->destroy = MatDestroy_SeqAIJCUSPARSE; 1694 B->ops->getvecs = MatCreateVecs_SeqAIJCUSPARSE; 1695 B->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE; 1696 B->ops->mult = MatMult_SeqAIJCUSPARSE; 1697 B->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1698 B->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1699 B->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1700 1701 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1702 1703 B->valid_GPU_matrix = PETSC_CUDA_UNALLOCATED; 1704 1705 ierr = PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE);CHKERRQ(ierr); 1706 PetscFunctionReturn(0); 1707 } 1708 1709 /*M 1710 MATSEQAIJCUSPARSE - MATAIJCUSPARSE = "(seq)aijcusparse" - A matrix type to be used for sparse matrices. 1711 1712 A matrix type type whose data resides on Nvidia GPUs. These matrices can be in either 1713 CSR, ELL, or Hybrid format. The ELL and HYB formats require CUDA 4.2 or later. 1714 All matrix calculations are performed on Nvidia GPUs using the CUSPARSE library. 1715 1716 Options Database Keys: 1717 + -mat_type aijcusparse - sets the matrix type to "seqaijcusparse" during a call to MatSetFromOptions() 1718 . -mat_cusparse_storage_format csr - sets the storage format of matrices (for MatMult and factors in MatSolve) during a call to MatSetFromOptions(). Other options include ell (ellpack) or hyb (hybrid). 1719 . -mat_cusparse_mult_storage_format csr - sets the storage format of matrices (for MatMult) during a call to MatSetFromOptions(). Other options include ell (ellpack) or hyb (hybrid). 1720 1721 Level: beginner 1722 1723 .seealso: MatCreateSeqAIJCUSPARSE(), MATAIJCUSPARSE, MatCreateAIJCUSPARSE(), MatCUSPARSESetFormat(), MatCUSPARSEStorageFormat, MatCUSPARSEFormatOperation 1724 M*/ 1725 1726 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat,MatFactorType,Mat*); 1727 1728 1729 PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_CUSPARSE(void) 1730 { 1731 PetscErrorCode ierr; 1732 1733 PetscFunctionBegin; 1734 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_LU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1735 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_CHOLESKY,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1736 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ILU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1737 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ICC,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1738 PetscFunctionReturn(0); 1739 } 1740 1741 1742 static PetscErrorCode Mat_SeqAIJCUSPARSE_Destroy(Mat_SeqAIJCUSPARSE **cusparsestruct) 1743 { 1744 cusparseStatus_t stat; 1745 cusparseHandle_t handle; 1746 1747 PetscFunctionBegin; 1748 if (*cusparsestruct) { 1749 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&(*cusparsestruct)->mat,(*cusparsestruct)->format); 1750 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&(*cusparsestruct)->matTranspose,(*cusparsestruct)->format); 1751 delete (*cusparsestruct)->workVector; 1752 if (handle = (*cusparsestruct)->handle) { 1753 stat = cusparseDestroy(handle);CHKERRCUDA(stat); 1754 } 1755 delete *cusparsestruct; 1756 *cusparsestruct = 0; 1757 } 1758 PetscFunctionReturn(0); 1759 } 1760 1761 static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **mat) 1762 { 1763 PetscFunctionBegin; 1764 if (*mat) { 1765 delete (*mat)->values; 1766 delete (*mat)->column_indices; 1767 delete (*mat)->row_offsets; 1768 delete *mat; 1769 *mat = 0; 1770 } 1771 PetscFunctionReturn(0); 1772 } 1773 1774 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **trifactor) 1775 { 1776 cusparseStatus_t stat; 1777 PetscErrorCode ierr; 1778 1779 PetscFunctionBegin; 1780 if (*trifactor) { 1781 if ((*trifactor)->descr) { stat = cusparseDestroyMatDescr((*trifactor)->descr);CHKERRCUDA(stat); } 1782 if ((*trifactor)->solveInfo) { stat = cusparseDestroySolveAnalysisInfo((*trifactor)->solveInfo);CHKERRCUDA(stat); } 1783 ierr = CsrMatrix_Destroy(&(*trifactor)->csrMat);CHKERRQ(ierr); 1784 delete *trifactor; 1785 *trifactor = 0; 1786 } 1787 PetscFunctionReturn(0); 1788 } 1789 1790 static PetscErrorCode Mat_SeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **matstruct,MatCUSPARSEStorageFormat format) 1791 { 1792 CsrMatrix *mat; 1793 cusparseStatus_t stat; 1794 cudaError_t err; 1795 1796 PetscFunctionBegin; 1797 if (*matstruct) { 1798 if ((*matstruct)->mat) { 1799 if (format==MAT_CUSPARSE_ELL || format==MAT_CUSPARSE_HYB) { 1800 cusparseHybMat_t hybMat = (cusparseHybMat_t)(*matstruct)->mat; 1801 stat = cusparseDestroyHybMat(hybMat);CHKERRCUDA(stat); 1802 } else { 1803 mat = (CsrMatrix*)(*matstruct)->mat; 1804 CsrMatrix_Destroy(&mat); 1805 } 1806 } 1807 if ((*matstruct)->descr) { stat = cusparseDestroyMatDescr((*matstruct)->descr);CHKERRCUDA(stat); } 1808 delete (*matstruct)->cprowIndices; 1809 if ((*matstruct)->alpha) { err=cudaFree((*matstruct)->alpha);CHKERRCUDA(err); } 1810 if ((*matstruct)->beta) { err=cudaFree((*matstruct)->beta);CHKERRCUDA(err); } 1811 delete *matstruct; 1812 *matstruct = 0; 1813 } 1814 PetscFunctionReturn(0); 1815 } 1816 1817 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors** trifactors) 1818 { 1819 cusparseHandle_t handle; 1820 cusparseStatus_t stat; 1821 1822 PetscFunctionBegin; 1823 if (*trifactors) { 1824 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->loTriFactorPtr); 1825 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->upTriFactorPtr); 1826 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->loTriFactorPtrTranspose); 1827 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->upTriFactorPtrTranspose); 1828 delete (*trifactors)->rpermIndices; 1829 delete (*trifactors)->cpermIndices; 1830 delete (*trifactors)->workVector; 1831 if (handle = (*trifactors)->handle) { 1832 stat = cusparseDestroy(handle);CHKERRCUDA(stat); 1833 } 1834 delete *trifactors; 1835 *trifactors = 0; 1836 } 1837 PetscFunctionReturn(0); 1838 } 1839 1840