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 1091 /* Get the GPU pointers */ 1092 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1093 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1094 xGPU = thrust::device_pointer_cast(xarray); 1095 bGPU = thrust::device_pointer_cast(barray); 1096 1097 /* First, reorder with the row permutation */ 1098 thrust::copy(thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), 1099 thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->end()), 1100 xGPU); 1101 1102 /* Next, solve L */ 1103 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1104 loTriFactor->csrMat->num_rows, &ALPHA, loTriFactor->descr, 1105 loTriFactor->csrMat->values->data().get(), 1106 loTriFactor->csrMat->row_offsets->data().get(), 1107 loTriFactor->csrMat->column_indices->data().get(), 1108 loTriFactor->solveInfo, 1109 xarray, tempGPU->data().get());CHKERRCUDA(stat); 1110 1111 /* Then, solve U */ 1112 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1113 upTriFactor->csrMat->num_rows, &ALPHA, upTriFactor->descr, 1114 upTriFactor->csrMat->values->data().get(), 1115 upTriFactor->csrMat->row_offsets->data().get(), 1116 upTriFactor->csrMat->column_indices->data().get(), 1117 upTriFactor->solveInfo, 1118 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1119 1120 /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */ 1121 thrust::copy(thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), 1122 thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->end()), 1123 tempGPU->begin()); 1124 1125 /* Copy the temporary to the full solution. */ 1126 thrust::copy(tempGPU->begin(), tempGPU->end(), xGPU); 1127 1128 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1129 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1130 ierr = WaitForGPU();CHKERRCUDA(ierr); 1131 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1132 PetscFunctionReturn(0); 1133 } 1134 1135 static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A,Vec bb,Vec xx) 1136 { 1137 const PetscScalar *barray; 1138 PetscScalar *xarray; 1139 cusparseStatus_t stat; 1140 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1141 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 1142 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 1143 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1144 PetscErrorCode ierr; 1145 1146 PetscFunctionBegin; 1147 /* Get the GPU pointers */ 1148 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1149 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1150 1151 /* First, solve L */ 1152 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1153 loTriFactor->csrMat->num_rows, &ALPHA, loTriFactor->descr, 1154 loTriFactor->csrMat->values->data().get(), 1155 loTriFactor->csrMat->row_offsets->data().get(), 1156 loTriFactor->csrMat->column_indices->data().get(), 1157 loTriFactor->solveInfo, 1158 barray, tempGPU->data().get());CHKERRCUDA(stat); 1159 1160 /* Next, solve U */ 1161 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1162 upTriFactor->csrMat->num_rows, &ALPHA, upTriFactor->descr, 1163 upTriFactor->csrMat->values->data().get(), 1164 upTriFactor->csrMat->row_offsets->data().get(), 1165 upTriFactor->csrMat->column_indices->data().get(), 1166 upTriFactor->solveInfo, 1167 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1168 1169 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1170 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1171 ierr = WaitForGPU();CHKERRCUDA(ierr); 1172 ierr = PetscLogFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1173 PetscFunctionReturn(0); 1174 } 1175 1176 static PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A) 1177 { 1178 1179 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1180 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1181 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1182 PetscInt m = A->rmap->n,*ii,*ridx; 1183 PetscErrorCode ierr; 1184 cusparseStatus_t stat; 1185 cudaError_t err; 1186 1187 PetscFunctionBegin; 1188 if (A->valid_GPU_matrix == PETSC_CUDA_UNALLOCATED || A->valid_GPU_matrix == PETSC_CUDA_CPU) { 1189 ierr = PetscLogEventBegin(MAT_CUSPARSECopyToGPU,A,0,0,0);CHKERRQ(ierr); 1190 if (A->assembled && A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { 1191 CsrMatrix *matrix = (CsrMatrix*)matstruct->mat; 1192 /* copy values only */ 1193 matrix->values->assign(a->a, a->a+a->nz); 1194 } else { 1195 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&matstruct,cusparsestruct->format); 1196 try { 1197 cusparsestruct->nonzerorow=0; 1198 for (int j = 0; j<m; j++) cusparsestruct->nonzerorow += ((a->i[j+1]-a->i[j])>0); 1199 1200 if (a->compressedrow.use) { 1201 m = a->compressedrow.nrows; 1202 ii = a->compressedrow.i; 1203 ridx = a->compressedrow.rindex; 1204 } else { 1205 /* Forcing compressed row on the GPU */ 1206 int k=0; 1207 ierr = PetscMalloc1(cusparsestruct->nonzerorow+1, &ii);CHKERRQ(ierr); 1208 ierr = PetscMalloc1(cusparsestruct->nonzerorow, &ridx);CHKERRQ(ierr); 1209 ii[0]=0; 1210 for (int j = 0; j<m; j++) { 1211 if ((a->i[j+1]-a->i[j])>0) { 1212 ii[k] = a->i[j]; 1213 ridx[k]= j; 1214 k++; 1215 } 1216 } 1217 ii[cusparsestruct->nonzerorow] = a->nz; 1218 m = cusparsestruct->nonzerorow; 1219 } 1220 1221 /* allocate space for the triangular factor information */ 1222 matstruct = new Mat_SeqAIJCUSPARSEMultStruct; 1223 stat = cusparseCreateMatDescr(&matstruct->descr);CHKERRCUDA(stat); 1224 stat = cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 1225 stat = cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL);CHKERRCUDA(stat); 1226 1227 err = cudaMalloc((void **)&(matstruct->alpha),sizeof(PetscScalar));CHKERRCUDA(err); 1228 err = cudaMemcpy(matstruct->alpha,&ALPHA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1229 err = cudaMalloc((void **)&(matstruct->beta),sizeof(PetscScalar));CHKERRCUDA(err); 1230 err = cudaMemcpy(matstruct->beta,&BETA,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1231 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 1232 1233 /* Build a hybrid/ellpack matrix if this option is chosen for the storage */ 1234 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1235 /* set the matrix */ 1236 CsrMatrix *matrix= new CsrMatrix; 1237 matrix->num_rows = m; 1238 matrix->num_cols = A->cmap->n; 1239 matrix->num_entries = a->nz; 1240 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1241 matrix->row_offsets->assign(ii, ii + m+1); 1242 1243 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1244 matrix->column_indices->assign(a->j, a->j+a->nz); 1245 1246 matrix->values = new THRUSTARRAY(a->nz); 1247 matrix->values->assign(a->a, a->a+a->nz); 1248 1249 /* assign the pointer */ 1250 matstruct->mat = matrix; 1251 1252 } else if (cusparsestruct->format==MAT_CUSPARSE_ELL || cusparsestruct->format==MAT_CUSPARSE_HYB) { 1253 #if CUDA_VERSION>=4020 1254 CsrMatrix *matrix= new CsrMatrix; 1255 matrix->num_rows = m; 1256 matrix->num_cols = A->cmap->n; 1257 matrix->num_entries = a->nz; 1258 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1259 matrix->row_offsets->assign(ii, ii + m+1); 1260 1261 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1262 matrix->column_indices->assign(a->j, a->j+a->nz); 1263 1264 matrix->values = new THRUSTARRAY(a->nz); 1265 matrix->values->assign(a->a, a->a+a->nz); 1266 1267 cusparseHybMat_t hybMat; 1268 stat = cusparseCreateHybMat(&hybMat);CHKERRCUDA(stat); 1269 cusparseHybPartition_t partition = cusparsestruct->format==MAT_CUSPARSE_ELL ? 1270 CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO; 1271 stat = cusparse_csr2hyb(cusparsestruct->handle, matrix->num_rows, matrix->num_cols, 1272 matstruct->descr, matrix->values->data().get(), 1273 matrix->row_offsets->data().get(), 1274 matrix->column_indices->data().get(), 1275 hybMat, 0, partition);CHKERRCUDA(stat); 1276 /* assign the pointer */ 1277 matstruct->mat = hybMat; 1278 1279 if (matrix) { 1280 if (matrix->values) delete (THRUSTARRAY*)matrix->values; 1281 if (matrix->column_indices) delete (THRUSTINTARRAY32*)matrix->column_indices; 1282 if (matrix->row_offsets) delete (THRUSTINTARRAY32*)matrix->row_offsets; 1283 delete (CsrMatrix*)matrix; 1284 } 1285 #endif 1286 } 1287 1288 /* assign the compressed row indices */ 1289 matstruct->cprowIndices = new THRUSTINTARRAY(m); 1290 matstruct->cprowIndices->assign(ridx,ridx+m); 1291 1292 /* assign the pointer */ 1293 cusparsestruct->mat = matstruct; 1294 1295 if (!a->compressedrow.use) { 1296 ierr = PetscFree(ii);CHKERRQ(ierr); 1297 ierr = PetscFree(ridx);CHKERRQ(ierr); 1298 } 1299 cusparsestruct->workVector = new THRUSTARRAY; 1300 cusparsestruct->workVector->resize(m); 1301 } catch(char *ex) { 1302 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1303 } 1304 cusparsestruct->nonzerostate = A->nonzerostate; 1305 } 1306 ierr = WaitForGPU();CHKERRCUDA(ierr); 1307 A->valid_GPU_matrix = PETSC_CUDA_BOTH; 1308 ierr = PetscLogEventEnd(MAT_CUSPARSECopyToGPU,A,0,0,0);CHKERRQ(ierr); 1309 } 1310 PetscFunctionReturn(0); 1311 } 1312 1313 static PetscErrorCode MatCreateVecs_SeqAIJCUSPARSE(Mat mat, Vec *right, Vec *left) 1314 { 1315 PetscErrorCode ierr; 1316 PetscInt rbs,cbs; 1317 1318 PetscFunctionBegin; 1319 ierr = MatGetBlockSizes(mat,&rbs,&cbs);CHKERRQ(ierr); 1320 if (right) { 1321 ierr = VecCreate(PetscObjectComm((PetscObject)mat),right);CHKERRQ(ierr); 1322 ierr = VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 1323 ierr = VecSetBlockSize(*right,cbs);CHKERRQ(ierr); 1324 ierr = VecSetType(*right,VECSEQCUDA);CHKERRQ(ierr); 1325 ierr = PetscLayoutReference(mat->cmap,&(*right)->map);CHKERRQ(ierr); 1326 } 1327 if (left) { 1328 ierr = VecCreate(PetscObjectComm((PetscObject)mat),left);CHKERRQ(ierr); 1329 ierr = VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 1330 ierr = VecSetBlockSize(*left,rbs);CHKERRQ(ierr); 1331 ierr = VecSetType(*left,VECSEQCUDA);CHKERRQ(ierr); 1332 ierr = PetscLayoutReference(mat->rmap,&(*left)->map);CHKERRQ(ierr); 1333 } 1334 PetscFunctionReturn(0); 1335 } 1336 1337 struct VecCUDAPlusEquals 1338 { 1339 template <typename Tuple> 1340 __host__ __device__ 1341 void operator()(Tuple t) 1342 { 1343 thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t); 1344 } 1345 }; 1346 1347 static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy) 1348 { 1349 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1350 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1351 Mat_SeqAIJCUSPARSEMultStruct *matstruct; /* Do not initialize yet, because GPU data may not be available yet */ 1352 const PetscScalar *xarray; 1353 PetscScalar *yarray; 1354 PetscErrorCode ierr; 1355 cusparseStatus_t stat; 1356 1357 PetscFunctionBegin; 1358 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1359 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1360 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1361 ierr = VecSet(yy,0);CHKERRQ(ierr); 1362 ierr = VecCUDAGetArrayWrite(yy,&yarray);CHKERRQ(ierr); 1363 matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1364 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1365 CsrMatrix *mat = (CsrMatrix*)matstruct->mat; 1366 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1367 mat->num_rows, mat->num_cols, mat->num_entries, 1368 matstruct->alpha, matstruct->descr, mat->values->data().get(), mat->row_offsets->data().get(), 1369 mat->column_indices->data().get(), xarray, matstruct->beta, 1370 yarray);CHKERRCUDA(stat); 1371 } else { 1372 #if CUDA_VERSION>=4020 1373 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat; 1374 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1375 matstruct->alpha, matstruct->descr, hybMat, 1376 xarray, matstruct->beta, 1377 yarray);CHKERRCUDA(stat); 1378 #endif 1379 } 1380 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1381 ierr = VecCUDARestoreArrayWrite(yy,&yarray);CHKERRQ(ierr); 1382 if (!cusparsestruct->stream) { 1383 ierr = WaitForGPU();CHKERRCUDA(ierr); 1384 } 1385 ierr = PetscLogFlops(2.0*a->nz - cusparsestruct->nonzerorow);CHKERRQ(ierr); 1386 PetscFunctionReturn(0); 1387 } 1388 1389 static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy) 1390 { 1391 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1392 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1393 Mat_SeqAIJCUSPARSEMultStruct *matstructT; 1394 const PetscScalar *xarray; 1395 PetscScalar *yarray; 1396 PetscErrorCode ierr; 1397 cusparseStatus_t stat; 1398 1399 PetscFunctionBegin; 1400 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1401 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1402 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 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; 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 matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1453 try { 1454 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1455 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1456 ierr = VecCUDAGetArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1457 zptr = thrust::device_pointer_cast(zarray); 1458 1459 /* multiply add */ 1460 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1461 CsrMatrix *mat = (CsrMatrix*)matstruct->mat; 1462 /* here we need to be careful to set the number of rows in the multiply to the 1463 number of compressed rows in the matrix ... which is equivalent to the 1464 size of the workVector */ 1465 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1466 mat->num_rows, mat->num_cols, 1467 mat->num_entries, matstruct->alpha, matstruct->descr, 1468 mat->values->data().get(), mat->row_offsets->data().get(), 1469 mat->column_indices->data().get(), xarray, matstruct->beta, 1470 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1471 } else { 1472 #if CUDA_VERSION>=4020 1473 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat; 1474 if (cusparsestruct->workVector->size()) { 1475 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1476 matstruct->alpha, matstruct->descr, hybMat, 1477 xarray, matstruct->beta, 1478 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1479 } 1480 #endif 1481 } 1482 1483 /* scatter the data from the temporary into the full vector with a += operation */ 1484 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))), 1485 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))) + cusparsestruct->workVector->size(), 1486 VecCUDAPlusEquals()); 1487 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1488 ierr = VecCUDARestoreArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1489 1490 } catch(char *ex) { 1491 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1492 } 1493 ierr = WaitForGPU();CHKERRCUDA(ierr); 1494 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1495 PetscFunctionReturn(0); 1496 } 1497 1498 static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy,Vec zz) 1499 { 1500 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1501 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1502 Mat_SeqAIJCUSPARSEMultStruct *matstructT; 1503 thrust::device_ptr<PetscScalar> zptr; 1504 const PetscScalar *xarray; 1505 PetscScalar *zarray; 1506 PetscErrorCode ierr; 1507 cusparseStatus_t stat; 1508 1509 PetscFunctionBegin; 1510 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1511 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1512 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1513 if (!matstructT) { 1514 ierr = MatSeqAIJCUSPARSEGenerateTransposeForMult(A);CHKERRQ(ierr); 1515 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1516 } 1517 1518 try { 1519 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1520 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1521 ierr = VecCUDAGetArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1522 zptr = thrust::device_pointer_cast(zarray); 1523 1524 /* multiply add with matrix transpose */ 1525 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1526 CsrMatrix *mat = (CsrMatrix*)matstructT->mat; 1527 /* here we need to be careful to set the number of rows in the multiply to the 1528 number of compressed rows in the matrix ... which is equivalent to the 1529 size of the workVector */ 1530 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1531 mat->num_rows, mat->num_cols, 1532 mat->num_entries, matstructT->alpha, matstructT->descr, 1533 mat->values->data().get(), mat->row_offsets->data().get(), 1534 mat->column_indices->data().get(), xarray, matstructT->beta, 1535 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1536 } else { 1537 #if CUDA_VERSION>=4020 1538 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstructT->mat; 1539 if (cusparsestruct->workVector->size()) { 1540 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1541 matstructT->alpha, matstructT->descr, hybMat, 1542 xarray, matstructT->beta, 1543 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1544 } 1545 #endif 1546 } 1547 1548 /* scatter the data from the temporary into the full vector with a += operation */ 1549 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))), 1550 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))) + cusparsestruct->workVector->size(), 1551 VecCUDAPlusEquals()); 1552 1553 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1554 ierr = VecCUDARestoreArrayReadWrite(zz,&zarray);CHKERRQ(ierr); 1555 1556 } catch(char *ex) { 1557 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1558 } 1559 ierr = WaitForGPU();CHKERRCUDA(ierr); 1560 ierr = PetscLogFlops(2.0*a->nz);CHKERRQ(ierr); 1561 PetscFunctionReturn(0); 1562 } 1563 1564 static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A,MatAssemblyType mode) 1565 { 1566 PetscErrorCode ierr; 1567 1568 PetscFunctionBegin; 1569 ierr = MatAssemblyEnd_SeqAIJ(A,mode);CHKERRQ(ierr); 1570 if (A->factortype==MAT_FACTOR_NONE) { 1571 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1572 } 1573 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 1574 A->ops->mult = MatMult_SeqAIJCUSPARSE; 1575 A->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1576 A->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1577 A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1578 PetscFunctionReturn(0); 1579 } 1580 1581 /* --------------------------------------------------------------------------------*/ 1582 /*@ 1583 MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in AIJ (compressed row) format 1584 (the default parallel PETSc format). This matrix will ultimately pushed down 1585 to NVidia GPUs and use the CUSPARSE library for calculations. For good matrix 1586 assembly performance the user should preallocate the matrix storage by setting 1587 the parameter nz (or the array nnz). By setting these parameters accurately, 1588 performance during matrix assembly can be increased by more than a factor of 50. 1589 1590 Collective on MPI_Comm 1591 1592 Input Parameters: 1593 + comm - MPI communicator, set to PETSC_COMM_SELF 1594 . m - number of rows 1595 . n - number of columns 1596 . nz - number of nonzeros per row (same for all rows) 1597 - nnz - array containing the number of nonzeros in the various rows 1598 (possibly different for each row) or NULL 1599 1600 Output Parameter: 1601 . A - the matrix 1602 1603 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 1604 MatXXXXSetPreallocation() paradgm instead of this routine directly. 1605 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 1606 1607 Notes: 1608 If nnz is given then nz is ignored 1609 1610 The AIJ format (also called the Yale sparse matrix format or 1611 compressed row storage), is fully compatible with standard Fortran 77 1612 storage. That is, the stored row and column indices can begin at 1613 either one (as in Fortran) or zero. See the users' manual for details. 1614 1615 Specify the preallocated storage with either nz or nnz (not both). 1616 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 1617 allocation. For large problems you MUST preallocate memory or you 1618 will get TERRIBLE performance, see the users' manual chapter on matrices. 1619 1620 By default, this format uses inodes (identical nodes) when possible, to 1621 improve numerical efficiency of matrix-vector products and solves. We 1622 search for consecutive rows with the same nonzero structure, thereby 1623 reusing matrix information to achieve increased efficiency. 1624 1625 Level: intermediate 1626 1627 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATSEQAIJCUSPARSE, MATAIJCUSPARSE 1628 @*/ 1629 PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 1630 { 1631 PetscErrorCode ierr; 1632 1633 PetscFunctionBegin; 1634 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1635 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 1636 ierr = MatSetType(*A,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1637 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);CHKERRQ(ierr); 1638 PetscFunctionReturn(0); 1639 } 1640 1641 static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A) 1642 { 1643 PetscErrorCode ierr; 1644 1645 PetscFunctionBegin; 1646 if (A->factortype==MAT_FACTOR_NONE) { 1647 if (A->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) { 1648 ierr = Mat_SeqAIJCUSPARSE_Destroy((Mat_SeqAIJCUSPARSE**)&A->spptr);CHKERRQ(ierr); 1649 } 1650 } else { 1651 ierr = Mat_SeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors**)&A->spptr);CHKERRQ(ierr); 1652 } 1653 ierr = MatDestroy_SeqAIJ(A);CHKERRQ(ierr); 1654 PetscFunctionReturn(0); 1655 } 1656 1657 static PetscErrorCode MatDuplicate_SeqAIJCUSPARSE(Mat A,MatDuplicateOption cpvalues,Mat *B) 1658 { 1659 PetscErrorCode ierr; 1660 Mat C; 1661 cusparseStatus_t stat; 1662 cusparseHandle_t handle=0; 1663 1664 PetscFunctionBegin; 1665 ierr = MatDuplicate_SeqAIJ(A,cpvalues,B);CHKERRQ(ierr); 1666 C = *B; 1667 /* inject CUSPARSE-specific stuff */ 1668 if (C->factortype==MAT_FACTOR_NONE) { 1669 /* you cannot check the inode.use flag here since the matrix was just created. 1670 now build a GPU matrix data structure */ 1671 C->spptr = new Mat_SeqAIJCUSPARSE; 1672 ((Mat_SeqAIJCUSPARSE*)C->spptr)->mat = 0; 1673 ((Mat_SeqAIJCUSPARSE*)C->spptr)->matTranspose = 0; 1674 ((Mat_SeqAIJCUSPARSE*)C->spptr)->workVector = 0; 1675 ((Mat_SeqAIJCUSPARSE*)C->spptr)->format = MAT_CUSPARSE_CSR; 1676 ((Mat_SeqAIJCUSPARSE*)C->spptr)->stream = 0; 1677 ((Mat_SeqAIJCUSPARSE*)C->spptr)->handle = 0; 1678 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1679 ((Mat_SeqAIJCUSPARSE*)C->spptr)->handle = handle; 1680 ((Mat_SeqAIJCUSPARSE*)C->spptr)->stream = 0; 1681 ((Mat_SeqAIJCUSPARSE*)C->spptr)->nonzerostate = 0; 1682 } else { 1683 /* NEXT, set the pointers to the triangular factors */ 1684 C->spptr = new Mat_SeqAIJCUSPARSETriFactors; 1685 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->loTriFactorPtr = 0; 1686 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->upTriFactorPtr = 0; 1687 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->loTriFactorPtrTranspose = 0; 1688 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->upTriFactorPtrTranspose = 0; 1689 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->rpermIndices = 0; 1690 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->cpermIndices = 0; 1691 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->workVector = 0; 1692 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->handle = 0; 1693 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1694 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->handle = handle; 1695 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->nnz = 0; 1696 } 1697 1698 C->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE; 1699 C->ops->destroy = MatDestroy_SeqAIJCUSPARSE; 1700 C->ops->getvecs = MatCreateVecs_SeqAIJCUSPARSE; 1701 C->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE; 1702 C->ops->mult = MatMult_SeqAIJCUSPARSE; 1703 C->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1704 C->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1705 C->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1706 C->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE; 1707 1708 ierr = PetscObjectChangeTypeName((PetscObject)C,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1709 1710 C->valid_GPU_matrix = PETSC_CUDA_UNALLOCATED; 1711 1712 ierr = PetscObjectComposeFunction((PetscObject)C, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE);CHKERRQ(ierr); 1713 PetscFunctionReturn(0); 1714 } 1715 1716 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B) 1717 { 1718 PetscErrorCode ierr; 1719 cusparseStatus_t stat; 1720 cusparseHandle_t handle=0; 1721 1722 PetscFunctionBegin; 1723 ierr = MatCreate_SeqAIJ(B);CHKERRQ(ierr); 1724 if (B->factortype==MAT_FACTOR_NONE) { 1725 /* you cannot check the inode.use flag here since the matrix was just created. 1726 now build a GPU matrix data structure */ 1727 B->spptr = new Mat_SeqAIJCUSPARSE; 1728 ((Mat_SeqAIJCUSPARSE*)B->spptr)->mat = 0; 1729 ((Mat_SeqAIJCUSPARSE*)B->spptr)->matTranspose = 0; 1730 ((Mat_SeqAIJCUSPARSE*)B->spptr)->workVector = 0; 1731 ((Mat_SeqAIJCUSPARSE*)B->spptr)->format = MAT_CUSPARSE_CSR; 1732 ((Mat_SeqAIJCUSPARSE*)B->spptr)->stream = 0; 1733 ((Mat_SeqAIJCUSPARSE*)B->spptr)->handle = 0; 1734 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1735 ((Mat_SeqAIJCUSPARSE*)B->spptr)->handle = handle; 1736 ((Mat_SeqAIJCUSPARSE*)B->spptr)->stream = 0; 1737 ((Mat_SeqAIJCUSPARSE*)B->spptr)->nonzerostate = 0; 1738 } else { 1739 /* NEXT, set the pointers to the triangular factors */ 1740 B->spptr = new Mat_SeqAIJCUSPARSETriFactors; 1741 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtr = 0; 1742 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtr = 0; 1743 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtrTranspose = 0; 1744 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtrTranspose = 0; 1745 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->rpermIndices = 0; 1746 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->cpermIndices = 0; 1747 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->workVector = 0; 1748 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->handle = 0; 1749 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1750 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->handle = handle; 1751 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->nnz = 0; 1752 } 1753 1754 B->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE; 1755 B->ops->destroy = MatDestroy_SeqAIJCUSPARSE; 1756 B->ops->getvecs = MatCreateVecs_SeqAIJCUSPARSE; 1757 B->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE; 1758 B->ops->mult = MatMult_SeqAIJCUSPARSE; 1759 B->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1760 B->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1761 B->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1762 B->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE; 1763 1764 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1765 1766 B->valid_GPU_matrix = PETSC_CUDA_UNALLOCATED; 1767 1768 ierr = PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE);CHKERRQ(ierr); 1769 PetscFunctionReturn(0); 1770 } 1771 1772 /*M 1773 MATSEQAIJCUSPARSE - MATAIJCUSPARSE = "(seq)aijcusparse" - A matrix type to be used for sparse matrices. 1774 1775 A matrix type type whose data resides on Nvidia GPUs. These matrices can be in either 1776 CSR, ELL, or Hybrid format. The ELL and HYB formats require CUDA 4.2 or later. 1777 All matrix calculations are performed on Nvidia GPUs using the CUSPARSE library. 1778 1779 Options Database Keys: 1780 + -mat_type aijcusparse - sets the matrix type to "seqaijcusparse" during a call to MatSetFromOptions() 1781 . -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). 1782 . -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). 1783 1784 Level: beginner 1785 1786 .seealso: MatCreateSeqAIJCUSPARSE(), MATAIJCUSPARSE, MatCreateAIJCUSPARSE(), MatCUSPARSESetFormat(), MatCUSPARSEStorageFormat, MatCUSPARSEFormatOperation 1787 M*/ 1788 1789 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat,MatFactorType,Mat*); 1790 1791 1792 PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_CUSPARSE(void) 1793 { 1794 PetscErrorCode ierr; 1795 1796 PetscFunctionBegin; 1797 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_LU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1798 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_CHOLESKY,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1799 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ILU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1800 ierr = MatSolverPackageRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ICC,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1801 PetscFunctionReturn(0); 1802 } 1803 1804 1805 static PetscErrorCode Mat_SeqAIJCUSPARSE_Destroy(Mat_SeqAIJCUSPARSE **cusparsestruct) 1806 { 1807 cusparseStatus_t stat; 1808 cusparseHandle_t handle; 1809 1810 PetscFunctionBegin; 1811 if (*cusparsestruct) { 1812 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&(*cusparsestruct)->mat,(*cusparsestruct)->format); 1813 Mat_SeqAIJCUSPARSEMultStruct_Destroy(&(*cusparsestruct)->matTranspose,(*cusparsestruct)->format); 1814 delete (*cusparsestruct)->workVector; 1815 if (handle = (*cusparsestruct)->handle) { 1816 stat = cusparseDestroy(handle);CHKERRCUDA(stat); 1817 } 1818 delete *cusparsestruct; 1819 *cusparsestruct = 0; 1820 } 1821 PetscFunctionReturn(0); 1822 } 1823 1824 static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **mat) 1825 { 1826 PetscFunctionBegin; 1827 if (*mat) { 1828 delete (*mat)->values; 1829 delete (*mat)->column_indices; 1830 delete (*mat)->row_offsets; 1831 delete *mat; 1832 *mat = 0; 1833 } 1834 PetscFunctionReturn(0); 1835 } 1836 1837 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **trifactor) 1838 { 1839 cusparseStatus_t stat; 1840 PetscErrorCode ierr; 1841 1842 PetscFunctionBegin; 1843 if (*trifactor) { 1844 if ((*trifactor)->descr) { stat = cusparseDestroyMatDescr((*trifactor)->descr);CHKERRCUDA(stat); } 1845 if ((*trifactor)->solveInfo) { stat = cusparseDestroySolveAnalysisInfo((*trifactor)->solveInfo);CHKERRCUDA(stat); } 1846 ierr = CsrMatrix_Destroy(&(*trifactor)->csrMat);CHKERRQ(ierr); 1847 delete *trifactor; 1848 *trifactor = 0; 1849 } 1850 PetscFunctionReturn(0); 1851 } 1852 1853 static PetscErrorCode Mat_SeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **matstruct,MatCUSPARSEStorageFormat format) 1854 { 1855 CsrMatrix *mat; 1856 cusparseStatus_t stat; 1857 cudaError_t err; 1858 1859 PetscFunctionBegin; 1860 if (*matstruct) { 1861 if ((*matstruct)->mat) { 1862 if (format==MAT_CUSPARSE_ELL || format==MAT_CUSPARSE_HYB) { 1863 cusparseHybMat_t hybMat = (cusparseHybMat_t)(*matstruct)->mat; 1864 stat = cusparseDestroyHybMat(hybMat);CHKERRCUDA(stat); 1865 } else { 1866 mat = (CsrMatrix*)(*matstruct)->mat; 1867 CsrMatrix_Destroy(&mat); 1868 } 1869 } 1870 if ((*matstruct)->descr) { stat = cusparseDestroyMatDescr((*matstruct)->descr);CHKERRCUDA(stat); } 1871 delete (*matstruct)->cprowIndices; 1872 if ((*matstruct)->alpha) { err=cudaFree((*matstruct)->alpha);CHKERRCUDA(err); } 1873 if ((*matstruct)->beta) { err=cudaFree((*matstruct)->beta);CHKERRCUDA(err); } 1874 delete *matstruct; 1875 *matstruct = 0; 1876 } 1877 PetscFunctionReturn(0); 1878 } 1879 1880 static PetscErrorCode Mat_SeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors** trifactors) 1881 { 1882 cusparseHandle_t handle; 1883 cusparseStatus_t stat; 1884 1885 PetscFunctionBegin; 1886 if (*trifactors) { 1887 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->loTriFactorPtr); 1888 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->upTriFactorPtr); 1889 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->loTriFactorPtrTranspose); 1890 Mat_SeqAIJCUSPARSETriFactorStruct_Destroy(&(*trifactors)->upTriFactorPtrTranspose); 1891 delete (*trifactors)->rpermIndices; 1892 delete (*trifactors)->cpermIndices; 1893 delete (*trifactors)->workVector; 1894 if (handle = (*trifactors)->handle) { 1895 stat = cusparseDestroy(handle);CHKERRCUDA(stat); 1896 } 1897 delete *trifactors; 1898 *trifactors = 0; 1899 } 1900 PetscFunctionReturn(0); 1901 } 1902 1903