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 MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct**); 37 static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct**,MatCUSPARSEStorageFormat); 38 static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors**); 39 static PetscErrorCode MatSeqAIJCUSPARSE_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 MatFactorGetSolverType_seqaij_cusparse(Mat A,MatSolverType *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: PCFactorSetMatSolverType(), MatSolverType, 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),"MatFactorGetSolverType_C",MatFactorGetSolverType_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 if (A->factortype==MAT_FACTOR_NONE) { 181 ierr = PetscOptionsEnum("-mat_cusparse_mult_storage_format","sets storage format of (seq)aijcusparse gpu matrices for SpMV", 182 "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)cusparsestruct->format,(PetscEnum*)&format,&flg);CHKERRQ(ierr); 183 if (flg) { 184 ierr = MatCUSPARSESetFormat(A,MAT_CUSPARSE_MULT,format);CHKERRQ(ierr); 185 } 186 } 187 ierr = PetscOptionsEnum("-mat_cusparse_storage_format","sets storage format of (seq)aijcusparse gpu matrices for SpMV and TriSolve", 188 "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)cusparsestruct->format,(PetscEnum*)&format,&flg);CHKERRQ(ierr); 189 if (flg) { 190 ierr = MatCUSPARSESetFormat(A,MAT_CUSPARSE_ALL,format);CHKERRQ(ierr); 191 } 192 ierr = PetscOptionsTail();CHKERRQ(ierr); 193 PetscFunctionReturn(0); 194 195 } 196 197 static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS isrow,IS iscol,const MatFactorInfo *info) 198 { 199 PetscErrorCode ierr; 200 201 PetscFunctionBegin; 202 ierr = MatILUFactorSymbolic_SeqAIJ(B,A,isrow,iscol,info);CHKERRQ(ierr); 203 B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE; 204 PetscFunctionReturn(0); 205 } 206 207 static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS isrow,IS iscol,const MatFactorInfo *info) 208 { 209 PetscErrorCode ierr; 210 211 PetscFunctionBegin; 212 ierr = MatLUFactorSymbolic_SeqAIJ(B,A,isrow,iscol,info);CHKERRQ(ierr); 213 B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE; 214 PetscFunctionReturn(0); 215 } 216 217 static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS perm,const MatFactorInfo *info) 218 { 219 PetscErrorCode ierr; 220 221 PetscFunctionBegin; 222 ierr = MatICCFactorSymbolic_SeqAIJ(B,A,perm,info);CHKERRQ(ierr); 223 B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE; 224 PetscFunctionReturn(0); 225 } 226 227 static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B,Mat A,IS perm,const MatFactorInfo *info) 228 { 229 PetscErrorCode ierr; 230 231 PetscFunctionBegin; 232 ierr = MatCholeskyFactorSymbolic_SeqAIJ(B,A,perm,info);CHKERRQ(ierr); 233 B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE; 234 PetscFunctionReturn(0); 235 } 236 237 static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A) 238 { 239 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 240 PetscInt n = A->rmap->n; 241 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 242 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 243 cusparseStatus_t stat; 244 const PetscInt *ai = a->i,*aj = a->j,*vi; 245 const MatScalar *aa = a->a,*v; 246 PetscInt *AiLo, *AjLo; 247 PetscScalar *AALo; 248 PetscInt i,nz, nzLower, offset, rowOffset; 249 PetscErrorCode ierr; 250 251 PetscFunctionBegin; 252 if (A->valid_GPU_matrix == PETSC_OFFLOAD_UNALLOCATED || A->valid_GPU_matrix == PETSC_OFFLOAD_CPU) { 253 try { 254 /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */ 255 nzLower=n+ai[n]-ai[1]; 256 257 /* Allocate Space for the lower triangular matrix */ 258 ierr = cudaMallocHost((void**) &AiLo, (n+1)*sizeof(PetscInt));CHKERRCUDA(ierr); 259 ierr = cudaMallocHost((void**) &AjLo, nzLower*sizeof(PetscInt));CHKERRCUDA(ierr); 260 ierr = cudaMallocHost((void**) &AALo, nzLower*sizeof(PetscScalar));CHKERRCUDA(ierr); 261 262 /* Fill the lower triangular matrix */ 263 AiLo[0] = (PetscInt) 0; 264 AiLo[n] = nzLower; 265 AjLo[0] = (PetscInt) 0; 266 AALo[0] = (MatScalar) 1.0; 267 v = aa; 268 vi = aj; 269 offset = 1; 270 rowOffset= 1; 271 for (i=1; i<n; i++) { 272 nz = ai[i+1] - ai[i]; 273 /* additional 1 for the term on the diagonal */ 274 AiLo[i] = rowOffset; 275 rowOffset += nz+1; 276 277 ierr = PetscArraycpy(&(AjLo[offset]), vi, nz);CHKERRQ(ierr); 278 ierr = PetscArraycpy(&(AALo[offset]), v, nz);CHKERRQ(ierr); 279 280 offset += nz; 281 AjLo[offset] = (PetscInt) i; 282 AALo[offset] = (MatScalar) 1.0; 283 offset += 1; 284 285 v += nz; 286 vi += nz; 287 } 288 289 /* allocate space for the triangular factor information */ 290 loTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 291 292 /* Create the matrix description */ 293 stat = cusparseCreateMatDescr(&loTriFactor->descr);CHKERRCUDA(stat); 294 stat = cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 295 stat = cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 296 stat = cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER);CHKERRCUDA(stat); 297 stat = cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT);CHKERRCUDA(stat); 298 299 /* Create the solve analysis information */ 300 stat = cusparseCreateSolveAnalysisInfo(&loTriFactor->solveInfo);CHKERRCUDA(stat); 301 302 /* set the operation */ 303 loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 304 305 /* set the matrix */ 306 loTriFactor->csrMat = new CsrMatrix; 307 loTriFactor->csrMat->num_rows = n; 308 loTriFactor->csrMat->num_cols = n; 309 loTriFactor->csrMat->num_entries = nzLower; 310 311 loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n+1); 312 loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo+n+1); 313 314 loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower); 315 loTriFactor->csrMat->column_indices->assign(AjLo, AjLo+nzLower); 316 317 loTriFactor->csrMat->values = new THRUSTARRAY(nzLower); 318 loTriFactor->csrMat->values->assign(AALo, AALo+nzLower); 319 320 /* perform the solve analysis */ 321 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, 322 loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, 323 loTriFactor->csrMat->values->data().get(), loTriFactor->csrMat->row_offsets->data().get(), 324 loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo);CHKERRCUDA(stat); 325 326 /* assign the pointer. Is this really necessary? */ 327 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtr = loTriFactor; 328 329 ierr = cudaFreeHost(AiLo);CHKERRCUDA(ierr); 330 ierr = cudaFreeHost(AjLo);CHKERRCUDA(ierr); 331 ierr = cudaFreeHost(AALo);CHKERRCUDA(ierr); 332 ierr = PetscLogCpuToGpu((n+1+nzLower)*sizeof(int)+nzLower*sizeof(PetscScalar));CHKERRQ(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_OFFLOAD_UNALLOCATED || A->valid_GPU_matrix == PETSC_OFFLOAD_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 = PetscArraycpy(&(AjUp[offset+1]), vi, nz);CHKERRQ(ierr); 385 ierr = PetscArraycpy(&(AAUp[offset+1]), v, nz);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 ierr = PetscLogCpuToGpu((n+1+nzUpper)*sizeof(int)+nzUpper*sizeof(PetscScalar));CHKERRQ(ierr); 432 } catch(char *ex) { 433 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 434 } 435 } 436 PetscFunctionReturn(0); 437 } 438 439 static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A) 440 { 441 PetscErrorCode ierr; 442 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 443 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 444 IS isrow = a->row,iscol = a->icol; 445 PetscBool row_identity,col_identity; 446 const PetscInt *r,*c; 447 PetscInt n = A->rmap->n; 448 449 PetscFunctionBegin; 450 ierr = MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A);CHKERRQ(ierr); 451 ierr = MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A);CHKERRQ(ierr); 452 453 cusparseTriFactors->workVector = new THRUSTARRAY(n); 454 cusparseTriFactors->nnz=a->nz; 455 456 A->valid_GPU_matrix = PETSC_OFFLOAD_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 } 473 474 if(!row_identity && !col_identity) { 475 ierr = PetscLogCpuToGpu(2*n*sizeof(PetscInt));CHKERRQ(ierr); 476 } else if(!row_identity) { 477 ierr = PetscLogCpuToGpu(n*sizeof(PetscInt));CHKERRQ(ierr); 478 } else if(!col_identity) { 479 ierr = PetscLogCpuToGpu(n*sizeof(PetscInt));CHKERRQ(ierr); 480 } 481 482 ierr = ISRestoreIndices(iscol,&c);CHKERRQ(ierr); 483 PetscFunctionReturn(0); 484 } 485 486 static PetscErrorCode MatSeqAIJCUSPARSEBuildICCTriMatrices(Mat A) 487 { 488 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 489 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 490 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 491 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 492 cusparseStatus_t stat; 493 PetscErrorCode ierr; 494 PetscInt *AiUp, *AjUp; 495 PetscScalar *AAUp; 496 PetscScalar *AALo; 497 PetscInt nzUpper = a->nz,n = A->rmap->n,i,offset,nz,j; 498 Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ*)A->data; 499 const PetscInt *ai = b->i,*aj = b->j,*vj; 500 const MatScalar *aa = b->a,*v; 501 502 PetscFunctionBegin; 503 if (A->valid_GPU_matrix == PETSC_OFFLOAD_UNALLOCATED || A->valid_GPU_matrix == PETSC_OFFLOAD_CPU) { 504 try { 505 /* Allocate Space for the upper triangular matrix */ 506 ierr = cudaMallocHost((void**) &AiUp, (n+1)*sizeof(PetscInt));CHKERRCUDA(ierr); 507 ierr = cudaMallocHost((void**) &AjUp, nzUpper*sizeof(PetscInt));CHKERRCUDA(ierr); 508 ierr = cudaMallocHost((void**) &AAUp, nzUpper*sizeof(PetscScalar));CHKERRCUDA(ierr); 509 ierr = cudaMallocHost((void**) &AALo, nzUpper*sizeof(PetscScalar));CHKERRCUDA(ierr); 510 511 /* Fill the upper triangular matrix */ 512 AiUp[0]=(PetscInt) 0; 513 AiUp[n]=nzUpper; 514 offset = 0; 515 for (i=0; i<n; i++) { 516 /* set the pointers */ 517 v = aa + ai[i]; 518 vj = aj + ai[i]; 519 nz = ai[i+1] - ai[i] - 1; /* exclude diag[i] */ 520 521 /* first, set the diagonal elements */ 522 AjUp[offset] = (PetscInt) i; 523 AAUp[offset] = (MatScalar)1.0/v[nz]; 524 AiUp[i] = offset; 525 AALo[offset] = (MatScalar)1.0/v[nz]; 526 527 offset+=1; 528 if (nz>0) { 529 ierr = PetscArraycpy(&(AjUp[offset]), vj, nz);CHKERRQ(ierr); 530 ierr = PetscArraycpy(&(AAUp[offset]), v, nz);CHKERRQ(ierr); 531 for (j=offset; j<offset+nz; j++) { 532 AAUp[j] = -AAUp[j]; 533 AALo[j] = AAUp[j]/v[nz]; 534 } 535 offset+=nz; 536 } 537 } 538 539 /* allocate space for the triangular factor information */ 540 upTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 541 542 /* Create the matrix description */ 543 stat = cusparseCreateMatDescr(&upTriFactor->descr);CHKERRCUDA(stat); 544 stat = cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 545 stat = cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 546 stat = cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER);CHKERRCUDA(stat); 547 stat = cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT);CHKERRCUDA(stat); 548 549 /* Create the solve analysis information */ 550 stat = cusparseCreateSolveAnalysisInfo(&upTriFactor->solveInfo);CHKERRCUDA(stat); 551 552 /* set the operation */ 553 upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 554 555 /* set the matrix */ 556 upTriFactor->csrMat = new CsrMatrix; 557 upTriFactor->csrMat->num_rows = A->rmap->n; 558 upTriFactor->csrMat->num_cols = A->cmap->n; 559 upTriFactor->csrMat->num_entries = a->nz; 560 561 upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 562 upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp+A->rmap->n+1); 563 564 upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz); 565 upTriFactor->csrMat->column_indices->assign(AjUp, AjUp+a->nz); 566 567 upTriFactor->csrMat->values = new THRUSTARRAY(a->nz); 568 upTriFactor->csrMat->values->assign(AAUp, AAUp+a->nz); 569 570 /* perform the solve analysis */ 571 stat = cusparse_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, 572 upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, 573 upTriFactor->csrMat->values->data().get(), upTriFactor->csrMat->row_offsets->data().get(), 574 upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo);CHKERRCUDA(stat); 575 576 /* assign the pointer. Is this really necessary? */ 577 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->upTriFactorPtr = upTriFactor; 578 579 /* allocate space for the triangular factor information */ 580 loTriFactor = new Mat_SeqAIJCUSPARSETriFactorStruct; 581 582 /* Create the matrix description */ 583 stat = cusparseCreateMatDescr(&loTriFactor->descr);CHKERRCUDA(stat); 584 stat = cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 585 stat = cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR);CHKERRCUDA(stat); 586 stat = cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_UPPER);CHKERRCUDA(stat); 587 stat = cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT);CHKERRCUDA(stat); 588 589 /* Create the solve analysis information */ 590 stat = cusparseCreateSolveAnalysisInfo(&loTriFactor->solveInfo);CHKERRCUDA(stat); 591 592 /* set the operation */ 593 loTriFactor->solveOp = CUSPARSE_OPERATION_TRANSPOSE; 594 595 /* set the matrix */ 596 loTriFactor->csrMat = new CsrMatrix; 597 loTriFactor->csrMat->num_rows = A->rmap->n; 598 loTriFactor->csrMat->num_cols = A->cmap->n; 599 loTriFactor->csrMat->num_entries = a->nz; 600 601 loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 602 loTriFactor->csrMat->row_offsets->assign(AiUp, AiUp+A->rmap->n+1); 603 604 loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz); 605 loTriFactor->csrMat->column_indices->assign(AjUp, AjUp+a->nz); 606 607 loTriFactor->csrMat->values = new THRUSTARRAY(a->nz); 608 loTriFactor->csrMat->values->assign(AALo, AALo+a->nz); 609 ierr = PetscLogCpuToGpu(2*(((A->rmap->n+1)+(a->nz))*sizeof(int)+(a->nz)*sizeof(PetscScalar)));CHKERRQ(ierr); 610 611 /* perform the solve analysis */ 612 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, 613 loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, 614 loTriFactor->csrMat->values->data().get(), loTriFactor->csrMat->row_offsets->data().get(), 615 loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo);CHKERRCUDA(stat); 616 617 /* assign the pointer. Is this really necessary? */ 618 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtr = loTriFactor; 619 620 A->valid_GPU_matrix = PETSC_OFFLOAD_BOTH; 621 ierr = cudaFreeHost(AiUp);CHKERRCUDA(ierr); 622 ierr = cudaFreeHost(AjUp);CHKERRCUDA(ierr); 623 ierr = cudaFreeHost(AAUp);CHKERRCUDA(ierr); 624 ierr = cudaFreeHost(AALo);CHKERRCUDA(ierr); 625 } catch(char *ex) { 626 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 627 } 628 } 629 PetscFunctionReturn(0); 630 } 631 632 static PetscErrorCode MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(Mat A) 633 { 634 PetscErrorCode ierr; 635 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 636 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 637 IS ip = a->row; 638 const PetscInt *rip; 639 PetscBool perm_identity; 640 PetscInt n = A->rmap->n; 641 642 PetscFunctionBegin; 643 ierr = MatSeqAIJCUSPARSEBuildICCTriMatrices(A);CHKERRQ(ierr); 644 cusparseTriFactors->workVector = new THRUSTARRAY(n); 645 cusparseTriFactors->nnz=(a->nz-n)*2 + n; 646 647 /*lower triangular indices */ 648 ierr = ISGetIndices(ip,&rip);CHKERRQ(ierr); 649 ierr = ISIdentity(ip,&perm_identity);CHKERRQ(ierr); 650 if (!perm_identity) { 651 cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n); 652 cusparseTriFactors->rpermIndices->assign(rip, rip+n); 653 cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n); 654 cusparseTriFactors->cpermIndices->assign(rip, rip+n); 655 ierr = PetscLogCpuToGpu(2*n*sizeof(PetscInt));CHKERRQ(ierr); 656 } 657 ierr = ISRestoreIndices(ip,&rip);CHKERRQ(ierr); 658 PetscFunctionReturn(0); 659 } 660 661 static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B,Mat A,const MatFactorInfo *info) 662 { 663 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 664 IS isrow = b->row,iscol = b->col; 665 PetscBool row_identity,col_identity; 666 PetscErrorCode ierr; 667 668 PetscFunctionBegin; 669 ierr = MatLUFactorNumeric_SeqAIJ(B,A,info);CHKERRQ(ierr); 670 /* determine which version of MatSolve needs to be used. */ 671 ierr = ISIdentity(isrow,&row_identity);CHKERRQ(ierr); 672 ierr = ISIdentity(iscol,&col_identity);CHKERRQ(ierr); 673 if (row_identity && col_identity) { 674 B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering; 675 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering; 676 } else { 677 B->ops->solve = MatSolve_SeqAIJCUSPARSE; 678 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE; 679 } 680 681 /* get the triangular factors */ 682 ierr = MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B);CHKERRQ(ierr); 683 PetscFunctionReturn(0); 684 } 685 686 static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat B,Mat A,const MatFactorInfo *info) 687 { 688 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 689 IS ip = b->row; 690 PetscBool perm_identity; 691 PetscErrorCode ierr; 692 693 PetscFunctionBegin; 694 ierr = MatCholeskyFactorNumeric_SeqAIJ(B,A,info);CHKERRQ(ierr); 695 696 /* determine which version of MatSolve needs to be used. */ 697 ierr = ISIdentity(ip,&perm_identity);CHKERRQ(ierr); 698 if (perm_identity) { 699 B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering; 700 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering; 701 } else { 702 B->ops->solve = MatSolve_SeqAIJCUSPARSE; 703 B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE; 704 } 705 706 /* get the triangular factors */ 707 ierr = MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(B);CHKERRQ(ierr); 708 PetscFunctionReturn(0); 709 } 710 711 static PetscErrorCode MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(Mat A) 712 { 713 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 714 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 715 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 716 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 717 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 718 cusparseStatus_t stat; 719 cusparseIndexBase_t indexBase; 720 cusparseMatrixType_t matrixType; 721 cusparseFillMode_t fillMode; 722 cusparseDiagType_t diagType; 723 724 PetscFunctionBegin; 725 726 /*********************************************/ 727 /* Now the Transpose of the Lower Tri Factor */ 728 /*********************************************/ 729 730 /* allocate space for the transpose of the lower triangular factor */ 731 loTriFactorT = new Mat_SeqAIJCUSPARSETriFactorStruct; 732 733 /* set the matrix descriptors of the lower triangular factor */ 734 matrixType = cusparseGetMatType(loTriFactor->descr); 735 indexBase = cusparseGetMatIndexBase(loTriFactor->descr); 736 fillMode = cusparseGetMatFillMode(loTriFactor->descr)==CUSPARSE_FILL_MODE_UPPER ? 737 CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER; 738 diagType = cusparseGetMatDiagType(loTriFactor->descr); 739 740 /* Create the matrix description */ 741 stat = cusparseCreateMatDescr(&loTriFactorT->descr);CHKERRCUDA(stat); 742 stat = cusparseSetMatIndexBase(loTriFactorT->descr, indexBase);CHKERRCUDA(stat); 743 stat = cusparseSetMatType(loTriFactorT->descr, matrixType);CHKERRCUDA(stat); 744 stat = cusparseSetMatFillMode(loTriFactorT->descr, fillMode);CHKERRCUDA(stat); 745 stat = cusparseSetMatDiagType(loTriFactorT->descr, diagType);CHKERRCUDA(stat); 746 747 /* Create the solve analysis information */ 748 stat = cusparseCreateSolveAnalysisInfo(&loTriFactorT->solveInfo);CHKERRCUDA(stat); 749 750 /* set the operation */ 751 loTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 752 753 /* allocate GPU space for the CSC of the lower triangular factor*/ 754 loTriFactorT->csrMat = new CsrMatrix; 755 loTriFactorT->csrMat->num_rows = loTriFactor->csrMat->num_rows; 756 loTriFactorT->csrMat->num_cols = loTriFactor->csrMat->num_cols; 757 loTriFactorT->csrMat->num_entries = loTriFactor->csrMat->num_entries; 758 loTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(loTriFactor->csrMat->num_rows+1); 759 loTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(loTriFactor->csrMat->num_entries); 760 loTriFactorT->csrMat->values = new THRUSTARRAY(loTriFactor->csrMat->num_entries); 761 762 /* compute the transpose of the lower triangular factor, i.e. the CSC */ 763 stat = cusparse_csr2csc(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, 764 loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, 765 loTriFactor->csrMat->values->data().get(), 766 loTriFactor->csrMat->row_offsets->data().get(), 767 loTriFactor->csrMat->column_indices->data().get(), 768 loTriFactorT->csrMat->values->data().get(), 769 loTriFactorT->csrMat->column_indices->data().get(), 770 loTriFactorT->csrMat->row_offsets->data().get(), 771 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 772 773 /* perform the solve analysis on the transposed matrix */ 774 stat = cusparse_analysis(cusparseTriFactors->handle, loTriFactorT->solveOp, 775 loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, 776 loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(), 777 loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), 778 loTriFactorT->solveInfo);CHKERRCUDA(stat); 779 780 /* assign the pointer. Is this really necessary? */ 781 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->loTriFactorPtrTranspose = loTriFactorT; 782 783 /*********************************************/ 784 /* Now the Transpose of the Upper Tri Factor */ 785 /*********************************************/ 786 787 /* allocate space for the transpose of the upper triangular factor */ 788 upTriFactorT = new Mat_SeqAIJCUSPARSETriFactorStruct; 789 790 /* set the matrix descriptors of the upper triangular factor */ 791 matrixType = cusparseGetMatType(upTriFactor->descr); 792 indexBase = cusparseGetMatIndexBase(upTriFactor->descr); 793 fillMode = cusparseGetMatFillMode(upTriFactor->descr)==CUSPARSE_FILL_MODE_UPPER ? 794 CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER; 795 diagType = cusparseGetMatDiagType(upTriFactor->descr); 796 797 /* Create the matrix description */ 798 stat = cusparseCreateMatDescr(&upTriFactorT->descr);CHKERRCUDA(stat); 799 stat = cusparseSetMatIndexBase(upTriFactorT->descr, indexBase);CHKERRCUDA(stat); 800 stat = cusparseSetMatType(upTriFactorT->descr, matrixType);CHKERRCUDA(stat); 801 stat = cusparseSetMatFillMode(upTriFactorT->descr, fillMode);CHKERRCUDA(stat); 802 stat = cusparseSetMatDiagType(upTriFactorT->descr, diagType);CHKERRCUDA(stat); 803 804 /* Create the solve analysis information */ 805 stat = cusparseCreateSolveAnalysisInfo(&upTriFactorT->solveInfo);CHKERRCUDA(stat); 806 807 /* set the operation */ 808 upTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE; 809 810 /* allocate GPU space for the CSC of the upper triangular factor*/ 811 upTriFactorT->csrMat = new CsrMatrix; 812 upTriFactorT->csrMat->num_rows = upTriFactor->csrMat->num_rows; 813 upTriFactorT->csrMat->num_cols = upTriFactor->csrMat->num_cols; 814 upTriFactorT->csrMat->num_entries = upTriFactor->csrMat->num_entries; 815 upTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(upTriFactor->csrMat->num_rows+1); 816 upTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(upTriFactor->csrMat->num_entries); 817 upTriFactorT->csrMat->values = new THRUSTARRAY(upTriFactor->csrMat->num_entries); 818 819 /* compute the transpose of the upper triangular factor, i.e. the CSC */ 820 stat = cusparse_csr2csc(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, 821 upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, 822 upTriFactor->csrMat->values->data().get(), 823 upTriFactor->csrMat->row_offsets->data().get(), 824 upTriFactor->csrMat->column_indices->data().get(), 825 upTriFactorT->csrMat->values->data().get(), 826 upTriFactorT->csrMat->column_indices->data().get(), 827 upTriFactorT->csrMat->row_offsets->data().get(), 828 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 829 830 /* perform the solve analysis on the transposed matrix */ 831 stat = cusparse_analysis(cusparseTriFactors->handle, upTriFactorT->solveOp, 832 upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, 833 upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(), 834 upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), 835 upTriFactorT->solveInfo);CHKERRCUDA(stat); 836 837 /* assign the pointer. Is this really necessary? */ 838 ((Mat_SeqAIJCUSPARSETriFactors*)A->spptr)->upTriFactorPtrTranspose = upTriFactorT; 839 PetscFunctionReturn(0); 840 } 841 842 static PetscErrorCode MatSeqAIJCUSPARSEGenerateTransposeForMult(Mat A) 843 { 844 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 845 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 846 Mat_SeqAIJCUSPARSEMultStruct *matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 847 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 848 cusparseStatus_t stat; 849 cusparseIndexBase_t indexBase; 850 cudaError_t err; 851 PetscErrorCode ierr; 852 853 PetscFunctionBegin; 854 855 /* allocate space for the triangular factor information */ 856 matstructT = new Mat_SeqAIJCUSPARSEMultStruct; 857 stat = cusparseCreateMatDescr(&matstructT->descr);CHKERRCUDA(stat); 858 indexBase = cusparseGetMatIndexBase(matstruct->descr); 859 stat = cusparseSetMatIndexBase(matstructT->descr, indexBase);CHKERRCUDA(stat); 860 stat = cusparseSetMatType(matstructT->descr, CUSPARSE_MATRIX_TYPE_GENERAL);CHKERRCUDA(stat); 861 862 /* set alpha and beta */ 863 err = cudaMalloc((void **)&(matstructT->alpha), sizeof(PetscScalar));CHKERRCUDA(err); 864 err = cudaMalloc((void **)&(matstructT->beta_zero),sizeof(PetscScalar));CHKERRCUDA(err); 865 err = cudaMalloc((void **)&(matstructT->beta_one), sizeof(PetscScalar));CHKERRCUDA(err); 866 err = cudaMemcpy(matstructT->alpha, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 867 err = cudaMemcpy(matstructT->beta_zero,&PETSC_CUSPARSE_ZERO,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 868 err = cudaMemcpy(matstructT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 869 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 870 871 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 872 CsrMatrix *matrix = (CsrMatrix*)matstruct->mat; 873 CsrMatrix *matrixT= new CsrMatrix; 874 matrixT->num_rows = A->cmap->n; 875 matrixT->num_cols = A->rmap->n; 876 matrixT->num_entries = a->nz; 877 matrixT->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 878 matrixT->column_indices = new THRUSTINTARRAY32(a->nz); 879 matrixT->values = new THRUSTARRAY(a->nz); 880 881 /* compute the transpose of the upper triangular factor, i.e. the CSC */ 882 indexBase = cusparseGetMatIndexBase(matstruct->descr); 883 stat = cusparse_csr2csc(cusparsestruct->handle, matrix->num_rows, 884 matrix->num_cols, matrix->num_entries, 885 matrix->values->data().get(), 886 matrix->row_offsets->data().get(), 887 matrix->column_indices->data().get(), 888 matrixT->values->data().get(), 889 matrixT->column_indices->data().get(), 890 matrixT->row_offsets->data().get(), 891 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 892 893 /* assign the pointer */ 894 matstructT->mat = matrixT; 895 ierr = PetscLogCpuToGpu(((A->rmap->n+1)+(a->nz))*sizeof(int)+(3+a->nz)*sizeof(PetscScalar));CHKERRQ(ierr); 896 } else if (cusparsestruct->format==MAT_CUSPARSE_ELL || cusparsestruct->format==MAT_CUSPARSE_HYB) { 897 #if CUDA_VERSION>=5000 898 /* First convert HYB to CSR */ 899 CsrMatrix *temp= new CsrMatrix; 900 temp->num_rows = A->rmap->n; 901 temp->num_cols = A->cmap->n; 902 temp->num_entries = a->nz; 903 temp->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 904 temp->column_indices = new THRUSTINTARRAY32(a->nz); 905 temp->values = new THRUSTARRAY(a->nz); 906 907 908 stat = cusparse_hyb2csr(cusparsestruct->handle, 909 matstruct->descr, (cusparseHybMat_t)matstruct->mat, 910 temp->values->data().get(), 911 temp->row_offsets->data().get(), 912 temp->column_indices->data().get());CHKERRCUDA(stat); 913 914 /* Next, convert CSR to CSC (i.e. the matrix transpose) */ 915 CsrMatrix *tempT= new CsrMatrix; 916 tempT->num_rows = A->rmap->n; 917 tempT->num_cols = A->cmap->n; 918 tempT->num_entries = a->nz; 919 tempT->row_offsets = new THRUSTINTARRAY32(A->rmap->n+1); 920 tempT->column_indices = new THRUSTINTARRAY32(a->nz); 921 tempT->values = new THRUSTARRAY(a->nz); 922 923 stat = cusparse_csr2csc(cusparsestruct->handle, temp->num_rows, 924 temp->num_cols, temp->num_entries, 925 temp->values->data().get(), 926 temp->row_offsets->data().get(), 927 temp->column_indices->data().get(), 928 tempT->values->data().get(), 929 tempT->column_indices->data().get(), 930 tempT->row_offsets->data().get(), 931 CUSPARSE_ACTION_NUMERIC, indexBase);CHKERRCUDA(stat); 932 933 /* Last, convert CSC to HYB */ 934 cusparseHybMat_t hybMat; 935 stat = cusparseCreateHybMat(&hybMat);CHKERRCUDA(stat); 936 cusparseHybPartition_t partition = cusparsestruct->format==MAT_CUSPARSE_ELL ? 937 CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO; 938 stat = cusparse_csr2hyb(cusparsestruct->handle, A->rmap->n, A->cmap->n, 939 matstructT->descr, tempT->values->data().get(), 940 tempT->row_offsets->data().get(), 941 tempT->column_indices->data().get(), 942 hybMat, 0, partition);CHKERRCUDA(stat); 943 944 /* assign the pointer */ 945 matstructT->mat = hybMat; 946 ierr = PetscLogCpuToGpu((2*(((A->rmap->n+1)+(a->nz))*sizeof(int)+(a->nz)*sizeof(PetscScalar)))+3*sizeof(PetscScalar));CHKERRQ(ierr); 947 948 /* delete temporaries */ 949 if (tempT) { 950 if (tempT->values) delete (THRUSTARRAY*) tempT->values; 951 if (tempT->column_indices) delete (THRUSTINTARRAY32*) tempT->column_indices; 952 if (tempT->row_offsets) delete (THRUSTINTARRAY32*) tempT->row_offsets; 953 delete (CsrMatrix*) tempT; 954 } 955 if (temp) { 956 if (temp->values) delete (THRUSTARRAY*) temp->values; 957 if (temp->column_indices) delete (THRUSTINTARRAY32*) temp->column_indices; 958 if (temp->row_offsets) delete (THRUSTINTARRAY32*) temp->row_offsets; 959 delete (CsrMatrix*) temp; 960 } 961 #else 962 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."); 963 #endif 964 } 965 /* assign the compressed row indices */ 966 matstructT->cprowIndices = new THRUSTINTARRAY; 967 matstructT->cprowIndices->resize(A->cmap->n); 968 thrust::sequence(matstructT->cprowIndices->begin(), matstructT->cprowIndices->end()); 969 /* assign the pointer */ 970 ((Mat_SeqAIJCUSPARSE*)A->spptr)->matTranspose = matstructT; 971 PetscFunctionReturn(0); 972 } 973 974 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat A,Vec bb,Vec xx) 975 { 976 PetscInt n = xx->map->n; 977 const PetscScalar *barray; 978 PetscScalar *xarray; 979 thrust::device_ptr<const PetscScalar> bGPU; 980 thrust::device_ptr<PetscScalar> xGPU; 981 cusparseStatus_t stat; 982 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 983 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 984 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 985 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 986 PetscErrorCode ierr; 987 988 PetscFunctionBegin; 989 /* Analyze the matrix and create the transpose ... on the fly */ 990 if (!loTriFactorT && !upTriFactorT) { 991 ierr = MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A);CHKERRQ(ierr); 992 loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 993 upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 994 } 995 996 /* Get the GPU pointers */ 997 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 998 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 999 xGPU = thrust::device_pointer_cast(xarray); 1000 bGPU = thrust::device_pointer_cast(barray); 1001 1002 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1003 /* First, reorder with the row permutation */ 1004 thrust::copy(thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), 1005 thrust::make_permutation_iterator(bGPU+n, cusparseTriFactors->rpermIndices->end()), 1006 xGPU); 1007 1008 /* First, solve U */ 1009 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, 1010 upTriFactorT->csrMat->num_rows, &PETSC_CUSPARSE_ONE, upTriFactorT->descr, 1011 upTriFactorT->csrMat->values->data().get(), 1012 upTriFactorT->csrMat->row_offsets->data().get(), 1013 upTriFactorT->csrMat->column_indices->data().get(), 1014 upTriFactorT->solveInfo, 1015 xarray, tempGPU->data().get());CHKERRCUDA(stat); 1016 1017 /* Then, solve L */ 1018 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, 1019 loTriFactorT->csrMat->num_rows, &PETSC_CUSPARSE_ONE, loTriFactorT->descr, 1020 loTriFactorT->csrMat->values->data().get(), 1021 loTriFactorT->csrMat->row_offsets->data().get(), 1022 loTriFactorT->csrMat->column_indices->data().get(), 1023 loTriFactorT->solveInfo, 1024 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1025 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1026 1027 /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */ 1028 thrust::copy(thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), 1029 thrust::make_permutation_iterator(xGPU+n, cusparseTriFactors->cpermIndices->end()), 1030 tempGPU->begin()); 1031 1032 /* Copy the temporary to the full solution. */ 1033 thrust::copy(tempGPU->begin(), tempGPU->end(), xGPU); 1034 1035 /* restore */ 1036 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1037 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1038 ierr = WaitForGPU();CHKERRCUDA(ierr); 1039 ierr = PetscLogGpuFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1040 PetscFunctionReturn(0); 1041 } 1042 1043 static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat A,Vec bb,Vec xx) 1044 { 1045 const PetscScalar *barray; 1046 PetscScalar *xarray; 1047 cusparseStatus_t stat; 1048 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1049 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 1050 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 1051 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1052 PetscErrorCode ierr; 1053 1054 PetscFunctionBegin; 1055 /* Analyze the matrix and create the transpose ... on the fly */ 1056 if (!loTriFactorT && !upTriFactorT) { 1057 ierr = MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A);CHKERRQ(ierr); 1058 loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtrTranspose; 1059 upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtrTranspose; 1060 } 1061 1062 /* Get the GPU pointers */ 1063 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1064 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1065 1066 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1067 /* First, solve U */ 1068 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, 1069 upTriFactorT->csrMat->num_rows, &PETSC_CUSPARSE_ONE, upTriFactorT->descr, 1070 upTriFactorT->csrMat->values->data().get(), 1071 upTriFactorT->csrMat->row_offsets->data().get(), 1072 upTriFactorT->csrMat->column_indices->data().get(), 1073 upTriFactorT->solveInfo, 1074 barray, tempGPU->data().get());CHKERRCUDA(stat); 1075 1076 /* Then, solve L */ 1077 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, 1078 loTriFactorT->csrMat->num_rows, &PETSC_CUSPARSE_ONE, loTriFactorT->descr, 1079 loTriFactorT->csrMat->values->data().get(), 1080 loTriFactorT->csrMat->row_offsets->data().get(), 1081 loTriFactorT->csrMat->column_indices->data().get(), 1082 loTriFactorT->solveInfo, 1083 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1084 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1085 1086 /* restore */ 1087 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1088 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1089 ierr = WaitForGPU();CHKERRCUDA(ierr); 1090 ierr = PetscLogGpuFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1091 PetscFunctionReturn(0); 1092 } 1093 1094 static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat A,Vec bb,Vec xx) 1095 { 1096 const PetscScalar *barray; 1097 PetscScalar *xarray; 1098 thrust::device_ptr<const PetscScalar> bGPU; 1099 thrust::device_ptr<PetscScalar> xGPU; 1100 cusparseStatus_t stat; 1101 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1102 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 1103 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 1104 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1105 PetscErrorCode ierr; 1106 1107 PetscFunctionBegin; 1108 1109 /* Get the GPU pointers */ 1110 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1111 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1112 xGPU = thrust::device_pointer_cast(xarray); 1113 bGPU = thrust::device_pointer_cast(barray); 1114 1115 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1116 /* First, reorder with the row permutation */ 1117 thrust::copy(thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), 1118 thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->end()), 1119 xGPU); 1120 1121 /* Next, solve L */ 1122 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1123 loTriFactor->csrMat->num_rows, &PETSC_CUSPARSE_ONE, loTriFactor->descr, 1124 loTriFactor->csrMat->values->data().get(), 1125 loTriFactor->csrMat->row_offsets->data().get(), 1126 loTriFactor->csrMat->column_indices->data().get(), 1127 loTriFactor->solveInfo, 1128 xarray, tempGPU->data().get());CHKERRCUDA(stat); 1129 1130 /* Then, solve U */ 1131 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1132 upTriFactor->csrMat->num_rows, &PETSC_CUSPARSE_ONE, upTriFactor->descr, 1133 upTriFactor->csrMat->values->data().get(), 1134 upTriFactor->csrMat->row_offsets->data().get(), 1135 upTriFactor->csrMat->column_indices->data().get(), 1136 upTriFactor->solveInfo, 1137 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1138 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1139 1140 /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */ 1141 thrust::copy(thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), 1142 thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->end()), 1143 tempGPU->begin()); 1144 1145 /* Copy the temporary to the full solution. */ 1146 thrust::copy(tempGPU->begin(), tempGPU->end(), xGPU); 1147 1148 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1149 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1150 ierr = WaitForGPU();CHKERRCUDA(ierr); 1151 ierr = PetscLogGpuFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1152 PetscFunctionReturn(0); 1153 } 1154 1155 static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A,Vec bb,Vec xx) 1156 { 1157 const PetscScalar *barray; 1158 PetscScalar *xarray; 1159 cusparseStatus_t stat; 1160 Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors*)A->spptr; 1161 Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->loTriFactorPtr; 1162 Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct*)cusparseTriFactors->upTriFactorPtr; 1163 THRUSTARRAY *tempGPU = (THRUSTARRAY*)cusparseTriFactors->workVector; 1164 PetscErrorCode ierr; 1165 1166 PetscFunctionBegin; 1167 /* Get the GPU pointers */ 1168 ierr = VecCUDAGetArrayWrite(xx,&xarray);CHKERRQ(ierr); 1169 ierr = VecCUDAGetArrayRead(bb,&barray);CHKERRQ(ierr); 1170 1171 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1172 /* First, solve L */ 1173 stat = cusparse_solve(cusparseTriFactors->handle, loTriFactor->solveOp, 1174 loTriFactor->csrMat->num_rows, &PETSC_CUSPARSE_ONE, loTriFactor->descr, 1175 loTriFactor->csrMat->values->data().get(), 1176 loTriFactor->csrMat->row_offsets->data().get(), 1177 loTriFactor->csrMat->column_indices->data().get(), 1178 loTriFactor->solveInfo, 1179 barray, tempGPU->data().get());CHKERRCUDA(stat); 1180 1181 /* Next, solve U */ 1182 stat = cusparse_solve(cusparseTriFactors->handle, upTriFactor->solveOp, 1183 upTriFactor->csrMat->num_rows, &PETSC_CUSPARSE_ONE, upTriFactor->descr, 1184 upTriFactor->csrMat->values->data().get(), 1185 upTriFactor->csrMat->row_offsets->data().get(), 1186 upTriFactor->csrMat->column_indices->data().get(), 1187 upTriFactor->solveInfo, 1188 tempGPU->data().get(), xarray);CHKERRCUDA(stat); 1189 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1190 1191 ierr = VecCUDARestoreArrayRead(bb,&barray);CHKERRQ(ierr); 1192 ierr = VecCUDARestoreArrayWrite(xx,&xarray);CHKERRQ(ierr); 1193 ierr = WaitForGPU();CHKERRCUDA(ierr); 1194 ierr = PetscLogGpuFlops(2.0*cusparseTriFactors->nnz - A->cmap->n);CHKERRQ(ierr); 1195 PetscFunctionReturn(0); 1196 } 1197 1198 static PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A) 1199 { 1200 1201 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1202 Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1203 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1204 PetscInt m = A->rmap->n,*ii,*ridx; 1205 PetscErrorCode ierr; 1206 cusparseStatus_t stat; 1207 cudaError_t err; 1208 1209 PetscFunctionBegin; 1210 if (A->pinnedtocpu) PetscFunctionReturn(0); 1211 if (A->valid_GPU_matrix == PETSC_OFFLOAD_UNALLOCATED || A->valid_GPU_matrix == PETSC_OFFLOAD_CPU) { 1212 ierr = PetscLogEventBegin(MAT_CUSPARSECopyToGPU,A,0,0,0);CHKERRQ(ierr); 1213 if (A->assembled && A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { 1214 CsrMatrix *matrix = (CsrMatrix*)matstruct->mat; 1215 /* copy values only */ 1216 matrix->values->assign(a->a, a->a+a->nz); 1217 ierr = PetscLogCpuToGpu((a->nz)*sizeof(PetscScalar));CHKERRQ(ierr); 1218 } else { 1219 MatSeqAIJCUSPARSEMultStruct_Destroy(&matstruct,cusparsestruct->format); 1220 try { 1221 cusparsestruct->nonzerorow=0; 1222 for (int j = 0; j<m; j++) cusparsestruct->nonzerorow += ((a->i[j+1]-a->i[j])>0); 1223 1224 if (a->compressedrow.use) { 1225 m = a->compressedrow.nrows; 1226 ii = a->compressedrow.i; 1227 ridx = a->compressedrow.rindex; 1228 } else { 1229 /* Forcing compressed row on the GPU */ 1230 int k=0; 1231 ierr = PetscMalloc1(cusparsestruct->nonzerorow+1, &ii);CHKERRQ(ierr); 1232 ierr = PetscMalloc1(cusparsestruct->nonzerorow, &ridx);CHKERRQ(ierr); 1233 ii[0]=0; 1234 for (int j = 0; j<m; j++) { 1235 if ((a->i[j+1]-a->i[j])>0) { 1236 ii[k] = a->i[j]; 1237 ridx[k]= j; 1238 k++; 1239 } 1240 } 1241 ii[cusparsestruct->nonzerorow] = a->nz; 1242 m = cusparsestruct->nonzerorow; 1243 } 1244 1245 /* allocate space for the triangular factor information */ 1246 matstruct = new Mat_SeqAIJCUSPARSEMultStruct; 1247 stat = cusparseCreateMatDescr(&matstruct->descr);CHKERRCUDA(stat); 1248 stat = cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO);CHKERRCUDA(stat); 1249 stat = cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL);CHKERRCUDA(stat); 1250 1251 err = cudaMalloc((void **)&(matstruct->alpha), sizeof(PetscScalar));CHKERRCUDA(err); 1252 err = cudaMalloc((void **)&(matstruct->beta_zero),sizeof(PetscScalar));CHKERRCUDA(err); 1253 err = cudaMalloc((void **)&(matstruct->beta_one), sizeof(PetscScalar));CHKERRCUDA(err); 1254 err = cudaMemcpy(matstruct->alpha, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1255 err = cudaMemcpy(matstruct->beta_zero,&PETSC_CUSPARSE_ZERO,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1256 err = cudaMemcpy(matstruct->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(err); 1257 stat = cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE);CHKERRCUDA(stat); 1258 1259 /* Build a hybrid/ellpack matrix if this option is chosen for the storage */ 1260 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1261 /* set the matrix */ 1262 CsrMatrix *matrix= new CsrMatrix; 1263 matrix->num_rows = m; 1264 matrix->num_cols = A->cmap->n; 1265 matrix->num_entries = a->nz; 1266 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1267 matrix->row_offsets->assign(ii, ii + m+1); 1268 1269 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1270 matrix->column_indices->assign(a->j, a->j+a->nz); 1271 1272 matrix->values = new THRUSTARRAY(a->nz); 1273 matrix->values->assign(a->a, a->a+a->nz); 1274 1275 /* assign the pointer */ 1276 matstruct->mat = matrix; 1277 1278 } else if (cusparsestruct->format==MAT_CUSPARSE_ELL || cusparsestruct->format==MAT_CUSPARSE_HYB) { 1279 #if CUDA_VERSION>=4020 1280 CsrMatrix *matrix= new CsrMatrix; 1281 matrix->num_rows = m; 1282 matrix->num_cols = A->cmap->n; 1283 matrix->num_entries = a->nz; 1284 matrix->row_offsets = new THRUSTINTARRAY32(m+1); 1285 matrix->row_offsets->assign(ii, ii + m+1); 1286 1287 matrix->column_indices = new THRUSTINTARRAY32(a->nz); 1288 matrix->column_indices->assign(a->j, a->j+a->nz); 1289 1290 matrix->values = new THRUSTARRAY(a->nz); 1291 matrix->values->assign(a->a, a->a+a->nz); 1292 1293 cusparseHybMat_t hybMat; 1294 stat = cusparseCreateHybMat(&hybMat);CHKERRCUDA(stat); 1295 cusparseHybPartition_t partition = cusparsestruct->format==MAT_CUSPARSE_ELL ? 1296 CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO; 1297 stat = cusparse_csr2hyb(cusparsestruct->handle, matrix->num_rows, matrix->num_cols, 1298 matstruct->descr, matrix->values->data().get(), 1299 matrix->row_offsets->data().get(), 1300 matrix->column_indices->data().get(), 1301 hybMat, 0, partition);CHKERRCUDA(stat); 1302 /* assign the pointer */ 1303 matstruct->mat = hybMat; 1304 1305 if (matrix) { 1306 if (matrix->values) delete (THRUSTARRAY*)matrix->values; 1307 if (matrix->column_indices) delete (THRUSTINTARRAY32*)matrix->column_indices; 1308 if (matrix->row_offsets) delete (THRUSTINTARRAY32*)matrix->row_offsets; 1309 delete (CsrMatrix*)matrix; 1310 } 1311 #endif 1312 } 1313 1314 /* assign the compressed row indices */ 1315 matstruct->cprowIndices = new THRUSTINTARRAY(m); 1316 matstruct->cprowIndices->assign(ridx,ridx+m); 1317 ierr = PetscLogCpuToGpu(((m+1)+(a->nz))*sizeof(int)+m*sizeof(PetscInt)+(3+(a->nz))*sizeof(PetscScalar));CHKERRQ(ierr); 1318 1319 /* assign the pointer */ 1320 cusparsestruct->mat = matstruct; 1321 1322 if (!a->compressedrow.use) { 1323 ierr = PetscFree(ii);CHKERRQ(ierr); 1324 ierr = PetscFree(ridx);CHKERRQ(ierr); 1325 } 1326 cusparsestruct->workVector = new THRUSTARRAY(m); 1327 } catch(char *ex) { 1328 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1329 } 1330 cusparsestruct->nonzerostate = A->nonzerostate; 1331 } 1332 ierr = WaitForGPU();CHKERRCUDA(ierr); 1333 A->valid_GPU_matrix = PETSC_OFFLOAD_BOTH; 1334 ierr = PetscLogEventEnd(MAT_CUSPARSECopyToGPU,A,0,0,0);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 PetscErrorCode ierr; 1352 1353 PetscFunctionBegin; 1354 ierr = MatMultAdd_SeqAIJCUSPARSE(A,xx,NULL,yy);CHKERRQ(ierr); 1355 PetscFunctionReturn(0); 1356 } 1357 1358 static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy) 1359 { 1360 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1361 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1362 Mat_SeqAIJCUSPARSEMultStruct *matstructT; 1363 const PetscScalar *xarray; 1364 PetscScalar *yarray; 1365 PetscErrorCode ierr; 1366 cusparseStatus_t stat; 1367 1368 PetscFunctionBegin; 1369 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1370 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1371 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1372 if (!matstructT) { 1373 ierr = MatSeqAIJCUSPARSEGenerateTransposeForMult(A);CHKERRQ(ierr); 1374 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1375 } 1376 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1377 ierr = VecSet(yy,0);CHKERRQ(ierr); 1378 ierr = VecCUDAGetArrayWrite(yy,&yarray);CHKERRQ(ierr); 1379 1380 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1381 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1382 CsrMatrix *mat = (CsrMatrix*)matstructT->mat; 1383 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1384 mat->num_rows, mat->num_cols, 1385 mat->num_entries, matstructT->alpha, matstructT->descr, 1386 mat->values->data().get(), mat->row_offsets->data().get(), 1387 mat->column_indices->data().get(), xarray, matstructT->beta_zero, 1388 yarray);CHKERRCUDA(stat); 1389 } else { 1390 #if CUDA_VERSION>=4020 1391 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstructT->mat; 1392 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1393 matstructT->alpha, matstructT->descr, hybMat, 1394 xarray, matstructT->beta_zero, 1395 yarray);CHKERRCUDA(stat); 1396 #endif 1397 } 1398 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1399 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1400 ierr = VecCUDARestoreArrayWrite(yy,&yarray);CHKERRQ(ierr); 1401 if (!cusparsestruct->stream) { 1402 ierr = WaitForGPU();CHKERRCUDA(ierr); 1403 } 1404 ierr = PetscLogGpuFlops(2.0*a->nz - cusparsestruct->nonzerorow);CHKERRQ(ierr); 1405 PetscFunctionReturn(0); 1406 } 1407 1408 1409 static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy,Vec zz) 1410 { 1411 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1412 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1413 Mat_SeqAIJCUSPARSEMultStruct *matstruct; 1414 const PetscScalar *xarray; 1415 PetscScalar *zarray,*dptr,*beta; 1416 PetscErrorCode ierr; 1417 cusparseStatus_t stat; 1418 1419 PetscFunctionBegin; 1420 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1421 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1422 matstruct = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->mat; 1423 try { 1424 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1425 ierr = VecCUDAGetArray(zz,&zarray);CHKERRQ(ierr); 1426 dptr = cusparsestruct->workVector->size() == (thrust::detail::vector_base<PetscScalar, thrust::device_malloc_allocator<PetscScalar> >::size_type)(A->rmap->n) ? zarray : cusparsestruct->workVector->data().get(); 1427 beta = (yy == zz && dptr == zarray) ? matstruct->beta_one : matstruct->beta_zero; 1428 1429 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1430 /* csr_spmv is multiply add */ 1431 if (cusparsestruct->format == MAT_CUSPARSE_CSR) { 1432 /* here we need to be careful to set the number of rows in the multiply to the 1433 number of compressed rows in the matrix ... which is equivalent to the 1434 size of the workVector */ 1435 CsrMatrix *mat = (CsrMatrix*)matstruct->mat; 1436 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1437 mat->num_rows, mat->num_cols, 1438 mat->num_entries, matstruct->alpha, matstruct->descr, 1439 mat->values->data().get(), mat->row_offsets->data().get(), 1440 mat->column_indices->data().get(), xarray, beta, 1441 dptr);CHKERRCUDA(stat); 1442 } else { 1443 #if CUDA_VERSION>=4020 1444 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat; 1445 if (cusparsestruct->workVector->size()) { 1446 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1447 matstruct->alpha, matstruct->descr, hybMat, 1448 xarray, beta, 1449 dptr);CHKERRCUDA(stat); 1450 } 1451 #endif 1452 } 1453 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1454 1455 if (yy) { 1456 if (dptr != zarray) { 1457 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1458 } else if (zz != yy) { 1459 ierr = VecAXPY_SeqCUDA(zz,1.0,yy);CHKERRQ(ierr); 1460 } 1461 } else if (dptr != zarray) { 1462 ierr = VecSet(zz,0);CHKERRQ(ierr); 1463 } 1464 /* scatter the data from the temporary into the full vector with a += operation */ 1465 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1466 if (dptr != zarray) { 1467 thrust::device_ptr<PetscScalar> zptr; 1468 1469 zptr = thrust::device_pointer_cast(zarray); 1470 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))), 1471 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))) + cusparsestruct->workVector->size(), 1472 VecCUDAPlusEquals()); 1473 } 1474 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1475 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1476 ierr = VecCUDARestoreArray(zz,&zarray);CHKERRQ(ierr); 1477 } catch(char *ex) { 1478 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1479 } 1480 if (!yy) { /* MatMult */ 1481 if (!cusparsestruct->stream) { 1482 ierr = WaitForGPU();CHKERRCUDA(ierr); 1483 } 1484 } 1485 ierr = PetscLogGpuFlops(2.0*a->nz);CHKERRQ(ierr); 1486 PetscFunctionReturn(0); 1487 } 1488 1489 static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A,Vec xx,Vec yy,Vec zz) 1490 { 1491 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1492 Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE*)A->spptr; 1493 Mat_SeqAIJCUSPARSEMultStruct *matstructT; 1494 thrust::device_ptr<PetscScalar> zptr; 1495 const PetscScalar *xarray; 1496 PetscScalar *zarray; 1497 PetscErrorCode ierr; 1498 cusparseStatus_t stat; 1499 1500 PetscFunctionBegin; 1501 /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */ 1502 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1503 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1504 if (!matstructT) { 1505 ierr = MatSeqAIJCUSPARSEGenerateTransposeForMult(A);CHKERRQ(ierr); 1506 matstructT = (Mat_SeqAIJCUSPARSEMultStruct*)cusparsestruct->matTranspose; 1507 } 1508 1509 try { 1510 ierr = VecCopy_SeqCUDA(yy,zz);CHKERRQ(ierr); 1511 ierr = VecCUDAGetArrayRead(xx,&xarray);CHKERRQ(ierr); 1512 ierr = VecCUDAGetArray(zz,&zarray);CHKERRQ(ierr); 1513 zptr = thrust::device_pointer_cast(zarray); 1514 1515 ierr = PetscLogGpuTimeBegin();CHKERRQ(ierr); 1516 /* multiply add with matrix transpose */ 1517 if (cusparsestruct->format==MAT_CUSPARSE_CSR) { 1518 CsrMatrix *mat = (CsrMatrix*)matstructT->mat; 1519 /* here we need to be careful to set the number of rows in the multiply to the 1520 number of compressed rows in the matrix ... which is equivalent to the 1521 size of the workVector */ 1522 stat = cusparse_csr_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1523 mat->num_rows, mat->num_cols, 1524 mat->num_entries, matstructT->alpha, matstructT->descr, 1525 mat->values->data().get(), mat->row_offsets->data().get(), 1526 mat->column_indices->data().get(), xarray, matstructT->beta_zero, 1527 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1528 } else { 1529 #if CUDA_VERSION>=4020 1530 cusparseHybMat_t hybMat = (cusparseHybMat_t)matstructT->mat; 1531 if (cusparsestruct->workVector->size()) { 1532 stat = cusparse_hyb_spmv(cusparsestruct->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, 1533 matstructT->alpha, matstructT->descr, hybMat, 1534 xarray, matstructT->beta_zero, 1535 cusparsestruct->workVector->data().get());CHKERRCUDA(stat); 1536 } 1537 #endif 1538 } 1539 1540 /* scatter the data from the temporary into the full vector with a += operation */ 1541 thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))), 1542 thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstructT->cprowIndices->begin()))) + A->cmap->n, 1543 VecCUDAPlusEquals()); 1544 ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr); 1545 ierr = VecCUDARestoreArrayRead(xx,&xarray);CHKERRQ(ierr); 1546 ierr = VecCUDARestoreArray(zz,&zarray);CHKERRQ(ierr); 1547 1548 } catch(char *ex) { 1549 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"CUSPARSE error: %s", ex); 1550 } 1551 ierr = WaitForGPU();CHKERRCUDA(ierr); 1552 ierr = PetscLogGpuFlops(2.0*a->nz);CHKERRQ(ierr); 1553 PetscFunctionReturn(0); 1554 } 1555 1556 static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A,MatAssemblyType mode) 1557 { 1558 PetscErrorCode ierr; 1559 1560 PetscFunctionBegin; 1561 ierr = MatAssemblyEnd_SeqAIJ(A,mode);CHKERRQ(ierr); 1562 if (A->factortype==MAT_FACTOR_NONE) { 1563 ierr = MatSeqAIJCUSPARSECopyToGPU(A);CHKERRQ(ierr); 1564 } 1565 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 1566 A->ops->mult = MatMult_SeqAIJCUSPARSE; 1567 A->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1568 A->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1569 A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1570 PetscFunctionReturn(0); 1571 } 1572 1573 /* --------------------------------------------------------------------------------*/ 1574 /*@ 1575 MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in AIJ (compressed row) format 1576 (the default parallel PETSc format). This matrix will ultimately pushed down 1577 to NVidia GPUs and use the CUSPARSE library for calculations. For good matrix 1578 assembly performance the user should preallocate the matrix storage by setting 1579 the parameter nz (or the array nnz). By setting these parameters accurately, 1580 performance during matrix assembly can be increased by more than a factor of 50. 1581 1582 Collective 1583 1584 Input Parameters: 1585 + comm - MPI communicator, set to PETSC_COMM_SELF 1586 . m - number of rows 1587 . n - number of columns 1588 . nz - number of nonzeros per row (same for all rows) 1589 - nnz - array containing the number of nonzeros in the various rows 1590 (possibly different for each row) or NULL 1591 1592 Output Parameter: 1593 . A - the matrix 1594 1595 It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(), 1596 MatXXXXSetPreallocation() paradgm instead of this routine directly. 1597 [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation] 1598 1599 Notes: 1600 If nnz is given then nz is ignored 1601 1602 The AIJ format (also called the Yale sparse matrix format or 1603 compressed row storage), is fully compatible with standard Fortran 77 1604 storage. That is, the stored row and column indices can begin at 1605 either one (as in Fortran) or zero. See the users' manual for details. 1606 1607 Specify the preallocated storage with either nz or nnz (not both). 1608 Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory 1609 allocation. For large problems you MUST preallocate memory or you 1610 will get TERRIBLE performance, see the users' manual chapter on matrices. 1611 1612 By default, this format uses inodes (identical nodes) when possible, to 1613 improve numerical efficiency of matrix-vector products and solves. We 1614 search for consecutive rows with the same nonzero structure, thereby 1615 reusing matrix information to achieve increased efficiency. 1616 1617 Level: intermediate 1618 1619 .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATSEQAIJCUSPARSE, MATAIJCUSPARSE 1620 @*/ 1621 PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 1622 { 1623 PetscErrorCode ierr; 1624 1625 PetscFunctionBegin; 1626 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1627 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 1628 ierr = MatSetType(*A,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1629 ierr = MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);CHKERRQ(ierr); 1630 PetscFunctionReturn(0); 1631 } 1632 1633 static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A) 1634 { 1635 PetscErrorCode ierr; 1636 1637 PetscFunctionBegin; 1638 if (A->factortype==MAT_FACTOR_NONE) { 1639 if (A->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) { 1640 ierr = MatSeqAIJCUSPARSE_Destroy((Mat_SeqAIJCUSPARSE**)&A->spptr);CHKERRQ(ierr); 1641 } 1642 } else { 1643 ierr = MatSeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors**)&A->spptr);CHKERRQ(ierr); 1644 } 1645 ierr = MatDestroy_SeqAIJ(A);CHKERRQ(ierr); 1646 PetscFunctionReturn(0); 1647 } 1648 1649 static PetscErrorCode MatDuplicate_SeqAIJCUSPARSE(Mat A,MatDuplicateOption cpvalues,Mat *B) 1650 { 1651 PetscErrorCode ierr; 1652 Mat C; 1653 cusparseStatus_t stat; 1654 cusparseHandle_t handle=0; 1655 1656 PetscFunctionBegin; 1657 ierr = MatDuplicate_SeqAIJ(A,cpvalues,B);CHKERRQ(ierr); 1658 C = *B; 1659 ierr = PetscFree(C->defaultvectype);CHKERRQ(ierr); 1660 ierr = PetscStrallocpy(VECCUDA,&C->defaultvectype);CHKERRQ(ierr); 1661 1662 /* inject CUSPARSE-specific stuff */ 1663 if (C->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 C->spptr = new Mat_SeqAIJCUSPARSE; 1667 ((Mat_SeqAIJCUSPARSE*)C->spptr)->mat = 0; 1668 ((Mat_SeqAIJCUSPARSE*)C->spptr)->matTranspose = 0; 1669 ((Mat_SeqAIJCUSPARSE*)C->spptr)->workVector = 0; 1670 ((Mat_SeqAIJCUSPARSE*)C->spptr)->format = MAT_CUSPARSE_CSR; 1671 ((Mat_SeqAIJCUSPARSE*)C->spptr)->stream = 0; 1672 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1673 ((Mat_SeqAIJCUSPARSE*)C->spptr)->handle = handle; 1674 ((Mat_SeqAIJCUSPARSE*)C->spptr)->nonzerostate = 0; 1675 } else { 1676 /* NEXT, set the pointers to the triangular factors */ 1677 C->spptr = new Mat_SeqAIJCUSPARSETriFactors; 1678 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->loTriFactorPtr = 0; 1679 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->upTriFactorPtr = 0; 1680 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->loTriFactorPtrTranspose = 0; 1681 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->upTriFactorPtrTranspose = 0; 1682 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->rpermIndices = 0; 1683 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->cpermIndices = 0; 1684 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->workVector = 0; 1685 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->handle = 0; 1686 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1687 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->handle = handle; 1688 ((Mat_SeqAIJCUSPARSETriFactors*)C->spptr)->nnz = 0; 1689 } 1690 1691 C->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE; 1692 C->ops->destroy = MatDestroy_SeqAIJCUSPARSE; 1693 C->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE; 1694 C->ops->mult = MatMult_SeqAIJCUSPARSE; 1695 C->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1696 C->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1697 C->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1698 C->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE; 1699 1700 ierr = PetscObjectChangeTypeName((PetscObject)C,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1701 1702 C->valid_GPU_matrix = PETSC_OFFLOAD_UNALLOCATED; 1703 1704 ierr = PetscObjectComposeFunction((PetscObject)C, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE);CHKERRQ(ierr); 1705 PetscFunctionReturn(0); 1706 } 1707 1708 PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat B) 1709 { 1710 PetscErrorCode ierr; 1711 cusparseStatus_t stat; 1712 cusparseHandle_t handle=0; 1713 1714 PetscFunctionBegin; 1715 ierr = PetscFree(B->defaultvectype);CHKERRQ(ierr); 1716 ierr = PetscStrallocpy(VECCUDA,&B->defaultvectype);CHKERRQ(ierr); 1717 1718 if (B->factortype==MAT_FACTOR_NONE) { 1719 /* you cannot check the inode.use flag here since the matrix was just created. 1720 now build a GPU matrix data structure */ 1721 B->spptr = new Mat_SeqAIJCUSPARSE; 1722 ((Mat_SeqAIJCUSPARSE*)B->spptr)->mat = 0; 1723 ((Mat_SeqAIJCUSPARSE*)B->spptr)->matTranspose = 0; 1724 ((Mat_SeqAIJCUSPARSE*)B->spptr)->workVector = 0; 1725 ((Mat_SeqAIJCUSPARSE*)B->spptr)->format = MAT_CUSPARSE_CSR; 1726 ((Mat_SeqAIJCUSPARSE*)B->spptr)->stream = 0; 1727 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1728 ((Mat_SeqAIJCUSPARSE*)B->spptr)->handle = handle; 1729 ((Mat_SeqAIJCUSPARSE*)B->spptr)->nonzerostate = 0; 1730 } else { 1731 /* NEXT, set the pointers to the triangular factors */ 1732 B->spptr = new Mat_SeqAIJCUSPARSETriFactors; 1733 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtr = 0; 1734 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtr = 0; 1735 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->loTriFactorPtrTranspose = 0; 1736 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->upTriFactorPtrTranspose = 0; 1737 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->rpermIndices = 0; 1738 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->cpermIndices = 0; 1739 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->workVector = 0; 1740 stat = cusparseCreate(&handle);CHKERRCUDA(stat); 1741 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->handle = handle; 1742 ((Mat_SeqAIJCUSPARSETriFactors*)B->spptr)->nnz = 0; 1743 } 1744 1745 B->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE; 1746 B->ops->destroy = MatDestroy_SeqAIJCUSPARSE; 1747 B->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE; 1748 B->ops->mult = MatMult_SeqAIJCUSPARSE; 1749 B->ops->multadd = MatMultAdd_SeqAIJCUSPARSE; 1750 B->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE; 1751 B->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE; 1752 B->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE; 1753 1754 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJCUSPARSE);CHKERRQ(ierr); 1755 1756 B->valid_GPU_matrix = PETSC_OFFLOAD_UNALLOCATED; 1757 1758 ierr = PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE);CHKERRQ(ierr); 1759 PetscFunctionReturn(0); 1760 } 1761 1762 PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B) 1763 { 1764 PetscErrorCode ierr; 1765 1766 PetscFunctionBegin; 1767 ierr = MatCreate_SeqAIJ(B);CHKERRQ(ierr); 1768 ierr = MatConvert_SeqAIJ_SeqAIJCUSPARSE(B); 1769 PetscFunctionReturn(0); 1770 } 1771 1772 /*MC 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 MatSolverTypeRegister_CUSPARSE(void) 1793 { 1794 PetscErrorCode ierr; 1795 1796 PetscFunctionBegin; 1797 ierr = MatSolverTypeRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_LU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1798 ierr = MatSolverTypeRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_CHOLESKY,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1799 ierr = MatSolverTypeRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ILU,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1800 ierr = MatSolverTypeRegister(MATSOLVERCUSPARSE,MATSEQAIJCUSPARSE,MAT_FACTOR_ICC,MatGetFactor_seqaijcusparse_cusparse);CHKERRQ(ierr); 1801 PetscFunctionReturn(0); 1802 } 1803 1804 1805 static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat_SeqAIJCUSPARSE **cusparsestruct) 1806 { 1807 cusparseStatus_t stat; 1808 cusparseHandle_t handle; 1809 1810 PetscFunctionBegin; 1811 if (*cusparsestruct) { 1812 MatSeqAIJCUSPARSEMultStruct_Destroy(&(*cusparsestruct)->mat,(*cusparsestruct)->format); 1813 MatSeqAIJCUSPARSEMultStruct_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 MatSeqAIJCUSPARSEMultStruct_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 MatSeqAIJCUSPARSEMultStruct_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_zero) { err=cudaFree((*matstruct)->beta_zero);CHKERRCUDA(err); } 1874 if ((*matstruct)->beta_one) { err=cudaFree((*matstruct)->beta_one);CHKERRCUDA(err); } 1875 delete *matstruct; 1876 *matstruct = 0; 1877 } 1878 PetscFunctionReturn(0); 1879 } 1880 1881 static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors** trifactors) 1882 { 1883 cusparseHandle_t handle; 1884 cusparseStatus_t stat; 1885 1886 PetscFunctionBegin; 1887 if (*trifactors) { 1888 MatSeqAIJCUSPARSEMultStruct_Destroy(&(*trifactors)->loTriFactorPtr); 1889 MatSeqAIJCUSPARSEMultStruct_Destroy(&(*trifactors)->upTriFactorPtr); 1890 MatSeqAIJCUSPARSEMultStruct_Destroy(&(*trifactors)->loTriFactorPtrTranspose); 1891 MatSeqAIJCUSPARSEMultStruct_Destroy(&(*trifactors)->upTriFactorPtrTranspose); 1892 delete (*trifactors)->rpermIndices; 1893 delete (*trifactors)->cpermIndices; 1894 delete (*trifactors)->workVector; 1895 if (handle = (*trifactors)->handle) { 1896 stat = cusparseDestroy(handle);CHKERRCUDA(stat); 1897 } 1898 delete *trifactors; 1899 *trifactors = 0; 1900 } 1901 PetscFunctionReturn(0); 1902 } 1903 1904