Actual source code: aijcusparse.cu

  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_IMMINTRIN_H_CUDAWORKAROUND 1

  7: #include <petscconf.h>
  8: #include <../src/mat/impls/aij/seq/aij.h>
  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: #include <thrust/adjacent_difference.h>
 15: #if PETSC_CPP_VERSION >= 14
 16:   #define PETSC_HAVE_THRUST_ASYNC 1
 17: // thrust::for_each(thrust::cuda::par.on()) requires C++14
 18: #endif
 19: #include <thrust/iterator/constant_iterator.h>
 20: #include <thrust/remove.h>
 21: #include <thrust/sort.h>
 22: #include <thrust/unique.h>
 23: #if PETSC_PKG_CUDA_VERSION_GE(12, 9, 0) && !PetscDefined(HAVE_THRUST)
 24:   #include <cuda/std/functional>
 25: #endif

 27: const char *const MatCUSPARSEStorageFormats[] = {"CSR", "ELL", "HYB", "MatCUSPARSEStorageFormat", "MAT_CUSPARSE_", 0};
 28: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
 29: /*
 30:   The following are copied from cusparse.h in CUDA-11.0. In MatCUSPARSESpMVAlgorithms[] etc, we copy them in
 31:   0-based integer value order, since we want to use PetscOptionsEnum() to parse user command line options for them.
 32: */
 33: const char *const MatCUSPARSESpMVAlgorithms[]    = {"MV_ALG_DEFAULT", "COOMV_ALG", "CSRMV_ALG1", "CSRMV_ALG2", "cusparseSpMVAlg_t", "CUSPARSE_", 0};
 34: const char *const MatCUSPARSESpMMAlgorithms[]    = {"ALG_DEFAULT", "COO_ALG1", "COO_ALG2", "COO_ALG3", "CSR_ALG1", "COO_ALG4", "CSR_ALG2", "cusparseSpMMAlg_t", "CUSPARSE_SPMM_", 0};
 35: const char *const MatCUSPARSECsr2CscAlgorithms[] = {"INVALID" /*cusparse does not have enum 0! We created one*/, "ALG1", "ALG2", "cusparseCsr2CscAlg_t", "CUSPARSE_CSR2CSC_", 0};
 36: #endif

 38: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
 39: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
 40: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat, Mat, const MatFactorInfo *);
 41: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, IS, const MatFactorInfo *);
 42: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
 43: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat, Vec, Vec);
 44: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
 45: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
 46: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
 47: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **);
 48: #endif
 49: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat, PetscOptionItems PetscOptionsObject);
 50: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat, PetscScalar, Mat, MatStructure);
 51: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat, PetscScalar);
 52: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat, Vec, Vec);
 53: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
 54: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
 55: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
 56: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
 57: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
 58: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec, PetscBool, PetscBool);

 60: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **);
 61: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **, MatCUSPARSEStorageFormat);
 62: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **);
 63: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat);

 65: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat);
 66: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat, PetscBool);

 68: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat, PetscInt, const PetscInt[], PetscScalar[]);
 69: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat, PetscCount, PetscInt[], PetscInt[]);
 70: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat, const PetscScalar[], InsertMode);

 72: PETSC_INTERN PetscErrorCode MatCUSPARSESetFormat_SeqAIJCUSPARSE(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
 73: {
 74:   Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;

 76:   PetscFunctionBegin;
 77:   switch (op) {
 78:   case MAT_CUSPARSE_MULT:
 79:     cusparsestruct->format = format;
 80:     break;
 81:   case MAT_CUSPARSE_ALL:
 82:     cusparsestruct->format = format;
 83:     break;
 84:   default:
 85:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported operation %d for MatCUSPARSEFormatOperation. MAT_CUSPARSE_MULT and MAT_CUSPARSE_ALL are currently supported.", op);
 86:   }
 87:   PetscFunctionReturn(PETSC_SUCCESS);
 88: }

 90: /*@
 91:   MatCUSPARSESetFormat - Sets the storage format of `MATSEQCUSPARSE` matrices for a particular
 92:   operation. Only the `MatMult()` operation can use different GPU storage formats

 94:   Not Collective

 96:   Input Parameters:
 97: + A      - Matrix of type `MATSEQAIJCUSPARSE`
 98: . op     - `MatCUSPARSEFormatOperation`. `MATSEQAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT` and `MAT_CUSPARSE_ALL`.
 99:         `MATMPIAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT_DIAG`,`MAT_CUSPARSE_MULT_OFFDIAG`, and `MAT_CUSPARSE_ALL`.
100: - format - `MatCUSPARSEStorageFormat` (one of `MAT_CUSPARSE_CSR`, `MAT_CUSPARSE_ELL`, `MAT_CUSPARSE_HYB`.)

102:   Level: intermediate

104: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
105: @*/
106: PetscErrorCode MatCUSPARSESetFormat(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
107: {
108:   PetscFunctionBegin;
110:   PetscTryMethod(A, "MatCUSPARSESetFormat_C", (Mat, MatCUSPARSEFormatOperation, MatCUSPARSEStorageFormat), (A, op, format));
111:   PetscFunctionReturn(PETSC_SUCCESS);
112: }

114: PETSC_INTERN PetscErrorCode MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE(Mat A, PetscBool use_cpu)
115: {
116:   Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;

118:   PetscFunctionBegin;
119:   cusparsestruct->use_cpu_solve = use_cpu;
120:   PetscFunctionReturn(PETSC_SUCCESS);
121: }

123: /*@
124:   MatCUSPARSESetUseCPUSolve - Sets to use CPU `MatSolve()`.

126:   Input Parameters:
127: + A       - Matrix of type `MATSEQAIJCUSPARSE`
128: - use_cpu - set flag for using the built-in CPU `MatSolve()`

130:   Level: intermediate

132:   Note:
133:   The cuSparse LU solver currently computes the factors with the built-in CPU method
134:   and moves the factors to the GPU for the solve. We have observed better performance keeping the data on the CPU and computing the solve there.
135:   This method to specify if the solve is done on the CPU or GPU (GPU is the default).

137: .seealso: [](ch_matrices), `Mat`, `MatSolve()`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
138: @*/
139: PetscErrorCode MatCUSPARSESetUseCPUSolve(Mat A, PetscBool use_cpu)
140: {
141:   PetscFunctionBegin;
143:   PetscTryMethod(A, "MatCUSPARSESetUseCPUSolve_C", (Mat, PetscBool), (A, use_cpu));
144:   PetscFunctionReturn(PETSC_SUCCESS);
145: }

147: static PetscErrorCode MatSetOption_SeqAIJCUSPARSE(Mat A, MatOption op, PetscBool flg)
148: {
149:   PetscFunctionBegin;
150:   switch (op) {
151:   case MAT_FORM_EXPLICIT_TRANSPOSE:
152:     /* need to destroy the transpose matrix if present to prevent from logic errors if flg is set to true later */
153:     if (A->form_explicit_transpose && !flg) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
154:     A->form_explicit_transpose = flg;
155:     break;
156:   default:
157:     PetscCall(MatSetOption_SeqAIJ(A, op, flg));
158:     break;
159:   }
160:   PetscFunctionReturn(PETSC_SUCCESS);
161: }

163: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat A, PetscOptionItems PetscOptionsObject)
164: {
165:   MatCUSPARSEStorageFormat format;
166:   PetscBool                flg;
167:   Mat_SeqAIJCUSPARSE      *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;

169:   PetscFunctionBegin;
170:   PetscOptionsHeadBegin(PetscOptionsObject, "SeqAIJCUSPARSE options");
171:   if (A->factortype == MAT_FACTOR_NONE) {
172:     PetscCall(PetscOptionsEnum("-mat_cusparse_mult_storage_format", "sets storage format of (seq)aijcusparse gpu matrices for SpMV", "MatCUSPARSESetFormat", MatCUSPARSEStorageFormats, (PetscEnum)cusparsestruct->format, (PetscEnum *)&format, &flg));
173:     if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_MULT, format));

175:     PetscCall(PetscOptionsEnum("-mat_cusparse_storage_format", "sets storage format of (seq)aijcusparse gpu matrices for SpMV and TriSolve", "MatCUSPARSESetFormat", MatCUSPARSEStorageFormats, (PetscEnum)cusparsestruct->format, (PetscEnum *)&format, &flg));
176:     if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_ALL, format));
177:     PetscCall(PetscOptionsBool("-mat_cusparse_use_cpu_solve", "Use CPU (I)LU solve", "MatCUSPARSESetUseCPUSolve", cusparsestruct->use_cpu_solve, &cusparsestruct->use_cpu_solve, &flg));
178:     if (flg) PetscCall(MatCUSPARSESetUseCPUSolve(A, cusparsestruct->use_cpu_solve));
179: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
180:     PetscCall(PetscOptionsEnum("-mat_cusparse_spmv_alg", "sets cuSPARSE algorithm used in sparse-mat dense-vector multiplication (SpMV)", "cusparseSpMVAlg_t", MatCUSPARSESpMVAlgorithms, (PetscEnum)cusparsestruct->spmvAlg, (PetscEnum *)&cusparsestruct->spmvAlg, &flg));
181:     /* If user did use this option, check its consistency with cuSPARSE, since PetscOptionsEnum() sets enum values based on their position in MatCUSPARSESpMVAlgorithms[] */
182:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
183:     PetscCheck(!flg || CUSPARSE_SPMV_CSR_ALG1 == 2, PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSPARSE enum cusparseSpMVAlg_t has been changed but PETSc has not been updated accordingly");
184:   #else
185:     PetscCheck(!flg || CUSPARSE_CSRMV_ALG1 == 2, PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSPARSE enum cusparseSpMVAlg_t has been changed but PETSc has not been updated accordingly");
186:   #endif
187:     PetscCall(PetscOptionsEnum("-mat_cusparse_spmm_alg", "sets cuSPARSE algorithm used in sparse-mat dense-mat multiplication (SpMM)", "cusparseSpMMAlg_t", MatCUSPARSESpMMAlgorithms, (PetscEnum)cusparsestruct->spmmAlg, (PetscEnum *)&cusparsestruct->spmmAlg, &flg));
188:     PetscCheck(!flg || CUSPARSE_SPMM_CSR_ALG1 == 4, PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSPARSE enum cusparseSpMMAlg_t has been changed but PETSc has not been updated accordingly");

190:     PetscCall(
191:       PetscOptionsEnum("-mat_cusparse_csr2csc_alg", "sets cuSPARSE algorithm used in converting CSR matrices to CSC matrices", "cusparseCsr2CscAlg_t", MatCUSPARSECsr2CscAlgorithms, (PetscEnum)cusparsestruct->csr2cscAlg, (PetscEnum *)&cusparsestruct->csr2cscAlg, &flg));
192:     PetscCheck(!flg || CUSPARSE_CSR2CSC_ALG1 == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSPARSE enum cusparseCsr2CscAlg_t has been changed but PETSc has not been updated accordingly");
193: #endif
194:   }
195:   PetscOptionsHeadEnd();
196:   PetscFunctionReturn(PETSC_SUCCESS);
197: }

199: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
200: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(Mat A)
201: {
202:   Mat_SeqAIJ                   *a  = static_cast<Mat_SeqAIJ *>(A->data);
203:   PetscInt                      m  = A->rmap->n;
204:   Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
205:   const PetscInt               *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
206:   const MatScalar              *Aa = a->a;
207:   PetscInt                     *Mi, *Mj, Mnz;
208:   PetscScalar                  *Ma;

210:   PetscFunctionBegin;
211:   if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
212:     if (!fs->csrRowPtr) {                    // Is't the first time to do the setup? Use csrRowPtr since it is not null even when m=0
213:       // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host
214:       Mnz = (Ai[m] - Ai[0]) + (Adiag[0] - Adiag[m]); // Lnz (without the unit diagonal) + Unz (with the non-unit diagonal)
215:       PetscCall(PetscMalloc1(m + 1, &Mi));
216:       PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj is temp
217:       PetscCall(PetscMalloc1(Mnz, &Ma));
218:       Mi[0] = 0;
219:       for (PetscInt i = 0; i < m; i++) {
220:         PetscInt llen = Ai[i + 1] - Ai[i];
221:         PetscInt ulen = Adiag[i] - Adiag[i + 1];
222:         PetscCall(PetscArraycpy(Mj + Mi[i], Aj + Ai[i], llen));                           // entries of L
223:         Mj[Mi[i] + llen] = i;                                                             // diagonal entry
224:         PetscCall(PetscArraycpy(Mj + Mi[i] + llen + 1, Aj + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
225:         Mi[i + 1] = Mi[i] + llen + ulen;
226:       }
227:       // Copy M (L,U) from host to device
228:       PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
229:       PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
230:       PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
231:       PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Mi, sizeof(*fs->csrRowPtr) * (m + 1), cudaMemcpyHostToDevice));
232:       PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*fs->csrColIdx) * Mnz, cudaMemcpyHostToDevice));

234:       // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
235:       // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
236:       // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
237:       // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
238:       // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
239:       cusparseFillMode_t        fillMode  = CUSPARSE_FILL_MODE_LOWER;
240:       cusparseDiagType_t        diagType  = CUSPARSE_DIAG_TYPE_UNIT;
241:       const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;

243:       PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
244:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
245:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

247:       fillMode = CUSPARSE_FILL_MODE_UPPER;
248:       diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
249:       PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
250:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
251:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

253:       // Allocate work vectors in SpSv
254:       PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
255:       PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));

257:       PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
258:       PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));

260:       // Query buffer sizes for SpSV and then allocate buffers, temporarily assuming opA = CUSPARSE_OPERATION_NON_TRANSPOSE
261:       PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
262:       PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, &fs->spsvBufferSize_L));
263:       PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
264:       PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, &fs->spsvBufferSize_U));
265:       PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
266:       PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));

268:       // Record for reuse
269:       fs->csrRowPtr_h = Mi;
270:       fs->csrVal_h    = Ma;
271:       PetscCall(PetscFree(Mj));
272:     }
273:     // Copy the value
274:     Mi  = fs->csrRowPtr_h;
275:     Ma  = fs->csrVal_h;
276:     Mnz = Mi[m];
277:     for (PetscInt i = 0; i < m; i++) {
278:       PetscInt llen = Ai[i + 1] - Ai[i];
279:       PetscInt ulen = Adiag[i] - Adiag[i + 1];
280:       PetscCall(PetscArraycpy(Ma + Mi[i], Aa + Ai[i], llen));                           // entries of L
281:       Ma[Mi[i] + llen] = (MatScalar)1.0 / Aa[Adiag[i]];                                 // recover the diagonal entry
282:       PetscCall(PetscArraycpy(Ma + Mi[i] + llen + 1, Aa + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
283:     }
284:     PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));

286:   #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
287:     if (fs->updatedSpSVAnalysis) { // have done cusparseSpSV_analysis before, and only matrix values changed?
288:       // Otherwise cusparse would error out: "On entry to cusparseSpSV_updateMatrix() parameter number 3 (newValues) had an illegal value: NULL pointer"
289:       if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
290:       if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
291:     } else
292:   #endif
293:     {
294:       // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
295:       PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, fs->spsvBuffer_L));

297:       PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, fs->spsvBuffer_U));
298:       fs->updatedSpSVAnalysis          = PETSC_TRUE;
299:       fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
300:     }
301:   }
302:   PetscFunctionReturn(PETSC_SUCCESS);
303: }
304: #else
305: static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A)
306: {
307:   Mat_SeqAIJ                        *a                  = (Mat_SeqAIJ *)A->data;
308:   PetscInt                           n                  = A->rmap->n;
309:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
310:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
311:   const PetscInt                    *ai = a->i, *aj = a->j, *vi;
312:   const MatScalar                   *aa = a->a, *v;
313:   PetscInt                          *AiLo, *AjLo;
314:   PetscInt                           i, nz, nzLower, offset, rowOffset;

316:   PetscFunctionBegin;
317:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
318:   if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
319:     try {
320:       /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */
321:       nzLower = n + ai[n] - ai[1];
322:       if (!loTriFactor) {
323:         PetscScalar *AALo;

325:         PetscCallCUDA(cudaMallocHost((void **)&AALo, nzLower * sizeof(PetscScalar)));

327:         /* Allocate Space for the lower triangular matrix */
328:         PetscCallCUDA(cudaMallocHost((void **)&AiLo, (n + 1) * sizeof(PetscInt)));
329:         PetscCallCUDA(cudaMallocHost((void **)&AjLo, nzLower * sizeof(PetscInt)));

331:         /* Fill the lower triangular matrix */
332:         AiLo[0]   = (PetscInt)0;
333:         AiLo[n]   = nzLower;
334:         AjLo[0]   = (PetscInt)0;
335:         AALo[0]   = (MatScalar)1.0;
336:         v         = aa;
337:         vi        = aj;
338:         offset    = 1;
339:         rowOffset = 1;
340:         for (i = 1; i < n; i++) {
341:           nz = ai[i + 1] - ai[i];
342:           /* additional 1 for the term on the diagonal */
343:           AiLo[i] = rowOffset;
344:           rowOffset += nz + 1;

346:           PetscCall(PetscArraycpy(&AjLo[offset], vi, nz));
347:           PetscCall(PetscArraycpy(&AALo[offset], v, nz));

349:           offset += nz;
350:           AjLo[offset] = (PetscInt)i;
351:           AALo[offset] = (MatScalar)1.0;
352:           offset += 1;

354:           v += nz;
355:           vi += nz;
356:         }

358:         /* allocate space for the triangular factor information */
359:         PetscCall(PetscNew(&loTriFactor));
360:         loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
361:         /* Create the matrix description */
362:         PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
363:         PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
364:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
365:         PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
366:   #else
367:         PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
368:   #endif
369:         PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER));
370:         PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));

372:         /* set the operation */
373:         loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;

375:         /* set the matrix */
376:         loTriFactor->csrMat              = new CsrMatrix;
377:         loTriFactor->csrMat->num_rows    = n;
378:         loTriFactor->csrMat->num_cols    = n;
379:         loTriFactor->csrMat->num_entries = nzLower;

381:         loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
382:         loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo + n + 1);

384:         loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower);
385:         loTriFactor->csrMat->column_indices->assign(AjLo, AjLo + nzLower);

387:         loTriFactor->csrMat->values = new THRUSTARRAY(nzLower);
388:         loTriFactor->csrMat->values->assign(AALo, AALo + nzLower);

390:         /* Create the solve analysis information */
391:         PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
392:         PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
393:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
394:         PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
395:                                                   loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
396:         PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
397:   #endif

399:         /* perform the solve analysis */
400:         PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
401:                                                   loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
402:         PetscCallCUDA(WaitForCUDA());
403:         PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

405:         /* assign the pointer */
406:         ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
407:         loTriFactor->AA_h                                          = AALo;
408:         PetscCallCUDA(cudaFreeHost(AiLo));
409:         PetscCallCUDA(cudaFreeHost(AjLo));
410:         PetscCall(PetscLogCpuToGpu((n + 1 + nzLower) * sizeof(int) + nzLower * sizeof(PetscScalar)));
411:       } else { /* update values only */
412:         if (!loTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&loTriFactor->AA_h, nzLower * sizeof(PetscScalar)));
413:         /* Fill the lower triangular matrix */
414:         loTriFactor->AA_h[0] = 1.0;
415:         v                    = aa;
416:         vi                   = aj;
417:         offset               = 1;
418:         for (i = 1; i < n; i++) {
419:           nz = ai[i + 1] - ai[i];
420:           PetscCall(PetscArraycpy(&loTriFactor->AA_h[offset], v, nz));
421:           offset += nz;
422:           loTriFactor->AA_h[offset] = 1.0;
423:           offset += 1;
424:           v += nz;
425:         }
426:         loTriFactor->csrMat->values->assign(loTriFactor->AA_h, loTriFactor->AA_h + nzLower);
427:         PetscCall(PetscLogCpuToGpu(nzLower * sizeof(PetscScalar)));
428:       }
429:     } catch (char *ex) {
430:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
431:     }
432:   }
433:   PetscFunctionReturn(PETSC_SUCCESS);
434: }

436: static PetscErrorCode MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(Mat A)
437: {
438:   Mat_SeqAIJ                        *a                  = (Mat_SeqAIJ *)A->data;
439:   PetscInt                           n                  = A->rmap->n;
440:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
441:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
442:   const PetscInt                    *aj = a->j, *adiag = a->diag, *vi;
443:   const MatScalar                   *aa = a->a, *v;
444:   PetscInt                          *AiUp, *AjUp;
445:   PetscInt                           i, nz, nzUpper, offset;

447:   PetscFunctionBegin;
448:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
449:   if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
450:     try {
451:       /* next, figure out the number of nonzeros in the upper triangular matrix. */
452:       nzUpper = adiag[0] - adiag[n];
453:       if (!upTriFactor) {
454:         PetscScalar *AAUp;

456:         PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));

458:         /* Allocate Space for the upper triangular matrix */
459:         PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
460:         PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));

462:         /* Fill the upper triangular matrix */
463:         AiUp[0] = (PetscInt)0;
464:         AiUp[n] = nzUpper;
465:         offset  = nzUpper;
466:         for (i = n - 1; i >= 0; i--) {
467:           v  = aa + adiag[i + 1] + 1;
468:           vi = aj + adiag[i + 1] + 1;

470:           /* number of elements NOT on the diagonal */
471:           nz = adiag[i] - adiag[i + 1] - 1;

473:           /* decrement the offset */
474:           offset -= (nz + 1);

476:           /* first, set the diagonal elements */
477:           AjUp[offset] = (PetscInt)i;
478:           AAUp[offset] = (MatScalar)1. / v[nz];
479:           AiUp[i]      = AiUp[i + 1] - (nz + 1);

481:           PetscCall(PetscArraycpy(&AjUp[offset + 1], vi, nz));
482:           PetscCall(PetscArraycpy(&AAUp[offset + 1], v, nz));
483:         }

485:         /* allocate space for the triangular factor information */
486:         PetscCall(PetscNew(&upTriFactor));
487:         upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;

489:         /* Create the matrix description */
490:         PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
491:         PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
492:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
493:         PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
494:   #else
495:         PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
496:   #endif
497:         PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
498:         PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));

500:         /* set the operation */
501:         upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;

503:         /* set the matrix */
504:         upTriFactor->csrMat              = new CsrMatrix;
505:         upTriFactor->csrMat->num_rows    = n;
506:         upTriFactor->csrMat->num_cols    = n;
507:         upTriFactor->csrMat->num_entries = nzUpper;

509:         upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
510:         upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + n + 1);

512:         upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzUpper);
513:         upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + nzUpper);

515:         upTriFactor->csrMat->values = new THRUSTARRAY(nzUpper);
516:         upTriFactor->csrMat->values->assign(AAUp, AAUp + nzUpper);

518:         /* Create the solve analysis information */
519:         PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
520:         PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
521:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
522:         PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
523:                                                   upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
524:         PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
525:   #endif

527:         /* perform the solve analysis */
528:         PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
529:                                                   upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));

531:         PetscCallCUDA(WaitForCUDA());
532:         PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

534:         /* assign the pointer */
535:         ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
536:         upTriFactor->AA_h                                          = AAUp;
537:         PetscCallCUDA(cudaFreeHost(AiUp));
538:         PetscCallCUDA(cudaFreeHost(AjUp));
539:         PetscCall(PetscLogCpuToGpu((n + 1 + nzUpper) * sizeof(int) + nzUpper * sizeof(PetscScalar)));
540:       } else {
541:         if (!upTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&upTriFactor->AA_h, nzUpper * sizeof(PetscScalar)));
542:         /* Fill the upper triangular matrix */
543:         offset = nzUpper;
544:         for (i = n - 1; i >= 0; i--) {
545:           v = aa + adiag[i + 1] + 1;

547:           /* number of elements NOT on the diagonal */
548:           nz = adiag[i] - adiag[i + 1] - 1;

550:           /* decrement the offset */
551:           offset -= (nz + 1);

553:           /* first, set the diagonal elements */
554:           upTriFactor->AA_h[offset] = 1. / v[nz];
555:           PetscCall(PetscArraycpy(&upTriFactor->AA_h[offset + 1], v, nz));
556:         }
557:         upTriFactor->csrMat->values->assign(upTriFactor->AA_h, upTriFactor->AA_h + nzUpper);
558:         PetscCall(PetscLogCpuToGpu(nzUpper * sizeof(PetscScalar)));
559:       }
560:     } catch (char *ex) {
561:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
562:     }
563:   }
564:   PetscFunctionReturn(PETSC_SUCCESS);
565: }
566: #endif

568: static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A)
569: {
570:   Mat_SeqAIJ                   *a                  = (Mat_SeqAIJ *)A->data;
571:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
572:   IS                            isrow = a->row, isicol = a->icol;
573:   PetscBool                     row_identity, col_identity;
574:   PetscInt                      n = A->rmap->n;

576:   PetscFunctionBegin;
577:   PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
578: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
579:   PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(A));
580: #else
581:   PetscCall(MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A));
582:   PetscCall(MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A));
583:   if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
584: #endif

586:   cusparseTriFactors->nnz = a->nz;

588:   A->offloadmask = PETSC_OFFLOAD_BOTH; // factored matrix is sync'ed to GPU
589:   /* lower triangular indices */
590:   PetscCall(ISIdentity(isrow, &row_identity));
591:   if (!row_identity && !cusparseTriFactors->rpermIndices) {
592:     const PetscInt *r;

594:     PetscCall(ISGetIndices(isrow, &r));
595:     cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
596:     cusparseTriFactors->rpermIndices->assign(r, r + n);
597:     PetscCall(ISRestoreIndices(isrow, &r));
598:     PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
599:   }

601:   /* upper triangular indices */
602:   PetscCall(ISIdentity(isicol, &col_identity));
603:   if (!col_identity && !cusparseTriFactors->cpermIndices) {
604:     const PetscInt *c;

606:     PetscCall(ISGetIndices(isicol, &c));
607:     cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
608:     cusparseTriFactors->cpermIndices->assign(c, c + n);
609:     PetscCall(ISRestoreIndices(isicol, &c));
610:     PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
611:   }
612:   PetscFunctionReturn(PETSC_SUCCESS);
613: }

615: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
616: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_Cheolesky(Mat A)
617: {
618:   Mat_SeqAIJ                   *a  = static_cast<Mat_SeqAIJ *>(A->data);
619:   PetscInt                      m  = A->rmap->n;
620:   Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
621:   const PetscInt               *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
622:   const MatScalar              *Aa = a->a;
623:   PetscInt                     *Mj, Mnz;
624:   PetscScalar                  *Ma, *D;

626:   PetscFunctionBegin;
627:   if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
628:     if (!fs->csrRowPtr) {                    // Is't the first time to do the setup? Use csrRowPtr since it is not null even m=0
629:       // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host.
630:       // See comments at MatICCFactorSymbolic_SeqAIJ() on the layout of the factored matrix (U) on host.
631:       Mnz = Ai[m]; // Unz (with the unit diagonal)
632:       PetscCall(PetscMalloc1(Mnz, &Ma));
633:       PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj[] is temp
634:       PetscCall(PetscMalloc1(m, &D));    // the diagonal
635:       for (PetscInt i = 0; i < m; i++) {
636:         PetscInt ulen = Ai[i + 1] - Ai[i];
637:         Mj[Ai[i]]     = i;                                              // diagonal entry
638:         PetscCall(PetscArraycpy(Mj + Ai[i] + 1, Aj + Ai[i], ulen - 1)); // entries of U on the right of the diagonal
639:       }
640:       // Copy M (U) from host to device
641:       PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
642:       PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
643:       PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
644:       PetscCallCUDA(cudaMalloc(&fs->diag, sizeof(*fs->diag) * m));
645:       PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyHostToDevice));
646:       PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*Mj) * Mnz, cudaMemcpyHostToDevice));

648:       // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
649:       // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
650:       // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
651:       // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
652:       // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
653:       cusparseFillMode_t        fillMode  = CUSPARSE_FILL_MODE_UPPER;
654:       cusparseDiagType_t        diagType  = CUSPARSE_DIAG_TYPE_UNIT; // U is unit diagonal
655:       const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;

657:       PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
658:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
659:       PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

661:       // Allocate work vectors in SpSv
662:       PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
663:       PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));

665:       PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
666:       PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));

668:       // Query buffer sizes for SpSV and then allocate buffers
669:       PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
670:       PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, &fs->spsvBufferSize_U));
671:       PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));

673:       PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut)); // Ut solve uses the same matrix (spMatDescr_U), but different descr and buffer
674:       PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Ut, &fs->spsvBufferSize_Ut));
675:       PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));

677:       // Record for reuse
678:       fs->csrVal_h = Ma;
679:       fs->diag_h   = D;
680:       PetscCall(PetscFree(Mj));
681:     }
682:     // Copy the value
683:     Ma  = fs->csrVal_h;
684:     D   = fs->diag_h;
685:     Mnz = Ai[m];
686:     for (PetscInt i = 0; i < m; i++) {
687:       D[i]      = Aa[Adiag[i]];   // actually Aa[Adiag[i]] is the inverse of the diagonal
688:       Ma[Ai[i]] = (MatScalar)1.0; // set the unit diagonal, which is cosmetic since cusparse does not really read it given CUSPARSE_DIAG_TYPE_UNIT
689:       for (PetscInt k = 0; k < Ai[i + 1] - Ai[i] - 1; k++) Ma[Ai[i] + 1 + k] = -Aa[Ai[i] + k];
690:     }
691:     PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
692:     PetscCallCUDA(cudaMemcpy(fs->diag, D, sizeof(*D) * m, cudaMemcpyHostToDevice));

694:   #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
695:     if (fs->updatedSpSVAnalysis) {
696:       if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
697:       if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_Ut, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
698:     } else
699:   #endif
700:     {
701:       // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
702:       PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, fs->spsvBuffer_U));
703:       PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Ut, fs->spsvBuffer_Ut));
704:       fs->updatedSpSVAnalysis = PETSC_TRUE;
705:     }
706:   }
707:   PetscFunctionReturn(PETSC_SUCCESS);
708: }

710: // Solve Ut D U x = b
711: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_Cholesky(Mat A, Vec b, Vec x)
712: {
713:   Mat_SeqAIJCUSPARSETriFactors         *fs  = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
714:   Mat_SeqAIJ                           *aij = static_cast<Mat_SeqAIJ *>(A->data);
715:   const PetscScalar                    *barray;
716:   PetscScalar                          *xarray;
717:   thrust::device_ptr<const PetscScalar> bGPU;
718:   thrust::device_ptr<PetscScalar>       xGPU;
719:   const cusparseSpSVAlg_t               alg = CUSPARSE_SPSV_ALG_DEFAULT;
720:   PetscInt                              m   = A->rmap->n;

722:   PetscFunctionBegin;
723:   PetscCall(PetscLogGpuTimeBegin());
724:   PetscCall(VecCUDAGetArrayWrite(x, &xarray));
725:   PetscCall(VecCUDAGetArrayRead(b, &barray));
726:   xGPU = thrust::device_pointer_cast(xarray);
727:   bGPU = thrust::device_pointer_cast(barray);

729:   // Reorder b with the row permutation if needed, and wrap the result in fs->X
730:   if (fs->rpermIndices) {
731:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->begin()), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->end()), thrust::device_pointer_cast(fs->X)));
732:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
733:   } else {
734:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
735:   }

737:   // Solve Ut Y = X
738:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
739:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut));

741:   // Solve diag(D) Z = Y. Actually just do Y = Y*D since D is already inverted in MatCholeskyFactorNumeric_SeqAIJ().
742:   // It is basically a vector element-wise multiplication, but cublas does not have it!
743:   PetscCallThrust(thrust::transform(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::device_pointer_cast(fs->Y), thrust::device_pointer_cast(fs->Y + m), thrust::device_pointer_cast(fs->diag), thrust::device_pointer_cast(fs->Y), thrust::multiplies<PetscScalar>()));

745:   // Solve U X = Y
746:   if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
747:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
748:   } else {
749:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
750:   }
751:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_U));

753:   // Reorder X with the column permutation if needed, and put the result back to x
754:   if (fs->cpermIndices) {
755:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
756:                                  thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
757:   }

759:   PetscCall(VecCUDARestoreArrayRead(b, &barray));
760:   PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
761:   PetscCall(PetscLogGpuTimeEnd());
762:   PetscCall(PetscLogGpuFlops(4.0 * aij->nz - A->rmap->n));
763:   PetscFunctionReturn(PETSC_SUCCESS);
764: }
765: #else
766: static PetscErrorCode MatSeqAIJCUSPARSEBuildICCTriMatrices(Mat A)
767: {
768:   Mat_SeqAIJ                        *a                  = (Mat_SeqAIJ *)A->data;
769:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
770:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
771:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
772:   PetscInt                          *AiUp, *AjUp;
773:   PetscScalar                       *AAUp;
774:   PetscScalar                       *AALo;
775:   PetscInt                           nzUpper = a->nz, n = A->rmap->n, i, offset, nz, j;
776:   Mat_SeqSBAIJ                      *b  = (Mat_SeqSBAIJ *)A->data;
777:   const PetscInt                    *ai = b->i, *aj = b->j, *vj;
778:   const MatScalar                   *aa = b->a, *v;

780:   PetscFunctionBegin;
781:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
782:   if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
783:     try {
784:       PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
785:       PetscCallCUDA(cudaMallocHost((void **)&AALo, nzUpper * sizeof(PetscScalar)));
786:       if (!upTriFactor && !loTriFactor) {
787:         /* Allocate Space for the upper triangular matrix */
788:         PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
789:         PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));

791:         /* Fill the upper triangular matrix */
792:         AiUp[0] = (PetscInt)0;
793:         AiUp[n] = nzUpper;
794:         offset  = 0;
795:         for (i = 0; i < n; i++) {
796:           /* set the pointers */
797:           v  = aa + ai[i];
798:           vj = aj + ai[i];
799:           nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */

801:           /* first, set the diagonal elements */
802:           AjUp[offset] = (PetscInt)i;
803:           AAUp[offset] = (MatScalar)1.0 / v[nz];
804:           AiUp[i]      = offset;
805:           AALo[offset] = (MatScalar)1.0 / v[nz];

807:           offset += 1;
808:           if (nz > 0) {
809:             PetscCall(PetscArraycpy(&AjUp[offset], vj, nz));
810:             PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
811:             for (j = offset; j < offset + nz; j++) {
812:               AAUp[j] = -AAUp[j];
813:               AALo[j] = AAUp[j] / v[nz];
814:             }
815:             offset += nz;
816:           }
817:         }

819:         /* allocate space for the triangular factor information */
820:         PetscCall(PetscNew(&upTriFactor));
821:         upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;

823:         /* Create the matrix description */
824:         PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
825:         PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
826:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
827:         PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
828:   #else
829:         PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
830:   #endif
831:         PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
832:         PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));

834:         /* set the matrix */
835:         upTriFactor->csrMat              = new CsrMatrix;
836:         upTriFactor->csrMat->num_rows    = A->rmap->n;
837:         upTriFactor->csrMat->num_cols    = A->cmap->n;
838:         upTriFactor->csrMat->num_entries = a->nz;

840:         upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
841:         upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);

843:         upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
844:         upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);

846:         upTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
847:         upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);

849:         /* set the operation */
850:         upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;

852:         /* Create the solve analysis information */
853:         PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
854:         PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
855:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
856:         PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
857:                                                   upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
858:         PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
859:   #endif

861:         /* perform the solve analysis */
862:         PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
863:                                                   upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));

865:         PetscCallCUDA(WaitForCUDA());
866:         PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

868:         /* assign the pointer */
869:         ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;

871:         /* allocate space for the triangular factor information */
872:         PetscCall(PetscNew(&loTriFactor));
873:         loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;

875:         /* Create the matrix description */
876:         PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
877:         PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
878:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
879:         PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
880:   #else
881:         PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
882:   #endif
883:         PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
884:         PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));

886:         /* set the operation */
887:         loTriFactor->solveOp = CUSPARSE_OPERATION_TRANSPOSE;

889:         /* set the matrix */
890:         loTriFactor->csrMat              = new CsrMatrix;
891:         loTriFactor->csrMat->num_rows    = A->rmap->n;
892:         loTriFactor->csrMat->num_cols    = A->cmap->n;
893:         loTriFactor->csrMat->num_entries = a->nz;

895:         loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
896:         loTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);

898:         loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
899:         loTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);

901:         loTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
902:         loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);

904:         /* Create the solve analysis information */
905:         PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
906:         PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
907:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
908:         PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
909:                                                   loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
910:         PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
911:   #endif

913:         /* perform the solve analysis */
914:         PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
915:                                                   loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));

917:         PetscCallCUDA(WaitForCUDA());
918:         PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

920:         /* assign the pointer */
921:         ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;

923:         PetscCall(PetscLogCpuToGpu(2 * (((A->rmap->n + 1) + (a->nz)) * sizeof(int) + (a->nz) * sizeof(PetscScalar))));
924:         PetscCallCUDA(cudaFreeHost(AiUp));
925:         PetscCallCUDA(cudaFreeHost(AjUp));
926:       } else {
927:         /* Fill the upper triangular matrix */
928:         offset = 0;
929:         for (i = 0; i < n; i++) {
930:           /* set the pointers */
931:           v  = aa + ai[i];
932:           nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */

934:           /* first, set the diagonal elements */
935:           AAUp[offset] = 1.0 / v[nz];
936:           AALo[offset] = 1.0 / v[nz];

938:           offset += 1;
939:           if (nz > 0) {
940:             PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
941:             for (j = offset; j < offset + nz; j++) {
942:               AAUp[j] = -AAUp[j];
943:               AALo[j] = AAUp[j] / v[nz];
944:             }
945:             offset += nz;
946:           }
947:         }
948:         PetscCheck(upTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
949:         PetscCheck(loTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
950:         upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);
951:         loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);
952:         PetscCall(PetscLogCpuToGpu(2 * (a->nz) * sizeof(PetscScalar)));
953:       }
954:       PetscCallCUDA(cudaFreeHost(AAUp));
955:       PetscCallCUDA(cudaFreeHost(AALo));
956:     } catch (char *ex) {
957:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
958:     }
959:   }
960:   PetscFunctionReturn(PETSC_SUCCESS);
961: }
962: #endif

964: static PetscErrorCode MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(Mat A)
965: {
966:   Mat_SeqAIJ                   *a                  = (Mat_SeqAIJ *)A->data;
967:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
968:   IS                            ip                 = a->row;
969:   PetscBool                     perm_identity;
970:   PetscInt                      n = A->rmap->n;

972:   PetscFunctionBegin;
973:   PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");

975: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
976:   PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_Cheolesky(A));
977: #else
978:   PetscCall(MatSeqAIJCUSPARSEBuildICCTriMatrices(A));
979:   if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
980: #endif
981:   cusparseTriFactors->nnz = (a->nz - n) * 2 + n;

983:   A->offloadmask = PETSC_OFFLOAD_BOTH;

985:   /* lower triangular indices */
986:   PetscCall(ISIdentity(ip, &perm_identity));
987:   if (!perm_identity) {
988:     IS              iip;
989:     const PetscInt *irip, *rip;

991:     PetscCall(ISInvertPermutation(ip, PETSC_DECIDE, &iip));
992:     PetscCall(ISGetIndices(iip, &irip));
993:     PetscCall(ISGetIndices(ip, &rip));
994:     cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
995:     cusparseTriFactors->rpermIndices->assign(rip, rip + n);
996:     cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
997:     cusparseTriFactors->cpermIndices->assign(irip, irip + n);
998:     PetscCall(ISRestoreIndices(iip, &irip));
999:     PetscCall(ISDestroy(&iip));
1000:     PetscCall(ISRestoreIndices(ip, &rip));
1001:     PetscCall(PetscLogCpuToGpu(2. * n * sizeof(PetscInt)));
1002:   }
1003:   PetscFunctionReturn(PETSC_SUCCESS);
1004: }

1006: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
1007: {
1008:   PetscFunctionBegin;
1009:   PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
1010:   PetscCall(MatCholeskyFactorNumeric_SeqAIJ(B, A, info));
1011:   B->offloadmask = PETSC_OFFLOAD_CPU;

1013: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1014:   B->ops->solve          = MatSolve_SeqAIJCUSPARSE_Cholesky;
1015:   B->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_Cholesky;
1016: #else
1017:   /* determine which version of MatSolve needs to be used. */
1018:   Mat_SeqAIJ *b  = (Mat_SeqAIJ *)B->data;
1019:   IS          ip = b->row;
1020:   PetscBool   perm_identity;

1022:   PetscCall(ISIdentity(ip, &perm_identity));
1023:   if (perm_identity) {
1024:     B->ops->solve          = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
1025:     B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
1026:   } else {
1027:     B->ops->solve          = MatSolve_SeqAIJCUSPARSE;
1028:     B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
1029:   }
1030: #endif
1031:   B->ops->matsolve          = NULL;
1032:   B->ops->matsolvetranspose = NULL;

1034:   /* get the triangular factors */
1035:   PetscCall(MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(B));
1036:   PetscFunctionReturn(PETSC_SUCCESS);
1037: }

1039: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1040: static PetscErrorCode MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(Mat A)
1041: {
1042:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1043:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1044:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1045:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT;
1046:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT;
1047:   cusparseIndexBase_t                indexBase;
1048:   cusparseMatrixType_t               matrixType;
1049:   cusparseFillMode_t                 fillMode;
1050:   cusparseDiagType_t                 diagType;

1052:   PetscFunctionBegin;
1053:   /* allocate space for the transpose of the lower triangular factor */
1054:   PetscCall(PetscNew(&loTriFactorT));
1055:   loTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;

1057:   /* set the matrix descriptors of the lower triangular factor */
1058:   matrixType = cusparseGetMatType(loTriFactor->descr);
1059:   indexBase  = cusparseGetMatIndexBase(loTriFactor->descr);
1060:   fillMode   = cusparseGetMatFillMode(loTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1061:   diagType   = cusparseGetMatDiagType(loTriFactor->descr);

1063:   /* Create the matrix description */
1064:   PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactorT->descr));
1065:   PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactorT->descr, indexBase));
1066:   PetscCallCUSPARSE(cusparseSetMatType(loTriFactorT->descr, matrixType));
1067:   PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactorT->descr, fillMode));
1068:   PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactorT->descr, diagType));

1070:   /* set the operation */
1071:   loTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;

1073:   /* allocate GPU space for the CSC of the lower triangular factor*/
1074:   loTriFactorT->csrMat                 = new CsrMatrix;
1075:   loTriFactorT->csrMat->num_rows       = loTriFactor->csrMat->num_cols;
1076:   loTriFactorT->csrMat->num_cols       = loTriFactor->csrMat->num_rows;
1077:   loTriFactorT->csrMat->num_entries    = loTriFactor->csrMat->num_entries;
1078:   loTriFactorT->csrMat->row_offsets    = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_rows + 1);
1079:   loTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_entries);
1080:   loTriFactorT->csrMat->values         = new THRUSTARRAY(loTriFactorT->csrMat->num_entries);

1082:   /* compute the transpose of the lower triangular factor, i.e. the CSC */
1083:   #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1084:   PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, loTriFactor->csrMat->values->data().get(),
1085:                                                   loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(), loTriFactorT->csrMat->row_offsets->data().get(),
1086:                                                   loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &loTriFactor->csr2cscBufferSize));
1087:   PetscCallCUDA(cudaMalloc(&loTriFactor->csr2cscBuffer, loTriFactor->csr2cscBufferSize));
1088:   #endif

1090:   PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1091:   {
1092:     // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1093:     auto stat = cusparse_csr2csc(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, loTriFactor->csrMat->values->data().get(), loTriFactor->csrMat->row_offsets->data().get(),
1094:                                  loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(),
1095:   #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1096:                                  loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, loTriFactor->csr2cscBuffer);
1097:   #else
1098:                                  loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1099:   #endif
1100:     PetscCallCUSPARSE(stat);
1101:   }

1103:   PetscCallCUDA(WaitForCUDA());
1104:   PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));

1106:   /* Create the solve analysis information */
1107:   PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1108:   PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactorT->solveInfo));
1109:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1110:   PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1111:                                             loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, &loTriFactorT->solveBufferSize));
1112:   PetscCallCUDA(cudaMalloc(&loTriFactorT->solveBuffer, loTriFactorT->solveBufferSize));
1113:   #endif

1115:   /* perform the solve analysis */
1116:   PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1117:                                             loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));

1119:   PetscCallCUDA(WaitForCUDA());
1120:   PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

1122:   /* assign the pointer */
1123:   ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtrTranspose = loTriFactorT;

1125:   /*********************************************/
1126:   /* Now the Transpose of the Upper Tri Factor */
1127:   /*********************************************/

1129:   /* allocate space for the transpose of the upper triangular factor */
1130:   PetscCall(PetscNew(&upTriFactorT));
1131:   upTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;

1133:   /* set the matrix descriptors of the upper triangular factor */
1134:   matrixType = cusparseGetMatType(upTriFactor->descr);
1135:   indexBase  = cusparseGetMatIndexBase(upTriFactor->descr);
1136:   fillMode   = cusparseGetMatFillMode(upTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1137:   diagType   = cusparseGetMatDiagType(upTriFactor->descr);

1139:   /* Create the matrix description */
1140:   PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactorT->descr));
1141:   PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactorT->descr, indexBase));
1142:   PetscCallCUSPARSE(cusparseSetMatType(upTriFactorT->descr, matrixType));
1143:   PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactorT->descr, fillMode));
1144:   PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactorT->descr, diagType));

1146:   /* set the operation */
1147:   upTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;

1149:   /* allocate GPU space for the CSC of the upper triangular factor*/
1150:   upTriFactorT->csrMat                 = new CsrMatrix;
1151:   upTriFactorT->csrMat->num_rows       = upTriFactor->csrMat->num_cols;
1152:   upTriFactorT->csrMat->num_cols       = upTriFactor->csrMat->num_rows;
1153:   upTriFactorT->csrMat->num_entries    = upTriFactor->csrMat->num_entries;
1154:   upTriFactorT->csrMat->row_offsets    = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_rows + 1);
1155:   upTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_entries);
1156:   upTriFactorT->csrMat->values         = new THRUSTARRAY(upTriFactorT->csrMat->num_entries);

1158:   /* compute the transpose of the upper triangular factor, i.e. the CSC */
1159:   #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1160:   PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, upTriFactor->csrMat->values->data().get(),
1161:                                                   upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(), upTriFactorT->csrMat->row_offsets->data().get(),
1162:                                                   upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &upTriFactor->csr2cscBufferSize));
1163:   PetscCallCUDA(cudaMalloc(&upTriFactor->csr2cscBuffer, upTriFactor->csr2cscBufferSize));
1164:   #endif

1166:   PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1167:   {
1168:     // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1169:     auto stat = cusparse_csr2csc(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, upTriFactor->csrMat->values->data().get(), upTriFactor->csrMat->row_offsets->data().get(),
1170:                                  upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(),
1171:   #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1172:                                  upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, upTriFactor->csr2cscBuffer);
1173:   #else
1174:                                  upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1175:   #endif
1176:     PetscCallCUSPARSE(stat);
1177:   }

1179:   PetscCallCUDA(WaitForCUDA());
1180:   PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));

1182:   /* Create the solve analysis information */
1183:   PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1184:   PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactorT->solveInfo));
1185:   #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1186:   PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1187:                                             upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, &upTriFactorT->solveBufferSize));
1188:   PetscCallCUDA(cudaMalloc(&upTriFactorT->solveBuffer, upTriFactorT->solveBufferSize));
1189:   #endif

1191:   /* perform the solve analysis */
1192:   /* christ, would it have killed you to put this stuff in a function????????? */
1193:   PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1194:                                             upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));

1196:   PetscCallCUDA(WaitForCUDA());
1197:   PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));

1199:   /* assign the pointer */
1200:   ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtrTranspose = upTriFactorT;
1201:   PetscFunctionReturn(PETSC_SUCCESS);
1202: }
1203: #endif

1205: struct PetscScalarToPetscInt {
1206:   __host__ __device__ PetscInt operator()(PetscScalar s) { return (PetscInt)PetscRealPart(s); }
1207: };

1209: static PetscErrorCode MatSeqAIJCUSPARSEFormExplicitTranspose(Mat A)
1210: {
1211:   Mat_SeqAIJCUSPARSE           *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
1212:   Mat_SeqAIJCUSPARSEMultStruct *matstruct, *matstructT;
1213:   Mat_SeqAIJ                   *a = (Mat_SeqAIJ *)A->data;
1214:   cusparseStatus_t              stat;
1215:   cusparseIndexBase_t           indexBase;

1217:   PetscFunctionBegin;
1218:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1219:   matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
1220:   PetscCheck(matstruct, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing mat struct");
1221:   matstructT = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
1222:   PetscCheck(!A->transupdated || matstructT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing matTranspose struct");
1223:   if (A->transupdated) PetscFunctionReturn(PETSC_SUCCESS);
1224:   PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1225:   PetscCall(PetscLogGpuTimeBegin());
1226:   if (cusparsestruct->format != MAT_CUSPARSE_CSR) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
1227:   if (!cusparsestruct->matTranspose) { /* create cusparse matrix */
1228:     matstructT = new Mat_SeqAIJCUSPARSEMultStruct;
1229:     PetscCallCUSPARSE(cusparseCreateMatDescr(&matstructT->descr));
1230:     indexBase = cusparseGetMatIndexBase(matstruct->descr);
1231:     PetscCallCUSPARSE(cusparseSetMatIndexBase(matstructT->descr, indexBase));
1232:     PetscCallCUSPARSE(cusparseSetMatType(matstructT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));

1234:     /* set alpha and beta */
1235:     PetscCallCUDA(cudaMalloc((void **)&matstructT->alpha_one, sizeof(PetscScalar)));
1236:     PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_zero, sizeof(PetscScalar)));
1237:     PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_one, sizeof(PetscScalar)));
1238:     PetscCallCUDA(cudaMemcpy(matstructT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1239:     PetscCallCUDA(cudaMemcpy(matstructT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1240:     PetscCallCUDA(cudaMemcpy(matstructT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));

1242:     if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
1243:       CsrMatrix *matrixT      = new CsrMatrix;
1244:       matstructT->mat         = matrixT;
1245:       matrixT->num_rows       = A->cmap->n;
1246:       matrixT->num_cols       = A->rmap->n;
1247:       matrixT->num_entries    = a->nz;
1248:       matrixT->row_offsets    = new THRUSTINTARRAY32(matrixT->num_rows + 1);
1249:       matrixT->column_indices = new THRUSTINTARRAY32(a->nz);
1250:       matrixT->values         = new THRUSTARRAY(a->nz);

1252:       if (!cusparsestruct->rowoffsets_gpu) cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1253:       cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);

1255: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1256:   #if PETSC_PKG_CUDA_VERSION_GE(11, 2, 1)
1257:       stat = cusparseCreateCsr(&matstructT->matDescr, matrixT->num_rows, matrixT->num_cols, matrixT->num_entries, matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), matrixT->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, /* row offset, col idx type due to THRUSTINTARRAY32 */
1258:                                indexBase, cusparse_scalartype);
1259:       PetscCallCUSPARSE(stat);
1260:   #else
1261:       /* cusparse-11.x returns errors with zero-sized matrices until 11.2.1,
1262:            see https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cusparse-11.2.1

1264:            I don't know what a proper value should be for matstructT->matDescr with empty matrices, so I just set
1265:            it to NULL to blow it up if one relies on it. Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2,
1266:            when nnz = 0, matrixT->row_offsets[] should be filled with indexBase. So I also set it accordingly.
1267:         */
1268:       if (matrixT->num_entries) {
1269:         stat = cusparseCreateCsr(&matstructT->matDescr, matrixT->num_rows, matrixT->num_cols, matrixT->num_entries, matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), matrixT->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, indexBase, cusparse_scalartype);
1270:         PetscCallCUSPARSE(stat);

1272:       } else {
1273:         matstructT->matDescr = NULL;
1274:         matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1275:       }
1276:   #endif
1277: #endif
1278:     } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
1279: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1280:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
1281: #else
1282:       CsrMatrix *temp  = new CsrMatrix;
1283:       CsrMatrix *tempT = new CsrMatrix;
1284:       /* First convert HYB to CSR */
1285:       temp->num_rows       = A->rmap->n;
1286:       temp->num_cols       = A->cmap->n;
1287:       temp->num_entries    = a->nz;
1288:       temp->row_offsets    = new THRUSTINTARRAY32(A->rmap->n + 1);
1289:       temp->column_indices = new THRUSTINTARRAY32(a->nz);
1290:       temp->values         = new THRUSTARRAY(a->nz);

1292:       stat = cusparse_hyb2csr(cusparsestruct->handle, matstruct->descr, (cusparseHybMat_t)matstruct->mat, temp->values->data().get(), temp->row_offsets->data().get(), temp->column_indices->data().get());
1293:       PetscCallCUSPARSE(stat);

1295:       /* Next, convert CSR to CSC (i.e. the matrix transpose) */
1296:       tempT->num_rows       = A->rmap->n;
1297:       tempT->num_cols       = A->cmap->n;
1298:       tempT->num_entries    = a->nz;
1299:       tempT->row_offsets    = new THRUSTINTARRAY32(A->rmap->n + 1);
1300:       tempT->column_indices = new THRUSTINTARRAY32(a->nz);
1301:       tempT->values         = new THRUSTARRAY(a->nz);

1303:       stat = cusparse_csr2csc(cusparsestruct->handle, temp->num_rows, temp->num_cols, temp->num_entries, temp->values->data().get(), temp->row_offsets->data().get(), temp->column_indices->data().get(), tempT->values->data().get(),
1304:                               tempT->column_indices->data().get(), tempT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1305:       PetscCallCUSPARSE(stat);

1307:       /* Last, convert CSC to HYB */
1308:       cusparseHybMat_t hybMat;
1309:       PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
1310:       cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
1311:       stat                             = cusparse_csr2hyb(cusparsestruct->handle, A->rmap->n, A->cmap->n, matstructT->descr, tempT->values->data().get(), tempT->row_offsets->data().get(), tempT->column_indices->data().get(), hybMat, 0, partition);
1312:       PetscCallCUSPARSE(stat);

1314:       /* assign the pointer */
1315:       matstructT->mat = hybMat;
1316:       A->transupdated = PETSC_TRUE;
1317:       /* delete temporaries */
1318:       if (tempT) {
1319:         if (tempT->values) delete (THRUSTARRAY *)tempT->values;
1320:         if (tempT->column_indices) delete (THRUSTINTARRAY32 *)tempT->column_indices;
1321:         if (tempT->row_offsets) delete (THRUSTINTARRAY32 *)tempT->row_offsets;
1322:         delete (CsrMatrix *)tempT;
1323:       }
1324:       if (temp) {
1325:         if (temp->values) delete (THRUSTARRAY *)temp->values;
1326:         if (temp->column_indices) delete (THRUSTINTARRAY32 *)temp->column_indices;
1327:         if (temp->row_offsets) delete (THRUSTINTARRAY32 *)temp->row_offsets;
1328:         delete (CsrMatrix *)temp;
1329:       }
1330: #endif
1331:     }
1332:   }
1333:   if (cusparsestruct->format == MAT_CUSPARSE_CSR) { /* transpose mat struct may be already present, update data */
1334:     CsrMatrix *matrix  = (CsrMatrix *)matstruct->mat;
1335:     CsrMatrix *matrixT = (CsrMatrix *)matstructT->mat;
1336:     PetscCheck(matrix, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix");
1337:     PetscCheck(matrix->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix rows");
1338:     PetscCheck(matrix->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix cols");
1339:     PetscCheck(matrix->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix values");
1340:     PetscCheck(matrixT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT");
1341:     PetscCheck(matrixT->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT rows");
1342:     PetscCheck(matrixT->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT cols");
1343:     PetscCheck(matrixT->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT values");
1344:     if (!cusparsestruct->rowoffsets_gpu) { /* this may be absent when we did not construct the transpose with csr2csc */
1345:       cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1346:       cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
1347:       PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
1348:     }
1349:     if (!cusparsestruct->csr2csc_i) {
1350:       THRUSTARRAY csr2csc_a(matrix->num_entries);
1351:       PetscCallThrust(thrust::sequence(thrust::device, csr2csc_a.begin(), csr2csc_a.end(), 0.0));

1353:       indexBase = cusparseGetMatIndexBase(matstruct->descr);
1354: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1355:       void  *csr2cscBuffer;
1356:       size_t csr2cscBufferSize;
1357:       stat = cusparseCsr2cscEx2_bufferSize(cusparsestruct->handle, A->rmap->n, A->cmap->n, matrix->num_entries, matrix->values->data().get(), cusparsestruct->rowoffsets_gpu->data().get(), matrix->column_indices->data().get(), matrixT->values->data().get(),
1358:                                            matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, &csr2cscBufferSize);
1359:       PetscCallCUSPARSE(stat);
1360:       PetscCallCUDA(cudaMalloc(&csr2cscBuffer, csr2cscBufferSize));
1361: #endif

1363:       if (matrix->num_entries) {
1364:         /* When there are no nonzeros, this routine mistakenly returns CUSPARSE_STATUS_INVALID_VALUE in
1365:            mat_tests-ex62_15_mpiaijcusparse on ranks 0 and 2 with CUDA-11. But CUDA-10 is OK.
1366:            I checked every parameters and they were just fine. I have no clue why cusparse complains.

1368:            Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2, when nnz = 0, matrixT->row_offsets[]
1369:            should be filled with indexBase. So I just take a shortcut here.
1370:         */
1371:         stat = cusparse_csr2csc(cusparsestruct->handle, A->rmap->n, A->cmap->n, matrix->num_entries, csr2csc_a.data().get(), cusparsestruct->rowoffsets_gpu->data().get(), matrix->column_indices->data().get(), matrixT->values->data().get(),
1372: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1373:                                 matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, csr2cscBuffer);
1374:         PetscCallCUSPARSE(stat);
1375: #else
1376:                                 matrixT->column_indices->data().get(), matrixT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1377:         PetscCallCUSPARSE(stat);
1378: #endif
1379:       } else {
1380:         matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1381:       }

1383:       cusparsestruct->csr2csc_i = new THRUSTINTARRAY(matrix->num_entries);
1384:       PetscCallThrust(thrust::transform(thrust::device, matrixT->values->begin(), matrixT->values->end(), cusparsestruct->csr2csc_i->begin(), PetscScalarToPetscInt()));
1385: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1386:       PetscCallCUDA(cudaFree(csr2cscBuffer));
1387: #endif
1388:     }
1389:     PetscCallThrust(
1390:       thrust::copy(thrust::device, thrust::make_permutation_iterator(matrix->values->begin(), cusparsestruct->csr2csc_i->begin()), thrust::make_permutation_iterator(matrix->values->begin(), cusparsestruct->csr2csc_i->end()), matrixT->values->begin()));
1391:   }
1392:   PetscCall(PetscLogGpuTimeEnd());
1393:   PetscCall(PetscLogEventEnd(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1394:   /* the compressed row indices is not used for matTranspose */
1395:   matstructT->cprowIndices = NULL;
1396:   /* assign the pointer */
1397:   ((Mat_SeqAIJCUSPARSE *)A->spptr)->matTranspose = matstructT;
1398:   A->transupdated                                = PETSC_TRUE;
1399:   PetscFunctionReturn(PETSC_SUCCESS);
1400: }

1402: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1403: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1404: {
1405:   const PetscScalar                    *barray;
1406:   PetscScalar                          *xarray;
1407:   thrust::device_ptr<const PetscScalar> bGPU;
1408:   thrust::device_ptr<PetscScalar>       xGPU;
1409:   Mat_SeqAIJCUSPARSETriFactors         *fs  = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1410:   const Mat_SeqAIJ                     *aij = static_cast<Mat_SeqAIJ *>(A->data);
1411:   const cusparseOperation_t             op  = CUSPARSE_OPERATION_NON_TRANSPOSE;
1412:   const cusparseSpSVAlg_t               alg = CUSPARSE_SPSV_ALG_DEFAULT;
1413:   PetscInt                              m   = A->rmap->n;

1415:   PetscFunctionBegin;
1416:   PetscCall(PetscLogGpuTimeBegin());
1417:   PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1418:   PetscCall(VecCUDAGetArrayRead(b, &barray));
1419:   xGPU = thrust::device_pointer_cast(xarray);
1420:   bGPU = thrust::device_pointer_cast(barray);

1422:   // Reorder b with the row permutation if needed, and wrap the result in fs->X
1423:   if (fs->rpermIndices) {
1424:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->begin()), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->end()), thrust::device_pointer_cast(fs->X)));
1425:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1426:   } else {
1427:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1428:   }

1430:   // Solve L Y = X
1431:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1432:   // Note that cusparseSpSV_solve() secretly uses the external buffer used in cusparseSpSV_analysis()!
1433:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_L));

1435:   // Solve U X = Y
1436:   if (fs->cpermIndices) {
1437:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1438:   } else {
1439:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1440:   }
1441:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_U));

1443:   // Reorder X with the column permutation if needed, and put the result back to x
1444:   if (fs->cpermIndices) {
1445:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1446:                                  thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1447:   }
1448:   PetscCall(VecCUDARestoreArrayRead(b, &barray));
1449:   PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1450:   PetscCall(PetscLogGpuTimeEnd());
1451:   PetscCall(PetscLogGpuFlops(2.0 * aij->nz - m));
1452:   PetscFunctionReturn(PETSC_SUCCESS);
1453: }

1455: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1456: {
1457:   Mat_SeqAIJCUSPARSETriFactors         *fs  = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1458:   Mat_SeqAIJ                           *aij = static_cast<Mat_SeqAIJ *>(A->data);
1459:   const PetscScalar                    *barray;
1460:   PetscScalar                          *xarray;
1461:   thrust::device_ptr<const PetscScalar> bGPU;
1462:   thrust::device_ptr<PetscScalar>       xGPU;
1463:   const cusparseOperation_t             opA = CUSPARSE_OPERATION_TRANSPOSE;
1464:   const cusparseSpSVAlg_t               alg = CUSPARSE_SPSV_ALG_DEFAULT;
1465:   PetscInt                              m   = A->rmap->n;

1467:   PetscFunctionBegin;
1468:   PetscCall(PetscLogGpuTimeBegin());
1469:   if (!fs->createdTransposeSpSVDescr) { // Call MatSolveTranspose() for the first time
1470:     PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
1471:     PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* The matrix is still L. We only do transpose solve with it */
1472:                                               fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Lt, &fs->spsvBufferSize_Lt));

1474:     PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut));
1475:     PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut, &fs->spsvBufferSize_Ut));
1476:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
1477:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
1478:     fs->createdTransposeSpSVDescr = PETSC_TRUE;
1479:   }

1481:   if (!fs->updatedTransposeSpSVAnalysis) {
1482:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Lt, fs->spsvBuffer_Lt));

1484:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut, fs->spsvBuffer_Ut));
1485:     fs->updatedTransposeSpSVAnalysis = PETSC_TRUE;
1486:   }

1488:   PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1489:   PetscCall(VecCUDAGetArrayRead(b, &barray));
1490:   xGPU = thrust::device_pointer_cast(xarray);
1491:   bGPU = thrust::device_pointer_cast(barray);

1493:   // Reorder b with the row permutation if needed, and wrap the result in fs->X
1494:   if (fs->rpermIndices) {
1495:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->begin()), thrust::make_permutation_iterator(bGPU, fs->rpermIndices->end()), thrust::device_pointer_cast(fs->X)));
1496:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1497:   } else {
1498:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1499:   }

1501:   // Solve Ut Y = X
1502:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1503:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut));

1505:   // Solve Lt X = Y
1506:   if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
1507:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1508:   } else {
1509:     PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1510:   }
1511:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_Lt));

1513:   // Reorder X with the column permutation if needed, and put the result back to x
1514:   if (fs->cpermIndices) {
1515:     PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1516:                                  thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1517:   }

1519:   PetscCall(VecCUDARestoreArrayRead(b, &barray));
1520:   PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1521:   PetscCall(PetscLogGpuTimeEnd());
1522:   PetscCall(PetscLogGpuFlops(2.0 * aij->nz - A->rmap->n));
1523:   PetscFunctionReturn(PETSC_SUCCESS);
1524: }
1525: #else
1526: /* Why do we need to analyze the transposed matrix again? Can't we just use op(A) = CUSPARSE_OPERATION_TRANSPOSE in MatSolve_SeqAIJCUSPARSE? */
1527: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1528: {
1529:   PetscInt                              n = xx->map->n;
1530:   const PetscScalar                    *barray;
1531:   PetscScalar                          *xarray;
1532:   thrust::device_ptr<const PetscScalar> bGPU;
1533:   thrust::device_ptr<PetscScalar>       xGPU;
1534:   Mat_SeqAIJCUSPARSETriFactors         *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1535:   Mat_SeqAIJCUSPARSETriFactorStruct    *loTriFactorT       = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1536:   Mat_SeqAIJCUSPARSETriFactorStruct    *upTriFactorT       = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1537:   THRUSTARRAY                          *tempGPU            = (THRUSTARRAY *)cusparseTriFactors->workVector;

1539:   PetscFunctionBegin;
1540:   /* Analyze the matrix and create the transpose ... on the fly */
1541:   if (!loTriFactorT && !upTriFactorT) {
1542:     PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1543:     loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1544:     upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1545:   }

1547:   /* Get the GPU pointers */
1548:   PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1549:   PetscCall(VecCUDAGetArrayRead(bb, &barray));
1550:   xGPU = thrust::device_pointer_cast(xarray);
1551:   bGPU = thrust::device_pointer_cast(barray);

1553:   PetscCall(PetscLogGpuTimeBegin());
1554:   /* First, reorder with the row permutation */
1555:   thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), thrust::make_permutation_iterator(bGPU + n, cusparseTriFactors->rpermIndices->end()), xGPU);

1557:   /* First, solve U */
1558:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, &PETSC_CUSPARSE_ONE, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1559:                                          upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, xarray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));

1561:   /* Then, solve L */
1562:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, &PETSC_CUSPARSE_ONE, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1563:                                          loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));

1565:   /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */
1566:   thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(xGPU, cusparseTriFactors->cpermIndices->begin()), thrust::make_permutation_iterator(xGPU + n, cusparseTriFactors->cpermIndices->end()), tempGPU->begin());

1568:   /* Copy the temporary to the full solution. */
1569:   thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), tempGPU->begin(), tempGPU->end(), xGPU);

1571:   /* restore */
1572:   PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1573:   PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1574:   PetscCall(PetscLogGpuTimeEnd());
1575:   PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1576:   PetscFunctionReturn(PETSC_SUCCESS);
1577: }

1579: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1580: {
1581:   const PetscScalar                 *barray;
1582:   PetscScalar                       *xarray;
1583:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1584:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT       = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1585:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT       = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1586:   THRUSTARRAY                       *tempGPU            = (THRUSTARRAY *)cusparseTriFactors->workVector;

1588:   PetscFunctionBegin;
1589:   /* Analyze the matrix and create the transpose ... on the fly */
1590:   if (!loTriFactorT && !upTriFactorT) {
1591:     PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1592:     loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1593:     upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1594:   }

1596:   /* Get the GPU pointers */
1597:   PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1598:   PetscCall(VecCUDAGetArrayRead(bb, &barray));

1600:   PetscCall(PetscLogGpuTimeBegin());
1601:   /* First, solve U */
1602:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, &PETSC_CUSPARSE_ONE, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1603:                                          upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, barray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));

1605:   /* Then, solve L */
1606:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, &PETSC_CUSPARSE_ONE, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1607:                                          loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));

1609:   /* restore */
1610:   PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1611:   PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1612:   PetscCall(PetscLogGpuTimeEnd());
1613:   PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1614:   PetscFunctionReturn(PETSC_SUCCESS);
1615: }

1617: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1618: {
1619:   const PetscScalar                    *barray;
1620:   PetscScalar                          *xarray;
1621:   thrust::device_ptr<const PetscScalar> bGPU;
1622:   thrust::device_ptr<PetscScalar>       xGPU;
1623:   Mat_SeqAIJCUSPARSETriFactors         *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1624:   Mat_SeqAIJCUSPARSETriFactorStruct    *loTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1625:   Mat_SeqAIJCUSPARSETriFactorStruct    *upTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1626:   THRUSTARRAY                          *tempGPU            = (THRUSTARRAY *)cusparseTriFactors->workVector;

1628:   PetscFunctionBegin;
1629:   /* Get the GPU pointers */
1630:   PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1631:   PetscCall(VecCUDAGetArrayRead(bb, &barray));
1632:   xGPU = thrust::device_pointer_cast(xarray);
1633:   bGPU = thrust::device_pointer_cast(barray);

1635:   PetscCall(PetscLogGpuTimeBegin());
1636:   /* First, reorder with the row permutation */
1637:   thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->begin()), thrust::make_permutation_iterator(bGPU, cusparseTriFactors->rpermIndices->end()), tempGPU->begin());

1639:   /* Next, solve L */
1640:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, &PETSC_CUSPARSE_ONE, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
1641:                                          loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, tempGPU->data().get(), xarray, loTriFactor->solvePolicy, loTriFactor->solveBuffer));

1643:   /* Then, solve U */
1644:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, &PETSC_CUSPARSE_ONE, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
1645:                                          upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, xarray, tempGPU->data().get(), upTriFactor->solvePolicy, upTriFactor->solveBuffer));

1647:   /* Last, reorder with the column permutation */
1648:   thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(tempGPU->begin(), cusparseTriFactors->cpermIndices->begin()), thrust::make_permutation_iterator(tempGPU->begin(), cusparseTriFactors->cpermIndices->end()), xGPU);

1650:   PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1651:   PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1652:   PetscCall(PetscLogGpuTimeEnd());
1653:   PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1654:   PetscFunctionReturn(PETSC_SUCCESS);
1655: }

1657: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1658: {
1659:   const PetscScalar                 *barray;
1660:   PetscScalar                       *xarray;
1661:   Mat_SeqAIJCUSPARSETriFactors      *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1662:   Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1663:   Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor        = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1664:   THRUSTARRAY                       *tempGPU            = (THRUSTARRAY *)cusparseTriFactors->workVector;

1666:   PetscFunctionBegin;
1667:   /* Get the GPU pointers */
1668:   PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1669:   PetscCall(VecCUDAGetArrayRead(bb, &barray));

1671:   PetscCall(PetscLogGpuTimeBegin());
1672:   /* First, solve L */
1673:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, &PETSC_CUSPARSE_ONE, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
1674:                                          loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, barray, tempGPU->data().get(), loTriFactor->solvePolicy, loTriFactor->solveBuffer));

1676:   /* Next, solve U */
1677:   PetscCallCUSPARSE(cusparseXcsrsv_solve(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, &PETSC_CUSPARSE_ONE, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
1678:                                          upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, tempGPU->data().get(), xarray, upTriFactor->solvePolicy, upTriFactor->solveBuffer));

1680:   PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1681:   PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1682:   PetscCall(PetscLogGpuTimeEnd());
1683:   PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1684:   PetscFunctionReturn(PETSC_SUCCESS);
1685: }
1686: #endif

1688: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1689: static PetscErrorCode MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, const MatFactorInfo *)
1690: {
1691:   Mat_SeqAIJCUSPARSETriFactors *fs    = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1692:   Mat_SeqAIJ                   *aij   = (Mat_SeqAIJ *)fact->data;
1693:   Mat_SeqAIJCUSPARSE           *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1694:   CsrMatrix                    *Acsr;
1695:   PetscInt                      m, nz;
1696:   PetscBool                     flg;

1698:   PetscFunctionBegin;
1699:   if (PetscDefined(USE_DEBUG)) {
1700:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1701:     PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1702:   }

1704:   /* Copy A's value to fact */
1705:   m  = fact->rmap->n;
1706:   nz = aij->nz;
1707:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1708:   Acsr = (CsrMatrix *)Acusp->mat->mat;
1709:   PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));

1711:   PetscCall(PetscLogGpuTimeBegin());
1712:   /* Factorize fact inplace */
1713:   if (m)
1714:     PetscCallCUSPARSE(cusparseXcsrilu02(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1715:                                         fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1716:   if (PetscDefined(USE_DEBUG)) {
1717:     int              numerical_zero;
1718:     cusparseStatus_t status;
1719:     status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &numerical_zero);
1720:     PetscAssert(CUSPARSE_STATUS_ZERO_PIVOT != status, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "Numerical zero pivot detected in csrilu02: A(%d,%d) is zero", numerical_zero, numerical_zero);
1721:   }

1723:   #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
1724:   if (fs->updatedSpSVAnalysis) {
1725:     if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1726:     if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1727:   } else
1728:   #endif
1729:   {
1730:     /* cusparseSpSV_analysis() is numeric, i.e., it requires valid matrix values, therefore, we do it after cusparseXcsrilu02()
1731:      See discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/78
1732:     */
1733:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, fs->spsvBuffer_L));

1735:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, fs->spsvBuffer_U));

1737:     fs->updatedSpSVAnalysis = PETSC_TRUE;
1738:     /* L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve */
1739:     fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
1740:   }

1742:   fact->offloadmask            = PETSC_OFFLOAD_GPU;
1743:   fact->ops->solve             = MatSolve_SeqAIJCUSPARSE_LU; // spMatDescr_L/U uses 32-bit indices, but cusparseSpSV_solve() supports both 32 and 64. The info is encoded in cusparseSpMatDescr_t.
1744:   fact->ops->solvetranspose    = MatSolveTranspose_SeqAIJCUSPARSE_LU;
1745:   fact->ops->matsolve          = NULL;
1746:   fact->ops->matsolvetranspose = NULL;
1747:   PetscCall(PetscLogGpuTimeEnd());
1748:   PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1749:   PetscFunctionReturn(PETSC_SUCCESS);
1750: }

1752: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, IS, IS, const MatFactorInfo *info)
1753: {
1754:   Mat_SeqAIJCUSPARSETriFactors *fs  = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1755:   Mat_SeqAIJ                   *aij = (Mat_SeqAIJ *)fact->data;
1756:   PetscInt                      m, nz;

1758:   PetscFunctionBegin;
1759:   if (PetscDefined(USE_DEBUG)) {
1760:     PetscInt  i;
1761:     PetscBool flg, missing;

1763:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1764:     PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1765:     PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
1766:     PetscCall(MatMissingDiagonal(A, &missing, &i));
1767:     PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
1768:   }

1770:   /* Free the old stale stuff */
1771:   PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));

1773:   /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
1774:      but they will not be used. Allocate them just for easy debugging.
1775:    */
1776:   PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));

1778:   fact->offloadmask            = PETSC_OFFLOAD_BOTH;
1779:   fact->factortype             = MAT_FACTOR_ILU;
1780:   fact->info.factor_mallocs    = 0;
1781:   fact->info.fill_ratio_given  = info->fill;
1782:   fact->info.fill_ratio_needed = 1.0;

1784:   aij->row = NULL;
1785:   aij->col = NULL;

1787:   /* ====================================================================== */
1788:   /* Copy A's i, j to fact and also allocate the value array of fact.       */
1789:   /* We'll do in-place factorization on fact                                */
1790:   /* ====================================================================== */
1791:   const int *Ai, *Aj;

1793:   m  = fact->rmap->n;
1794:   nz = aij->nz;

1796:   PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
1797:   PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
1798:   PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(*fs->csrVal) * nz));
1799:   PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai.  The returned Ai, Aj are 32-bit */
1800:   PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1801:   PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));

1803:   /* ====================================================================== */
1804:   /* Create descriptors for M, L, U                                         */
1805:   /* ====================================================================== */
1806:   cusparseFillMode_t fillMode;
1807:   cusparseDiagType_t diagType;

1809:   PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
1810:   PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
1811:   PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));

1813:   /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
1814:     cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
1815:     assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
1816:     all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
1817:     assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
1818:   */
1819:   fillMode = CUSPARSE_FILL_MODE_LOWER;
1820:   diagType = CUSPARSE_DIAG_TYPE_UNIT;
1821:   PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, nz, fs->csrRowPtr32, fs->csrColIdx32, fs->csrVal, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
1822:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1823:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

1825:   fillMode = CUSPARSE_FILL_MODE_UPPER;
1826:   diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
1827:   PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, nz, fs->csrRowPtr32, fs->csrColIdx32, fs->csrVal, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
1828:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1829:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

1831:   /* ========================================================================= */
1832:   /* Query buffer sizes for csrilu0, SpSV and allocate buffers                 */
1833:   /* ========================================================================= */
1834:   PetscCallCUSPARSE(cusparseCreateCsrilu02Info(&fs->ilu0Info_M));
1835:   if (m)
1836:     PetscCallCUSPARSE(cusparseXcsrilu02_bufferSize(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1837:                                                    fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, &fs->factBufferSize_M));

1839:   PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
1840:   PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));

1842:   PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
1843:   PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));

1845:   PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
1846:   PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, &fs->spsvBufferSize_L));

1848:   PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
1849:   PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, &fs->spsvBufferSize_U));

1851:   /* From my experiment with the example at https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSE/bicgstab,
1852:      and discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/77,
1853:      spsvBuffer_L/U can not be shared (i.e., the same) for our case, but factBuffer_M can share with either of spsvBuffer_L/U.
1854:      To save memory, we make factBuffer_M share with the bigger of spsvBuffer_L/U.
1855:    */
1856:   if (fs->spsvBufferSize_L > fs->spsvBufferSize_U) {
1857:     PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
1858:     fs->spsvBuffer_L = fs->factBuffer_M;
1859:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
1860:   } else {
1861:     PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_U, (size_t)fs->factBufferSize_M)));
1862:     fs->spsvBuffer_U = fs->factBuffer_M;
1863:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
1864:   }

1866:   /* ========================================================================== */
1867:   /* Perform analysis of ilu0 on M, SpSv on L and U                             */
1868:   /* The lower(upper) triangular part of M has the same sparsity pattern as L(U)*/
1869:   /* ========================================================================== */
1870:   int              structural_zero;
1871:   cusparseStatus_t status;

1873:   fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1874:   if (m)
1875:     PetscCallCUSPARSE(cusparseXcsrilu02_analysis(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1876:                                                  fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1877:   if (PetscDefined(USE_DEBUG)) {
1878:     /* cusparseXcsrilu02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
1879:     status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &structural_zero);
1880:     PetscCheck(CUSPARSE_STATUS_ZERO_PIVOT != status, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "Structural zero pivot detected in csrilu02: A(%d,%d) is missing", structural_zero, structural_zero);
1881:   }

1883:   /* Estimate FLOPs of the numeric factorization */
1884:   {
1885:     Mat_SeqAIJ    *Aseq = (Mat_SeqAIJ *)A->data;
1886:     PetscInt      *Ai, *Adiag, nzRow, nzLeft;
1887:     PetscLogDouble flops = 0.0;

1889:     PetscCall(MatMarkDiagonal_SeqAIJ(A));
1890:     Ai    = Aseq->i;
1891:     Adiag = Aseq->diag;
1892:     for (PetscInt i = 0; i < m; i++) {
1893:       if (Ai[i] < Adiag[i] && Adiag[i] < Ai[i + 1]) { /* There are nonzeros left to the diagonal of row i */
1894:         nzRow  = Ai[i + 1] - Ai[i];
1895:         nzLeft = Adiag[i] - Ai[i];
1896:         /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
1897:           and include the eliminated one will be updated, which incurs a multiplication and an addition.
1898:         */
1899:         nzLeft = (nzRow - 1) / 2;
1900:         flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
1901:       }
1902:     }
1903:     fs->numericFactFlops = flops;
1904:   }
1905:   fact->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0;
1906:   PetscFunctionReturn(PETSC_SUCCESS);
1907: }

1909: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_ICC0(Mat fact, Vec b, Vec x)
1910: {
1911:   Mat_SeqAIJCUSPARSETriFactors *fs  = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1912:   Mat_SeqAIJ                   *aij = (Mat_SeqAIJ *)fact->data;
1913:   const PetscScalar            *barray;
1914:   PetscScalar                  *xarray;

1916:   PetscFunctionBegin;
1917:   PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1918:   PetscCall(VecCUDAGetArrayRead(b, &barray));
1919:   PetscCall(PetscLogGpuTimeBegin());

1921:   /* Solve L*y = b */
1922:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1923:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1924:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* L Y = X */
1925:                                        fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L));

1927:   /* Solve Lt*x = y */
1928:   PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1929:   PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* Lt X = Y */
1930:                                        fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt));

1932:   PetscCall(VecCUDARestoreArrayRead(b, &barray));
1933:   PetscCall(VecCUDARestoreArrayWrite(x, &xarray));

1935:   PetscCall(PetscLogGpuTimeEnd());
1936:   PetscCall(PetscLogGpuFlops(2.0 * aij->nz - fact->rmap->n));
1937:   PetscFunctionReturn(PETSC_SUCCESS);
1938: }

1940: static PetscErrorCode MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, const MatFactorInfo *)
1941: {
1942:   Mat_SeqAIJCUSPARSETriFactors *fs    = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1943:   Mat_SeqAIJ                   *aij   = (Mat_SeqAIJ *)fact->data;
1944:   Mat_SeqAIJCUSPARSE           *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1945:   CsrMatrix                    *Acsr;
1946:   PetscInt                      m, nz;
1947:   PetscBool                     flg;

1949:   PetscFunctionBegin;
1950:   if (PetscDefined(USE_DEBUG)) {
1951:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1952:     PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1953:   }

1955:   /* Copy A's value to fact */
1956:   m  = fact->rmap->n;
1957:   nz = aij->nz;
1958:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1959:   Acsr = (CsrMatrix *)Acusp->mat->mat;
1960:   PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));

1962:   /* Factorize fact inplace */
1963:   /* https://docs.nvidia.com/cuda/cusparse/index.html#csric02_solve
1964:      csric02() only takes the lower triangular part of matrix A to perform factorization.
1965:      The matrix type must be CUSPARSE_MATRIX_TYPE_GENERAL, the fill mode and diagonal type are ignored,
1966:      and the strictly upper triangular part is ignored and never touched. It does not matter if A is Hermitian or not.
1967:      In other words, from the point of view of csric02() A is Hermitian and only the lower triangular part is provided.
1968:    */
1969:   if (m) PetscCallCUSPARSE(cusparseXcsric02(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, fs->policy_M, fs->factBuffer_M));
1970:   if (PetscDefined(USE_DEBUG)) {
1971:     int              numerical_zero;
1972:     cusparseStatus_t status;
1973:     status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &numerical_zero);
1974:     PetscAssert(CUSPARSE_STATUS_ZERO_PIVOT != status, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "Numerical zero pivot detected in csric02: A(%d,%d) is zero", numerical_zero, numerical_zero);
1975:   }

1977:   #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
1978:   if (fs->updatedSpSVAnalysis) {
1979:     if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1980:     if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_Lt, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1981:   } else
1982:   #endif
1983:   {
1984:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, fs->spsvBuffer_L));

1986:     /* Note that cusparse reports this error if we use double and CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE
1987:     ** On entry to cusparseSpSV_analysis(): conjugate transpose (opA) is not supported for matA data type, current -> CUDA_R_64F
1988:   */
1989:     PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt, fs->spsvBuffer_Lt));
1990:     fs->updatedSpSVAnalysis = PETSC_TRUE;
1991:   }

1993:   fact->offloadmask            = PETSC_OFFLOAD_GPU;
1994:   fact->ops->solve             = MatSolve_SeqAIJCUSPARSE_ICC0;
1995:   fact->ops->solvetranspose    = MatSolve_SeqAIJCUSPARSE_ICC0;
1996:   fact->ops->matsolve          = NULL;
1997:   fact->ops->matsolvetranspose = NULL;
1998:   PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1999:   PetscFunctionReturn(PETSC_SUCCESS);
2000: }

2002: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, IS, const MatFactorInfo *info)
2003: {
2004:   Mat_SeqAIJCUSPARSETriFactors *fs  = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
2005:   Mat_SeqAIJ                   *aij = (Mat_SeqAIJ *)fact->data;
2006:   PetscInt                      m, nz;

2008:   PetscFunctionBegin;
2009:   if (PetscDefined(USE_DEBUG)) {
2010:     PetscInt  i;
2011:     PetscBool flg, missing;

2013:     PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2014:     PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
2015:     PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
2016:     PetscCall(MatMissingDiagonal(A, &missing, &i));
2017:     PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
2018:   }

2020:   /* Free the old stale stuff */
2021:   PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));

2023:   /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
2024:      but they will not be used. Allocate them just for easy debugging.
2025:    */
2026:   PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));

2028:   fact->offloadmask            = PETSC_OFFLOAD_BOTH;
2029:   fact->factortype             = MAT_FACTOR_ICC;
2030:   fact->info.factor_mallocs    = 0;
2031:   fact->info.fill_ratio_given  = info->fill;
2032:   fact->info.fill_ratio_needed = 1.0;

2034:   aij->row = NULL;
2035:   aij->col = NULL;

2037:   /* ====================================================================== */
2038:   /* Copy A's i, j to fact and also allocate the value array of fact.       */
2039:   /* We'll do in-place factorization on fact                                */
2040:   /* ====================================================================== */
2041:   const int *Ai, *Aj;

2043:   m  = fact->rmap->n;
2044:   nz = aij->nz;

2046:   PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
2047:   PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
2048:   PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(PetscScalar) * nz));
2049:   PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai */
2050:   PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2051:   PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));

2053:   /* ====================================================================== */
2054:   /* Create mat descriptors for M, L                                        */
2055:   /* ====================================================================== */
2056:   cusparseFillMode_t fillMode;
2057:   cusparseDiagType_t diagType;

2059:   PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
2060:   PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
2061:   PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));

2063:   /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
2064:     cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
2065:     assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
2066:     all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
2067:     assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
2068:   */
2069:   fillMode = CUSPARSE_FILL_MODE_LOWER;
2070:   diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
2071:   PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, nz, fs->csrRowPtr32, fs->csrColIdx32, fs->csrVal, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
2072:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
2073:   PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));

2075:   /* ========================================================================= */
2076:   /* Query buffer sizes for csric0, SpSV of L and Lt, and allocate buffers     */
2077:   /* ========================================================================= */
2078:   PetscCallCUSPARSE(cusparseCreateCsric02Info(&fs->ic0Info_M));
2079:   if (m) PetscCallCUSPARSE(cusparseXcsric02_bufferSize(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, &fs->factBufferSize_M));

2081:   PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
2082:   PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));

2084:   PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
2085:   PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));

2087:   PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
2088:   PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, &fs->spsvBufferSize_L));

2090:   PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
2091:   PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt, &fs->spsvBufferSize_Lt));

2093:   /* To save device memory, we make the factorization buffer share with one of the solver buffer.
2094:      See also comments in MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0().
2095:    */
2096:   if (fs->spsvBufferSize_L > fs->spsvBufferSize_Lt) {
2097:     PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
2098:     fs->spsvBuffer_L = fs->factBuffer_M;
2099:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
2100:   } else {
2101:     PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_Lt, (size_t)fs->factBufferSize_M)));
2102:     fs->spsvBuffer_Lt = fs->factBuffer_M;
2103:     PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
2104:   }

2106:   /* ========================================================================== */
2107:   /* Perform analysis of ic0 on M                                               */
2108:   /* The lower triangular part of M has the same sparsity pattern as L          */
2109:   /* ========================================================================== */
2110:   int              structural_zero;
2111:   cusparseStatus_t status;

2113:   fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
2114:   if (m) PetscCallCUSPARSE(cusparseXcsric02_analysis(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, fs->policy_M, fs->factBuffer_M));
2115:   if (PetscDefined(USE_DEBUG)) {
2116:     /* cusparseXcsric02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
2117:     status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &structural_zero);
2118:     PetscCheck(CUSPARSE_STATUS_ZERO_PIVOT != status, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "Structural zero pivot detected in csric02: A(%d,%d) is missing", structural_zero, structural_zero);
2119:   }

2121:   /* Estimate FLOPs of the numeric factorization */
2122:   {
2123:     Mat_SeqAIJ    *Aseq = (Mat_SeqAIJ *)A->data;
2124:     PetscInt      *Ai, nzRow, nzLeft;
2125:     PetscLogDouble flops = 0.0;

2127:     Ai = Aseq->i;
2128:     for (PetscInt i = 0; i < m; i++) {
2129:       nzRow = Ai[i + 1] - Ai[i];
2130:       if (nzRow > 1) {
2131:         /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
2132:           and include the eliminated one will be updated, which incurs a multiplication and an addition.
2133:         */
2134:         nzLeft = (nzRow - 1) / 2;
2135:         flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
2136:       }
2137:     }
2138:     fs->numericFactFlops = flops;
2139:   }
2140:   fact->ops->choleskyfactornumeric = MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0;
2141:   PetscFunctionReturn(PETSC_SUCCESS);
2142: }
2143: #endif

2145: static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
2146: {
2147:   // use_cpu_solve is a field in Mat_SeqAIJCUSPARSE. B, a factored matrix, uses Mat_SeqAIJCUSPARSETriFactors.
2148:   Mat_SeqAIJCUSPARSE *cusparsestruct = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);

2150:   PetscFunctionBegin;
2151:   PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2152:   PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
2153:   B->offloadmask = PETSC_OFFLOAD_CPU;

2155:   if (!cusparsestruct->use_cpu_solve) {
2156: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2157:     B->ops->solve          = MatSolve_SeqAIJCUSPARSE_LU;
2158:     B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
2159: #else
2160:     /* determine which version of MatSolve needs to be used. */
2161:     Mat_SeqAIJ *b     = (Mat_SeqAIJ *)B->data;
2162:     IS          isrow = b->row, iscol = b->col;
2163:     PetscBool   row_identity, col_identity;

2165:     PetscCall(ISIdentity(isrow, &row_identity));
2166:     PetscCall(ISIdentity(iscol, &col_identity));
2167:     if (row_identity && col_identity) {
2168:       B->ops->solve          = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
2169:       B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
2170:     } else {
2171:       B->ops->solve          = MatSolve_SeqAIJCUSPARSE;
2172:       B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
2173:     }
2174: #endif
2175:   }
2176:   B->ops->matsolve          = NULL;
2177:   B->ops->matsolvetranspose = NULL;

2179:   /* get the triangular factors */
2180:   if (!cusparsestruct->use_cpu_solve) PetscCall(MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B));
2181:   PetscFunctionReturn(PETSC_SUCCESS);
2182: }

2184: static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2185: {
2186:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(B->spptr);

2188:   PetscFunctionBegin;
2189:   PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2190:   PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2191:   B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2192:   PetscFunctionReturn(PETSC_SUCCESS);
2193: }

2195: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2196: {
2197:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;

2199:   PetscFunctionBegin;
2200: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2201:   PetscBool row_identity = PETSC_FALSE, col_identity = PETSC_FALSE;
2202:   if (!info->factoronhost) {
2203:     PetscCall(ISIdentity(isrow, &row_identity));
2204:     PetscCall(ISIdentity(iscol, &col_identity));
2205:   }
2206:   if (!info->levels && row_identity && col_identity) {
2207:     PetscCall(MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(B, A, isrow, iscol, info));
2208:   } else
2209: #endif
2210:   {
2211:     PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2212:     PetscCall(MatILUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2213:     B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2214:   }
2215:   PetscFunctionReturn(PETSC_SUCCESS);
2216: }

2218: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2219: {
2220:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;

2222:   PetscFunctionBegin;
2223: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2224:   PetscBool perm_identity = PETSC_FALSE;
2225:   if (!info->factoronhost) PetscCall(ISIdentity(perm, &perm_identity));
2226:   if (!info->levels && perm_identity) {
2227:     PetscCall(MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(B, A, perm, info));
2228:   } else
2229: #endif
2230:   {
2231:     PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2232:     PetscCall(MatICCFactorSymbolic_SeqAIJ(B, A, perm, info));
2233:     B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2234:   }
2235:   PetscFunctionReturn(PETSC_SUCCESS);
2236: }

2238: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2239: {
2240:   Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;

2242:   PetscFunctionBegin;
2243:   PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2244:   PetscCall(MatCholeskyFactorSymbolic_SeqAIJ(B, A, perm, info));
2245:   B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2246:   PetscFunctionReturn(PETSC_SUCCESS);
2247: }

2249: static PetscErrorCode MatFactorGetSolverType_seqaij_cusparse(Mat, MatSolverType *type)
2250: {
2251:   PetscFunctionBegin;
2252:   *type = MATSOLVERCUSPARSE;
2253:   PetscFunctionReturn(PETSC_SUCCESS);
2254: }

2256: /*MC
2257:   MATSOLVERCUSPARSE = "cusparse" - A matrix type providing triangular solvers for seq matrices
2258:   on a single GPU of type, `MATSEQAIJCUSPARSE`. Currently supported
2259:   algorithms are ILU(k) and ICC(k). Typically, deeper factorizations (larger k) results in poorer
2260:   performance in the triangular solves. Full LU, and Cholesky decompositions can be solved through the
2261:   CuSPARSE triangular solve algorithm. However, the performance can be quite poor and thus these
2262:   algorithms are not recommended. This class does NOT support direct solver operations.

2264:   Level: beginner

2266: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJCUSPARSE()`,
2267:           `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
2268: M*/

2270: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat A, MatFactorType ftype, Mat *B)
2271: {
2272:   PetscInt n = A->rmap->n;

2274:   PetscFunctionBegin;
2275:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2276:   PetscCall(MatSetSizes(*B, n, n, n, n));
2277:   (*B)->factortype = ftype; // factortype makes MatSetType() allocate spptr of type Mat_SeqAIJCUSPARSETriFactors
2278:   PetscCall(MatSetType(*B, MATSEQAIJCUSPARSE));

2280:   if (A->boundtocpu && A->bindingpropagates) PetscCall(MatBindToCPU(*B, PETSC_TRUE));
2281:   if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
2282:     PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2283:     if (!A->boundtocpu) {
2284:       (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJCUSPARSE;
2285:       (*B)->ops->lufactorsymbolic  = MatLUFactorSymbolic_SeqAIJCUSPARSE;
2286:     } else {
2287:       (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJ;
2288:       (*B)->ops->lufactorsymbolic  = MatLUFactorSymbolic_SeqAIJ;
2289:     }
2290:     PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
2291:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILU]));
2292:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILUDT]));
2293:   } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
2294:     if (!A->boundtocpu) {
2295:       (*B)->ops->iccfactorsymbolic      = MatICCFactorSymbolic_SeqAIJCUSPARSE;
2296:       (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJCUSPARSE;
2297:     } else {
2298:       (*B)->ops->iccfactorsymbolic      = MatICCFactorSymbolic_SeqAIJ;
2299:       (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJ;
2300:     }
2301:     PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
2302:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
2303:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported for CUSPARSE Matrix Types");

2305:   PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
2306:   (*B)->canuseordering = PETSC_TRUE;
2307:   PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_cusparse));
2308:   PetscFunctionReturn(PETSC_SUCCESS);
2309: }

2311: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat A)
2312: {
2313:   Mat_SeqAIJ         *a    = (Mat_SeqAIJ *)A->data;
2314:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2315: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2316:   Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
2317: #endif

2319:   PetscFunctionBegin;
2320:   if (A->offloadmask == PETSC_OFFLOAD_GPU) {
2321:     PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2322:     if (A->factortype == MAT_FACTOR_NONE) {
2323:       CsrMatrix *matrix = (CsrMatrix *)cusp->mat->mat;
2324:       PetscCallCUDA(cudaMemcpy(a->a, matrix->values->data().get(), a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2325:     }
2326: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2327:     else if (fs->csrVal) {
2328:       /* We have a factorized matrix on device and are able to copy it to host */
2329:       PetscCallCUDA(cudaMemcpy(a->a, fs->csrVal, a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2330:     }
2331: #endif
2332:     else
2333:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for copying this type of factorized matrix from device to host");
2334:     PetscCall(PetscLogGpuToCpu(a->nz * sizeof(PetscScalar)));
2335:     PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2336:     A->offloadmask = PETSC_OFFLOAD_BOTH;
2337:   }
2338:   PetscFunctionReturn(PETSC_SUCCESS);
2339: }

2341: static PetscErrorCode MatSeqAIJGetArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2342: {
2343:   PetscFunctionBegin;
2344:   PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2345:   *array = ((Mat_SeqAIJ *)A->data)->a;
2346:   PetscFunctionReturn(PETSC_SUCCESS);
2347: }

2349: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2350: {
2351:   PetscFunctionBegin;
2352:   A->offloadmask = PETSC_OFFLOAD_CPU;
2353:   *array         = NULL;
2354:   PetscFunctionReturn(PETSC_SUCCESS);
2355: }

2357: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJCUSPARSE(Mat A, const PetscScalar *array[])
2358: {
2359:   PetscFunctionBegin;
2360:   PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2361:   *array = ((Mat_SeqAIJ *)A->data)->a;
2362:   PetscFunctionReturn(PETSC_SUCCESS);
2363: }

2365: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE(Mat, const PetscScalar *array[])
2366: {
2367:   PetscFunctionBegin;
2368:   *array = NULL;
2369:   PetscFunctionReturn(PETSC_SUCCESS);
2370: }

2372: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2373: {
2374:   PetscFunctionBegin;
2375:   *array = ((Mat_SeqAIJ *)A->data)->a;
2376:   PetscFunctionReturn(PETSC_SUCCESS);
2377: }

2379: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2380: {
2381:   PetscFunctionBegin;
2382:   A->offloadmask = PETSC_OFFLOAD_CPU;
2383:   *array         = NULL;
2384:   PetscFunctionReturn(PETSC_SUCCESS);
2385: }

2387: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
2388: {
2389:   Mat_SeqAIJCUSPARSE *cusp;
2390:   CsrMatrix          *matrix;

2392:   PetscFunctionBegin;
2393:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2394:   PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2395:   cusp = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2396:   PetscCheck(cusp != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "cusp is NULL");
2397:   matrix = (CsrMatrix *)cusp->mat->mat;

2399:   if (i) {
2400: #if !defined(PETSC_USE_64BIT_INDICES)
2401:     *i = matrix->row_offsets->data().get();
2402: #else
2403:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2404: #endif
2405:   }
2406:   if (j) {
2407: #if !defined(PETSC_USE_64BIT_INDICES)
2408:     *j = matrix->column_indices->data().get();
2409: #else
2410:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2411: #endif
2412:   }
2413:   if (a) *a = matrix->values->data().get();
2414:   if (mtype) *mtype = PETSC_MEMTYPE_CUDA;
2415:   PetscFunctionReturn(PETSC_SUCCESS);
2416: }

2418: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A)
2419: {
2420:   Mat_SeqAIJCUSPARSE           *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
2421:   Mat_SeqAIJCUSPARSEMultStruct *matstruct      = cusparsestruct->mat;
2422:   Mat_SeqAIJ                   *a              = (Mat_SeqAIJ *)A->data;
2423:   PetscInt                      m              = A->rmap->n, *ii, *ridx, tmp;
2424:   cusparseStatus_t              stat;
2425:   PetscBool                     both = PETSC_TRUE;

2427:   PetscFunctionBegin;
2428:   PetscCheck(!A->boundtocpu, PETSC_COMM_SELF, PETSC_ERR_GPU, "Cannot copy to GPU");
2429:   if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
2430:     if (A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { /* Copy values only */
2431:       CsrMatrix *matrix;
2432:       matrix = (CsrMatrix *)cusparsestruct->mat->mat;

2434:       PetscCheck(!a->nz || a->a, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR values");
2435:       PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2436:       matrix->values->assign(a->a, a->a + a->nz);
2437:       PetscCallCUDA(WaitForCUDA());
2438:       PetscCall(PetscLogCpuToGpu(a->nz * sizeof(PetscScalar)));
2439:       PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2440:       PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
2441:     } else {
2442:       PetscInt nnz;
2443:       PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2444:       PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusparsestruct->mat, cusparsestruct->format));
2445:       PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
2446:       delete cusparsestruct->workVector;
2447:       delete cusparsestruct->rowoffsets_gpu;
2448:       cusparsestruct->workVector     = NULL;
2449:       cusparsestruct->rowoffsets_gpu = NULL;
2450:       try {
2451:         if (a->compressedrow.use) {
2452:           m    = a->compressedrow.nrows;
2453:           ii   = a->compressedrow.i;
2454:           ridx = a->compressedrow.rindex;
2455:         } else {
2456:           m    = A->rmap->n;
2457:           ii   = a->i;
2458:           ridx = NULL;
2459:         }
2460:         PetscCheck(ii, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR row data");
2461:         if (!a->a) {
2462:           nnz  = ii[m];
2463:           both = PETSC_FALSE;
2464:         } else nnz = a->nz;
2465:         PetscCheck(!nnz || a->j, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR column data");

2467:         /* create cusparse matrix */
2468:         cusparsestruct->nrows = m;
2469:         matstruct             = new Mat_SeqAIJCUSPARSEMultStruct;
2470:         PetscCallCUSPARSE(cusparseCreateMatDescr(&matstruct->descr));
2471:         PetscCallCUSPARSE(cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO));
2472:         PetscCallCUSPARSE(cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL));

2474:         PetscCallCUDA(cudaMalloc((void **)&matstruct->alpha_one, sizeof(PetscScalar)));
2475:         PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_zero, sizeof(PetscScalar)));
2476:         PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_one, sizeof(PetscScalar)));
2477:         PetscCallCUDA(cudaMemcpy(matstruct->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2478:         PetscCallCUDA(cudaMemcpy(matstruct->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2479:         PetscCallCUDA(cudaMemcpy(matstruct->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2480:         PetscCallCUSPARSE(cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE));

2482:         /* Build a hybrid/ellpack matrix if this option is chosen for the storage */
2483:         if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
2484:           /* set the matrix */
2485:           CsrMatrix *mat   = new CsrMatrix;
2486:           mat->num_rows    = m;
2487:           mat->num_cols    = A->cmap->n;
2488:           mat->num_entries = nnz;
2489:           PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2490:           mat->row_offsets->assign(ii, ii + m + 1);

2492:           PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2493:           mat->column_indices->assign(a->j, a->j + nnz);

2495:           PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2496:           if (a->a) mat->values->assign(a->a, a->a + nnz);

2498:           /* assign the pointer */
2499:           matstruct->mat = mat;
2500: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2501:           if (mat->num_rows) { /* cusparse errors on empty matrices! */
2502:             stat = cusparseCreateCsr(&matstruct->matDescr, mat->num_rows, mat->num_cols, mat->num_entries, mat->row_offsets->data().get(), mat->column_indices->data().get(), mat->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, /* row offset, col idx types due to THRUSTINTARRAY32 */
2503:                                      CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2504:             PetscCallCUSPARSE(stat);
2505:           }
2506: #endif
2507:         } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
2508: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2509:           SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
2510: #else
2511:           CsrMatrix *mat   = new CsrMatrix;
2512:           mat->num_rows    = m;
2513:           mat->num_cols    = A->cmap->n;
2514:           mat->num_entries = nnz;
2515:           PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2516:           mat->row_offsets->assign(ii, ii + m + 1);

2518:           PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2519:           mat->column_indices->assign(a->j, a->j + nnz);

2521:           PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2522:           if (a->a) mat->values->assign(a->a, a->a + nnz);

2524:           cusparseHybMat_t hybMat;
2525:           PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
2526:           cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
2527:           stat                             = cusparse_csr2hyb(cusparsestruct->handle, mat->num_rows, mat->num_cols, matstruct->descr, mat->values->data().get(), mat->row_offsets->data().get(), mat->column_indices->data().get(), hybMat, 0, partition);
2528:           PetscCallCUSPARSE(stat);
2529:           /* assign the pointer */
2530:           matstruct->mat = hybMat;

2532:           if (mat) {
2533:             if (mat->values) delete (THRUSTARRAY *)mat->values;
2534:             if (mat->column_indices) delete (THRUSTINTARRAY32 *)mat->column_indices;
2535:             if (mat->row_offsets) delete (THRUSTINTARRAY32 *)mat->row_offsets;
2536:             delete (CsrMatrix *)mat;
2537:           }
2538: #endif
2539:         }

2541:         /* assign the compressed row indices */
2542:         if (a->compressedrow.use) {
2543:           PetscCallCXX(cusparsestruct->workVector = new THRUSTARRAY(m));
2544:           PetscCallCXX(matstruct->cprowIndices = new THRUSTINTARRAY(m));
2545:           matstruct->cprowIndices->assign(ridx, ridx + m);
2546:           tmp = m;
2547:         } else {
2548:           cusparsestruct->workVector = NULL;
2549:           matstruct->cprowIndices    = NULL;
2550:           tmp                        = 0;
2551:         }
2552:         PetscCall(PetscLogCpuToGpu(((m + 1) + (a->nz)) * sizeof(int) + tmp * sizeof(PetscInt) + (3 + (a->nz)) * sizeof(PetscScalar)));

2554:         /* assign the pointer */
2555:         cusparsestruct->mat = matstruct;
2556:       } catch (char *ex) {
2557:         SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
2558:       }
2559:       PetscCallCUDA(WaitForCUDA());
2560:       PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2561:       cusparsestruct->nonzerostate = A->nonzerostate;
2562:     }
2563:     if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
2564:   }
2565:   PetscFunctionReturn(PETSC_SUCCESS);
2566: }

2568: struct VecCUDAPlusEquals {
2569:   template <typename Tuple>
2570:   __host__ __device__ void operator()(Tuple t)
2571:   {
2572:     thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t);
2573:   }
2574: };

2576: struct VecCUDAEquals {
2577:   template <typename Tuple>
2578:   __host__ __device__ void operator()(Tuple t)
2579:   {
2580:     thrust::get<1>(t) = thrust::get<0>(t);
2581:   }
2582: };

2584: struct VecCUDAEqualsReverse {
2585:   template <typename Tuple>
2586:   __host__ __device__ void operator()(Tuple t)
2587:   {
2588:     thrust::get<0>(t) = thrust::get<1>(t);
2589:   }
2590: };

2592: struct MatMatCusparse {
2593:   PetscBool      cisdense;
2594:   PetscScalar   *Bt;
2595:   Mat            X;
2596:   PetscBool      reusesym; /* Cusparse does not have split symbolic and numeric phases for sparse matmat operations */
2597:   PetscLogDouble flops;
2598:   CsrMatrix     *Bcsr;

2600: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2601:   cusparseSpMatDescr_t matSpBDescr;
2602:   PetscBool            initialized; /* C = alpha op(A) op(B) + beta C */
2603:   cusparseDnMatDescr_t matBDescr;
2604:   cusparseDnMatDescr_t matCDescr;
2605:   PetscInt             Blda, Clda; /* Record leading dimensions of B and C here to detect changes*/
2606:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2607:   void *dBuffer4;
2608:   void *dBuffer5;
2609:   #endif
2610:   size_t                mmBufferSize;
2611:   void                 *mmBuffer;
2612:   void                 *mmBuffer2; /* SpGEMM WorkEstimation buffer */
2613:   cusparseSpGEMMDescr_t spgemmDesc;
2614: #endif
2615: };

2617: static PetscErrorCode MatDestroy_MatMatCusparse(void *data)
2618: {
2619:   MatMatCusparse *mmdata = (MatMatCusparse *)data;

2621:   PetscFunctionBegin;
2622:   PetscCallCUDA(cudaFree(mmdata->Bt));
2623:   delete mmdata->Bcsr;
2624: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2625:   if (mmdata->matSpBDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mmdata->matSpBDescr));
2626:   if (mmdata->matBDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2627:   if (mmdata->matCDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2628:   if (mmdata->spgemmDesc) PetscCallCUSPARSE(cusparseSpGEMM_destroyDescr(mmdata->spgemmDesc));
2629:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2630:   if (mmdata->dBuffer4) PetscCallCUDA(cudaFree(mmdata->dBuffer4));
2631:   if (mmdata->dBuffer5) PetscCallCUDA(cudaFree(mmdata->dBuffer5));
2632:   #endif
2633:   if (mmdata->mmBuffer) PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2634:   if (mmdata->mmBuffer2) PetscCallCUDA(cudaFree(mmdata->mmBuffer2));
2635: #endif
2636:   PetscCall(MatDestroy(&mmdata->X));
2637:   PetscCall(PetscFree(data));
2638:   PetscFunctionReturn(PETSC_SUCCESS);
2639: }

2641: #include <../src/mat/impls/dense/seq/dense.h>

2643: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2644: {
2645:   Mat_Product                  *product = C->product;
2646:   Mat                           A, B;
2647:   PetscInt                      m, n, blda, clda;
2648:   PetscBool                     flg, biscuda;
2649:   Mat_SeqAIJCUSPARSE           *cusp;
2650:   cusparseStatus_t              stat;
2651:   cusparseOperation_t           opA;
2652:   const PetscScalar            *barray;
2653:   PetscScalar                  *carray;
2654:   MatMatCusparse               *mmdata;
2655:   Mat_SeqAIJCUSPARSEMultStruct *mat;
2656:   CsrMatrix                    *csrmat;

2658:   PetscFunctionBegin;
2659:   MatCheckProduct(C, 1);
2660:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2661:   mmdata = (MatMatCusparse *)product->data;
2662:   A      = product->A;
2663:   B      = product->B;
2664:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2665:   PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2666:   /* currently CopyToGpu does not copy if the matrix is bound to CPU
2667:      Instead of silently accepting the wrong answer, I prefer to raise the error */
2668:   PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2669:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2670:   cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2671:   switch (product->type) {
2672:   case MATPRODUCT_AB:
2673:   case MATPRODUCT_PtAP:
2674:     mat = cusp->mat;
2675:     opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2676:     m   = A->rmap->n;
2677:     n   = B->cmap->n;
2678:     break;
2679:   case MATPRODUCT_AtB:
2680:     if (!A->form_explicit_transpose) {
2681:       mat = cusp->mat;
2682:       opA = CUSPARSE_OPERATION_TRANSPOSE;
2683:     } else {
2684:       PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
2685:       mat = cusp->matTranspose;
2686:       opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2687:     }
2688:     m = A->cmap->n;
2689:     n = B->cmap->n;
2690:     break;
2691:   case MATPRODUCT_ABt:
2692:   case MATPRODUCT_RARt:
2693:     mat = cusp->mat;
2694:     opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2695:     m   = A->rmap->n;
2696:     n   = B->rmap->n;
2697:     break;
2698:   default:
2699:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2700:   }
2701:   PetscCheck(mat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing Mat_SeqAIJCUSPARSEMultStruct");
2702:   csrmat = (CsrMatrix *)mat->mat;
2703:   /* if the user passed a CPU matrix, copy the data to the GPU */
2704:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSECUDA, &biscuda));
2705:   if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSECUDA, MAT_INPLACE_MATRIX, &B));
2706:   PetscCall(MatDenseGetArrayReadAndMemType(B, &barray, nullptr));

2708:   PetscCall(MatDenseGetLDA(B, &blda));
2709:   if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2710:     PetscCall(MatDenseGetArrayWriteAndMemType(mmdata->X, &carray, nullptr));
2711:     PetscCall(MatDenseGetLDA(mmdata->X, &clda));
2712:   } else {
2713:     PetscCall(MatDenseGetArrayWriteAndMemType(C, &carray, nullptr));
2714:     PetscCall(MatDenseGetLDA(C, &clda));
2715:   }

2717:   PetscCall(PetscLogGpuTimeBegin());
2718: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2719:   cusparseOperation_t opB = (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE;
2720:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
2721:   cusparseSpMatDescr_t &matADescr = mat->matDescr_SpMM[opA];
2722:   #else
2723:   cusparseSpMatDescr_t &matADescr = mat->matDescr;
2724:   #endif

2726:   /* (re)allocate mmBuffer if not initialized or LDAs are different */
2727:   if (!mmdata->initialized || mmdata->Blda != blda || mmdata->Clda != clda) {
2728:     size_t mmBufferSize;
2729:     if (mmdata->initialized && mmdata->Blda != blda) {
2730:       PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2731:       mmdata->matBDescr = NULL;
2732:     }
2733:     if (!mmdata->matBDescr) {
2734:       PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matBDescr, B->rmap->n, B->cmap->n, blda, (void *)barray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2735:       mmdata->Blda = blda;
2736:     }

2738:     if (mmdata->initialized && mmdata->Clda != clda) {
2739:       PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2740:       mmdata->matCDescr = NULL;
2741:     }
2742:     if (!mmdata->matCDescr) { /* matCDescr is for C or mmdata->X */
2743:       PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matCDescr, m, n, clda, (void *)carray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2744:       mmdata->Clda = clda;
2745:     }

2747:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
2748:     if (matADescr) {
2749:       PetscCallCUSPARSE(cusparseDestroySpMat(matADescr)); // Because I find I could not reuse matADescr. It could be a cusparse bug
2750:       matADescr = NULL;
2751:     }
2752:   #endif

2754:     if (!matADescr) {
2755:       stat = cusparseCreateCsr(&matADescr, csrmat->num_rows, csrmat->num_cols, csrmat->num_entries, csrmat->row_offsets->data().get(), csrmat->column_indices->data().get(), csrmat->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, /* row offset, col idx types due to THRUSTINTARRAY32 */
2756:                                CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2757:       PetscCallCUSPARSE(stat);
2758:     }

2760:     PetscCallCUSPARSE(cusparseSpMM_bufferSize(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, &mmBufferSize));

2762:     if ((mmdata->mmBuffer && mmdata->mmBufferSize < mmBufferSize) || !mmdata->mmBuffer) {
2763:       PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2764:       PetscCallCUDA(cudaMalloc(&mmdata->mmBuffer, mmBufferSize));
2765:       mmdata->mmBufferSize = mmBufferSize;
2766:     }

2768:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // the _preprocess was added in 11.2.1, but PETSc worked without it until 12.4.0
2769:     PetscCallCUSPARSE(cusparseSpMM_preprocess(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2770:   #endif

2772:     mmdata->initialized = PETSC_TRUE;
2773:   } else {
2774:     /* to be safe, always update pointers of the mats */
2775:     PetscCallCUSPARSE(cusparseSpMatSetValues(matADescr, csrmat->values->data().get()));
2776:     PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matBDescr, (void *)barray));
2777:     PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matCDescr, (void *)carray));
2778:   }

2780:   /* do cusparseSpMM, which supports transpose on B */
2781:   PetscCallCUSPARSE(cusparseSpMM(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2782: #else
2783:   PetscInt k;
2784:   /* cusparseXcsrmm does not support transpose on B */
2785:   if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) {
2786:     cublasHandle_t cublasv2handle;
2787:     cublasStatus_t cerr;

2789:     PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
2790:     cerr = cublasXgeam(cublasv2handle, CUBLAS_OP_T, CUBLAS_OP_T, B->cmap->n, B->rmap->n, &PETSC_CUSPARSE_ONE, barray, blda, &PETSC_CUSPARSE_ZERO, barray, blda, mmdata->Bt, B->cmap->n);
2791:     PetscCallCUBLAS(cerr);
2792:     blda = B->cmap->n;
2793:     k    = B->cmap->n;
2794:   } else {
2795:     k = B->rmap->n;
2796:   }

2798:   /* perform the MatMat operation, op(A) is m x k, op(B) is k x n */
2799:   stat = cusparse_csr_spmm(cusp->handle, opA, m, n, k, csrmat->num_entries, mat->alpha_one, mat->descr, csrmat->values->data().get(), csrmat->row_offsets->data().get(), csrmat->column_indices->data().get(), mmdata->Bt ? mmdata->Bt : barray, blda, mat->beta_zero, carray, clda);
2800:   PetscCallCUSPARSE(stat);
2801: #endif
2802:   PetscCall(PetscLogGpuTimeEnd());
2803:   PetscCall(PetscLogGpuFlops(n * 2.0 * csrmat->num_entries));
2804:   PetscCall(MatDenseRestoreArrayReadAndMemType(B, &barray));
2805:   if (product->type == MATPRODUCT_RARt) {
2806:     PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2807:     PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_FALSE, PETSC_FALSE));
2808:   } else if (product->type == MATPRODUCT_PtAP) {
2809:     PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2810:     PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_TRUE, PETSC_FALSE));
2811:   } else {
2812:     PetscCall(MatDenseRestoreArrayWriteAndMemType(C, &carray));
2813:   }
2814:   if (mmdata->cisdense) PetscCall(MatConvert(C, MATSEQDENSE, MAT_INPLACE_MATRIX, &C));
2815:   if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
2816:   PetscFunctionReturn(PETSC_SUCCESS);
2817: }

2819: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2820: {
2821:   Mat_Product        *product = C->product;
2822:   Mat                 A, B;
2823:   PetscInt            m, n;
2824:   PetscBool           cisdense, flg;
2825:   MatMatCusparse     *mmdata;
2826:   Mat_SeqAIJCUSPARSE *cusp;

2828:   PetscFunctionBegin;
2829:   MatCheckProduct(C, 1);
2830:   PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
2831:   A = product->A;
2832:   B = product->B;
2833:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2834:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2835:   cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2836:   PetscCheck(cusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2837:   switch (product->type) {
2838:   case MATPRODUCT_AB:
2839:     m = A->rmap->n;
2840:     n = B->cmap->n;
2841:     PetscCall(MatSetBlockSizesFromMats(C, A, B));
2842:     break;
2843:   case MATPRODUCT_AtB:
2844:     m = A->cmap->n;
2845:     n = B->cmap->n;
2846:     if (A->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->cmap->bs));
2847:     if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2848:     break;
2849:   case MATPRODUCT_ABt:
2850:     m = A->rmap->n;
2851:     n = B->rmap->n;
2852:     if (A->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->rmap->bs));
2853:     if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2854:     break;
2855:   case MATPRODUCT_PtAP:
2856:     m = B->cmap->n;
2857:     n = B->cmap->n;
2858:     if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->cmap->bs));
2859:     if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2860:     break;
2861:   case MATPRODUCT_RARt:
2862:     m = B->rmap->n;
2863:     n = B->rmap->n;
2864:     if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->rmap->bs));
2865:     if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2866:     break;
2867:   default:
2868:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2869:   }
2870:   PetscCall(MatSetSizes(C, m, n, m, n));
2871:   /* if C is of type MATSEQDENSE (CPU), perform the operation on the GPU and then copy on the CPU */
2872:   PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQDENSE, &cisdense));
2873:   PetscCall(MatSetType(C, MATSEQDENSECUDA));

2875:   /* product data */
2876:   PetscCall(PetscNew(&mmdata));
2877:   mmdata->cisdense = cisdense;
2878: #if PETSC_PKG_CUDA_VERSION_LT(11, 0, 0)
2879:   /* cusparseXcsrmm does not support transpose on B, so we allocate buffer to store B^T */
2880:   if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) PetscCallCUDA(cudaMalloc((void **)&mmdata->Bt, (size_t)B->rmap->n * (size_t)B->cmap->n * sizeof(PetscScalar)));
2881: #endif
2882:   /* for these products we need intermediate storage */
2883:   if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2884:     PetscCall(MatCreate(PetscObjectComm((PetscObject)C), &mmdata->X));
2885:     PetscCall(MatSetType(mmdata->X, MATSEQDENSECUDA));
2886:     if (product->type == MATPRODUCT_RARt) { /* do not preallocate, since the first call to MatDenseCUDAGetArray will preallocate on the GPU for us */
2887:       PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->rmap->n, A->rmap->n, B->rmap->n));
2888:     } else {
2889:       PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->cmap->n, A->rmap->n, B->cmap->n));
2890:     }
2891:   }
2892:   C->product->data    = mmdata;
2893:   C->product->destroy = MatDestroy_MatMatCusparse;

2895:   C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA;
2896:   PetscFunctionReturn(PETSC_SUCCESS);
2897: }

2899: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
2900: {
2901:   Mat_Product                  *product = C->product;
2902:   Mat                           A, B;
2903:   Mat_SeqAIJCUSPARSE           *Acusp, *Bcusp, *Ccusp;
2904:   Mat_SeqAIJ                   *c = (Mat_SeqAIJ *)C->data;
2905:   Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
2906:   CsrMatrix                    *Acsr, *Bcsr, *Ccsr;
2907:   PetscBool                     flg;
2908:   cusparseStatus_t              stat;
2909:   MatProductType                ptype;
2910:   MatMatCusparse               *mmdata;
2911: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2912:   cusparseSpMatDescr_t BmatSpDescr;
2913: #endif
2914:   cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */

2916:   PetscFunctionBegin;
2917:   MatCheckProduct(C, 1);
2918:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2919:   PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQAIJCUSPARSE, &flg));
2920:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for C of type %s", ((PetscObject)C)->type_name);
2921:   mmdata = (MatMatCusparse *)C->product->data;
2922:   A      = product->A;
2923:   B      = product->B;
2924:   if (mmdata->reusesym) { /* this happens when api_user is true, meaning that the matrix values have been already computed in the MatProductSymbolic phase */
2925:     mmdata->reusesym = PETSC_FALSE;
2926:     Ccusp            = (Mat_SeqAIJCUSPARSE *)C->spptr;
2927:     PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2928:     Cmat = Ccusp->mat;
2929:     PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[C->product->type]);
2930:     Ccsr = (CsrMatrix *)Cmat->mat;
2931:     PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2932:     goto finalize;
2933:   }
2934:   if (!c->nz) goto finalize;
2935:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2936:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2937:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
2938:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
2939:   PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2940:   PetscCheck(!B->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2941:   Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2942:   Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
2943:   Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2944:   PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2945:   PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2946:   PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2947:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2948:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));

2950:   ptype = product->type;
2951:   if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
2952:     ptype = MATPRODUCT_AB;
2953:     PetscCheck(product->symbolic_used_the_fact_A_is_symmetric, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Symbolic should have been built using the fact that A is symmetric");
2954:   }
2955:   if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
2956:     ptype = MATPRODUCT_AB;
2957:     PetscCheck(product->symbolic_used_the_fact_B_is_symmetric, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Symbolic should have been built using the fact that B is symmetric");
2958:   }
2959:   switch (ptype) {
2960:   case MATPRODUCT_AB:
2961:     Amat = Acusp->mat;
2962:     Bmat = Bcusp->mat;
2963:     break;
2964:   case MATPRODUCT_AtB:
2965:     Amat = Acusp->matTranspose;
2966:     Bmat = Bcusp->mat;
2967:     break;
2968:   case MATPRODUCT_ABt:
2969:     Amat = Acusp->mat;
2970:     Bmat = Bcusp->matTranspose;
2971:     break;
2972:   default:
2973:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2974:   }
2975:   Cmat = Ccusp->mat;
2976:   PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
2977:   PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
2978:   PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[ptype]);
2979:   Acsr = (CsrMatrix *)Amat->mat;
2980:   Bcsr = mmdata->Bcsr ? mmdata->Bcsr : (CsrMatrix *)Bmat->mat; /* B may be in compressed row storage */
2981:   Ccsr = (CsrMatrix *)Cmat->mat;
2982:   PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
2983:   PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
2984:   PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2985:   PetscCall(PetscLogGpuTimeBegin());
2986: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2987:   BmatSpDescr = mmdata->Bcsr ? mmdata->matSpBDescr : Bmat->matDescr; /* B may be in compressed row storage */
2988:   PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
2989:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2990:   stat = cusparseSpGEMMreuse_compute(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc);
2991:   PetscCallCUSPARSE(stat);
2992:   #else
2993:   stat = cusparseSpGEMM_compute(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &mmdata->mmBufferSize, mmdata->mmBuffer);
2994:   PetscCallCUSPARSE(stat);
2995:   stat = cusparseSpGEMM_copy(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc);
2996:   PetscCallCUSPARSE(stat);
2997:   #endif
2998: #else
2999:   stat = cusparse_csr_spgemm(Ccusp->handle, opA, opB, Acsr->num_rows, Bcsr->num_cols, Acsr->num_cols, Amat->descr, Acsr->num_entries, Acsr->values->data().get(), Acsr->row_offsets->data().get(), Acsr->column_indices->data().get(), Bmat->descr, Bcsr->num_entries,
3000:                              Bcsr->values->data().get(), Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->values->data().get(), Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get());
3001:   PetscCallCUSPARSE(stat);
3002: #endif
3003:   PetscCall(PetscLogGpuFlops(mmdata->flops));
3004:   PetscCallCUDA(WaitForCUDA());
3005:   PetscCall(PetscLogGpuTimeEnd());
3006:   C->offloadmask = PETSC_OFFLOAD_GPU;
3007: finalize:
3008:   /* shorter version of MatAssemblyEnd_SeqAIJ */
3009:   PetscCall(PetscInfo(C, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: 0 unneeded,%" PetscInt_FMT " used\n", C->rmap->n, C->cmap->n, c->nz));
3010:   PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
3011:   PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
3012:   c->reallocs = 0;
3013:   C->info.mallocs += 0;
3014:   C->info.nz_unneeded = 0;
3015:   C->assembled = C->was_assembled = PETSC_TRUE;
3016:   C->num_ass++;
3017:   PetscFunctionReturn(PETSC_SUCCESS);
3018: }

3020: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
3021: {
3022:   Mat_Product                  *product = C->product;
3023:   Mat                           A, B;
3024:   Mat_SeqAIJCUSPARSE           *Acusp, *Bcusp, *Ccusp;
3025:   Mat_SeqAIJ                   *a, *b, *c;
3026:   Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
3027:   CsrMatrix                    *Acsr, *Bcsr, *Ccsr;
3028:   PetscInt                      i, j, m, n, k;
3029:   PetscBool                     flg;
3030:   cusparseStatus_t              stat;
3031:   MatProductType                ptype;
3032:   MatMatCusparse               *mmdata;
3033:   PetscLogDouble                flops;
3034:   PetscBool                     biscompressed, ciscompressed;
3035: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3036:   int64_t              C_num_rows1, C_num_cols1, C_nnz1;
3037:   cusparseSpMatDescr_t BmatSpDescr;
3038: #else
3039:   int cnz;
3040: #endif
3041:   cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */

3043:   PetscFunctionBegin;
3044:   MatCheckProduct(C, 1);
3045:   PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
3046:   A = product->A;
3047:   B = product->B;
3048:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
3049:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
3050:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
3051:   PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
3052:   a = (Mat_SeqAIJ *)A->data;
3053:   b = (Mat_SeqAIJ *)B->data;
3054:   /* product data */
3055:   PetscCall(PetscNew(&mmdata));
3056:   C->product->data    = mmdata;
3057:   C->product->destroy = MatDestroy_MatMatCusparse;

3059:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3060:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
3061:   Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr; /* Access spptr after MatSeqAIJCUSPARSECopyToGPU, not before */
3062:   Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
3063:   PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3064:   PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");

3066:   ptype = product->type;
3067:   if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
3068:     ptype                                          = MATPRODUCT_AB;
3069:     product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
3070:   }
3071:   if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
3072:     ptype                                          = MATPRODUCT_AB;
3073:     product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
3074:   }
3075:   biscompressed = PETSC_FALSE;
3076:   ciscompressed = PETSC_FALSE;
3077:   switch (ptype) {
3078:   case MATPRODUCT_AB:
3079:     m    = A->rmap->n;
3080:     n    = B->cmap->n;
3081:     k    = A->cmap->n;
3082:     Amat = Acusp->mat;
3083:     Bmat = Bcusp->mat;
3084:     if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3085:     if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3086:     break;
3087:   case MATPRODUCT_AtB:
3088:     m = A->cmap->n;
3089:     n = B->cmap->n;
3090:     k = A->rmap->n;
3091:     PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3092:     Amat = Acusp->matTranspose;
3093:     Bmat = Bcusp->mat;
3094:     if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3095:     break;
3096:   case MATPRODUCT_ABt:
3097:     m = A->rmap->n;
3098:     n = B->rmap->n;
3099:     k = A->cmap->n;
3100:     PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
3101:     Amat = Acusp->mat;
3102:     Bmat = Bcusp->matTranspose;
3103:     if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3104:     break;
3105:   default:
3106:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
3107:   }

3109:   /* create cusparse matrix */
3110:   PetscCall(MatSetSizes(C, m, n, m, n));
3111:   PetscCall(MatSetType(C, MATSEQAIJCUSPARSE));
3112:   c     = (Mat_SeqAIJ *)C->data;
3113:   Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
3114:   Cmat  = new Mat_SeqAIJCUSPARSEMultStruct;
3115:   Ccsr  = new CsrMatrix;

3117:   c->compressedrow.use = ciscompressed;
3118:   if (c->compressedrow.use) { /* if a is in compressed row, than c will be in compressed row format */
3119:     c->compressedrow.nrows = a->compressedrow.nrows;
3120:     PetscCall(PetscMalloc2(c->compressedrow.nrows + 1, &c->compressedrow.i, c->compressedrow.nrows, &c->compressedrow.rindex));
3121:     PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, c->compressedrow.nrows));
3122:     Ccusp->workVector  = new THRUSTARRAY(c->compressedrow.nrows);
3123:     Cmat->cprowIndices = new THRUSTINTARRAY(c->compressedrow.nrows);
3124:     Cmat->cprowIndices->assign(c->compressedrow.rindex, c->compressedrow.rindex + c->compressedrow.nrows);
3125:   } else {
3126:     c->compressedrow.nrows  = 0;
3127:     c->compressedrow.i      = NULL;
3128:     c->compressedrow.rindex = NULL;
3129:     Ccusp->workVector       = NULL;
3130:     Cmat->cprowIndices      = NULL;
3131:   }
3132:   Ccusp->nrows      = ciscompressed ? c->compressedrow.nrows : m;
3133:   Ccusp->mat        = Cmat;
3134:   Ccusp->mat->mat   = Ccsr;
3135:   Ccsr->num_rows    = Ccusp->nrows;
3136:   Ccsr->num_cols    = n;
3137:   Ccsr->row_offsets = new THRUSTINTARRAY32(Ccusp->nrows + 1);
3138:   PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
3139:   PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
3140:   PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
3141:   PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
3142:   PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
3143:   PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
3144:   PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3145:   PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3146:   PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3147:   if (!Ccsr->num_rows || !Ccsr->num_cols || !a->nz || !b->nz) { /* cusparse raise errors in different calls when matrices have zero rows/columns! */
3148:     PetscCallThrust(thrust::fill(thrust::device, Ccsr->row_offsets->begin(), Ccsr->row_offsets->end(), 0));
3149:     c->nz                = 0;
3150:     Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3151:     Ccsr->values         = new THRUSTARRAY(c->nz);
3152:     goto finalizesym;
3153:   }

3155:   PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
3156:   PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
3157:   Acsr = (CsrMatrix *)Amat->mat;
3158:   if (!biscompressed) {
3159:     Bcsr = (CsrMatrix *)Bmat->mat;
3160: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3161:     BmatSpDescr = Bmat->matDescr;
3162: #endif
3163:   } else { /* we need to use row offsets for the full matrix */
3164:     CsrMatrix *cBcsr     = (CsrMatrix *)Bmat->mat;
3165:     Bcsr                 = new CsrMatrix;
3166:     Bcsr->num_rows       = B->rmap->n;
3167:     Bcsr->num_cols       = cBcsr->num_cols;
3168:     Bcsr->num_entries    = cBcsr->num_entries;
3169:     Bcsr->column_indices = cBcsr->column_indices;
3170:     Bcsr->values         = cBcsr->values;
3171:     if (!Bcusp->rowoffsets_gpu) {
3172:       Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
3173:       Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
3174:       PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
3175:     }
3176:     Bcsr->row_offsets = Bcusp->rowoffsets_gpu;
3177:     mmdata->Bcsr      = Bcsr;
3178: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3179:     if (Bcsr->num_rows && Bcsr->num_cols) {
3180:       stat = cusparseCreateCsr(&mmdata->matSpBDescr, Bcsr->num_rows, Bcsr->num_cols, Bcsr->num_entries, Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Bcsr->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
3181:       PetscCallCUSPARSE(stat);
3182:     }
3183:     BmatSpDescr = mmdata->matSpBDescr;
3184: #endif
3185:   }
3186:   PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
3187:   PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
3188:   /* precompute flops count */
3189:   if (ptype == MATPRODUCT_AB) {
3190:     for (i = 0, flops = 0; i < A->rmap->n; i++) {
3191:       const PetscInt st = a->i[i];
3192:       const PetscInt en = a->i[i + 1];
3193:       for (j = st; j < en; j++) {
3194:         const PetscInt brow = a->j[j];
3195:         flops += 2. * (b->i[brow + 1] - b->i[brow]);
3196:       }
3197:     }
3198:   } else if (ptype == MATPRODUCT_AtB) {
3199:     for (i = 0, flops = 0; i < A->rmap->n; i++) {
3200:       const PetscInt anzi = a->i[i + 1] - a->i[i];
3201:       const PetscInt bnzi = b->i[i + 1] - b->i[i];
3202:       flops += (2. * anzi) * bnzi;
3203:     }
3204:   } else { /* TODO */
3205:     flops = 0.;
3206:   }

3208:   mmdata->flops = flops;
3209:   PetscCall(PetscLogGpuTimeBegin());

3211: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3212:   PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3213:   // cuda-12.2 requires non-null csrRowOffsets
3214:   stat = cusparseCreateCsr(&Cmat->matDescr, Ccsr->num_rows, Ccsr->num_cols, 0, Ccsr->row_offsets->data().get(), NULL, NULL, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
3215:   PetscCallCUSPARSE(stat);
3216:   PetscCallCUSPARSE(cusparseSpGEMM_createDescr(&mmdata->spgemmDesc));
3217:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
3218:   {
3219:     /* cusparseSpGEMMreuse has more reasonable APIs than cusparseSpGEMM, so we prefer to use it.
3220:      We follow the sample code at https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSPARSE/spgemm_reuse
3221:   */
3222:     void *dBuffer1 = NULL;
3223:     void *dBuffer2 = NULL;
3224:     void *dBuffer3 = NULL;
3225:     /* dBuffer4, dBuffer5 are needed by cusparseSpGEMMreuse_compute, and therefore are stored in mmdata */
3226:     size_t bufferSize1 = 0;
3227:     size_t bufferSize2 = 0;
3228:     size_t bufferSize3 = 0;
3229:     size_t bufferSize4 = 0;
3230:     size_t bufferSize5 = 0;

3232:     /* ask bufferSize1 bytes for external memory */
3233:     stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, NULL);
3234:     PetscCallCUSPARSE(stat);
3235:     PetscCallCUDA(cudaMalloc((void **)&dBuffer1, bufferSize1));
3236:     /* inspect the matrices A and B to understand the memory requirement for the next step */
3237:     stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, dBuffer1);
3238:     PetscCallCUSPARSE(stat);

3240:     stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, NULL, &bufferSize3, NULL, &bufferSize4, NULL);
3241:     PetscCallCUSPARSE(stat);
3242:     PetscCallCUDA(cudaMalloc((void **)&dBuffer2, bufferSize2));
3243:     PetscCallCUDA(cudaMalloc((void **)&dBuffer3, bufferSize3));
3244:     PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer4, bufferSize4));
3245:     stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, dBuffer2, &bufferSize3, dBuffer3, &bufferSize4, mmdata->dBuffer4);
3246:     PetscCallCUSPARSE(stat);
3247:     PetscCallCUDA(cudaFree(dBuffer1));
3248:     PetscCallCUDA(cudaFree(dBuffer2));

3250:     /* get matrix C non-zero entries C_nnz1 */
3251:     PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3252:     c->nz = (PetscInt)C_nnz1;
3253:     /* allocate matrix C */
3254:     Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3255:     PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3256:     Ccsr->values = new THRUSTARRAY(c->nz);
3257:     PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3258:     /* update matC with the new pointers */
3259:     stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3260:     PetscCallCUSPARSE(stat);

3262:     stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, NULL);
3263:     PetscCallCUSPARSE(stat);
3264:     PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer5, bufferSize5));
3265:     stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, mmdata->dBuffer5);
3266:     PetscCallCUSPARSE(stat);
3267:     PetscCallCUDA(cudaFree(dBuffer3));
3268:     stat = cusparseSpGEMMreuse_compute(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc);
3269:     PetscCallCUSPARSE(stat);
3270:     PetscCall(PetscInfo(C, "Buffer sizes for type %s, result %" PetscInt_FMT " x %" PetscInt_FMT " (k %" PetscInt_FMT ", nzA %" PetscInt_FMT ", nzB %" PetscInt_FMT ", nzC %" PetscInt_FMT ") are: %ldKB %ldKB\n", MatProductTypes[ptype], m, n, k, a->nz, b->nz, c->nz, bufferSize4 / 1024, bufferSize5 / 1024));
3271:   }
3272:   #else
3273:   size_t bufSize2;
3274:   /* ask bufferSize bytes for external memory */
3275:   stat = cusparseSpGEMM_workEstimation(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufSize2, NULL);
3276:   PetscCallCUSPARSE(stat);
3277:   PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer2, bufSize2));
3278:   /* inspect the matrices A and B to understand the memory requirement for the next step */
3279:   stat = cusparseSpGEMM_workEstimation(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufSize2, mmdata->mmBuffer2);
3280:   PetscCallCUSPARSE(stat);
3281:   /* ask bufferSize again bytes for external memory */
3282:   stat = cusparseSpGEMM_compute(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &mmdata->mmBufferSize, NULL);
3283:   PetscCallCUSPARSE(stat);
3284:   /* The CUSPARSE documentation is not clear, nor the API
3285:      We need both buffers to perform the operations properly!
3286:      mmdata->mmBuffer2 does not appear anywhere in the compute/copy API
3287:      it only appears for the workEstimation stuff, but it seems it is needed in compute, so probably the address
3288:      is stored in the descriptor! What a messy API... */
3289:   PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer, mmdata->mmBufferSize));
3290:   /* compute the intermediate product of A * B */
3291:   stat = cusparseSpGEMM_compute(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &mmdata->mmBufferSize, mmdata->mmBuffer);
3292:   PetscCallCUSPARSE(stat);
3293:   /* get matrix C non-zero entries C_nnz1 */
3294:   PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3295:   c->nz = (PetscInt)C_nnz1;
3296:   PetscCall(PetscInfo(C, "Buffer sizes for type %s, result %" PetscInt_FMT " x %" PetscInt_FMT " (k %" PetscInt_FMT ", nzA %" PetscInt_FMT ", nzB %" PetscInt_FMT ", nzC %" PetscInt_FMT ") are: %ldKB %ldKB\n", MatProductTypes[ptype], m, n, k, a->nz, b->nz, c->nz, bufSize2 / 1024,
3297:                       mmdata->mmBufferSize / 1024));
3298:   Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3299:   PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3300:   Ccsr->values = new THRUSTARRAY(c->nz);
3301:   PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3302:   stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3303:   PetscCallCUSPARSE(stat);
3304:   stat = cusparseSpGEMM_copy(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc);
3305:   PetscCallCUSPARSE(stat);
3306:   #endif // PETSC_PKG_CUDA_VERSION_GE(11,4,0)
3307: #else
3308:   PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_HOST));
3309:   stat = cusparseXcsrgemmNnz(Ccusp->handle, opA, opB, Acsr->num_rows, Bcsr->num_cols, Acsr->num_cols, Amat->descr, Acsr->num_entries, Acsr->row_offsets->data().get(), Acsr->column_indices->data().get(), Bmat->descr, Bcsr->num_entries,
3310:                              Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->row_offsets->data().get(), &cnz);
3311:   PetscCallCUSPARSE(stat);
3312:   c->nz                = cnz;
3313:   Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3314:   PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3315:   Ccsr->values = new THRUSTARRAY(c->nz);
3316:   PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */

3318:   PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3319:   /* with the old gemm interface (removed from 11.0 on) we cannot compute the symbolic factorization only.
3320:      I have tried using the gemm2 interface (alpha * A * B + beta * D), which allows to do symbolic by passing NULL for values, but it seems quite buggy when
3321:      D is NULL, despite the fact that CUSPARSE documentation claims it is supported! */
3322:   stat = cusparse_csr_spgemm(Ccusp->handle, opA, opB, Acsr->num_rows, Bcsr->num_cols, Acsr->num_cols, Amat->descr, Acsr->num_entries, Acsr->values->data().get(), Acsr->row_offsets->data().get(), Acsr->column_indices->data().get(), Bmat->descr, Bcsr->num_entries,
3323:                              Bcsr->values->data().get(), Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->values->data().get(), Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get());
3324:   PetscCallCUSPARSE(stat);
3325: #endif
3326:   PetscCall(PetscLogGpuFlops(mmdata->flops));
3327:   PetscCall(PetscLogGpuTimeEnd());
3328: finalizesym:
3329:   c->free_a = PETSC_TRUE;
3330:   PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
3331:   PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
3332:   c->free_ij = PETSC_TRUE;
3333:   if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
3334:     PetscInt      *d_i = c->i;
3335:     THRUSTINTARRAY ii(Ccsr->row_offsets->size());
3336:     THRUSTINTARRAY jj(Ccsr->column_indices->size());
3337:     ii = *Ccsr->row_offsets;
3338:     jj = *Ccsr->column_indices;
3339:     if (ciscompressed) d_i = c->compressedrow.i;
3340:     PetscCallCUDA(cudaMemcpy(d_i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3341:     PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3342:   } else {
3343:     PetscInt *d_i = c->i;
3344:     if (ciscompressed) d_i = c->compressedrow.i;
3345:     PetscCallCUDA(cudaMemcpy(d_i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3346:     PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3347:   }
3348:   if (ciscompressed) { /* need to expand host row offsets */
3349:     PetscInt r = 0;
3350:     c->i[0]    = 0;
3351:     for (k = 0; k < c->compressedrow.nrows; k++) {
3352:       const PetscInt next = c->compressedrow.rindex[k];
3353:       const PetscInt old  = c->compressedrow.i[k];
3354:       for (; r < next; r++) c->i[r + 1] = old;
3355:     }
3356:     for (; r < m; r++) c->i[r + 1] = c->compressedrow.i[c->compressedrow.nrows];
3357:   }
3358:   PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
3359:   PetscCall(PetscMalloc1(m, &c->ilen));
3360:   PetscCall(PetscMalloc1(m, &c->imax));
3361:   c->maxnz         = c->nz;
3362:   c->nonzerorowcnt = 0;
3363:   c->rmax          = 0;
3364:   for (k = 0; k < m; k++) {
3365:     const PetscInt nn = c->i[k + 1] - c->i[k];
3366:     c->ilen[k] = c->imax[k] = nn;
3367:     c->nonzerorowcnt += (PetscInt)!!nn;
3368:     c->rmax = PetscMax(c->rmax, nn);
3369:   }
3370:   PetscCall(MatMarkDiagonal_SeqAIJ(C));
3371:   PetscCall(PetscMalloc1(c->nz, &c->a));
3372:   Ccsr->num_entries = c->nz;

3374:   C->nonzerostate++;
3375:   PetscCall(PetscLayoutSetUp(C->rmap));
3376:   PetscCall(PetscLayoutSetUp(C->cmap));
3377:   Ccusp->nonzerostate = C->nonzerostate;
3378:   C->offloadmask      = PETSC_OFFLOAD_UNALLOCATED;
3379:   C->preallocated     = PETSC_TRUE;
3380:   C->assembled        = PETSC_FALSE;
3381:   C->was_assembled    = PETSC_FALSE;
3382:   if (product->api_user && A->offloadmask == PETSC_OFFLOAD_BOTH && B->offloadmask == PETSC_OFFLOAD_BOTH) { /* flag the matrix C values as computed, so that the numeric phase will only call MatAssembly */
3383:     mmdata->reusesym = PETSC_TRUE;
3384:     C->offloadmask   = PETSC_OFFLOAD_GPU;
3385:   }
3386:   C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3387:   PetscFunctionReturn(PETSC_SUCCESS);
3388: }

3390: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat);

3392: /* handles sparse or dense B */
3393: static PetscErrorCode MatProductSetFromOptions_SeqAIJCUSPARSE(Mat mat)
3394: {
3395:   Mat_Product *product = mat->product;
3396:   PetscBool    isdense = PETSC_FALSE, Biscusp = PETSC_FALSE, Ciscusp = PETSC_TRUE;

3398:   PetscFunctionBegin;
3399:   MatCheckProduct(mat, 1);
3400:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)product->B, MATSEQDENSE, &isdense));
3401:   if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJCUSPARSE, &Biscusp));
3402:   if (product->type == MATPRODUCT_ABC) {
3403:     Ciscusp = PETSC_FALSE;
3404:     if (!product->C->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJCUSPARSE, &Ciscusp));
3405:   }
3406:   if (Biscusp && Ciscusp) { /* we can always select the CPU backend */
3407:     PetscBool usecpu = PETSC_FALSE;
3408:     switch (product->type) {
3409:     case MATPRODUCT_AB:
3410:       if (product->api_user) {
3411:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
3412:         PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3413:         PetscOptionsEnd();
3414:       } else {
3415:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
3416:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3417:         PetscOptionsEnd();
3418:       }
3419:       break;
3420:     case MATPRODUCT_AtB:
3421:       if (product->api_user) {
3422:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
3423:         PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3424:         PetscOptionsEnd();
3425:       } else {
3426:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
3427:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3428:         PetscOptionsEnd();
3429:       }
3430:       break;
3431:     case MATPRODUCT_PtAP:
3432:       if (product->api_user) {
3433:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
3434:         PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3435:         PetscOptionsEnd();
3436:       } else {
3437:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
3438:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3439:         PetscOptionsEnd();
3440:       }
3441:       break;
3442:     case MATPRODUCT_RARt:
3443:       if (product->api_user) {
3444:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatRARt", "Mat");
3445:         PetscCall(PetscOptionsBool("-matrart_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3446:         PetscOptionsEnd();
3447:       } else {
3448:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_RARt", "Mat");
3449:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3450:         PetscOptionsEnd();
3451:       }
3452:       break;
3453:     case MATPRODUCT_ABC:
3454:       if (product->api_user) {
3455:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMatMult", "Mat");
3456:         PetscCall(PetscOptionsBool("-matmatmatmult_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3457:         PetscOptionsEnd();
3458:       } else {
3459:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_ABC", "Mat");
3460:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3461:         PetscOptionsEnd();
3462:       }
3463:       break;
3464:     default:
3465:       break;
3466:     }
3467:     if (usecpu) Biscusp = Ciscusp = PETSC_FALSE;
3468:   }
3469:   /* dispatch */
3470:   if (isdense) {
3471:     switch (product->type) {
3472:     case MATPRODUCT_AB:
3473:     case MATPRODUCT_AtB:
3474:     case MATPRODUCT_ABt:
3475:     case MATPRODUCT_PtAP:
3476:     case MATPRODUCT_RARt:
3477:       if (product->A->boundtocpu) {
3478:         PetscCall(MatProductSetFromOptions_SeqAIJ_SeqDense(mat));
3479:       } else {
3480:         mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA;
3481:       }
3482:       break;
3483:     case MATPRODUCT_ABC:
3484:       mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3485:       break;
3486:     default:
3487:       break;
3488:     }
3489:   } else if (Biscusp && Ciscusp) {
3490:     switch (product->type) {
3491:     case MATPRODUCT_AB:
3492:     case MATPRODUCT_AtB:
3493:     case MATPRODUCT_ABt:
3494:       mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3495:       break;
3496:     case MATPRODUCT_PtAP:
3497:     case MATPRODUCT_RARt:
3498:     case MATPRODUCT_ABC:
3499:       mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3500:       break;
3501:     default:
3502:       break;
3503:     }
3504:   } else { /* fallback for AIJ */
3505:     PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
3506:   }
3507:   PetscFunctionReturn(PETSC_SUCCESS);
3508: }

3510: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3511: {
3512:   PetscFunctionBegin;
3513:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_FALSE, PETSC_FALSE));
3514:   PetscFunctionReturn(PETSC_SUCCESS);
3515: }

3517: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3518: {
3519:   PetscFunctionBegin;
3520:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_FALSE, PETSC_FALSE));
3521:   PetscFunctionReturn(PETSC_SUCCESS);
3522: }

3524: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3525: {
3526:   PetscFunctionBegin;
3527:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_TRUE));
3528:   PetscFunctionReturn(PETSC_SUCCESS);
3529: }

3531: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3532: {
3533:   PetscFunctionBegin;
3534:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_TRUE));
3535:   PetscFunctionReturn(PETSC_SUCCESS);
3536: }

3538: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3539: {
3540:   PetscFunctionBegin;
3541:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_FALSE));
3542:   PetscFunctionReturn(PETSC_SUCCESS);
3543: }

3545: __global__ static void ScatterAdd(PetscInt n, PetscInt *idx, const PetscScalar *x, PetscScalar *y)
3546: {
3547:   int i = blockIdx.x * blockDim.x + threadIdx.x;
3548:   if (i < n) y[idx[i]] += x[i];
3549: }

3551: /* z = op(A) x + y. If trans & !herm, op = ^T; if trans & herm, op = ^H; if !trans, op = no-op */
3552: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz, PetscBool trans, PetscBool herm)
3553: {
3554:   Mat_SeqAIJ                   *a              = (Mat_SeqAIJ *)A->data;
3555:   Mat_SeqAIJCUSPARSE           *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3556:   Mat_SeqAIJCUSPARSEMultStruct *matstruct;
3557:   PetscScalar                  *xarray, *zarray, *dptr, *beta, *xptr;
3558:   cusparseOperation_t           opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
3559:   PetscBool                     compressed;
3560: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3561:   PetscInt nx, ny;
3562: #endif

3564:   PetscFunctionBegin;
3565:   PetscCheck(!herm || trans, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Hermitian and not transpose not supported");
3566:   if (!a->nz) {
3567:     if (yy) PetscCall(VecSeq_CUDA::Copy(yy, zz));
3568:     else PetscCall(VecSeq_CUDA::Set(zz, 0));
3569:     PetscFunctionReturn(PETSC_SUCCESS);
3570:   }
3571:   /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
3572:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3573:   if (!trans) {
3574:     matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3575:     PetscCheck(matstruct, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "SeqAIJCUSPARSE does not have a 'mat' (need to fix)");
3576:   } else {
3577:     if (herm || !A->form_explicit_transpose) {
3578:       opA       = herm ? CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
3579:       matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3580:     } else {
3581:       if (!cusparsestruct->matTranspose) PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3582:       matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
3583:     }
3584:   }
3585:   /* Does the matrix use compressed rows (i.e., drop zero rows)? */
3586:   compressed = matstruct->cprowIndices ? PETSC_TRUE : PETSC_FALSE;

3588:   try {
3589:     PetscCall(VecCUDAGetArrayRead(xx, (const PetscScalar **)&xarray));
3590:     if (yy == zz) PetscCall(VecCUDAGetArray(zz, &zarray)); /* read & write zz, so need to get up-to-date zarray on GPU */
3591:     else PetscCall(VecCUDAGetArrayWrite(zz, &zarray));     /* write zz, so no need to init zarray on GPU */

3593:     PetscCall(PetscLogGpuTimeBegin());
3594:     if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3595:       /* z = A x + beta y.
3596:          If A is compressed (with less rows), then Ax is shorter than the full z, so we need a work vector to store Ax.
3597:          When A is non-compressed, and z = y, we can set beta=1 to compute y = Ax + y in one call.
3598:       */
3599:       xptr = xarray;
3600:       dptr = compressed ? cusparsestruct->workVector->data().get() : zarray;
3601:       beta = (yy == zz && !compressed) ? matstruct->beta_one : matstruct->beta_zero;
3602: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3603:       /* Get length of x, y for y=Ax. ny might be shorter than the work vector's allocated length, since the work vector is
3604:           allocated to accommodate different uses. So we get the length info directly from mat.
3605:        */
3606:       if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3607:         CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3608:         nx             = mat->num_cols; // since y = Ax
3609:         ny             = mat->num_rows;
3610:       }
3611: #endif
3612:     } else {
3613:       /* z = A^T x + beta y
3614:          If A is compressed, then we need a work vector as the shorter version of x to compute A^T x.
3615:          Note A^Tx is of full length, so we set beta to 1.0 if y exists.
3616:        */
3617:       xptr = compressed ? cusparsestruct->workVector->data().get() : xarray;
3618:       dptr = zarray;
3619:       beta = yy ? matstruct->beta_one : matstruct->beta_zero;
3620:       if (compressed) { /* Scatter x to work vector */
3621:         thrust::device_ptr<PetscScalar> xarr = thrust::device_pointer_cast(xarray);

3623:         thrust::for_each(
3624: #if PetscDefined(HAVE_THRUST_ASYNC)
3625:           thrust::cuda::par.on(PetscDefaultCudaStream),
3626: #endif
3627:           thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))),
3628:           thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))) + matstruct->cprowIndices->size(), VecCUDAEqualsReverse());
3629:       }
3630: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3631:       if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3632:         CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3633:         nx             = mat->num_rows; // since y = A^T x
3634:         ny             = mat->num_cols;
3635:       }
3636: #endif
3637:     }

3639:     /* csr_spmv does y = alpha op(A) x + beta y */
3640:     if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3641: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3642:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3643:       cusparseSpMatDescr_t &matDescr = matstruct->matDescr_SpMV[opA]; // All opA's should use the same matDescr, but the cusparse issue/bug (#212) after 12.4 forced us to create a new one for each opA.
3644:   #else
3645:       cusparseSpMatDescr_t &matDescr = matstruct->matDescr;
3646:   #endif

3648:       PetscCheck(opA >= 0 && opA <= 2, PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSPARSE ABI on cusparseOperation_t has changed and PETSc has not been updated accordingly");
3649:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3650:       if (!matDescr) {
3651:         CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3652:         PetscCallCUSPARSE(cusparseCreateCsr(&matDescr, mat->num_rows, mat->num_cols, mat->num_entries, mat->row_offsets->data().get(), mat->column_indices->data().get(), mat->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
3653:       }
3654:   #endif

3656:       if (!matstruct->cuSpMV[opA].initialized) { /* built on demand */
3657:         PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecXDescr, nx, xptr, cusparse_scalartype));
3658:         PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecYDescr, ny, dptr, cusparse_scalartype));
3659:         PetscCallCUSPARSE(
3660:           cusparseSpMV_bufferSize(cusparsestruct->handle, opA, matstruct->alpha_one, matDescr, matstruct->cuSpMV[opA].vecXDescr, beta, matstruct->cuSpMV[opA].vecYDescr, cusparse_scalartype, cusparsestruct->spmvAlg, &matstruct->cuSpMV[opA].spmvBufferSize));
3661:         PetscCallCUDA(cudaMalloc(&matstruct->cuSpMV[opA].spmvBuffer, matstruct->cuSpMV[opA].spmvBufferSize));
3662:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // cusparseSpMV_preprocess is added in 12.4
3663:         PetscCallCUSPARSE(
3664:           cusparseSpMV_preprocess(cusparsestruct->handle, opA, matstruct->alpha_one, matDescr, matstruct->cuSpMV[opA].vecXDescr, beta, matstruct->cuSpMV[opA].vecYDescr, cusparse_scalartype, cusparsestruct->spmvAlg, matstruct->cuSpMV[opA].spmvBuffer));
3665:   #endif
3666:         matstruct->cuSpMV[opA].initialized = PETSC_TRUE;
3667:       } else {
3668:         /* x, y's value pointers might change between calls, but their shape is kept, so we just update pointers */
3669:         PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecXDescr, xptr));
3670:         PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecYDescr, dptr));
3671:       }

3673:       PetscCallCUSPARSE(cusparseSpMV(cusparsestruct->handle, opA, matstruct->alpha_one, matDescr, matstruct->cuSpMV[opA].vecXDescr, beta, matstruct->cuSpMV[opA].vecYDescr, cusparse_scalartype, cusparsestruct->spmvAlg, matstruct->cuSpMV[opA].spmvBuffer));
3674: #else
3675:       CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3676:       PetscCallCUSPARSE(cusparse_csr_spmv(cusparsestruct->handle, opA, mat->num_rows, mat->num_cols, mat->num_entries, matstruct->alpha_one, matstruct->descr, mat->values->data().get(), mat->row_offsets->data().get(), mat->column_indices->data().get(), xptr, beta, dptr));
3677: #endif
3678:     } else {
3679:       if (cusparsestruct->nrows) {
3680: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3681:         SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
3682: #else
3683:         cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat;
3684:         PetscCallCUSPARSE(cusparse_hyb_spmv(cusparsestruct->handle, opA, matstruct->alpha_one, matstruct->descr, hybMat, xptr, beta, dptr));
3685: #endif
3686:       }
3687:     }
3688:     PetscCall(PetscLogGpuTimeEnd());

3690:     if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3691:       if (yy) {                                      /* MatMultAdd: zz = A*xx + yy */
3692:         if (compressed) {                            /* A is compressed. We first copy yy to zz, then ScatterAdd the work vector to zz */
3693:           PetscCall(VecSeq_CUDA::Copy(yy, zz));      /* zz = yy */
3694:         } else if (zz != yy) {                       /* A is not compressed. zz already contains A*xx, and we just need to add yy */
3695:           PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3696:         }
3697:       } else if (compressed) { /* MatMult: zz = A*xx. A is compressed, so we zero zz first, then ScatterAdd the work vector to zz */
3698:         PetscCall(VecSeq_CUDA::Set(zz, 0));
3699:       }

3701:       /* ScatterAdd the result from work vector into the full vector when A is compressed */
3702:       if (compressed) {
3703:         PetscCall(PetscLogGpuTimeBegin());
3704:         PetscInt n = (PetscInt)matstruct->cprowIndices->size();
3705:         ScatterAdd<<<(int)((n + 255) / 256), 256, 0, PetscDefaultCudaStream>>>(n, matstruct->cprowIndices->data().get(), cusparsestruct->workVector->data().get(), zarray);
3706:         PetscCall(PetscLogGpuTimeEnd());
3707:       }
3708:     } else {
3709:       if (yy && yy != zz) PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3710:     }
3711:     PetscCall(VecCUDARestoreArrayRead(xx, (const PetscScalar **)&xarray));
3712:     if (yy == zz) PetscCall(VecCUDARestoreArray(zz, &zarray));
3713:     else PetscCall(VecCUDARestoreArrayWrite(zz, &zarray));
3714:   } catch (char *ex) {
3715:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
3716:   }
3717:   if (yy) {
3718:     PetscCall(PetscLogGpuFlops(2.0 * a->nz));
3719:   } else {
3720:     PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
3721:   }
3722:   PetscFunctionReturn(PETSC_SUCCESS);
3723: }

3725: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3726: {
3727:   PetscFunctionBegin;
3728:   PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_FALSE));
3729:   PetscFunctionReturn(PETSC_SUCCESS);
3730: }

3732: static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A, MatAssemblyType mode)
3733: {
3734:   PetscFunctionBegin;
3735:   PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
3736:   PetscFunctionReturn(PETSC_SUCCESS);
3737: }

3739: /*@
3740:   MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in `MATAIJCUSPARSE` (compressed row) format
3741:   (the default parallel PETSc format).

3743:   Collective

3745:   Input Parameters:
3746: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3747: . m    - number of rows
3748: . n    - number of columns
3749: . nz   - number of nonzeros per row (same for all rows), ignored if `nnz` is provide
3750: - nnz  - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`

3752:   Output Parameter:
3753: . A - the matrix

3755:   Level: intermediate

3757:   Notes:
3758:   This matrix will ultimately pushed down to NVIDIA GPUs and use the CuSPARSE library for
3759:   calculations. For good matrix assembly performance the user should preallocate the matrix
3760:   storage by setting the parameter `nz` (or the array `nnz`).

3762:   It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3763:   MatXXXXSetPreallocation() paradgm instead of this routine directly.
3764:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

3766:   The AIJ format, also called
3767:   compressed row storage, is fully compatible with standard Fortran
3768:   storage.  That is, the stored row and column indices can begin at
3769:   either one (as in Fortran) or zero.

3771:   Specify the preallocated storage with either nz or nnz (not both).
3772:   Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3773:   allocation.

3775: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATAIJCUSPARSE`
3776: @*/
3777: PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3778: {
3779:   PetscFunctionBegin;
3780:   PetscCall(MatCreate(comm, A));
3781:   PetscCall(MatSetSizes(*A, m, n, m, n));
3782:   PetscCall(MatSetType(*A, MATSEQAIJCUSPARSE));
3783:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
3784:   PetscFunctionReturn(PETSC_SUCCESS);
3785: }

3787: static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A)
3788: {
3789:   PetscFunctionBegin;
3790:   if (A->factortype == MAT_FACTOR_NONE) {
3791:     PetscCall(MatSeqAIJCUSPARSE_Destroy(A));
3792:   } else {
3793:     PetscCall(MatSeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors **)&A->spptr));
3794:   }
3795:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3796:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetFormat_C", NULL));
3797:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetUseCPUSolve_C", NULL));
3798:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3799:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3800:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3801:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
3802:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3803:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3804:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijcusparse_hypre_C", NULL));
3805:   PetscCall(MatDestroy_SeqAIJ(A));
3806:   PetscFunctionReturn(PETSC_SUCCESS);
3807: }

3809: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
3810: static PetscErrorCode       MatBindToCPU_SeqAIJCUSPARSE(Mat, PetscBool);
3811: static PetscErrorCode       MatDuplicate_SeqAIJCUSPARSE(Mat A, MatDuplicateOption cpvalues, Mat *B)
3812: {
3813:   PetscFunctionBegin;
3814:   PetscCall(MatDuplicate_SeqAIJ(A, cpvalues, B));
3815:   PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(*B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, B));
3816:   PetscFunctionReturn(PETSC_SUCCESS);
3817: }

3819: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat Y, PetscScalar a, Mat X, MatStructure str)
3820: {
3821:   Mat_SeqAIJ         *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3822:   Mat_SeqAIJCUSPARSE *cy;
3823:   Mat_SeqAIJCUSPARSE *cx;
3824:   PetscScalar        *ay;
3825:   const PetscScalar  *ax;
3826:   CsrMatrix          *csry, *csrx;

3828:   PetscFunctionBegin;
3829:   cy = (Mat_SeqAIJCUSPARSE *)Y->spptr;
3830:   cx = (Mat_SeqAIJCUSPARSE *)X->spptr;
3831:   if (X->ops->axpy != Y->ops->axpy) {
3832:     PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3833:     PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3834:     PetscFunctionReturn(PETSC_SUCCESS);
3835:   }
3836:   /* if we are here, it means both matrices are bound to GPU */
3837:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(Y));
3838:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(X));
3839:   PetscCheck(cy->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)Y), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3840:   PetscCheck(cx->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)X), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3841:   csry = (CsrMatrix *)cy->mat->mat;
3842:   csrx = (CsrMatrix *)cx->mat->mat;
3843:   /* see if we can turn this into a cublas axpy */
3844:   if (str != SAME_NONZERO_PATTERN && x->nz == y->nz && !x->compressedrow.use && !y->compressedrow.use) {
3845:     bool eq = thrust::equal(thrust::device, csry->row_offsets->begin(), csry->row_offsets->end(), csrx->row_offsets->begin());
3846:     if (eq) eq = thrust::equal(thrust::device, csry->column_indices->begin(), csry->column_indices->end(), csrx->column_indices->begin());
3847:     if (eq) str = SAME_NONZERO_PATTERN;
3848:   }
3849:   /* spgeam is buggy with one column */
3850:   if (Y->cmap->n == 1 && str != SAME_NONZERO_PATTERN) str = DIFFERENT_NONZERO_PATTERN;

3852:   if (str == SUBSET_NONZERO_PATTERN) {
3853:     PetscScalar b = 1.0;
3854: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3855:     size_t bufferSize;
3856:     void  *buffer;
3857: #endif

3859:     PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3860:     PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3861:     PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_HOST));
3862: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3863:     PetscCallCUSPARSE(cusparse_csr_spgeam_bufferSize(cy->handle, Y->rmap->n, Y->cmap->n, &a, cx->mat->descr, x->nz, ax, csrx->row_offsets->data().get(), csrx->column_indices->data().get(), &b, cy->mat->descr, y->nz, ay, csry->row_offsets->data().get(),
3864:                                                      csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), &bufferSize));
3865:     PetscCallCUDA(cudaMalloc(&buffer, bufferSize));
3866:     PetscCall(PetscLogGpuTimeBegin());
3867:     PetscCallCUSPARSE(cusparse_csr_spgeam(cy->handle, Y->rmap->n, Y->cmap->n, &a, cx->mat->descr, x->nz, ax, csrx->row_offsets->data().get(), csrx->column_indices->data().get(), &b, cy->mat->descr, y->nz, ay, csry->row_offsets->data().get(),
3868:                                           csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), buffer));
3869:     PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3870:     PetscCall(PetscLogGpuTimeEnd());
3871:     PetscCallCUDA(cudaFree(buffer));
3872: #else
3873:     PetscCall(PetscLogGpuTimeBegin());
3874:     PetscCallCUSPARSE(cusparse_csr_spgeam(cy->handle, Y->rmap->n, Y->cmap->n, &a, cx->mat->descr, x->nz, ax, csrx->row_offsets->data().get(), csrx->column_indices->data().get(), &b, cy->mat->descr, y->nz, ay, csry->row_offsets->data().get(),
3875:                                           csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get()));
3876:     PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3877:     PetscCall(PetscLogGpuTimeEnd());
3878: #endif
3879:     PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_DEVICE));
3880:     PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3881:     PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3882:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3883:   } else if (str == SAME_NONZERO_PATTERN) {
3884:     cublasHandle_t cublasv2handle;
3885:     PetscBLASInt   one = 1, bnz = 1;

3887:     PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3888:     PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3889:     PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3890:     PetscCall(PetscBLASIntCast(x->nz, &bnz));
3891:     PetscCall(PetscLogGpuTimeBegin());
3892:     PetscCallCUBLAS(cublasXaxpy(cublasv2handle, bnz, &a, ax, one, ay, one));
3893:     PetscCall(PetscLogGpuFlops(2.0 * bnz));
3894:     PetscCall(PetscLogGpuTimeEnd());
3895:     PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3896:     PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3897:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3898:   } else {
3899:     PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3900:     PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3901:   }
3902:   PetscFunctionReturn(PETSC_SUCCESS);
3903: }

3905: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat Y, PetscScalar a)
3906: {
3907:   Mat_SeqAIJ    *y = (Mat_SeqAIJ *)Y->data;
3908:   PetscScalar   *ay;
3909:   cublasHandle_t cublasv2handle;
3910:   PetscBLASInt   one = 1, bnz = 1;

3912:   PetscFunctionBegin;
3913:   PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3914:   PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3915:   PetscCall(PetscBLASIntCast(y->nz, &bnz));
3916:   PetscCall(PetscLogGpuTimeBegin());
3917:   PetscCallCUBLAS(cublasXscal(cublasv2handle, bnz, &a, ay, one));
3918:   PetscCall(PetscLogGpuFlops(bnz));
3919:   PetscCall(PetscLogGpuTimeEnd());
3920:   PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3921:   PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3922:   PetscFunctionReturn(PETSC_SUCCESS);
3923: }

3925: static PetscErrorCode MatZeroEntries_SeqAIJCUSPARSE(Mat A)
3926: {
3927:   PetscBool   both = PETSC_FALSE;
3928:   Mat_SeqAIJ *a    = (Mat_SeqAIJ *)A->data;

3930:   PetscFunctionBegin;
3931:   if (A->factortype == MAT_FACTOR_NONE) {
3932:     Mat_SeqAIJCUSPARSE *spptr = (Mat_SeqAIJCUSPARSE *)A->spptr;
3933:     if (spptr->mat) {
3934:       CsrMatrix *matrix = (CsrMatrix *)spptr->mat->mat;
3935:       if (matrix->values) {
3936:         both = PETSC_TRUE;
3937:         thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3938:       }
3939:     }
3940:     if (spptr->matTranspose) {
3941:       CsrMatrix *matrix = (CsrMatrix *)spptr->matTranspose->mat;
3942:       if (matrix->values) thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3943:     }
3944:   }
3945:   PetscCall(PetscArrayzero(a->a, a->i[A->rmap->n]));
3946:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
3947:   if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
3948:   else A->offloadmask = PETSC_OFFLOAD_CPU;
3949:   PetscFunctionReturn(PETSC_SUCCESS);
3950: }

3952: static PetscErrorCode MatGetCurrentMemType_SeqAIJCUSPARSE(PETSC_UNUSED Mat A, PetscMemType *m)
3953: {
3954:   PetscFunctionBegin;
3955:   *m = PETSC_MEMTYPE_CUDA;
3956:   PetscFunctionReturn(PETSC_SUCCESS);
3957: }

3959: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat A, PetscBool flg)
3960: {
3961:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

3963:   PetscFunctionBegin;
3964:   if (A->factortype != MAT_FACTOR_NONE) {
3965:     A->boundtocpu = flg;
3966:     PetscFunctionReturn(PETSC_SUCCESS);
3967:   }
3968:   if (flg) {
3969:     PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));

3971:     A->ops->scale                     = MatScale_SeqAIJ;
3972:     A->ops->axpy                      = MatAXPY_SeqAIJ;
3973:     A->ops->zeroentries               = MatZeroEntries_SeqAIJ;
3974:     A->ops->mult                      = MatMult_SeqAIJ;
3975:     A->ops->multadd                   = MatMultAdd_SeqAIJ;
3976:     A->ops->multtranspose             = MatMultTranspose_SeqAIJ;
3977:     A->ops->multtransposeadd          = MatMultTransposeAdd_SeqAIJ;
3978:     A->ops->multhermitiantranspose    = NULL;
3979:     A->ops->multhermitiantransposeadd = NULL;
3980:     A->ops->productsetfromoptions     = MatProductSetFromOptions_SeqAIJ;
3981:     A->ops->getcurrentmemtype         = NULL;
3982:     PetscCall(PetscMemzero(a->ops, sizeof(Mat_SeqAIJOps)));
3983:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3984:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3985:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3986:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3987:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3988:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3989:   } else {
3990:     A->ops->scale                     = MatScale_SeqAIJCUSPARSE;
3991:     A->ops->axpy                      = MatAXPY_SeqAIJCUSPARSE;
3992:     A->ops->zeroentries               = MatZeroEntries_SeqAIJCUSPARSE;
3993:     A->ops->mult                      = MatMult_SeqAIJCUSPARSE;
3994:     A->ops->multadd                   = MatMultAdd_SeqAIJCUSPARSE;
3995:     A->ops->multtranspose             = MatMultTranspose_SeqAIJCUSPARSE;
3996:     A->ops->multtransposeadd          = MatMultTransposeAdd_SeqAIJCUSPARSE;
3997:     A->ops->multhermitiantranspose    = MatMultHermitianTranspose_SeqAIJCUSPARSE;
3998:     A->ops->multhermitiantransposeadd = MatMultHermitianTransposeAdd_SeqAIJCUSPARSE;
3999:     A->ops->productsetfromoptions     = MatProductSetFromOptions_SeqAIJCUSPARSE;
4000:     A->ops->getcurrentmemtype         = MatGetCurrentMemType_SeqAIJCUSPARSE;
4001:     a->ops->getarray                  = MatSeqAIJGetArray_SeqAIJCUSPARSE;
4002:     a->ops->restorearray              = MatSeqAIJRestoreArray_SeqAIJCUSPARSE;
4003:     a->ops->getarrayread              = MatSeqAIJGetArrayRead_SeqAIJCUSPARSE;
4004:     a->ops->restorearrayread          = MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE;
4005:     a->ops->getarraywrite             = MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE;
4006:     a->ops->restorearraywrite         = MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE;
4007:     a->ops->getcsrandmemtype          = MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE;

4009:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", MatSeqAIJCopySubArray_SeqAIJCUSPARSE));
4010:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4011:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4012:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJCUSPARSE));
4013:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJCUSPARSE));
4014:     PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4015:   }
4016:   A->boundtocpu = flg;
4017:   if (flg && a->inode.size_csr) {
4018:     a->inode.use = PETSC_TRUE;
4019:   } else {
4020:     a->inode.use = PETSC_FALSE;
4021:   }
4022:   PetscFunctionReturn(PETSC_SUCCESS);
4023: }

4025: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat A, MatType, MatReuse reuse, Mat *newmat)
4026: {
4027:   Mat B;

4029:   PetscFunctionBegin;
4030:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUDA)); /* first use of CUSPARSE may be via MatConvert */
4031:   if (reuse == MAT_INITIAL_MATRIX) {
4032:     PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
4033:   } else if (reuse == MAT_REUSE_MATRIX) {
4034:     PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN));
4035:   }
4036:   B = *newmat;

4038:   PetscCall(PetscFree(B->defaultvectype));
4039:   PetscCall(PetscStrallocpy(VECCUDA, &B->defaultvectype));

4041:   if (reuse != MAT_REUSE_MATRIX && !B->spptr) {
4042:     if (B->factortype == MAT_FACTOR_NONE) {
4043:       Mat_SeqAIJCUSPARSE *spptr;
4044:       PetscCall(PetscNew(&spptr));
4045:       PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4046:       PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4047:       spptr->format = MAT_CUSPARSE_CSR;
4048: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4049:   #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4050:       spptr->spmvAlg = CUSPARSE_SPMV_CSR_ALG1; /* default, since we only support csr */
4051:   #else
4052:       spptr->spmvAlg = CUSPARSE_CSRMV_ALG1; /* default, since we only support csr */
4053:   #endif
4054:       spptr->spmmAlg    = CUSPARSE_SPMM_CSR_ALG1; /* default, only support column-major dense matrix B */
4055:       spptr->csr2cscAlg = CUSPARSE_CSR2CSC_ALG1;
4056: #endif
4057:       B->spptr = spptr;
4058:     } else {
4059:       Mat_SeqAIJCUSPARSETriFactors *spptr;

4061:       PetscCall(PetscNew(&spptr));
4062:       PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4063:       PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4064:       B->spptr = spptr;
4065:     }
4066:     B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
4067:   }
4068:   B->ops->assemblyend       = MatAssemblyEnd_SeqAIJCUSPARSE;
4069:   B->ops->destroy           = MatDestroy_SeqAIJCUSPARSE;
4070:   B->ops->setoption         = MatSetOption_SeqAIJCUSPARSE;
4071:   B->ops->setfromoptions    = MatSetFromOptions_SeqAIJCUSPARSE;
4072:   B->ops->bindtocpu         = MatBindToCPU_SeqAIJCUSPARSE;
4073:   B->ops->duplicate         = MatDuplicate_SeqAIJCUSPARSE;
4074:   B->ops->getcurrentmemtype = MatGetCurrentMemType_SeqAIJCUSPARSE;

4076:   PetscCall(MatBindToCPU_SeqAIJCUSPARSE(B, PETSC_FALSE));
4077:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJCUSPARSE));
4078:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE));
4079: #if defined(PETSC_HAVE_HYPRE)
4080:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaijcusparse_hypre_C", MatConvert_AIJ_HYPRE));
4081: #endif
4082:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetUseCPUSolve_C", MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE));
4083:   PetscFunctionReturn(PETSC_SUCCESS);
4084: }

4086: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B)
4087: {
4088:   PetscFunctionBegin;
4089:   PetscCall(MatCreate_SeqAIJ(B));
4090:   PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, &B));
4091:   PetscFunctionReturn(PETSC_SUCCESS);
4092: }

4094: /*MC
4095:    MATSEQAIJCUSPARSE - MATAIJCUSPARSE = "(seq)aijcusparse" - A matrix type to be used for sparse matrices.

4097:    A matrix type whose data resides on NVIDIA GPUs. These matrices can be in either
4098:    CSR, ELL, or Hybrid format.
4099:    All matrix calculations are performed on NVIDIA GPUs using the CuSPARSE library.

4101:    Options Database Keys:
4102: +  -mat_type aijcusparse - sets the matrix type to "seqaijcusparse" during a call to `MatSetFromOptions()`
4103: .  -mat_cusparse_storage_format csr - sets the storage format of matrices (for `MatMult()` and factors in `MatSolve()`).
4104:                                       Other options include ell (ellpack) or hyb (hybrid).
4105: .  -mat_cusparse_mult_storage_format csr - sets the storage format of matrices (for `MatMult()`). Other options include ell (ellpack) or hyb (hybrid).
4106: -  -mat_cusparse_use_cpu_solve - Do `MatSolve()` on CPU

4108:   Level: beginner

4110: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJCUSPARSE()`, `MatCUSPARSESetUseCPUSolve()`, `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
4111: M*/

4113: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_CUSPARSE(void)
4114: {
4115:   PetscFunctionBegin;
4116:   PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_LU, MatGetFactor_seqaijcusparse_cusparse));
4117:   PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_CHOLESKY, MatGetFactor_seqaijcusparse_cusparse));
4118:   PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ILU, MatGetFactor_seqaijcusparse_cusparse));
4119:   PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ICC, MatGetFactor_seqaijcusparse_cusparse));
4120:   PetscFunctionReturn(PETSC_SUCCESS);
4121: }

4123: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat mat)
4124: {
4125:   Mat_SeqAIJCUSPARSE *cusp = static_cast<Mat_SeqAIJCUSPARSE *>(mat->spptr);

4127:   PetscFunctionBegin;
4128:   if (cusp) {
4129:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->mat, cusp->format));
4130:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4131:     delete cusp->workVector;
4132:     delete cusp->rowoffsets_gpu;
4133:     delete cusp->csr2csc_i;
4134:     delete cusp->coords;
4135:     if (cusp->handle) PetscCallCUSPARSE(cusparseDestroy(cusp->handle));
4136:     PetscCall(PetscFree(mat->spptr));
4137:   }
4138:   PetscFunctionReturn(PETSC_SUCCESS);
4139: }

4141: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **mat)
4142: {
4143:   PetscFunctionBegin;
4144:   if (*mat) {
4145:     delete (*mat)->values;
4146:     delete (*mat)->column_indices;
4147:     delete (*mat)->row_offsets;
4148:     delete *mat;
4149:     *mat = 0;
4150:   }
4151:   PetscFunctionReturn(PETSC_SUCCESS);
4152: }

4154: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4155: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **trifactor)
4156: {
4157:   PetscFunctionBegin;
4158:   if (*trifactor) {
4159:     if ((*trifactor)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*trifactor)->descr));
4160:     if ((*trifactor)->solveInfo) PetscCallCUSPARSE(cusparseDestroyCsrsvInfo((*trifactor)->solveInfo));
4161:     PetscCall(CsrMatrix_Destroy(&(*trifactor)->csrMat));
4162:     if ((*trifactor)->solveBuffer) PetscCallCUDA(cudaFree((*trifactor)->solveBuffer));
4163:     if ((*trifactor)->AA_h) PetscCallCUDA(cudaFreeHost((*trifactor)->AA_h));
4164:   #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4165:     if ((*trifactor)->csr2cscBuffer) PetscCallCUDA(cudaFree((*trifactor)->csr2cscBuffer));
4166:   #endif
4167:     PetscCall(PetscFree(*trifactor));
4168:   }
4169:   PetscFunctionReturn(PETSC_SUCCESS);
4170: }
4171: #endif

4173: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **matstruct, MatCUSPARSEStorageFormat format)
4174: {
4175:   CsrMatrix *mat;

4177:   PetscFunctionBegin;
4178:   if (*matstruct) {
4179:     if ((*matstruct)->mat) {
4180:       if (format == MAT_CUSPARSE_ELL || format == MAT_CUSPARSE_HYB) {
4181: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4182:         SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
4183: #else
4184:         cusparseHybMat_t hybMat = (cusparseHybMat_t)(*matstruct)->mat;
4185:         PetscCallCUSPARSE(cusparseDestroyHybMat(hybMat));
4186: #endif
4187:       } else {
4188:         mat = (CsrMatrix *)(*matstruct)->mat;
4189:         PetscCall(CsrMatrix_Destroy(&mat));
4190:       }
4191:     }
4192:     if ((*matstruct)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*matstruct)->descr));
4193:     delete (*matstruct)->cprowIndices;
4194:     if ((*matstruct)->alpha_one) PetscCallCUDA(cudaFree((*matstruct)->alpha_one));
4195:     if ((*matstruct)->beta_zero) PetscCallCUDA(cudaFree((*matstruct)->beta_zero));
4196:     if ((*matstruct)->beta_one) PetscCallCUDA(cudaFree((*matstruct)->beta_one));

4198: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4199:     Mat_SeqAIJCUSPARSEMultStruct *mdata = *matstruct;
4200:     if (mdata->matDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr));

4202:     for (int i = 0; i < 3; i++) {
4203:       if (mdata->cuSpMV[i].initialized) {
4204:         PetscCallCUDA(cudaFree(mdata->cuSpMV[i].spmvBuffer));
4205:         PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecXDescr));
4206:         PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecYDescr));
4207:   #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
4208:         if (mdata->matDescr_SpMV[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMV[i]));
4209:         if (mdata->matDescr_SpMM[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMM[i]));
4210:   #endif
4211:       }
4212:     }
4213: #endif
4214:     delete *matstruct;
4215:     *matstruct = NULL;
4216:   }
4217:   PetscFunctionReturn(PETSC_SUCCESS);
4218: }

4220: PetscErrorCode MatSeqAIJCUSPARSETriFactors_Reset(Mat_SeqAIJCUSPARSETriFactors_p *trifactors)
4221: {
4222:   Mat_SeqAIJCUSPARSETriFactors *fs = *trifactors;

4224:   PetscFunctionBegin;
4225:   if (fs) {
4226: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4227:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtr));
4228:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtr));
4229:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtrTranspose));
4230:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtrTranspose));
4231:     delete fs->workVector;
4232:     fs->workVector = NULL;
4233: #endif
4234:     delete fs->rpermIndices;
4235:     delete fs->cpermIndices;
4236:     fs->rpermIndices  = NULL;
4237:     fs->cpermIndices  = NULL;
4238:     fs->init_dev_prop = PETSC_FALSE;
4239: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4240:     PetscCallCUDA(cudaFree(fs->csrRowPtr));
4241:     PetscCallCUDA(cudaFree(fs->csrColIdx));
4242:     PetscCallCUDA(cudaFree(fs->csrRowPtr32));
4243:     PetscCallCUDA(cudaFree(fs->csrColIdx32));
4244:     PetscCallCUDA(cudaFree(fs->csrVal));
4245:     PetscCallCUDA(cudaFree(fs->diag));
4246:     PetscCallCUDA(cudaFree(fs->X));
4247:     PetscCallCUDA(cudaFree(fs->Y));
4248:     // PetscCallCUDA(cudaFree(fs->factBuffer_M)); /* No needed since factBuffer_M shares with one of spsvBuffer_L/U */
4249:     PetscCallCUDA(cudaFree(fs->spsvBuffer_L));
4250:     PetscCallCUDA(cudaFree(fs->spsvBuffer_U));
4251:     PetscCallCUDA(cudaFree(fs->spsvBuffer_Lt));
4252:     PetscCallCUDA(cudaFree(fs->spsvBuffer_Ut));
4253:     PetscCallCUSPARSE(cusparseDestroyMatDescr(fs->matDescr_M));
4254:     PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_L));
4255:     PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_U));
4256:     PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_L));
4257:     PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Lt));
4258:     PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_U));
4259:     PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Ut));
4260:     PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_X));
4261:     PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_Y));
4262:     PetscCallCUSPARSE(cusparseDestroyCsrilu02Info(fs->ilu0Info_M));
4263:     PetscCallCUSPARSE(cusparseDestroyCsric02Info(fs->ic0Info_M));
4264:     PetscCall(PetscFree(fs->csrRowPtr_h));
4265:     PetscCall(PetscFree(fs->csrVal_h));
4266:     PetscCall(PetscFree(fs->diag_h));
4267:     fs->createdTransposeSpSVDescr    = PETSC_FALSE;
4268:     fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
4269: #endif
4270:   }
4271:   PetscFunctionReturn(PETSC_SUCCESS);
4272: }

4274: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **trifactors)
4275: {
4276:   PetscFunctionBegin;
4277:   if (*trifactors) {
4278:     PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(trifactors));
4279:     PetscCallCUSPARSE(cusparseDestroy((*trifactors)->handle));
4280:     PetscCall(PetscFree(*trifactors));
4281:   }
4282:   PetscFunctionReturn(PETSC_SUCCESS);
4283: }

4285: struct IJCompare {
4286:   __host__ __device__ inline bool operator()(const thrust::tuple<PetscInt, PetscInt> &t1, const thrust::tuple<PetscInt, PetscInt> &t2)
4287:   {
4288:     if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4289:     if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4290:     return false;
4291:   }
4292: };

4294: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat A, PetscBool destroy)
4295: {
4296:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;

4298:   PetscFunctionBegin;
4299:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4300:   if (!cusp) PetscFunctionReturn(PETSC_SUCCESS);
4301:   if (destroy) {
4302:     PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4303:     delete cusp->csr2csc_i;
4304:     cusp->csr2csc_i = NULL;
4305:   }
4306:   A->transupdated = PETSC_FALSE;
4307:   PetscFunctionReturn(PETSC_SUCCESS);
4308: }

4310: static PetscErrorCode MatCOOStructDestroy_SeqAIJCUSPARSE(void **data)
4311: {
4312:   MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;

4314:   PetscFunctionBegin;
4315:   PetscCallCUDA(cudaFree(coo->perm));
4316:   PetscCallCUDA(cudaFree(coo->jmap));
4317:   PetscCall(PetscFree(coo));
4318:   PetscFunctionReturn(PETSC_SUCCESS);
4319: }

4321: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4322: {
4323:   PetscBool            dev_ij = PETSC_FALSE;
4324:   PetscMemType         mtype  = PETSC_MEMTYPE_HOST;
4325:   PetscInt            *i, *j;
4326:   PetscContainer       container_h;
4327:   MatCOOStruct_SeqAIJ *coo_h, *coo_d;

4329:   PetscFunctionBegin;
4330:   PetscCall(PetscGetMemType(coo_i, &mtype));
4331:   if (PetscMemTypeDevice(mtype)) {
4332:     dev_ij = PETSC_TRUE;
4333:     PetscCall(PetscMalloc2(coo_n, &i, coo_n, &j));
4334:     PetscCallCUDA(cudaMemcpy(i, coo_i, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4335:     PetscCallCUDA(cudaMemcpy(j, coo_j, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4336:   } else {
4337:     i = coo_i;
4338:     j = coo_j;
4339:   }

4341:   PetscCall(MatSetPreallocationCOO_SeqAIJ(mat, coo_n, i, j));
4342:   if (dev_ij) PetscCall(PetscFree2(i, j));
4343:   mat->offloadmask = PETSC_OFFLOAD_CPU;
4344:   // Create the GPU memory
4345:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(mat));

4347:   // Copy the COO struct to device
4348:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
4349:   PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
4350:   PetscCall(PetscMalloc1(1, &coo_d));
4351:   *coo_d = *coo_h; // do a shallow copy and then amend some fields that need to be different
4352:   PetscCallCUDA(cudaMalloc((void **)&coo_d->jmap, (coo_h->nz + 1) * sizeof(PetscCount)));
4353:   PetscCallCUDA(cudaMemcpy(coo_d->jmap, coo_h->jmap, (coo_h->nz + 1) * sizeof(PetscCount), cudaMemcpyHostToDevice));
4354:   PetscCallCUDA(cudaMalloc((void **)&coo_d->perm, coo_h->Atot * sizeof(PetscCount)));
4355:   PetscCallCUDA(cudaMemcpy(coo_d->perm, coo_h->perm, coo_h->Atot * sizeof(PetscCount), cudaMemcpyHostToDevice));

4357:   // Put the COO struct in a container and then attach that to the matrix
4358:   PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", coo_d, MatCOOStructDestroy_SeqAIJCUSPARSE));
4359:   PetscFunctionReturn(PETSC_SUCCESS);
4360: }

4362: __global__ static void MatAddCOOValues(const PetscScalar kv[], PetscCount nnz, const PetscCount jmap[], const PetscCount perm[], InsertMode imode, PetscScalar a[])
4363: {
4364:   PetscCount       i         = blockIdx.x * blockDim.x + threadIdx.x;
4365:   const PetscCount grid_size = gridDim.x * blockDim.x;
4366:   for (; i < nnz; i += grid_size) {
4367:     PetscScalar sum = 0.0;
4368:     for (PetscCount k = jmap[i]; k < jmap[i + 1]; k++) sum += kv[perm[k]];
4369:     a[i] = (imode == INSERT_VALUES ? 0.0 : a[i]) + sum;
4370:   }
4371: }

4373: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat A, const PetscScalar v[], InsertMode imode)
4374: {
4375:   Mat_SeqAIJ          *seq  = (Mat_SeqAIJ *)A->data;
4376:   Mat_SeqAIJCUSPARSE  *dev  = (Mat_SeqAIJCUSPARSE *)A->spptr;
4377:   PetscCount           Annz = seq->nz;
4378:   PetscMemType         memtype;
4379:   const PetscScalar   *v1 = v;
4380:   PetscScalar         *Aa;
4381:   PetscContainer       container;
4382:   MatCOOStruct_SeqAIJ *coo;

4384:   PetscFunctionBegin;
4385:   if (!dev->mat) PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));

4387:   PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
4388:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));

4390:   PetscCall(PetscGetMemType(v, &memtype));
4391:   if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
4392:     PetscCallCUDA(cudaMalloc((void **)&v1, coo->n * sizeof(PetscScalar)));
4393:     PetscCallCUDA(cudaMemcpy((void *)v1, v, coo->n * sizeof(PetscScalar), cudaMemcpyHostToDevice));
4394:   }

4396:   if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSEGetArrayWrite(A, &Aa));
4397:   else PetscCall(MatSeqAIJCUSPARSEGetArray(A, &Aa));

4399:   PetscCall(PetscLogGpuTimeBegin());
4400:   if (Annz) {
4401:     MatAddCOOValues<<<((int)(Annz + 255) / 256), 256>>>(v1, Annz, coo->jmap, coo->perm, imode, Aa);
4402:     PetscCallCUDA(cudaPeekAtLastError());
4403:   }
4404:   PetscCall(PetscLogGpuTimeEnd());

4406:   if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSERestoreArrayWrite(A, &Aa));
4407:   else PetscCall(MatSeqAIJCUSPARSERestoreArray(A, &Aa));

4409:   if (PetscMemTypeHost(memtype)) PetscCallCUDA(cudaFree((void *)v1));
4410:   PetscFunctionReturn(PETSC_SUCCESS);
4411: }

4413: /*@C
4414:   MatSeqAIJCUSPARSEGetIJ - returns the device row storage `i` and `j` indices for `MATSEQAIJCUSPARSE` matrices.

4416:   Not Collective

4418:   Input Parameters:
4419: + A          - the matrix
4420: - compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form

4422:   Output Parameters:
4423: + i - the CSR row pointers
4424: - j - the CSR column indices

4426:   Level: developer

4428:   Note:
4429:   When compressed is true, the CSR structure does not contain empty rows

4431: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSERestoreIJ()`, `MatSeqAIJCUSPARSEGetArrayRead()`
4432: @*/
4433: PetscErrorCode MatSeqAIJCUSPARSEGetIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4434: {
4435:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4436:   CsrMatrix          *csr;
4437:   Mat_SeqAIJ         *a = (Mat_SeqAIJ *)A->data;

4439:   PetscFunctionBegin;
4441:   if (!i || !j) PetscFunctionReturn(PETSC_SUCCESS);
4442:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4443:   PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4444:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4445:   PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4446:   csr = (CsrMatrix *)cusp->mat->mat;
4447:   if (i) {
4448:     if (!compressed && a->compressedrow.use) { /* need full row offset */
4449:       if (!cusp->rowoffsets_gpu) {
4450:         cusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4451:         cusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4452:         PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4453:       }
4454:       *i = cusp->rowoffsets_gpu->data().get();
4455:     } else *i = csr->row_offsets->data().get();
4456:   }
4457:   if (j) *j = csr->column_indices->data().get();
4458:   PetscFunctionReturn(PETSC_SUCCESS);
4459: }

4461: /*@C
4462:   MatSeqAIJCUSPARSERestoreIJ - restore the device row storage `i` and `j` indices obtained with `MatSeqAIJCUSPARSEGetIJ()`

4464:   Not Collective

4466:   Input Parameters:
4467: + A          - the matrix
4468: . compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4469: . i          - the CSR row pointers
4470: - j          - the CSR column indices

4472:   Level: developer

4474: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetIJ()`
4475: @*/
4476: PetscErrorCode MatSeqAIJCUSPARSERestoreIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4477: {
4478:   PetscFunctionBegin;
4480:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4481:   if (i) *i = NULL;
4482:   if (j) *j = NULL;
4483:   (void)compressed;
4484:   PetscFunctionReturn(PETSC_SUCCESS);
4485: }

4487: /*@C
4488:   MatSeqAIJCUSPARSEGetArrayRead - gives read-only access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored

4490:   Not Collective

4492:   Input Parameter:
4493: . A - a `MATSEQAIJCUSPARSE` matrix

4495:   Output Parameter:
4496: . a - pointer to the device data

4498:   Level: developer

4500:   Note:
4501:   May trigger host-device copies if up-to-date matrix data is on host

4503: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArrayRead()`
4504: @*/
4505: PetscErrorCode MatSeqAIJCUSPARSEGetArrayRead(Mat A, const PetscScalar **a)
4506: {
4507:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4508:   CsrMatrix          *csr;

4510:   PetscFunctionBegin;
4512:   PetscAssertPointer(a, 2);
4513:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4514:   PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4515:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4516:   PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4517:   csr = (CsrMatrix *)cusp->mat->mat;
4518:   PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4519:   *a = csr->values->data().get();
4520:   PetscFunctionReturn(PETSC_SUCCESS);
4521: }

4523: /*@C
4524:   MatSeqAIJCUSPARSERestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJCUSPARSEGetArrayRead()`

4526:   Not Collective

4528:   Input Parameters:
4529: + A - a `MATSEQAIJCUSPARSE` matrix
4530: - a - pointer to the device data

4532:   Level: developer

4534: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`
4535: @*/
4536: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayRead(Mat A, const PetscScalar **a)
4537: {
4538:   PetscFunctionBegin;
4540:   PetscAssertPointer(a, 2);
4541:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4542:   *a = NULL;
4543:   PetscFunctionReturn(PETSC_SUCCESS);
4544: }

4546: /*@C
4547:   MatSeqAIJCUSPARSEGetArray - gives read-write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored

4549:   Not Collective

4551:   Input Parameter:
4552: . A - a `MATSEQAIJCUSPARSE` matrix

4554:   Output Parameter:
4555: . a - pointer to the device data

4557:   Level: developer

4559:   Note:
4560:   May trigger host-device copies if up-to-date matrix data is on host

4562: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArray()`
4563: @*/
4564: PetscErrorCode MatSeqAIJCUSPARSEGetArray(Mat A, PetscScalar **a)
4565: {
4566:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4567:   CsrMatrix          *csr;

4569:   PetscFunctionBegin;
4571:   PetscAssertPointer(a, 2);
4572:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4573:   PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4574:   PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4575:   PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4576:   csr = (CsrMatrix *)cusp->mat->mat;
4577:   PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4578:   *a             = csr->values->data().get();
4579:   A->offloadmask = PETSC_OFFLOAD_GPU;
4580:   PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4581:   PetscFunctionReturn(PETSC_SUCCESS);
4582: }
4583: /*@C
4584:   MatSeqAIJCUSPARSERestoreArray - restore the read-write access array obtained from `MatSeqAIJCUSPARSEGetArray()`

4586:   Not Collective

4588:   Input Parameters:
4589: + A - a `MATSEQAIJCUSPARSE` matrix
4590: - a - pointer to the device data

4592:   Level: developer

4594: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`
4595: @*/
4596: PetscErrorCode MatSeqAIJCUSPARSERestoreArray(Mat A, PetscScalar **a)
4597: {
4598:   PetscFunctionBegin;
4600:   PetscAssertPointer(a, 2);
4601:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4602:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4603:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4604:   *a = NULL;
4605:   PetscFunctionReturn(PETSC_SUCCESS);
4606: }

4608: /*@C
4609:   MatSeqAIJCUSPARSEGetArrayWrite - gives write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored

4611:   Not Collective

4613:   Input Parameter:
4614: . A - a `MATSEQAIJCUSPARSE` matrix

4616:   Output Parameter:
4617: . a - pointer to the device data

4619:   Level: developer

4621:   Note:
4622:   Does not trigger host-device copies and flags data validity on the GPU

4624: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSERestoreArrayWrite()`
4625: @*/
4626: PetscErrorCode MatSeqAIJCUSPARSEGetArrayWrite(Mat A, PetscScalar **a)
4627: {
4628:   Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4629:   CsrMatrix          *csr;

4631:   PetscFunctionBegin;
4633:   PetscAssertPointer(a, 2);
4634:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4635:   PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4636:   PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4637:   csr = (CsrMatrix *)cusp->mat->mat;
4638:   PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4639:   *a             = csr->values->data().get();
4640:   A->offloadmask = PETSC_OFFLOAD_GPU;
4641:   PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4642:   PetscFunctionReturn(PETSC_SUCCESS);
4643: }

4645: /*@C
4646:   MatSeqAIJCUSPARSERestoreArrayWrite - restore the write-only access array obtained from `MatSeqAIJCUSPARSEGetArrayWrite()`

4648:   Not Collective

4650:   Input Parameters:
4651: + A - a `MATSEQAIJCUSPARSE` matrix
4652: - a - pointer to the device data

4654:   Level: developer

4656: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayWrite()`
4657: @*/
4658: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayWrite(Mat A, PetscScalar **a)
4659: {
4660:   PetscFunctionBegin;
4662:   PetscAssertPointer(a, 2);
4663:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4664:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4665:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4666:   *a = NULL;
4667:   PetscFunctionReturn(PETSC_SUCCESS);
4668: }

4670: struct IJCompare4 {
4671:   __host__ __device__ inline bool operator()(const thrust::tuple<int, int, PetscScalar, int> &t1, const thrust::tuple<int, int, PetscScalar, int> &t2)
4672:   {
4673:     if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4674:     if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4675:     return false;
4676:   }
4677: };

4679: struct Shift {
4680:   int _shift;

4682:   Shift(int shift) : _shift(shift) { }
4683:   __host__ __device__ inline int operator()(const int &c) { return c + _shift; }
4684: };

4686: /* merges two SeqAIJCUSPARSE matrices A, B by concatenating their rows. [A';B']' operation in MATLAB notation */
4687: PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
4688: {
4689:   Mat_SeqAIJ                   *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data, *c;
4690:   Mat_SeqAIJCUSPARSE           *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr, *Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr, *Ccusp;
4691:   Mat_SeqAIJCUSPARSEMultStruct *Cmat;
4692:   CsrMatrix                    *Acsr, *Bcsr, *Ccsr;
4693:   PetscInt                      Annz, Bnnz;
4694:   cusparseStatus_t              stat;
4695:   PetscInt                      i, m, n, zero = 0;

4697:   PetscFunctionBegin;
4700:   PetscAssertPointer(C, 4);
4701:   PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4702:   PetscCheckTypeName(B, MATSEQAIJCUSPARSE);
4703:   PetscCheck(A->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Invalid number or rows %" PetscInt_FMT " != %" PetscInt_FMT, A->rmap->n, B->rmap->n);
4704:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
4705:   PetscCheck(Acusp->format != MAT_CUSPARSE_ELL && Acusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4706:   PetscCheck(Bcusp->format != MAT_CUSPARSE_ELL && Bcusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4707:   if (reuse == MAT_INITIAL_MATRIX) {
4708:     m = A->rmap->n;
4709:     n = A->cmap->n + B->cmap->n;
4710:     PetscCall(MatCreate(PETSC_COMM_SELF, C));
4711:     PetscCall(MatSetSizes(*C, m, n, m, n));
4712:     PetscCall(MatSetType(*C, MATSEQAIJCUSPARSE));
4713:     c                       = (Mat_SeqAIJ *)(*C)->data;
4714:     Ccusp                   = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4715:     Cmat                    = new Mat_SeqAIJCUSPARSEMultStruct;
4716:     Ccsr                    = new CsrMatrix;
4717:     Cmat->cprowIndices      = NULL;
4718:     c->compressedrow.use    = PETSC_FALSE;
4719:     c->compressedrow.nrows  = 0;
4720:     c->compressedrow.i      = NULL;
4721:     c->compressedrow.rindex = NULL;
4722:     Ccusp->workVector       = NULL;
4723:     Ccusp->nrows            = m;
4724:     Ccusp->mat              = Cmat;
4725:     Ccusp->mat->mat         = Ccsr;
4726:     Ccsr->num_rows          = m;
4727:     Ccsr->num_cols          = n;
4728:     PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
4729:     PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
4730:     PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4731:     PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
4732:     PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
4733:     PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
4734:     PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4735:     PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4736:     PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4737:     PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4738:     PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4739:     PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4740:     PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");

4742:     Acsr                 = (CsrMatrix *)Acusp->mat->mat;
4743:     Bcsr                 = (CsrMatrix *)Bcusp->mat->mat;
4744:     Annz                 = (PetscInt)Acsr->column_indices->size();
4745:     Bnnz                 = (PetscInt)Bcsr->column_indices->size();
4746:     c->nz                = Annz + Bnnz;
4747:     Ccsr->row_offsets    = new THRUSTINTARRAY32(m + 1);
4748:     Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
4749:     Ccsr->values         = new THRUSTARRAY(c->nz);
4750:     Ccsr->num_entries    = c->nz;
4751:     Ccusp->coords        = new THRUSTINTARRAY(c->nz);
4752:     if (c->nz) {
4753:       auto              Acoo = new THRUSTINTARRAY32(Annz);
4754:       auto              Bcoo = new THRUSTINTARRAY32(Bnnz);
4755:       auto              Ccoo = new THRUSTINTARRAY32(c->nz);
4756:       THRUSTINTARRAY32 *Aroff, *Broff;

4758:       if (a->compressedrow.use) { /* need full row offset */
4759:         if (!Acusp->rowoffsets_gpu) {
4760:           Acusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4761:           Acusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4762:           PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4763:         }
4764:         Aroff = Acusp->rowoffsets_gpu;
4765:       } else Aroff = Acsr->row_offsets;
4766:       if (b->compressedrow.use) { /* need full row offset */
4767:         if (!Bcusp->rowoffsets_gpu) {
4768:           Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
4769:           Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
4770:           PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
4771:         }
4772:         Broff = Bcusp->rowoffsets_gpu;
4773:       } else Broff = Bcsr->row_offsets;
4774:       PetscCall(PetscLogGpuTimeBegin());
4775:       stat = cusparseXcsr2coo(Acusp->handle, Aroff->data().get(), Annz, m, Acoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4776:       PetscCallCUSPARSE(stat);
4777:       stat = cusparseXcsr2coo(Bcusp->handle, Broff->data().get(), Bnnz, m, Bcoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4778:       PetscCallCUSPARSE(stat);
4779:       /* Issues when using bool with large matrices on SUMMIT 10.2.89 */
4780:       auto Aperm = thrust::make_constant_iterator(1);
4781:       auto Bperm = thrust::make_constant_iterator(0);
4782: #if PETSC_PKG_CUDA_VERSION_GE(10, 0, 0)
4783:       auto Bcib = thrust::make_transform_iterator(Bcsr->column_indices->begin(), Shift(A->cmap->n));
4784:       auto Bcie = thrust::make_transform_iterator(Bcsr->column_indices->end(), Shift(A->cmap->n));
4785: #else
4786:       /* there are issues instantiating the merge operation using a transform iterator for the columns of B */
4787:       auto Bcib = Bcsr->column_indices->begin();
4788:       auto Bcie = Bcsr->column_indices->end();
4789:       thrust::transform(Bcib, Bcie, Bcib, Shift(A->cmap->n));
4790: #endif
4791:       auto wPerm = new THRUSTINTARRAY32(Annz + Bnnz);
4792:       auto Azb   = thrust::make_zip_iterator(thrust::make_tuple(Acoo->begin(), Acsr->column_indices->begin(), Acsr->values->begin(), Aperm));
4793:       auto Aze   = thrust::make_zip_iterator(thrust::make_tuple(Acoo->end(), Acsr->column_indices->end(), Acsr->values->end(), Aperm));
4794:       auto Bzb   = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->begin(), Bcib, Bcsr->values->begin(), Bperm));
4795:       auto Bze   = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->end(), Bcie, Bcsr->values->end(), Bperm));
4796:       auto Czb   = thrust::make_zip_iterator(thrust::make_tuple(Ccoo->begin(), Ccsr->column_indices->begin(), Ccsr->values->begin(), wPerm->begin()));
4797:       auto p1    = Ccusp->coords->begin();
4798:       auto p2    = Ccusp->coords->begin();
4799:       thrust::advance(p2, Annz);
4800:       PetscCallThrust(thrust::merge(thrust::device, Azb, Aze, Bzb, Bze, Czb, IJCompare4()));
4801: #if PETSC_PKG_CUDA_VERSION_LT(10, 0, 0)
4802:       thrust::transform(Bcib, Bcie, Bcib, Shift(-A->cmap->n));
4803: #endif
4804:       auto cci = thrust::make_counting_iterator(zero);
4805:       auto cce = thrust::make_counting_iterator(c->nz);
4806: #if 0 //Errors on SUMMIT cuda 11.1.0
4807:       PetscCallThrust(thrust::partition_copy(thrust::device,cci,cce,wPerm->begin(),p1,p2,thrust::identity<int>()));
4808: #else
4809:   #if PETSC_PKG_CUDA_VERSION_LT(12, 9, 0) || PetscDefined(HAVE_THRUST)
4810:       auto pred = thrust::identity<int>();
4811:   #else
4812:       auto pred = cuda::std::identity();
4813:   #endif
4814:       PetscCallThrust(thrust::copy_if(thrust::device, cci, cce, wPerm->begin(), p1, pred));
4815:       PetscCallThrust(thrust::remove_copy_if(thrust::device, cci, cce, wPerm->begin(), p2, pred));
4816: #endif
4817:       stat = cusparseXcoo2csr(Ccusp->handle, Ccoo->data().get(), c->nz, m, Ccsr->row_offsets->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4818:       PetscCallCUSPARSE(stat);
4819:       PetscCall(PetscLogGpuTimeEnd());
4820:       delete wPerm;
4821:       delete Acoo;
4822:       delete Bcoo;
4823:       delete Ccoo;
4824: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4825:       stat = cusparseCreateCsr(&Cmat->matDescr, Ccsr->num_rows, Ccsr->num_cols, Ccsr->num_entries, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
4826:       PetscCallCUSPARSE(stat);
4827: #endif
4828:       if (A->form_explicit_transpose && B->form_explicit_transpose) { /* if A and B have the transpose, generate C transpose too */
4829:         PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
4830:         PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
4831:         PetscBool                     AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4832:         Mat_SeqAIJCUSPARSEMultStruct *CmatT = new Mat_SeqAIJCUSPARSEMultStruct;
4833:         CsrMatrix                    *CcsrT = new CsrMatrix;
4834:         CsrMatrix                    *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4835:         CsrMatrix                    *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;

4837:         (*C)->form_explicit_transpose = PETSC_TRUE;
4838:         (*C)->transupdated            = PETSC_TRUE;
4839:         Ccusp->rowoffsets_gpu         = NULL;
4840:         CmatT->cprowIndices           = NULL;
4841:         CmatT->mat                    = CcsrT;
4842:         CcsrT->num_rows               = n;
4843:         CcsrT->num_cols               = m;
4844:         CcsrT->num_entries            = c->nz;

4846:         CcsrT->row_offsets    = new THRUSTINTARRAY32(n + 1);
4847:         CcsrT->column_indices = new THRUSTINTARRAY32(c->nz);
4848:         CcsrT->values         = new THRUSTARRAY(c->nz);

4850:         PetscCall(PetscLogGpuTimeBegin());
4851:         auto rT = CcsrT->row_offsets->begin();
4852:         if (AT) {
4853:           rT = thrust::copy(AcsrT->row_offsets->begin(), AcsrT->row_offsets->end(), rT);
4854:           thrust::advance(rT, -1);
4855:         }
4856:         if (BT) {
4857:           auto titb = thrust::make_transform_iterator(BcsrT->row_offsets->begin(), Shift(a->nz));
4858:           auto tite = thrust::make_transform_iterator(BcsrT->row_offsets->end(), Shift(a->nz));
4859:           thrust::copy(titb, tite, rT);
4860:         }
4861:         auto cT = CcsrT->column_indices->begin();
4862:         if (AT) cT = thrust::copy(AcsrT->column_indices->begin(), AcsrT->column_indices->end(), cT);
4863:         if (BT) thrust::copy(BcsrT->column_indices->begin(), BcsrT->column_indices->end(), cT);
4864:         auto vT = CcsrT->values->begin();
4865:         if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4866:         if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4867:         PetscCall(PetscLogGpuTimeEnd());

4869:         PetscCallCUSPARSE(cusparseCreateMatDescr(&CmatT->descr));
4870:         PetscCallCUSPARSE(cusparseSetMatIndexBase(CmatT->descr, CUSPARSE_INDEX_BASE_ZERO));
4871:         PetscCallCUSPARSE(cusparseSetMatType(CmatT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4872:         PetscCallCUDA(cudaMalloc((void **)&CmatT->alpha_one, sizeof(PetscScalar)));
4873:         PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_zero, sizeof(PetscScalar)));
4874:         PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_one, sizeof(PetscScalar)));
4875:         PetscCallCUDA(cudaMemcpy(CmatT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4876:         PetscCallCUDA(cudaMemcpy(CmatT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4877:         PetscCallCUDA(cudaMemcpy(CmatT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4878: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4879:         stat = cusparseCreateCsr(&CmatT->matDescr, CcsrT->num_rows, CcsrT->num_cols, CcsrT->num_entries, CcsrT->row_offsets->data().get(), CcsrT->column_indices->data().get(), CcsrT->values->data().get(), CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
4880:         PetscCallCUSPARSE(stat);
4881: #endif
4882:         Ccusp->matTranspose = CmatT;
4883:       }
4884:     }

4886:     c->free_a = PETSC_TRUE;
4887:     PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
4888:     PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4889:     c->free_ij = PETSC_TRUE;
4890:     if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
4891:       THRUSTINTARRAY ii(Ccsr->row_offsets->size());
4892:       THRUSTINTARRAY jj(Ccsr->column_indices->size());
4893:       ii = *Ccsr->row_offsets;
4894:       jj = *Ccsr->column_indices;
4895:       PetscCallCUDA(cudaMemcpy(c->i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4896:       PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4897:     } else {
4898:       PetscCallCUDA(cudaMemcpy(c->i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4899:       PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4900:     }
4901:     PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
4902:     PetscCall(PetscMalloc1(m, &c->ilen));
4903:     PetscCall(PetscMalloc1(m, &c->imax));
4904:     c->maxnz         = c->nz;
4905:     c->nonzerorowcnt = 0;
4906:     c->rmax          = 0;
4907:     for (i = 0; i < m; i++) {
4908:       const PetscInt nn = c->i[i + 1] - c->i[i];
4909:       c->ilen[i] = c->imax[i] = nn;
4910:       c->nonzerorowcnt += (PetscInt)!!nn;
4911:       c->rmax = PetscMax(c->rmax, nn);
4912:     }
4913:     PetscCall(MatMarkDiagonal_SeqAIJ(*C));
4914:     PetscCall(PetscMalloc1(c->nz, &c->a));
4915:     (*C)->nonzerostate++;
4916:     PetscCall(PetscLayoutSetUp((*C)->rmap));
4917:     PetscCall(PetscLayoutSetUp((*C)->cmap));
4918:     Ccusp->nonzerostate = (*C)->nonzerostate;
4919:     (*C)->preallocated  = PETSC_TRUE;
4920:   } else {
4921:     PetscCheck((*C)->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Invalid number or rows %" PetscInt_FMT " != %" PetscInt_FMT, (*C)->rmap->n, B->rmap->n);
4922:     c = (Mat_SeqAIJ *)(*C)->data;
4923:     if (c->nz) {
4924:       Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4925:       PetscCheck(Ccusp->coords, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing coords");
4926:       PetscCheck(Ccusp->format != MAT_CUSPARSE_ELL && Ccusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4927:       PetscCheck(Ccusp->nonzerostate == (*C)->nonzerostate, PETSC_COMM_SELF, PETSC_ERR_COR, "Wrong nonzerostate");
4928:       PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4929:       PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4930:       PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4931:       PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4932:       Acsr = (CsrMatrix *)Acusp->mat->mat;
4933:       Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4934:       Ccsr = (CsrMatrix *)Ccusp->mat->mat;
4935:       PetscCheck(Acsr->num_entries == (PetscInt)Acsr->values->size(), PETSC_COMM_SELF, PETSC_ERR_COR, "A nnz %" PetscInt_FMT " != %" PetscInt_FMT, Acsr->num_entries, (PetscInt)Acsr->values->size());
4936:       PetscCheck(Bcsr->num_entries == (PetscInt)Bcsr->values->size(), PETSC_COMM_SELF, PETSC_ERR_COR, "B nnz %" PetscInt_FMT " != %" PetscInt_FMT, Bcsr->num_entries, (PetscInt)Bcsr->values->size());
4937:       PetscCheck(Ccsr->num_entries == (PetscInt)Ccsr->values->size(), PETSC_COMM_SELF, PETSC_ERR_COR, "C nnz %" PetscInt_FMT " != %" PetscInt_FMT, Ccsr->num_entries, (PetscInt)Ccsr->values->size());
4938:       PetscCheck(Ccsr->num_entries == Acsr->num_entries + Bcsr->num_entries, PETSC_COMM_SELF, PETSC_ERR_COR, "C nnz %" PetscInt_FMT " != %" PetscInt_FMT " + %" PetscInt_FMT, Ccsr->num_entries, Acsr->num_entries, Bcsr->num_entries);
4939:       PetscCheck(Ccusp->coords->size() == Ccsr->values->size(), PETSC_COMM_SELF, PETSC_ERR_COR, "permSize %" PetscInt_FMT " != %" PetscInt_FMT, (PetscInt)Ccusp->coords->size(), (PetscInt)Ccsr->values->size());
4940:       auto pmid = Ccusp->coords->begin();
4941:       thrust::advance(pmid, Acsr->num_entries);
4942:       PetscCall(PetscLogGpuTimeBegin());
4943:       auto zibait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->begin())));
4944:       auto zieait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4945:       thrust::for_each(zibait, zieait, VecCUDAEquals());
4946:       auto zibbit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4947:       auto ziebit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->end())));
4948:       thrust::for_each(zibbit, ziebit, VecCUDAEquals());
4949:       PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(*C, PETSC_FALSE));
4950:       if (A->form_explicit_transpose && B->form_explicit_transpose && (*C)->form_explicit_transpose) {
4951:         PetscCheck(Ccusp->matTranspose, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing transpose Mat_SeqAIJCUSPARSEMultStruct");
4952:         PetscBool  AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4953:         CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4954:         CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4955:         CsrMatrix *CcsrT = (CsrMatrix *)Ccusp->matTranspose->mat;
4956:         auto       vT    = CcsrT->values->begin();
4957:         if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4958:         if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4959:         (*C)->transupdated = PETSC_TRUE;
4960:       }
4961:       PetscCall(PetscLogGpuTimeEnd());
4962:     }
4963:   }
4964:   PetscCall(PetscObjectStateIncrease((PetscObject)*C));
4965:   (*C)->assembled     = PETSC_TRUE;
4966:   (*C)->was_assembled = PETSC_FALSE;
4967:   (*C)->offloadmask   = PETSC_OFFLOAD_GPU;
4968:   PetscFunctionReturn(PETSC_SUCCESS);
4969: }

4971: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
4972: {
4973:   bool               dmem;
4974:   const PetscScalar *av;

4976:   PetscFunctionBegin;
4977:   dmem = isCudaMem(v);
4978:   PetscCall(MatSeqAIJCUSPARSEGetArrayRead(A, &av));
4979:   if (n && idx) {
4980:     THRUSTINTARRAY widx(n);
4981:     widx.assign(idx, idx + n);
4982:     PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));

4984:     THRUSTARRAY                    *w = NULL;
4985:     thrust::device_ptr<PetscScalar> dv;
4986:     if (dmem) {
4987:       dv = thrust::device_pointer_cast(v);
4988:     } else {
4989:       w  = new THRUSTARRAY(n);
4990:       dv = w->data();
4991:     }
4992:     thrust::device_ptr<const PetscScalar> dav = thrust::device_pointer_cast(av);

4994:     auto zibit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.begin()), dv));
4995:     auto zieit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.end()), dv + n));
4996:     thrust::for_each(zibit, zieit, VecCUDAEquals());
4997:     if (w) PetscCallCUDA(cudaMemcpy(v, w->data().get(), n * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
4998:     delete w;
4999:   } else {
5000:     PetscCallCUDA(cudaMemcpy(v, av, n * sizeof(PetscScalar), dmem ? cudaMemcpyDeviceToDevice : cudaMemcpyDeviceToHost));
5001:   }
5002:   if (!dmem) PetscCall(PetscLogCpuToGpu(n * sizeof(PetscScalar)));
5003:   PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(A, &av));
5004:   PetscFunctionReturn(PETSC_SUCCESS);
5005: }