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 NVIDIA 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 performing 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);
2491: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2492: mat->column_indices->assign(a->j, a->j + nnz);
2494: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2495: if (a->a) mat->values->assign(a->a, a->a + nnz);
2497: /* assign the pointer */
2498: matstruct->mat = mat;
2499: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2500: if (mat->num_rows) { /* cusparse errors on empty matrices! */
2501: 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 */
2502: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2503: PetscCallCUSPARSE(stat);
2504: }
2505: #endif
2506: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
2507: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2508: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
2509: #else
2510: CsrMatrix *mat = new CsrMatrix;
2511: mat->num_rows = m;
2512: mat->num_cols = A->cmap->n;
2513: mat->num_entries = nnz;
2514: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2515: mat->row_offsets->assign(ii, ii + m + 1);
2517: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2518: mat->column_indices->assign(a->j, a->j + nnz);
2520: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2521: if (a->a) mat->values->assign(a->a, a->a + nnz);
2523: cusparseHybMat_t hybMat;
2524: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
2525: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
2526: 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);
2527: PetscCallCUSPARSE(stat);
2528: /* assign the pointer */
2529: matstruct->mat = hybMat;
2531: if (mat) {
2532: if (mat->values) delete (THRUSTARRAY *)mat->values;
2533: if (mat->column_indices) delete (THRUSTINTARRAY32 *)mat->column_indices;
2534: if (mat->row_offsets) delete (THRUSTINTARRAY32 *)mat->row_offsets;
2535: delete (CsrMatrix *)mat;
2536: }
2537: #endif
2538: }
2540: /* assign the compressed row indices */
2541: if (a->compressedrow.use) {
2542: PetscCallCXX(cusparsestruct->workVector = new THRUSTARRAY(m));
2543: PetscCallCXX(matstruct->cprowIndices = new THRUSTINTARRAY(m));
2544: matstruct->cprowIndices->assign(ridx, ridx + m);
2545: tmp = m;
2546: } else {
2547: cusparsestruct->workVector = NULL;
2548: matstruct->cprowIndices = NULL;
2549: tmp = 0;
2550: }
2551: PetscCall(PetscLogCpuToGpu(((m + 1) + (a->nz)) * sizeof(int) + tmp * sizeof(PetscInt) + (3 + (a->nz)) * sizeof(PetscScalar)));
2553: /* assign the pointer */
2554: cusparsestruct->mat = matstruct;
2555: } catch (char *ex) {
2556: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
2557: }
2558: PetscCallCUDA(WaitForCUDA());
2559: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2560: cusparsestruct->nonzerostate = A->nonzerostate;
2561: }
2562: if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
2563: }
2564: PetscFunctionReturn(PETSC_SUCCESS);
2565: }
2567: struct VecCUDAPlusEquals {
2568: template <typename Tuple>
2569: __host__ __device__ void operator()(Tuple t)
2570: {
2571: thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t);
2572: }
2573: };
2575: struct VecCUDAEquals {
2576: template <typename Tuple>
2577: __host__ __device__ void operator()(Tuple t)
2578: {
2579: thrust::get<1>(t) = thrust::get<0>(t);
2580: }
2581: };
2583: struct VecCUDAEqualsReverse {
2584: template <typename Tuple>
2585: __host__ __device__ void operator()(Tuple t)
2586: {
2587: thrust::get<0>(t) = thrust::get<1>(t);
2588: }
2589: };
2591: struct MatMatCusparse {
2592: PetscBool cisdense;
2593: PetscScalar *Bt;
2594: Mat X;
2595: PetscBool reusesym; /* Cusparse does not have split symbolic and numeric phases for sparse matmat operations */
2596: PetscLogDouble flops;
2597: CsrMatrix *Bcsr;
2599: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2600: cusparseSpMatDescr_t matSpBDescr;
2601: PetscBool initialized; /* C = alpha op(A) op(B) + beta C */
2602: cusparseDnMatDescr_t matBDescr;
2603: cusparseDnMatDescr_t matCDescr;
2604: PetscInt Blda, Clda; /* Record leading dimensions of B and C here to detect changes*/
2605: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2606: void *dBuffer4;
2607: void *dBuffer5;
2608: #endif
2609: size_t mmBufferSize;
2610: void *mmBuffer;
2611: void *mmBuffer2; /* SpGEMM WorkEstimation buffer */
2612: cusparseSpGEMMDescr_t spgemmDesc;
2613: #endif
2614: };
2616: static PetscErrorCode MatDestroy_MatMatCusparse(void *data)
2617: {
2618: MatMatCusparse *mmdata = (MatMatCusparse *)data;
2620: PetscFunctionBegin;
2621: PetscCallCUDA(cudaFree(mmdata->Bt));
2622: delete mmdata->Bcsr;
2623: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2624: if (mmdata->matSpBDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mmdata->matSpBDescr));
2625: if (mmdata->matBDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2626: if (mmdata->matCDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2627: if (mmdata->spgemmDesc) PetscCallCUSPARSE(cusparseSpGEMM_destroyDescr(mmdata->spgemmDesc));
2628: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2629: if (mmdata->dBuffer4) PetscCallCUDA(cudaFree(mmdata->dBuffer4));
2630: if (mmdata->dBuffer5) PetscCallCUDA(cudaFree(mmdata->dBuffer5));
2631: #endif
2632: if (mmdata->mmBuffer) PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2633: if (mmdata->mmBuffer2) PetscCallCUDA(cudaFree(mmdata->mmBuffer2));
2634: #endif
2635: PetscCall(MatDestroy(&mmdata->X));
2636: PetscCall(PetscFree(data));
2637: PetscFunctionReturn(PETSC_SUCCESS);
2638: }
2640: #include <../src/mat/impls/dense/seq/dense.h>
2642: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2643: {
2644: Mat_Product *product = C->product;
2645: Mat A, B;
2646: PetscInt m, n, blda, clda;
2647: PetscBool flg, biscuda;
2648: Mat_SeqAIJCUSPARSE *cusp;
2649: cusparseStatus_t stat;
2650: cusparseOperation_t opA;
2651: const PetscScalar *barray;
2652: PetscScalar *carray;
2653: MatMatCusparse *mmdata;
2654: Mat_SeqAIJCUSPARSEMultStruct *mat;
2655: CsrMatrix *csrmat;
2657: PetscFunctionBegin;
2658: MatCheckProduct(C, 1);
2659: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2660: mmdata = (MatMatCusparse *)product->data;
2661: A = product->A;
2662: B = product->B;
2663: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2664: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2665: /* currently CopyToGpu does not copy if the matrix is bound to CPU
2666: Instead of silently accepting the wrong answer, I prefer to raise the error */
2667: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2668: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2669: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2670: switch (product->type) {
2671: case MATPRODUCT_AB:
2672: case MATPRODUCT_PtAP:
2673: mat = cusp->mat;
2674: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2675: m = A->rmap->n;
2676: n = B->cmap->n;
2677: break;
2678: case MATPRODUCT_AtB:
2679: if (!A->form_explicit_transpose) {
2680: mat = cusp->mat;
2681: opA = CUSPARSE_OPERATION_TRANSPOSE;
2682: } else {
2683: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
2684: mat = cusp->matTranspose;
2685: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2686: }
2687: m = A->cmap->n;
2688: n = B->cmap->n;
2689: break;
2690: case MATPRODUCT_ABt:
2691: case MATPRODUCT_RARt:
2692: mat = cusp->mat;
2693: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2694: m = A->rmap->n;
2695: n = B->rmap->n;
2696: break;
2697: default:
2698: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2699: }
2700: PetscCheck(mat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing Mat_SeqAIJCUSPARSEMultStruct");
2701: csrmat = (CsrMatrix *)mat->mat;
2702: /* if the user passed a CPU matrix, copy the data to the GPU */
2703: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSECUDA, &biscuda));
2704: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSECUDA, MAT_INPLACE_MATRIX, &B));
2705: PetscCall(MatDenseGetArrayReadAndMemType(B, &barray, nullptr));
2707: PetscCall(MatDenseGetLDA(B, &blda));
2708: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2709: PetscCall(MatDenseGetArrayWriteAndMemType(mmdata->X, &carray, nullptr));
2710: PetscCall(MatDenseGetLDA(mmdata->X, &clda));
2711: } else {
2712: PetscCall(MatDenseGetArrayWriteAndMemType(C, &carray, nullptr));
2713: PetscCall(MatDenseGetLDA(C, &clda));
2714: }
2716: PetscCall(PetscLogGpuTimeBegin());
2717: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2718: cusparseOperation_t opB = (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE;
2719: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
2720: cusparseSpMatDescr_t &matADescr = mat->matDescr_SpMM[opA];
2721: #else
2722: cusparseSpMatDescr_t &matADescr = mat->matDescr;
2723: #endif
2725: /* (re)allocate mmBuffer if not initialized or LDAs are different */
2726: if (!mmdata->initialized || mmdata->Blda != blda || mmdata->Clda != clda) {
2727: size_t mmBufferSize;
2728: if (mmdata->initialized && mmdata->Blda != blda) {
2729: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2730: mmdata->matBDescr = NULL;
2731: }
2732: if (!mmdata->matBDescr) {
2733: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matBDescr, B->rmap->n, B->cmap->n, blda, (void *)barray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2734: mmdata->Blda = blda;
2735: }
2737: if (mmdata->initialized && mmdata->Clda != clda) {
2738: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2739: mmdata->matCDescr = NULL;
2740: }
2741: if (!mmdata->matCDescr) { /* matCDescr is for C or mmdata->X */
2742: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matCDescr, m, n, clda, (void *)carray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2743: mmdata->Clda = clda;
2744: }
2746: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
2747: if (matADescr) {
2748: PetscCallCUSPARSE(cusparseDestroySpMat(matADescr)); // Because I find I could not reuse matADescr. It could be a cusparse bug
2749: matADescr = NULL;
2750: }
2751: #endif
2753: if (!matADescr) {
2754: 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 */
2755: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2756: PetscCallCUSPARSE(stat);
2757: }
2759: PetscCallCUSPARSE(cusparseSpMM_bufferSize(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, &mmBufferSize));
2761: if ((mmdata->mmBuffer && mmdata->mmBufferSize < mmBufferSize) || !mmdata->mmBuffer) {
2762: PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2763: PetscCallCUDA(cudaMalloc(&mmdata->mmBuffer, mmBufferSize));
2764: mmdata->mmBufferSize = mmBufferSize;
2765: }
2767: #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
2768: PetscCallCUSPARSE(cusparseSpMM_preprocess(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2769: #endif
2771: mmdata->initialized = PETSC_TRUE;
2772: } else {
2773: /* to be safe, always update pointers of the mats */
2774: PetscCallCUSPARSE(cusparseSpMatSetValues(matADescr, csrmat->values->data().get()));
2775: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matBDescr, (void *)barray));
2776: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matCDescr, (void *)carray));
2777: }
2779: /* do cusparseSpMM, which supports transpose on B */
2780: PetscCallCUSPARSE(cusparseSpMM(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2781: #else
2782: PetscInt k;
2783: /* cusparseXcsrmm does not support transpose on B */
2784: if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) {
2785: cublasHandle_t cublasv2handle;
2786: cublasStatus_t cerr;
2788: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
2789: 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);
2790: PetscCallCUBLAS(cerr);
2791: blda = B->cmap->n;
2792: k = B->cmap->n;
2793: } else {
2794: k = B->rmap->n;
2795: }
2797: /* perform the MatMat operation, op(A) is m x k, op(B) is k x n */
2798: 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);
2799: PetscCallCUSPARSE(stat);
2800: #endif
2801: PetscCall(PetscLogGpuTimeEnd());
2802: PetscCall(PetscLogGpuFlops(n * 2.0 * csrmat->num_entries));
2803: PetscCall(MatDenseRestoreArrayReadAndMemType(B, &barray));
2804: if (product->type == MATPRODUCT_RARt) {
2805: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2806: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_FALSE, PETSC_FALSE));
2807: } else if (product->type == MATPRODUCT_PtAP) {
2808: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2809: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_TRUE, PETSC_FALSE));
2810: } else {
2811: PetscCall(MatDenseRestoreArrayWriteAndMemType(C, &carray));
2812: }
2813: if (mmdata->cisdense) PetscCall(MatConvert(C, MATSEQDENSE, MAT_INPLACE_MATRIX, &C));
2814: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
2815: PetscFunctionReturn(PETSC_SUCCESS);
2816: }
2818: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2819: {
2820: Mat_Product *product = C->product;
2821: Mat A, B;
2822: PetscInt m, n;
2823: PetscBool cisdense, flg;
2824: MatMatCusparse *mmdata;
2825: Mat_SeqAIJCUSPARSE *cusp;
2827: PetscFunctionBegin;
2828: MatCheckProduct(C, 1);
2829: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
2830: A = product->A;
2831: B = product->B;
2832: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2833: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2834: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2835: PetscCheck(cusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2836: switch (product->type) {
2837: case MATPRODUCT_AB:
2838: m = A->rmap->n;
2839: n = B->cmap->n;
2840: PetscCall(MatSetBlockSizesFromMats(C, A, B));
2841: break;
2842: case MATPRODUCT_AtB:
2843: m = A->cmap->n;
2844: n = B->cmap->n;
2845: if (A->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->cmap->bs));
2846: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2847: break;
2848: case MATPRODUCT_ABt:
2849: m = A->rmap->n;
2850: n = B->rmap->n;
2851: if (A->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->rmap->bs));
2852: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2853: break;
2854: case MATPRODUCT_PtAP:
2855: m = B->cmap->n;
2856: n = B->cmap->n;
2857: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->cmap->bs));
2858: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2859: break;
2860: case MATPRODUCT_RARt:
2861: m = B->rmap->n;
2862: n = B->rmap->n;
2863: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->rmap->bs));
2864: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2865: break;
2866: default:
2867: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2868: }
2869: PetscCall(MatSetSizes(C, m, n, m, n));
2870: /* if C is of type MATSEQDENSE (CPU), perform the operation on the GPU and then copy on the CPU */
2871: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQDENSE, &cisdense));
2872: PetscCall(MatSetType(C, MATSEQDENSECUDA));
2874: /* product data */
2875: PetscCall(PetscNew(&mmdata));
2876: mmdata->cisdense = cisdense;
2877: #if PETSC_PKG_CUDA_VERSION_LT(11, 0, 0)
2878: /* cusparseXcsrmm does not support transpose on B, so we allocate buffer to store B^T */
2879: 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)));
2880: #endif
2881: /* for these products we need intermediate storage */
2882: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2883: PetscCall(MatCreate(PetscObjectComm((PetscObject)C), &mmdata->X));
2884: PetscCall(MatSetType(mmdata->X, MATSEQDENSECUDA));
2885: if (product->type == MATPRODUCT_RARt) { /* do not preallocate, since the first call to MatDenseCUDAGetArray will preallocate on the GPU for us */
2886: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->rmap->n, A->rmap->n, B->rmap->n));
2887: } else {
2888: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->cmap->n, A->rmap->n, B->cmap->n));
2889: }
2890: }
2891: C->product->data = mmdata;
2892: C->product->destroy = MatDestroy_MatMatCusparse;
2894: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA;
2895: PetscFunctionReturn(PETSC_SUCCESS);
2896: }
2898: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
2899: {
2900: Mat_Product *product = C->product;
2901: Mat A, B;
2902: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
2903: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2904: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
2905: CsrMatrix *Acsr, *Bcsr, *Ccsr;
2906: PetscBool flg;
2907: cusparseStatus_t stat;
2908: MatProductType ptype;
2909: MatMatCusparse *mmdata;
2910: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2911: cusparseSpMatDescr_t BmatSpDescr;
2912: #endif
2913: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
2915: PetscFunctionBegin;
2916: MatCheckProduct(C, 1);
2917: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2918: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQAIJCUSPARSE, &flg));
2919: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for C of type %s", ((PetscObject)C)->type_name);
2920: mmdata = (MatMatCusparse *)C->product->data;
2921: A = product->A;
2922: B = product->B;
2923: if (mmdata->reusesym) { /* this happens when api_user is true, meaning that the matrix values have been already computed in the MatProductSymbolic phase */
2924: mmdata->reusesym = PETSC_FALSE;
2925: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2926: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2927: Cmat = Ccusp->mat;
2928: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[C->product->type]);
2929: Ccsr = (CsrMatrix *)Cmat->mat;
2930: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2931: goto finalize;
2932: }
2933: if (!c->nz) goto finalize;
2934: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2935: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2936: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
2937: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
2938: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2939: PetscCheck(!B->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2940: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2941: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
2942: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2943: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2944: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2945: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2946: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2947: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
2949: ptype = product->type;
2950: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
2951: ptype = MATPRODUCT_AB;
2952: 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");
2953: }
2954: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
2955: ptype = MATPRODUCT_AB;
2956: 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");
2957: }
2958: switch (ptype) {
2959: case MATPRODUCT_AB:
2960: Amat = Acusp->mat;
2961: Bmat = Bcusp->mat;
2962: break;
2963: case MATPRODUCT_AtB:
2964: Amat = Acusp->matTranspose;
2965: Bmat = Bcusp->mat;
2966: break;
2967: case MATPRODUCT_ABt:
2968: Amat = Acusp->mat;
2969: Bmat = Bcusp->matTranspose;
2970: break;
2971: default:
2972: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2973: }
2974: Cmat = Ccusp->mat;
2975: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
2976: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
2977: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[ptype]);
2978: Acsr = (CsrMatrix *)Amat->mat;
2979: Bcsr = mmdata->Bcsr ? mmdata->Bcsr : (CsrMatrix *)Bmat->mat; /* B may be in compressed row storage */
2980: Ccsr = (CsrMatrix *)Cmat->mat;
2981: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
2982: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
2983: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2984: PetscCall(PetscLogGpuTimeBegin());
2985: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2986: BmatSpDescr = mmdata->Bcsr ? mmdata->matSpBDescr : Bmat->matDescr; /* B may be in compressed row storage */
2987: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
2988: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2989: 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);
2990: PetscCallCUSPARSE(stat);
2991: #else
2992: 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);
2993: PetscCallCUSPARSE(stat);
2994: 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);
2995: PetscCallCUSPARSE(stat);
2996: #endif
2997: #else
2998: 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,
2999: 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());
3000: PetscCallCUSPARSE(stat);
3001: #endif
3002: PetscCall(PetscLogGpuFlops(mmdata->flops));
3003: PetscCallCUDA(WaitForCUDA());
3004: PetscCall(PetscLogGpuTimeEnd());
3005: C->offloadmask = PETSC_OFFLOAD_GPU;
3006: finalize:
3007: /* shorter version of MatAssemblyEnd_SeqAIJ */
3008: 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));
3009: PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
3010: PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
3011: c->reallocs = 0;
3012: C->info.mallocs += 0;
3013: C->info.nz_unneeded = 0;
3014: C->assembled = C->was_assembled = PETSC_TRUE;
3015: C->num_ass++;
3016: PetscFunctionReturn(PETSC_SUCCESS);
3017: }
3019: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
3020: {
3021: Mat_Product *product = C->product;
3022: Mat A, B;
3023: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
3024: Mat_SeqAIJ *a, *b, *c;
3025: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
3026: CsrMatrix *Acsr, *Bcsr, *Ccsr;
3027: PetscInt i, j, m, n, k;
3028: PetscBool flg;
3029: cusparseStatus_t stat;
3030: MatProductType ptype;
3031: MatMatCusparse *mmdata;
3032: PetscLogDouble flops;
3033: PetscBool biscompressed, ciscompressed;
3034: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3035: int64_t C_num_rows1, C_num_cols1, C_nnz1;
3036: cusparseSpMatDescr_t BmatSpDescr;
3037: #else
3038: int cnz;
3039: #endif
3040: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
3042: PetscFunctionBegin;
3043: MatCheckProduct(C, 1);
3044: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
3045: A = product->A;
3046: B = product->B;
3047: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
3048: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
3049: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
3050: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
3051: a = (Mat_SeqAIJ *)A->data;
3052: b = (Mat_SeqAIJ *)B->data;
3053: /* product data */
3054: PetscCall(PetscNew(&mmdata));
3055: C->product->data = mmdata;
3056: C->product->destroy = MatDestroy_MatMatCusparse;
3058: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3059: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
3060: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr; /* Access spptr after MatSeqAIJCUSPARSECopyToGPU, not before */
3061: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
3062: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3063: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3065: ptype = product->type;
3066: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
3067: ptype = MATPRODUCT_AB;
3068: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
3069: }
3070: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
3071: ptype = MATPRODUCT_AB;
3072: product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
3073: }
3074: biscompressed = PETSC_FALSE;
3075: ciscompressed = PETSC_FALSE;
3076: switch (ptype) {
3077: case MATPRODUCT_AB:
3078: m = A->rmap->n;
3079: n = B->cmap->n;
3080: k = A->cmap->n;
3081: Amat = Acusp->mat;
3082: Bmat = Bcusp->mat;
3083: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3084: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3085: break;
3086: case MATPRODUCT_AtB:
3087: m = A->cmap->n;
3088: n = B->cmap->n;
3089: k = A->rmap->n;
3090: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3091: Amat = Acusp->matTranspose;
3092: Bmat = Bcusp->mat;
3093: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3094: break;
3095: case MATPRODUCT_ABt:
3096: m = A->rmap->n;
3097: n = B->rmap->n;
3098: k = A->cmap->n;
3099: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
3100: Amat = Acusp->mat;
3101: Bmat = Bcusp->matTranspose;
3102: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3103: break;
3104: default:
3105: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
3106: }
3108: /* create cusparse matrix */
3109: PetscCall(MatSetSizes(C, m, n, m, n));
3110: PetscCall(MatSetType(C, MATSEQAIJCUSPARSE));
3111: c = (Mat_SeqAIJ *)C->data;
3112: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
3113: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
3114: Ccsr = new CsrMatrix;
3116: c->compressedrow.use = ciscompressed;
3117: if (c->compressedrow.use) { /* if a is in compressed row, than c will be in compressed row format */
3118: c->compressedrow.nrows = a->compressedrow.nrows;
3119: PetscCall(PetscMalloc2(c->compressedrow.nrows + 1, &c->compressedrow.i, c->compressedrow.nrows, &c->compressedrow.rindex));
3120: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, c->compressedrow.nrows));
3121: Ccusp->workVector = new THRUSTARRAY(c->compressedrow.nrows);
3122: Cmat->cprowIndices = new THRUSTINTARRAY(c->compressedrow.nrows);
3123: Cmat->cprowIndices->assign(c->compressedrow.rindex, c->compressedrow.rindex + c->compressedrow.nrows);
3124: } else {
3125: c->compressedrow.nrows = 0;
3126: c->compressedrow.i = NULL;
3127: c->compressedrow.rindex = NULL;
3128: Ccusp->workVector = NULL;
3129: Cmat->cprowIndices = NULL;
3130: }
3131: Ccusp->nrows = ciscompressed ? c->compressedrow.nrows : m;
3132: Ccusp->mat = Cmat;
3133: Ccusp->mat->mat = Ccsr;
3134: Ccsr->num_rows = Ccusp->nrows;
3135: Ccsr->num_cols = n;
3136: Ccsr->row_offsets = new THRUSTINTARRAY32(Ccusp->nrows + 1);
3137: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
3138: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
3139: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
3140: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
3141: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
3142: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
3143: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3144: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3145: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3146: if (!Ccsr->num_rows || !Ccsr->num_cols || !a->nz || !b->nz) { /* cusparse raise errors in different calls when matrices have zero rows/columns! */
3147: PetscCallThrust(thrust::fill(thrust::device, Ccsr->row_offsets->begin(), Ccsr->row_offsets->end(), 0));
3148: c->nz = 0;
3149: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3150: Ccsr->values = new THRUSTARRAY(c->nz);
3151: goto finalizesym;
3152: }
3154: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
3155: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
3156: Acsr = (CsrMatrix *)Amat->mat;
3157: if (!biscompressed) {
3158: Bcsr = (CsrMatrix *)Bmat->mat;
3159: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3160: BmatSpDescr = Bmat->matDescr;
3161: #endif
3162: } else { /* we need to use row offsets for the full matrix */
3163: CsrMatrix *cBcsr = (CsrMatrix *)Bmat->mat;
3164: Bcsr = new CsrMatrix;
3165: Bcsr->num_rows = B->rmap->n;
3166: Bcsr->num_cols = cBcsr->num_cols;
3167: Bcsr->num_entries = cBcsr->num_entries;
3168: Bcsr->column_indices = cBcsr->column_indices;
3169: Bcsr->values = cBcsr->values;
3170: if (!Bcusp->rowoffsets_gpu) {
3171: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
3172: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
3173: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
3174: }
3175: Bcsr->row_offsets = Bcusp->rowoffsets_gpu;
3176: mmdata->Bcsr = Bcsr;
3177: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3178: if (Bcsr->num_rows && Bcsr->num_cols) {
3179: 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);
3180: PetscCallCUSPARSE(stat);
3181: }
3182: BmatSpDescr = mmdata->matSpBDescr;
3183: #endif
3184: }
3185: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
3186: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
3187: /* precompute flops count */
3188: if (ptype == MATPRODUCT_AB) {
3189: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3190: const PetscInt st = a->i[i];
3191: const PetscInt en = a->i[i + 1];
3192: for (j = st; j < en; j++) {
3193: const PetscInt brow = a->j[j];
3194: flops += 2. * (b->i[brow + 1] - b->i[brow]);
3195: }
3196: }
3197: } else if (ptype == MATPRODUCT_AtB) {
3198: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3199: const PetscInt anzi = a->i[i + 1] - a->i[i];
3200: const PetscInt bnzi = b->i[i + 1] - b->i[i];
3201: flops += (2. * anzi) * bnzi;
3202: }
3203: } else { /* TODO */
3204: flops = 0.;
3205: }
3207: mmdata->flops = flops;
3208: PetscCall(PetscLogGpuTimeBegin());
3210: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3211: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3212: // cuda-12.2 requires non-null csrRowOffsets
3213: 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);
3214: PetscCallCUSPARSE(stat);
3215: PetscCallCUSPARSE(cusparseSpGEMM_createDescr(&mmdata->spgemmDesc));
3216: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
3217: {
3218: /* cusparseSpGEMMreuse has more reasonable APIs than cusparseSpGEMM, so we prefer to use it.
3219: We follow the sample code at https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSPARSE/spgemm_reuse
3220: */
3221: void *dBuffer1 = NULL;
3222: void *dBuffer2 = NULL;
3223: void *dBuffer3 = NULL;
3224: /* dBuffer4, dBuffer5 are needed by cusparseSpGEMMreuse_compute, and therefore are stored in mmdata */
3225: size_t bufferSize1 = 0;
3226: size_t bufferSize2 = 0;
3227: size_t bufferSize3 = 0;
3228: size_t bufferSize4 = 0;
3229: size_t bufferSize5 = 0;
3231: /* ask bufferSize1 bytes for external memory */
3232: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, NULL);
3233: PetscCallCUSPARSE(stat);
3234: PetscCallCUDA(cudaMalloc((void **)&dBuffer1, bufferSize1));
3235: /* inspect the matrices A and B to understand the memory requirement for the next step */
3236: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, dBuffer1);
3237: PetscCallCUSPARSE(stat);
3239: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, NULL, &bufferSize3, NULL, &bufferSize4, NULL);
3240: PetscCallCUSPARSE(stat);
3241: PetscCallCUDA(cudaMalloc((void **)&dBuffer2, bufferSize2));
3242: PetscCallCUDA(cudaMalloc((void **)&dBuffer3, bufferSize3));
3243: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer4, bufferSize4));
3244: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, dBuffer2, &bufferSize3, dBuffer3, &bufferSize4, mmdata->dBuffer4);
3245: PetscCallCUSPARSE(stat);
3246: PetscCallCUDA(cudaFree(dBuffer1));
3247: PetscCallCUDA(cudaFree(dBuffer2));
3249: /* get matrix C non-zero entries C_nnz1 */
3250: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3251: c->nz = (PetscInt)C_nnz1;
3252: /* allocate matrix C */
3253: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3254: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3255: Ccsr->values = new THRUSTARRAY(c->nz);
3256: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3257: /* update matC with the new pointers */
3258: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3259: PetscCallCUSPARSE(stat);
3261: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, NULL);
3262: PetscCallCUSPARSE(stat);
3263: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer5, bufferSize5));
3264: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, mmdata->dBuffer5);
3265: PetscCallCUSPARSE(stat);
3266: PetscCallCUDA(cudaFree(dBuffer3));
3267: 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);
3268: PetscCallCUSPARSE(stat);
3269: 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));
3270: }
3271: #else
3272: size_t bufSize2;
3273: /* ask bufferSize bytes for external memory */
3274: 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);
3275: PetscCallCUSPARSE(stat);
3276: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer2, bufSize2));
3277: /* inspect the matrices A and B to understand the memory requirement for the next step */
3278: 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);
3279: PetscCallCUSPARSE(stat);
3280: /* ask bufferSize again bytes for external memory */
3281: 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);
3282: PetscCallCUSPARSE(stat);
3283: /* The CUSPARSE documentation is not clear, nor the API
3284: We need both buffers to perform the operations properly!
3285: mmdata->mmBuffer2 does not appear anywhere in the compute/copy API
3286: it only appears for the workEstimation stuff, but it seems it is needed in compute, so probably the address
3287: is stored in the descriptor! What a messy API... */
3288: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer, mmdata->mmBufferSize));
3289: /* compute the intermediate product of A * B */
3290: 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);
3291: PetscCallCUSPARSE(stat);
3292: /* get matrix C non-zero entries C_nnz1 */
3293: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3294: c->nz = (PetscInt)C_nnz1;
3295: 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,
3296: mmdata->mmBufferSize / 1024));
3297: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3298: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3299: Ccsr->values = new THRUSTARRAY(c->nz);
3300: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3301: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3302: PetscCallCUSPARSE(stat);
3303: 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);
3304: PetscCallCUSPARSE(stat);
3305: #endif // PETSC_PKG_CUDA_VERSION_GE(11,4,0)
3306: #else
3307: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_HOST));
3308: 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,
3309: Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->row_offsets->data().get(), &cnz);
3310: PetscCallCUSPARSE(stat);
3311: c->nz = cnz;
3312: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3313: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3314: Ccsr->values = new THRUSTARRAY(c->nz);
3315: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3317: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3318: /* with the old gemm interface (removed from 11.0 on) we cannot compute the symbolic factorization only.
3319: 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
3320: D is NULL, despite the fact that CUSPARSE documentation claims it is supported! */
3321: 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,
3322: 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());
3323: PetscCallCUSPARSE(stat);
3324: #endif
3325: PetscCall(PetscLogGpuFlops(mmdata->flops));
3326: PetscCall(PetscLogGpuTimeEnd());
3327: finalizesym:
3328: c->free_a = PETSC_TRUE;
3329: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
3330: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
3331: c->free_ij = PETSC_TRUE;
3332: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
3333: PetscInt *d_i = c->i;
3334: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
3335: THRUSTINTARRAY jj(Ccsr->column_indices->size());
3336: ii = *Ccsr->row_offsets;
3337: jj = *Ccsr->column_indices;
3338: if (ciscompressed) d_i = c->compressedrow.i;
3339: PetscCallCUDA(cudaMemcpy(d_i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3340: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3341: } else {
3342: PetscInt *d_i = c->i;
3343: if (ciscompressed) d_i = c->compressedrow.i;
3344: PetscCallCUDA(cudaMemcpy(d_i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3345: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3346: }
3347: if (ciscompressed) { /* need to expand host row offsets */
3348: PetscInt r = 0;
3349: c->i[0] = 0;
3350: for (k = 0; k < c->compressedrow.nrows; k++) {
3351: const PetscInt next = c->compressedrow.rindex[k];
3352: const PetscInt old = c->compressedrow.i[k];
3353: for (; r < next; r++) c->i[r + 1] = old;
3354: }
3355: for (; r < m; r++) c->i[r + 1] = c->compressedrow.i[c->compressedrow.nrows];
3356: }
3357: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
3358: PetscCall(PetscMalloc1(m, &c->ilen));
3359: PetscCall(PetscMalloc1(m, &c->imax));
3360: c->maxnz = c->nz;
3361: c->nonzerorowcnt = 0;
3362: c->rmax = 0;
3363: for (k = 0; k < m; k++) {
3364: const PetscInt nn = c->i[k + 1] - c->i[k];
3365: c->ilen[k] = c->imax[k] = nn;
3366: c->nonzerorowcnt += (PetscInt)!!nn;
3367: c->rmax = PetscMax(c->rmax, nn);
3368: }
3369: PetscCall(MatMarkDiagonal_SeqAIJ(C));
3370: PetscCall(PetscMalloc1(c->nz, &c->a));
3371: Ccsr->num_entries = c->nz;
3373: C->nonzerostate++;
3374: PetscCall(PetscLayoutSetUp(C->rmap));
3375: PetscCall(PetscLayoutSetUp(C->cmap));
3376: Ccusp->nonzerostate = C->nonzerostate;
3377: C->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
3378: C->preallocated = PETSC_TRUE;
3379: C->assembled = PETSC_FALSE;
3380: C->was_assembled = PETSC_FALSE;
3381: 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 */
3382: mmdata->reusesym = PETSC_TRUE;
3383: C->offloadmask = PETSC_OFFLOAD_GPU;
3384: }
3385: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3386: PetscFunctionReturn(PETSC_SUCCESS);
3387: }
3389: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat);
3391: /* handles sparse or dense B */
3392: static PetscErrorCode MatProductSetFromOptions_SeqAIJCUSPARSE(Mat mat)
3393: {
3394: Mat_Product *product = mat->product;
3395: PetscBool isdense = PETSC_FALSE, Biscusp = PETSC_FALSE, Ciscusp = PETSC_TRUE;
3397: PetscFunctionBegin;
3398: MatCheckProduct(mat, 1);
3399: PetscCall(PetscObjectBaseTypeCompare((PetscObject)product->B, MATSEQDENSE, &isdense));
3400: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJCUSPARSE, &Biscusp));
3401: if (product->type == MATPRODUCT_ABC) {
3402: Ciscusp = PETSC_FALSE;
3403: if (!product->C->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJCUSPARSE, &Ciscusp));
3404: }
3405: if (Biscusp && Ciscusp) { /* we can always select the CPU backend */
3406: PetscBool usecpu = PETSC_FALSE;
3407: switch (product->type) {
3408: case MATPRODUCT_AB:
3409: if (product->api_user) {
3410: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
3411: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3412: PetscOptionsEnd();
3413: } else {
3414: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
3415: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3416: PetscOptionsEnd();
3417: }
3418: break;
3419: case MATPRODUCT_AtB:
3420: if (product->api_user) {
3421: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
3422: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3423: PetscOptionsEnd();
3424: } else {
3425: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
3426: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3427: PetscOptionsEnd();
3428: }
3429: break;
3430: case MATPRODUCT_PtAP:
3431: if (product->api_user) {
3432: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
3433: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3434: PetscOptionsEnd();
3435: } else {
3436: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
3437: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3438: PetscOptionsEnd();
3439: }
3440: break;
3441: case MATPRODUCT_RARt:
3442: if (product->api_user) {
3443: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatRARt", "Mat");
3444: PetscCall(PetscOptionsBool("-matrart_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3445: PetscOptionsEnd();
3446: } else {
3447: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_RARt", "Mat");
3448: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3449: PetscOptionsEnd();
3450: }
3451: break;
3452: case MATPRODUCT_ABC:
3453: if (product->api_user) {
3454: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMatMult", "Mat");
3455: PetscCall(PetscOptionsBool("-matmatmatmult_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3456: PetscOptionsEnd();
3457: } else {
3458: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_ABC", "Mat");
3459: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3460: PetscOptionsEnd();
3461: }
3462: break;
3463: default:
3464: break;
3465: }
3466: if (usecpu) Biscusp = Ciscusp = PETSC_FALSE;
3467: }
3468: /* dispatch */
3469: if (isdense) {
3470: switch (product->type) {
3471: case MATPRODUCT_AB:
3472: case MATPRODUCT_AtB:
3473: case MATPRODUCT_ABt:
3474: case MATPRODUCT_PtAP:
3475: case MATPRODUCT_RARt:
3476: if (product->A->boundtocpu) {
3477: PetscCall(MatProductSetFromOptions_SeqAIJ_SeqDense(mat));
3478: } else {
3479: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA;
3480: }
3481: break;
3482: case MATPRODUCT_ABC:
3483: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3484: break;
3485: default:
3486: break;
3487: }
3488: } else if (Biscusp && Ciscusp) {
3489: switch (product->type) {
3490: case MATPRODUCT_AB:
3491: case MATPRODUCT_AtB:
3492: case MATPRODUCT_ABt:
3493: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3494: break;
3495: case MATPRODUCT_PtAP:
3496: case MATPRODUCT_RARt:
3497: case MATPRODUCT_ABC:
3498: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3499: break;
3500: default:
3501: break;
3502: }
3503: } else { /* fallback for AIJ */
3504: PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
3505: }
3506: PetscFunctionReturn(PETSC_SUCCESS);
3507: }
3509: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3510: {
3511: PetscFunctionBegin;
3512: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_FALSE, PETSC_FALSE));
3513: PetscFunctionReturn(PETSC_SUCCESS);
3514: }
3516: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3517: {
3518: PetscFunctionBegin;
3519: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_FALSE, PETSC_FALSE));
3520: PetscFunctionReturn(PETSC_SUCCESS);
3521: }
3523: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3524: {
3525: PetscFunctionBegin;
3526: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_TRUE));
3527: PetscFunctionReturn(PETSC_SUCCESS);
3528: }
3530: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3531: {
3532: PetscFunctionBegin;
3533: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_TRUE));
3534: PetscFunctionReturn(PETSC_SUCCESS);
3535: }
3537: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3538: {
3539: PetscFunctionBegin;
3540: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_FALSE));
3541: PetscFunctionReturn(PETSC_SUCCESS);
3542: }
3544: __global__ static void ScatterAdd(PetscInt n, PetscInt *idx, const PetscScalar *x, PetscScalar *y)
3545: {
3546: int i = blockIdx.x * blockDim.x + threadIdx.x;
3547: if (i < n) y[idx[i]] += x[i];
3548: }
3550: /* z = op(A) x + y. If trans & !herm, op = ^T; if trans & herm, op = ^H; if !trans, op = no-op */
3551: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz, PetscBool trans, PetscBool herm)
3552: {
3553: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3554: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3555: Mat_SeqAIJCUSPARSEMultStruct *matstruct;
3556: PetscScalar *xarray, *zarray, *dptr, *beta, *xptr;
3557: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
3558: PetscBool compressed;
3559: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3560: PetscInt nx, ny;
3561: #endif
3563: PetscFunctionBegin;
3564: PetscCheck(!herm || trans, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Hermitian and not transpose not supported");
3565: if (!a->nz) {
3566: if (yy) PetscCall(VecSeq_CUDA::Copy(yy, zz));
3567: else PetscCall(VecSeq_CUDA::Set(zz, 0));
3568: PetscFunctionReturn(PETSC_SUCCESS);
3569: }
3570: /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
3571: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3572: if (!trans) {
3573: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3574: PetscCheck(matstruct, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "SeqAIJCUSPARSE does not have a 'mat' (need to fix)");
3575: } else {
3576: if (herm || !A->form_explicit_transpose) {
3577: opA = herm ? CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
3578: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3579: } else {
3580: if (!cusparsestruct->matTranspose) PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3581: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
3582: }
3583: }
3584: /* Does the matrix use compressed rows (i.e., drop zero rows)? */
3585: compressed = matstruct->cprowIndices ? PETSC_TRUE : PETSC_FALSE;
3587: try {
3588: PetscCall(VecCUDAGetArrayRead(xx, (const PetscScalar **)&xarray));
3589: if (yy == zz) PetscCall(VecCUDAGetArray(zz, &zarray)); /* read & write zz, so need to get up-to-date zarray on GPU */
3590: else PetscCall(VecCUDAGetArrayWrite(zz, &zarray)); /* write zz, so no need to init zarray on GPU */
3592: PetscCall(PetscLogGpuTimeBegin());
3593: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3594: /* z = A x + beta y.
3595: If A is compressed (with less rows), then Ax is shorter than the full z, so we need a work vector to store Ax.
3596: When A is non-compressed, and z = y, we can set beta=1 to compute y = Ax + y in one call.
3597: */
3598: xptr = xarray;
3599: dptr = compressed ? cusparsestruct->workVector->data().get() : zarray;
3600: beta = (yy == zz && !compressed) ? matstruct->beta_one : matstruct->beta_zero;
3601: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3602: /* Get length of x, y for y=Ax. ny might be shorter than the work vector's allocated length, since the work vector is
3603: allocated to accommodate different uses. So we get the length info directly from mat.
3604: */
3605: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3606: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3607: nx = mat->num_cols; // since y = Ax
3608: ny = mat->num_rows;
3609: }
3610: #endif
3611: } else {
3612: /* z = A^T x + beta y
3613: If A is compressed, then we need a work vector as the shorter version of x to compute A^T x.
3614: Note A^Tx is of full length, so we set beta to 1.0 if y exists.
3615: */
3616: xptr = compressed ? cusparsestruct->workVector->data().get() : xarray;
3617: dptr = zarray;
3618: beta = yy ? matstruct->beta_one : matstruct->beta_zero;
3619: if (compressed) { /* Scatter x to work vector */
3620: thrust::device_ptr<PetscScalar> xarr = thrust::device_pointer_cast(xarray);
3622: thrust::for_each(
3623: #if PetscDefined(HAVE_THRUST_ASYNC)
3624: thrust::cuda::par.on(PetscDefaultCudaStream),
3625: #endif
3626: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))),
3627: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))) + matstruct->cprowIndices->size(), VecCUDAEqualsReverse());
3628: }
3629: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3630: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3631: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3632: nx = mat->num_rows; // since y = A^T x
3633: ny = mat->num_cols;
3634: }
3635: #endif
3636: }
3638: /* csr_spmv does y = alpha op(A) x + beta y */
3639: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3640: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3641: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3642: 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.
3643: #else
3644: cusparseSpMatDescr_t &matDescr = matstruct->matDescr;
3645: #endif
3647: 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");
3648: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3649: if (!matDescr) {
3650: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3651: 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));
3652: }
3653: #endif
3655: if (!matstruct->cuSpMV[opA].initialized) { /* built on demand */
3656: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecXDescr, nx, xptr, cusparse_scalartype));
3657: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecYDescr, ny, dptr, cusparse_scalartype));
3658: PetscCallCUSPARSE(
3659: 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));
3660: PetscCallCUDA(cudaMalloc(&matstruct->cuSpMV[opA].spmvBuffer, matstruct->cuSpMV[opA].spmvBufferSize));
3661: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // cusparseSpMV_preprocess is added in 12.4
3662: PetscCallCUSPARSE(
3663: 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));
3664: #endif
3665: matstruct->cuSpMV[opA].initialized = PETSC_TRUE;
3666: } else {
3667: /* x, y's value pointers might change between calls, but their shape is kept, so we just update pointers */
3668: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecXDescr, xptr));
3669: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecYDescr, dptr));
3670: }
3672: 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));
3673: #else
3674: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3675: 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));
3676: #endif
3677: } else {
3678: if (cusparsestruct->nrows) {
3679: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3680: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
3681: #else
3682: cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat;
3683: PetscCallCUSPARSE(cusparse_hyb_spmv(cusparsestruct->handle, opA, matstruct->alpha_one, matstruct->descr, hybMat, xptr, beta, dptr));
3684: #endif
3685: }
3686: }
3687: PetscCall(PetscLogGpuTimeEnd());
3689: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3690: if (yy) { /* MatMultAdd: zz = A*xx + yy */
3691: if (compressed) { /* A is compressed. We first copy yy to zz, then ScatterAdd the work vector to zz */
3692: PetscCall(VecSeq_CUDA::Copy(yy, zz)); /* zz = yy */
3693: } else if (zz != yy) { /* A is not compressed. zz already contains A*xx, and we just need to add yy */
3694: PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3695: }
3696: } else if (compressed) { /* MatMult: zz = A*xx. A is compressed, so we zero zz first, then ScatterAdd the work vector to zz */
3697: PetscCall(VecSeq_CUDA::Set(zz, 0));
3698: }
3700: /* ScatterAdd the result from work vector into the full vector when A is compressed */
3701: if (compressed) {
3702: PetscCall(PetscLogGpuTimeBegin());
3703: PetscInt n = (PetscInt)matstruct->cprowIndices->size();
3704: ScatterAdd<<<(int)((n + 255) / 256), 256, 0, PetscDefaultCudaStream>>>(n, matstruct->cprowIndices->data().get(), cusparsestruct->workVector->data().get(), zarray);
3705: PetscCall(PetscLogGpuTimeEnd());
3706: }
3707: } else {
3708: if (yy && yy != zz) PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3709: }
3710: PetscCall(VecCUDARestoreArrayRead(xx, (const PetscScalar **)&xarray));
3711: if (yy == zz) PetscCall(VecCUDARestoreArray(zz, &zarray));
3712: else PetscCall(VecCUDARestoreArrayWrite(zz, &zarray));
3713: } catch (char *ex) {
3714: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
3715: }
3716: if (yy) {
3717: PetscCall(PetscLogGpuFlops(2.0 * a->nz));
3718: } else {
3719: PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
3720: }
3721: PetscFunctionReturn(PETSC_SUCCESS);
3722: }
3724: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3725: {
3726: PetscFunctionBegin;
3727: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_FALSE));
3728: PetscFunctionReturn(PETSC_SUCCESS);
3729: }
3731: PETSC_INTERN PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec xx);
3733: __global__ static void GetDiagonal_CSR(const int *row, const int *col, const PetscScalar *val, const PetscInt len, PetscScalar *diag)
3734: {
3735: const size_t x = blockIdx.x * blockDim.x + threadIdx.x;
3737: if (x < len) {
3738: const PetscInt rowx = row[x], num_non0_row = row[x + 1] - rowx;
3739: PetscScalar d = 0.0;
3741: for (PetscInt i = 0; i < num_non0_row; i++) {
3742: if (col[i + rowx] == x) {
3743: d = val[i + rowx];
3744: break;
3745: }
3746: }
3747: diag[x] = d;
3748: }
3749: }
3751: static PetscErrorCode MatGetDiagonal_SeqAIJCUSPARSE(Mat A, Vec diag)
3752: {
3753: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3754: Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3755: PetscScalar *darray;
3757: PetscFunctionBegin;
3758: if (A->offloadmask == PETSC_OFFLOAD_BOTH || A->offloadmask == PETSC_OFFLOAD_GPU) {
3759: PetscInt n = A->rmap->n;
3760: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3762: PetscCheck(cusparsestruct->format == MAT_CUSPARSE_CSR, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only CSR format supported");
3763: if (n > 0) {
3764: PetscCall(VecCUDAGetArrayWrite(diag, &darray));
3765: GetDiagonal_CSR<<<(int)((n + 255) / 256), 256, 0, PetscDefaultCudaStream>>>(mat->row_offsets->data().get(), mat->column_indices->data().get(), mat->values->data().get(), n, darray);
3766: PetscCallCUDA(cudaPeekAtLastError());
3767: PetscCall(VecCUDARestoreArrayWrite(diag, &darray));
3768: }
3769: } else PetscCall(MatGetDiagonal_SeqAIJ(A, diag));
3770: PetscFunctionReturn(PETSC_SUCCESS);
3771: }
3773: static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A, MatAssemblyType mode)
3774: {
3775: PetscFunctionBegin;
3776: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
3777: PetscFunctionReturn(PETSC_SUCCESS);
3778: }
3780: /*@
3781: MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in `MATAIJCUSPARSE` (compressed row) format for use on NVIDIA GPUs
3783: Collective
3785: Input Parameters:
3786: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3787: . m - number of rows
3788: . n - number of columns
3789: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is provide
3790: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
3792: Output Parameter:
3793: . A - the matrix
3795: Level: intermediate
3797: Notes:
3798: This matrix will ultimately pushed down to NVIDIA GPUs and use the CuSPARSE library for
3799: calculations. For good matrix assembly performance the user should preallocate the matrix
3800: storage by setting the parameter `nz` (or the array `nnz`).
3802: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3803: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3804: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
3806: The AIJ format, also called
3807: compressed row storage, is fully compatible with standard Fortran
3808: storage. That is, the stored row and column indices can begin at
3809: either one (as in Fortran) or zero.
3811: Specify the preallocated storage with either nz or nnz (not both).
3812: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3813: allocation.
3815: When working with matrices for GPUs, it is often better to use the `MatSetPreallocationCOO()` and `MatSetValuesCOO()` paradigm rather than using this routine and `MatSetValues()`
3817: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATAIJCUSPARSE`,
3818: `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
3819: @*/
3820: PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3821: {
3822: PetscFunctionBegin;
3823: PetscCall(MatCreate(comm, A));
3824: PetscCall(MatSetSizes(*A, m, n, m, n));
3825: PetscCall(MatSetType(*A, MATSEQAIJCUSPARSE));
3826: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
3827: PetscFunctionReturn(PETSC_SUCCESS);
3828: }
3830: static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A)
3831: {
3832: PetscFunctionBegin;
3833: if (A->factortype == MAT_FACTOR_NONE) {
3834: PetscCall(MatSeqAIJCUSPARSE_Destroy(A));
3835: } else {
3836: PetscCall(MatSeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors **)&A->spptr));
3837: }
3838: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3839: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetFormat_C", NULL));
3840: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetUseCPUSolve_C", NULL));
3841: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3842: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3843: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3844: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
3845: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3846: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3847: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijcusparse_hypre_C", NULL));
3848: PetscCall(MatDestroy_SeqAIJ(A));
3849: PetscFunctionReturn(PETSC_SUCCESS);
3850: }
3852: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
3853: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat, PetscBool);
3854: static PetscErrorCode MatDuplicate_SeqAIJCUSPARSE(Mat A, MatDuplicateOption cpvalues, Mat *B)
3855: {
3856: PetscFunctionBegin;
3857: PetscCall(MatDuplicate_SeqAIJ(A, cpvalues, B));
3858: PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(*B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, B));
3859: PetscFunctionReturn(PETSC_SUCCESS);
3860: }
3862: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat Y, PetscScalar a, Mat X, MatStructure str)
3863: {
3864: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3865: Mat_SeqAIJCUSPARSE *cy;
3866: Mat_SeqAIJCUSPARSE *cx;
3867: PetscScalar *ay;
3868: const PetscScalar *ax;
3869: CsrMatrix *csry, *csrx;
3871: PetscFunctionBegin;
3872: cy = (Mat_SeqAIJCUSPARSE *)Y->spptr;
3873: cx = (Mat_SeqAIJCUSPARSE *)X->spptr;
3874: if (X->ops->axpy != Y->ops->axpy) {
3875: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3876: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3877: PetscFunctionReturn(PETSC_SUCCESS);
3878: }
3879: /* if we are here, it means both matrices are bound to GPU */
3880: PetscCall(MatSeqAIJCUSPARSECopyToGPU(Y));
3881: PetscCall(MatSeqAIJCUSPARSECopyToGPU(X));
3882: PetscCheck(cy->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)Y), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3883: PetscCheck(cx->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)X), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3884: csry = (CsrMatrix *)cy->mat->mat;
3885: csrx = (CsrMatrix *)cx->mat->mat;
3886: /* see if we can turn this into a cublas axpy */
3887: if (str != SAME_NONZERO_PATTERN && x->nz == y->nz && !x->compressedrow.use && !y->compressedrow.use) {
3888: bool eq = thrust::equal(thrust::device, csry->row_offsets->begin(), csry->row_offsets->end(), csrx->row_offsets->begin());
3889: if (eq) eq = thrust::equal(thrust::device, csry->column_indices->begin(), csry->column_indices->end(), csrx->column_indices->begin());
3890: if (eq) str = SAME_NONZERO_PATTERN;
3891: }
3892: /* spgeam is buggy with one column */
3893: if (Y->cmap->n == 1 && str != SAME_NONZERO_PATTERN) str = DIFFERENT_NONZERO_PATTERN;
3895: if (str == SUBSET_NONZERO_PATTERN) {
3896: PetscScalar b = 1.0;
3897: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3898: size_t bufferSize;
3899: void *buffer;
3900: #endif
3902: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3903: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3904: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_HOST));
3905: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3906: 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(),
3907: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), &bufferSize));
3908: PetscCallCUDA(cudaMalloc(&buffer, bufferSize));
3909: PetscCall(PetscLogGpuTimeBegin());
3910: 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(),
3911: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), buffer));
3912: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3913: PetscCall(PetscLogGpuTimeEnd());
3914: PetscCallCUDA(cudaFree(buffer));
3915: #else
3916: PetscCall(PetscLogGpuTimeBegin());
3917: 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(),
3918: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get()));
3919: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3920: PetscCall(PetscLogGpuTimeEnd());
3921: #endif
3922: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_DEVICE));
3923: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3924: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3925: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3926: } else if (str == SAME_NONZERO_PATTERN) {
3927: cublasHandle_t cublasv2handle;
3928: PetscBLASInt one = 1, bnz = 1;
3930: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3931: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3932: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3933: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3934: PetscCall(PetscLogGpuTimeBegin());
3935: PetscCallCUBLAS(cublasXaxpy(cublasv2handle, bnz, &a, ax, one, ay, one));
3936: PetscCall(PetscLogGpuFlops(2.0 * bnz));
3937: PetscCall(PetscLogGpuTimeEnd());
3938: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3939: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3940: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3941: } else {
3942: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3943: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3944: }
3945: PetscFunctionReturn(PETSC_SUCCESS);
3946: }
3948: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat Y, PetscScalar a)
3949: {
3950: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3951: PetscScalar *ay;
3952: cublasHandle_t cublasv2handle;
3953: PetscBLASInt one = 1, bnz = 1;
3955: PetscFunctionBegin;
3956: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3957: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3958: PetscCall(PetscBLASIntCast(y->nz, &bnz));
3959: PetscCall(PetscLogGpuTimeBegin());
3960: PetscCallCUBLAS(cublasXscal(cublasv2handle, bnz, &a, ay, one));
3961: PetscCall(PetscLogGpuFlops(bnz));
3962: PetscCall(PetscLogGpuTimeEnd());
3963: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3964: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3965: PetscFunctionReturn(PETSC_SUCCESS);
3966: }
3968: static PetscErrorCode MatZeroEntries_SeqAIJCUSPARSE(Mat A)
3969: {
3970: PetscBool gpu = PETSC_FALSE;
3971: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3973: PetscFunctionBegin;
3974: if (A->factortype == MAT_FACTOR_NONE) {
3975: Mat_SeqAIJCUSPARSE *spptr = (Mat_SeqAIJCUSPARSE *)A->spptr;
3976: if (spptr->mat) {
3977: CsrMatrix *matrix = (CsrMatrix *)spptr->mat->mat;
3978: if (matrix->values) {
3979: gpu = PETSC_TRUE;
3980: thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3981: }
3982: }
3983: if (spptr->matTranspose) {
3984: CsrMatrix *matrix = (CsrMatrix *)spptr->matTranspose->mat;
3985: if (matrix->values) thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3986: }
3987: }
3988: if (gpu) A->offloadmask = PETSC_OFFLOAD_GPU;
3989: else {
3990: PetscCall(PetscArrayzero(a->a, a->i[A->rmap->n]));
3991: A->offloadmask = PETSC_OFFLOAD_CPU;
3992: }
3993: PetscCall(MatSeqAIJInvalidateDiagonal(A));
3994: PetscFunctionReturn(PETSC_SUCCESS);
3995: }
3997: static PetscErrorCode MatGetCurrentMemType_SeqAIJCUSPARSE(PETSC_UNUSED Mat A, PetscMemType *m)
3998: {
3999: PetscFunctionBegin;
4000: *m = PETSC_MEMTYPE_CUDA;
4001: PetscFunctionReturn(PETSC_SUCCESS);
4002: }
4004: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat A, PetscBool flg)
4005: {
4006: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4008: PetscFunctionBegin;
4009: if (A->factortype != MAT_FACTOR_NONE) {
4010: A->boundtocpu = flg;
4011: PetscFunctionReturn(PETSC_SUCCESS);
4012: }
4013: if (flg) {
4014: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
4016: A->ops->scale = MatScale_SeqAIJ;
4017: A->ops->getdiagonal = MatGetDiagonal_SeqAIJ;
4018: A->ops->axpy = MatAXPY_SeqAIJ;
4019: A->ops->zeroentries = MatZeroEntries_SeqAIJ;
4020: A->ops->mult = MatMult_SeqAIJ;
4021: A->ops->multadd = MatMultAdd_SeqAIJ;
4022: A->ops->multtranspose = MatMultTranspose_SeqAIJ;
4023: A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJ;
4024: A->ops->multhermitiantranspose = NULL;
4025: A->ops->multhermitiantransposeadd = NULL;
4026: A->ops->productsetfromoptions = MatProductSetFromOptions_SeqAIJ;
4027: A->ops->getcurrentmemtype = NULL;
4028: PetscCall(PetscMemzero(a->ops, sizeof(Mat_SeqAIJOps)));
4029: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
4030: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
4031: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
4032: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
4033: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
4034: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
4035: } else {
4036: A->ops->scale = MatScale_SeqAIJCUSPARSE;
4037: A->ops->getdiagonal = MatGetDiagonal_SeqAIJCUSPARSE;
4038: A->ops->axpy = MatAXPY_SeqAIJCUSPARSE;
4039: A->ops->zeroentries = MatZeroEntries_SeqAIJCUSPARSE;
4040: A->ops->mult = MatMult_SeqAIJCUSPARSE;
4041: A->ops->multadd = MatMultAdd_SeqAIJCUSPARSE;
4042: A->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE;
4043: A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE;
4044: A->ops->multhermitiantranspose = MatMultHermitianTranspose_SeqAIJCUSPARSE;
4045: A->ops->multhermitiantransposeadd = MatMultHermitianTransposeAdd_SeqAIJCUSPARSE;
4046: A->ops->productsetfromoptions = MatProductSetFromOptions_SeqAIJCUSPARSE;
4047: A->ops->getcurrentmemtype = MatGetCurrentMemType_SeqAIJCUSPARSE;
4048: a->ops->getarray = MatSeqAIJGetArray_SeqAIJCUSPARSE;
4049: a->ops->restorearray = MatSeqAIJRestoreArray_SeqAIJCUSPARSE;
4050: a->ops->getarrayread = MatSeqAIJGetArrayRead_SeqAIJCUSPARSE;
4051: a->ops->restorearrayread = MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE;
4052: a->ops->getarraywrite = MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE;
4053: a->ops->restorearraywrite = MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE;
4054: a->ops->getcsrandmemtype = MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE;
4056: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", MatSeqAIJCopySubArray_SeqAIJCUSPARSE));
4057: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4058: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4059: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJCUSPARSE));
4060: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJCUSPARSE));
4061: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4062: }
4063: A->boundtocpu = flg;
4064: if (flg && a->inode.size_csr) {
4065: a->inode.use = PETSC_TRUE;
4066: } else {
4067: a->inode.use = PETSC_FALSE;
4068: }
4069: PetscFunctionReturn(PETSC_SUCCESS);
4070: }
4072: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat A, MatType, MatReuse reuse, Mat *newmat)
4073: {
4074: Mat B;
4076: PetscFunctionBegin;
4077: PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUDA)); /* first use of CUSPARSE may be via MatConvert */
4078: if (reuse == MAT_INITIAL_MATRIX) {
4079: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
4080: } else if (reuse == MAT_REUSE_MATRIX) {
4081: PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN));
4082: }
4083: B = *newmat;
4085: PetscCall(PetscFree(B->defaultvectype));
4086: PetscCall(PetscStrallocpy(VECCUDA, &B->defaultvectype));
4088: if (reuse != MAT_REUSE_MATRIX && !B->spptr) {
4089: if (B->factortype == MAT_FACTOR_NONE) {
4090: Mat_SeqAIJCUSPARSE *spptr;
4091: PetscCall(PetscNew(&spptr));
4092: PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4093: PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4094: spptr->format = MAT_CUSPARSE_CSR;
4095: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4096: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4097: spptr->spmvAlg = CUSPARSE_SPMV_CSR_ALG1; /* default, since we only support csr */
4098: #else
4099: spptr->spmvAlg = CUSPARSE_CSRMV_ALG1; /* default, since we only support csr */
4100: #endif
4101: spptr->spmmAlg = CUSPARSE_SPMM_CSR_ALG1; /* default, only support column-major dense matrix B */
4102: spptr->csr2cscAlg = CUSPARSE_CSR2CSC_ALG1;
4103: #endif
4104: B->spptr = spptr;
4105: } else {
4106: Mat_SeqAIJCUSPARSETriFactors *spptr;
4108: PetscCall(PetscNew(&spptr));
4109: PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4110: PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4111: B->spptr = spptr;
4112: }
4113: B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
4114: }
4115: B->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE;
4116: B->ops->destroy = MatDestroy_SeqAIJCUSPARSE;
4117: B->ops->setoption = MatSetOption_SeqAIJCUSPARSE;
4118: B->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE;
4119: B->ops->bindtocpu = MatBindToCPU_SeqAIJCUSPARSE;
4120: B->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE;
4121: B->ops->getcurrentmemtype = MatGetCurrentMemType_SeqAIJCUSPARSE;
4123: PetscCall(MatBindToCPU_SeqAIJCUSPARSE(B, PETSC_FALSE));
4124: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJCUSPARSE));
4125: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE));
4126: #if defined(PETSC_HAVE_HYPRE)
4127: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaijcusparse_hypre_C", MatConvert_AIJ_HYPRE));
4128: #endif
4129: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetUseCPUSolve_C", MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE));
4130: PetscFunctionReturn(PETSC_SUCCESS);
4131: }
4133: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B)
4134: {
4135: PetscFunctionBegin;
4136: PetscCall(MatCreate_SeqAIJ(B));
4137: PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, &B));
4138: PetscFunctionReturn(PETSC_SUCCESS);
4139: }
4141: /*MC
4142: MATSEQAIJCUSPARSE - MATAIJCUSPARSE = "(seq)aijcusparse" - A matrix type to be used for sparse matrices on NVIDIA GPUs.
4144: Options Database Keys:
4145: + -mat_type aijcusparse - Sets the matrix type to "seqaijcusparse" during a call to `MatSetFromOptions()`
4146: . -mat_cusparse_storage_format csr - Sets the storage format of matrices (for `MatMult()` and factors in `MatSolve()`).
4147: Other options include ell (ellpack) or hyb (hybrid).
4148: . -mat_cusparse_mult_storage_format csr - Sets the storage format of matrices (for `MatMult()`). Other options include ell (ellpack) or hyb (hybrid).
4149: - -mat_cusparse_use_cpu_solve - Performs the `MatSolve()` on the CPU
4151: Level: beginner
4153: Notes:
4154: These matrices can be in either CSR, ELL, or HYB format.
4156: All matrix calculations are performed on NVIDIA GPUs using the cuSPARSE library.
4158: Uses 32-bit integers internally. If PETSc is configured `--with-64-bit-indices`, the integer row and column indices are stored on the GPU with `int`. It is unclear what happens
4159: if some integer values passed in do not fit in `int`.
4161: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJCUSPARSE()`, `MatCUSPARSESetUseCPUSolve()`, `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
4162: M*/
4164: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_CUSPARSE(void)
4165: {
4166: PetscFunctionBegin;
4167: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_LU, MatGetFactor_seqaijcusparse_cusparse));
4168: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_CHOLESKY, MatGetFactor_seqaijcusparse_cusparse));
4169: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ILU, MatGetFactor_seqaijcusparse_cusparse));
4170: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ICC, MatGetFactor_seqaijcusparse_cusparse));
4171: PetscFunctionReturn(PETSC_SUCCESS);
4172: }
4174: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat mat)
4175: {
4176: Mat_SeqAIJCUSPARSE *cusp = static_cast<Mat_SeqAIJCUSPARSE *>(mat->spptr);
4178: PetscFunctionBegin;
4179: if (cusp) {
4180: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->mat, cusp->format));
4181: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4182: delete cusp->workVector;
4183: delete cusp->rowoffsets_gpu;
4184: delete cusp->csr2csc_i;
4185: delete cusp->coords;
4186: if (cusp->handle) PetscCallCUSPARSE(cusparseDestroy(cusp->handle));
4187: PetscCall(PetscFree(mat->spptr));
4188: }
4189: PetscFunctionReturn(PETSC_SUCCESS);
4190: }
4192: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **mat)
4193: {
4194: PetscFunctionBegin;
4195: if (*mat) {
4196: delete (*mat)->values;
4197: delete (*mat)->column_indices;
4198: delete (*mat)->row_offsets;
4199: delete *mat;
4200: *mat = 0;
4201: }
4202: PetscFunctionReturn(PETSC_SUCCESS);
4203: }
4205: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4206: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **trifactor)
4207: {
4208: PetscFunctionBegin;
4209: if (*trifactor) {
4210: if ((*trifactor)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*trifactor)->descr));
4211: if ((*trifactor)->solveInfo) PetscCallCUSPARSE(cusparseDestroyCsrsvInfo((*trifactor)->solveInfo));
4212: PetscCall(CsrMatrix_Destroy(&(*trifactor)->csrMat));
4213: if ((*trifactor)->solveBuffer) PetscCallCUDA(cudaFree((*trifactor)->solveBuffer));
4214: if ((*trifactor)->AA_h) PetscCallCUDA(cudaFreeHost((*trifactor)->AA_h));
4215: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4216: if ((*trifactor)->csr2cscBuffer) PetscCallCUDA(cudaFree((*trifactor)->csr2cscBuffer));
4217: #endif
4218: PetscCall(PetscFree(*trifactor));
4219: }
4220: PetscFunctionReturn(PETSC_SUCCESS);
4221: }
4222: #endif
4224: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **matstruct, MatCUSPARSEStorageFormat format)
4225: {
4226: CsrMatrix *mat;
4228: PetscFunctionBegin;
4229: if (*matstruct) {
4230: if ((*matstruct)->mat) {
4231: if (format == MAT_CUSPARSE_ELL || format == MAT_CUSPARSE_HYB) {
4232: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4233: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
4234: #else
4235: cusparseHybMat_t hybMat = (cusparseHybMat_t)(*matstruct)->mat;
4236: PetscCallCUSPARSE(cusparseDestroyHybMat(hybMat));
4237: #endif
4238: } else {
4239: mat = (CsrMatrix *)(*matstruct)->mat;
4240: PetscCall(CsrMatrix_Destroy(&mat));
4241: }
4242: }
4243: if ((*matstruct)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*matstruct)->descr));
4244: delete (*matstruct)->cprowIndices;
4245: if ((*matstruct)->alpha_one) PetscCallCUDA(cudaFree((*matstruct)->alpha_one));
4246: if ((*matstruct)->beta_zero) PetscCallCUDA(cudaFree((*matstruct)->beta_zero));
4247: if ((*matstruct)->beta_one) PetscCallCUDA(cudaFree((*matstruct)->beta_one));
4249: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4250: Mat_SeqAIJCUSPARSEMultStruct *mdata = *matstruct;
4251: if (mdata->matDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr));
4253: for (int i = 0; i < 3; i++) {
4254: if (mdata->cuSpMV[i].initialized) {
4255: PetscCallCUDA(cudaFree(mdata->cuSpMV[i].spmvBuffer));
4256: PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecXDescr));
4257: PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecYDescr));
4258: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
4259: if (mdata->matDescr_SpMV[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMV[i]));
4260: if (mdata->matDescr_SpMM[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMM[i]));
4261: #endif
4262: }
4263: }
4264: #endif
4265: delete *matstruct;
4266: *matstruct = NULL;
4267: }
4268: PetscFunctionReturn(PETSC_SUCCESS);
4269: }
4271: PetscErrorCode MatSeqAIJCUSPARSETriFactors_Reset(Mat_SeqAIJCUSPARSETriFactors_p *trifactors)
4272: {
4273: Mat_SeqAIJCUSPARSETriFactors *fs = *trifactors;
4275: PetscFunctionBegin;
4276: if (fs) {
4277: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4278: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtr));
4279: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtr));
4280: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtrTranspose));
4281: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtrTranspose));
4282: delete fs->workVector;
4283: fs->workVector = NULL;
4284: #endif
4285: delete fs->rpermIndices;
4286: delete fs->cpermIndices;
4287: fs->rpermIndices = NULL;
4288: fs->cpermIndices = NULL;
4289: fs->init_dev_prop = PETSC_FALSE;
4290: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4291: PetscCallCUDA(cudaFree(fs->csrRowPtr));
4292: PetscCallCUDA(cudaFree(fs->csrColIdx));
4293: PetscCallCUDA(cudaFree(fs->csrRowPtr32));
4294: PetscCallCUDA(cudaFree(fs->csrColIdx32));
4295: PetscCallCUDA(cudaFree(fs->csrVal));
4296: PetscCallCUDA(cudaFree(fs->diag));
4297: PetscCallCUDA(cudaFree(fs->X));
4298: PetscCallCUDA(cudaFree(fs->Y));
4299: // PetscCallCUDA(cudaFree(fs->factBuffer_M)); /* No needed since factBuffer_M shares with one of spsvBuffer_L/U */
4300: PetscCallCUDA(cudaFree(fs->spsvBuffer_L));
4301: PetscCallCUDA(cudaFree(fs->spsvBuffer_U));
4302: PetscCallCUDA(cudaFree(fs->spsvBuffer_Lt));
4303: PetscCallCUDA(cudaFree(fs->spsvBuffer_Ut));
4304: PetscCallCUSPARSE(cusparseDestroyMatDescr(fs->matDescr_M));
4305: PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_L));
4306: PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_U));
4307: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_L));
4308: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Lt));
4309: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_U));
4310: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Ut));
4311: PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_X));
4312: PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_Y));
4313: PetscCallCUSPARSE(cusparseDestroyCsrilu02Info(fs->ilu0Info_M));
4314: PetscCallCUSPARSE(cusparseDestroyCsric02Info(fs->ic0Info_M));
4315: PetscCall(PetscFree(fs->csrRowPtr_h));
4316: PetscCall(PetscFree(fs->csrVal_h));
4317: PetscCall(PetscFree(fs->diag_h));
4318: fs->createdTransposeSpSVDescr = PETSC_FALSE;
4319: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
4320: #endif
4321: }
4322: PetscFunctionReturn(PETSC_SUCCESS);
4323: }
4325: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **trifactors)
4326: {
4327: PetscFunctionBegin;
4328: if (*trifactors) {
4329: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(trifactors));
4330: PetscCallCUSPARSE(cusparseDestroy((*trifactors)->handle));
4331: PetscCall(PetscFree(*trifactors));
4332: }
4333: PetscFunctionReturn(PETSC_SUCCESS);
4334: }
4336: struct IJCompare {
4337: __host__ __device__ inline bool operator()(const thrust::tuple<PetscInt, PetscInt> &t1, const thrust::tuple<PetscInt, PetscInt> &t2)
4338: {
4339: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4340: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4341: return false;
4342: }
4343: };
4345: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat A, PetscBool destroy)
4346: {
4347: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4349: PetscFunctionBegin;
4350: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4351: if (!cusp) PetscFunctionReturn(PETSC_SUCCESS);
4352: if (destroy) {
4353: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4354: delete cusp->csr2csc_i;
4355: cusp->csr2csc_i = NULL;
4356: }
4357: A->transupdated = PETSC_FALSE;
4358: PetscFunctionReturn(PETSC_SUCCESS);
4359: }
4361: static PetscErrorCode MatCOOStructDestroy_SeqAIJCUSPARSE(void **data)
4362: {
4363: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;
4365: PetscFunctionBegin;
4366: PetscCallCUDA(cudaFree(coo->perm));
4367: PetscCallCUDA(cudaFree(coo->jmap));
4368: PetscCall(PetscFree(coo));
4369: PetscFunctionReturn(PETSC_SUCCESS);
4370: }
4372: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4373: {
4374: PetscBool dev_ij = PETSC_FALSE;
4375: PetscMemType mtype = PETSC_MEMTYPE_HOST;
4376: PetscInt *i, *j;
4377: PetscContainer container_h;
4378: MatCOOStruct_SeqAIJ *coo_h, *coo_d;
4380: PetscFunctionBegin;
4381: PetscCall(PetscGetMemType(coo_i, &mtype));
4382: if (PetscMemTypeDevice(mtype)) {
4383: dev_ij = PETSC_TRUE;
4384: PetscCall(PetscMalloc2(coo_n, &i, coo_n, &j));
4385: PetscCallCUDA(cudaMemcpy(i, coo_i, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4386: PetscCallCUDA(cudaMemcpy(j, coo_j, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4387: } else {
4388: i = coo_i;
4389: j = coo_j;
4390: }
4392: PetscCall(MatSetPreallocationCOO_SeqAIJ(mat, coo_n, i, j));
4393: if (dev_ij) PetscCall(PetscFree2(i, j));
4394: mat->offloadmask = PETSC_OFFLOAD_CPU;
4395: // Create the GPU memory
4396: PetscCall(MatSeqAIJCUSPARSECopyToGPU(mat));
4398: // Copy the COO struct to device
4399: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
4400: PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
4401: PetscCall(PetscMalloc1(1, &coo_d));
4402: *coo_d = *coo_h; // do a shallow copy and then amend some fields that need to be different
4403: PetscCallCUDA(cudaMalloc((void **)&coo_d->jmap, (coo_h->nz + 1) * sizeof(PetscCount)));
4404: PetscCallCUDA(cudaMemcpy(coo_d->jmap, coo_h->jmap, (coo_h->nz + 1) * sizeof(PetscCount), cudaMemcpyHostToDevice));
4405: PetscCallCUDA(cudaMalloc((void **)&coo_d->perm, coo_h->Atot * sizeof(PetscCount)));
4406: PetscCallCUDA(cudaMemcpy(coo_d->perm, coo_h->perm, coo_h->Atot * sizeof(PetscCount), cudaMemcpyHostToDevice));
4408: // Put the COO struct in a container and then attach that to the matrix
4409: PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", coo_d, MatCOOStructDestroy_SeqAIJCUSPARSE));
4410: PetscFunctionReturn(PETSC_SUCCESS);
4411: }
4413: __global__ static void MatAddCOOValues(const PetscScalar kv[], PetscCount nnz, const PetscCount jmap[], const PetscCount perm[], InsertMode imode, PetscScalar a[])
4414: {
4415: PetscCount i = blockIdx.x * blockDim.x + threadIdx.x;
4416: const PetscCount grid_size = gridDim.x * blockDim.x;
4417: for (; i < nnz; i += grid_size) {
4418: PetscScalar sum = 0.0;
4419: for (PetscCount k = jmap[i]; k < jmap[i + 1]; k++) sum += kv[perm[k]];
4420: a[i] = (imode == INSERT_VALUES ? 0.0 : a[i]) + sum;
4421: }
4422: }
4424: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat A, const PetscScalar v[], InsertMode imode)
4425: {
4426: Mat_SeqAIJ *seq = (Mat_SeqAIJ *)A->data;
4427: Mat_SeqAIJCUSPARSE *dev = (Mat_SeqAIJCUSPARSE *)A->spptr;
4428: PetscCount Annz = seq->nz;
4429: PetscMemType memtype;
4430: const PetscScalar *v1 = v;
4431: PetscScalar *Aa;
4432: PetscContainer container;
4433: MatCOOStruct_SeqAIJ *coo;
4435: PetscFunctionBegin;
4436: if (!dev->mat) PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4438: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
4439: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4441: PetscCall(PetscGetMemType(v, &memtype));
4442: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
4443: PetscCallCUDA(cudaMalloc((void **)&v1, coo->n * sizeof(PetscScalar)));
4444: PetscCallCUDA(cudaMemcpy((void *)v1, v, coo->n * sizeof(PetscScalar), cudaMemcpyHostToDevice));
4445: }
4447: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSEGetArrayWrite(A, &Aa));
4448: else PetscCall(MatSeqAIJCUSPARSEGetArray(A, &Aa));
4450: PetscCall(PetscLogGpuTimeBegin());
4451: if (Annz) {
4452: MatAddCOOValues<<<((int)(Annz + 255) / 256), 256>>>(v1, Annz, coo->jmap, coo->perm, imode, Aa);
4453: PetscCallCUDA(cudaPeekAtLastError());
4454: }
4455: PetscCall(PetscLogGpuTimeEnd());
4457: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSERestoreArrayWrite(A, &Aa));
4458: else PetscCall(MatSeqAIJCUSPARSERestoreArray(A, &Aa));
4460: if (PetscMemTypeHost(memtype)) PetscCallCUDA(cudaFree((void *)v1));
4461: PetscFunctionReturn(PETSC_SUCCESS);
4462: }
4464: /*@C
4465: MatSeqAIJCUSPARSEGetIJ - returns the device row storage `i` and `j` indices for `MATSEQAIJCUSPARSE` matrices.
4467: Not Collective
4469: Input Parameters:
4470: + A - the matrix
4471: - compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4473: Output Parameters:
4474: + i - the CSR row pointers, these are always `int` even when PETSc is configured with `--with-64-bit-indices`
4475: - j - the CSR column indices, these are always `int` even when PETSc is configured with `--with-64-bit-indices`
4477: Level: developer
4479: Note:
4480: When compressed is true, the CSR structure does not contain empty rows
4482: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSERestoreIJ()`, `MatSeqAIJCUSPARSEGetArrayRead()`
4483: @*/
4484: PetscErrorCode MatSeqAIJCUSPARSEGetIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4485: {
4486: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4487: CsrMatrix *csr;
4488: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4490: PetscFunctionBegin;
4492: if (!i || !j) PetscFunctionReturn(PETSC_SUCCESS);
4493: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4494: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4495: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4496: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4497: csr = (CsrMatrix *)cusp->mat->mat;
4498: if (i) {
4499: if (!compressed && a->compressedrow.use) { /* need full row offset */
4500: if (!cusp->rowoffsets_gpu) {
4501: cusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4502: cusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4503: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4504: }
4505: *i = cusp->rowoffsets_gpu->data().get();
4506: } else *i = csr->row_offsets->data().get();
4507: }
4508: if (j) *j = csr->column_indices->data().get();
4509: PetscFunctionReturn(PETSC_SUCCESS);
4510: }
4512: /*@C
4513: MatSeqAIJCUSPARSERestoreIJ - restore the device row storage `i` and `j` indices obtained with `MatSeqAIJCUSPARSEGetIJ()`
4515: Not Collective
4517: Input Parameters:
4518: + A - the matrix
4519: . compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4520: . i - the CSR row pointers
4521: - j - the CSR column indices
4523: Level: developer
4525: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetIJ()`
4526: @*/
4527: PetscErrorCode MatSeqAIJCUSPARSERestoreIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4528: {
4529: PetscFunctionBegin;
4531: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4532: if (i) *i = NULL;
4533: if (j) *j = NULL;
4534: (void)compressed;
4535: PetscFunctionReturn(PETSC_SUCCESS);
4536: }
4538: /*@C
4539: MatSeqAIJCUSPARSEGetArrayRead - gives read-only access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix nonzero entries are stored
4541: Not Collective
4543: Input Parameter:
4544: . A - a `MATSEQAIJCUSPARSE` matrix
4546: Output Parameter:
4547: . a - pointer to the device data
4549: Level: developer
4551: Note:
4552: Will trigger host-to-device copies if the most up-to-date matrix data is on the host
4554: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArrayRead()`
4555: @*/
4556: PetscErrorCode MatSeqAIJCUSPARSEGetArrayRead(Mat A, const PetscScalar **a)
4557: {
4558: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4559: CsrMatrix *csr;
4561: PetscFunctionBegin;
4563: PetscAssertPointer(a, 2);
4564: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4565: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4566: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4567: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4568: csr = (CsrMatrix *)cusp->mat->mat;
4569: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4570: *a = csr->values->data().get();
4571: PetscFunctionReturn(PETSC_SUCCESS);
4572: }
4574: /*@C
4575: MatSeqAIJCUSPARSERestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJCUSPARSEGetArrayRead()`
4577: Not Collective
4579: Input Parameters:
4580: + A - a `MATSEQAIJCUSPARSE` matrix
4581: - a - pointer to the device data
4583: Level: developer
4585: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`
4586: @*/
4587: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayRead(Mat A, const PetscScalar **a)
4588: {
4589: PetscFunctionBegin;
4591: PetscAssertPointer(a, 2);
4592: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4593: *a = NULL;
4594: PetscFunctionReturn(PETSC_SUCCESS);
4595: }
4597: /*@C
4598: MatSeqAIJCUSPARSEGetArray - gives read-write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4600: Not Collective
4602: Input Parameter:
4603: . A - a `MATSEQAIJCUSPARSE` matrix
4605: Output Parameter:
4606: . a - pointer to the device data
4608: Level: developer
4610: Note:
4611: Will trigger host-to-device copies if the most up-to-date matrix data is on the host
4613: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArray()`
4614: @*/
4615: PetscErrorCode MatSeqAIJCUSPARSEGetArray(Mat A, PetscScalar **a)
4616: {
4617: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4618: CsrMatrix *csr;
4620: PetscFunctionBegin;
4622: PetscAssertPointer(a, 2);
4623: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4624: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4625: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4626: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4627: csr = (CsrMatrix *)cusp->mat->mat;
4628: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4629: *a = csr->values->data().get();
4630: A->offloadmask = PETSC_OFFLOAD_GPU;
4631: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4632: PetscFunctionReturn(PETSC_SUCCESS);
4633: }
4634: /*@C
4635: MatSeqAIJCUSPARSERestoreArray - restore the read-write access array obtained from `MatSeqAIJCUSPARSEGetArray()`
4637: Not Collective
4639: Input Parameters:
4640: + A - a `MATSEQAIJCUSPARSE` matrix
4641: - a - pointer to the device data
4643: Level: developer
4645: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`
4646: @*/
4647: PetscErrorCode MatSeqAIJCUSPARSERestoreArray(Mat A, PetscScalar **a)
4648: {
4649: PetscFunctionBegin;
4651: PetscAssertPointer(a, 2);
4652: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4653: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4654: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4655: *a = NULL;
4656: PetscFunctionReturn(PETSC_SUCCESS);
4657: }
4659: /*@C
4660: MatSeqAIJCUSPARSEGetArrayWrite - gives write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4662: Not Collective
4664: Input Parameter:
4665: . A - a `MATSEQAIJCUSPARSE` matrix
4667: Output Parameter:
4668: . a - pointer to the device data
4670: Level: developer
4672: Note:
4673: Does not trigger any host to device copies.
4675: It marks the data GPU valid so users must set all the values in `a` to ensure out-of-date data is not considered current
4677: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSERestoreArrayWrite()`
4678: @*/
4679: PetscErrorCode MatSeqAIJCUSPARSEGetArrayWrite(Mat A, PetscScalar **a)
4680: {
4681: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4682: CsrMatrix *csr;
4684: PetscFunctionBegin;
4686: PetscAssertPointer(a, 2);
4687: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4688: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4689: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4690: csr = (CsrMatrix *)cusp->mat->mat;
4691: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4692: *a = csr->values->data().get();
4693: A->offloadmask = PETSC_OFFLOAD_GPU;
4694: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4695: PetscFunctionReturn(PETSC_SUCCESS);
4696: }
4698: /*@C
4699: MatSeqAIJCUSPARSERestoreArrayWrite - restore the write-only access array obtained from `MatSeqAIJCUSPARSEGetArrayWrite()`
4701: Not Collective
4703: Input Parameters:
4704: + A - a `MATSEQAIJCUSPARSE` matrix
4705: - a - pointer to the device data
4707: Level: developer
4709: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayWrite()`
4710: @*/
4711: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayWrite(Mat A, PetscScalar **a)
4712: {
4713: PetscFunctionBegin;
4715: PetscAssertPointer(a, 2);
4716: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4717: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4718: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4719: *a = NULL;
4720: PetscFunctionReturn(PETSC_SUCCESS);
4721: }
4723: struct IJCompare4 {
4724: __host__ __device__ inline bool operator()(const thrust::tuple<int, int, PetscScalar, int> &t1, const thrust::tuple<int, int, PetscScalar, int> &t2)
4725: {
4726: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4727: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4728: return false;
4729: }
4730: };
4732: struct Shift {
4733: int _shift;
4735: Shift(int shift) : _shift(shift) { }
4736: __host__ __device__ inline int operator()(const int &c) { return c + _shift; }
4737: };
4739: /* merges two SeqAIJCUSPARSE matrices A, B by concatenating their rows. [A';B']' operation in MATLAB notation */
4740: PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
4741: {
4742: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data, *c;
4743: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr, *Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr, *Ccusp;
4744: Mat_SeqAIJCUSPARSEMultStruct *Cmat;
4745: CsrMatrix *Acsr, *Bcsr, *Ccsr;
4746: PetscInt Annz, Bnnz;
4747: cusparseStatus_t stat;
4748: PetscInt i, m, n, zero = 0;
4750: PetscFunctionBegin;
4753: PetscAssertPointer(C, 4);
4754: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4755: PetscCheckTypeName(B, MATSEQAIJCUSPARSE);
4756: 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);
4757: PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
4758: PetscCheck(Acusp->format != MAT_CUSPARSE_ELL && Acusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4759: PetscCheck(Bcusp->format != MAT_CUSPARSE_ELL && Bcusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4760: if (reuse == MAT_INITIAL_MATRIX) {
4761: m = A->rmap->n;
4762: n = A->cmap->n + B->cmap->n;
4763: PetscCall(MatCreate(PETSC_COMM_SELF, C));
4764: PetscCall(MatSetSizes(*C, m, n, m, n));
4765: PetscCall(MatSetType(*C, MATSEQAIJCUSPARSE));
4766: c = (Mat_SeqAIJ *)(*C)->data;
4767: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4768: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
4769: Ccsr = new CsrMatrix;
4770: Cmat->cprowIndices = NULL;
4771: c->compressedrow.use = PETSC_FALSE;
4772: c->compressedrow.nrows = 0;
4773: c->compressedrow.i = NULL;
4774: c->compressedrow.rindex = NULL;
4775: Ccusp->workVector = NULL;
4776: Ccusp->nrows = m;
4777: Ccusp->mat = Cmat;
4778: Ccusp->mat->mat = Ccsr;
4779: Ccsr->num_rows = m;
4780: Ccsr->num_cols = n;
4781: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
4782: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
4783: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4784: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
4785: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
4786: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
4787: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4788: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4789: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4790: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4791: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4792: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4793: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4795: Acsr = (CsrMatrix *)Acusp->mat->mat;
4796: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4797: Annz = (PetscInt)Acsr->column_indices->size();
4798: Bnnz = (PetscInt)Bcsr->column_indices->size();
4799: c->nz = Annz + Bnnz;
4800: Ccsr->row_offsets = new THRUSTINTARRAY32(m + 1);
4801: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
4802: Ccsr->values = new THRUSTARRAY(c->nz);
4803: Ccsr->num_entries = c->nz;
4804: Ccusp->coords = new THRUSTINTARRAY(c->nz);
4805: if (c->nz) {
4806: auto Acoo = new THRUSTINTARRAY32(Annz);
4807: auto Bcoo = new THRUSTINTARRAY32(Bnnz);
4808: auto Ccoo = new THRUSTINTARRAY32(c->nz);
4809: THRUSTINTARRAY32 *Aroff, *Broff;
4811: if (a->compressedrow.use) { /* need full row offset */
4812: if (!Acusp->rowoffsets_gpu) {
4813: Acusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4814: Acusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4815: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4816: }
4817: Aroff = Acusp->rowoffsets_gpu;
4818: } else Aroff = Acsr->row_offsets;
4819: if (b->compressedrow.use) { /* need full row offset */
4820: if (!Bcusp->rowoffsets_gpu) {
4821: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
4822: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
4823: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
4824: }
4825: Broff = Bcusp->rowoffsets_gpu;
4826: } else Broff = Bcsr->row_offsets;
4827: PetscCall(PetscLogGpuTimeBegin());
4828: stat = cusparseXcsr2coo(Acusp->handle, Aroff->data().get(), Annz, m, Acoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4829: PetscCallCUSPARSE(stat);
4830: stat = cusparseXcsr2coo(Bcusp->handle, Broff->data().get(), Bnnz, m, Bcoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4831: PetscCallCUSPARSE(stat);
4832: /* Issues when using bool with large matrices on SUMMIT 10.2.89 */
4833: auto Aperm = thrust::make_constant_iterator(1);
4834: auto Bperm = thrust::make_constant_iterator(0);
4835: #if PETSC_PKG_CUDA_VERSION_GE(10, 0, 0)
4836: auto Bcib = thrust::make_transform_iterator(Bcsr->column_indices->begin(), Shift(A->cmap->n));
4837: auto Bcie = thrust::make_transform_iterator(Bcsr->column_indices->end(), Shift(A->cmap->n));
4838: #else
4839: /* there are issues instantiating the merge operation using a transform iterator for the columns of B */
4840: auto Bcib = Bcsr->column_indices->begin();
4841: auto Bcie = Bcsr->column_indices->end();
4842: thrust::transform(Bcib, Bcie, Bcib, Shift(A->cmap->n));
4843: #endif
4844: auto wPerm = new THRUSTINTARRAY32(Annz + Bnnz);
4845: auto Azb = thrust::make_zip_iterator(thrust::make_tuple(Acoo->begin(), Acsr->column_indices->begin(), Acsr->values->begin(), Aperm));
4846: auto Aze = thrust::make_zip_iterator(thrust::make_tuple(Acoo->end(), Acsr->column_indices->end(), Acsr->values->end(), Aperm));
4847: auto Bzb = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->begin(), Bcib, Bcsr->values->begin(), Bperm));
4848: auto Bze = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->end(), Bcie, Bcsr->values->end(), Bperm));
4849: auto Czb = thrust::make_zip_iterator(thrust::make_tuple(Ccoo->begin(), Ccsr->column_indices->begin(), Ccsr->values->begin(), wPerm->begin()));
4850: auto p1 = Ccusp->coords->begin();
4851: auto p2 = Ccusp->coords->begin();
4852: thrust::advance(p2, Annz);
4853: PetscCallThrust(thrust::merge(thrust::device, Azb, Aze, Bzb, Bze, Czb, IJCompare4()));
4854: #if PETSC_PKG_CUDA_VERSION_LT(10, 0, 0)
4855: thrust::transform(Bcib, Bcie, Bcib, Shift(-A->cmap->n));
4856: #endif
4857: auto cci = thrust::make_counting_iterator(zero);
4858: auto cce = thrust::make_counting_iterator(c->nz);
4859: #if 0 //Errors on SUMMIT cuda 11.1.0
4860: PetscCallThrust(thrust::partition_copy(thrust::device,cci,cce,wPerm->begin(),p1,p2,thrust::identity<int>()));
4861: #else
4862: #if PETSC_PKG_CUDA_VERSION_LT(12, 9, 0) || PetscDefined(HAVE_THRUST)
4863: auto pred = thrust::identity<int>();
4864: #else
4865: auto pred = cuda::std::identity();
4866: #endif
4867: PetscCallThrust(thrust::copy_if(thrust::device, cci, cce, wPerm->begin(), p1, pred));
4868: PetscCallThrust(thrust::remove_copy_if(thrust::device, cci, cce, wPerm->begin(), p2, pred));
4869: #endif
4870: stat = cusparseXcoo2csr(Ccusp->handle, Ccoo->data().get(), c->nz, m, Ccsr->row_offsets->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4871: PetscCallCUSPARSE(stat);
4872: PetscCall(PetscLogGpuTimeEnd());
4873: delete wPerm;
4874: delete Acoo;
4875: delete Bcoo;
4876: delete Ccoo;
4877: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4878: 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);
4879: PetscCallCUSPARSE(stat);
4880: #endif
4881: if (A->form_explicit_transpose && B->form_explicit_transpose) { /* if A and B have the transpose, generate C transpose too */
4882: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
4883: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
4884: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4885: Mat_SeqAIJCUSPARSEMultStruct *CmatT = new Mat_SeqAIJCUSPARSEMultStruct;
4886: CsrMatrix *CcsrT = new CsrMatrix;
4887: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4888: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4890: (*C)->form_explicit_transpose = PETSC_TRUE;
4891: (*C)->transupdated = PETSC_TRUE;
4892: Ccusp->rowoffsets_gpu = NULL;
4893: CmatT->cprowIndices = NULL;
4894: CmatT->mat = CcsrT;
4895: CcsrT->num_rows = n;
4896: CcsrT->num_cols = m;
4897: CcsrT->num_entries = c->nz;
4899: CcsrT->row_offsets = new THRUSTINTARRAY32(n + 1);
4900: CcsrT->column_indices = new THRUSTINTARRAY32(c->nz);
4901: CcsrT->values = new THRUSTARRAY(c->nz);
4903: PetscCall(PetscLogGpuTimeBegin());
4904: auto rT = CcsrT->row_offsets->begin();
4905: if (AT) {
4906: rT = thrust::copy(AcsrT->row_offsets->begin(), AcsrT->row_offsets->end(), rT);
4907: thrust::advance(rT, -1);
4908: }
4909: if (BT) {
4910: auto titb = thrust::make_transform_iterator(BcsrT->row_offsets->begin(), Shift(a->nz));
4911: auto tite = thrust::make_transform_iterator(BcsrT->row_offsets->end(), Shift(a->nz));
4912: thrust::copy(titb, tite, rT);
4913: }
4914: auto cT = CcsrT->column_indices->begin();
4915: if (AT) cT = thrust::copy(AcsrT->column_indices->begin(), AcsrT->column_indices->end(), cT);
4916: if (BT) thrust::copy(BcsrT->column_indices->begin(), BcsrT->column_indices->end(), cT);
4917: auto vT = CcsrT->values->begin();
4918: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4919: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4920: PetscCall(PetscLogGpuTimeEnd());
4922: PetscCallCUSPARSE(cusparseCreateMatDescr(&CmatT->descr));
4923: PetscCallCUSPARSE(cusparseSetMatIndexBase(CmatT->descr, CUSPARSE_INDEX_BASE_ZERO));
4924: PetscCallCUSPARSE(cusparseSetMatType(CmatT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4925: PetscCallCUDA(cudaMalloc((void **)&CmatT->alpha_one, sizeof(PetscScalar)));
4926: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_zero, sizeof(PetscScalar)));
4927: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_one, sizeof(PetscScalar)));
4928: PetscCallCUDA(cudaMemcpy(CmatT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4929: PetscCallCUDA(cudaMemcpy(CmatT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4930: PetscCallCUDA(cudaMemcpy(CmatT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4931: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4932: 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);
4933: PetscCallCUSPARSE(stat);
4934: #endif
4935: Ccusp->matTranspose = CmatT;
4936: }
4937: }
4939: c->free_a = PETSC_TRUE;
4940: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
4941: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4942: c->free_ij = PETSC_TRUE;
4943: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
4944: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
4945: THRUSTINTARRAY jj(Ccsr->column_indices->size());
4946: ii = *Ccsr->row_offsets;
4947: jj = *Ccsr->column_indices;
4948: PetscCallCUDA(cudaMemcpy(c->i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4949: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4950: } else {
4951: PetscCallCUDA(cudaMemcpy(c->i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4952: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4953: }
4954: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
4955: PetscCall(PetscMalloc1(m, &c->ilen));
4956: PetscCall(PetscMalloc1(m, &c->imax));
4957: c->maxnz = c->nz;
4958: c->nonzerorowcnt = 0;
4959: c->rmax = 0;
4960: for (i = 0; i < m; i++) {
4961: const PetscInt nn = c->i[i + 1] - c->i[i];
4962: c->ilen[i] = c->imax[i] = nn;
4963: c->nonzerorowcnt += (PetscInt)!!nn;
4964: c->rmax = PetscMax(c->rmax, nn);
4965: }
4966: PetscCall(MatMarkDiagonal_SeqAIJ(*C));
4967: PetscCall(PetscMalloc1(c->nz, &c->a));
4968: (*C)->nonzerostate++;
4969: PetscCall(PetscLayoutSetUp((*C)->rmap));
4970: PetscCall(PetscLayoutSetUp((*C)->cmap));
4971: Ccusp->nonzerostate = (*C)->nonzerostate;
4972: (*C)->preallocated = PETSC_TRUE;
4973: } else {
4974: 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);
4975: c = (Mat_SeqAIJ *)(*C)->data;
4976: if (c->nz) {
4977: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4978: PetscCheck(Ccusp->coords, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing coords");
4979: PetscCheck(Ccusp->format != MAT_CUSPARSE_ELL && Ccusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4980: PetscCheck(Ccusp->nonzerostate == (*C)->nonzerostate, PETSC_COMM_SELF, PETSC_ERR_COR, "Wrong nonzerostate");
4981: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4982: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4983: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4984: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4985: Acsr = (CsrMatrix *)Acusp->mat->mat;
4986: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4987: Ccsr = (CsrMatrix *)Ccusp->mat->mat;
4988: 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());
4989: 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());
4990: 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());
4991: 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);
4992: 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());
4993: auto pmid = Ccusp->coords->begin();
4994: thrust::advance(pmid, Acsr->num_entries);
4995: PetscCall(PetscLogGpuTimeBegin());
4996: auto zibait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->begin())));
4997: auto zieait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4998: thrust::for_each(zibait, zieait, VecCUDAEquals());
4999: auto zibbit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
5000: auto ziebit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->end())));
5001: thrust::for_each(zibbit, ziebit, VecCUDAEquals());
5002: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(*C, PETSC_FALSE));
5003: if (A->form_explicit_transpose && B->form_explicit_transpose && (*C)->form_explicit_transpose) {
5004: PetscCheck(Ccusp->matTranspose, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing transpose Mat_SeqAIJCUSPARSEMultStruct");
5005: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
5006: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
5007: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
5008: CsrMatrix *CcsrT = (CsrMatrix *)Ccusp->matTranspose->mat;
5009: auto vT = CcsrT->values->begin();
5010: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
5011: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
5012: (*C)->transupdated = PETSC_TRUE;
5013: }
5014: PetscCall(PetscLogGpuTimeEnd());
5015: }
5016: }
5017: PetscCall(PetscObjectStateIncrease((PetscObject)*C));
5018: (*C)->assembled = PETSC_TRUE;
5019: (*C)->was_assembled = PETSC_FALSE;
5020: (*C)->offloadmask = PETSC_OFFLOAD_GPU;
5021: PetscFunctionReturn(PETSC_SUCCESS);
5022: }
5024: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
5025: {
5026: bool dmem;
5027: const PetscScalar *av;
5029: PetscFunctionBegin;
5030: dmem = isCudaMem(v);
5031: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(A, &av));
5032: if (n && idx) {
5033: THRUSTINTARRAY widx(n);
5034: widx.assign(idx, idx + n);
5035: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
5037: THRUSTARRAY *w = NULL;
5038: thrust::device_ptr<PetscScalar> dv;
5039: if (dmem) {
5040: dv = thrust::device_pointer_cast(v);
5041: } else {
5042: w = new THRUSTARRAY(n);
5043: dv = w->data();
5044: }
5045: thrust::device_ptr<const PetscScalar> dav = thrust::device_pointer_cast(av);
5047: auto zibit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.begin()), dv));
5048: auto zieit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.end()), dv + n));
5049: thrust::for_each(zibit, zieit, VecCUDAEquals());
5050: if (w) PetscCallCUDA(cudaMemcpy(v, w->data().get(), n * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
5051: delete w;
5052: } else {
5053: PetscCallCUDA(cudaMemcpy(v, av, n * sizeof(PetscScalar), dmem ? cudaMemcpyDeviceToDevice : cudaMemcpyDeviceToHost));
5054: }
5055: if (!dmem) PetscCall(PetscLogCpuToGpu(n * sizeof(PetscScalar)));
5056: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(A, &av));
5057: PetscFunctionReturn(PETSC_SUCCESS);
5058: }