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;
206: const MatScalar *Aa = a->a;
207: PetscInt *Mi, *Mj, Mnz;
208: PetscScalar *Ma;
210: PetscFunctionBegin;
211: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
212: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
213: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even when m=0
214: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host
215: Mnz = (Ai[m] - Ai[0]) + (adiag[0] - adiag[m]); // Lnz (without the unit diagonal) + Unz (with the non-unit diagonal)
216: PetscCall(PetscMalloc1(m + 1, &Mi));
217: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj is temp
218: PetscCall(PetscMalloc1(Mnz, &Ma));
219: Mi[0] = 0;
220: for (PetscInt i = 0; i < m; i++) {
221: PetscInt llen = Ai[i + 1] - Ai[i];
222: PetscInt ulen = adiag[i] - adiag[i + 1];
223: PetscCall(PetscArraycpy(Mj + Mi[i], Aj + Ai[i], llen)); // entries of L
224: Mj[Mi[i] + llen] = i; // diagonal entry
225: PetscCall(PetscArraycpy(Mj + Mi[i] + llen + 1, Aj + adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
226: Mi[i + 1] = Mi[i] + llen + ulen;
227: }
228: // Copy M (L,U) from host to device
229: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
230: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
231: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
232: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Mi, sizeof(*fs->csrRowPtr) * (m + 1), cudaMemcpyHostToDevice));
233: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*fs->csrColIdx) * Mnz, cudaMemcpyHostToDevice));
235: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
236: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
237: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
238: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
239: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
240: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_LOWER;
241: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT;
242: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
244: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
245: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
246: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
248: fillMode = CUSPARSE_FILL_MODE_UPPER;
249: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
250: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
251: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
252: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
254: // Allocate work vectors in SpSv
255: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
256: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
258: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
259: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
261: // Query buffer sizes for SpSV and then allocate buffers, temporarily assuming opA = CUSPARSE_OPERATION_NON_TRANSPOSE
262: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
263: 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));
264: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
265: 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));
266: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
267: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
269: // Record for reuse
270: fs->csrRowPtr_h = Mi;
271: fs->csrVal_h = Ma;
272: PetscCall(PetscFree(Mj));
273: }
274: // Copy the value
275: Mi = fs->csrRowPtr_h;
276: Ma = fs->csrVal_h;
277: Mnz = Mi[m];
278: for (PetscInt i = 0; i < m; i++) {
279: PetscInt llen = Ai[i + 1] - Ai[i];
280: PetscInt ulen = adiag[i] - adiag[i + 1];
281: PetscCall(PetscArraycpy(Ma + Mi[i], Aa + Ai[i], llen)); // entries of L
282: Ma[Mi[i] + llen] = (MatScalar)1.0 / Aa[adiag[i]]; // recover the diagonal entry
283: PetscCall(PetscArraycpy(Ma + Mi[i] + llen + 1, Aa + adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
284: }
285: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
287: #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
288: if (fs->updatedSpSVAnalysis) { // have done cusparseSpSV_analysis before, and only matrix values changed?
289: // Otherwise cusparse would error out: "On entry to cusparseSpSV_updateMatrix() parameter number 3 (newValues) had an illegal value: NULL pointer"
290: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
291: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
292: } else
293: #endif
294: {
295: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
296: 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));
298: 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));
299: fs->updatedSpSVAnalysis = PETSC_TRUE;
300: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
301: }
302: }
303: PetscFunctionReturn(PETSC_SUCCESS);
304: }
305: #else
306: static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A)
307: {
308: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
309: PetscInt n = A->rmap->n;
310: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
311: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
312: const PetscInt *ai = a->i, *aj = a->j, *vi;
313: const MatScalar *aa = a->a, *v;
314: PetscInt *AiLo, *AjLo;
315: PetscInt i, nz, nzLower, offset, rowOffset;
317: PetscFunctionBegin;
318: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
319: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
320: try {
321: /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */
322: nzLower = n + ai[n] - ai[1];
323: if (!loTriFactor) {
324: PetscScalar *AALo;
326: PetscCallCUDA(cudaMallocHost((void **)&AALo, nzLower * sizeof(PetscScalar)));
328: /* Allocate Space for the lower triangular matrix */
329: PetscCallCUDA(cudaMallocHost((void **)&AiLo, (n + 1) * sizeof(PetscInt)));
330: PetscCallCUDA(cudaMallocHost((void **)&AjLo, nzLower * sizeof(PetscInt)));
332: /* Fill the lower triangular matrix */
333: AiLo[0] = (PetscInt)0;
334: AiLo[n] = nzLower;
335: AjLo[0] = (PetscInt)0;
336: AALo[0] = (MatScalar)1.0;
337: v = aa;
338: vi = aj;
339: offset = 1;
340: rowOffset = 1;
341: for (i = 1; i < n; i++) {
342: nz = ai[i + 1] - ai[i];
343: /* additional 1 for the term on the diagonal */
344: AiLo[i] = rowOffset;
345: rowOffset += nz + 1;
347: PetscCall(PetscArraycpy(&AjLo[offset], vi, nz));
348: PetscCall(PetscArraycpy(&AALo[offset], v, nz));
350: offset += nz;
351: AjLo[offset] = (PetscInt)i;
352: AALo[offset] = (MatScalar)1.0;
353: offset += 1;
355: v += nz;
356: vi += nz;
357: }
359: /* allocate space for the triangular factor information */
360: PetscCall(PetscNew(&loTriFactor));
361: loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
362: /* Create the matrix description */
363: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
364: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
365: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
366: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
367: #else
368: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
369: #endif
370: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER));
371: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));
373: /* set the operation */
374: loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
376: /* set the matrix */
377: loTriFactor->csrMat = new CsrMatrix;
378: loTriFactor->csrMat->num_rows = n;
379: loTriFactor->csrMat->num_cols = n;
380: loTriFactor->csrMat->num_entries = nzLower;
382: loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
383: loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo + n + 1);
385: loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower);
386: loTriFactor->csrMat->column_indices->assign(AjLo, AjLo + nzLower);
388: loTriFactor->csrMat->values = new THRUSTARRAY(nzLower);
389: loTriFactor->csrMat->values->assign(AALo, AALo + nzLower);
391: /* Create the solve analysis information */
392: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
393: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
394: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
395: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
396: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
397: PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
398: #endif
400: /* perform the solve analysis */
401: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
402: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
403: PetscCallCUDA(WaitForCUDA());
404: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
406: /* assign the pointer */
407: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
408: loTriFactor->AA_h = AALo;
409: PetscCallCUDA(cudaFreeHost(AiLo));
410: PetscCallCUDA(cudaFreeHost(AjLo));
411: PetscCall(PetscLogCpuToGpu((n + 1 + nzLower) * sizeof(int) + nzLower * sizeof(PetscScalar)));
412: } else { /* update values only */
413: if (!loTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&loTriFactor->AA_h, nzLower * sizeof(PetscScalar)));
414: /* Fill the lower triangular matrix */
415: loTriFactor->AA_h[0] = 1.0;
416: v = aa;
417: vi = aj;
418: offset = 1;
419: for (i = 1; i < n; i++) {
420: nz = ai[i + 1] - ai[i];
421: PetscCall(PetscArraycpy(&loTriFactor->AA_h[offset], v, nz));
422: offset += nz;
423: loTriFactor->AA_h[offset] = 1.0;
424: offset += 1;
425: v += nz;
426: }
427: loTriFactor->csrMat->values->assign(loTriFactor->AA_h, loTriFactor->AA_h + nzLower);
428: PetscCall(PetscLogCpuToGpu(nzLower * sizeof(PetscScalar)));
429: }
430: } catch (char *ex) {
431: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
432: }
433: }
434: PetscFunctionReturn(PETSC_SUCCESS);
435: }
437: static PetscErrorCode MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(Mat A)
438: {
439: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
440: PetscInt n = A->rmap->n;
441: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
442: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
443: const PetscInt *aj = a->j, *adiag, *vi;
444: const MatScalar *aa = a->a, *v;
445: PetscInt *AiUp, *AjUp;
446: PetscInt i, nz, nzUpper, offset;
448: PetscFunctionBegin;
449: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
450: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
451: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
452: try {
453: /* next, figure out the number of nonzeros in the upper triangular matrix. */
454: nzUpper = adiag[0] - adiag[n];
455: if (!upTriFactor) {
456: PetscScalar *AAUp;
458: PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
460: /* Allocate Space for the upper triangular matrix */
461: PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
462: PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));
464: /* Fill the upper triangular matrix */
465: AiUp[0] = (PetscInt)0;
466: AiUp[n] = nzUpper;
467: offset = nzUpper;
468: for (i = n - 1; i >= 0; i--) {
469: v = aa + adiag[i + 1] + 1;
470: vi = aj + adiag[i + 1] + 1;
472: /* number of elements NOT on the diagonal */
473: nz = adiag[i] - adiag[i + 1] - 1;
475: /* decrement the offset */
476: offset -= (nz + 1);
478: /* first, set the diagonal elements */
479: AjUp[offset] = (PetscInt)i;
480: AAUp[offset] = (MatScalar)1. / v[nz];
481: AiUp[i] = AiUp[i + 1] - (nz + 1);
483: PetscCall(PetscArraycpy(&AjUp[offset + 1], vi, nz));
484: PetscCall(PetscArraycpy(&AAUp[offset + 1], v, nz));
485: }
487: /* allocate space for the triangular factor information */
488: PetscCall(PetscNew(&upTriFactor));
489: upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
491: /* Create the matrix description */
492: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
493: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
494: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
495: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
496: #else
497: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
498: #endif
499: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
500: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));
502: /* set the operation */
503: upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
505: /* set the matrix */
506: upTriFactor->csrMat = new CsrMatrix;
507: upTriFactor->csrMat->num_rows = n;
508: upTriFactor->csrMat->num_cols = n;
509: upTriFactor->csrMat->num_entries = nzUpper;
511: upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
512: upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + n + 1);
514: upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzUpper);
515: upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + nzUpper);
517: upTriFactor->csrMat->values = new THRUSTARRAY(nzUpper);
518: upTriFactor->csrMat->values->assign(AAUp, AAUp + nzUpper);
520: /* Create the solve analysis information */
521: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
522: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
523: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
524: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
525: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
526: PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
527: #endif
529: /* perform the solve analysis */
530: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
531: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
533: PetscCallCUDA(WaitForCUDA());
534: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
536: /* assign the pointer */
537: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
538: upTriFactor->AA_h = AAUp;
539: PetscCallCUDA(cudaFreeHost(AiUp));
540: PetscCallCUDA(cudaFreeHost(AjUp));
541: PetscCall(PetscLogCpuToGpu((n + 1 + nzUpper) * sizeof(int) + nzUpper * sizeof(PetscScalar)));
542: } else {
543: if (!upTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&upTriFactor->AA_h, nzUpper * sizeof(PetscScalar)));
544: /* Fill the upper triangular matrix */
545: offset = nzUpper;
546: for (i = n - 1; i >= 0; i--) {
547: v = aa + adiag[i + 1] + 1;
549: /* number of elements NOT on the diagonal */
550: nz = adiag[i] - adiag[i + 1] - 1;
552: /* decrement the offset */
553: offset -= (nz + 1);
555: /* first, set the diagonal elements */
556: upTriFactor->AA_h[offset] = 1. / v[nz];
557: PetscCall(PetscArraycpy(&upTriFactor->AA_h[offset + 1], v, nz));
558: }
559: upTriFactor->csrMat->values->assign(upTriFactor->AA_h, upTriFactor->AA_h + nzUpper);
560: PetscCall(PetscLogCpuToGpu(nzUpper * sizeof(PetscScalar)));
561: }
562: } catch (char *ex) {
563: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
564: }
565: }
566: PetscFunctionReturn(PETSC_SUCCESS);
567: }
568: #endif
570: static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A)
571: {
572: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
573: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
574: IS isrow = a->row, isicol = a->icol;
575: PetscBool row_identity, col_identity;
576: PetscInt n = A->rmap->n;
578: PetscFunctionBegin;
579: PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
580: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
581: PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(A));
582: #else
583: PetscCall(MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A));
584: PetscCall(MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A));
585: if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
586: #endif
588: cusparseTriFactors->nnz = a->nz;
590: A->offloadmask = PETSC_OFFLOAD_BOTH; // factored matrix is sync'ed to GPU
591: /* lower triangular indices */
592: PetscCall(ISIdentity(isrow, &row_identity));
593: if (!row_identity && !cusparseTriFactors->rpermIndices) {
594: const PetscInt *r;
596: PetscCall(ISGetIndices(isrow, &r));
597: cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
598: cusparseTriFactors->rpermIndices->assign(r, r + n);
599: PetscCall(ISRestoreIndices(isrow, &r));
600: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
601: }
603: /* upper triangular indices */
604: PetscCall(ISIdentity(isicol, &col_identity));
605: if (!col_identity && !cusparseTriFactors->cpermIndices) {
606: const PetscInt *c;
608: PetscCall(ISGetIndices(isicol, &c));
609: cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
610: cusparseTriFactors->cpermIndices->assign(c, c + n);
611: PetscCall(ISRestoreIndices(isicol, &c));
612: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
613: }
614: PetscFunctionReturn(PETSC_SUCCESS);
615: }
617: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
618: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_Cholesky(Mat A)
619: {
620: Mat_SeqAIJ *a = static_cast<Mat_SeqAIJ *>(A->data);
621: PetscInt m = A->rmap->n;
622: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
623: const PetscInt *Ai = a->i, *Aj = a->j, *adiag;
624: const MatScalar *Aa = a->a;
625: PetscInt *Mj, Mnz;
626: PetscScalar *Ma, *D;
628: PetscFunctionBegin;
629: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
630: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
631: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even m=0
632: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host.
633: // See comments at MatICCFactorSymbolic_SeqAIJ() on the layout of the factored matrix (U) on host.
634: Mnz = Ai[m]; // Unz (with the unit diagonal)
635: PetscCall(PetscMalloc1(Mnz, &Ma));
636: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj[] is temp
637: PetscCall(PetscMalloc1(m, &D)); // the diagonal
638: for (PetscInt i = 0; i < m; i++) {
639: PetscInt ulen = Ai[i + 1] - Ai[i];
640: Mj[Ai[i]] = i; // diagonal entry
641: PetscCall(PetscArraycpy(Mj + Ai[i] + 1, Aj + Ai[i], ulen - 1)); // entries of U on the right of the diagonal
642: }
643: // Copy M (U) from host to device
644: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
645: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
646: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
647: PetscCallCUDA(cudaMalloc(&fs->diag, sizeof(*fs->diag) * m));
648: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyHostToDevice));
649: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*Mj) * Mnz, cudaMemcpyHostToDevice));
651: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
652: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
653: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
654: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
655: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
656: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_UPPER;
657: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT; // U is unit diagonal
658: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
660: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
661: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
662: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
664: // Allocate work vectors in SpSv
665: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
666: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
668: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
669: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
671: // Query buffer sizes for SpSV and then allocate buffers
672: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
673: 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));
674: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
676: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut)); // Ut solve uses the same matrix (spMatDescr_U), but different descr and buffer
677: 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));
678: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
680: // Record for reuse
681: fs->csrVal_h = Ma;
682: fs->diag_h = D;
683: PetscCall(PetscFree(Mj));
684: }
685: // Copy the value
686: Ma = fs->csrVal_h;
687: D = fs->diag_h;
688: Mnz = Ai[m];
689: for (PetscInt i = 0; i < m; i++) {
690: D[i] = Aa[adiag[i]]; // actually Aa[adiag[i]] is the inverse of the diagonal
691: 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
692: for (PetscInt k = 0; k < Ai[i + 1] - Ai[i] - 1; k++) Ma[Ai[i] + 1 + k] = -Aa[Ai[i] + k];
693: }
694: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
695: PetscCallCUDA(cudaMemcpy(fs->diag, D, sizeof(*D) * m, cudaMemcpyHostToDevice));
697: #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
698: if (fs->updatedSpSVAnalysis) {
699: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
700: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_Ut, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
701: } else
702: #endif
703: {
704: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
705: 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));
706: 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));
707: fs->updatedSpSVAnalysis = PETSC_TRUE;
708: }
709: }
710: PetscFunctionReturn(PETSC_SUCCESS);
711: }
713: // Solve Ut D U x = b
714: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_Cholesky(Mat A, Vec b, Vec x)
715: {
716: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
717: Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
718: const PetscScalar *barray;
719: PetscScalar *xarray;
720: thrust::device_ptr<const PetscScalar> bGPU;
721: thrust::device_ptr<PetscScalar> xGPU;
722: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
723: PetscInt m = A->rmap->n;
725: PetscFunctionBegin;
726: PetscCall(PetscLogGpuTimeBegin());
727: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
728: PetscCall(VecCUDAGetArrayRead(b, &barray));
729: xGPU = thrust::device_pointer_cast(xarray);
730: bGPU = thrust::device_pointer_cast(barray);
732: // Reorder b with the row permutation if needed, and wrap the result in fs->X
733: if (fs->rpermIndices) {
734: 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)));
735: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
736: } else {
737: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
738: }
740: // Solve Ut Y = X
741: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
742: 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));
744: // Solve diag(D) Z = Y. Actually just do Y = Y*D since D is already inverted in MatCholeskyFactorNumeric_SeqAIJ().
745: // It is basically a vector element-wise multiplication, but cublas does not have it!
746: #if CCCL_VERSION >= 3001000
747: 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), cuda::std::multiplies<PetscScalar>()));
748: #else
749: 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>()));
750: #endif
752: // Solve U X = Y
753: if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
754: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
755: } else {
756: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
757: }
758: 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));
760: // Reorder X with the column permutation if needed, and put the result back to x
761: if (fs->cpermIndices) {
762: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
763: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
764: }
766: PetscCall(VecCUDARestoreArrayRead(b, &barray));
767: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
768: PetscCall(PetscLogGpuTimeEnd());
769: PetscCall(PetscLogGpuFlops(4.0 * aij->nz - A->rmap->n));
770: PetscFunctionReturn(PETSC_SUCCESS);
771: }
772: #else
773: static PetscErrorCode MatSeqAIJCUSPARSEBuildICCTriMatrices(Mat A)
774: {
775: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
776: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
777: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
778: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
779: PetscInt *AiUp, *AjUp;
780: PetscScalar *AAUp;
781: PetscScalar *AALo;
782: PetscInt nzUpper = a->nz, n = A->rmap->n, i, offset, nz, j;
783: Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ *)A->data;
784: const PetscInt *ai = b->i, *aj = b->j, *vj;
785: const MatScalar *aa = b->a, *v;
787: PetscFunctionBegin;
788: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
789: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
790: try {
791: PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
792: PetscCallCUDA(cudaMallocHost((void **)&AALo, nzUpper * sizeof(PetscScalar)));
793: if (!upTriFactor && !loTriFactor) {
794: /* Allocate Space for the upper triangular matrix */
795: PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
796: PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));
798: /* Fill the upper triangular matrix */
799: AiUp[0] = (PetscInt)0;
800: AiUp[n] = nzUpper;
801: offset = 0;
802: for (i = 0; i < n; i++) {
803: /* set the pointers */
804: v = aa + ai[i];
805: vj = aj + ai[i];
806: nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */
808: /* first, set the diagonal elements */
809: AjUp[offset] = (PetscInt)i;
810: AAUp[offset] = (MatScalar)1.0 / v[nz];
811: AiUp[i] = offset;
812: AALo[offset] = (MatScalar)1.0 / v[nz];
814: offset += 1;
815: if (nz > 0) {
816: PetscCall(PetscArraycpy(&AjUp[offset], vj, nz));
817: PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
818: for (j = offset; j < offset + nz; j++) {
819: AAUp[j] = -AAUp[j];
820: AALo[j] = AAUp[j] / v[nz];
821: }
822: offset += nz;
823: }
824: }
826: /* allocate space for the triangular factor information */
827: PetscCall(PetscNew(&upTriFactor));
828: upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
830: /* Create the matrix description */
831: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
832: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
833: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
834: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
835: #else
836: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
837: #endif
838: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
839: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));
841: /* set the matrix */
842: upTriFactor->csrMat = new CsrMatrix;
843: upTriFactor->csrMat->num_rows = A->rmap->n;
844: upTriFactor->csrMat->num_cols = A->cmap->n;
845: upTriFactor->csrMat->num_entries = a->nz;
847: upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
848: upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);
850: upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
851: upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);
853: upTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
854: upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);
856: /* set the operation */
857: upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
859: /* Create the solve analysis information */
860: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
861: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
862: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
863: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
864: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
865: PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
866: #endif
868: /* perform the solve analysis */
869: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
870: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
872: PetscCallCUDA(WaitForCUDA());
873: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
875: /* assign the pointer */
876: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
878: /* allocate space for the triangular factor information */
879: PetscCall(PetscNew(&loTriFactor));
880: loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
882: /* Create the matrix description */
883: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
884: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
885: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
886: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
887: #else
888: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
889: #endif
890: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
891: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));
893: /* set the operation */
894: loTriFactor->solveOp = CUSPARSE_OPERATION_TRANSPOSE;
896: /* set the matrix */
897: loTriFactor->csrMat = new CsrMatrix;
898: loTriFactor->csrMat->num_rows = A->rmap->n;
899: loTriFactor->csrMat->num_cols = A->cmap->n;
900: loTriFactor->csrMat->num_entries = a->nz;
902: loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
903: loTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);
905: loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
906: loTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);
908: loTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
909: loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);
911: /* Create the solve analysis information */
912: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
913: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
914: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
915: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
916: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
917: PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
918: #endif
920: /* perform the solve analysis */
921: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
922: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
924: PetscCallCUDA(WaitForCUDA());
925: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
927: /* assign the pointer */
928: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
930: PetscCall(PetscLogCpuToGpu(2 * (((A->rmap->n + 1) + (a->nz)) * sizeof(int) + (a->nz) * sizeof(PetscScalar))));
931: PetscCallCUDA(cudaFreeHost(AiUp));
932: PetscCallCUDA(cudaFreeHost(AjUp));
933: } else {
934: /* Fill the upper triangular matrix */
935: offset = 0;
936: for (i = 0; i < n; i++) {
937: /* set the pointers */
938: v = aa + ai[i];
939: nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */
941: /* first, set the diagonal elements */
942: AAUp[offset] = 1.0 / v[nz];
943: AALo[offset] = 1.0 / v[nz];
945: offset += 1;
946: if (nz > 0) {
947: PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
948: for (j = offset; j < offset + nz; j++) {
949: AAUp[j] = -AAUp[j];
950: AALo[j] = AAUp[j] / v[nz];
951: }
952: offset += nz;
953: }
954: }
955: PetscCheck(upTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
956: PetscCheck(loTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
957: upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);
958: loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);
959: PetscCall(PetscLogCpuToGpu(2 * (a->nz) * sizeof(PetscScalar)));
960: }
961: PetscCallCUDA(cudaFreeHost(AAUp));
962: PetscCallCUDA(cudaFreeHost(AALo));
963: } catch (char *ex) {
964: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
965: }
966: }
967: PetscFunctionReturn(PETSC_SUCCESS);
968: }
969: #endif
971: static PetscErrorCode MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(Mat A)
972: {
973: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
974: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
975: IS ip = a->row;
976: PetscBool perm_identity;
977: PetscInt n = A->rmap->n;
979: PetscFunctionBegin;
980: PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
982: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
983: PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_Cholesky(A));
984: #else
985: PetscCall(MatSeqAIJCUSPARSEBuildICCTriMatrices(A));
986: if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
987: #endif
988: cusparseTriFactors->nnz = (a->nz - n) * 2 + n;
990: A->offloadmask = PETSC_OFFLOAD_BOTH;
992: /* lower triangular indices */
993: PetscCall(ISIdentity(ip, &perm_identity));
994: if (!perm_identity) {
995: IS iip;
996: const PetscInt *irip, *rip;
998: PetscCall(ISInvertPermutation(ip, PETSC_DECIDE, &iip));
999: PetscCall(ISGetIndices(iip, &irip));
1000: PetscCall(ISGetIndices(ip, &rip));
1001: cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
1002: cusparseTriFactors->rpermIndices->assign(rip, rip + n);
1003: cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
1004: cusparseTriFactors->cpermIndices->assign(irip, irip + n);
1005: PetscCall(ISRestoreIndices(iip, &irip));
1006: PetscCall(ISDestroy(&iip));
1007: PetscCall(ISRestoreIndices(ip, &rip));
1008: PetscCall(PetscLogCpuToGpu(2. * n * sizeof(PetscInt)));
1009: }
1010: PetscFunctionReturn(PETSC_SUCCESS);
1011: }
1013: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
1014: {
1015: PetscFunctionBegin;
1016: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
1017: PetscCall(MatCholeskyFactorNumeric_SeqAIJ(B, A, info));
1018: B->offloadmask = PETSC_OFFLOAD_CPU;
1020: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1021: B->ops->solve = MatSolve_SeqAIJCUSPARSE_Cholesky;
1022: B->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_Cholesky;
1023: #else
1024: /* determine which version of MatSolve needs to be used. */
1025: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
1026: IS ip = b->row;
1027: PetscBool perm_identity;
1029: PetscCall(ISIdentity(ip, &perm_identity));
1030: if (perm_identity) {
1031: B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
1032: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
1033: } else {
1034: B->ops->solve = MatSolve_SeqAIJCUSPARSE;
1035: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
1036: }
1037: #endif
1038: B->ops->matsolve = NULL;
1039: B->ops->matsolvetranspose = NULL;
1041: /* get the triangular factors */
1042: PetscCall(MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(B));
1043: PetscFunctionReturn(PETSC_SUCCESS);
1044: }
1046: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1047: static PetscErrorCode MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(Mat A)
1048: {
1049: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1050: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1051: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1052: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT;
1053: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT;
1054: cusparseIndexBase_t indexBase;
1055: cusparseMatrixType_t matrixType;
1056: cusparseFillMode_t fillMode;
1057: cusparseDiagType_t diagType;
1059: PetscFunctionBegin;
1060: /* allocate space for the transpose of the lower triangular factor */
1061: PetscCall(PetscNew(&loTriFactorT));
1062: loTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1064: /* set the matrix descriptors of the lower triangular factor */
1065: matrixType = cusparseGetMatType(loTriFactor->descr);
1066: indexBase = cusparseGetMatIndexBase(loTriFactor->descr);
1067: fillMode = cusparseGetMatFillMode(loTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1068: diagType = cusparseGetMatDiagType(loTriFactor->descr);
1070: /* Create the matrix description */
1071: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactorT->descr));
1072: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactorT->descr, indexBase));
1073: PetscCallCUSPARSE(cusparseSetMatType(loTriFactorT->descr, matrixType));
1074: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactorT->descr, fillMode));
1075: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactorT->descr, diagType));
1077: /* set the operation */
1078: loTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
1080: /* allocate GPU space for the CSC of the lower triangular factor*/
1081: loTriFactorT->csrMat = new CsrMatrix;
1082: loTriFactorT->csrMat->num_rows = loTriFactor->csrMat->num_cols;
1083: loTriFactorT->csrMat->num_cols = loTriFactor->csrMat->num_rows;
1084: loTriFactorT->csrMat->num_entries = loTriFactor->csrMat->num_entries;
1085: loTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_rows + 1);
1086: loTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_entries);
1087: loTriFactorT->csrMat->values = new THRUSTARRAY(loTriFactorT->csrMat->num_entries);
1089: /* compute the transpose of the lower triangular factor, i.e. the CSC */
1090: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1091: PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, loTriFactor->csrMat->values->data().get(),
1092: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(), loTriFactorT->csrMat->row_offsets->data().get(),
1093: loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &loTriFactor->csr2cscBufferSize));
1094: PetscCallCUDA(cudaMalloc(&loTriFactor->csr2cscBuffer, loTriFactor->csr2cscBufferSize));
1095: #endif
1097: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1098: {
1099: // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1100: 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(),
1101: loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(),
1102: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1103: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, loTriFactor->csr2cscBuffer);
1104: #else
1105: loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1106: #endif
1107: PetscCallCUSPARSE(stat);
1108: }
1110: PetscCallCUDA(WaitForCUDA());
1111: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1113: /* Create the solve analysis information */
1114: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1115: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactorT->solveInfo));
1116: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1117: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1118: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, &loTriFactorT->solveBufferSize));
1119: PetscCallCUDA(cudaMalloc(&loTriFactorT->solveBuffer, loTriFactorT->solveBufferSize));
1120: #endif
1122: /* perform the solve analysis */
1123: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1124: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1126: PetscCallCUDA(WaitForCUDA());
1127: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1129: /* assign the pointer */
1130: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtrTranspose = loTriFactorT;
1132: /*********************************************/
1133: /* Now the Transpose of the Upper Tri Factor */
1134: /*********************************************/
1136: /* allocate space for the transpose of the upper triangular factor */
1137: PetscCall(PetscNew(&upTriFactorT));
1138: upTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1140: /* set the matrix descriptors of the upper triangular factor */
1141: matrixType = cusparseGetMatType(upTriFactor->descr);
1142: indexBase = cusparseGetMatIndexBase(upTriFactor->descr);
1143: fillMode = cusparseGetMatFillMode(upTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1144: diagType = cusparseGetMatDiagType(upTriFactor->descr);
1146: /* Create the matrix description */
1147: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactorT->descr));
1148: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactorT->descr, indexBase));
1149: PetscCallCUSPARSE(cusparseSetMatType(upTriFactorT->descr, matrixType));
1150: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactorT->descr, fillMode));
1151: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactorT->descr, diagType));
1153: /* set the operation */
1154: upTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
1156: /* allocate GPU space for the CSC of the upper triangular factor*/
1157: upTriFactorT->csrMat = new CsrMatrix;
1158: upTriFactorT->csrMat->num_rows = upTriFactor->csrMat->num_cols;
1159: upTriFactorT->csrMat->num_cols = upTriFactor->csrMat->num_rows;
1160: upTriFactorT->csrMat->num_entries = upTriFactor->csrMat->num_entries;
1161: upTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_rows + 1);
1162: upTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_entries);
1163: upTriFactorT->csrMat->values = new THRUSTARRAY(upTriFactorT->csrMat->num_entries);
1165: /* compute the transpose of the upper triangular factor, i.e. the CSC */
1166: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1167: PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, upTriFactor->csrMat->values->data().get(),
1168: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(), upTriFactorT->csrMat->row_offsets->data().get(),
1169: upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &upTriFactor->csr2cscBufferSize));
1170: PetscCallCUDA(cudaMalloc(&upTriFactor->csr2cscBuffer, upTriFactor->csr2cscBufferSize));
1171: #endif
1173: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1174: {
1175: // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1176: 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(),
1177: upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(),
1178: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1179: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, upTriFactor->csr2cscBuffer);
1180: #else
1181: upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1182: #endif
1183: PetscCallCUSPARSE(stat);
1184: }
1186: PetscCallCUDA(WaitForCUDA());
1187: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1189: /* Create the solve analysis information */
1190: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1191: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactorT->solveInfo));
1192: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1193: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(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->solveBufferSize));
1195: PetscCallCUDA(cudaMalloc(&upTriFactorT->solveBuffer, upTriFactorT->solveBufferSize));
1196: #endif
1198: /* perform the solve analysis */
1199: /* christ, would it have killed you to put this stuff in a function????????? */
1200: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1201: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1203: PetscCallCUDA(WaitForCUDA());
1204: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1206: /* assign the pointer */
1207: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtrTranspose = upTriFactorT;
1208: PetscFunctionReturn(PETSC_SUCCESS);
1209: }
1210: #endif
1212: struct PetscScalarToPetscInt {
1213: __host__ __device__ PetscInt operator()(PetscScalar s) { return (PetscInt)PetscRealPart(s); }
1214: };
1216: static PetscErrorCode MatSeqAIJCUSPARSEFormExplicitTranspose(Mat A)
1217: {
1218: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
1219: Mat_SeqAIJCUSPARSEMultStruct *matstruct, *matstructT;
1220: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1221: cusparseStatus_t stat;
1222: cusparseIndexBase_t indexBase;
1224: PetscFunctionBegin;
1225: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1226: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
1227: PetscCheck(matstruct, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing mat struct");
1228: matstructT = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
1229: PetscCheck(!A->transupdated || matstructT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing matTranspose struct");
1230: if (A->transupdated) PetscFunctionReturn(PETSC_SUCCESS);
1231: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1232: PetscCall(PetscLogGpuTimeBegin());
1233: if (cusparsestruct->format != MAT_CUSPARSE_CSR) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
1234: if (!cusparsestruct->matTranspose) { /* create cusparse matrix */
1235: matstructT = new Mat_SeqAIJCUSPARSEMultStruct;
1236: PetscCallCUSPARSE(cusparseCreateMatDescr(&matstructT->descr));
1237: indexBase = cusparseGetMatIndexBase(matstruct->descr);
1238: PetscCallCUSPARSE(cusparseSetMatIndexBase(matstructT->descr, indexBase));
1239: PetscCallCUSPARSE(cusparseSetMatType(matstructT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
1241: /* set alpha and beta */
1242: PetscCallCUDA(cudaMalloc((void **)&matstructT->alpha_one, sizeof(PetscScalar)));
1243: PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_zero, sizeof(PetscScalar)));
1244: PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_one, sizeof(PetscScalar)));
1245: PetscCallCUDA(cudaMemcpy(matstructT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1246: PetscCallCUDA(cudaMemcpy(matstructT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1247: PetscCallCUDA(cudaMemcpy(matstructT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1249: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
1250: CsrMatrix *matrixT = new CsrMatrix;
1251: matstructT->mat = matrixT;
1252: matrixT->num_rows = A->cmap->n;
1253: matrixT->num_cols = A->rmap->n;
1254: matrixT->num_entries = a->nz;
1255: matrixT->row_offsets = new THRUSTINTARRAY32(matrixT->num_rows + 1);
1256: matrixT->column_indices = new THRUSTINTARRAY32(a->nz);
1257: matrixT->values = new THRUSTARRAY(a->nz);
1259: if (!cusparsestruct->rowoffsets_gpu) cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1260: cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
1262: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1263: #if PETSC_PKG_CUDA_VERSION_GE(11, 2, 1)
1264: 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 */
1265: indexBase, cusparse_scalartype);
1266: PetscCallCUSPARSE(stat);
1267: #else
1268: /* cusparse-11.x returns errors with zero-sized matrices until 11.2.1,
1269: see https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cusparse-11.2.1
1271: I don't know what a proper value should be for matstructT->matDescr with empty matrices, so I just set
1272: it to NULL to blow it up if one relies on it. Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2,
1273: when nnz = 0, matrixT->row_offsets[] should be filled with indexBase. So I also set it accordingly.
1274: */
1275: if (matrixT->num_entries) {
1276: 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);
1277: PetscCallCUSPARSE(stat);
1279: } else {
1280: matstructT->matDescr = NULL;
1281: matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1282: }
1283: #endif
1284: #endif
1285: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
1286: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1287: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
1288: #else
1289: CsrMatrix *temp = new CsrMatrix;
1290: CsrMatrix *tempT = new CsrMatrix;
1291: /* First convert HYB to CSR */
1292: temp->num_rows = A->rmap->n;
1293: temp->num_cols = A->cmap->n;
1294: temp->num_entries = a->nz;
1295: temp->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
1296: temp->column_indices = new THRUSTINTARRAY32(a->nz);
1297: temp->values = new THRUSTARRAY(a->nz);
1299: 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());
1300: PetscCallCUSPARSE(stat);
1302: /* Next, convert CSR to CSC (i.e. the matrix transpose) */
1303: tempT->num_rows = A->rmap->n;
1304: tempT->num_cols = A->cmap->n;
1305: tempT->num_entries = a->nz;
1306: tempT->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
1307: tempT->column_indices = new THRUSTINTARRAY32(a->nz);
1308: tempT->values = new THRUSTARRAY(a->nz);
1310: 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(),
1311: tempT->column_indices->data().get(), tempT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1312: PetscCallCUSPARSE(stat);
1314: /* Last, convert CSC to HYB */
1315: cusparseHybMat_t hybMat;
1316: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
1317: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
1318: 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);
1319: PetscCallCUSPARSE(stat);
1321: /* assign the pointer */
1322: matstructT->mat = hybMat;
1323: A->transupdated = PETSC_TRUE;
1324: /* delete temporaries */
1325: if (tempT) {
1326: if (tempT->values) delete (THRUSTARRAY *)tempT->values;
1327: if (tempT->column_indices) delete (THRUSTINTARRAY32 *)tempT->column_indices;
1328: if (tempT->row_offsets) delete (THRUSTINTARRAY32 *)tempT->row_offsets;
1329: delete (CsrMatrix *)tempT;
1330: }
1331: if (temp) {
1332: if (temp->values) delete (THRUSTARRAY *)temp->values;
1333: if (temp->column_indices) delete (THRUSTINTARRAY32 *)temp->column_indices;
1334: if (temp->row_offsets) delete (THRUSTINTARRAY32 *)temp->row_offsets;
1335: delete (CsrMatrix *)temp;
1336: }
1337: #endif
1338: }
1339: }
1340: if (cusparsestruct->format == MAT_CUSPARSE_CSR) { /* transpose mat struct may be already present, update data */
1341: CsrMatrix *matrix = (CsrMatrix *)matstruct->mat;
1342: CsrMatrix *matrixT = (CsrMatrix *)matstructT->mat;
1343: PetscCheck(matrix, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix");
1344: PetscCheck(matrix->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix rows");
1345: PetscCheck(matrix->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix cols");
1346: PetscCheck(matrix->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix values");
1347: PetscCheck(matrixT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT");
1348: PetscCheck(matrixT->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT rows");
1349: PetscCheck(matrixT->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT cols");
1350: PetscCheck(matrixT->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT values");
1351: if (!cusparsestruct->rowoffsets_gpu) { /* this may be absent when we did not construct the transpose with csr2csc */
1352: cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1353: cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
1354: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
1355: }
1356: if (!cusparsestruct->csr2csc_i) {
1357: THRUSTARRAY csr2csc_a(matrix->num_entries);
1358: PetscCallThrust(thrust::sequence(thrust::device, csr2csc_a.begin(), csr2csc_a.end(), 0.0));
1360: indexBase = cusparseGetMatIndexBase(matstruct->descr);
1361: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1362: void *csr2cscBuffer;
1363: size_t csr2cscBufferSize;
1364: 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(),
1365: matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, &csr2cscBufferSize);
1366: PetscCallCUSPARSE(stat);
1367: PetscCallCUDA(cudaMalloc(&csr2cscBuffer, csr2cscBufferSize));
1368: #endif
1370: if (matrix->num_entries) {
1371: /* When there are no nonzeros, this routine mistakenly returns CUSPARSE_STATUS_INVALID_VALUE in
1372: mat_tests-ex62_15_mpiaijcusparse on ranks 0 and 2 with CUDA-11. But CUDA-10 is OK.
1373: I checked every parameters and they were just fine. I have no clue why cusparse complains.
1375: Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2, when nnz = 0, matrixT->row_offsets[]
1376: should be filled with indexBase. So I just take a shortcut here.
1377: */
1378: 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(),
1379: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1380: matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, csr2cscBuffer);
1381: PetscCallCUSPARSE(stat);
1382: #else
1383: matrixT->column_indices->data().get(), matrixT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1384: PetscCallCUSPARSE(stat);
1385: #endif
1386: } else {
1387: matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1388: }
1390: cusparsestruct->csr2csc_i = new THRUSTINTARRAY(matrix->num_entries);
1391: PetscCallThrust(thrust::transform(thrust::device, matrixT->values->begin(), matrixT->values->end(), cusparsestruct->csr2csc_i->begin(), PetscScalarToPetscInt()));
1392: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1393: PetscCallCUDA(cudaFree(csr2cscBuffer));
1394: #endif
1395: }
1396: PetscCallThrust(
1397: 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()));
1398: }
1399: PetscCall(PetscLogGpuTimeEnd());
1400: PetscCall(PetscLogEventEnd(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1401: /* the compressed row indices is not used for matTranspose */
1402: matstructT->cprowIndices = NULL;
1403: /* assign the pointer */
1404: ((Mat_SeqAIJCUSPARSE *)A->spptr)->matTranspose = matstructT;
1405: A->transupdated = PETSC_TRUE;
1406: PetscFunctionReturn(PETSC_SUCCESS);
1407: }
1409: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1410: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1411: {
1412: const PetscScalar *barray;
1413: PetscScalar *xarray;
1414: thrust::device_ptr<const PetscScalar> bGPU;
1415: thrust::device_ptr<PetscScalar> xGPU;
1416: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1417: const Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
1418: const cusparseOperation_t op = CUSPARSE_OPERATION_NON_TRANSPOSE;
1419: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
1420: PetscInt m = A->rmap->n;
1422: PetscFunctionBegin;
1423: PetscCall(PetscLogGpuTimeBegin());
1424: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1425: PetscCall(VecCUDAGetArrayRead(b, &barray));
1426: xGPU = thrust::device_pointer_cast(xarray);
1427: bGPU = thrust::device_pointer_cast(barray);
1429: // Reorder b with the row permutation if needed, and wrap the result in fs->X
1430: if (fs->rpermIndices) {
1431: 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)));
1432: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1433: } else {
1434: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1435: }
1437: // Solve L Y = X
1438: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1439: // Note that cusparseSpSV_solve() secretly uses the external buffer used in cusparseSpSV_analysis()!
1440: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_L));
1442: // Solve U X = Y
1443: if (fs->cpermIndices) {
1444: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1445: } else {
1446: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1447: }
1448: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_U));
1450: // Reorder X with the column permutation if needed, and put the result back to x
1451: if (fs->cpermIndices) {
1452: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1453: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1454: }
1455: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1456: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1457: PetscCall(PetscLogGpuTimeEnd());
1458: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - m));
1459: PetscFunctionReturn(PETSC_SUCCESS);
1460: }
1462: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1463: {
1464: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1465: Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
1466: const PetscScalar *barray;
1467: PetscScalar *xarray;
1468: thrust::device_ptr<const PetscScalar> bGPU;
1469: thrust::device_ptr<PetscScalar> xGPU;
1470: const cusparseOperation_t opA = CUSPARSE_OPERATION_TRANSPOSE;
1471: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
1472: PetscInt m = A->rmap->n;
1474: PetscFunctionBegin;
1475: PetscCall(PetscLogGpuTimeBegin());
1476: if (!fs->createdTransposeSpSVDescr) { // Call MatSolveTranspose() for the first time
1477: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
1478: PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* The matrix is still L. We only do transpose solve with it */
1479: fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Lt, &fs->spsvBufferSize_Lt));
1481: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut));
1482: 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));
1483: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
1484: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
1485: fs->createdTransposeSpSVDescr = PETSC_TRUE;
1486: }
1488: if (!fs->updatedTransposeSpSVAnalysis) {
1489: 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));
1491: 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));
1492: fs->updatedTransposeSpSVAnalysis = PETSC_TRUE;
1493: }
1495: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1496: PetscCall(VecCUDAGetArrayRead(b, &barray));
1497: xGPU = thrust::device_pointer_cast(xarray);
1498: bGPU = thrust::device_pointer_cast(barray);
1500: // Reorder b with the row permutation if needed, and wrap the result in fs->X
1501: if (fs->rpermIndices) {
1502: 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)));
1503: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1504: } else {
1505: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1506: }
1508: // Solve Ut Y = X
1509: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1510: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut));
1512: // Solve Lt X = Y
1513: if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
1514: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1515: } else {
1516: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1517: }
1518: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_Lt));
1520: // Reorder X with the column permutation if needed, and put the result back to x
1521: if (fs->cpermIndices) {
1522: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1523: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1524: }
1526: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1527: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1528: PetscCall(PetscLogGpuTimeEnd());
1529: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - A->rmap->n));
1530: PetscFunctionReturn(PETSC_SUCCESS);
1531: }
1532: #else
1533: /* Why do we need to analyze the transposed matrix again? Can't we just use op(A) = CUSPARSE_OPERATION_TRANSPOSE in MatSolve_SeqAIJCUSPARSE? */
1534: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1535: {
1536: PetscInt n = xx->map->n;
1537: const PetscScalar *barray;
1538: PetscScalar *xarray;
1539: thrust::device_ptr<const PetscScalar> bGPU;
1540: thrust::device_ptr<PetscScalar> xGPU;
1541: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1542: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1543: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1544: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1546: PetscFunctionBegin;
1547: /* Analyze the matrix and create the transpose ... on the fly */
1548: if (!loTriFactorT && !upTriFactorT) {
1549: PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1550: loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1551: upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1552: }
1554: /* Get the GPU pointers */
1555: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1556: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1557: xGPU = thrust::device_pointer_cast(xarray);
1558: bGPU = thrust::device_pointer_cast(barray);
1560: PetscCall(PetscLogGpuTimeBegin());
1561: /* First, reorder with the row permutation */
1562: 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);
1564: /* First, solve U */
1565: 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(),
1566: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, xarray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1568: /* Then, solve L */
1569: 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(),
1570: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1572: /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */
1573: 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());
1575: /* Copy the temporary to the full solution. */
1576: thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), tempGPU->begin(), tempGPU->end(), xGPU);
1578: /* restore */
1579: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1580: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1581: PetscCall(PetscLogGpuTimeEnd());
1582: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1583: PetscFunctionReturn(PETSC_SUCCESS);
1584: }
1586: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1587: {
1588: const PetscScalar *barray;
1589: PetscScalar *xarray;
1590: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1591: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1592: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1593: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1595: PetscFunctionBegin;
1596: /* Analyze the matrix and create the transpose ... on the fly */
1597: if (!loTriFactorT && !upTriFactorT) {
1598: PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1599: loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1600: upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1601: }
1603: /* Get the GPU pointers */
1604: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1605: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1607: PetscCall(PetscLogGpuTimeBegin());
1608: /* First, solve U */
1609: 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(),
1610: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, barray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1612: /* Then, solve L */
1613: 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(),
1614: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1616: /* restore */
1617: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1618: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1619: PetscCall(PetscLogGpuTimeEnd());
1620: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1621: PetscFunctionReturn(PETSC_SUCCESS);
1622: }
1624: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1625: {
1626: const PetscScalar *barray;
1627: PetscScalar *xarray;
1628: thrust::device_ptr<const PetscScalar> bGPU;
1629: thrust::device_ptr<PetscScalar> xGPU;
1630: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1631: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1632: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1633: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1635: PetscFunctionBegin;
1636: /* Get the GPU pointers */
1637: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1638: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1639: xGPU = thrust::device_pointer_cast(xarray);
1640: bGPU = thrust::device_pointer_cast(barray);
1642: PetscCall(PetscLogGpuTimeBegin());
1643: /* First, reorder with the row permutation */
1644: 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());
1646: /* Next, solve L */
1647: 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(),
1648: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, tempGPU->data().get(), xarray, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
1650: /* Then, solve U */
1651: 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(),
1652: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, xarray, tempGPU->data().get(), upTriFactor->solvePolicy, upTriFactor->solveBuffer));
1654: /* Last, reorder with the column permutation */
1655: 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);
1657: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1658: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1659: PetscCall(PetscLogGpuTimeEnd());
1660: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1661: PetscFunctionReturn(PETSC_SUCCESS);
1662: }
1664: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1665: {
1666: const PetscScalar *barray;
1667: PetscScalar *xarray;
1668: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1669: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1670: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1671: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1673: PetscFunctionBegin;
1674: /* Get the GPU pointers */
1675: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1676: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1678: PetscCall(PetscLogGpuTimeBegin());
1679: /* First, solve L */
1680: 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(),
1681: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, barray, tempGPU->data().get(), loTriFactor->solvePolicy, loTriFactor->solveBuffer));
1683: /* Next, solve U */
1684: 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(),
1685: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, tempGPU->data().get(), xarray, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
1687: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1688: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1689: PetscCall(PetscLogGpuTimeEnd());
1690: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1691: PetscFunctionReturn(PETSC_SUCCESS);
1692: }
1693: #endif
1695: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1696: static PetscErrorCode MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, const MatFactorInfo *)
1697: {
1698: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1699: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1700: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1701: CsrMatrix *Acsr;
1702: PetscInt m, nz;
1703: PetscBool flg;
1705: PetscFunctionBegin;
1706: if (PetscDefined(USE_DEBUG)) {
1707: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1708: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1709: }
1711: /* Copy A's value to fact */
1712: m = fact->rmap->n;
1713: nz = aij->nz;
1714: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1715: Acsr = (CsrMatrix *)Acusp->mat->mat;
1716: PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1718: PetscCall(PetscLogGpuTimeBegin());
1719: /* Factorize fact inplace */
1720: if (m)
1721: PetscCallCUSPARSE(cusparseXcsrilu02(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1722: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1723: if (PetscDefined(USE_DEBUG)) {
1724: int numerical_zero;
1725: cusparseStatus_t status;
1726: status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &numerical_zero);
1727: 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);
1728: }
1730: #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
1731: if (fs->updatedSpSVAnalysis) {
1732: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1733: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_U, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1734: } else
1735: #endif
1736: {
1737: /* cusparseSpSV_analysis() is numeric, i.e., it requires valid matrix values, therefore, we do it after cusparseXcsrilu02()
1738: See discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/78
1739: */
1740: 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));
1742: 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));
1744: fs->updatedSpSVAnalysis = PETSC_TRUE;
1745: /* L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve */
1746: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
1747: }
1749: fact->offloadmask = PETSC_OFFLOAD_GPU;
1750: 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.
1751: fact->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
1752: fact->ops->matsolve = NULL;
1753: fact->ops->matsolvetranspose = NULL;
1754: PetscCall(PetscLogGpuTimeEnd());
1755: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1756: PetscFunctionReturn(PETSC_SUCCESS);
1757: }
1759: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, IS, IS, const MatFactorInfo *info)
1760: {
1761: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1762: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1763: PetscInt m, nz;
1765: PetscFunctionBegin;
1766: if (PetscDefined(USE_DEBUG)) {
1767: PetscBool flg, diagDense;
1769: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1770: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1771: 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);
1772: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, NULL, &diagDense));
1773: PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing a diagonal entry");
1774: }
1776: /* Free the old stale stuff */
1777: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
1779: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
1780: but they will not be used. Allocate them just for easy debugging.
1781: */
1782: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
1784: fact->offloadmask = PETSC_OFFLOAD_BOTH;
1785: fact->factortype = MAT_FACTOR_ILU;
1786: fact->info.factor_mallocs = 0;
1787: fact->info.fill_ratio_given = info->fill;
1788: fact->info.fill_ratio_needed = 1.0;
1790: aij->row = NULL;
1791: aij->col = NULL;
1793: /* ====================================================================== */
1794: /* Copy A's i, j to fact and also allocate the value array of fact. */
1795: /* We'll do in-place factorization on fact */
1796: /* ====================================================================== */
1797: const int *Ai, *Aj;
1799: m = fact->rmap->n;
1800: nz = aij->nz;
1802: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
1803: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
1804: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(*fs->csrVal) * nz));
1805: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai. The returned Ai, Aj are 32-bit */
1806: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1807: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1809: /* ====================================================================== */
1810: /* Create descriptors for M, L, U */
1811: /* ====================================================================== */
1812: cusparseFillMode_t fillMode;
1813: cusparseDiagType_t diagType;
1815: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
1816: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
1817: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
1819: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
1820: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
1821: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
1822: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
1823: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
1824: */
1825: fillMode = CUSPARSE_FILL_MODE_LOWER;
1826: diagType = CUSPARSE_DIAG_TYPE_UNIT;
1827: 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));
1828: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1829: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1831: fillMode = CUSPARSE_FILL_MODE_UPPER;
1832: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
1833: 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));
1834: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1835: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1837: /* ========================================================================= */
1838: /* Query buffer sizes for csrilu0, SpSV and allocate buffers */
1839: /* ========================================================================= */
1840: PetscCallCUSPARSE(cusparseCreateCsrilu02Info(&fs->ilu0Info_M));
1841: if (m)
1842: PetscCallCUSPARSE(cusparseXcsrilu02_bufferSize(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1843: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, &fs->factBufferSize_M));
1845: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
1846: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
1848: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
1849: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
1851: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
1852: 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));
1854: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
1855: 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));
1857: /* From my experiment with the example at https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSE/bicgstab,
1858: and discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/77,
1859: 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.
1860: To save memory, we make factBuffer_M share with the bigger of spsvBuffer_L/U.
1861: */
1862: if (fs->spsvBufferSize_L > fs->spsvBufferSize_U) {
1863: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
1864: fs->spsvBuffer_L = fs->factBuffer_M;
1865: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
1866: } else {
1867: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_U, (size_t)fs->factBufferSize_M)));
1868: fs->spsvBuffer_U = fs->factBuffer_M;
1869: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
1870: }
1872: /* ========================================================================== */
1873: /* Perform analysis of ilu0 on M, SpSv on L and U */
1874: /* The lower(upper) triangular part of M has the same sparsity pattern as L(U)*/
1875: /* ========================================================================== */
1876: int structural_zero;
1877: cusparseStatus_t status;
1879: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1880: if (m)
1881: PetscCallCUSPARSE(cusparseXcsrilu02_analysis(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1882: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1883: if (PetscDefined(USE_DEBUG)) {
1884: /* cusparseXcsrilu02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
1885: status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &structural_zero);
1886: 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);
1887: }
1889: /* Estimate FLOPs of the numeric factorization */
1890: {
1891: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
1892: PetscInt *Ai, nzRow, nzLeft;
1893: const PetscInt *adiag;
1894: PetscLogDouble flops = 0.0;
1896: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
1897: Ai = Aseq->i;
1898: for (PetscInt i = 0; i < m; i++) {
1899: if (Ai[i] < adiag[i] && adiag[i] < Ai[i + 1]) { /* There are nonzeros left to the diagonal of row i */
1900: nzRow = Ai[i + 1] - Ai[i];
1901: nzLeft = adiag[i] - Ai[i];
1902: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
1903: and include the eliminated one will be updated, which incurs a multiplication and an addition.
1904: */
1905: nzLeft = (nzRow - 1) / 2;
1906: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
1907: }
1908: }
1909: fs->numericFactFlops = flops;
1910: }
1911: fact->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0;
1912: PetscFunctionReturn(PETSC_SUCCESS);
1913: }
1915: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_ICC0(Mat fact, Vec b, Vec x)
1916: {
1917: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1918: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1919: const PetscScalar *barray;
1920: PetscScalar *xarray;
1922: PetscFunctionBegin;
1923: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1924: PetscCall(VecCUDAGetArrayRead(b, &barray));
1925: PetscCall(PetscLogGpuTimeBegin());
1927: /* Solve L*y = b */
1928: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1929: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1930: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* L Y = X */
1931: fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L));
1933: /* Solve Lt*x = y */
1934: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1935: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* Lt X = Y */
1936: fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt));
1938: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1939: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1941: PetscCall(PetscLogGpuTimeEnd());
1942: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - fact->rmap->n));
1943: PetscFunctionReturn(PETSC_SUCCESS);
1944: }
1946: static PetscErrorCode MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, const MatFactorInfo *)
1947: {
1948: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1949: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1950: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1951: CsrMatrix *Acsr;
1952: PetscInt m, nz;
1953: PetscBool flg;
1955: PetscFunctionBegin;
1956: if (PetscDefined(USE_DEBUG)) {
1957: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1958: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1959: }
1961: /* Copy A's value to fact */
1962: m = fact->rmap->n;
1963: nz = aij->nz;
1964: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1965: Acsr = (CsrMatrix *)Acusp->mat->mat;
1966: PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1968: /* Factorize fact inplace */
1969: /* https://docs.nvidia.com/cuda/cusparse/index.html#csric02_solve
1970: csric02() only takes the lower triangular part of matrix A to perform factorization.
1971: The matrix type must be CUSPARSE_MATRIX_TYPE_GENERAL, the fill mode and diagonal type are ignored,
1972: and the strictly upper triangular part is ignored and never touched. It does not matter if A is Hermitian or not.
1973: In other words, from the point of view of csric02() A is Hermitian and only the lower triangular part is provided.
1974: */
1975: 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));
1976: if (PetscDefined(USE_DEBUG)) {
1977: int numerical_zero;
1978: cusparseStatus_t status;
1979: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &numerical_zero);
1980: 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);
1981: }
1983: #if PETSC_PKG_CUDA_VERSION_GE(12, 1, 1)
1984: if (fs->updatedSpSVAnalysis) {
1985: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_L, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1986: if (fs->csrVal) PetscCallCUSPARSE(cusparseSpSV_updateMatrix(fs->handle, fs->spsvDescr_Lt, fs->csrVal, CUSPARSE_SPSV_UPDATE_GENERAL));
1987: } else
1988: #endif
1989: {
1990: 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));
1992: /* Note that cusparse reports this error if we use double and CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE
1993: ** On entry to cusparseSpSV_analysis(): conjugate transpose (opA) is not supported for matA data type, current -> CUDA_R_64F
1994: */
1995: 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));
1996: fs->updatedSpSVAnalysis = PETSC_TRUE;
1997: }
1999: fact->offloadmask = PETSC_OFFLOAD_GPU;
2000: fact->ops->solve = MatSolve_SeqAIJCUSPARSE_ICC0;
2001: fact->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_ICC0;
2002: fact->ops->matsolve = NULL;
2003: fact->ops->matsolvetranspose = NULL;
2004: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
2005: PetscFunctionReturn(PETSC_SUCCESS);
2006: }
2008: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, IS, const MatFactorInfo *info)
2009: {
2010: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
2011: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
2012: PetscInt m, nz;
2014: PetscFunctionBegin;
2015: if (PetscDefined(USE_DEBUG)) {
2016: PetscBool flg, diagDense;
2018: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2019: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
2020: 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);
2021: PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, NULL, &diagDense));
2022: PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");
2023: }
2025: /* Free the old stale stuff */
2026: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
2028: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
2029: but they will not be used. Allocate them just for easy debugging.
2030: */
2031: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
2033: fact->offloadmask = PETSC_OFFLOAD_BOTH;
2034: fact->factortype = MAT_FACTOR_ICC;
2035: fact->info.factor_mallocs = 0;
2036: fact->info.fill_ratio_given = info->fill;
2037: fact->info.fill_ratio_needed = 1.0;
2039: aij->row = NULL;
2040: aij->col = NULL;
2042: /* ====================================================================== */
2043: /* Copy A's i, j to fact and also allocate the value array of fact. */
2044: /* We'll do in-place factorization on fact */
2045: /* ====================================================================== */
2046: const int *Ai, *Aj;
2048: m = fact->rmap->n;
2049: nz = aij->nz;
2051: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
2052: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
2053: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(PetscScalar) * nz));
2054: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai */
2055: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2056: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2058: /* ====================================================================== */
2059: /* Create mat descriptors for M, L */
2060: /* ====================================================================== */
2061: cusparseFillMode_t fillMode;
2062: cusparseDiagType_t diagType;
2064: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
2065: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
2066: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
2068: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
2069: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
2070: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
2071: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
2072: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
2073: */
2074: fillMode = CUSPARSE_FILL_MODE_LOWER;
2075: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
2076: 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));
2077: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
2078: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
2080: /* ========================================================================= */
2081: /* Query buffer sizes for csric0, SpSV of L and Lt, and allocate buffers */
2082: /* ========================================================================= */
2083: PetscCallCUSPARSE(cusparseCreateCsric02Info(&fs->ic0Info_M));
2084: if (m) PetscCallCUSPARSE(cusparseXcsric02_bufferSize(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, &fs->factBufferSize_M));
2086: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
2087: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
2089: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
2090: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
2092: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
2093: 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));
2095: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
2096: 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));
2098: /* To save device memory, we make the factorization buffer share with one of the solver buffer.
2099: See also comments in MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0().
2100: */
2101: if (fs->spsvBufferSize_L > fs->spsvBufferSize_Lt) {
2102: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
2103: fs->spsvBuffer_L = fs->factBuffer_M;
2104: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
2105: } else {
2106: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_Lt, (size_t)fs->factBufferSize_M)));
2107: fs->spsvBuffer_Lt = fs->factBuffer_M;
2108: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
2109: }
2111: /* ========================================================================== */
2112: /* Perform analysis of ic0 on M */
2113: /* The lower triangular part of M has the same sparsity pattern as L */
2114: /* ========================================================================== */
2115: int structural_zero;
2116: cusparseStatus_t status;
2118: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
2119: 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));
2120: if (PetscDefined(USE_DEBUG)) {
2121: /* cusparseXcsric02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
2122: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &structural_zero);
2123: 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);
2124: }
2126: /* Estimate FLOPs of the numeric factorization */
2127: {
2128: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
2129: PetscInt *Ai, nzRow, nzLeft;
2130: PetscLogDouble flops = 0.0;
2132: Ai = Aseq->i;
2133: for (PetscInt i = 0; i < m; i++) {
2134: nzRow = Ai[i + 1] - Ai[i];
2135: if (nzRow > 1) {
2136: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
2137: and include the eliminated one will be updated, which incurs a multiplication and an addition.
2138: */
2139: nzLeft = (nzRow - 1) / 2;
2140: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
2141: }
2142: }
2143: fs->numericFactFlops = flops;
2144: }
2145: fact->ops->choleskyfactornumeric = MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0;
2146: PetscFunctionReturn(PETSC_SUCCESS);
2147: }
2148: #endif
2150: static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
2151: {
2152: // use_cpu_solve is a field in Mat_SeqAIJCUSPARSE. B, a factored matrix, uses Mat_SeqAIJCUSPARSETriFactors.
2153: Mat_SeqAIJCUSPARSE *cusparsestruct = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2155: PetscFunctionBegin;
2156: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2157: PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
2158: B->offloadmask = PETSC_OFFLOAD_CPU;
2160: if (!cusparsestruct->use_cpu_solve) {
2161: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2162: B->ops->solve = MatSolve_SeqAIJCUSPARSE_LU;
2163: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
2164: #else
2165: /* determine which version of MatSolve needs to be used. */
2166: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2167: IS isrow = b->row, iscol = b->col;
2168: PetscBool row_identity, col_identity;
2170: PetscCall(ISIdentity(isrow, &row_identity));
2171: PetscCall(ISIdentity(iscol, &col_identity));
2172: if (row_identity && col_identity) {
2173: B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
2174: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
2175: } else {
2176: B->ops->solve = MatSolve_SeqAIJCUSPARSE;
2177: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
2178: }
2179: #endif
2180: }
2181: B->ops->matsolve = NULL;
2182: B->ops->matsolvetranspose = NULL;
2184: /* get the triangular factors */
2185: if (!cusparsestruct->use_cpu_solve) PetscCall(MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B));
2186: PetscFunctionReturn(PETSC_SUCCESS);
2187: }
2189: static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2190: {
2191: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(B->spptr);
2193: PetscFunctionBegin;
2194: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2195: PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2196: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2197: PetscFunctionReturn(PETSC_SUCCESS);
2198: }
2200: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2201: {
2202: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2204: PetscFunctionBegin;
2205: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2206: PetscBool row_identity = PETSC_FALSE, col_identity = PETSC_FALSE;
2207: if (!info->factoronhost) {
2208: PetscCall(ISIdentity(isrow, &row_identity));
2209: PetscCall(ISIdentity(iscol, &col_identity));
2210: }
2211: if (!info->levels && row_identity && col_identity) {
2212: PetscCall(MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(B, A, isrow, iscol, info));
2213: } else
2214: #endif
2215: {
2216: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2217: PetscCall(MatILUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2218: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2219: }
2220: PetscFunctionReturn(PETSC_SUCCESS);
2221: }
2223: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2224: {
2225: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2227: PetscFunctionBegin;
2228: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2229: PetscBool perm_identity = PETSC_FALSE;
2230: if (!info->factoronhost) PetscCall(ISIdentity(perm, &perm_identity));
2231: if (!info->levels && perm_identity) {
2232: PetscCall(MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(B, A, perm, info));
2233: } else
2234: #endif
2235: {
2236: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2237: PetscCall(MatICCFactorSymbolic_SeqAIJ(B, A, perm, info));
2238: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2239: }
2240: PetscFunctionReturn(PETSC_SUCCESS);
2241: }
2243: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2244: {
2245: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2247: PetscFunctionBegin;
2248: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2249: PetscCall(MatCholeskyFactorSymbolic_SeqAIJ(B, A, perm, info));
2250: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2251: PetscFunctionReturn(PETSC_SUCCESS);
2252: }
2254: static PetscErrorCode MatFactorGetSolverType_seqaij_cusparse(Mat, MatSolverType *type)
2255: {
2256: PetscFunctionBegin;
2257: *type = MATSOLVERCUSPARSE;
2258: PetscFunctionReturn(PETSC_SUCCESS);
2259: }
2261: /*MC
2262: MATSOLVERCUSPARSE = "cusparse" - A matrix type providing triangular solvers for seq matrices
2263: on a single GPU of type, `MATSEQAIJCUSPARSE`. Currently supported
2264: algorithms are ILU(k) and ICC(k). Typically, deeper factorizations (larger k) results in poorer
2265: performance in the triangular solves. Full LU, and Cholesky decompositions can be solved through the
2266: CuSPARSE triangular solve algorithm. However, the performance can be quite poor and thus these
2267: algorithms are not recommended. This class does NOT support direct solver operations.
2269: Level: beginner
2271: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJCUSPARSE()`,
2272: `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
2273: M*/
2275: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat A, MatFactorType ftype, Mat *B)
2276: {
2277: PetscInt n = A->rmap->n;
2279: PetscFunctionBegin;
2280: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2281: PetscCall(MatSetSizes(*B, n, n, n, n));
2282: (*B)->factortype = ftype; // factortype makes MatSetType() allocate spptr of type Mat_SeqAIJCUSPARSETriFactors
2283: PetscCall(MatSetType(*B, MATSEQAIJCUSPARSE));
2285: if (A->boundtocpu && A->bindingpropagates) PetscCall(MatBindToCPU(*B, PETSC_TRUE));
2286: if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
2287: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2288: if (!A->boundtocpu) {
2289: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJCUSPARSE;
2290: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJCUSPARSE;
2291: } else {
2292: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJ;
2293: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJ;
2294: }
2295: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
2296: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILU]));
2297: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILUDT]));
2298: } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
2299: if (!A->boundtocpu) {
2300: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJCUSPARSE;
2301: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJCUSPARSE;
2302: } else {
2303: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJ;
2304: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJ;
2305: }
2306: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
2307: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
2308: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported for CUSPARSE Matrix Types");
2310: PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
2311: (*B)->canuseordering = PETSC_TRUE;
2312: PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_cusparse));
2313: PetscFunctionReturn(PETSC_SUCCESS);
2314: }
2316: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat A)
2317: {
2318: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2319: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2320: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2321: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
2322: #endif
2324: PetscFunctionBegin;
2325: if (A->offloadmask == PETSC_OFFLOAD_GPU) {
2326: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2327: if (A->factortype == MAT_FACTOR_NONE) {
2328: CsrMatrix *matrix = (CsrMatrix *)cusp->mat->mat;
2329: PetscCallCUDA(cudaMemcpy(a->a, matrix->values->data().get(), a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2330: }
2331: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2332: else if (fs->csrVal) {
2333: /* We have a factorized matrix on device and are able to copy it to host */
2334: PetscCallCUDA(cudaMemcpy(a->a, fs->csrVal, a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2335: }
2336: #endif
2337: else
2338: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for copying this type of factorized matrix from device to host");
2339: PetscCall(PetscLogGpuToCpu(a->nz * sizeof(PetscScalar)));
2340: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2341: A->offloadmask = PETSC_OFFLOAD_BOTH;
2342: }
2343: PetscFunctionReturn(PETSC_SUCCESS);
2344: }
2346: static PetscErrorCode MatSeqAIJGetArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2347: {
2348: PetscFunctionBegin;
2349: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2350: *array = ((Mat_SeqAIJ *)A->data)->a;
2351: PetscFunctionReturn(PETSC_SUCCESS);
2352: }
2354: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2355: {
2356: PetscFunctionBegin;
2357: A->offloadmask = PETSC_OFFLOAD_CPU;
2358: *array = NULL;
2359: PetscFunctionReturn(PETSC_SUCCESS);
2360: }
2362: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJCUSPARSE(Mat A, const PetscScalar *array[])
2363: {
2364: PetscFunctionBegin;
2365: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2366: *array = ((Mat_SeqAIJ *)A->data)->a;
2367: PetscFunctionReturn(PETSC_SUCCESS);
2368: }
2370: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE(Mat, const PetscScalar *array[])
2371: {
2372: PetscFunctionBegin;
2373: *array = NULL;
2374: PetscFunctionReturn(PETSC_SUCCESS);
2375: }
2377: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2378: {
2379: PetscFunctionBegin;
2380: *array = ((Mat_SeqAIJ *)A->data)->a;
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2384: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2385: {
2386: PetscFunctionBegin;
2387: A->offloadmask = PETSC_OFFLOAD_CPU;
2388: *array = NULL;
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2392: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
2393: {
2394: Mat_SeqAIJCUSPARSE *cusp;
2395: CsrMatrix *matrix;
2397: PetscFunctionBegin;
2398: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2399: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2400: cusp = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2401: PetscCheck(cusp != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "cusp is NULL");
2402: matrix = (CsrMatrix *)cusp->mat->mat;
2404: if (i) {
2405: #if !defined(PETSC_USE_64BIT_INDICES)
2406: *i = matrix->row_offsets->data().get();
2407: #else
2408: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2409: #endif
2410: }
2411: if (j) {
2412: #if !defined(PETSC_USE_64BIT_INDICES)
2413: *j = matrix->column_indices->data().get();
2414: #else
2415: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2416: #endif
2417: }
2418: if (a) *a = matrix->values->data().get();
2419: if (mtype) *mtype = PETSC_MEMTYPE_CUDA;
2420: PetscFunctionReturn(PETSC_SUCCESS);
2421: }
2423: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A)
2424: {
2425: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
2426: Mat_SeqAIJCUSPARSEMultStruct *matstruct = cusparsestruct->mat;
2427: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2428: PetscInt m = A->rmap->n, *ii, *ridx, tmp;
2429: cusparseStatus_t stat;
2430: PetscBool both = PETSC_TRUE;
2432: PetscFunctionBegin;
2433: PetscCheck(!A->boundtocpu, PETSC_COMM_SELF, PETSC_ERR_GPU, "Cannot copy to GPU");
2434: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
2435: if (A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { /* Copy values only */
2436: CsrMatrix *matrix;
2437: matrix = (CsrMatrix *)cusparsestruct->mat->mat;
2439: PetscCheck(!a->nz || a->a, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR values");
2440: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2441: matrix->values->assign(a->a, a->a + a->nz);
2442: PetscCallCUDA(WaitForCUDA());
2443: PetscCall(PetscLogCpuToGpu(a->nz * sizeof(PetscScalar)));
2444: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2445: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
2446: } else {
2447: PetscInt nnz;
2448: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2449: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusparsestruct->mat, cusparsestruct->format));
2450: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
2451: delete cusparsestruct->workVector;
2452: delete cusparsestruct->rowoffsets_gpu;
2453: cusparsestruct->workVector = NULL;
2454: cusparsestruct->rowoffsets_gpu = NULL;
2455: try {
2456: if (a->compressedrow.use) {
2457: m = a->compressedrow.nrows;
2458: ii = a->compressedrow.i;
2459: ridx = a->compressedrow.rindex;
2460: } else {
2461: m = A->rmap->n;
2462: ii = a->i;
2463: ridx = NULL;
2464: }
2465: PetscCheck(ii, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR row data");
2466: if (!a->a) {
2467: nnz = ii[m];
2468: both = PETSC_FALSE;
2469: } else nnz = a->nz;
2470: PetscCheck(!nnz || a->j, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR column data");
2472: /* create cusparse matrix */
2473: cusparsestruct->nrows = m;
2474: matstruct = new Mat_SeqAIJCUSPARSEMultStruct;
2475: PetscCallCUSPARSE(cusparseCreateMatDescr(&matstruct->descr));
2476: PetscCallCUSPARSE(cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO));
2477: PetscCallCUSPARSE(cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
2479: PetscCallCUDA(cudaMalloc((void **)&matstruct->alpha_one, sizeof(PetscScalar)));
2480: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_zero, sizeof(PetscScalar)));
2481: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_one, sizeof(PetscScalar)));
2482: PetscCallCUDA(cudaMemcpy(matstruct->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2483: PetscCallCUDA(cudaMemcpy(matstruct->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2484: PetscCallCUDA(cudaMemcpy(matstruct->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2485: PetscCallCUSPARSE(cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE));
2487: /* Build a hybrid/ellpack matrix if this option is chosen for the storage */
2488: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
2489: /* set the matrix */
2490: CsrMatrix *mat = new CsrMatrix;
2491: mat->num_rows = m;
2492: mat->num_cols = A->cmap->n;
2493: mat->num_entries = nnz;
2494: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2495: mat->row_offsets->assign(ii, ii + m + 1);
2496: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2497: mat->column_indices->assign(a->j, a->j + nnz);
2499: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2500: if (a->a) mat->values->assign(a->a, a->a + nnz);
2502: /* assign the pointer */
2503: matstruct->mat = mat;
2504: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2505: if (mat->num_rows) { /* cusparse errors on empty matrices! */
2506: 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 */
2507: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2508: PetscCallCUSPARSE(stat);
2509: }
2510: #endif
2511: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
2512: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2513: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
2514: #else
2515: CsrMatrix *mat = new CsrMatrix;
2516: mat->num_rows = m;
2517: mat->num_cols = A->cmap->n;
2518: mat->num_entries = nnz;
2519: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2520: mat->row_offsets->assign(ii, ii + m + 1);
2522: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2523: mat->column_indices->assign(a->j, a->j + nnz);
2525: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2526: if (a->a) mat->values->assign(a->a, a->a + nnz);
2528: cusparseHybMat_t hybMat;
2529: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
2530: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
2531: 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);
2532: PetscCallCUSPARSE(stat);
2533: /* assign the pointer */
2534: matstruct->mat = hybMat;
2536: if (mat) {
2537: if (mat->values) delete (THRUSTARRAY *)mat->values;
2538: if (mat->column_indices) delete (THRUSTINTARRAY32 *)mat->column_indices;
2539: if (mat->row_offsets) delete (THRUSTINTARRAY32 *)mat->row_offsets;
2540: delete (CsrMatrix *)mat;
2541: }
2542: #endif
2543: }
2545: /* assign the compressed row indices */
2546: if (a->compressedrow.use) {
2547: PetscCallCXX(cusparsestruct->workVector = new THRUSTARRAY(m));
2548: PetscCallCXX(matstruct->cprowIndices = new THRUSTINTARRAY(m));
2549: matstruct->cprowIndices->assign(ridx, ridx + m);
2550: tmp = m;
2551: } else {
2552: cusparsestruct->workVector = NULL;
2553: matstruct->cprowIndices = NULL;
2554: tmp = 0;
2555: }
2556: PetscCall(PetscLogCpuToGpu(((m + 1) + (a->nz)) * sizeof(int) + tmp * sizeof(PetscInt) + (3 + (a->nz)) * sizeof(PetscScalar)));
2558: /* assign the pointer */
2559: cusparsestruct->mat = matstruct;
2560: } catch (char *ex) {
2561: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
2562: }
2563: PetscCallCUDA(WaitForCUDA());
2564: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2565: cusparsestruct->nonzerostate = A->nonzerostate;
2566: }
2567: if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
2568: }
2569: PetscFunctionReturn(PETSC_SUCCESS);
2570: }
2572: struct VecCUDAPlusEquals {
2573: template <typename Tuple>
2574: __host__ __device__ void operator()(Tuple t)
2575: {
2576: thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t);
2577: }
2578: };
2580: struct VecCUDAEquals {
2581: template <typename Tuple>
2582: __host__ __device__ void operator()(Tuple t)
2583: {
2584: thrust::get<1>(t) = thrust::get<0>(t);
2585: }
2586: };
2588: struct VecCUDAEqualsReverse {
2589: template <typename Tuple>
2590: __host__ __device__ void operator()(Tuple t)
2591: {
2592: thrust::get<0>(t) = thrust::get<1>(t);
2593: }
2594: };
2596: struct MatProductCtx_MatMatCusparse {
2597: PetscBool cisdense;
2598: PetscScalar *Bt;
2599: Mat X;
2600: PetscBool reusesym; /* Cusparse does not have split symbolic and numeric phases for sparse matmat operations */
2601: PetscLogDouble flops;
2602: CsrMatrix *Bcsr;
2604: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2605: cusparseSpMatDescr_t matSpBDescr;
2606: PetscBool initialized; /* C = alpha op(A) op(B) + beta C */
2607: cusparseDnMatDescr_t matBDescr;
2608: cusparseDnMatDescr_t matCDescr;
2609: PetscInt Blda, Clda; /* Record leading dimensions of B and C here to detect changes*/
2610: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2611: void *dBuffer4;
2612: void *dBuffer5;
2613: #endif
2614: size_t mmBufferSize;
2615: void *mmBuffer;
2616: void *mmBuffer2; /* SpGEMM WorkEstimation buffer */
2617: cusparseSpGEMMDescr_t spgemmDesc;
2618: #endif
2619: };
2621: static PetscErrorCode MatProductCtxDestroy_MatMatCusparse(void **data)
2622: {
2623: MatProductCtx_MatMatCusparse *mmdata = *(MatProductCtx_MatMatCusparse **)data;
2625: PetscFunctionBegin;
2626: PetscCallCUDA(cudaFree(mmdata->Bt));
2627: delete mmdata->Bcsr;
2628: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2629: if (mmdata->matSpBDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mmdata->matSpBDescr));
2630: if (mmdata->matBDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2631: if (mmdata->matCDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2632: if (mmdata->spgemmDesc) PetscCallCUSPARSE(cusparseSpGEMM_destroyDescr(mmdata->spgemmDesc));
2633: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2634: if (mmdata->dBuffer4) PetscCallCUDA(cudaFree(mmdata->dBuffer4));
2635: if (mmdata->dBuffer5) PetscCallCUDA(cudaFree(mmdata->dBuffer5));
2636: #endif
2637: if (mmdata->mmBuffer) PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2638: if (mmdata->mmBuffer2) PetscCallCUDA(cudaFree(mmdata->mmBuffer2));
2639: #endif
2640: PetscCall(MatDestroy(&mmdata->X));
2641: PetscCall(PetscFree(*data));
2642: PetscFunctionReturn(PETSC_SUCCESS);
2643: }
2645: #include <../src/mat/impls/dense/seq/dense.h>
2647: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2648: {
2649: Mat_Product *product = C->product;
2650: Mat A, B;
2651: PetscInt m, n, blda, clda;
2652: PetscBool flg, biscuda;
2653: Mat_SeqAIJCUSPARSE *cusp;
2654: cusparseStatus_t stat;
2655: cusparseOperation_t opA;
2656: const PetscScalar *barray;
2657: PetscScalar *carray;
2658: MatProductCtx_MatMatCusparse *mmdata;
2659: Mat_SeqAIJCUSPARSEMultStruct *mat;
2660: CsrMatrix *csrmat;
2662: PetscFunctionBegin;
2663: MatCheckProduct(C, 1);
2664: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2665: mmdata = (MatProductCtx_MatMatCusparse *)product->data;
2666: A = product->A;
2667: B = product->B;
2668: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2669: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2670: /* currently CopyToGpu does not copy if the matrix is bound to CPU
2671: Instead of silently accepting the wrong answer, I prefer to raise the error */
2672: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2673: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2674: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2675: switch (product->type) {
2676: case MATPRODUCT_AB:
2677: case MATPRODUCT_PtAP:
2678: mat = cusp->mat;
2679: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2680: m = A->rmap->n;
2681: n = B->cmap->n;
2682: break;
2683: case MATPRODUCT_AtB:
2684: if (!A->form_explicit_transpose) {
2685: mat = cusp->mat;
2686: opA = CUSPARSE_OPERATION_TRANSPOSE;
2687: } else {
2688: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
2689: mat = cusp->matTranspose;
2690: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2691: }
2692: m = A->cmap->n;
2693: n = B->cmap->n;
2694: break;
2695: case MATPRODUCT_ABt:
2696: case MATPRODUCT_RARt:
2697: mat = cusp->mat;
2698: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2699: m = A->rmap->n;
2700: n = B->rmap->n;
2701: break;
2702: default:
2703: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2704: }
2705: PetscCheck(mat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing Mat_SeqAIJCUSPARSEMultStruct");
2706: csrmat = (CsrMatrix *)mat->mat;
2707: /* if the user passed a CPU matrix, copy the data to the GPU */
2708: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSECUDA, &biscuda));
2709: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSECUDA, MAT_INPLACE_MATRIX, &B));
2710: PetscCall(MatDenseGetArrayReadAndMemType(B, &barray, nullptr));
2712: PetscCall(MatDenseGetLDA(B, &blda));
2713: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2714: PetscCall(MatDenseGetArrayWriteAndMemType(mmdata->X, &carray, nullptr));
2715: PetscCall(MatDenseGetLDA(mmdata->X, &clda));
2716: } else {
2717: PetscCall(MatDenseGetArrayWriteAndMemType(C, &carray, nullptr));
2718: PetscCall(MatDenseGetLDA(C, &clda));
2719: }
2721: PetscCall(PetscLogGpuTimeBegin());
2722: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2723: cusparseOperation_t opB = (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE;
2724: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
2725: cusparseSpMatDescr_t &matADescr = mat->matDescr_SpMM[opA];
2726: #else
2727: cusparseSpMatDescr_t &matADescr = mat->matDescr;
2728: #endif
2730: /* (re)allocate mmBuffer if not initialized or LDAs are different */
2731: if (!mmdata->initialized || mmdata->Blda != blda || mmdata->Clda != clda) {
2732: size_t mmBufferSize;
2733: if (mmdata->initialized && mmdata->Blda != blda) {
2734: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2735: mmdata->matBDescr = NULL;
2736: }
2737: if (!mmdata->matBDescr) {
2738: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matBDescr, B->rmap->n, B->cmap->n, blda, (void *)barray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2739: mmdata->Blda = blda;
2740: }
2742: if (mmdata->initialized && mmdata->Clda != clda) {
2743: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2744: mmdata->matCDescr = NULL;
2745: }
2746: if (!mmdata->matCDescr) { /* matCDescr is for C or mmdata->X */
2747: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matCDescr, m, n, clda, (void *)carray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2748: mmdata->Clda = clda;
2749: }
2751: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
2752: if (matADescr) {
2753: PetscCallCUSPARSE(cusparseDestroySpMat(matADescr)); // Because I find I could not reuse matADescr. It could be a cusparse bug
2754: matADescr = NULL;
2755: }
2756: #endif
2758: if (!matADescr) {
2759: 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 */
2760: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2761: PetscCallCUSPARSE(stat);
2762: }
2764: PetscCallCUSPARSE(cusparseSpMM_bufferSize(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, &mmBufferSize));
2766: if ((mmdata->mmBuffer && mmdata->mmBufferSize < mmBufferSize) || !mmdata->mmBuffer) {
2767: PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2768: PetscCallCUDA(cudaMalloc(&mmdata->mmBuffer, mmBufferSize));
2769: mmdata->mmBufferSize = mmBufferSize;
2770: }
2772: #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
2773: PetscCallCUSPARSE(cusparseSpMM_preprocess(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2774: #endif
2776: mmdata->initialized = PETSC_TRUE;
2777: } else {
2778: /* to be safe, always update pointers of the mats */
2779: PetscCallCUSPARSE(cusparseSpMatSetValues(matADescr, csrmat->values->data().get()));
2780: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matBDescr, (void *)barray));
2781: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matCDescr, (void *)carray));
2782: }
2784: /* do cusparseSpMM, which supports transpose on B */
2785: PetscCallCUSPARSE(cusparseSpMM(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2786: #else
2787: PetscInt k;
2788: /* cusparseXcsrmm does not support transpose on B */
2789: if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) {
2790: cublasHandle_t cublasv2handle;
2791: cublasStatus_t cerr;
2793: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
2794: 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);
2795: PetscCallCUBLAS(cerr);
2796: blda = B->cmap->n;
2797: k = B->cmap->n;
2798: } else {
2799: k = B->rmap->n;
2800: }
2802: /* perform the MatMat operation, op(A) is m x k, op(B) is k x n */
2803: 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);
2804: PetscCallCUSPARSE(stat);
2805: #endif
2806: PetscCall(PetscLogGpuTimeEnd());
2807: PetscCall(PetscLogGpuFlops(n * 2.0 * csrmat->num_entries));
2808: PetscCall(MatDenseRestoreArrayReadAndMemType(B, &barray));
2809: if (product->type == MATPRODUCT_RARt) {
2810: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2811: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_FALSE, PETSC_FALSE));
2812: } else if (product->type == MATPRODUCT_PtAP) {
2813: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2814: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_TRUE, PETSC_FALSE));
2815: } else {
2816: PetscCall(MatDenseRestoreArrayWriteAndMemType(C, &carray));
2817: }
2818: if (mmdata->cisdense) PetscCall(MatConvert(C, MATSEQDENSE, MAT_INPLACE_MATRIX, &C));
2819: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
2820: PetscFunctionReturn(PETSC_SUCCESS);
2821: }
2823: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2824: {
2825: Mat_Product *product = C->product;
2826: Mat A, B;
2827: PetscInt m, n;
2828: PetscBool cisdense, flg;
2829: MatProductCtx_MatMatCusparse *mmdata;
2830: Mat_SeqAIJCUSPARSE *cusp;
2832: PetscFunctionBegin;
2833: MatCheckProduct(C, 1);
2834: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
2835: A = product->A;
2836: B = product->B;
2837: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2838: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2839: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2840: PetscCheck(cusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2841: switch (product->type) {
2842: case MATPRODUCT_AB:
2843: m = A->rmap->n;
2844: n = B->cmap->n;
2845: PetscCall(MatSetBlockSizesFromMats(C, A, B));
2846: break;
2847: case MATPRODUCT_AtB:
2848: m = A->cmap->n;
2849: n = B->cmap->n;
2850: if (A->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->cmap->bs));
2851: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2852: break;
2853: case MATPRODUCT_ABt:
2854: m = A->rmap->n;
2855: n = B->rmap->n;
2856: if (A->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, A->rmap->bs));
2857: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2858: break;
2859: case MATPRODUCT_PtAP:
2860: m = B->cmap->n;
2861: n = B->cmap->n;
2862: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->cmap->bs));
2863: if (B->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->cmap->bs));
2864: break;
2865: case MATPRODUCT_RARt:
2866: m = B->rmap->n;
2867: n = B->rmap->n;
2868: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->rmap, B->rmap->bs));
2869: if (B->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(C->cmap, B->rmap->bs));
2870: break;
2871: default:
2872: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2873: }
2874: PetscCall(MatSetSizes(C, m, n, m, n));
2875: /* if C is of type MATSEQDENSE (CPU), perform the operation on the GPU and then copy on the CPU */
2876: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQDENSE, &cisdense));
2877: PetscCall(MatSetType(C, MATSEQDENSECUDA));
2879: /* product data */
2880: PetscCall(PetscNew(&mmdata));
2881: mmdata->cisdense = cisdense;
2882: #if PETSC_PKG_CUDA_VERSION_LT(11, 0, 0)
2883: /* cusparseXcsrmm does not support transpose on B, so we allocate buffer to store B^T */
2884: 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)));
2885: #endif
2886: /* for these products we need intermediate storage */
2887: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2888: PetscCall(MatCreate(PetscObjectComm((PetscObject)C), &mmdata->X));
2889: PetscCall(MatSetType(mmdata->X, MATSEQDENSECUDA));
2890: if (product->type == MATPRODUCT_RARt) { /* do not preallocate, since the first call to MatDenseCUDAGetArray will preallocate on the GPU for us */
2891: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->rmap->n, A->rmap->n, B->rmap->n));
2892: } else {
2893: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->cmap->n, A->rmap->n, B->cmap->n));
2894: }
2895: }
2896: C->product->data = mmdata;
2897: C->product->destroy = MatProductCtxDestroy_MatMatCusparse;
2899: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA;
2900: PetscFunctionReturn(PETSC_SUCCESS);
2901: }
2903: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
2904: {
2905: Mat_Product *product = C->product;
2906: Mat A, B;
2907: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
2908: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2909: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
2910: CsrMatrix *Acsr, *Bcsr, *Ccsr;
2911: PetscBool flg;
2912: cusparseStatus_t stat;
2913: MatProductType ptype;
2914: MatProductCtx_MatMatCusparse *mmdata;
2915: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2916: cusparseSpMatDescr_t BmatSpDescr;
2917: #endif
2918: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
2920: PetscFunctionBegin;
2921: MatCheckProduct(C, 1);
2922: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2923: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQAIJCUSPARSE, &flg));
2924: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for C of type %s", ((PetscObject)C)->type_name);
2925: mmdata = (MatProductCtx_MatMatCusparse *)C->product->data;
2926: A = product->A;
2927: B = product->B;
2928: if (mmdata->reusesym) { /* this happens when api_user is true, meaning that the matrix values have been already computed in the MatProductSymbolic phase */
2929: mmdata->reusesym = PETSC_FALSE;
2930: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2931: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2932: Cmat = Ccusp->mat;
2933: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[C->product->type]);
2934: Ccsr = (CsrMatrix *)Cmat->mat;
2935: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2936: goto finalize;
2937: }
2938: if (!c->nz) goto finalize;
2939: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2940: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2941: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
2942: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
2943: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2944: PetscCheck(!B->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2945: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2946: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
2947: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2948: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2949: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2950: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2951: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2952: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
2954: ptype = product->type;
2955: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
2956: ptype = MATPRODUCT_AB;
2957: 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");
2958: }
2959: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
2960: ptype = MATPRODUCT_AB;
2961: 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");
2962: }
2963: switch (ptype) {
2964: case MATPRODUCT_AB:
2965: Amat = Acusp->mat;
2966: Bmat = Bcusp->mat;
2967: break;
2968: case MATPRODUCT_AtB:
2969: Amat = Acusp->matTranspose;
2970: Bmat = Bcusp->mat;
2971: break;
2972: case MATPRODUCT_ABt:
2973: Amat = Acusp->mat;
2974: Bmat = Bcusp->matTranspose;
2975: break;
2976: default:
2977: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2978: }
2979: Cmat = Ccusp->mat;
2980: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
2981: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
2982: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[ptype]);
2983: Acsr = (CsrMatrix *)Amat->mat;
2984: Bcsr = mmdata->Bcsr ? mmdata->Bcsr : (CsrMatrix *)Bmat->mat; /* B may be in compressed row storage */
2985: Ccsr = (CsrMatrix *)Cmat->mat;
2986: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
2987: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
2988: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2989: PetscCall(PetscLogGpuTimeBegin());
2990: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2991: BmatSpDescr = mmdata->Bcsr ? mmdata->matSpBDescr : Bmat->matDescr; /* B may be in compressed row storage */
2992: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
2993: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2994: 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);
2995: PetscCallCUSPARSE(stat);
2996: #else
2997: 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);
2998: PetscCallCUSPARSE(stat);
2999: 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);
3000: PetscCallCUSPARSE(stat);
3001: #endif
3002: #else
3003: 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,
3004: 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());
3005: PetscCallCUSPARSE(stat);
3006: #endif
3007: PetscCall(PetscLogGpuFlops(mmdata->flops));
3008: PetscCallCUDA(WaitForCUDA());
3009: PetscCall(PetscLogGpuTimeEnd());
3010: C->offloadmask = PETSC_OFFLOAD_GPU;
3011: finalize:
3012: /* shorter version of MatAssemblyEnd_SeqAIJ */
3013: 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));
3014: PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
3015: PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
3016: c->reallocs = 0;
3017: C->info.mallocs += 0;
3018: C->info.nz_unneeded = 0;
3019: C->assembled = C->was_assembled = PETSC_TRUE;
3020: C->num_ass++;
3021: PetscFunctionReturn(PETSC_SUCCESS);
3022: }
3024: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
3025: {
3026: Mat_Product *product = C->product;
3027: Mat A, B;
3028: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
3029: Mat_SeqAIJ *a, *b, *c;
3030: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
3031: CsrMatrix *Acsr, *Bcsr, *Ccsr;
3032: PetscInt i, j, m, n, k;
3033: PetscBool flg;
3034: cusparseStatus_t stat;
3035: MatProductType ptype;
3036: MatProductCtx_MatMatCusparse *mmdata;
3037: PetscLogDouble flops;
3038: PetscBool biscompressed, ciscompressed;
3039: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3040: int64_t C_num_rows1, C_num_cols1, C_nnz1;
3041: cusparseSpMatDescr_t BmatSpDescr;
3042: #else
3043: int cnz;
3044: #endif
3045: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
3047: PetscFunctionBegin;
3048: MatCheckProduct(C, 1);
3049: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
3050: A = product->A;
3051: B = product->B;
3052: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
3053: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
3054: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
3055: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
3056: a = (Mat_SeqAIJ *)A->data;
3057: b = (Mat_SeqAIJ *)B->data;
3058: /* product data */
3059: PetscCall(PetscNew(&mmdata));
3060: C->product->data = mmdata;
3061: C->product->destroy = MatProductCtxDestroy_MatMatCusparse;
3063: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3064: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
3065: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr; /* Access spptr after MatSeqAIJCUSPARSECopyToGPU, not before */
3066: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
3067: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3068: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3070: ptype = product->type;
3071: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
3072: ptype = MATPRODUCT_AB;
3073: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
3074: }
3075: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
3076: ptype = MATPRODUCT_AB;
3077: product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
3078: }
3079: biscompressed = PETSC_FALSE;
3080: ciscompressed = PETSC_FALSE;
3081: switch (ptype) {
3082: case MATPRODUCT_AB:
3083: m = A->rmap->n;
3084: n = B->cmap->n;
3085: k = A->cmap->n;
3086: Amat = Acusp->mat;
3087: Bmat = Bcusp->mat;
3088: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3089: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3090: break;
3091: case MATPRODUCT_AtB:
3092: m = A->cmap->n;
3093: n = B->cmap->n;
3094: k = A->rmap->n;
3095: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3096: Amat = Acusp->matTranspose;
3097: Bmat = Bcusp->mat;
3098: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3099: break;
3100: case MATPRODUCT_ABt:
3101: m = A->rmap->n;
3102: n = B->rmap->n;
3103: k = A->cmap->n;
3104: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
3105: Amat = Acusp->mat;
3106: Bmat = Bcusp->matTranspose;
3107: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3108: break;
3109: default:
3110: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
3111: }
3113: /* create cusparse matrix */
3114: PetscCall(MatSetSizes(C, m, n, m, n));
3115: PetscCall(MatSetType(C, MATSEQAIJCUSPARSE));
3116: c = (Mat_SeqAIJ *)C->data;
3117: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
3118: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
3119: Ccsr = new CsrMatrix;
3121: c->compressedrow.use = ciscompressed;
3122: if (c->compressedrow.use) { /* if a is in compressed row, than c will be in compressed row format */
3123: c->compressedrow.nrows = a->compressedrow.nrows;
3124: PetscCall(PetscMalloc2(c->compressedrow.nrows + 1, &c->compressedrow.i, c->compressedrow.nrows, &c->compressedrow.rindex));
3125: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, c->compressedrow.nrows));
3126: Ccusp->workVector = new THRUSTARRAY(c->compressedrow.nrows);
3127: Cmat->cprowIndices = new THRUSTINTARRAY(c->compressedrow.nrows);
3128: Cmat->cprowIndices->assign(c->compressedrow.rindex, c->compressedrow.rindex + c->compressedrow.nrows);
3129: } else {
3130: c->compressedrow.nrows = 0;
3131: c->compressedrow.i = NULL;
3132: c->compressedrow.rindex = NULL;
3133: Ccusp->workVector = NULL;
3134: Cmat->cprowIndices = NULL;
3135: }
3136: Ccusp->nrows = ciscompressed ? c->compressedrow.nrows : m;
3137: Ccusp->mat = Cmat;
3138: Ccusp->mat->mat = Ccsr;
3139: Ccsr->num_rows = Ccusp->nrows;
3140: Ccsr->num_cols = n;
3141: Ccsr->row_offsets = new THRUSTINTARRAY32(Ccusp->nrows + 1);
3142: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
3143: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
3144: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
3145: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
3146: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
3147: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
3148: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3149: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3150: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3151: if (!Ccsr->num_rows || !Ccsr->num_cols || !a->nz || !b->nz) { /* cusparse raise errors in different calls when matrices have zero rows/columns! */
3152: PetscCallThrust(thrust::fill(thrust::device, Ccsr->row_offsets->begin(), Ccsr->row_offsets->end(), 0));
3153: c->nz = 0;
3154: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3155: Ccsr->values = new THRUSTARRAY(c->nz);
3156: goto finalizesym;
3157: }
3159: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
3160: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
3161: Acsr = (CsrMatrix *)Amat->mat;
3162: if (!biscompressed) {
3163: Bcsr = (CsrMatrix *)Bmat->mat;
3164: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3165: BmatSpDescr = Bmat->matDescr;
3166: #endif
3167: } else { /* we need to use row offsets for the full matrix */
3168: CsrMatrix *cBcsr = (CsrMatrix *)Bmat->mat;
3169: Bcsr = new CsrMatrix;
3170: Bcsr->num_rows = B->rmap->n;
3171: Bcsr->num_cols = cBcsr->num_cols;
3172: Bcsr->num_entries = cBcsr->num_entries;
3173: Bcsr->column_indices = cBcsr->column_indices;
3174: Bcsr->values = cBcsr->values;
3175: if (!Bcusp->rowoffsets_gpu) {
3176: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
3177: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
3178: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
3179: }
3180: Bcsr->row_offsets = Bcusp->rowoffsets_gpu;
3181: mmdata->Bcsr = Bcsr;
3182: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3183: if (Bcsr->num_rows && Bcsr->num_cols) {
3184: 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);
3185: PetscCallCUSPARSE(stat);
3186: }
3187: BmatSpDescr = mmdata->matSpBDescr;
3188: #endif
3189: }
3190: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
3191: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
3192: /* precompute flops count */
3193: if (ptype == MATPRODUCT_AB) {
3194: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3195: const PetscInt st = a->i[i];
3196: const PetscInt en = a->i[i + 1];
3197: for (j = st; j < en; j++) {
3198: const PetscInt brow = a->j[j];
3199: flops += 2. * (b->i[brow + 1] - b->i[brow]);
3200: }
3201: }
3202: } else if (ptype == MATPRODUCT_AtB) {
3203: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3204: const PetscInt anzi = a->i[i + 1] - a->i[i];
3205: const PetscInt bnzi = b->i[i + 1] - b->i[i];
3206: flops += (2. * anzi) * bnzi;
3207: }
3208: } else { /* TODO */
3209: flops = 0.;
3210: }
3212: mmdata->flops = flops;
3213: PetscCall(PetscLogGpuTimeBegin());
3215: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3216: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3217: // cuda-12.2 requires non-null csrRowOffsets
3218: 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);
3219: PetscCallCUSPARSE(stat);
3220: PetscCallCUSPARSE(cusparseSpGEMM_createDescr(&mmdata->spgemmDesc));
3221: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
3222: {
3223: /* cusparseSpGEMMreuse has more reasonable APIs than cusparseSpGEMM, so we prefer to use it.
3224: We follow the sample code at https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSPARSE/spgemm_reuse
3225: */
3226: void *dBuffer1 = NULL;
3227: void *dBuffer2 = NULL;
3228: void *dBuffer3 = NULL;
3229: /* dBuffer4, dBuffer5 are needed by cusparseSpGEMMreuse_compute, and therefore are stored in mmdata */
3230: size_t bufferSize1 = 0;
3231: size_t bufferSize2 = 0;
3232: size_t bufferSize3 = 0;
3233: size_t bufferSize4 = 0;
3234: size_t bufferSize5 = 0;
3236: /* ask bufferSize1 bytes for external memory */
3237: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, NULL);
3238: PetscCallCUSPARSE(stat);
3239: PetscCallCUDA(cudaMalloc((void **)&dBuffer1, bufferSize1));
3240: /* inspect the matrices A and B to understand the memory requirement for the next step */
3241: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, dBuffer1);
3242: PetscCallCUSPARSE(stat);
3244: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, NULL, &bufferSize3, NULL, &bufferSize4, NULL);
3245: PetscCallCUSPARSE(stat);
3246: PetscCallCUDA(cudaMalloc((void **)&dBuffer2, bufferSize2));
3247: PetscCallCUDA(cudaMalloc((void **)&dBuffer3, bufferSize3));
3248: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer4, bufferSize4));
3249: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, dBuffer2, &bufferSize3, dBuffer3, &bufferSize4, mmdata->dBuffer4);
3250: PetscCallCUSPARSE(stat);
3251: PetscCallCUDA(cudaFree(dBuffer1));
3252: PetscCallCUDA(cudaFree(dBuffer2));
3254: /* get matrix C non-zero entries C_nnz1 */
3255: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3256: c->nz = (PetscInt)C_nnz1;
3257: /* allocate matrix C */
3258: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3259: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3260: Ccsr->values = new THRUSTARRAY(c->nz);
3261: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3262: /* update matC with the new pointers */
3263: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3264: PetscCallCUSPARSE(stat);
3266: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, NULL);
3267: PetscCallCUSPARSE(stat);
3268: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer5, bufferSize5));
3269: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, mmdata->dBuffer5);
3270: PetscCallCUSPARSE(stat);
3271: PetscCallCUDA(cudaFree(dBuffer3));
3272: 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);
3273: PetscCallCUSPARSE(stat);
3274: 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));
3275: }
3276: #else
3277: size_t bufSize2;
3278: /* ask bufferSize bytes for external memory */
3279: stat = cusparseSpGEMM_workEstimation(Ccusp->handle, opA, opB, Cmat->alpha_one, Amat->matDescr, BmatSpDescr, Cmat->beta_zero, Cmat->matDescr, cusparse_scalartype, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufSize2, NULL);
3280: PetscCallCUSPARSE(stat);
3281: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer2, bufSize2));
3282: /* inspect the matrices A and B to understand the memory requirement for the next step */
3283: 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);
3284: PetscCallCUSPARSE(stat);
3285: /* ask bufferSize again bytes for external memory */
3286: 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);
3287: PetscCallCUSPARSE(stat);
3288: /* The CUSPARSE documentation is not clear, nor the API
3289: We need both buffers to perform the operations properly!
3290: mmdata->mmBuffer2 does not appear anywhere in the compute/copy API
3291: it only appears for the workEstimation stuff, but it seems it is needed in compute, so probably the address
3292: is stored in the descriptor! What a messy API... */
3293: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer, mmdata->mmBufferSize));
3294: /* compute the intermediate product of A * B */
3295: 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);
3296: PetscCallCUSPARSE(stat);
3297: /* get matrix C non-zero entries C_nnz1 */
3298: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3299: c->nz = (PetscInt)C_nnz1;
3300: 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,
3301: mmdata->mmBufferSize / 1024));
3302: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3303: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3304: Ccsr->values = new THRUSTARRAY(c->nz);
3305: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3306: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3307: PetscCallCUSPARSE(stat);
3308: 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);
3309: PetscCallCUSPARSE(stat);
3310: #endif // PETSC_PKG_CUDA_VERSION_GE(11,4,0)
3311: #else
3312: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_HOST));
3313: 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,
3314: Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->row_offsets->data().get(), &cnz);
3315: PetscCallCUSPARSE(stat);
3316: c->nz = cnz;
3317: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3318: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3319: Ccsr->values = new THRUSTARRAY(c->nz);
3320: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3322: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3323: /* with the old gemm interface (removed from 11.0 on) we cannot compute the symbolic factorization only.
3324: 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
3325: D is NULL, despite the fact that CUSPARSE documentation claims it is supported! */
3326: 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,
3327: 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());
3328: PetscCallCUSPARSE(stat);
3329: #endif
3330: PetscCall(PetscLogGpuFlops(mmdata->flops));
3331: PetscCall(PetscLogGpuTimeEnd());
3332: finalizesym:
3333: c->free_a = PETSC_TRUE;
3334: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
3335: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
3336: c->free_ij = PETSC_TRUE;
3337: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
3338: PetscInt *d_i = c->i;
3339: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
3340: THRUSTINTARRAY jj(Ccsr->column_indices->size());
3341: ii = *Ccsr->row_offsets;
3342: jj = *Ccsr->column_indices;
3343: if (ciscompressed) d_i = c->compressedrow.i;
3344: PetscCallCUDA(cudaMemcpy(d_i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3345: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3346: } else {
3347: PetscInt *d_i = c->i;
3348: if (ciscompressed) d_i = c->compressedrow.i;
3349: PetscCallCUDA(cudaMemcpy(d_i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3350: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3351: }
3352: if (ciscompressed) { /* need to expand host row offsets */
3353: PetscInt r = 0;
3354: c->i[0] = 0;
3355: for (k = 0; k < c->compressedrow.nrows; k++) {
3356: const PetscInt next = c->compressedrow.rindex[k];
3357: const PetscInt old = c->compressedrow.i[k];
3358: for (; r < next; r++) c->i[r + 1] = old;
3359: }
3360: for (; r < m; r++) c->i[r + 1] = c->compressedrow.i[c->compressedrow.nrows];
3361: }
3362: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
3363: PetscCall(PetscMalloc1(m, &c->ilen));
3364: PetscCall(PetscMalloc1(m, &c->imax));
3365: c->maxnz = c->nz;
3366: c->nonzerorowcnt = 0;
3367: c->rmax = 0;
3368: for (k = 0; k < m; k++) {
3369: const PetscInt nn = c->i[k + 1] - c->i[k];
3370: c->ilen[k] = c->imax[k] = nn;
3371: c->nonzerorowcnt += (PetscInt)!!nn;
3372: c->rmax = PetscMax(c->rmax, nn);
3373: }
3374: PetscCall(PetscMalloc1(c->nz, &c->a));
3375: Ccsr->num_entries = c->nz;
3377: C->nonzerostate++;
3378: PetscCall(PetscLayoutSetUp(C->rmap));
3379: PetscCall(PetscLayoutSetUp(C->cmap));
3380: Ccusp->nonzerostate = C->nonzerostate;
3381: C->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
3382: C->preallocated = PETSC_TRUE;
3383: C->assembled = PETSC_FALSE;
3384: C->was_assembled = PETSC_FALSE;
3385: 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 */
3386: mmdata->reusesym = PETSC_TRUE;
3387: C->offloadmask = PETSC_OFFLOAD_GPU;
3388: }
3389: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3390: PetscFunctionReturn(PETSC_SUCCESS);
3391: }
3393: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat);
3395: /* handles sparse or dense B */
3396: static PetscErrorCode MatProductSetFromOptions_SeqAIJCUSPARSE(Mat mat)
3397: {
3398: Mat_Product *product = mat->product;
3399: PetscBool isdense = PETSC_FALSE, Biscusp = PETSC_FALSE, Ciscusp = PETSC_TRUE;
3401: PetscFunctionBegin;
3402: MatCheckProduct(mat, 1);
3403: PetscCall(PetscObjectBaseTypeCompare((PetscObject)product->B, MATSEQDENSE, &isdense));
3404: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJCUSPARSE, &Biscusp));
3405: if (product->type == MATPRODUCT_ABC) {
3406: Ciscusp = PETSC_FALSE;
3407: if (!product->C->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJCUSPARSE, &Ciscusp));
3408: }
3409: if (Biscusp && Ciscusp) { /* we can always select the CPU backend */
3410: PetscBool usecpu = PETSC_FALSE;
3411: switch (product->type) {
3412: case MATPRODUCT_AB:
3413: if (product->api_user) {
3414: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
3415: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3416: PetscOptionsEnd();
3417: } else {
3418: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
3419: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3420: PetscOptionsEnd();
3421: }
3422: break;
3423: case MATPRODUCT_AtB:
3424: if (product->api_user) {
3425: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
3426: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3427: PetscOptionsEnd();
3428: } else {
3429: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
3430: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3431: PetscOptionsEnd();
3432: }
3433: break;
3434: case MATPRODUCT_PtAP:
3435: if (product->api_user) {
3436: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
3437: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3438: PetscOptionsEnd();
3439: } else {
3440: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
3441: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3442: PetscOptionsEnd();
3443: }
3444: break;
3445: case MATPRODUCT_RARt:
3446: if (product->api_user) {
3447: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatRARt", "Mat");
3448: PetscCall(PetscOptionsBool("-matrart_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3449: PetscOptionsEnd();
3450: } else {
3451: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_RARt", "Mat");
3452: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3453: PetscOptionsEnd();
3454: }
3455: break;
3456: case MATPRODUCT_ABC:
3457: if (product->api_user) {
3458: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMatMult", "Mat");
3459: PetscCall(PetscOptionsBool("-matmatmatmult_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3460: PetscOptionsEnd();
3461: } else {
3462: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_ABC", "Mat");
3463: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3464: PetscOptionsEnd();
3465: }
3466: break;
3467: default:
3468: break;
3469: }
3470: if (usecpu) Biscusp = Ciscusp = PETSC_FALSE;
3471: }
3472: /* dispatch */
3473: if (isdense) {
3474: switch (product->type) {
3475: case MATPRODUCT_AB:
3476: case MATPRODUCT_AtB:
3477: case MATPRODUCT_ABt:
3478: case MATPRODUCT_PtAP:
3479: case MATPRODUCT_RARt:
3480: if (product->A->boundtocpu) {
3481: PetscCall(MatProductSetFromOptions_SeqAIJ_SeqDense(mat));
3482: } else {
3483: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA;
3484: }
3485: break;
3486: case MATPRODUCT_ABC:
3487: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3488: break;
3489: default:
3490: break;
3491: }
3492: } else if (Biscusp && Ciscusp) {
3493: switch (product->type) {
3494: case MATPRODUCT_AB:
3495: case MATPRODUCT_AtB:
3496: case MATPRODUCT_ABt:
3497: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3498: break;
3499: case MATPRODUCT_PtAP:
3500: case MATPRODUCT_RARt:
3501: case MATPRODUCT_ABC:
3502: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3503: break;
3504: default:
3505: break;
3506: }
3507: } else { /* fallback for AIJ */
3508: PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
3509: }
3510: PetscFunctionReturn(PETSC_SUCCESS);
3511: }
3513: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3514: {
3515: PetscFunctionBegin;
3516: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_FALSE, PETSC_FALSE));
3517: PetscFunctionReturn(PETSC_SUCCESS);
3518: }
3520: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3521: {
3522: PetscFunctionBegin;
3523: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_FALSE, PETSC_FALSE));
3524: PetscFunctionReturn(PETSC_SUCCESS);
3525: }
3527: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3528: {
3529: PetscFunctionBegin;
3530: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_TRUE));
3531: PetscFunctionReturn(PETSC_SUCCESS);
3532: }
3534: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3535: {
3536: PetscFunctionBegin;
3537: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_TRUE));
3538: PetscFunctionReturn(PETSC_SUCCESS);
3539: }
3541: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3542: {
3543: PetscFunctionBegin;
3544: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_FALSE));
3545: PetscFunctionReturn(PETSC_SUCCESS);
3546: }
3548: __global__ static void ScatterAdd(PetscInt n, PetscInt *idx, const PetscScalar *x, PetscScalar *y)
3549: {
3550: int i = blockIdx.x * blockDim.x + threadIdx.x;
3551: if (i < n) y[idx[i]] += x[i];
3552: }
3554: /* z = op(A) x + y. If trans & !herm, op = ^T; if trans & herm, op = ^H; if !trans, op = no-op */
3555: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz, PetscBool trans, PetscBool herm)
3556: {
3557: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3558: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3559: Mat_SeqAIJCUSPARSEMultStruct *matstruct;
3560: PetscScalar *xarray, *zarray, *dptr, *beta, *xptr;
3561: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
3562: PetscBool compressed;
3563: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3564: PetscInt nx, ny;
3565: #endif
3567: PetscFunctionBegin;
3568: PetscCheck(!herm || trans, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Hermitian and not transpose not supported");
3569: if (!a->nz) {
3570: if (yy) PetscCall(VecSeq_CUDA::Copy(yy, zz));
3571: else PetscCall(VecSeq_CUDA::Set(zz, 0));
3572: PetscFunctionReturn(PETSC_SUCCESS);
3573: }
3574: /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
3575: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3576: if (!trans) {
3577: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3578: PetscCheck(matstruct, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "SeqAIJCUSPARSE does not have a 'mat' (need to fix)");
3579: } else {
3580: if (herm || !A->form_explicit_transpose) {
3581: opA = herm ? CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
3582: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3583: } else {
3584: if (!cusparsestruct->matTranspose) PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3585: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
3586: }
3587: }
3588: /* Does the matrix use compressed rows (i.e., drop zero rows)? */
3589: compressed = matstruct->cprowIndices ? PETSC_TRUE : PETSC_FALSE;
3591: try {
3592: PetscCall(VecCUDAGetArrayRead(xx, (const PetscScalar **)&xarray));
3593: if (yy == zz) PetscCall(VecCUDAGetArray(zz, &zarray)); /* read & write zz, so need to get up-to-date zarray on GPU */
3594: else PetscCall(VecCUDAGetArrayWrite(zz, &zarray)); /* write zz, so no need to init zarray on GPU */
3596: PetscCall(PetscLogGpuTimeBegin());
3597: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3598: /* z = A x + beta y.
3599: If A is compressed (with less rows), then Ax is shorter than the full z, so we need a work vector to store Ax.
3600: When A is non-compressed, and z = y, we can set beta=1 to compute y = Ax + y in one call.
3601: */
3602: xptr = xarray;
3603: dptr = compressed ? cusparsestruct->workVector->data().get() : zarray;
3604: beta = (yy == zz && !compressed) ? matstruct->beta_one : matstruct->beta_zero;
3605: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3606: /* Get length of x, y for y=Ax. ny might be shorter than the work vector's allocated length, since the work vector is
3607: allocated to accommodate different uses. So we get the length info directly from mat.
3608: */
3609: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3610: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3611: nx = mat->num_cols; // since y = Ax
3612: ny = mat->num_rows;
3613: }
3614: #endif
3615: } else {
3616: /* z = A^T x + beta y
3617: If A is compressed, then we need a work vector as the shorter version of x to compute A^T x.
3618: Note A^Tx is of full length, so we set beta to 1.0 if y exists.
3619: */
3620: xptr = compressed ? cusparsestruct->workVector->data().get() : xarray;
3621: dptr = zarray;
3622: beta = yy ? matstruct->beta_one : matstruct->beta_zero;
3623: if (compressed) { /* Scatter x to work vector */
3624: thrust::device_ptr<PetscScalar> xarr = thrust::device_pointer_cast(xarray);
3626: thrust::for_each(
3627: #if PetscDefined(HAVE_THRUST_ASYNC)
3628: thrust::cuda::par.on(PetscDefaultCudaStream),
3629: #endif
3630: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))),
3631: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))) + matstruct->cprowIndices->size(), VecCUDAEqualsReverse());
3632: }
3633: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3634: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3635: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3636: nx = mat->num_rows; // since y = A^T x
3637: ny = mat->num_cols;
3638: }
3639: #endif
3640: }
3642: /* csr_spmv does y = alpha op(A) x + beta y */
3643: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3644: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3645: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3646: 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.
3647: #else
3648: cusparseSpMatDescr_t &matDescr = matstruct->matDescr;
3649: #endif
3651: 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");
3652: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3653: if (!matDescr) {
3654: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3655: 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));
3656: }
3657: #endif
3659: if (!matstruct->cuSpMV[opA].initialized) { /* built on demand */
3660: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecXDescr, nx, xptr, cusparse_scalartype));
3661: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecYDescr, ny, dptr, cusparse_scalartype));
3662: PetscCallCUSPARSE(
3663: 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));
3664: PetscCallCUDA(cudaMalloc(&matstruct->cuSpMV[opA].spmvBuffer, matstruct->cuSpMV[opA].spmvBufferSize));
3665: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // cusparseSpMV_preprocess is added in 12.4
3666: PetscCallCUSPARSE(
3667: 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));
3668: #endif
3669: matstruct->cuSpMV[opA].initialized = PETSC_TRUE;
3670: } else {
3671: /* x, y's value pointers might change between calls, but their shape is kept, so we just update pointers */
3672: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecXDescr, xptr));
3673: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecYDescr, dptr));
3674: }
3676: 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));
3677: #else
3678: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3679: 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));
3680: #endif
3681: } else {
3682: if (cusparsestruct->nrows) {
3683: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3684: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
3685: #else
3686: cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat;
3687: PetscCallCUSPARSE(cusparse_hyb_spmv(cusparsestruct->handle, opA, matstruct->alpha_one, matstruct->descr, hybMat, xptr, beta, dptr));
3688: #endif
3689: }
3690: }
3691: PetscCall(PetscLogGpuTimeEnd());
3693: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3694: if (yy) { /* MatMultAdd: zz = A*xx + yy */
3695: if (compressed) { /* A is compressed. We first copy yy to zz, then ScatterAdd the work vector to zz */
3696: PetscCall(VecSeq_CUDA::Copy(yy, zz)); /* zz = yy */
3697: } else if (zz != yy) { /* A is not compressed. zz already contains A*xx, and we just need to add yy */
3698: PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3699: }
3700: } else if (compressed) { /* MatMult: zz = A*xx. A is compressed, so we zero zz first, then ScatterAdd the work vector to zz */
3701: PetscCall(VecSeq_CUDA::Set(zz, 0));
3702: }
3704: /* ScatterAdd the result from work vector into the full vector when A is compressed */
3705: if (compressed) {
3706: PetscCall(PetscLogGpuTimeBegin());
3707: PetscInt n = (PetscInt)matstruct->cprowIndices->size();
3708: ScatterAdd<<<(int)((n + 255) / 256), 256, 0, PetscDefaultCudaStream>>>(n, matstruct->cprowIndices->data().get(), cusparsestruct->workVector->data().get(), zarray);
3709: PetscCall(PetscLogGpuTimeEnd());
3710: }
3711: } else {
3712: if (yy && yy != zz) PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3713: }
3714: PetscCall(VecCUDARestoreArrayRead(xx, (const PetscScalar **)&xarray));
3715: if (yy == zz) PetscCall(VecCUDARestoreArray(zz, &zarray));
3716: else PetscCall(VecCUDARestoreArrayWrite(zz, &zarray));
3717: } catch (char *ex) {
3718: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
3719: }
3720: if (yy) {
3721: PetscCall(PetscLogGpuFlops(2.0 * a->nz));
3722: } else {
3723: PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
3724: }
3725: PetscFunctionReturn(PETSC_SUCCESS);
3726: }
3728: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3729: {
3730: PetscFunctionBegin;
3731: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_FALSE));
3732: PetscFunctionReturn(PETSC_SUCCESS);
3733: }
3735: PETSC_INTERN PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec xx);
3737: __global__ static void GetDiagonal_CSR(const int *row, const int *col, const PetscScalar *val, const PetscInt len, PetscScalar *diag)
3738: {
3739: const size_t x = blockIdx.x * blockDim.x + threadIdx.x;
3741: if (x < len) {
3742: const PetscInt rowx = row[x], num_non0_row = row[x + 1] - rowx;
3743: PetscScalar d = 0.0;
3745: for (PetscInt i = 0; i < num_non0_row; i++) {
3746: if (col[i + rowx] == x) {
3747: d = val[i + rowx];
3748: break;
3749: }
3750: }
3751: diag[x] = d;
3752: }
3753: }
3755: static PetscErrorCode MatGetDiagonal_SeqAIJCUSPARSE(Mat A, Vec diag)
3756: {
3757: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3758: Mat_SeqAIJCUSPARSEMultStruct *matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3759: PetscScalar *darray;
3761: PetscFunctionBegin;
3762: if (A->offloadmask == PETSC_OFFLOAD_BOTH || A->offloadmask == PETSC_OFFLOAD_GPU) {
3763: PetscInt n = A->rmap->n;
3764: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3766: PetscCheck(cusparsestruct->format == MAT_CUSPARSE_CSR, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only CSR format supported");
3767: if (n > 0) {
3768: PetscCall(VecCUDAGetArrayWrite(diag, &darray));
3769: 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);
3770: PetscCallCUDA(cudaPeekAtLastError());
3771: PetscCall(VecCUDARestoreArrayWrite(diag, &darray));
3772: }
3773: } else PetscCall(MatGetDiagonal_SeqAIJ(A, diag));
3774: PetscFunctionReturn(PETSC_SUCCESS);
3775: }
3777: static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A, MatAssemblyType mode)
3778: {
3779: PetscFunctionBegin;
3780: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
3781: PetscFunctionReturn(PETSC_SUCCESS);
3782: }
3784: /*@
3785: MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in `MATAIJCUSPARSE` (compressed row) format for use on NVIDIA GPUs
3787: Collective
3789: Input Parameters:
3790: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3791: . m - number of rows
3792: . n - number of columns
3793: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is provide
3794: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
3796: Output Parameter:
3797: . A - the matrix
3799: Level: intermediate
3801: Notes:
3802: This matrix will ultimately pushed down to NVIDIA GPUs and use the CuSPARSE library for
3803: calculations. For good matrix assembly performance the user should preallocate the matrix
3804: storage by setting the parameter `nz` (or the array `nnz`).
3806: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3807: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3808: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
3810: The AIJ format, also called
3811: compressed row storage, is fully compatible with standard Fortran
3812: storage. That is, the stored row and column indices can begin at
3813: either one (as in Fortran) or zero.
3815: Specify the preallocated storage with either nz or nnz (not both).
3816: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3817: allocation.
3819: When working with matrices for GPUs, it is often better to use the `MatSetPreallocationCOO()` and `MatSetValuesCOO()` paradigm rather than using this routine and `MatSetValues()`
3821: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATAIJCUSPARSE`,
3822: `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
3823: @*/
3824: PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3825: {
3826: PetscFunctionBegin;
3827: PetscCall(MatCreate(comm, A));
3828: PetscCall(MatSetSizes(*A, m, n, m, n));
3829: PetscCall(MatSetType(*A, MATSEQAIJCUSPARSE));
3830: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
3831: PetscFunctionReturn(PETSC_SUCCESS);
3832: }
3834: static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A)
3835: {
3836: PetscFunctionBegin;
3837: if (A->factortype == MAT_FACTOR_NONE) {
3838: PetscCall(MatSeqAIJCUSPARSE_Destroy(A));
3839: } else {
3840: PetscCall(MatSeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors **)&A->spptr));
3841: }
3842: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3843: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetFormat_C", NULL));
3844: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetUseCPUSolve_C", NULL));
3845: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3846: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3847: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3848: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
3849: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3850: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3851: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijcusparse_hypre_C", NULL));
3852: PetscCall(MatDestroy_SeqAIJ(A));
3853: PetscFunctionReturn(PETSC_SUCCESS);
3854: }
3856: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
3857: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat, PetscBool);
3858: static PetscErrorCode MatDuplicate_SeqAIJCUSPARSE(Mat A, MatDuplicateOption cpvalues, Mat *B)
3859: {
3860: PetscFunctionBegin;
3861: PetscCall(MatDuplicate_SeqAIJ(A, cpvalues, B));
3862: PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(*B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, B));
3863: PetscFunctionReturn(PETSC_SUCCESS);
3864: }
3866: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat Y, PetscScalar a, Mat X, MatStructure str)
3867: {
3868: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3869: Mat_SeqAIJCUSPARSE *cy;
3870: Mat_SeqAIJCUSPARSE *cx;
3871: PetscScalar *ay;
3872: const PetscScalar *ax;
3873: CsrMatrix *csry, *csrx;
3875: PetscFunctionBegin;
3876: cy = (Mat_SeqAIJCUSPARSE *)Y->spptr;
3877: cx = (Mat_SeqAIJCUSPARSE *)X->spptr;
3878: if (X->ops->axpy != Y->ops->axpy) {
3879: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3880: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3881: PetscFunctionReturn(PETSC_SUCCESS);
3882: }
3883: /* if we are here, it means both matrices are bound to GPU */
3884: PetscCall(MatSeqAIJCUSPARSECopyToGPU(Y));
3885: PetscCall(MatSeqAIJCUSPARSECopyToGPU(X));
3886: PetscCheck(cy->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)Y), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3887: PetscCheck(cx->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)X), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3888: csry = (CsrMatrix *)cy->mat->mat;
3889: csrx = (CsrMatrix *)cx->mat->mat;
3890: /* see if we can turn this into a cublas axpy */
3891: if (str != SAME_NONZERO_PATTERN && x->nz == y->nz && !x->compressedrow.use && !y->compressedrow.use) {
3892: bool eq = thrust::equal(thrust::device, csry->row_offsets->begin(), csry->row_offsets->end(), csrx->row_offsets->begin());
3893: if (eq) eq = thrust::equal(thrust::device, csry->column_indices->begin(), csry->column_indices->end(), csrx->column_indices->begin());
3894: if (eq) str = SAME_NONZERO_PATTERN;
3895: }
3896: /* spgeam is buggy with one column */
3897: if (Y->cmap->n == 1 && str != SAME_NONZERO_PATTERN) str = DIFFERENT_NONZERO_PATTERN;
3899: if (str == SUBSET_NONZERO_PATTERN) {
3900: PetscScalar b = 1.0;
3901: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3902: size_t bufferSize;
3903: void *buffer;
3904: #endif
3906: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3907: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3908: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_HOST));
3909: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3910: 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(),
3911: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), &bufferSize));
3912: PetscCallCUDA(cudaMalloc(&buffer, bufferSize));
3913: PetscCall(PetscLogGpuTimeBegin());
3914: 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(),
3915: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), buffer));
3916: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3917: PetscCall(PetscLogGpuTimeEnd());
3918: PetscCallCUDA(cudaFree(buffer));
3919: #else
3920: PetscCall(PetscLogGpuTimeBegin());
3921: 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(),
3922: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get()));
3923: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3924: PetscCall(PetscLogGpuTimeEnd());
3925: #endif
3926: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_DEVICE));
3927: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3928: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3929: } else if (str == SAME_NONZERO_PATTERN) {
3930: cublasHandle_t cublasv2handle;
3931: PetscBLASInt one = 1, bnz = 1;
3933: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3934: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3935: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3936: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3937: PetscCall(PetscLogGpuTimeBegin());
3938: PetscCallCUBLAS(cublasXaxpy(cublasv2handle, bnz, &a, ax, one, ay, one));
3939: PetscCall(PetscLogGpuFlops(2.0 * bnz));
3940: PetscCall(PetscLogGpuTimeEnd());
3941: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3942: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3943: } else {
3944: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3945: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3946: }
3947: PetscFunctionReturn(PETSC_SUCCESS);
3948: }
3950: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat Y, PetscScalar a)
3951: {
3952: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3953: PetscScalar *ay;
3954: cublasHandle_t cublasv2handle;
3955: PetscBLASInt one = 1, bnz = 1;
3957: PetscFunctionBegin;
3958: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3959: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3960: PetscCall(PetscBLASIntCast(y->nz, &bnz));
3961: PetscCall(PetscLogGpuTimeBegin());
3962: PetscCallCUBLAS(cublasXscal(cublasv2handle, bnz, &a, ay, one));
3963: PetscCall(PetscLogGpuFlops(bnz));
3964: PetscCall(PetscLogGpuTimeEnd());
3965: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3966: PetscFunctionReturn(PETSC_SUCCESS);
3967: }
3969: static PetscErrorCode MatZeroEntries_SeqAIJCUSPARSE(Mat A)
3970: {
3971: PetscBool gpu = PETSC_FALSE;
3972: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3974: PetscFunctionBegin;
3975: if (A->factortype == MAT_FACTOR_NONE) {
3976: Mat_SeqAIJCUSPARSE *spptr = (Mat_SeqAIJCUSPARSE *)A->spptr;
3977: if (spptr->mat) {
3978: CsrMatrix *matrix = (CsrMatrix *)spptr->mat->mat;
3979: if (matrix->values) {
3980: gpu = PETSC_TRUE;
3981: thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3982: }
3983: }
3984: if (spptr->matTranspose) {
3985: CsrMatrix *matrix = (CsrMatrix *)spptr->matTranspose->mat;
3986: if (matrix->values) thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3987: }
3988: }
3989: if (gpu) A->offloadmask = PETSC_OFFLOAD_GPU;
3990: else {
3991: PetscCall(PetscArrayzero(a->a, a->i[A->rmap->n]));
3992: A->offloadmask = PETSC_OFFLOAD_CPU;
3993: }
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(PetscObjectStateIncrease((PetscObject)A));
4654: *a = NULL;
4655: PetscFunctionReturn(PETSC_SUCCESS);
4656: }
4658: /*@C
4659: MatSeqAIJCUSPARSEGetArrayWrite - gives write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4661: Not Collective
4663: Input Parameter:
4664: . A - a `MATSEQAIJCUSPARSE` matrix
4666: Output Parameter:
4667: . a - pointer to the device data
4669: Level: developer
4671: Note:
4672: Does not trigger any host to device copies.
4674: 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
4676: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSERestoreArrayWrite()`
4677: @*/
4678: PetscErrorCode MatSeqAIJCUSPARSEGetArrayWrite(Mat A, PetscScalar **a)
4679: {
4680: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4681: CsrMatrix *csr;
4683: PetscFunctionBegin;
4685: PetscAssertPointer(a, 2);
4686: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4687: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4688: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4689: csr = (CsrMatrix *)cusp->mat->mat;
4690: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4691: *a = csr->values->data().get();
4692: A->offloadmask = PETSC_OFFLOAD_GPU;
4693: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4694: PetscFunctionReturn(PETSC_SUCCESS);
4695: }
4697: /*@C
4698: MatSeqAIJCUSPARSERestoreArrayWrite - restore the write-only access array obtained from `MatSeqAIJCUSPARSEGetArrayWrite()`
4700: Not Collective
4702: Input Parameters:
4703: + A - a `MATSEQAIJCUSPARSE` matrix
4704: - a - pointer to the device data
4706: Level: developer
4708: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayWrite()`
4709: @*/
4710: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayWrite(Mat A, PetscScalar **a)
4711: {
4712: PetscFunctionBegin;
4714: PetscAssertPointer(a, 2);
4715: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4716: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4717: *a = NULL;
4718: PetscFunctionReturn(PETSC_SUCCESS);
4719: }
4721: struct IJCompare4 {
4722: __host__ __device__ inline bool operator()(const thrust::tuple<int, int, PetscScalar, int> &t1, const thrust::tuple<int, int, PetscScalar, int> &t2)
4723: {
4724: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4725: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4726: return false;
4727: }
4728: };
4730: struct Shift {
4731: int _shift;
4733: Shift(int shift) : _shift(shift) { }
4734: __host__ __device__ inline int operator()(const int &c) { return c + _shift; }
4735: };
4737: /* merges two SeqAIJCUSPARSE matrices A, B by concatenating their rows. [A';B']' operation in MATLAB notation */
4738: PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
4739: {
4740: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data, *c;
4741: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr, *Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr, *Ccusp;
4742: Mat_SeqAIJCUSPARSEMultStruct *Cmat;
4743: CsrMatrix *Acsr, *Bcsr, *Ccsr;
4744: PetscInt Annz, Bnnz;
4745: cusparseStatus_t stat;
4746: PetscInt i, m, n, zero = 0;
4748: PetscFunctionBegin;
4751: PetscAssertPointer(C, 4);
4752: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4753: PetscCheckTypeName(B, MATSEQAIJCUSPARSE);
4754: 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);
4755: PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
4756: PetscCheck(Acusp->format != MAT_CUSPARSE_ELL && Acusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4757: PetscCheck(Bcusp->format != MAT_CUSPARSE_ELL && Bcusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4758: if (reuse == MAT_INITIAL_MATRIX) {
4759: m = A->rmap->n;
4760: n = A->cmap->n + B->cmap->n;
4761: PetscCall(MatCreate(PETSC_COMM_SELF, C));
4762: PetscCall(MatSetSizes(*C, m, n, m, n));
4763: PetscCall(MatSetType(*C, MATSEQAIJCUSPARSE));
4764: c = (Mat_SeqAIJ *)(*C)->data;
4765: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4766: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
4767: Ccsr = new CsrMatrix;
4768: Cmat->cprowIndices = NULL;
4769: c->compressedrow.use = PETSC_FALSE;
4770: c->compressedrow.nrows = 0;
4771: c->compressedrow.i = NULL;
4772: c->compressedrow.rindex = NULL;
4773: Ccusp->workVector = NULL;
4774: Ccusp->nrows = m;
4775: Ccusp->mat = Cmat;
4776: Ccusp->mat->mat = Ccsr;
4777: Ccsr->num_rows = m;
4778: Ccsr->num_cols = n;
4779: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
4780: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
4781: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4782: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
4783: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
4784: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
4785: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4786: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4787: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4788: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4789: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4790: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4791: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4793: Acsr = (CsrMatrix *)Acusp->mat->mat;
4794: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4795: Annz = (PetscInt)Acsr->column_indices->size();
4796: Bnnz = (PetscInt)Bcsr->column_indices->size();
4797: c->nz = Annz + Bnnz;
4798: Ccsr->row_offsets = new THRUSTINTARRAY32(m + 1);
4799: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
4800: Ccsr->values = new THRUSTARRAY(c->nz);
4801: Ccsr->num_entries = c->nz;
4802: Ccusp->coords = new THRUSTINTARRAY(c->nz);
4803: if (c->nz) {
4804: auto Acoo = new THRUSTINTARRAY32(Annz);
4805: auto Bcoo = new THRUSTINTARRAY32(Bnnz);
4806: auto Ccoo = new THRUSTINTARRAY32(c->nz);
4807: THRUSTINTARRAY32 *Aroff, *Broff;
4809: if (a->compressedrow.use) { /* need full row offset */
4810: if (!Acusp->rowoffsets_gpu) {
4811: Acusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4812: Acusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4813: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4814: }
4815: Aroff = Acusp->rowoffsets_gpu;
4816: } else Aroff = Acsr->row_offsets;
4817: if (b->compressedrow.use) { /* need full row offset */
4818: if (!Bcusp->rowoffsets_gpu) {
4819: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
4820: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
4821: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
4822: }
4823: Broff = Bcusp->rowoffsets_gpu;
4824: } else Broff = Bcsr->row_offsets;
4825: PetscCall(PetscLogGpuTimeBegin());
4826: stat = cusparseXcsr2coo(Acusp->handle, Aroff->data().get(), Annz, m, Acoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4827: PetscCallCUSPARSE(stat);
4828: stat = cusparseXcsr2coo(Bcusp->handle, Broff->data().get(), Bnnz, m, Bcoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4829: PetscCallCUSPARSE(stat);
4830: /* Issues when using bool with large matrices on SUMMIT 10.2.89 */
4831: auto Aperm = thrust::make_constant_iterator(1);
4832: auto Bperm = thrust::make_constant_iterator(0);
4833: #if PETSC_PKG_CUDA_VERSION_GE(10, 0, 0)
4834: auto Bcib = thrust::make_transform_iterator(Bcsr->column_indices->begin(), Shift(A->cmap->n));
4835: auto Bcie = thrust::make_transform_iterator(Bcsr->column_indices->end(), Shift(A->cmap->n));
4836: #else
4837: /* there are issues instantiating the merge operation using a transform iterator for the columns of B */
4838: auto Bcib = Bcsr->column_indices->begin();
4839: auto Bcie = Bcsr->column_indices->end();
4840: thrust::transform(Bcib, Bcie, Bcib, Shift(A->cmap->n));
4841: #endif
4842: auto wPerm = new THRUSTINTARRAY32(Annz + Bnnz);
4843: auto Azb = thrust::make_zip_iterator(thrust::make_tuple(Acoo->begin(), Acsr->column_indices->begin(), Acsr->values->begin(), Aperm));
4844: auto Aze = thrust::make_zip_iterator(thrust::make_tuple(Acoo->end(), Acsr->column_indices->end(), Acsr->values->end(), Aperm));
4845: auto Bzb = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->begin(), Bcib, Bcsr->values->begin(), Bperm));
4846: auto Bze = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->end(), Bcie, Bcsr->values->end(), Bperm));
4847: auto Czb = thrust::make_zip_iterator(thrust::make_tuple(Ccoo->begin(), Ccsr->column_indices->begin(), Ccsr->values->begin(), wPerm->begin()));
4848: auto p1 = Ccusp->coords->begin();
4849: auto p2 = Ccusp->coords->begin();
4850: #if CCCL_VERSION >= 3001000
4851: cuda::std::advance(p2, Annz);
4852: #else
4853: thrust::advance(p2, Annz);
4854: #endif
4855: PetscCallThrust(thrust::merge(thrust::device, Azb, Aze, Bzb, Bze, Czb, IJCompare4()));
4856: #if PETSC_PKG_CUDA_VERSION_LT(10, 0, 0)
4857: thrust::transform(Bcib, Bcie, Bcib, Shift(-A->cmap->n));
4858: #endif
4859: auto cci = thrust::make_counting_iterator(zero);
4860: auto cce = thrust::make_counting_iterator(c->nz);
4861: #if 0 //Errors on SUMMIT cuda 11.1.0
4862: PetscCallThrust(thrust::partition_copy(thrust::device,cci,cce,wPerm->begin(),p1,p2,thrust::identity<int>()));
4863: #else
4864: #if PETSC_PKG_CUDA_VERSION_LT(12, 9, 0) || PetscDefined(HAVE_THRUST)
4865: auto pred = thrust::identity<int>();
4866: #else
4867: auto pred = cuda::std::identity();
4868: #endif
4869: PetscCallThrust(thrust::copy_if(thrust::device, cci, cce, wPerm->begin(), p1, pred));
4870: PetscCallThrust(thrust::remove_copy_if(thrust::device, cci, cce, wPerm->begin(), p2, pred));
4871: #endif
4872: stat = cusparseXcoo2csr(Ccusp->handle, Ccoo->data().get(), c->nz, m, Ccsr->row_offsets->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4873: PetscCallCUSPARSE(stat);
4874: PetscCall(PetscLogGpuTimeEnd());
4875: delete wPerm;
4876: delete Acoo;
4877: delete Bcoo;
4878: delete Ccoo;
4879: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4880: 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);
4881: PetscCallCUSPARSE(stat);
4882: #endif
4883: if (A->form_explicit_transpose && B->form_explicit_transpose) { /* if A and B have the transpose, generate C transpose too */
4884: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
4885: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
4886: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4887: Mat_SeqAIJCUSPARSEMultStruct *CmatT = new Mat_SeqAIJCUSPARSEMultStruct;
4888: CsrMatrix *CcsrT = new CsrMatrix;
4889: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4890: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4892: (*C)->form_explicit_transpose = PETSC_TRUE;
4893: (*C)->transupdated = PETSC_TRUE;
4894: Ccusp->rowoffsets_gpu = NULL;
4895: CmatT->cprowIndices = NULL;
4896: CmatT->mat = CcsrT;
4897: CcsrT->num_rows = n;
4898: CcsrT->num_cols = m;
4899: CcsrT->num_entries = c->nz;
4901: CcsrT->row_offsets = new THRUSTINTARRAY32(n + 1);
4902: CcsrT->column_indices = new THRUSTINTARRAY32(c->nz);
4903: CcsrT->values = new THRUSTARRAY(c->nz);
4905: PetscCall(PetscLogGpuTimeBegin());
4906: auto rT = CcsrT->row_offsets->begin();
4907: if (AT) {
4908: rT = thrust::copy(AcsrT->row_offsets->begin(), AcsrT->row_offsets->end(), rT);
4909: #if CCCL_VERSION >= 3001000
4910: cuda::std::advance(rT, -1);
4911: #else
4912: thrust::advance(rT, -1);
4913: #endif
4914: }
4915: if (BT) {
4916: auto titb = thrust::make_transform_iterator(BcsrT->row_offsets->begin(), Shift(a->nz));
4917: auto tite = thrust::make_transform_iterator(BcsrT->row_offsets->end(), Shift(a->nz));
4918: thrust::copy(titb, tite, rT);
4919: }
4920: auto cT = CcsrT->column_indices->begin();
4921: if (AT) cT = thrust::copy(AcsrT->column_indices->begin(), AcsrT->column_indices->end(), cT);
4922: if (BT) thrust::copy(BcsrT->column_indices->begin(), BcsrT->column_indices->end(), cT);
4923: auto vT = CcsrT->values->begin();
4924: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4925: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4926: PetscCall(PetscLogGpuTimeEnd());
4928: PetscCallCUSPARSE(cusparseCreateMatDescr(&CmatT->descr));
4929: PetscCallCUSPARSE(cusparseSetMatIndexBase(CmatT->descr, CUSPARSE_INDEX_BASE_ZERO));
4930: PetscCallCUSPARSE(cusparseSetMatType(CmatT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4931: PetscCallCUDA(cudaMalloc((void **)&CmatT->alpha_one, sizeof(PetscScalar)));
4932: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_zero, sizeof(PetscScalar)));
4933: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_one, sizeof(PetscScalar)));
4934: PetscCallCUDA(cudaMemcpy(CmatT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4935: PetscCallCUDA(cudaMemcpy(CmatT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4936: PetscCallCUDA(cudaMemcpy(CmatT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4937: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4938: 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);
4939: PetscCallCUSPARSE(stat);
4940: #endif
4941: Ccusp->matTranspose = CmatT;
4942: }
4943: }
4945: c->free_a = PETSC_TRUE;
4946: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
4947: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4948: c->free_ij = PETSC_TRUE;
4949: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
4950: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
4951: THRUSTINTARRAY jj(Ccsr->column_indices->size());
4952: ii = *Ccsr->row_offsets;
4953: jj = *Ccsr->column_indices;
4954: PetscCallCUDA(cudaMemcpy(c->i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4955: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4956: } else {
4957: PetscCallCUDA(cudaMemcpy(c->i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4958: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4959: }
4960: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
4961: PetscCall(PetscMalloc1(m, &c->ilen));
4962: PetscCall(PetscMalloc1(m, &c->imax));
4963: c->maxnz = c->nz;
4964: c->nonzerorowcnt = 0;
4965: c->rmax = 0;
4966: for (i = 0; i < m; i++) {
4967: const PetscInt nn = c->i[i + 1] - c->i[i];
4968: c->ilen[i] = c->imax[i] = nn;
4969: c->nonzerorowcnt += (PetscInt)!!nn;
4970: c->rmax = PetscMax(c->rmax, nn);
4971: }
4972: PetscCall(PetscMalloc1(c->nz, &c->a));
4973: (*C)->nonzerostate++;
4974: PetscCall(PetscLayoutSetUp((*C)->rmap));
4975: PetscCall(PetscLayoutSetUp((*C)->cmap));
4976: Ccusp->nonzerostate = (*C)->nonzerostate;
4977: (*C)->preallocated = PETSC_TRUE;
4978: } else {
4979: 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);
4980: c = (Mat_SeqAIJ *)(*C)->data;
4981: if (c->nz) {
4982: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4983: PetscCheck(Ccusp->coords, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing coords");
4984: PetscCheck(Ccusp->format != MAT_CUSPARSE_ELL && Ccusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4985: PetscCheck(Ccusp->nonzerostate == (*C)->nonzerostate, PETSC_COMM_SELF, PETSC_ERR_COR, "Wrong nonzerostate");
4986: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4987: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4988: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4989: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4990: Acsr = (CsrMatrix *)Acusp->mat->mat;
4991: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4992: Ccsr = (CsrMatrix *)Ccusp->mat->mat;
4993: 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());
4994: 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());
4995: 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());
4996: 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);
4997: 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());
4998: auto pmid = Ccusp->coords->begin();
4999: #if CCCL_VERSION >= 3001000
5000: cuda::std::advance(pmid, Acsr->num_entries);
5001: #else
5002: thrust::advance(pmid, Acsr->num_entries);
5003: #endif
5004: PetscCall(PetscLogGpuTimeBegin());
5005: auto zibait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->begin())));
5006: auto zieait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
5007: thrust::for_each(zibait, zieait, VecCUDAEquals());
5008: auto zibbit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
5009: auto ziebit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->end())));
5010: thrust::for_each(zibbit, ziebit, VecCUDAEquals());
5011: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(*C, PETSC_FALSE));
5012: if (A->form_explicit_transpose && B->form_explicit_transpose && (*C)->form_explicit_transpose) {
5013: PetscCheck(Ccusp->matTranspose, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing transpose Mat_SeqAIJCUSPARSEMultStruct");
5014: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
5015: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
5016: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
5017: CsrMatrix *CcsrT = (CsrMatrix *)Ccusp->matTranspose->mat;
5018: auto vT = CcsrT->values->begin();
5019: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
5020: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
5021: (*C)->transupdated = PETSC_TRUE;
5022: }
5023: PetscCall(PetscLogGpuTimeEnd());
5024: }
5025: }
5026: PetscCall(PetscObjectStateIncrease((PetscObject)*C));
5027: (*C)->assembled = PETSC_TRUE;
5028: (*C)->was_assembled = PETSC_FALSE;
5029: (*C)->offloadmask = PETSC_OFFLOAD_GPU;
5030: PetscFunctionReturn(PETSC_SUCCESS);
5031: }
5033: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
5034: {
5035: bool dmem;
5036: const PetscScalar *av;
5038: PetscFunctionBegin;
5039: dmem = isCudaMem(v);
5040: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(A, &av));
5041: if (n && idx) {
5042: THRUSTINTARRAY widx(n);
5043: widx.assign(idx, idx + n);
5044: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
5046: THRUSTARRAY *w = NULL;
5047: thrust::device_ptr<PetscScalar> dv;
5048: if (dmem) {
5049: dv = thrust::device_pointer_cast(v);
5050: } else {
5051: w = new THRUSTARRAY(n);
5052: dv = w->data();
5053: }
5054: thrust::device_ptr<const PetscScalar> dav = thrust::device_pointer_cast(av);
5056: auto zibit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.begin()), dv));
5057: auto zieit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.end()), dv + n));
5058: thrust::for_each(zibit, zieit, VecCUDAEquals());
5059: if (w) PetscCallCUDA(cudaMemcpy(v, w->data().get(), n * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
5060: delete w;
5061: } else {
5062: PetscCallCUDA(cudaMemcpy(v, av, n * sizeof(PetscScalar), dmem ? cudaMemcpyDeviceToDevice : cudaMemcpyDeviceToHost));
5063: }
5064: if (!dmem) PetscCall(PetscLogCpuToGpu(n * sizeof(PetscScalar)));
5065: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(A, &av));
5066: PetscFunctionReturn(PETSC_SUCCESS);
5067: }