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: #include <thrust/async/for_each.h>
19: #endif
20: #include <thrust/iterator/constant_iterator.h>
21: #include <thrust/remove.h>
22: #include <thrust/sort.h>
23: #include <thrust/unique.h>
25: const char *const MatCUSPARSEStorageFormats[] = {"CSR", "ELL", "HYB", "MatCUSPARSEStorageFormat", "MAT_CUSPARSE_", 0};
26: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
27: /* The following are copied from cusparse.h in CUDA-11.0. In MatCUSPARSESpMVAlgorithms[] etc, we copy them in
28: 0-based integer value order, since we want to use PetscOptionsEnum() to parse user command line options for them.
30: typedef enum {
31: CUSPARSE_MV_ALG_DEFAULT = 0,
32: CUSPARSE_COOMV_ALG = 1,
33: CUSPARSE_CSRMV_ALG1 = 2,
34: CUSPARSE_CSRMV_ALG2 = 3
35: } cusparseSpMVAlg_t;
37: typedef enum {
38: CUSPARSE_MM_ALG_DEFAULT CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_ALG_DEFAULT) = 0,
39: CUSPARSE_COOMM_ALG1 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG1) = 1,
40: CUSPARSE_COOMM_ALG2 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG2) = 2,
41: CUSPARSE_COOMM_ALG3 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG3) = 3,
42: CUSPARSE_CSRMM_ALG1 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_CSR_ALG1) = 4,
43: CUSPARSE_SPMM_ALG_DEFAULT = 0,
44: CUSPARSE_SPMM_COO_ALG1 = 1,
45: CUSPARSE_SPMM_COO_ALG2 = 2,
46: CUSPARSE_SPMM_COO_ALG3 = 3,
47: CUSPARSE_SPMM_COO_ALG4 = 5,
48: CUSPARSE_SPMM_CSR_ALG1 = 4,
49: CUSPARSE_SPMM_CSR_ALG2 = 6,
50: } cusparseSpMMAlg_t;
52: typedef enum {
53: CUSPARSE_CSR2CSC_ALG1 = 1, // faster than V2 (in general), deterministic
54: CUSPARSE_CSR2CSC_ALG2 = 2 // low memory requirement, non-deterministic
55: } cusparseCsr2CscAlg_t;
56: */
57: const char *const MatCUSPARSESpMVAlgorithms[] = {"MV_ALG_DEFAULT", "COOMV_ALG", "CSRMV_ALG1", "CSRMV_ALG2", "cusparseSpMVAlg_t", "CUSPARSE_", 0};
58: const char *const MatCUSPARSESpMMAlgorithms[] = {"ALG_DEFAULT", "COO_ALG1", "COO_ALG2", "COO_ALG3", "CSR_ALG1", "COO_ALG4", "CSR_ALG2", "cusparseSpMMAlg_t", "CUSPARSE_SPMM_", 0};
59: const char *const MatCUSPARSECsr2CscAlgorithms[] = {"INVALID" /*cusparse does not have enum 0! We created one*/, "ALG1", "ALG2", "cusparseCsr2CscAlg_t", "CUSPARSE_CSR2CSC_", 0};
60: #endif
62: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
63: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
64: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat, Mat, const MatFactorInfo *);
65: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, IS, const MatFactorInfo *);
66: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
67: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat, Vec, Vec);
68: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
69: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
70: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
71: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **);
72: #endif
73: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat, PetscOptionItems *PetscOptionsObject);
74: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat, PetscScalar, Mat, MatStructure);
75: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat, PetscScalar);
76: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat, Vec, Vec);
77: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
78: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
79: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
80: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
81: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
82: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec, PetscBool, PetscBool);
84: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **);
85: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **, MatCUSPARSEStorageFormat);
86: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **);
87: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat);
89: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat);
90: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat, PetscBool);
92: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat, PetscInt, const PetscInt[], PetscScalar[]);
93: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat, PetscCount, PetscInt[], PetscInt[]);
94: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat, const PetscScalar[], InsertMode);
96: PETSC_INTERN PetscErrorCode MatCUSPARSESetFormat_SeqAIJCUSPARSE(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
97: {
98: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
100: PetscFunctionBegin;
101: switch (op) {
102: case MAT_CUSPARSE_MULT:
103: cusparsestruct->format = format;
104: break;
105: case MAT_CUSPARSE_ALL:
106: cusparsestruct->format = format;
107: break;
108: default:
109: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported operation %d for MatCUSPARSEFormatOperation. MAT_CUSPARSE_MULT and MAT_CUSPARSE_ALL are currently supported.", op);
110: }
111: PetscFunctionReturn(PETSC_SUCCESS);
112: }
114: /*@
115: MatCUSPARSESetFormat - Sets the storage format of `MATSEQCUSPARSE` matrices for a particular
116: operation. Only the `MatMult()` operation can use different GPU storage formats
118: Not Collective
120: Input Parameters:
121: + A - Matrix of type `MATSEQAIJCUSPARSE`
122: . op - `MatCUSPARSEFormatOperation`. `MATSEQAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT` and `MAT_CUSPARSE_ALL`.
123: `MATMPIAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT_DIAG`,`MAT_CUSPARSE_MULT_OFFDIAG`, and `MAT_CUSPARSE_ALL`.
124: - format - `MatCUSPARSEStorageFormat` (one of `MAT_CUSPARSE_CSR`, `MAT_CUSPARSE_ELL`, `MAT_CUSPARSE_HYB`.)
126: Level: intermediate
128: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
129: @*/
130: PetscErrorCode MatCUSPARSESetFormat(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
131: {
132: PetscFunctionBegin;
134: PetscTryMethod(A, "MatCUSPARSESetFormat_C", (Mat, MatCUSPARSEFormatOperation, MatCUSPARSEStorageFormat), (A, op, format));
135: PetscFunctionReturn(PETSC_SUCCESS);
136: }
138: PETSC_INTERN PetscErrorCode MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE(Mat A, PetscBool use_cpu)
139: {
140: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
142: PetscFunctionBegin;
143: cusparsestruct->use_cpu_solve = use_cpu;
144: PetscFunctionReturn(PETSC_SUCCESS);
145: }
147: /*@
148: MatCUSPARSESetUseCPUSolve - Sets to use CPU `MatSolve()`.
150: Input Parameters:
151: + A - Matrix of type `MATSEQAIJCUSPARSE`
152: - use_cpu - set flag for using the built-in CPU `MatSolve()`
154: Level: intermediate
156: Note:
157: The cuSparse LU solver currently computes the factors with the built-in CPU method
158: and moves the factors to the GPU for the solve. We have observed better performance keeping the data on the CPU and computing the solve there.
159: This method to specify if the solve is done on the CPU or GPU (GPU is the default).
161: .seealso: [](ch_matrices), `Mat`, `MatSolve()`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
162: @*/
163: PetscErrorCode MatCUSPARSESetUseCPUSolve(Mat A, PetscBool use_cpu)
164: {
165: PetscFunctionBegin;
167: PetscTryMethod(A, "MatCUSPARSESetUseCPUSolve_C", (Mat, PetscBool), (A, use_cpu));
168: PetscFunctionReturn(PETSC_SUCCESS);
169: }
171: static PetscErrorCode MatSetOption_SeqAIJCUSPARSE(Mat A, MatOption op, PetscBool flg)
172: {
173: PetscFunctionBegin;
174: switch (op) {
175: case MAT_FORM_EXPLICIT_TRANSPOSE:
176: /* need to destroy the transpose matrix if present to prevent from logic errors if flg is set to true later */
177: if (A->form_explicit_transpose && !flg) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
178: A->form_explicit_transpose = flg;
179: break;
180: default:
181: PetscCall(MatSetOption_SeqAIJ(A, op, flg));
182: break;
183: }
184: PetscFunctionReturn(PETSC_SUCCESS);
185: }
187: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat A, PetscOptionItems *PetscOptionsObject)
188: {
189: MatCUSPARSEStorageFormat format;
190: PetscBool flg;
191: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
193: PetscFunctionBegin;
194: PetscOptionsHeadBegin(PetscOptionsObject, "SeqAIJCUSPARSE options");
195: if (A->factortype == MAT_FACTOR_NONE) {
196: 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));
197: if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_MULT, format));
199: 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));
200: if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_ALL, format));
201: PetscCall(PetscOptionsBool("-mat_cusparse_use_cpu_solve", "Use CPU (I)LU solve", "MatCUSPARSESetUseCPUSolve", cusparsestruct->use_cpu_solve, &cusparsestruct->use_cpu_solve, &flg));
202: if (flg) PetscCall(MatCUSPARSESetUseCPUSolve(A, cusparsestruct->use_cpu_solve));
203: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
204: 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));
205: /* If user did use this option, check its consistency with cuSPARSE, since PetscOptionsEnum() sets enum values based on their position in MatCUSPARSESpMVAlgorithms[] */
206: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
207: 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");
208: #else
209: 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");
210: #endif
211: 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));
212: 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");
214: PetscCall(
215: 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));
216: 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");
217: #endif
218: }
219: PetscOptionsHeadEnd();
220: PetscFunctionReturn(PETSC_SUCCESS);
221: }
223: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
224: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(Mat A)
225: {
226: Mat_SeqAIJ *a = static_cast<Mat_SeqAIJ *>(A->data);
227: PetscInt m = A->rmap->n;
228: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
229: const PetscInt *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
230: const MatScalar *Aa = a->a;
231: PetscInt *Mi, *Mj, Mnz;
232: PetscScalar *Ma;
234: PetscFunctionBegin;
235: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
236: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even when m=0
237: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host
238: Mnz = (Ai[m] - Ai[0]) + (Adiag[0] - Adiag[m]); // Lnz (without the unit diagonal) + Unz (with the non-unit diagonal)
239: PetscCall(PetscMalloc1(m + 1, &Mi));
240: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj is temp
241: PetscCall(PetscMalloc1(Mnz, &Ma));
242: Mi[0] = 0;
243: for (PetscInt i = 0; i < m; i++) {
244: PetscInt llen = Ai[i + 1] - Ai[i];
245: PetscInt ulen = Adiag[i] - Adiag[i + 1];
246: PetscCall(PetscArraycpy(Mj + Mi[i], Aj + Ai[i], llen)); // entries of L
247: Mj[Mi[i] + llen] = i; // diagonal entry
248: PetscCall(PetscArraycpy(Mj + Mi[i] + llen + 1, Aj + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
249: Mi[i + 1] = Mi[i] + llen + ulen;
250: }
251: // Copy M (L,U) from host to device
252: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
253: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
254: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
255: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Mi, sizeof(*fs->csrRowPtr) * (m + 1), cudaMemcpyHostToDevice));
256: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*fs->csrColIdx) * Mnz, cudaMemcpyHostToDevice));
258: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
259: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
260: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
261: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
262: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
263: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_LOWER;
264: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT;
265: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
267: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
268: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
269: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
271: fillMode = CUSPARSE_FILL_MODE_UPPER;
272: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
273: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
274: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
275: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
277: // Allocate work vectors in SpSv
278: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
279: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
281: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
282: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
284: // Query buffer sizes for SpSV and then allocate buffers, temporarily assuming opA = CUSPARSE_OPERATION_NON_TRANSPOSE
285: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
286: 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));
287: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
288: 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));
289: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
290: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
292: // Record for reuse
293: fs->csrRowPtr_h = Mi;
294: fs->csrVal_h = Ma;
295: PetscCall(PetscFree(Mj));
296: }
297: // Copy the value
298: Mi = fs->csrRowPtr_h;
299: Ma = fs->csrVal_h;
300: Mnz = Mi[m];
301: for (PetscInt i = 0; i < m; i++) {
302: PetscInt llen = Ai[i + 1] - Ai[i];
303: PetscInt ulen = Adiag[i] - Adiag[i + 1];
304: PetscCall(PetscArraycpy(Ma + Mi[i], Aa + Ai[i], llen)); // entries of L
305: Ma[Mi[i] + llen] = (MatScalar)1.0 / Aa[Adiag[i]]; // recover the diagonal entry
306: PetscCall(PetscArraycpy(Ma + Mi[i] + llen + 1, Aa + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
307: }
308: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
310: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
311: 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));
313: 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));
315: // L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve
316: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
317: }
318: PetscFunctionReturn(PETSC_SUCCESS);
319: }
320: #else
321: static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A)
322: {
323: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
324: PetscInt n = A->rmap->n;
325: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
326: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
327: const PetscInt *ai = a->i, *aj = a->j, *vi;
328: const MatScalar *aa = a->a, *v;
329: PetscInt *AiLo, *AjLo;
330: PetscInt i, nz, nzLower, offset, rowOffset;
332: PetscFunctionBegin;
333: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
334: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
335: try {
336: /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */
337: nzLower = n + ai[n] - ai[1];
338: if (!loTriFactor) {
339: PetscScalar *AALo;
341: PetscCallCUDA(cudaMallocHost((void **)&AALo, nzLower * sizeof(PetscScalar)));
343: /* Allocate Space for the lower triangular matrix */
344: PetscCallCUDA(cudaMallocHost((void **)&AiLo, (n + 1) * sizeof(PetscInt)));
345: PetscCallCUDA(cudaMallocHost((void **)&AjLo, nzLower * sizeof(PetscInt)));
347: /* Fill the lower triangular matrix */
348: AiLo[0] = (PetscInt)0;
349: AiLo[n] = nzLower;
350: AjLo[0] = (PetscInt)0;
351: AALo[0] = (MatScalar)1.0;
352: v = aa;
353: vi = aj;
354: offset = 1;
355: rowOffset = 1;
356: for (i = 1; i < n; i++) {
357: nz = ai[i + 1] - ai[i];
358: /* additional 1 for the term on the diagonal */
359: AiLo[i] = rowOffset;
360: rowOffset += nz + 1;
362: PetscCall(PetscArraycpy(&AjLo[offset], vi, nz));
363: PetscCall(PetscArraycpy(&AALo[offset], v, nz));
365: offset += nz;
366: AjLo[offset] = (PetscInt)i;
367: AALo[offset] = (MatScalar)1.0;
368: offset += 1;
370: v += nz;
371: vi += nz;
372: }
374: /* allocate space for the triangular factor information */
375: PetscCall(PetscNew(&loTriFactor));
376: loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
377: /* Create the matrix description */
378: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
379: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
380: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
381: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
382: #else
383: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
384: #endif
385: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER));
386: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));
388: /* set the operation */
389: loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
391: /* set the matrix */
392: loTriFactor->csrMat = new CsrMatrix;
393: loTriFactor->csrMat->num_rows = n;
394: loTriFactor->csrMat->num_cols = n;
395: loTriFactor->csrMat->num_entries = nzLower;
397: loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
398: loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo + n + 1);
400: loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower);
401: loTriFactor->csrMat->column_indices->assign(AjLo, AjLo + nzLower);
403: loTriFactor->csrMat->values = new THRUSTARRAY(nzLower);
404: loTriFactor->csrMat->values->assign(AALo, AALo + nzLower);
406: /* Create the solve analysis information */
407: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
408: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
409: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
410: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
411: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
412: PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
413: #endif
415: /* perform the solve analysis */
416: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
417: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
418: PetscCallCUDA(WaitForCUDA());
419: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
421: /* assign the pointer */
422: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
423: loTriFactor->AA_h = AALo;
424: PetscCallCUDA(cudaFreeHost(AiLo));
425: PetscCallCUDA(cudaFreeHost(AjLo));
426: PetscCall(PetscLogCpuToGpu((n + 1 + nzLower) * sizeof(int) + nzLower * sizeof(PetscScalar)));
427: } else { /* update values only */
428: if (!loTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&loTriFactor->AA_h, nzLower * sizeof(PetscScalar)));
429: /* Fill the lower triangular matrix */
430: loTriFactor->AA_h[0] = 1.0;
431: v = aa;
432: vi = aj;
433: offset = 1;
434: for (i = 1; i < n; i++) {
435: nz = ai[i + 1] - ai[i];
436: PetscCall(PetscArraycpy(&loTriFactor->AA_h[offset], v, nz));
437: offset += nz;
438: loTriFactor->AA_h[offset] = 1.0;
439: offset += 1;
440: v += nz;
441: }
442: loTriFactor->csrMat->values->assign(loTriFactor->AA_h, loTriFactor->AA_h + nzLower);
443: PetscCall(PetscLogCpuToGpu(nzLower * sizeof(PetscScalar)));
444: }
445: } catch (char *ex) {
446: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
447: }
448: }
449: PetscFunctionReturn(PETSC_SUCCESS);
450: }
452: static PetscErrorCode MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(Mat A)
453: {
454: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
455: PetscInt n = A->rmap->n;
456: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
457: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
458: const PetscInt *aj = a->j, *adiag = a->diag, *vi;
459: const MatScalar *aa = a->a, *v;
460: PetscInt *AiUp, *AjUp;
461: PetscInt i, nz, nzUpper, offset;
463: PetscFunctionBegin;
464: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
465: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
466: try {
467: /* next, figure out the number of nonzeros in the upper triangular matrix. */
468: nzUpper = adiag[0] - adiag[n];
469: if (!upTriFactor) {
470: PetscScalar *AAUp;
472: PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
474: /* Allocate Space for the upper triangular matrix */
475: PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
476: PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));
478: /* Fill the upper triangular matrix */
479: AiUp[0] = (PetscInt)0;
480: AiUp[n] = nzUpper;
481: offset = nzUpper;
482: for (i = n - 1; i >= 0; i--) {
483: v = aa + adiag[i + 1] + 1;
484: vi = aj + adiag[i + 1] + 1;
486: /* number of elements NOT on the diagonal */
487: nz = adiag[i] - adiag[i + 1] - 1;
489: /* decrement the offset */
490: offset -= (nz + 1);
492: /* first, set the diagonal elements */
493: AjUp[offset] = (PetscInt)i;
494: AAUp[offset] = (MatScalar)1. / v[nz];
495: AiUp[i] = AiUp[i + 1] - (nz + 1);
497: PetscCall(PetscArraycpy(&AjUp[offset + 1], vi, nz));
498: PetscCall(PetscArraycpy(&AAUp[offset + 1], v, nz));
499: }
501: /* allocate space for the triangular factor information */
502: PetscCall(PetscNew(&upTriFactor));
503: upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
505: /* Create the matrix description */
506: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
507: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
508: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
509: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
510: #else
511: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
512: #endif
513: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
514: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));
516: /* set the operation */
517: upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
519: /* set the matrix */
520: upTriFactor->csrMat = new CsrMatrix;
521: upTriFactor->csrMat->num_rows = n;
522: upTriFactor->csrMat->num_cols = n;
523: upTriFactor->csrMat->num_entries = nzUpper;
525: upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
526: upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + n + 1);
528: upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzUpper);
529: upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + nzUpper);
531: upTriFactor->csrMat->values = new THRUSTARRAY(nzUpper);
532: upTriFactor->csrMat->values->assign(AAUp, AAUp + nzUpper);
534: /* Create the solve analysis information */
535: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
536: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
537: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
538: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
539: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
540: PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
541: #endif
543: /* perform the solve analysis */
544: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
545: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
547: PetscCallCUDA(WaitForCUDA());
548: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
550: /* assign the pointer */
551: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
552: upTriFactor->AA_h = AAUp;
553: PetscCallCUDA(cudaFreeHost(AiUp));
554: PetscCallCUDA(cudaFreeHost(AjUp));
555: PetscCall(PetscLogCpuToGpu((n + 1 + nzUpper) * sizeof(int) + nzUpper * sizeof(PetscScalar)));
556: } else {
557: if (!upTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&upTriFactor->AA_h, nzUpper * sizeof(PetscScalar)));
558: /* Fill the upper triangular matrix */
559: offset = nzUpper;
560: for (i = n - 1; i >= 0; i--) {
561: v = aa + adiag[i + 1] + 1;
563: /* number of elements NOT on the diagonal */
564: nz = adiag[i] - adiag[i + 1] - 1;
566: /* decrement the offset */
567: offset -= (nz + 1);
569: /* first, set the diagonal elements */
570: upTriFactor->AA_h[offset] = 1. / v[nz];
571: PetscCall(PetscArraycpy(&upTriFactor->AA_h[offset + 1], v, nz));
572: }
573: upTriFactor->csrMat->values->assign(upTriFactor->AA_h, upTriFactor->AA_h + nzUpper);
574: PetscCall(PetscLogCpuToGpu(nzUpper * sizeof(PetscScalar)));
575: }
576: } catch (char *ex) {
577: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
578: }
579: }
580: PetscFunctionReturn(PETSC_SUCCESS);
581: }
582: #endif
584: static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A)
585: {
586: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
587: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
588: IS isrow = a->row, iscol = a->icol;
589: PetscBool row_identity, col_identity;
590: PetscInt n = A->rmap->n;
592: PetscFunctionBegin;
593: PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
594: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
595: PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(A));
596: #else
597: PetscCall(MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A));
598: PetscCall(MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A));
599: if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
600: #endif
602: cusparseTriFactors->nnz = a->nz;
604: A->offloadmask = PETSC_OFFLOAD_BOTH; // factored matrix is sync'ed to GPU
605: /* lower triangular indices */
606: PetscCall(ISIdentity(isrow, &row_identity));
607: if (!row_identity && !cusparseTriFactors->rpermIndices) {
608: const PetscInt *r;
610: PetscCall(ISGetIndices(isrow, &r));
611: cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
612: cusparseTriFactors->rpermIndices->assign(r, r + n);
613: PetscCall(ISRestoreIndices(isrow, &r));
614: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
615: }
617: /* upper triangular indices */
618: PetscCall(ISIdentity(iscol, &col_identity));
619: if (!col_identity && !cusparseTriFactors->cpermIndices) {
620: const PetscInt *c;
622: PetscCall(ISGetIndices(iscol, &c));
623: cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
624: cusparseTriFactors->cpermIndices->assign(c, c + n);
625: PetscCall(ISRestoreIndices(iscol, &c));
626: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
627: }
628: PetscFunctionReturn(PETSC_SUCCESS);
629: }
631: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
632: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_Cheolesky(Mat A)
633: {
634: Mat_SeqAIJ *a = static_cast<Mat_SeqAIJ *>(A->data);
635: PetscInt m = A->rmap->n;
636: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
637: const PetscInt *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
638: const MatScalar *Aa = a->a;
639: PetscInt *Mj, Mnz;
640: PetscScalar *Ma, *D;
642: PetscFunctionBegin;
643: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
644: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even m=0
645: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host.
646: // See comments at MatICCFactorSymbolic_SeqAIJ() on the layout of the factored matrix (U) on host.
647: Mnz = Ai[m]; // Unz (with the unit diagonal)
648: PetscCall(PetscMalloc1(Mnz, &Ma));
649: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj[] is temp
650: PetscCall(PetscMalloc1(m, &D)); // the diagonal
651: for (PetscInt i = 0; i < m; i++) {
652: PetscInt ulen = Ai[i + 1] - Ai[i];
653: Mj[Ai[i]] = i; // diagonal entry
654: PetscCall(PetscArraycpy(Mj + Ai[i] + 1, Aj + Ai[i], ulen - 1)); // entries of U on the right of the diagonal
655: }
656: // Copy M (U) from host to device
657: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
658: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
659: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
660: PetscCallCUDA(cudaMalloc(&fs->diag, sizeof(*fs->diag) * m));
661: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyHostToDevice));
662: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*Mj) * Mnz, cudaMemcpyHostToDevice));
664: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
665: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
666: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
667: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
668: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
669: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_UPPER;
670: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT; // U is unit diagonal
671: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
673: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
674: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
675: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
677: // Allocate work vectors in SpSv
678: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
679: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
681: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
682: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
684: // Query buffer sizes for SpSV and then allocate buffers
685: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
686: 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));
687: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
689: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut)); // Ut solve uses the same matrix (spMatDescr_U), but different descr and buffer
690: 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));
691: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
693: // Record for reuse
694: fs->csrVal_h = Ma;
695: fs->diag_h = D;
696: PetscCall(PetscFree(Mj));
697: }
698: // Copy the value
699: Ma = fs->csrVal_h;
700: D = fs->diag_h;
701: Mnz = Ai[m];
702: for (PetscInt i = 0; i < m; i++) {
703: D[i] = Aa[Adiag[i]]; // actually Aa[Adiag[i]] is the inverse of the diagonal
704: 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
705: for (PetscInt k = 0; k < Ai[i + 1] - Ai[i] - 1; k++) Ma[Ai[i] + 1 + k] = -Aa[Ai[i] + k];
706: }
707: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
708: PetscCallCUDA(cudaMemcpy(fs->diag, D, sizeof(*D) * m, cudaMemcpyHostToDevice));
710: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
711: 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));
712: 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));
713: }
714: PetscFunctionReturn(PETSC_SUCCESS);
715: }
717: // Solve Ut D U x = b
718: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_Cholesky(Mat A, Vec b, Vec x)
719: {
720: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
721: Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
722: const PetscScalar *barray;
723: PetscScalar *xarray;
724: thrust::device_ptr<const PetscScalar> bGPU;
725: thrust::device_ptr<PetscScalar> xGPU;
726: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
727: PetscInt m = A->rmap->n;
729: PetscFunctionBegin;
730: PetscCall(PetscLogGpuTimeBegin());
731: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
732: PetscCall(VecCUDAGetArrayRead(b, &barray));
733: xGPU = thrust::device_pointer_cast(xarray);
734: bGPU = thrust::device_pointer_cast(barray);
736: // Reorder b with the row permutation if needed, and wrap the result in fs->X
737: if (fs->rpermIndices) {
738: 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)));
739: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
740: } else {
741: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
742: }
744: // Solve Ut Y = X
745: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
746: 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));
748: // Solve diag(D) Z = Y. Actually just do Y = Y*D since D is already inverted in MatCholeskyFactorNumeric_SeqAIJ().
749: // It is basically a vector element-wise multiplication, but cublas does not have it!
750: 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>()));
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_Cheolesky(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: /* cusparseSpSV_analysis() is numeric, i.e., it requires valid matrix values, therefore, we do it after cusparseXcsrilu02()
1731: See discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/78
1732: */
1733: PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L, fs->spsvBuffer_L));
1735: PetscCallCUSPARSE(cusparseSpSV_analysis(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_U, fs->spsvBuffer_U));
1737: /* L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve */
1738: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
1740: fact->offloadmask = PETSC_OFFLOAD_GPU;
1741: 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.
1742: fact->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
1743: fact->ops->matsolve = NULL;
1744: fact->ops->matsolvetranspose = NULL;
1745: PetscCall(PetscLogGpuTimeEnd());
1746: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1747: PetscFunctionReturn(PETSC_SUCCESS);
1748: }
1750: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, IS, IS, const MatFactorInfo *info)
1751: {
1752: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1753: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1754: PetscInt m, nz;
1756: PetscFunctionBegin;
1757: if (PetscDefined(USE_DEBUG)) {
1758: PetscInt i;
1759: PetscBool flg, missing;
1761: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1762: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1763: 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);
1764: PetscCall(MatMissingDiagonal(A, &missing, &i));
1765: PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
1766: }
1768: /* Free the old stale stuff */
1769: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
1771: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
1772: but they will not be used. Allocate them just for easy debugging.
1773: */
1774: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
1776: fact->offloadmask = PETSC_OFFLOAD_BOTH;
1777: fact->factortype = MAT_FACTOR_ILU;
1778: fact->info.factor_mallocs = 0;
1779: fact->info.fill_ratio_given = info->fill;
1780: fact->info.fill_ratio_needed = 1.0;
1782: aij->row = NULL;
1783: aij->col = NULL;
1785: /* ====================================================================== */
1786: /* Copy A's i, j to fact and also allocate the value array of fact. */
1787: /* We'll do in-place factorization on fact */
1788: /* ====================================================================== */
1789: const int *Ai, *Aj;
1791: m = fact->rmap->n;
1792: nz = aij->nz;
1794: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
1795: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
1796: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(*fs->csrVal) * nz));
1797: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai. The returned Ai, Aj are 32-bit */
1798: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1799: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1801: /* ====================================================================== */
1802: /* Create descriptors for M, L, U */
1803: /* ====================================================================== */
1804: cusparseFillMode_t fillMode;
1805: cusparseDiagType_t diagType;
1807: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
1808: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
1809: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
1811: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
1812: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
1813: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
1814: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
1815: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
1816: */
1817: fillMode = CUSPARSE_FILL_MODE_LOWER;
1818: diagType = CUSPARSE_DIAG_TYPE_UNIT;
1819: 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));
1820: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1821: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1823: fillMode = CUSPARSE_FILL_MODE_UPPER;
1824: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
1825: 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));
1826: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1827: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1829: /* ========================================================================= */
1830: /* Query buffer sizes for csrilu0, SpSV and allocate buffers */
1831: /* ========================================================================= */
1832: PetscCallCUSPARSE(cusparseCreateCsrilu02Info(&fs->ilu0Info_M));
1833: if (m)
1834: PetscCallCUSPARSE(cusparseXcsrilu02_bufferSize(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1835: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, &fs->factBufferSize_M));
1837: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
1838: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
1840: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
1841: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
1843: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
1844: 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));
1846: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
1847: 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));
1849: /* From my experiment with the example at https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSE/bicgstab,
1850: and discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/77,
1851: 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.
1852: To save memory, we make factBuffer_M share with the bigger of spsvBuffer_L/U.
1853: */
1854: if (fs->spsvBufferSize_L > fs->spsvBufferSize_U) {
1855: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
1856: fs->spsvBuffer_L = fs->factBuffer_M;
1857: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
1858: } else {
1859: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_U, (size_t)fs->factBufferSize_M)));
1860: fs->spsvBuffer_U = fs->factBuffer_M;
1861: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
1862: }
1864: /* ========================================================================== */
1865: /* Perform analysis of ilu0 on M, SpSv on L and U */
1866: /* The lower(upper) triangular part of M has the same sparsity pattern as L(U)*/
1867: /* ========================================================================== */
1868: int structural_zero;
1869: cusparseStatus_t status;
1871: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1872: if (m)
1873: PetscCallCUSPARSE(cusparseXcsrilu02_analysis(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1874: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1875: if (PetscDefined(USE_DEBUG)) {
1876: /* Function cusparseXcsrilu02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
1877: status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &structural_zero);
1878: 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);
1879: }
1881: /* Estimate FLOPs of the numeric factorization */
1882: {
1883: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
1884: PetscInt *Ai, *Adiag, nzRow, nzLeft;
1885: PetscLogDouble flops = 0.0;
1887: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1888: Ai = Aseq->i;
1889: Adiag = Aseq->diag;
1890: for (PetscInt i = 0; i < m; i++) {
1891: if (Ai[i] < Adiag[i] && Adiag[i] < Ai[i + 1]) { /* There are nonzeros left to the diagonal of row i */
1892: nzRow = Ai[i + 1] - Ai[i];
1893: nzLeft = Adiag[i] - Ai[i];
1894: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
1895: and include the eliminated one will be updated, which incurs a multiplication and an addition.
1896: */
1897: nzLeft = (nzRow - 1) / 2;
1898: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
1899: }
1900: }
1901: fs->numericFactFlops = flops;
1902: }
1903: fact->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0;
1904: PetscFunctionReturn(PETSC_SUCCESS);
1905: }
1907: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_ICC0(Mat fact, Vec b, Vec x)
1908: {
1909: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1910: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1911: const PetscScalar *barray;
1912: PetscScalar *xarray;
1914: PetscFunctionBegin;
1915: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1916: PetscCall(VecCUDAGetArrayRead(b, &barray));
1917: PetscCall(PetscLogGpuTimeBegin());
1919: /* Solve L*y = b */
1920: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1921: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1922: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* L Y = X */
1923: fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L));
1925: /* Solve Lt*x = y */
1926: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1927: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* Lt X = Y */
1928: fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt));
1930: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1931: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1933: PetscCall(PetscLogGpuTimeEnd());
1934: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - fact->rmap->n));
1935: PetscFunctionReturn(PETSC_SUCCESS);
1936: }
1938: static PetscErrorCode MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, const MatFactorInfo *)
1939: {
1940: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1941: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1942: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1943: CsrMatrix *Acsr;
1944: PetscInt m, nz;
1945: PetscBool flg;
1947: PetscFunctionBegin;
1948: if (PetscDefined(USE_DEBUG)) {
1949: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1950: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1951: }
1953: /* Copy A's value to fact */
1954: m = fact->rmap->n;
1955: nz = aij->nz;
1956: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1957: Acsr = (CsrMatrix *)Acusp->mat->mat;
1958: PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1960: /* Factorize fact inplace */
1961: /* https://docs.nvidia.com/cuda/cusparse/index.html#csric02_solve
1962: Function csric02() only takes the lower triangular part of matrix A to perform factorization.
1963: The matrix type must be CUSPARSE_MATRIX_TYPE_GENERAL, the fill mode and diagonal type are ignored,
1964: and the strictly upper triangular part is ignored and never touched. It does not matter if A is Hermitian or not.
1965: In other words, from the point of view of csric02() A is Hermitian and only the lower triangular part is provided.
1966: */
1967: 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));
1968: if (PetscDefined(USE_DEBUG)) {
1969: int numerical_zero;
1970: cusparseStatus_t status;
1971: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &numerical_zero);
1972: 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);
1973: }
1975: 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));
1977: /* Note that cusparse reports this error if we use double and CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE
1978: ** On entry to cusparseSpSV_analysis(): conjugate transpose (opA) is not supported for matA data type, current -> CUDA_R_64F
1979: */
1980: 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));
1982: fact->offloadmask = PETSC_OFFLOAD_GPU;
1983: fact->ops->solve = MatSolve_SeqAIJCUSPARSE_ICC0;
1984: fact->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_ICC0;
1985: fact->ops->matsolve = NULL;
1986: fact->ops->matsolvetranspose = NULL;
1987: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1988: PetscFunctionReturn(PETSC_SUCCESS);
1989: }
1991: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, IS, const MatFactorInfo *info)
1992: {
1993: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1994: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1995: PetscInt m, nz;
1997: PetscFunctionBegin;
1998: if (PetscDefined(USE_DEBUG)) {
1999: PetscInt i;
2000: PetscBool flg, missing;
2002: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2003: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
2004: 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);
2005: PetscCall(MatMissingDiagonal(A, &missing, &i));
2006: PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
2007: }
2009: /* Free the old stale stuff */
2010: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
2012: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
2013: but they will not be used. Allocate them just for easy debugging.
2014: */
2015: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
2017: fact->offloadmask = PETSC_OFFLOAD_BOTH;
2018: fact->factortype = MAT_FACTOR_ICC;
2019: fact->info.factor_mallocs = 0;
2020: fact->info.fill_ratio_given = info->fill;
2021: fact->info.fill_ratio_needed = 1.0;
2023: aij->row = NULL;
2024: aij->col = NULL;
2026: /* ====================================================================== */
2027: /* Copy A's i, j to fact and also allocate the value array of fact. */
2028: /* We'll do in-place factorization on fact */
2029: /* ====================================================================== */
2030: const int *Ai, *Aj;
2032: m = fact->rmap->n;
2033: nz = aij->nz;
2035: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
2036: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
2037: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(PetscScalar) * nz));
2038: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai */
2039: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2040: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2042: /* ====================================================================== */
2043: /* Create mat descriptors for M, L */
2044: /* ====================================================================== */
2045: cusparseFillMode_t fillMode;
2046: cusparseDiagType_t diagType;
2048: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
2049: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
2050: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
2052: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
2053: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
2054: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
2055: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
2056: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
2057: */
2058: fillMode = CUSPARSE_FILL_MODE_LOWER;
2059: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
2060: 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));
2061: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
2062: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
2064: /* ========================================================================= */
2065: /* Query buffer sizes for csric0, SpSV of L and Lt, and allocate buffers */
2066: /* ========================================================================= */
2067: PetscCallCUSPARSE(cusparseCreateCsric02Info(&fs->ic0Info_M));
2068: if (m) PetscCallCUSPARSE(cusparseXcsric02_bufferSize(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, &fs->factBufferSize_M));
2070: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
2071: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
2073: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
2074: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
2076: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
2077: 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));
2079: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
2080: 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));
2082: /* To save device memory, we make the factorization buffer share with one of the solver buffer.
2083: See also comments in MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0().
2084: */
2085: if (fs->spsvBufferSize_L > fs->spsvBufferSize_Lt) {
2086: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
2087: fs->spsvBuffer_L = fs->factBuffer_M;
2088: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
2089: } else {
2090: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_Lt, (size_t)fs->factBufferSize_M)));
2091: fs->spsvBuffer_Lt = fs->factBuffer_M;
2092: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
2093: }
2095: /* ========================================================================== */
2096: /* Perform analysis of ic0 on M */
2097: /* The lower triangular part of M has the same sparsity pattern as L */
2098: /* ========================================================================== */
2099: int structural_zero;
2100: cusparseStatus_t status;
2102: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
2103: 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));
2104: if (PetscDefined(USE_DEBUG)) {
2105: /* Function cusparseXcsric02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
2106: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &structural_zero);
2107: 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);
2108: }
2110: /* Estimate FLOPs of the numeric factorization */
2111: {
2112: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
2113: PetscInt *Ai, nzRow, nzLeft;
2114: PetscLogDouble flops = 0.0;
2116: Ai = Aseq->i;
2117: for (PetscInt i = 0; i < m; i++) {
2118: nzRow = Ai[i + 1] - Ai[i];
2119: if (nzRow > 1) {
2120: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
2121: and include the eliminated one will be updated, which incurs a multiplication and an addition.
2122: */
2123: nzLeft = (nzRow - 1) / 2;
2124: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
2125: }
2126: }
2127: fs->numericFactFlops = flops;
2128: }
2129: fact->ops->choleskyfactornumeric = MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0;
2130: PetscFunctionReturn(PETSC_SUCCESS);
2131: }
2132: #endif
2134: static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
2135: {
2136: // use_cpu_solve is a field in Mat_SeqAIJCUSPARSE. B, a factored matrix, uses Mat_SeqAIJCUSPARSETriFactors.
2137: Mat_SeqAIJCUSPARSE *cusparsestruct = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2139: PetscFunctionBegin;
2140: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2141: PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
2142: B->offloadmask = PETSC_OFFLOAD_CPU;
2144: if (!cusparsestruct->use_cpu_solve) {
2145: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2146: B->ops->solve = MatSolve_SeqAIJCUSPARSE_LU;
2147: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
2148: #else
2149: /* determine which version of MatSolve needs to be used. */
2150: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2151: IS isrow = b->row, iscol = b->col;
2152: PetscBool row_identity, col_identity;
2154: PetscCall(ISIdentity(isrow, &row_identity));
2155: PetscCall(ISIdentity(iscol, &col_identity));
2156: if (row_identity && col_identity) {
2157: B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
2158: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
2159: } else {
2160: B->ops->solve = MatSolve_SeqAIJCUSPARSE;
2161: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
2162: }
2163: #endif
2164: }
2165: B->ops->matsolve = NULL;
2166: B->ops->matsolvetranspose = NULL;
2168: /* get the triangular factors */
2169: if (!cusparsestruct->use_cpu_solve) PetscCall(MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B));
2170: PetscFunctionReturn(PETSC_SUCCESS);
2171: }
2173: static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2174: {
2175: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(B->spptr);
2177: PetscFunctionBegin;
2178: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2179: PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2180: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2181: PetscFunctionReturn(PETSC_SUCCESS);
2182: }
2184: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2185: {
2186: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2188: PetscFunctionBegin;
2189: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2190: PetscBool row_identity = PETSC_FALSE, col_identity = PETSC_FALSE;
2191: if (cusparseTriFactors->factorizeOnDevice) {
2192: PetscCall(ISIdentity(isrow, &row_identity));
2193: PetscCall(ISIdentity(iscol, &col_identity));
2194: }
2195: if (!info->levels && row_identity && col_identity) {
2196: PetscCall(MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(B, A, isrow, iscol, info));
2197: } else
2198: #endif
2199: {
2200: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2201: PetscCall(MatILUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2202: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2203: }
2204: PetscFunctionReturn(PETSC_SUCCESS);
2205: }
2207: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2208: {
2209: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2211: PetscFunctionBegin;
2212: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2213: PetscBool perm_identity = PETSC_FALSE;
2214: if (cusparseTriFactors->factorizeOnDevice) PetscCall(ISIdentity(perm, &perm_identity));
2215: if (!info->levels && perm_identity) {
2216: PetscCall(MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(B, A, perm, info));
2217: } else
2218: #endif
2219: {
2220: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2221: PetscCall(MatICCFactorSymbolic_SeqAIJ(B, A, perm, info));
2222: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2223: }
2224: PetscFunctionReturn(PETSC_SUCCESS);
2225: }
2227: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2228: {
2229: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2231: PetscFunctionBegin;
2232: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2233: PetscCall(MatCholeskyFactorSymbolic_SeqAIJ(B, A, perm, info));
2234: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2235: PetscFunctionReturn(PETSC_SUCCESS);
2236: }
2238: static PetscErrorCode MatFactorGetSolverType_seqaij_cusparse(Mat, MatSolverType *type)
2239: {
2240: PetscFunctionBegin;
2241: *type = MATSOLVERCUSPARSE;
2242: PetscFunctionReturn(PETSC_SUCCESS);
2243: }
2245: /*MC
2246: MATSOLVERCUSPARSE = "cusparse" - A matrix type providing triangular solvers for seq matrices
2247: on a single GPU of type, `MATSEQAIJCUSPARSE`. Currently supported
2248: algorithms are ILU(k) and ICC(k). Typically, deeper factorizations (larger k) results in poorer
2249: performance in the triangular solves. Full LU, and Cholesky decompositions can be solved through the
2250: CuSPARSE triangular solve algorithm. However, the performance can be quite poor and thus these
2251: algorithms are not recommended. This class does NOT support direct solver operations.
2253: Level: beginner
2255: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJCUSPARSE()`,
2256: `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
2257: M*/
2259: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat A, MatFactorType ftype, Mat *B)
2260: {
2261: PetscInt n = A->rmap->n;
2262: PetscBool factOnDevice, factOnHost;
2263: char *prefix;
2264: char factPlace[32] = "device"; /* the default */
2266: PetscFunctionBegin;
2267: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2268: PetscCall(MatSetSizes(*B, n, n, n, n));
2269: (*B)->factortype = ftype; // factortype makes MatSetType() allocate spptr of type Mat_SeqAIJCUSPARSETriFactors
2270: PetscCall(MatSetType(*B, MATSEQAIJCUSPARSE));
2272: prefix = (*B)->factorprefix ? (*B)->factorprefix : ((PetscObject)A)->prefix;
2273: PetscOptionsBegin(PetscObjectComm((PetscObject)*B), prefix, "MatGetFactor", "Mat");
2274: PetscCall(PetscOptionsString("-mat_factor_bind_factorization", "Do matrix factorization on host or device when possible", "MatGetFactor", NULL, factPlace, sizeof(factPlace), NULL));
2275: PetscOptionsEnd();
2276: PetscCall(PetscStrcasecmp("device", factPlace, &factOnDevice));
2277: PetscCall(PetscStrcasecmp("host", factPlace, &factOnHost));
2278: PetscCheck(factOnDevice || factOnHost, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_OUTOFRANGE, "Wrong option %s to -mat_factor_bind_factorization <string>. Only host and device are allowed", factPlace);
2279: ((Mat_SeqAIJCUSPARSETriFactors *)(*B)->spptr)->factorizeOnDevice = factOnDevice;
2281: if (A->boundtocpu && A->bindingpropagates) PetscCall(MatBindToCPU(*B, PETSC_TRUE));
2282: if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
2283: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2284: if (!A->boundtocpu) {
2285: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJCUSPARSE;
2286: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJCUSPARSE;
2287: } else {
2288: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJ;
2289: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJ;
2290: }
2291: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
2292: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILU]));
2293: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILUDT]));
2294: } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
2295: if (!A->boundtocpu) {
2296: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJCUSPARSE;
2297: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJCUSPARSE;
2298: } else {
2299: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJ;
2300: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJ;
2301: }
2302: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
2303: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
2304: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported for CUSPARSE Matrix Types");
2306: PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
2307: (*B)->canuseordering = PETSC_TRUE;
2308: PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_cusparse));
2309: PetscFunctionReturn(PETSC_SUCCESS);
2310: }
2312: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat A)
2313: {
2314: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2315: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2316: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2317: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
2318: #endif
2320: PetscFunctionBegin;
2321: if (A->offloadmask == PETSC_OFFLOAD_GPU) {
2322: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2323: if (A->factortype == MAT_FACTOR_NONE) {
2324: CsrMatrix *matrix = (CsrMatrix *)cusp->mat->mat;
2325: PetscCallCUDA(cudaMemcpy(a->a, matrix->values->data().get(), a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2326: }
2327: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2328: else if (fs->csrVal) {
2329: /* We have a factorized matrix on device and are able to copy it to host */
2330: PetscCallCUDA(cudaMemcpy(a->a, fs->csrVal, a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2331: }
2332: #endif
2333: else
2334: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for copying this type of factorized matrix from device to host");
2335: PetscCall(PetscLogGpuToCpu(a->nz * sizeof(PetscScalar)));
2336: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2337: A->offloadmask = PETSC_OFFLOAD_BOTH;
2338: }
2339: PetscFunctionReturn(PETSC_SUCCESS);
2340: }
2342: static PetscErrorCode MatSeqAIJGetArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2343: {
2344: PetscFunctionBegin;
2345: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2346: *array = ((Mat_SeqAIJ *)A->data)->a;
2347: PetscFunctionReturn(PETSC_SUCCESS);
2348: }
2350: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2351: {
2352: PetscFunctionBegin;
2353: A->offloadmask = PETSC_OFFLOAD_CPU;
2354: *array = NULL;
2355: PetscFunctionReturn(PETSC_SUCCESS);
2356: }
2358: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJCUSPARSE(Mat A, const PetscScalar *array[])
2359: {
2360: PetscFunctionBegin;
2361: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2362: *array = ((Mat_SeqAIJ *)A->data)->a;
2363: PetscFunctionReturn(PETSC_SUCCESS);
2364: }
2366: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE(Mat, const PetscScalar *array[])
2367: {
2368: PetscFunctionBegin;
2369: *array = NULL;
2370: PetscFunctionReturn(PETSC_SUCCESS);
2371: }
2373: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2374: {
2375: PetscFunctionBegin;
2376: *array = ((Mat_SeqAIJ *)A->data)->a;
2377: PetscFunctionReturn(PETSC_SUCCESS);
2378: }
2380: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2381: {
2382: PetscFunctionBegin;
2383: A->offloadmask = PETSC_OFFLOAD_CPU;
2384: *array = NULL;
2385: PetscFunctionReturn(PETSC_SUCCESS);
2386: }
2388: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
2389: {
2390: Mat_SeqAIJCUSPARSE *cusp;
2391: CsrMatrix *matrix;
2393: PetscFunctionBegin;
2394: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2395: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2396: cusp = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2397: PetscCheck(cusp != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "cusp is NULL");
2398: matrix = (CsrMatrix *)cusp->mat->mat;
2400: if (i) {
2401: #if !defined(PETSC_USE_64BIT_INDICES)
2402: *i = matrix->row_offsets->data().get();
2403: #else
2404: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2405: #endif
2406: }
2407: if (j) {
2408: #if !defined(PETSC_USE_64BIT_INDICES)
2409: *j = matrix->column_indices->data().get();
2410: #else
2411: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2412: #endif
2413: }
2414: if (a) *a = matrix->values->data().get();
2415: if (mtype) *mtype = PETSC_MEMTYPE_CUDA;
2416: PetscFunctionReturn(PETSC_SUCCESS);
2417: }
2419: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A)
2420: {
2421: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
2422: Mat_SeqAIJCUSPARSEMultStruct *matstruct = cusparsestruct->mat;
2423: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2424: PetscInt m = A->rmap->n, *ii, *ridx, tmp;
2425: cusparseStatus_t stat;
2426: PetscBool both = PETSC_TRUE;
2428: PetscFunctionBegin;
2429: PetscCheck(!A->boundtocpu, PETSC_COMM_SELF, PETSC_ERR_GPU, "Cannot copy to GPU");
2430: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
2431: if (A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { /* Copy values only */
2432: CsrMatrix *matrix;
2433: matrix = (CsrMatrix *)cusparsestruct->mat->mat;
2435: PetscCheck(!a->nz || a->a, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR values");
2436: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2437: matrix->values->assign(a->a, a->a + a->nz);
2438: PetscCallCUDA(WaitForCUDA());
2439: PetscCall(PetscLogCpuToGpu(a->nz * sizeof(PetscScalar)));
2440: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2441: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
2442: } else {
2443: PetscInt nnz;
2444: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2445: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusparsestruct->mat, cusparsestruct->format));
2446: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
2447: delete cusparsestruct->workVector;
2448: delete cusparsestruct->rowoffsets_gpu;
2449: cusparsestruct->workVector = NULL;
2450: cusparsestruct->rowoffsets_gpu = NULL;
2451: try {
2452: if (a->compressedrow.use) {
2453: m = a->compressedrow.nrows;
2454: ii = a->compressedrow.i;
2455: ridx = a->compressedrow.rindex;
2456: } else {
2457: m = A->rmap->n;
2458: ii = a->i;
2459: ridx = NULL;
2460: }
2461: PetscCheck(ii, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR row data");
2462: if (!a->a) {
2463: nnz = ii[m];
2464: both = PETSC_FALSE;
2465: } else nnz = a->nz;
2466: PetscCheck(!nnz || a->j, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR column data");
2468: /* create cusparse matrix */
2469: cusparsestruct->nrows = m;
2470: matstruct = new Mat_SeqAIJCUSPARSEMultStruct;
2471: PetscCallCUSPARSE(cusparseCreateMatDescr(&matstruct->descr));
2472: PetscCallCUSPARSE(cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO));
2473: PetscCallCUSPARSE(cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
2475: PetscCallCUDA(cudaMalloc((void **)&matstruct->alpha_one, sizeof(PetscScalar)));
2476: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_zero, sizeof(PetscScalar)));
2477: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_one, sizeof(PetscScalar)));
2478: PetscCallCUDA(cudaMemcpy(matstruct->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2479: PetscCallCUDA(cudaMemcpy(matstruct->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2480: PetscCallCUDA(cudaMemcpy(matstruct->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2481: PetscCallCUSPARSE(cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE));
2483: /* Build a hybrid/ellpack matrix if this option is chosen for the storage */
2484: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
2485: /* set the matrix */
2486: CsrMatrix *mat = new CsrMatrix;
2487: mat->num_rows = m;
2488: mat->num_cols = A->cmap->n;
2489: mat->num_entries = nnz;
2490: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2491: mat->row_offsets->assign(ii, ii + m + 1);
2493: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2494: mat->column_indices->assign(a->j, a->j + nnz);
2496: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2497: if (a->a) mat->values->assign(a->a, a->a + nnz);
2499: /* assign the pointer */
2500: matstruct->mat = mat;
2501: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2502: if (mat->num_rows) { /* cusparse errors on empty matrices! */
2503: 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 */
2504: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2505: PetscCallCUSPARSE(stat);
2506: }
2507: #endif
2508: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
2509: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2510: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
2511: #else
2512: CsrMatrix *mat = new CsrMatrix;
2513: mat->num_rows = m;
2514: mat->num_cols = A->cmap->n;
2515: mat->num_entries = nnz;
2516: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2517: mat->row_offsets->assign(ii, ii + m + 1);
2519: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2520: mat->column_indices->assign(a->j, a->j + nnz);
2522: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2523: if (a->a) mat->values->assign(a->a, a->a + nnz);
2525: cusparseHybMat_t hybMat;
2526: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
2527: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
2528: 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);
2529: PetscCallCUSPARSE(stat);
2530: /* assign the pointer */
2531: matstruct->mat = hybMat;
2533: if (mat) {
2534: if (mat->values) delete (THRUSTARRAY *)mat->values;
2535: if (mat->column_indices) delete (THRUSTINTARRAY32 *)mat->column_indices;
2536: if (mat->row_offsets) delete (THRUSTINTARRAY32 *)mat->row_offsets;
2537: delete (CsrMatrix *)mat;
2538: }
2539: #endif
2540: }
2542: /* assign the compressed row indices */
2543: if (a->compressedrow.use) {
2544: PetscCallCXX(cusparsestruct->workVector = new THRUSTARRAY(m));
2545: PetscCallCXX(matstruct->cprowIndices = new THRUSTINTARRAY(m));
2546: matstruct->cprowIndices->assign(ridx, ridx + m);
2547: tmp = m;
2548: } else {
2549: cusparsestruct->workVector = NULL;
2550: matstruct->cprowIndices = NULL;
2551: tmp = 0;
2552: }
2553: PetscCall(PetscLogCpuToGpu(((m + 1) + (a->nz)) * sizeof(int) + tmp * sizeof(PetscInt) + (3 + (a->nz)) * sizeof(PetscScalar)));
2555: /* assign the pointer */
2556: cusparsestruct->mat = matstruct;
2557: } catch (char *ex) {
2558: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
2559: }
2560: PetscCallCUDA(WaitForCUDA());
2561: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2562: cusparsestruct->nonzerostate = A->nonzerostate;
2563: }
2564: if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
2565: }
2566: PetscFunctionReturn(PETSC_SUCCESS);
2567: }
2569: struct VecCUDAPlusEquals {
2570: template <typename Tuple>
2571: __host__ __device__ void operator()(Tuple t)
2572: {
2573: thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t);
2574: }
2575: };
2577: struct VecCUDAEquals {
2578: template <typename Tuple>
2579: __host__ __device__ void operator()(Tuple t)
2580: {
2581: thrust::get<1>(t) = thrust::get<0>(t);
2582: }
2583: };
2585: struct VecCUDAEqualsReverse {
2586: template <typename Tuple>
2587: __host__ __device__ void operator()(Tuple t)
2588: {
2589: thrust::get<0>(t) = thrust::get<1>(t);
2590: }
2591: };
2593: struct MatMatCusparse {
2594: PetscBool cisdense;
2595: PetscScalar *Bt;
2596: Mat X;
2597: PetscBool reusesym; /* Cusparse does not have split symbolic and numeric phases for sparse matmat operations */
2598: PetscLogDouble flops;
2599: CsrMatrix *Bcsr;
2601: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2602: cusparseSpMatDescr_t matSpBDescr;
2603: PetscBool initialized; /* C = alpha op(A) op(B) + beta C */
2604: cusparseDnMatDescr_t matBDescr;
2605: cusparseDnMatDescr_t matCDescr;
2606: PetscInt Blda, Clda; /* Record leading dimensions of B and C here to detect changes*/
2607: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2608: void *dBuffer4;
2609: void *dBuffer5;
2610: #endif
2611: size_t mmBufferSize;
2612: void *mmBuffer;
2613: void *mmBuffer2; /* SpGEMM WorkEstimation buffer */
2614: cusparseSpGEMMDescr_t spgemmDesc;
2615: #endif
2616: };
2618: static PetscErrorCode MatDestroy_MatMatCusparse(void *data)
2619: {
2620: MatMatCusparse *mmdata = (MatMatCusparse *)data;
2622: PetscFunctionBegin;
2623: PetscCallCUDA(cudaFree(mmdata->Bt));
2624: delete mmdata->Bcsr;
2625: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2626: if (mmdata->matSpBDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mmdata->matSpBDescr));
2627: if (mmdata->matBDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2628: if (mmdata->matCDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2629: if (mmdata->spgemmDesc) PetscCallCUSPARSE(cusparseSpGEMM_destroyDescr(mmdata->spgemmDesc));
2630: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2631: if (mmdata->dBuffer4) PetscCallCUDA(cudaFree(mmdata->dBuffer4));
2632: if (mmdata->dBuffer5) PetscCallCUDA(cudaFree(mmdata->dBuffer5));
2633: #endif
2634: if (mmdata->mmBuffer) PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2635: if (mmdata->mmBuffer2) PetscCallCUDA(cudaFree(mmdata->mmBuffer2));
2636: #endif
2637: PetscCall(MatDestroy(&mmdata->X));
2638: PetscCall(PetscFree(data));
2639: PetscFunctionReturn(PETSC_SUCCESS);
2640: }
2642: #include <../src/mat/impls/dense/seq/dense.h>
2644: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2645: {
2646: Mat_Product *product = C->product;
2647: Mat A, B;
2648: PetscInt m, n, blda, clda;
2649: PetscBool flg, biscuda;
2650: Mat_SeqAIJCUSPARSE *cusp;
2651: cusparseStatus_t stat;
2652: cusparseOperation_t opA;
2653: const PetscScalar *barray;
2654: PetscScalar *carray;
2655: MatMatCusparse *mmdata;
2656: Mat_SeqAIJCUSPARSEMultStruct *mat;
2657: CsrMatrix *csrmat;
2659: PetscFunctionBegin;
2660: MatCheckProduct(C, 1);
2661: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2662: mmdata = (MatMatCusparse *)product->data;
2663: A = product->A;
2664: B = product->B;
2665: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2666: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2667: /* currently CopyToGpu does not copy if the matrix is bound to CPU
2668: Instead of silently accepting the wrong answer, I prefer to raise the error */
2669: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2670: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2671: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2672: switch (product->type) {
2673: case MATPRODUCT_AB:
2674: case MATPRODUCT_PtAP:
2675: mat = cusp->mat;
2676: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2677: m = A->rmap->n;
2678: n = B->cmap->n;
2679: break;
2680: case MATPRODUCT_AtB:
2681: if (!A->form_explicit_transpose) {
2682: mat = cusp->mat;
2683: opA = CUSPARSE_OPERATION_TRANSPOSE;
2684: } else {
2685: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
2686: mat = cusp->matTranspose;
2687: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2688: }
2689: m = A->cmap->n;
2690: n = B->cmap->n;
2691: break;
2692: case MATPRODUCT_ABt:
2693: case MATPRODUCT_RARt:
2694: mat = cusp->mat;
2695: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2696: m = A->rmap->n;
2697: n = B->rmap->n;
2698: break;
2699: default:
2700: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2701: }
2702: PetscCheck(mat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing Mat_SeqAIJCUSPARSEMultStruct");
2703: csrmat = (CsrMatrix *)mat->mat;
2704: /* if the user passed a CPU matrix, copy the data to the GPU */
2705: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSECUDA, &biscuda));
2706: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSECUDA, MAT_INPLACE_MATRIX, &B));
2707: PetscCall(MatDenseGetArrayReadAndMemType(B, &barray, nullptr));
2709: PetscCall(MatDenseGetLDA(B, &blda));
2710: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2711: PetscCall(MatDenseGetArrayWriteAndMemType(mmdata->X, &carray, nullptr));
2712: PetscCall(MatDenseGetLDA(mmdata->X, &clda));
2713: } else {
2714: PetscCall(MatDenseGetArrayWriteAndMemType(C, &carray, nullptr));
2715: PetscCall(MatDenseGetLDA(C, &clda));
2716: }
2718: PetscCall(PetscLogGpuTimeBegin());
2719: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2720: cusparseOperation_t opB = (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE;
2721: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
2722: cusparseSpMatDescr_t &matADescr = mat->matDescr_SpMM[opA];
2723: #else
2724: cusparseSpMatDescr_t &matADescr = mat->matDescr;
2725: #endif
2727: /* (re)allocate mmBuffer if not initialized or LDAs are different */
2728: if (!mmdata->initialized || mmdata->Blda != blda || mmdata->Clda != clda) {
2729: size_t mmBufferSize;
2730: if (mmdata->initialized && mmdata->Blda != blda) {
2731: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2732: mmdata->matBDescr = NULL;
2733: }
2734: if (!mmdata->matBDescr) {
2735: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matBDescr, B->rmap->n, B->cmap->n, blda, (void *)barray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2736: mmdata->Blda = blda;
2737: }
2739: if (mmdata->initialized && mmdata->Clda != clda) {
2740: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2741: mmdata->matCDescr = NULL;
2742: }
2743: if (!mmdata->matCDescr) { /* matCDescr is for C or mmdata->X */
2744: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matCDescr, m, n, clda, (void *)carray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2745: mmdata->Clda = clda;
2746: }
2748: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
2749: if (matADescr) {
2750: PetscCallCUSPARSE(cusparseDestroySpMat(matADescr)); // Because I find I could not reuse matADescr. It could be a cusparse bug
2751: matADescr = NULL;
2752: }
2753: #endif
2755: if (!matADescr) {
2756: 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 */
2757: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2758: PetscCallCUSPARSE(stat);
2759: }
2761: PetscCallCUSPARSE(cusparseSpMM_bufferSize(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, &mmBufferSize));
2763: if ((mmdata->mmBuffer && mmdata->mmBufferSize < mmBufferSize) || !mmdata->mmBuffer) {
2764: PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2765: PetscCallCUDA(cudaMalloc(&mmdata->mmBuffer, mmBufferSize));
2766: mmdata->mmBufferSize = mmBufferSize;
2767: }
2769: #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
2770: PetscCallCUSPARSE(cusparseSpMM_preprocess(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2771: #endif
2773: mmdata->initialized = PETSC_TRUE;
2774: } else {
2775: /* to be safe, always update pointers of the mats */
2776: PetscCallCUSPARSE(cusparseSpMatSetValues(matADescr, csrmat->values->data().get()));
2777: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matBDescr, (void *)barray));
2778: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matCDescr, (void *)carray));
2779: }
2781: /* do cusparseSpMM, which supports transpose on B */
2782: PetscCallCUSPARSE(cusparseSpMM(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2783: #else
2784: PetscInt k;
2785: /* cusparseXcsrmm does not support transpose on B */
2786: if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) {
2787: cublasHandle_t cublasv2handle;
2788: cublasStatus_t cerr;
2790: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
2791: 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);
2792: PetscCallCUBLAS(cerr);
2793: blda = B->cmap->n;
2794: k = B->cmap->n;
2795: } else {
2796: k = B->rmap->n;
2797: }
2799: /* perform the MatMat operation, op(A) is m x k, op(B) is k x n */
2800: 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);
2801: PetscCallCUSPARSE(stat);
2802: #endif
2803: PetscCall(PetscLogGpuTimeEnd());
2804: PetscCall(PetscLogGpuFlops(n * 2.0 * csrmat->num_entries));
2805: PetscCall(MatDenseRestoreArrayReadAndMemType(B, &barray));
2806: if (product->type == MATPRODUCT_RARt) {
2807: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2808: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_FALSE, PETSC_FALSE));
2809: } else if (product->type == MATPRODUCT_PtAP) {
2810: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2811: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_TRUE, PETSC_FALSE));
2812: } else {
2813: PetscCall(MatDenseRestoreArrayWriteAndMemType(C, &carray));
2814: }
2815: if (mmdata->cisdense) PetscCall(MatConvert(C, MATSEQDENSE, MAT_INPLACE_MATRIX, &C));
2816: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
2817: PetscFunctionReturn(PETSC_SUCCESS);
2818: }
2820: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2821: {
2822: Mat_Product *product = C->product;
2823: Mat A, B;
2824: PetscInt m, n;
2825: PetscBool cisdense, flg;
2826: MatMatCusparse *mmdata;
2827: Mat_SeqAIJCUSPARSE *cusp;
2829: PetscFunctionBegin;
2830: MatCheckProduct(C, 1);
2831: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
2832: A = product->A;
2833: B = product->B;
2834: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2835: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2836: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2837: PetscCheck(cusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2838: switch (product->type) {
2839: case MATPRODUCT_AB:
2840: m = A->rmap->n;
2841: n = B->cmap->n;
2842: break;
2843: case MATPRODUCT_AtB:
2844: m = A->cmap->n;
2845: n = B->cmap->n;
2846: break;
2847: case MATPRODUCT_ABt:
2848: m = A->rmap->n;
2849: n = B->rmap->n;
2850: break;
2851: case MATPRODUCT_PtAP:
2852: m = B->cmap->n;
2853: n = B->cmap->n;
2854: break;
2855: case MATPRODUCT_RARt:
2856: m = B->rmap->n;
2857: n = B->rmap->n;
2858: break;
2859: default:
2860: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2861: }
2862: PetscCall(MatSetSizes(C, m, n, m, n));
2863: /* if C is of type MATSEQDENSE (CPU), perform the operation on the GPU and then copy on the CPU */
2864: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQDENSE, &cisdense));
2865: PetscCall(MatSetType(C, MATSEQDENSECUDA));
2867: /* product data */
2868: PetscCall(PetscNew(&mmdata));
2869: mmdata->cisdense = cisdense;
2870: #if PETSC_PKG_CUDA_VERSION_LT(11, 0, 0)
2871: /* cusparseXcsrmm does not support transpose on B, so we allocate buffer to store B^T */
2872: 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)));
2873: #endif
2874: /* for these products we need intermediate storage */
2875: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2876: PetscCall(MatCreate(PetscObjectComm((PetscObject)C), &mmdata->X));
2877: PetscCall(MatSetType(mmdata->X, MATSEQDENSECUDA));
2878: if (product->type == MATPRODUCT_RARt) { /* do not preallocate, since the first call to MatDenseCUDAGetArray will preallocate on the GPU for us */
2879: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->rmap->n, A->rmap->n, B->rmap->n));
2880: } else {
2881: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->cmap->n, A->rmap->n, B->cmap->n));
2882: }
2883: }
2884: C->product->data = mmdata;
2885: C->product->destroy = MatDestroy_MatMatCusparse;
2887: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA;
2888: PetscFunctionReturn(PETSC_SUCCESS);
2889: }
2891: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
2892: {
2893: Mat_Product *product = C->product;
2894: Mat A, B;
2895: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
2896: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2897: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
2898: CsrMatrix *Acsr, *Bcsr, *Ccsr;
2899: PetscBool flg;
2900: cusparseStatus_t stat;
2901: MatProductType ptype;
2902: MatMatCusparse *mmdata;
2903: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2904: cusparseSpMatDescr_t BmatSpDescr;
2905: #endif
2906: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
2908: PetscFunctionBegin;
2909: MatCheckProduct(C, 1);
2910: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2911: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQAIJCUSPARSE, &flg));
2912: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for C of type %s", ((PetscObject)C)->type_name);
2913: mmdata = (MatMatCusparse *)C->product->data;
2914: A = product->A;
2915: B = product->B;
2916: if (mmdata->reusesym) { /* this happens when api_user is true, meaning that the matrix values have been already computed in the MatProductSymbolic phase */
2917: mmdata->reusesym = PETSC_FALSE;
2918: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2919: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2920: Cmat = Ccusp->mat;
2921: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[C->product->type]);
2922: Ccsr = (CsrMatrix *)Cmat->mat;
2923: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2924: goto finalize;
2925: }
2926: if (!c->nz) goto finalize;
2927: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2928: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2929: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
2930: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
2931: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2932: PetscCheck(!B->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2933: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2934: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
2935: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2936: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2937: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2938: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2939: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2940: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
2942: ptype = product->type;
2943: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
2944: ptype = MATPRODUCT_AB;
2945: 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");
2946: }
2947: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
2948: ptype = MATPRODUCT_AB;
2949: 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");
2950: }
2951: switch (ptype) {
2952: case MATPRODUCT_AB:
2953: Amat = Acusp->mat;
2954: Bmat = Bcusp->mat;
2955: break;
2956: case MATPRODUCT_AtB:
2957: Amat = Acusp->matTranspose;
2958: Bmat = Bcusp->mat;
2959: break;
2960: case MATPRODUCT_ABt:
2961: Amat = Acusp->mat;
2962: Bmat = Bcusp->matTranspose;
2963: break;
2964: default:
2965: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2966: }
2967: Cmat = Ccusp->mat;
2968: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
2969: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
2970: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[ptype]);
2971: Acsr = (CsrMatrix *)Amat->mat;
2972: Bcsr = mmdata->Bcsr ? mmdata->Bcsr : (CsrMatrix *)Bmat->mat; /* B may be in compressed row storage */
2973: Ccsr = (CsrMatrix *)Cmat->mat;
2974: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
2975: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
2976: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2977: PetscCall(PetscLogGpuTimeBegin());
2978: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2979: BmatSpDescr = mmdata->Bcsr ? mmdata->matSpBDescr : Bmat->matDescr; /* B may be in compressed row storage */
2980: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
2981: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2982: 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);
2983: PetscCallCUSPARSE(stat);
2984: #else
2985: 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);
2986: PetscCallCUSPARSE(stat);
2987: 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);
2988: PetscCallCUSPARSE(stat);
2989: #endif
2990: #else
2991: 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,
2992: 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());
2993: PetscCallCUSPARSE(stat);
2994: #endif
2995: PetscCall(PetscLogGpuFlops(mmdata->flops));
2996: PetscCallCUDA(WaitForCUDA());
2997: PetscCall(PetscLogGpuTimeEnd());
2998: C->offloadmask = PETSC_OFFLOAD_GPU;
2999: finalize:
3000: /* shorter version of MatAssemblyEnd_SeqAIJ */
3001: 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));
3002: PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
3003: PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
3004: c->reallocs = 0;
3005: C->info.mallocs += 0;
3006: C->info.nz_unneeded = 0;
3007: C->assembled = C->was_assembled = PETSC_TRUE;
3008: C->num_ass++;
3009: PetscFunctionReturn(PETSC_SUCCESS);
3010: }
3012: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
3013: {
3014: Mat_Product *product = C->product;
3015: Mat A, B;
3016: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
3017: Mat_SeqAIJ *a, *b, *c;
3018: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
3019: CsrMatrix *Acsr, *Bcsr, *Ccsr;
3020: PetscInt i, j, m, n, k;
3021: PetscBool flg;
3022: cusparseStatus_t stat;
3023: MatProductType ptype;
3024: MatMatCusparse *mmdata;
3025: PetscLogDouble flops;
3026: PetscBool biscompressed, ciscompressed;
3027: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3028: int64_t C_num_rows1, C_num_cols1, C_nnz1;
3029: cusparseSpMatDescr_t BmatSpDescr;
3030: #else
3031: int cnz;
3032: #endif
3033: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
3035: PetscFunctionBegin;
3036: MatCheckProduct(C, 1);
3037: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
3038: A = product->A;
3039: B = product->B;
3040: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
3041: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
3042: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
3043: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
3044: a = (Mat_SeqAIJ *)A->data;
3045: b = (Mat_SeqAIJ *)B->data;
3046: /* product data */
3047: PetscCall(PetscNew(&mmdata));
3048: C->product->data = mmdata;
3049: C->product->destroy = MatDestroy_MatMatCusparse;
3051: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3052: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
3053: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr; /* Access spptr after MatSeqAIJCUSPARSECopyToGPU, not before */
3054: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
3055: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3056: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3058: ptype = product->type;
3059: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
3060: ptype = MATPRODUCT_AB;
3061: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
3062: }
3063: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
3064: ptype = MATPRODUCT_AB;
3065: product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
3066: }
3067: biscompressed = PETSC_FALSE;
3068: ciscompressed = PETSC_FALSE;
3069: switch (ptype) {
3070: case MATPRODUCT_AB:
3071: m = A->rmap->n;
3072: n = B->cmap->n;
3073: k = A->cmap->n;
3074: Amat = Acusp->mat;
3075: Bmat = Bcusp->mat;
3076: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3077: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3078: break;
3079: case MATPRODUCT_AtB:
3080: m = A->cmap->n;
3081: n = B->cmap->n;
3082: k = A->rmap->n;
3083: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3084: Amat = Acusp->matTranspose;
3085: Bmat = Bcusp->mat;
3086: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3087: break;
3088: case MATPRODUCT_ABt:
3089: m = A->rmap->n;
3090: n = B->rmap->n;
3091: k = A->cmap->n;
3092: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
3093: Amat = Acusp->mat;
3094: Bmat = Bcusp->matTranspose;
3095: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3096: break;
3097: default:
3098: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
3099: }
3101: /* create cusparse matrix */
3102: PetscCall(MatSetSizes(C, m, n, m, n));
3103: PetscCall(MatSetType(C, MATSEQAIJCUSPARSE));
3104: c = (Mat_SeqAIJ *)C->data;
3105: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
3106: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
3107: Ccsr = new CsrMatrix;
3109: c->compressedrow.use = ciscompressed;
3110: if (c->compressedrow.use) { /* if a is in compressed row, than c will be in compressed row format */
3111: c->compressedrow.nrows = a->compressedrow.nrows;
3112: PetscCall(PetscMalloc2(c->compressedrow.nrows + 1, &c->compressedrow.i, c->compressedrow.nrows, &c->compressedrow.rindex));
3113: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, c->compressedrow.nrows));
3114: Ccusp->workVector = new THRUSTARRAY(c->compressedrow.nrows);
3115: Cmat->cprowIndices = new THRUSTINTARRAY(c->compressedrow.nrows);
3116: Cmat->cprowIndices->assign(c->compressedrow.rindex, c->compressedrow.rindex + c->compressedrow.nrows);
3117: } else {
3118: c->compressedrow.nrows = 0;
3119: c->compressedrow.i = NULL;
3120: c->compressedrow.rindex = NULL;
3121: Ccusp->workVector = NULL;
3122: Cmat->cprowIndices = NULL;
3123: }
3124: Ccusp->nrows = ciscompressed ? c->compressedrow.nrows : m;
3125: Ccusp->mat = Cmat;
3126: Ccusp->mat->mat = Ccsr;
3127: Ccsr->num_rows = Ccusp->nrows;
3128: Ccsr->num_cols = n;
3129: Ccsr->row_offsets = new THRUSTINTARRAY32(Ccusp->nrows + 1);
3130: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
3131: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
3132: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
3133: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
3134: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
3135: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
3136: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3137: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3138: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3139: if (!Ccsr->num_rows || !Ccsr->num_cols || !a->nz || !b->nz) { /* cusparse raise errors in different calls when matrices have zero rows/columns! */
3140: PetscCallThrust(thrust::fill(thrust::device, Ccsr->row_offsets->begin(), Ccsr->row_offsets->end(), 0));
3141: c->nz = 0;
3142: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3143: Ccsr->values = new THRUSTARRAY(c->nz);
3144: goto finalizesym;
3145: }
3147: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
3148: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
3149: Acsr = (CsrMatrix *)Amat->mat;
3150: if (!biscompressed) {
3151: Bcsr = (CsrMatrix *)Bmat->mat;
3152: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3153: BmatSpDescr = Bmat->matDescr;
3154: #endif
3155: } else { /* we need to use row offsets for the full matrix */
3156: CsrMatrix *cBcsr = (CsrMatrix *)Bmat->mat;
3157: Bcsr = new CsrMatrix;
3158: Bcsr->num_rows = B->rmap->n;
3159: Bcsr->num_cols = cBcsr->num_cols;
3160: Bcsr->num_entries = cBcsr->num_entries;
3161: Bcsr->column_indices = cBcsr->column_indices;
3162: Bcsr->values = cBcsr->values;
3163: if (!Bcusp->rowoffsets_gpu) {
3164: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
3165: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
3166: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
3167: }
3168: Bcsr->row_offsets = Bcusp->rowoffsets_gpu;
3169: mmdata->Bcsr = Bcsr;
3170: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3171: if (Bcsr->num_rows && Bcsr->num_cols) {
3172: 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);
3173: PetscCallCUSPARSE(stat);
3174: }
3175: BmatSpDescr = mmdata->matSpBDescr;
3176: #endif
3177: }
3178: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
3179: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
3180: /* precompute flops count */
3181: if (ptype == MATPRODUCT_AB) {
3182: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3183: const PetscInt st = a->i[i];
3184: const PetscInt en = a->i[i + 1];
3185: for (j = st; j < en; j++) {
3186: const PetscInt brow = a->j[j];
3187: flops += 2. * (b->i[brow + 1] - b->i[brow]);
3188: }
3189: }
3190: } else if (ptype == MATPRODUCT_AtB) {
3191: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3192: const PetscInt anzi = a->i[i + 1] - a->i[i];
3193: const PetscInt bnzi = b->i[i + 1] - b->i[i];
3194: flops += (2. * anzi) * bnzi;
3195: }
3196: } else { /* TODO */
3197: flops = 0.;
3198: }
3200: mmdata->flops = flops;
3201: PetscCall(PetscLogGpuTimeBegin());
3203: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3204: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3205: // cuda-12.2 requires non-null csrRowOffsets
3206: 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);
3207: PetscCallCUSPARSE(stat);
3208: PetscCallCUSPARSE(cusparseSpGEMM_createDescr(&mmdata->spgemmDesc));
3209: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
3210: {
3211: /* cusparseSpGEMMreuse has more reasonable APIs than cusparseSpGEMM, so we prefer to use it.
3212: We follow the sample code at https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSPARSE/spgemm_reuse
3213: */
3214: void *dBuffer1 = NULL;
3215: void *dBuffer2 = NULL;
3216: void *dBuffer3 = NULL;
3217: /* dBuffer4, dBuffer5 are needed by cusparseSpGEMMreuse_compute, and therefore are stored in mmdata */
3218: size_t bufferSize1 = 0;
3219: size_t bufferSize2 = 0;
3220: size_t bufferSize3 = 0;
3221: size_t bufferSize4 = 0;
3222: size_t bufferSize5 = 0;
3224: /* ask bufferSize1 bytes for external memory */
3225: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, NULL);
3226: PetscCallCUSPARSE(stat);
3227: PetscCallCUDA(cudaMalloc((void **)&dBuffer1, bufferSize1));
3228: /* inspect the matrices A and B to understand the memory requirement for the next step */
3229: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, dBuffer1);
3230: PetscCallCUSPARSE(stat);
3232: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, NULL, &bufferSize3, NULL, &bufferSize4, NULL);
3233: PetscCallCUSPARSE(stat);
3234: PetscCallCUDA(cudaMalloc((void **)&dBuffer2, bufferSize2));
3235: PetscCallCUDA(cudaMalloc((void **)&dBuffer3, bufferSize3));
3236: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer4, bufferSize4));
3237: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, dBuffer2, &bufferSize3, dBuffer3, &bufferSize4, mmdata->dBuffer4);
3238: PetscCallCUSPARSE(stat);
3239: PetscCallCUDA(cudaFree(dBuffer1));
3240: PetscCallCUDA(cudaFree(dBuffer2));
3242: /* get matrix C non-zero entries C_nnz1 */
3243: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3244: c->nz = (PetscInt)C_nnz1;
3245: /* allocate matrix C */
3246: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3247: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3248: Ccsr->values = new THRUSTARRAY(c->nz);
3249: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3250: /* update matC with the new pointers */
3251: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3252: PetscCallCUSPARSE(stat);
3254: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, NULL);
3255: PetscCallCUSPARSE(stat);
3256: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer5, bufferSize5));
3257: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, mmdata->dBuffer5);
3258: PetscCallCUSPARSE(stat);
3259: PetscCallCUDA(cudaFree(dBuffer3));
3260: 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);
3261: PetscCallCUSPARSE(stat);
3262: 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));
3263: }
3264: #else
3265: size_t bufSize2;
3266: /* ask bufferSize bytes for external memory */
3267: 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);
3268: PetscCallCUSPARSE(stat);
3269: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer2, bufSize2));
3270: /* inspect the matrices A and B to understand the memory requirement for the next step */
3271: 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);
3272: PetscCallCUSPARSE(stat);
3273: /* ask bufferSize again bytes for external memory */
3274: 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);
3275: PetscCallCUSPARSE(stat);
3276: /* The CUSPARSE documentation is not clear, nor the API
3277: We need both buffers to perform the operations properly!
3278: mmdata->mmBuffer2 does not appear anywhere in the compute/copy API
3279: it only appears for the workEstimation stuff, but it seems it is needed in compute, so probably the address
3280: is stored in the descriptor! What a messy API... */
3281: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer, mmdata->mmBufferSize));
3282: /* compute the intermediate product of A * B */
3283: 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);
3284: PetscCallCUSPARSE(stat);
3285: /* get matrix C non-zero entries C_nnz1 */
3286: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3287: c->nz = (PetscInt)C_nnz1;
3288: 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,
3289: mmdata->mmBufferSize / 1024));
3290: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3291: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3292: Ccsr->values = new THRUSTARRAY(c->nz);
3293: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3294: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3295: PetscCallCUSPARSE(stat);
3296: 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);
3297: PetscCallCUSPARSE(stat);
3298: #endif // PETSC_PKG_CUDA_VERSION_GE(11,4,0)
3299: #else
3300: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_HOST));
3301: 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,
3302: Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->row_offsets->data().get(), &cnz);
3303: PetscCallCUSPARSE(stat);
3304: c->nz = cnz;
3305: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3306: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3307: Ccsr->values = new THRUSTARRAY(c->nz);
3308: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3310: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3311: /* with the old gemm interface (removed from 11.0 on) we cannot compute the symbolic factorization only.
3312: 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
3313: D is NULL, despite the fact that CUSPARSE documentation claims it is supported! */
3314: 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,
3315: 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());
3316: PetscCallCUSPARSE(stat);
3317: #endif
3318: PetscCall(PetscLogGpuFlops(mmdata->flops));
3319: PetscCall(PetscLogGpuTimeEnd());
3320: finalizesym:
3321: c->singlemalloc = PETSC_FALSE;
3322: c->free_a = PETSC_TRUE;
3323: c->free_ij = PETSC_TRUE;
3324: PetscCall(PetscMalloc1(m + 1, &c->i));
3325: PetscCall(PetscMalloc1(c->nz, &c->j));
3326: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
3327: PetscInt *d_i = c->i;
3328: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
3329: THRUSTINTARRAY jj(Ccsr->column_indices->size());
3330: ii = *Ccsr->row_offsets;
3331: jj = *Ccsr->column_indices;
3332: if (ciscompressed) d_i = c->compressedrow.i;
3333: PetscCallCUDA(cudaMemcpy(d_i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3334: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3335: } else {
3336: PetscInt *d_i = c->i;
3337: if (ciscompressed) d_i = c->compressedrow.i;
3338: PetscCallCUDA(cudaMemcpy(d_i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3339: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
3340: }
3341: if (ciscompressed) { /* need to expand host row offsets */
3342: PetscInt r = 0;
3343: c->i[0] = 0;
3344: for (k = 0; k < c->compressedrow.nrows; k++) {
3345: const PetscInt next = c->compressedrow.rindex[k];
3346: const PetscInt old = c->compressedrow.i[k];
3347: for (; r < next; r++) c->i[r + 1] = old;
3348: }
3349: for (; r < m; r++) c->i[r + 1] = c->compressedrow.i[c->compressedrow.nrows];
3350: }
3351: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
3352: PetscCall(PetscMalloc1(m, &c->ilen));
3353: PetscCall(PetscMalloc1(m, &c->imax));
3354: c->maxnz = c->nz;
3355: c->nonzerorowcnt = 0;
3356: c->rmax = 0;
3357: for (k = 0; k < m; k++) {
3358: const PetscInt nn = c->i[k + 1] - c->i[k];
3359: c->ilen[k] = c->imax[k] = nn;
3360: c->nonzerorowcnt += (PetscInt) !!nn;
3361: c->rmax = PetscMax(c->rmax, nn);
3362: }
3363: PetscCall(MatMarkDiagonal_SeqAIJ(C));
3364: PetscCall(PetscMalloc1(c->nz, &c->a));
3365: Ccsr->num_entries = c->nz;
3367: C->nonzerostate++;
3368: PetscCall(PetscLayoutSetUp(C->rmap));
3369: PetscCall(PetscLayoutSetUp(C->cmap));
3370: Ccusp->nonzerostate = C->nonzerostate;
3371: C->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
3372: C->preallocated = PETSC_TRUE;
3373: C->assembled = PETSC_FALSE;
3374: C->was_assembled = PETSC_FALSE;
3375: 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 */
3376: mmdata->reusesym = PETSC_TRUE;
3377: C->offloadmask = PETSC_OFFLOAD_GPU;
3378: }
3379: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3380: PetscFunctionReturn(PETSC_SUCCESS);
3381: }
3383: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat);
3385: /* handles sparse or dense B */
3386: static PetscErrorCode MatProductSetFromOptions_SeqAIJCUSPARSE(Mat mat)
3387: {
3388: Mat_Product *product = mat->product;
3389: PetscBool isdense = PETSC_FALSE, Biscusp = PETSC_FALSE, Ciscusp = PETSC_TRUE;
3391: PetscFunctionBegin;
3392: MatCheckProduct(mat, 1);
3393: PetscCall(PetscObjectBaseTypeCompare((PetscObject)product->B, MATSEQDENSE, &isdense));
3394: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJCUSPARSE, &Biscusp));
3395: if (product->type == MATPRODUCT_ABC) {
3396: Ciscusp = PETSC_FALSE;
3397: if (!product->C->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJCUSPARSE, &Ciscusp));
3398: }
3399: if (Biscusp && Ciscusp) { /* we can always select the CPU backend */
3400: PetscBool usecpu = PETSC_FALSE;
3401: switch (product->type) {
3402: case MATPRODUCT_AB:
3403: if (product->api_user) {
3404: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
3405: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3406: PetscOptionsEnd();
3407: } else {
3408: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
3409: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
3410: PetscOptionsEnd();
3411: }
3412: break;
3413: case MATPRODUCT_AtB:
3414: if (product->api_user) {
3415: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
3416: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3417: PetscOptionsEnd();
3418: } else {
3419: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
3420: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
3421: PetscOptionsEnd();
3422: }
3423: break;
3424: case MATPRODUCT_PtAP:
3425: if (product->api_user) {
3426: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
3427: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3428: PetscOptionsEnd();
3429: } else {
3430: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
3431: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
3432: PetscOptionsEnd();
3433: }
3434: break;
3435: case MATPRODUCT_RARt:
3436: if (product->api_user) {
3437: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatRARt", "Mat");
3438: PetscCall(PetscOptionsBool("-matrart_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3439: PetscOptionsEnd();
3440: } else {
3441: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_RARt", "Mat");
3442: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatRARt", usecpu, &usecpu, NULL));
3443: PetscOptionsEnd();
3444: }
3445: break;
3446: case MATPRODUCT_ABC:
3447: if (product->api_user) {
3448: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMatMult", "Mat");
3449: PetscCall(PetscOptionsBool("-matmatmatmult_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3450: PetscOptionsEnd();
3451: } else {
3452: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_ABC", "Mat");
3453: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMatMult", usecpu, &usecpu, NULL));
3454: PetscOptionsEnd();
3455: }
3456: break;
3457: default:
3458: break;
3459: }
3460: if (usecpu) Biscusp = Ciscusp = PETSC_FALSE;
3461: }
3462: /* dispatch */
3463: if (isdense) {
3464: switch (product->type) {
3465: case MATPRODUCT_AB:
3466: case MATPRODUCT_AtB:
3467: case MATPRODUCT_ABt:
3468: case MATPRODUCT_PtAP:
3469: case MATPRODUCT_RARt:
3470: if (product->A->boundtocpu) {
3471: PetscCall(MatProductSetFromOptions_SeqAIJ_SeqDense(mat));
3472: } else {
3473: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA;
3474: }
3475: break;
3476: case MATPRODUCT_ABC:
3477: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3478: break;
3479: default:
3480: break;
3481: }
3482: } else if (Biscusp && Ciscusp) {
3483: switch (product->type) {
3484: case MATPRODUCT_AB:
3485: case MATPRODUCT_AtB:
3486: case MATPRODUCT_ABt:
3487: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE;
3488: break;
3489: case MATPRODUCT_PtAP:
3490: case MATPRODUCT_RARt:
3491: case MATPRODUCT_ABC:
3492: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
3493: break;
3494: default:
3495: break;
3496: }
3497: } else { /* fallback for AIJ */
3498: PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
3499: }
3500: PetscFunctionReturn(PETSC_SUCCESS);
3501: }
3503: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3504: {
3505: PetscFunctionBegin;
3506: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_FALSE, PETSC_FALSE));
3507: PetscFunctionReturn(PETSC_SUCCESS);
3508: }
3510: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3511: {
3512: PetscFunctionBegin;
3513: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_FALSE, PETSC_FALSE));
3514: PetscFunctionReturn(PETSC_SUCCESS);
3515: }
3517: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3518: {
3519: PetscFunctionBegin;
3520: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_TRUE));
3521: PetscFunctionReturn(PETSC_SUCCESS);
3522: }
3524: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3525: {
3526: PetscFunctionBegin;
3527: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_TRUE));
3528: PetscFunctionReturn(PETSC_SUCCESS);
3529: }
3531: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy)
3532: {
3533: PetscFunctionBegin;
3534: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, NULL, yy, PETSC_TRUE, PETSC_FALSE));
3535: PetscFunctionReturn(PETSC_SUCCESS);
3536: }
3538: __global__ static void ScatterAdd(PetscInt n, PetscInt *idx, const PetscScalar *x, PetscScalar *y)
3539: {
3540: int i = blockIdx.x * blockDim.x + threadIdx.x;
3541: if (i < n) y[idx[i]] += x[i];
3542: }
3544: /* z = op(A) x + y. If trans & !herm, op = ^T; if trans & herm, op = ^H; if !trans, op = no-op */
3545: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz, PetscBool trans, PetscBool herm)
3546: {
3547: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3548: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
3549: Mat_SeqAIJCUSPARSEMultStruct *matstruct;
3550: PetscScalar *xarray, *zarray, *dptr, *beta, *xptr;
3551: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
3552: PetscBool compressed;
3553: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3554: PetscInt nx, ny;
3555: #endif
3557: PetscFunctionBegin;
3558: PetscCheck(!herm || trans, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Hermitian and not transpose not supported");
3559: if (!a->nz) {
3560: if (yy) PetscCall(VecSeq_CUDA::Copy(yy, zz));
3561: else PetscCall(VecSeq_CUDA::Set(zz, 0));
3562: PetscFunctionReturn(PETSC_SUCCESS);
3563: }
3564: /* The line below is necessary due to the operations that modify the matrix on the CPU (axpy, scale, etc) */
3565: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3566: if (!trans) {
3567: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3568: PetscCheck(matstruct, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "SeqAIJCUSPARSE does not have a 'mat' (need to fix)");
3569: } else {
3570: if (herm || !A->form_explicit_transpose) {
3571: opA = herm ? CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
3572: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
3573: } else {
3574: if (!cusparsestruct->matTranspose) PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3575: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
3576: }
3577: }
3578: /* Does the matrix use compressed rows (i.e., drop zero rows)? */
3579: compressed = matstruct->cprowIndices ? PETSC_TRUE : PETSC_FALSE;
3581: try {
3582: PetscCall(VecCUDAGetArrayRead(xx, (const PetscScalar **)&xarray));
3583: if (yy == zz) PetscCall(VecCUDAGetArray(zz, &zarray)); /* read & write zz, so need to get up-to-date zarray on GPU */
3584: else PetscCall(VecCUDAGetArrayWrite(zz, &zarray)); /* write zz, so no need to init zarray on GPU */
3586: PetscCall(PetscLogGpuTimeBegin());
3587: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3588: /* z = A x + beta y.
3589: If A is compressed (with less rows), then Ax is shorter than the full z, so we need a work vector to store Ax.
3590: When A is non-compressed, and z = y, we can set beta=1 to compute y = Ax + y in one call.
3591: */
3592: xptr = xarray;
3593: dptr = compressed ? cusparsestruct->workVector->data().get() : zarray;
3594: beta = (yy == zz && !compressed) ? matstruct->beta_one : matstruct->beta_zero;
3595: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3596: /* Get length of x, y for y=Ax. ny might be shorter than the work vector's allocated length, since the work vector is
3597: allocated to accommodate different uses. So we get the length info directly from mat.
3598: */
3599: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3600: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3601: nx = mat->num_cols; // since y = Ax
3602: ny = mat->num_rows;
3603: }
3604: #endif
3605: } else {
3606: /* z = A^T x + beta y
3607: If A is compressed, then we need a work vector as the shorter version of x to compute A^T x.
3608: Note A^Tx is of full length, so we set beta to 1.0 if y exists.
3609: */
3610: xptr = compressed ? cusparsestruct->workVector->data().get() : xarray;
3611: dptr = zarray;
3612: beta = yy ? matstruct->beta_one : matstruct->beta_zero;
3613: if (compressed) { /* Scatter x to work vector */
3614: thrust::device_ptr<PetscScalar> xarr = thrust::device_pointer_cast(xarray);
3616: thrust::for_each(
3617: #if PetscDefined(HAVE_THRUST_ASYNC)
3618: thrust::cuda::par.on(PetscDefaultCudaStream),
3619: #endif
3620: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))),
3621: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(xarr, matstruct->cprowIndices->begin()))) + matstruct->cprowIndices->size(), VecCUDAEqualsReverse());
3622: }
3623: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3624: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3625: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3626: nx = mat->num_rows; // since y = A^T x
3627: ny = mat->num_cols;
3628: }
3629: #endif
3630: }
3632: /* csr_spmv does y = alpha op(A) x + beta y */
3633: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
3634: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3635: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3636: 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.
3637: #else
3638: cusparseSpMatDescr_t &matDescr = matstruct->matDescr;
3639: #endif
3641: 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");
3642: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
3643: if (!matDescr) {
3644: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3645: 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));
3646: }
3647: #endif
3649: if (!matstruct->cuSpMV[opA].initialized) { /* built on demand */
3650: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecXDescr, nx, xptr, cusparse_scalartype));
3651: PetscCallCUSPARSE(cusparseCreateDnVec(&matstruct->cuSpMV[opA].vecYDescr, ny, dptr, cusparse_scalartype));
3652: PetscCallCUSPARSE(
3653: 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));
3654: PetscCallCUDA(cudaMalloc(&matstruct->cuSpMV[opA].spmvBuffer, matstruct->cuSpMV[opA].spmvBufferSize));
3655: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // cusparseSpMV_preprocess is added in 12.4
3656: PetscCallCUSPARSE(
3657: 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));
3658: #endif
3659: matstruct->cuSpMV[opA].initialized = PETSC_TRUE;
3660: } else {
3661: /* x, y's value pointers might change between calls, but their shape is kept, so we just update pointers */
3662: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecXDescr, xptr));
3663: PetscCallCUSPARSE(cusparseDnVecSetValues(matstruct->cuSpMV[opA].vecYDescr, dptr));
3664: }
3666: 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));
3667: #else
3668: CsrMatrix *mat = (CsrMatrix *)matstruct->mat;
3669: 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));
3670: #endif
3671: } else {
3672: if (cusparsestruct->nrows) {
3673: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3674: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
3675: #else
3676: cusparseHybMat_t hybMat = (cusparseHybMat_t)matstruct->mat;
3677: PetscCallCUSPARSE(cusparse_hyb_spmv(cusparsestruct->handle, opA, matstruct->alpha_one, matstruct->descr, hybMat, xptr, beta, dptr));
3678: #endif
3679: }
3680: }
3681: PetscCall(PetscLogGpuTimeEnd());
3683: if (opA == CUSPARSE_OPERATION_NON_TRANSPOSE) {
3684: if (yy) { /* MatMultAdd: zz = A*xx + yy */
3685: if (compressed) { /* A is compressed. We first copy yy to zz, then ScatterAdd the work vector to zz */
3686: PetscCall(VecSeq_CUDA::Copy(yy, zz)); /* zz = yy */
3687: } else if (zz != yy) { /* A is not compressed. zz already contains A*xx, and we just need to add yy */
3688: PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3689: }
3690: } else if (compressed) { /* MatMult: zz = A*xx. A is compressed, so we zero zz first, then ScatterAdd the work vector to zz */
3691: PetscCall(VecSeq_CUDA::Set(zz, 0));
3692: }
3694: /* ScatterAdd the result from work vector into the full vector when A is compressed */
3695: if (compressed) {
3696: PetscCall(PetscLogGpuTimeBegin());
3697: /* I wanted to make this for_each asynchronous but failed. thrust::async::for_each() returns an event (internally registered)
3698: and in the destructor of the scope, it will call cudaStreamSynchronize() on this stream. One has to store all events to
3699: prevent that. So I just add a ScatterAdd kernel.
3700: */
3701: #if 0
3702: thrust::device_ptr<PetscScalar> zptr = thrust::device_pointer_cast(zarray);
3703: thrust::async::for_each(thrust::cuda::par.on(cusparsestruct->stream),
3704: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))),
3705: thrust::make_zip_iterator(thrust::make_tuple(cusparsestruct->workVector->begin(), thrust::make_permutation_iterator(zptr, matstruct->cprowIndices->begin()))) + matstruct->cprowIndices->size(),
3706: VecCUDAPlusEquals());
3707: #else
3708: PetscInt n = matstruct->cprowIndices->size();
3709: ScatterAdd<<<(n + 255) / 256, 256, 0, PetscDefaultCudaStream>>>(n, matstruct->cprowIndices->data().get(), cusparsestruct->workVector->data().get(), zarray);
3710: #endif
3711: PetscCall(PetscLogGpuTimeEnd());
3712: }
3713: } else {
3714: if (yy && yy != zz) PetscCall(VecSeq_CUDA::AXPY(zz, 1.0, yy)); /* zz += yy */
3715: }
3716: PetscCall(VecCUDARestoreArrayRead(xx, (const PetscScalar **)&xarray));
3717: if (yy == zz) PetscCall(VecCUDARestoreArray(zz, &zarray));
3718: else PetscCall(VecCUDARestoreArrayWrite(zz, &zarray));
3719: } catch (char *ex) {
3720: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
3721: }
3722: if (yy) {
3723: PetscCall(PetscLogGpuFlops(2.0 * a->nz));
3724: } else {
3725: PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
3726: }
3727: PetscFunctionReturn(PETSC_SUCCESS);
3728: }
3730: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
3731: {
3732: PetscFunctionBegin;
3733: PetscCall(MatMultAddKernel_SeqAIJCUSPARSE(A, xx, yy, zz, PETSC_TRUE, PETSC_FALSE));
3734: PetscFunctionReturn(PETSC_SUCCESS);
3735: }
3737: static PetscErrorCode MatAssemblyEnd_SeqAIJCUSPARSE(Mat A, MatAssemblyType mode)
3738: {
3739: PetscFunctionBegin;
3740: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
3741: PetscFunctionReturn(PETSC_SUCCESS);
3742: }
3744: /*@
3745: MatCreateSeqAIJCUSPARSE - Creates a sparse matrix in `MATAIJCUSPARSE` (compressed row) format
3746: (the default parallel PETSc format).
3748: Collective
3750: Input Parameters:
3751: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3752: . m - number of rows
3753: . n - number of columns
3754: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is provide
3755: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
3757: Output Parameter:
3758: . A - the matrix
3760: Level: intermediate
3762: Notes:
3763: This matrix will ultimately pushed down to NVIDIA GPUs and use the CuSPARSE library for
3764: calculations. For good matrix assembly performance the user should preallocate the matrix
3765: storage by setting the parameter `nz` (or the array `nnz`).
3767: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3768: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3769: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
3771: The AIJ format, also called
3772: compressed row storage, is fully compatible with standard Fortran
3773: storage. That is, the stored row and column indices can begin at
3774: either one (as in Fortran) or zero.
3776: Specify the preallocated storage with either nz or nnz (not both).
3777: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3778: allocation.
3780: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATAIJCUSPARSE`
3781: @*/
3782: PetscErrorCode MatCreateSeqAIJCUSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3783: {
3784: PetscFunctionBegin;
3785: PetscCall(MatCreate(comm, A));
3786: PetscCall(MatSetSizes(*A, m, n, m, n));
3787: PetscCall(MatSetType(*A, MATSEQAIJCUSPARSE));
3788: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
3789: PetscFunctionReturn(PETSC_SUCCESS);
3790: }
3792: static PetscErrorCode MatDestroy_SeqAIJCUSPARSE(Mat A)
3793: {
3794: PetscFunctionBegin;
3795: if (A->factortype == MAT_FACTOR_NONE) {
3796: PetscCall(MatSeqAIJCUSPARSE_Destroy(A));
3797: } else {
3798: PetscCall(MatSeqAIJCUSPARSETriFactors_Destroy((Mat_SeqAIJCUSPARSETriFactors **)&A->spptr));
3799: }
3800: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3801: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetFormat_C", NULL));
3802: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatCUSPARSESetUseCPUSolve_C", NULL));
3803: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3804: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3805: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3806: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
3807: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3808: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3809: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijcusparse_hypre_C", NULL));
3810: PetscCall(MatDestroy_SeqAIJ(A));
3811: PetscFunctionReturn(PETSC_SUCCESS);
3812: }
3814: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
3815: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat, PetscBool);
3816: static PetscErrorCode MatDuplicate_SeqAIJCUSPARSE(Mat A, MatDuplicateOption cpvalues, Mat *B)
3817: {
3818: PetscFunctionBegin;
3819: PetscCall(MatDuplicate_SeqAIJ(A, cpvalues, B));
3820: PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(*B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, B));
3821: PetscFunctionReturn(PETSC_SUCCESS);
3822: }
3824: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat Y, PetscScalar a, Mat X, MatStructure str)
3825: {
3826: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3827: Mat_SeqAIJCUSPARSE *cy;
3828: Mat_SeqAIJCUSPARSE *cx;
3829: PetscScalar *ay;
3830: const PetscScalar *ax;
3831: CsrMatrix *csry, *csrx;
3833: PetscFunctionBegin;
3834: cy = (Mat_SeqAIJCUSPARSE *)Y->spptr;
3835: cx = (Mat_SeqAIJCUSPARSE *)X->spptr;
3836: if (X->ops->axpy != Y->ops->axpy) {
3837: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3838: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3839: PetscFunctionReturn(PETSC_SUCCESS);
3840: }
3841: /* if we are here, it means both matrices are bound to GPU */
3842: PetscCall(MatSeqAIJCUSPARSECopyToGPU(Y));
3843: PetscCall(MatSeqAIJCUSPARSECopyToGPU(X));
3844: PetscCheck(cy->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)Y), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3845: PetscCheck(cx->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)X), PETSC_ERR_GPU, "only MAT_CUSPARSE_CSR supported");
3846: csry = (CsrMatrix *)cy->mat->mat;
3847: csrx = (CsrMatrix *)cx->mat->mat;
3848: /* see if we can turn this into a cublas axpy */
3849: if (str != SAME_NONZERO_PATTERN && x->nz == y->nz && !x->compressedrow.use && !y->compressedrow.use) {
3850: bool eq = thrust::equal(thrust::device, csry->row_offsets->begin(), csry->row_offsets->end(), csrx->row_offsets->begin());
3851: if (eq) eq = thrust::equal(thrust::device, csry->column_indices->begin(), csry->column_indices->end(), csrx->column_indices->begin());
3852: if (eq) str = SAME_NONZERO_PATTERN;
3853: }
3854: /* spgeam is buggy with one column */
3855: if (Y->cmap->n == 1 && str != SAME_NONZERO_PATTERN) str = DIFFERENT_NONZERO_PATTERN;
3857: if (str == SUBSET_NONZERO_PATTERN) {
3858: PetscScalar b = 1.0;
3859: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3860: size_t bufferSize;
3861: void *buffer;
3862: #endif
3864: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3865: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3866: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_HOST));
3867: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3868: 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(),
3869: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), &bufferSize));
3870: PetscCallCUDA(cudaMalloc(&buffer, bufferSize));
3871: PetscCall(PetscLogGpuTimeBegin());
3872: 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(),
3873: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get(), buffer));
3874: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3875: PetscCall(PetscLogGpuTimeEnd());
3876: PetscCallCUDA(cudaFree(buffer));
3877: #else
3878: PetscCall(PetscLogGpuTimeBegin());
3879: 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(),
3880: csry->column_indices->data().get(), cy->mat->descr, ay, csry->row_offsets->data().get(), csry->column_indices->data().get()));
3881: PetscCall(PetscLogGpuFlops(x->nz + y->nz));
3882: PetscCall(PetscLogGpuTimeEnd());
3883: #endif
3884: PetscCallCUSPARSE(cusparseSetPointerMode(cy->handle, CUSPARSE_POINTER_MODE_DEVICE));
3885: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3886: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3887: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3888: } else if (str == SAME_NONZERO_PATTERN) {
3889: cublasHandle_t cublasv2handle;
3890: PetscBLASInt one = 1, bnz = 1;
3892: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(X, &ax));
3893: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3894: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3895: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3896: PetscCall(PetscLogGpuTimeBegin());
3897: PetscCallCUBLAS(cublasXaxpy(cublasv2handle, bnz, &a, ax, one, ay, one));
3898: PetscCall(PetscLogGpuFlops(2.0 * bnz));
3899: PetscCall(PetscLogGpuTimeEnd());
3900: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(X, &ax));
3901: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3902: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3903: } else {
3904: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(Y, PETSC_FALSE));
3905: PetscCall(MatAXPY_SeqAIJ(Y, a, X, str));
3906: }
3907: PetscFunctionReturn(PETSC_SUCCESS);
3908: }
3910: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat Y, PetscScalar a)
3911: {
3912: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3913: PetscScalar *ay;
3914: cublasHandle_t cublasv2handle;
3915: PetscBLASInt one = 1, bnz = 1;
3917: PetscFunctionBegin;
3918: PetscCall(MatSeqAIJCUSPARSEGetArray(Y, &ay));
3919: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
3920: PetscCall(PetscBLASIntCast(y->nz, &bnz));
3921: PetscCall(PetscLogGpuTimeBegin());
3922: PetscCallCUBLAS(cublasXscal(cublasv2handle, bnz, &a, ay, one));
3923: PetscCall(PetscLogGpuFlops(bnz));
3924: PetscCall(PetscLogGpuTimeEnd());
3925: PetscCall(MatSeqAIJCUSPARSERestoreArray(Y, &ay));
3926: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3927: PetscFunctionReturn(PETSC_SUCCESS);
3928: }
3930: static PetscErrorCode MatZeroEntries_SeqAIJCUSPARSE(Mat A)
3931: {
3932: PetscBool both = PETSC_FALSE;
3933: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3935: PetscFunctionBegin;
3936: if (A->factortype == MAT_FACTOR_NONE) {
3937: Mat_SeqAIJCUSPARSE *spptr = (Mat_SeqAIJCUSPARSE *)A->spptr;
3938: if (spptr->mat) {
3939: CsrMatrix *matrix = (CsrMatrix *)spptr->mat->mat;
3940: if (matrix->values) {
3941: both = PETSC_TRUE;
3942: thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3943: }
3944: }
3945: if (spptr->matTranspose) {
3946: CsrMatrix *matrix = (CsrMatrix *)spptr->matTranspose->mat;
3947: if (matrix->values) thrust::fill(thrust::device, matrix->values->begin(), matrix->values->end(), 0.);
3948: }
3949: }
3950: PetscCall(PetscArrayzero(a->a, a->i[A->rmap->n]));
3951: PetscCall(MatSeqAIJInvalidateDiagonal(A));
3952: if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
3953: else A->offloadmask = PETSC_OFFLOAD_CPU;
3954: PetscFunctionReturn(PETSC_SUCCESS);
3955: }
3957: static PetscErrorCode MatBindToCPU_SeqAIJCUSPARSE(Mat A, PetscBool flg)
3958: {
3959: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3961: PetscFunctionBegin;
3962: if (A->factortype != MAT_FACTOR_NONE) {
3963: A->boundtocpu = flg;
3964: PetscFunctionReturn(PETSC_SUCCESS);
3965: }
3966: if (flg) {
3967: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
3969: A->ops->scale = MatScale_SeqAIJ;
3970: A->ops->axpy = MatAXPY_SeqAIJ;
3971: A->ops->zeroentries = MatZeroEntries_SeqAIJ;
3972: A->ops->mult = MatMult_SeqAIJ;
3973: A->ops->multadd = MatMultAdd_SeqAIJ;
3974: A->ops->multtranspose = MatMultTranspose_SeqAIJ;
3975: A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJ;
3976: A->ops->multhermitiantranspose = NULL;
3977: A->ops->multhermitiantransposeadd = NULL;
3978: A->ops->productsetfromoptions = MatProductSetFromOptions_SeqAIJ;
3979: PetscCall(PetscMemzero(a->ops, sizeof(Mat_SeqAIJOps)));
3980: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", NULL));
3981: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", NULL));
3982: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", NULL));
3983: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
3984: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
3985: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", NULL));
3986: } else {
3987: A->ops->scale = MatScale_SeqAIJCUSPARSE;
3988: A->ops->axpy = MatAXPY_SeqAIJCUSPARSE;
3989: A->ops->zeroentries = MatZeroEntries_SeqAIJCUSPARSE;
3990: A->ops->mult = MatMult_SeqAIJCUSPARSE;
3991: A->ops->multadd = MatMultAdd_SeqAIJCUSPARSE;
3992: A->ops->multtranspose = MatMultTranspose_SeqAIJCUSPARSE;
3993: A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJCUSPARSE;
3994: A->ops->multhermitiantranspose = MatMultHermitianTranspose_SeqAIJCUSPARSE;
3995: A->ops->multhermitiantransposeadd = MatMultHermitianTransposeAdd_SeqAIJCUSPARSE;
3996: A->ops->productsetfromoptions = MatProductSetFromOptions_SeqAIJCUSPARSE;
3997: a->ops->getarray = MatSeqAIJGetArray_SeqAIJCUSPARSE;
3998: a->ops->restorearray = MatSeqAIJRestoreArray_SeqAIJCUSPARSE;
3999: a->ops->getarrayread = MatSeqAIJGetArrayRead_SeqAIJCUSPARSE;
4000: a->ops->restorearrayread = MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE;
4001: a->ops->getarraywrite = MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE;
4002: a->ops->restorearraywrite = MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE;
4003: a->ops->getcsrandmemtype = MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE;
4005: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", MatSeqAIJCopySubArray_SeqAIJCUSPARSE));
4006: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdensecuda_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4007: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqdense_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4008: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJCUSPARSE));
4009: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJCUSPARSE));
4010: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJCUSPARSE));
4011: }
4012: A->boundtocpu = flg;
4013: if (flg && a->inode.size) {
4014: a->inode.use = PETSC_TRUE;
4015: } else {
4016: a->inode.use = PETSC_FALSE;
4017: }
4018: PetscFunctionReturn(PETSC_SUCCESS);
4019: }
4021: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat A, MatType, MatReuse reuse, Mat *newmat)
4022: {
4023: Mat B;
4025: PetscFunctionBegin;
4026: PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUDA)); /* first use of CUSPARSE may be via MatConvert */
4027: if (reuse == MAT_INITIAL_MATRIX) {
4028: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
4029: } else if (reuse == MAT_REUSE_MATRIX) {
4030: PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN));
4031: }
4032: B = *newmat;
4034: PetscCall(PetscFree(B->defaultvectype));
4035: PetscCall(PetscStrallocpy(VECCUDA, &B->defaultvectype));
4037: if (reuse != MAT_REUSE_MATRIX && !B->spptr) {
4038: if (B->factortype == MAT_FACTOR_NONE) {
4039: Mat_SeqAIJCUSPARSE *spptr;
4040: PetscCall(PetscNew(&spptr));
4041: PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4042: PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4043: spptr->format = MAT_CUSPARSE_CSR;
4044: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4045: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4046: spptr->spmvAlg = CUSPARSE_SPMV_CSR_ALG1; /* default, since we only support csr */
4047: #else
4048: spptr->spmvAlg = CUSPARSE_CSRMV_ALG1; /* default, since we only support csr */
4049: #endif
4050: spptr->spmmAlg = CUSPARSE_SPMM_CSR_ALG1; /* default, only support column-major dense matrix B */
4051: spptr->csr2cscAlg = CUSPARSE_CSR2CSC_ALG1;
4052: #endif
4053: B->spptr = spptr;
4054: } else {
4055: Mat_SeqAIJCUSPARSETriFactors *spptr;
4057: PetscCall(PetscNew(&spptr));
4058: PetscCallCUSPARSE(cusparseCreate(&spptr->handle));
4059: PetscCallCUSPARSE(cusparseSetStream(spptr->handle, PetscDefaultCudaStream));
4060: B->spptr = spptr;
4061: }
4062: B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
4063: }
4064: B->ops->assemblyend = MatAssemblyEnd_SeqAIJCUSPARSE;
4065: B->ops->destroy = MatDestroy_SeqAIJCUSPARSE;
4066: B->ops->setoption = MatSetOption_SeqAIJCUSPARSE;
4067: B->ops->setfromoptions = MatSetFromOptions_SeqAIJCUSPARSE;
4068: B->ops->bindtocpu = MatBindToCPU_SeqAIJCUSPARSE;
4069: B->ops->duplicate = MatDuplicate_SeqAIJCUSPARSE;
4071: PetscCall(MatBindToCPU_SeqAIJCUSPARSE(B, PETSC_FALSE));
4072: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJCUSPARSE));
4073: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetFormat_C", MatCUSPARSESetFormat_SeqAIJCUSPARSE));
4074: #if defined(PETSC_HAVE_HYPRE)
4075: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaijcusparse_hypre_C", MatConvert_AIJ_HYPRE));
4076: #endif
4077: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatCUSPARSESetUseCPUSolve_C", MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE));
4078: PetscFunctionReturn(PETSC_SUCCESS);
4079: }
4081: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJCUSPARSE(Mat B)
4082: {
4083: PetscFunctionBegin;
4084: PetscCall(MatCreate_SeqAIJ(B));
4085: PetscCall(MatConvert_SeqAIJ_SeqAIJCUSPARSE(B, MATSEQAIJCUSPARSE, MAT_INPLACE_MATRIX, &B));
4086: PetscFunctionReturn(PETSC_SUCCESS);
4087: }
4089: /*MC
4090: MATSEQAIJCUSPARSE - MATAIJCUSPARSE = "(seq)aijcusparse" - A matrix type to be used for sparse matrices.
4092: A matrix type whose data resides on NVIDIA GPUs. These matrices can be in either
4093: CSR, ELL, or Hybrid format.
4094: All matrix calculations are performed on NVIDIA GPUs using the CuSPARSE library.
4096: Options Database Keys:
4097: + -mat_type aijcusparse - sets the matrix type to "seqaijcusparse" during a call to `MatSetFromOptions()`
4098: . -mat_cusparse_storage_format csr - sets the storage format of matrices (for `MatMult()` and factors in `MatSolve()`).
4099: Other options include ell (ellpack) or hyb (hybrid).
4100: . -mat_cusparse_mult_storage_format csr - sets the storage format of matrices (for `MatMult()`). Other options include ell (ellpack) or hyb (hybrid).
4101: - -mat_cusparse_use_cpu_solve - Do `MatSolve()` on CPU
4103: Level: beginner
4105: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJCUSPARSE()`, `MatCUSPARSESetUseCPUSolve()`, `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
4106: M*/
4108: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_CUSPARSE(void)
4109: {
4110: PetscFunctionBegin;
4111: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_LU, MatGetFactor_seqaijcusparse_cusparse));
4112: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_CHOLESKY, MatGetFactor_seqaijcusparse_cusparse));
4113: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ILU, MatGetFactor_seqaijcusparse_cusparse));
4114: PetscCall(MatSolverTypeRegister(MATSOLVERCUSPARSE, MATSEQAIJCUSPARSE, MAT_FACTOR_ICC, MatGetFactor_seqaijcusparse_cusparse));
4115: PetscFunctionReturn(PETSC_SUCCESS);
4116: }
4118: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat mat)
4119: {
4120: Mat_SeqAIJCUSPARSE *cusp = static_cast<Mat_SeqAIJCUSPARSE *>(mat->spptr);
4122: PetscFunctionBegin;
4123: if (cusp) {
4124: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->mat, cusp->format));
4125: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4126: delete cusp->workVector;
4127: delete cusp->rowoffsets_gpu;
4128: delete cusp->csr2csc_i;
4129: delete cusp->coords;
4130: if (cusp->handle) PetscCallCUSPARSE(cusparseDestroy(cusp->handle));
4131: PetscCall(PetscFree(mat->spptr));
4132: }
4133: PetscFunctionReturn(PETSC_SUCCESS);
4134: }
4136: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **mat)
4137: {
4138: PetscFunctionBegin;
4139: if (*mat) {
4140: delete (*mat)->values;
4141: delete (*mat)->column_indices;
4142: delete (*mat)->row_offsets;
4143: delete *mat;
4144: *mat = 0;
4145: }
4146: PetscFunctionReturn(PETSC_SUCCESS);
4147: }
4149: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4150: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **trifactor)
4151: {
4152: PetscFunctionBegin;
4153: if (*trifactor) {
4154: if ((*trifactor)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*trifactor)->descr));
4155: if ((*trifactor)->solveInfo) PetscCallCUSPARSE(cusparseDestroyCsrsvInfo((*trifactor)->solveInfo));
4156: PetscCall(CsrMatrix_Destroy(&(*trifactor)->csrMat));
4157: if ((*trifactor)->solveBuffer) PetscCallCUDA(cudaFree((*trifactor)->solveBuffer));
4158: if ((*trifactor)->AA_h) PetscCallCUDA(cudaFreeHost((*trifactor)->AA_h));
4159: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4160: if ((*trifactor)->csr2cscBuffer) PetscCallCUDA(cudaFree((*trifactor)->csr2cscBuffer));
4161: #endif
4162: PetscCall(PetscFree(*trifactor));
4163: }
4164: PetscFunctionReturn(PETSC_SUCCESS);
4165: }
4166: #endif
4168: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **matstruct, MatCUSPARSEStorageFormat format)
4169: {
4170: CsrMatrix *mat;
4172: PetscFunctionBegin;
4173: if (*matstruct) {
4174: if ((*matstruct)->mat) {
4175: if (format == MAT_CUSPARSE_ELL || format == MAT_CUSPARSE_HYB) {
4176: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4177: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
4178: #else
4179: cusparseHybMat_t hybMat = (cusparseHybMat_t)(*matstruct)->mat;
4180: PetscCallCUSPARSE(cusparseDestroyHybMat(hybMat));
4181: #endif
4182: } else {
4183: mat = (CsrMatrix *)(*matstruct)->mat;
4184: PetscCall(CsrMatrix_Destroy(&mat));
4185: }
4186: }
4187: if ((*matstruct)->descr) PetscCallCUSPARSE(cusparseDestroyMatDescr((*matstruct)->descr));
4188: delete (*matstruct)->cprowIndices;
4189: if ((*matstruct)->alpha_one) PetscCallCUDA(cudaFree((*matstruct)->alpha_one));
4190: if ((*matstruct)->beta_zero) PetscCallCUDA(cudaFree((*matstruct)->beta_zero));
4191: if ((*matstruct)->beta_one) PetscCallCUDA(cudaFree((*matstruct)->beta_one));
4193: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4194: Mat_SeqAIJCUSPARSEMultStruct *mdata = *matstruct;
4195: if (mdata->matDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr));
4197: for (int i = 0; i < 3; i++) {
4198: if (mdata->cuSpMV[i].initialized) {
4199: PetscCallCUDA(cudaFree(mdata->cuSpMV[i].spmvBuffer));
4200: PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecXDescr));
4201: PetscCallCUSPARSE(cusparseDestroyDnVec(mdata->cuSpMV[i].vecYDescr));
4202: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
4203: if (mdata->matDescr_SpMV[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMV[i]));
4204: if (mdata->matDescr_SpMM[i]) PetscCallCUSPARSE(cusparseDestroySpMat(mdata->matDescr_SpMM[i]));
4205: #endif
4206: }
4207: }
4208: #endif
4209: delete *matstruct;
4210: *matstruct = NULL;
4211: }
4212: PetscFunctionReturn(PETSC_SUCCESS);
4213: }
4215: PetscErrorCode MatSeqAIJCUSPARSETriFactors_Reset(Mat_SeqAIJCUSPARSETriFactors_p *trifactors)
4216: {
4217: Mat_SeqAIJCUSPARSETriFactors *fs = *trifactors;
4219: PetscFunctionBegin;
4220: if (fs) {
4221: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
4222: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtr));
4223: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtr));
4224: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->loTriFactorPtrTranspose));
4225: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&fs->upTriFactorPtrTranspose));
4226: delete fs->workVector;
4227: fs->workVector = NULL;
4228: #endif
4229: delete fs->rpermIndices;
4230: delete fs->cpermIndices;
4231: fs->rpermIndices = NULL;
4232: fs->cpermIndices = NULL;
4233: fs->init_dev_prop = PETSC_FALSE;
4234: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
4235: PetscCallCUDA(cudaFree(fs->csrRowPtr));
4236: PetscCallCUDA(cudaFree(fs->csrColIdx));
4237: PetscCallCUDA(cudaFree(fs->csrRowPtr32));
4238: PetscCallCUDA(cudaFree(fs->csrColIdx32));
4239: PetscCallCUDA(cudaFree(fs->csrVal));
4240: PetscCallCUDA(cudaFree(fs->diag));
4241: PetscCallCUDA(cudaFree(fs->X));
4242: PetscCallCUDA(cudaFree(fs->Y));
4243: // PetscCallCUDA(cudaFree(fs->factBuffer_M)); /* No needed since factBuffer_M shares with one of spsvBuffer_L/U */
4244: PetscCallCUDA(cudaFree(fs->spsvBuffer_L));
4245: PetscCallCUDA(cudaFree(fs->spsvBuffer_U));
4246: PetscCallCUDA(cudaFree(fs->spsvBuffer_Lt));
4247: PetscCallCUDA(cudaFree(fs->spsvBuffer_Ut));
4248: PetscCallCUSPARSE(cusparseDestroyMatDescr(fs->matDescr_M));
4249: PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_L));
4250: PetscCallCUSPARSE(cusparseDestroySpMat(fs->spMatDescr_U));
4251: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_L));
4252: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Lt));
4253: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_U));
4254: PetscCallCUSPARSE(cusparseSpSV_destroyDescr(fs->spsvDescr_Ut));
4255: PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_X));
4256: PetscCallCUSPARSE(cusparseDestroyDnVec(fs->dnVecDescr_Y));
4257: PetscCallCUSPARSE(cusparseDestroyCsrilu02Info(fs->ilu0Info_M));
4258: PetscCallCUSPARSE(cusparseDestroyCsric02Info(fs->ic0Info_M));
4259: PetscCall(PetscFree(fs->csrRowPtr_h));
4260: PetscCall(PetscFree(fs->csrVal_h));
4261: PetscCall(PetscFree(fs->diag_h));
4262: fs->createdTransposeSpSVDescr = PETSC_FALSE;
4263: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
4264: #endif
4265: }
4266: PetscFunctionReturn(PETSC_SUCCESS);
4267: }
4269: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **trifactors)
4270: {
4271: PetscFunctionBegin;
4272: if (*trifactors) {
4273: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(trifactors));
4274: PetscCallCUSPARSE(cusparseDestroy((*trifactors)->handle));
4275: PetscCall(PetscFree(*trifactors));
4276: }
4277: PetscFunctionReturn(PETSC_SUCCESS);
4278: }
4280: struct IJCompare {
4281: __host__ __device__ inline bool operator()(const thrust::tuple<PetscInt, PetscInt> &t1, const thrust::tuple<PetscInt, PetscInt> &t2)
4282: {
4283: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4284: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4285: return false;
4286: }
4287: };
4289: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat A, PetscBool destroy)
4290: {
4291: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4293: PetscFunctionBegin;
4294: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4295: if (!cusp) PetscFunctionReturn(PETSC_SUCCESS);
4296: if (destroy) {
4297: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusp->matTranspose, cusp->format));
4298: delete cusp->csr2csc_i;
4299: cusp->csr2csc_i = NULL;
4300: }
4301: A->transupdated = PETSC_FALSE;
4302: PetscFunctionReturn(PETSC_SUCCESS);
4303: }
4305: static PetscErrorCode MatCOOStructDestroy_SeqAIJCUSPARSE(void *data)
4306: {
4307: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;
4309: PetscFunctionBegin;
4310: PetscCallCUDA(cudaFree(coo->perm));
4311: PetscCallCUDA(cudaFree(coo->jmap));
4312: PetscCall(PetscFree(coo));
4313: PetscFunctionReturn(PETSC_SUCCESS);
4314: }
4316: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4317: {
4318: PetscBool dev_ij = PETSC_FALSE;
4319: PetscMemType mtype = PETSC_MEMTYPE_HOST;
4320: PetscInt *i, *j;
4321: PetscContainer container_h, container_d;
4322: MatCOOStruct_SeqAIJ *coo_h, *coo_d;
4324: PetscFunctionBegin;
4325: // The two MatResetPreallocationCOO_* must be done in order. The former relies on values that might be destroyed by the latter
4326: PetscCall(PetscGetMemType(coo_i, &mtype));
4327: if (PetscMemTypeDevice(mtype)) {
4328: dev_ij = PETSC_TRUE;
4329: PetscCall(PetscMalloc2(coo_n, &i, coo_n, &j));
4330: PetscCallCUDA(cudaMemcpy(i, coo_i, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4331: PetscCallCUDA(cudaMemcpy(j, coo_j, coo_n * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4332: } else {
4333: i = coo_i;
4334: j = coo_j;
4335: }
4337: PetscCall(MatSetPreallocationCOO_SeqAIJ(mat, coo_n, i, j));
4338: if (dev_ij) PetscCall(PetscFree2(i, j));
4339: mat->offloadmask = PETSC_OFFLOAD_CPU;
4340: // Create the GPU memory
4341: PetscCall(MatSeqAIJCUSPARSECopyToGPU(mat));
4343: // Copy the COO struct to device
4344: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
4345: PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
4346: PetscCall(PetscMalloc1(1, &coo_d));
4347: *coo_d = *coo_h; // do a shallow copy and then amend some fields that need to be different
4348: PetscCallCUDA(cudaMalloc((void **)&coo_d->jmap, (coo_h->nz + 1) * sizeof(PetscCount)));
4349: PetscCallCUDA(cudaMemcpy(coo_d->jmap, coo_h->jmap, (coo_h->nz + 1) * sizeof(PetscCount), cudaMemcpyHostToDevice));
4350: PetscCallCUDA(cudaMalloc((void **)&coo_d->perm, coo_h->Atot * sizeof(PetscCount)));
4351: PetscCallCUDA(cudaMemcpy(coo_d->perm, coo_h->perm, coo_h->Atot * sizeof(PetscCount), cudaMemcpyHostToDevice));
4353: // Put the COO struct in a container and then attach that to the matrix
4354: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container_d));
4355: PetscCall(PetscContainerSetPointer(container_d, coo_d));
4356: PetscCall(PetscContainerSetUserDestroy(container_d, MatCOOStructDestroy_SeqAIJCUSPARSE));
4357: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject)container_d));
4358: PetscCall(PetscContainerDestroy(&container_d));
4359: PetscFunctionReturn(PETSC_SUCCESS);
4360: }
4362: __global__ static void MatAddCOOValues(const PetscScalar kv[], PetscCount nnz, const PetscCount jmap[], const PetscCount perm[], InsertMode imode, PetscScalar a[])
4363: {
4364: PetscCount i = blockIdx.x * blockDim.x + threadIdx.x;
4365: const PetscCount grid_size = gridDim.x * blockDim.x;
4366: for (; i < nnz; i += grid_size) {
4367: PetscScalar sum = 0.0;
4368: for (PetscCount k = jmap[i]; k < jmap[i + 1]; k++) sum += kv[perm[k]];
4369: a[i] = (imode == INSERT_VALUES ? 0.0 : a[i]) + sum;
4370: }
4371: }
4373: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat A, const PetscScalar v[], InsertMode imode)
4374: {
4375: Mat_SeqAIJ *seq = (Mat_SeqAIJ *)A->data;
4376: Mat_SeqAIJCUSPARSE *dev = (Mat_SeqAIJCUSPARSE *)A->spptr;
4377: PetscCount Annz = seq->nz;
4378: PetscMemType memtype;
4379: const PetscScalar *v1 = v;
4380: PetscScalar *Aa;
4381: PetscContainer container;
4382: MatCOOStruct_SeqAIJ *coo;
4384: PetscFunctionBegin;
4385: if (!dev->mat) PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4387: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
4388: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4390: PetscCall(PetscGetMemType(v, &memtype));
4391: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
4392: PetscCallCUDA(cudaMalloc((void **)&v1, coo->n * sizeof(PetscScalar)));
4393: PetscCallCUDA(cudaMemcpy((void *)v1, v, coo->n * sizeof(PetscScalar), cudaMemcpyHostToDevice));
4394: }
4396: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSEGetArrayWrite(A, &Aa));
4397: else PetscCall(MatSeqAIJCUSPARSEGetArray(A, &Aa));
4399: PetscCall(PetscLogGpuTimeBegin());
4400: if (Annz) {
4401: MatAddCOOValues<<<(Annz + 255) / 256, 256>>>(v1, Annz, coo->jmap, coo->perm, imode, Aa);
4402: PetscCallCUDA(cudaPeekAtLastError());
4403: }
4404: PetscCall(PetscLogGpuTimeEnd());
4406: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSERestoreArrayWrite(A, &Aa));
4407: else PetscCall(MatSeqAIJCUSPARSERestoreArray(A, &Aa));
4409: if (PetscMemTypeHost(memtype)) PetscCallCUDA(cudaFree((void *)v1));
4410: PetscFunctionReturn(PETSC_SUCCESS);
4411: }
4413: /*@C
4414: MatSeqAIJCUSPARSEGetIJ - returns the device row storage `i` and `j` indices for `MATSEQAIJCUSPARSE` matrices.
4416: Not Collective
4418: Input Parameters:
4419: + A - the matrix
4420: - compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4422: Output Parameters:
4423: + i - the CSR row pointers
4424: - j - the CSR column indices
4426: Level: developer
4428: Note:
4429: When compressed is true, the CSR structure does not contain empty rows
4431: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSERestoreIJ()`, `MatSeqAIJCUSPARSEGetArrayRead()`
4432: @*/
4433: PetscErrorCode MatSeqAIJCUSPARSEGetIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4434: {
4435: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4436: CsrMatrix *csr;
4437: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4439: PetscFunctionBegin;
4441: if (!i || !j) PetscFunctionReturn(PETSC_SUCCESS);
4442: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4443: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4444: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4445: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4446: csr = (CsrMatrix *)cusp->mat->mat;
4447: if (i) {
4448: if (!compressed && a->compressedrow.use) { /* need full row offset */
4449: if (!cusp->rowoffsets_gpu) {
4450: cusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4451: cusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4452: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4453: }
4454: *i = cusp->rowoffsets_gpu->data().get();
4455: } else *i = csr->row_offsets->data().get();
4456: }
4457: if (j) *j = csr->column_indices->data().get();
4458: PetscFunctionReturn(PETSC_SUCCESS);
4459: }
4461: /*@C
4462: MatSeqAIJCUSPARSERestoreIJ - restore the device row storage `i` and `j` indices obtained with `MatSeqAIJCUSPARSEGetIJ()`
4464: Not Collective
4466: Input Parameters:
4467: + A - the matrix
4468: . compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4469: . i - the CSR row pointers
4470: - j - the CSR column indices
4472: Level: developer
4474: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetIJ()`
4475: @*/
4476: PetscErrorCode MatSeqAIJCUSPARSERestoreIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4477: {
4478: PetscFunctionBegin;
4480: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4481: if (i) *i = NULL;
4482: if (j) *j = NULL;
4483: (void)compressed;
4484: PetscFunctionReturn(PETSC_SUCCESS);
4485: }
4487: /*@C
4488: MatSeqAIJCUSPARSEGetArrayRead - gives read-only access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4490: Not Collective
4492: Input Parameter:
4493: . A - a `MATSEQAIJCUSPARSE` matrix
4495: Output Parameter:
4496: . a - pointer to the device data
4498: Level: developer
4500: Note:
4501: May trigger host-device copies if up-to-date matrix data is on host
4503: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArrayRead()`
4504: @*/
4505: PetscErrorCode MatSeqAIJCUSPARSEGetArrayRead(Mat A, const PetscScalar **a)
4506: {
4507: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4508: CsrMatrix *csr;
4510: PetscFunctionBegin;
4512: PetscAssertPointer(a, 2);
4513: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4514: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4515: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4516: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4517: csr = (CsrMatrix *)cusp->mat->mat;
4518: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4519: *a = csr->values->data().get();
4520: PetscFunctionReturn(PETSC_SUCCESS);
4521: }
4523: /*@C
4524: MatSeqAIJCUSPARSERestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJCUSPARSEGetArrayRead()`
4526: Not Collective
4528: Input Parameters:
4529: + A - a `MATSEQAIJCUSPARSE` matrix
4530: - a - pointer to the device data
4532: Level: developer
4534: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`
4535: @*/
4536: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayRead(Mat A, const PetscScalar **a)
4537: {
4538: PetscFunctionBegin;
4540: PetscAssertPointer(a, 2);
4541: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4542: *a = NULL;
4543: PetscFunctionReturn(PETSC_SUCCESS);
4544: }
4546: /*@C
4547: MatSeqAIJCUSPARSEGetArray - gives read-write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4549: Not Collective
4551: Input Parameter:
4552: . A - a `MATSEQAIJCUSPARSE` matrix
4554: Output Parameter:
4555: . a - pointer to the device data
4557: Level: developer
4559: Note:
4560: May trigger host-device copies if up-to-date matrix data is on host
4562: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArray()`
4563: @*/
4564: PetscErrorCode MatSeqAIJCUSPARSEGetArray(Mat A, PetscScalar **a)
4565: {
4566: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4567: CsrMatrix *csr;
4569: PetscFunctionBegin;
4571: PetscAssertPointer(a, 2);
4572: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4573: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4574: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4575: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4576: csr = (CsrMatrix *)cusp->mat->mat;
4577: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4578: *a = csr->values->data().get();
4579: A->offloadmask = PETSC_OFFLOAD_GPU;
4580: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4581: PetscFunctionReturn(PETSC_SUCCESS);
4582: }
4583: /*@C
4584: MatSeqAIJCUSPARSERestoreArray - restore the read-write access array obtained from `MatSeqAIJCUSPARSEGetArray()`
4586: Not Collective
4588: Input Parameters:
4589: + A - a `MATSEQAIJCUSPARSE` matrix
4590: - a - pointer to the device data
4592: Level: developer
4594: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`
4595: @*/
4596: PetscErrorCode MatSeqAIJCUSPARSERestoreArray(Mat A, PetscScalar **a)
4597: {
4598: PetscFunctionBegin;
4600: PetscAssertPointer(a, 2);
4601: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4602: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4603: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4604: *a = NULL;
4605: PetscFunctionReturn(PETSC_SUCCESS);
4606: }
4608: /*@C
4609: MatSeqAIJCUSPARSEGetArrayWrite - gives write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4611: Not Collective
4613: Input Parameter:
4614: . A - a `MATSEQAIJCUSPARSE` matrix
4616: Output Parameter:
4617: . a - pointer to the device data
4619: Level: developer
4621: Note:
4622: Does not trigger host-device copies and flags data validity on the GPU
4624: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSERestoreArrayWrite()`
4625: @*/
4626: PetscErrorCode MatSeqAIJCUSPARSEGetArrayWrite(Mat A, PetscScalar **a)
4627: {
4628: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4629: CsrMatrix *csr;
4631: PetscFunctionBegin;
4633: PetscAssertPointer(a, 2);
4634: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4635: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4636: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4637: csr = (CsrMatrix *)cusp->mat->mat;
4638: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4639: *a = csr->values->data().get();
4640: A->offloadmask = PETSC_OFFLOAD_GPU;
4641: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4642: PetscFunctionReturn(PETSC_SUCCESS);
4643: }
4645: /*@C
4646: MatSeqAIJCUSPARSERestoreArrayWrite - restore the write-only access array obtained from `MatSeqAIJCUSPARSEGetArrayWrite()`
4648: Not Collective
4650: Input Parameters:
4651: + A - a `MATSEQAIJCUSPARSE` matrix
4652: - a - pointer to the device data
4654: Level: developer
4656: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayWrite()`
4657: @*/
4658: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayWrite(Mat A, PetscScalar **a)
4659: {
4660: PetscFunctionBegin;
4662: PetscAssertPointer(a, 2);
4663: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4664: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4665: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4666: *a = NULL;
4667: PetscFunctionReturn(PETSC_SUCCESS);
4668: }
4670: struct IJCompare4 {
4671: __host__ __device__ inline bool operator()(const thrust::tuple<int, int, PetscScalar, int> &t1, const thrust::tuple<int, int, PetscScalar, int> &t2)
4672: {
4673: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4674: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4675: return false;
4676: }
4677: };
4679: struct Shift {
4680: int _shift;
4682: Shift(int shift) : _shift(shift) { }
4683: __host__ __device__ inline int operator()(const int &c) { return c + _shift; }
4684: };
4686: /* merges two SeqAIJCUSPARSE matrices A, B by concatenating their rows. [A';B']' operation in MATLAB notation */
4687: PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
4688: {
4689: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data, *c;
4690: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr, *Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr, *Ccusp;
4691: Mat_SeqAIJCUSPARSEMultStruct *Cmat;
4692: CsrMatrix *Acsr, *Bcsr, *Ccsr;
4693: PetscInt Annz, Bnnz;
4694: cusparseStatus_t stat;
4695: PetscInt i, m, n, zero = 0;
4697: PetscFunctionBegin;
4700: PetscAssertPointer(C, 4);
4701: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4702: PetscCheckTypeName(B, MATSEQAIJCUSPARSE);
4703: PetscCheck(A->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Invalid number or rows %" PetscInt_FMT " != %" PetscInt_FMT, A->rmap->n, B->rmap->n);
4704: PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
4705: PetscCheck(Acusp->format != MAT_CUSPARSE_ELL && Acusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4706: PetscCheck(Bcusp->format != MAT_CUSPARSE_ELL && Bcusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4707: if (reuse == MAT_INITIAL_MATRIX) {
4708: m = A->rmap->n;
4709: n = A->cmap->n + B->cmap->n;
4710: PetscCall(MatCreate(PETSC_COMM_SELF, C));
4711: PetscCall(MatSetSizes(*C, m, n, m, n));
4712: PetscCall(MatSetType(*C, MATSEQAIJCUSPARSE));
4713: c = (Mat_SeqAIJ *)(*C)->data;
4714: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4715: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
4716: Ccsr = new CsrMatrix;
4717: Cmat->cprowIndices = NULL;
4718: c->compressedrow.use = PETSC_FALSE;
4719: c->compressedrow.nrows = 0;
4720: c->compressedrow.i = NULL;
4721: c->compressedrow.rindex = NULL;
4722: Ccusp->workVector = NULL;
4723: Ccusp->nrows = m;
4724: Ccusp->mat = Cmat;
4725: Ccusp->mat->mat = Ccsr;
4726: Ccsr->num_rows = m;
4727: Ccsr->num_cols = n;
4728: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
4729: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
4730: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4731: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
4732: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
4733: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
4734: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4735: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4736: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4737: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4738: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4739: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4740: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4742: Acsr = (CsrMatrix *)Acusp->mat->mat;
4743: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4744: Annz = (PetscInt)Acsr->column_indices->size();
4745: Bnnz = (PetscInt)Bcsr->column_indices->size();
4746: c->nz = Annz + Bnnz;
4747: Ccsr->row_offsets = new THRUSTINTARRAY32(m + 1);
4748: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
4749: Ccsr->values = new THRUSTARRAY(c->nz);
4750: Ccsr->num_entries = c->nz;
4751: Ccusp->coords = new THRUSTINTARRAY(c->nz);
4752: if (c->nz) {
4753: auto Acoo = new THRUSTINTARRAY32(Annz);
4754: auto Bcoo = new THRUSTINTARRAY32(Bnnz);
4755: auto Ccoo = new THRUSTINTARRAY32(c->nz);
4756: THRUSTINTARRAY32 *Aroff, *Broff;
4758: if (a->compressedrow.use) { /* need full row offset */
4759: if (!Acusp->rowoffsets_gpu) {
4760: Acusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4761: Acusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4762: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4763: }
4764: Aroff = Acusp->rowoffsets_gpu;
4765: } else Aroff = Acsr->row_offsets;
4766: if (b->compressedrow.use) { /* need full row offset */
4767: if (!Bcusp->rowoffsets_gpu) {
4768: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
4769: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
4770: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
4771: }
4772: Broff = Bcusp->rowoffsets_gpu;
4773: } else Broff = Bcsr->row_offsets;
4774: PetscCall(PetscLogGpuTimeBegin());
4775: stat = cusparseXcsr2coo(Acusp->handle, Aroff->data().get(), Annz, m, Acoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4776: PetscCallCUSPARSE(stat);
4777: stat = cusparseXcsr2coo(Bcusp->handle, Broff->data().get(), Bnnz, m, Bcoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4778: PetscCallCUSPARSE(stat);
4779: /* Issues when using bool with large matrices on SUMMIT 10.2.89 */
4780: auto Aperm = thrust::make_constant_iterator(1);
4781: auto Bperm = thrust::make_constant_iterator(0);
4782: #if PETSC_PKG_CUDA_VERSION_GE(10, 0, 0)
4783: auto Bcib = thrust::make_transform_iterator(Bcsr->column_indices->begin(), Shift(A->cmap->n));
4784: auto Bcie = thrust::make_transform_iterator(Bcsr->column_indices->end(), Shift(A->cmap->n));
4785: #else
4786: /* there are issues instantiating the merge operation using a transform iterator for the columns of B */
4787: auto Bcib = Bcsr->column_indices->begin();
4788: auto Bcie = Bcsr->column_indices->end();
4789: thrust::transform(Bcib, Bcie, Bcib, Shift(A->cmap->n));
4790: #endif
4791: auto wPerm = new THRUSTINTARRAY32(Annz + Bnnz);
4792: auto Azb = thrust::make_zip_iterator(thrust::make_tuple(Acoo->begin(), Acsr->column_indices->begin(), Acsr->values->begin(), Aperm));
4793: auto Aze = thrust::make_zip_iterator(thrust::make_tuple(Acoo->end(), Acsr->column_indices->end(), Acsr->values->end(), Aperm));
4794: auto Bzb = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->begin(), Bcib, Bcsr->values->begin(), Bperm));
4795: auto Bze = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->end(), Bcie, Bcsr->values->end(), Bperm));
4796: auto Czb = thrust::make_zip_iterator(thrust::make_tuple(Ccoo->begin(), Ccsr->column_indices->begin(), Ccsr->values->begin(), wPerm->begin()));
4797: auto p1 = Ccusp->coords->begin();
4798: auto p2 = Ccusp->coords->begin();
4799: thrust::advance(p2, Annz);
4800: PetscCallThrust(thrust::merge(thrust::device, Azb, Aze, Bzb, Bze, Czb, IJCompare4()));
4801: #if PETSC_PKG_CUDA_VERSION_LT(10, 0, 0)
4802: thrust::transform(Bcib, Bcie, Bcib, Shift(-A->cmap->n));
4803: #endif
4804: auto cci = thrust::make_counting_iterator(zero);
4805: auto cce = thrust::make_counting_iterator(c->nz);
4806: #if 0 //Errors on SUMMIT cuda 11.1.0
4807: PetscCallThrust(thrust::partition_copy(thrust::device,cci,cce,wPerm->begin(),p1,p2,thrust::identity<int>()));
4808: #else
4809: auto pred = thrust::identity<int>();
4810: PetscCallThrust(thrust::copy_if(thrust::device, cci, cce, wPerm->begin(), p1, pred));
4811: PetscCallThrust(thrust::remove_copy_if(thrust::device, cci, cce, wPerm->begin(), p2, pred));
4812: #endif
4813: stat = cusparseXcoo2csr(Ccusp->handle, Ccoo->data().get(), c->nz, m, Ccsr->row_offsets->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4814: PetscCallCUSPARSE(stat);
4815: PetscCall(PetscLogGpuTimeEnd());
4816: delete wPerm;
4817: delete Acoo;
4818: delete Bcoo;
4819: delete Ccoo;
4820: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4821: 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);
4822: PetscCallCUSPARSE(stat);
4823: #endif
4824: if (A->form_explicit_transpose && B->form_explicit_transpose) { /* if A and B have the transpose, generate C transpose too */
4825: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
4826: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
4827: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4828: Mat_SeqAIJCUSPARSEMultStruct *CmatT = new Mat_SeqAIJCUSPARSEMultStruct;
4829: CsrMatrix *CcsrT = new CsrMatrix;
4830: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4831: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4833: (*C)->form_explicit_transpose = PETSC_TRUE;
4834: (*C)->transupdated = PETSC_TRUE;
4835: Ccusp->rowoffsets_gpu = NULL;
4836: CmatT->cprowIndices = NULL;
4837: CmatT->mat = CcsrT;
4838: CcsrT->num_rows = n;
4839: CcsrT->num_cols = m;
4840: CcsrT->num_entries = c->nz;
4842: CcsrT->row_offsets = new THRUSTINTARRAY32(n + 1);
4843: CcsrT->column_indices = new THRUSTINTARRAY32(c->nz);
4844: CcsrT->values = new THRUSTARRAY(c->nz);
4846: PetscCall(PetscLogGpuTimeBegin());
4847: auto rT = CcsrT->row_offsets->begin();
4848: if (AT) {
4849: rT = thrust::copy(AcsrT->row_offsets->begin(), AcsrT->row_offsets->end(), rT);
4850: thrust::advance(rT, -1);
4851: }
4852: if (BT) {
4853: auto titb = thrust::make_transform_iterator(BcsrT->row_offsets->begin(), Shift(a->nz));
4854: auto tite = thrust::make_transform_iterator(BcsrT->row_offsets->end(), Shift(a->nz));
4855: thrust::copy(titb, tite, rT);
4856: }
4857: auto cT = CcsrT->column_indices->begin();
4858: if (AT) cT = thrust::copy(AcsrT->column_indices->begin(), AcsrT->column_indices->end(), cT);
4859: if (BT) thrust::copy(BcsrT->column_indices->begin(), BcsrT->column_indices->end(), cT);
4860: auto vT = CcsrT->values->begin();
4861: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4862: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4863: PetscCall(PetscLogGpuTimeEnd());
4865: PetscCallCUSPARSE(cusparseCreateMatDescr(&CmatT->descr));
4866: PetscCallCUSPARSE(cusparseSetMatIndexBase(CmatT->descr, CUSPARSE_INDEX_BASE_ZERO));
4867: PetscCallCUSPARSE(cusparseSetMatType(CmatT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4868: PetscCallCUDA(cudaMalloc((void **)&CmatT->alpha_one, sizeof(PetscScalar)));
4869: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_zero, sizeof(PetscScalar)));
4870: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_one, sizeof(PetscScalar)));
4871: PetscCallCUDA(cudaMemcpy(CmatT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4872: PetscCallCUDA(cudaMemcpy(CmatT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4873: PetscCallCUDA(cudaMemcpy(CmatT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4874: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4875: 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);
4876: PetscCallCUSPARSE(stat);
4877: #endif
4878: Ccusp->matTranspose = CmatT;
4879: }
4880: }
4882: c->singlemalloc = PETSC_FALSE;
4883: c->free_a = PETSC_TRUE;
4884: c->free_ij = PETSC_TRUE;
4885: PetscCall(PetscMalloc1(m + 1, &c->i));
4886: PetscCall(PetscMalloc1(c->nz, &c->j));
4887: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
4888: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
4889: THRUSTINTARRAY jj(Ccsr->column_indices->size());
4890: ii = *Ccsr->row_offsets;
4891: jj = *Ccsr->column_indices;
4892: PetscCallCUDA(cudaMemcpy(c->i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4893: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4894: } else {
4895: PetscCallCUDA(cudaMemcpy(c->i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4896: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4897: }
4898: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
4899: PetscCall(PetscMalloc1(m, &c->ilen));
4900: PetscCall(PetscMalloc1(m, &c->imax));
4901: c->maxnz = c->nz;
4902: c->nonzerorowcnt = 0;
4903: c->rmax = 0;
4904: for (i = 0; i < m; i++) {
4905: const PetscInt nn = c->i[i + 1] - c->i[i];
4906: c->ilen[i] = c->imax[i] = nn;
4907: c->nonzerorowcnt += (PetscInt) !!nn;
4908: c->rmax = PetscMax(c->rmax, nn);
4909: }
4910: PetscCall(MatMarkDiagonal_SeqAIJ(*C));
4911: PetscCall(PetscMalloc1(c->nz, &c->a));
4912: (*C)->nonzerostate++;
4913: PetscCall(PetscLayoutSetUp((*C)->rmap));
4914: PetscCall(PetscLayoutSetUp((*C)->cmap));
4915: Ccusp->nonzerostate = (*C)->nonzerostate;
4916: (*C)->preallocated = PETSC_TRUE;
4917: } else {
4918: 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);
4919: c = (Mat_SeqAIJ *)(*C)->data;
4920: if (c->nz) {
4921: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4922: PetscCheck(Ccusp->coords, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing coords");
4923: PetscCheck(Ccusp->format != MAT_CUSPARSE_ELL && Ccusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4924: PetscCheck(Ccusp->nonzerostate == (*C)->nonzerostate, PETSC_COMM_SELF, PETSC_ERR_COR, "Wrong nonzerostate");
4925: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4926: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4927: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4928: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4929: Acsr = (CsrMatrix *)Acusp->mat->mat;
4930: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4931: Ccsr = (CsrMatrix *)Ccusp->mat->mat;
4932: 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());
4933: 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());
4934: 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());
4935: 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);
4936: 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());
4937: auto pmid = Ccusp->coords->begin();
4938: thrust::advance(pmid, Acsr->num_entries);
4939: PetscCall(PetscLogGpuTimeBegin());
4940: auto zibait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->begin())));
4941: auto zieait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4942: thrust::for_each(zibait, zieait, VecCUDAEquals());
4943: auto zibbit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4944: auto ziebit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->end())));
4945: thrust::for_each(zibbit, ziebit, VecCUDAEquals());
4946: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(*C, PETSC_FALSE));
4947: if (A->form_explicit_transpose && B->form_explicit_transpose && (*C)->form_explicit_transpose) {
4948: PetscCheck(Ccusp->matTranspose, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing transpose Mat_SeqAIJCUSPARSEMultStruct");
4949: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4950: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4951: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4952: CsrMatrix *CcsrT = (CsrMatrix *)Ccusp->matTranspose->mat;
4953: auto vT = CcsrT->values->begin();
4954: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4955: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4956: (*C)->transupdated = PETSC_TRUE;
4957: }
4958: PetscCall(PetscLogGpuTimeEnd());
4959: }
4960: }
4961: PetscCall(PetscObjectStateIncrease((PetscObject)*C));
4962: (*C)->assembled = PETSC_TRUE;
4963: (*C)->was_assembled = PETSC_FALSE;
4964: (*C)->offloadmask = PETSC_OFFLOAD_GPU;
4965: PetscFunctionReturn(PETSC_SUCCESS);
4966: }
4968: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
4969: {
4970: bool dmem;
4971: const PetscScalar *av;
4973: PetscFunctionBegin;
4974: dmem = isCudaMem(v);
4975: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(A, &av));
4976: if (n && idx) {
4977: THRUSTINTARRAY widx(n);
4978: widx.assign(idx, idx + n);
4979: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
4981: THRUSTARRAY *w = NULL;
4982: thrust::device_ptr<PetscScalar> dv;
4983: if (dmem) {
4984: dv = thrust::device_pointer_cast(v);
4985: } else {
4986: w = new THRUSTARRAY(n);
4987: dv = w->data();
4988: }
4989: thrust::device_ptr<const PetscScalar> dav = thrust::device_pointer_cast(av);
4991: auto zibit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.begin()), dv));
4992: auto zieit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.end()), dv + n));
4993: thrust::for_each(zibit, zieit, VecCUDAEquals());
4994: if (w) PetscCallCUDA(cudaMemcpy(v, w->data().get(), n * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
4995: delete w;
4996: } else {
4997: PetscCallCUDA(cudaMemcpy(v, av, n * sizeof(PetscScalar), dmem ? cudaMemcpyDeviceToDevice : cudaMemcpyDeviceToHost));
4998: }
4999: if (!dmem) PetscCall(PetscLogCpuToGpu(n * sizeof(PetscScalar)));
5000: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(A, &av));
5001: PetscFunctionReturn(PETSC_SUCCESS);
5002: }