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: PETSC_PRAGMA_DIAGNOSTIC_IGNORED_BEGIN("-Wdeprecated-declarations")
26: const char *const MatCUSPARSEStorageFormats[] = {"CSR", "ELL", "HYB", "MatCUSPARSEStorageFormat", "MAT_CUSPARSE_", 0};
27: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
28: /* The following are copied from cusparse.h in CUDA-11.0. In MatCUSPARSESpMVAlgorithms[] etc, we copy them in
29: 0-based integer value order, since we want to use PetscOptionsEnum() to parse user command line options for them.
31: typedef enum {
32: CUSPARSE_MV_ALG_DEFAULT = 0,
33: CUSPARSE_COOMV_ALG = 1,
34: CUSPARSE_CSRMV_ALG1 = 2,
35: CUSPARSE_CSRMV_ALG2 = 3
36: } cusparseSpMVAlg_t;
38: typedef enum {
39: CUSPARSE_MM_ALG_DEFAULT CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_ALG_DEFAULT) = 0,
40: CUSPARSE_COOMM_ALG1 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG1) = 1,
41: CUSPARSE_COOMM_ALG2 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG2) = 2,
42: CUSPARSE_COOMM_ALG3 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_COO_ALG3) = 3,
43: CUSPARSE_CSRMM_ALG1 CUSPARSE_DEPRECATED_ENUM(CUSPARSE_SPMM_CSR_ALG1) = 4,
44: CUSPARSE_SPMM_ALG_DEFAULT = 0,
45: CUSPARSE_SPMM_COO_ALG1 = 1,
46: CUSPARSE_SPMM_COO_ALG2 = 2,
47: CUSPARSE_SPMM_COO_ALG3 = 3,
48: CUSPARSE_SPMM_COO_ALG4 = 5,
49: CUSPARSE_SPMM_CSR_ALG1 = 4,
50: CUSPARSE_SPMM_CSR_ALG2 = 6,
51: } cusparseSpMMAlg_t;
53: typedef enum {
54: CUSPARSE_CSR2CSC_ALG1 = 1, // faster than V2 (in general), deterministic
55: CUSPARSE_CSR2CSC_ALG2 = 2 // low memory requirement, non-deterministic
56: } cusparseCsr2CscAlg_t;
57: */
58: const char *const MatCUSPARSESpMVAlgorithms[] = {"MV_ALG_DEFAULT", "COOMV_ALG", "CSRMV_ALG1", "CSRMV_ALG2", "cusparseSpMVAlg_t", "CUSPARSE_", 0};
59: const char *const MatCUSPARSESpMMAlgorithms[] = {"ALG_DEFAULT", "COO_ALG1", "COO_ALG2", "COO_ALG3", "CSR_ALG1", "COO_ALG4", "CSR_ALG2", "cusparseSpMMAlg_t", "CUSPARSE_SPMM_", 0};
60: const char *const MatCUSPARSECsr2CscAlgorithms[] = {"INVALID" /*cusparse does not have enum 0! We created one*/, "ALG1", "ALG2", "cusparseCsr2CscAlg_t", "CUSPARSE_CSR2CSC_", 0};
61: #endif
63: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
64: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, const MatFactorInfo *);
65: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat, Mat, const MatFactorInfo *);
66: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat, Mat, IS, IS, const MatFactorInfo *);
67: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
68: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat, Vec, Vec);
69: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
70: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
71: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat, Vec, Vec);
72: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSETriFactorStruct **);
73: #endif
74: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat, PetscOptionItems *PetscOptionsObject);
75: static PetscErrorCode MatAXPY_SeqAIJCUSPARSE(Mat, PetscScalar, Mat, MatStructure);
76: static PetscErrorCode MatScale_SeqAIJCUSPARSE(Mat, PetscScalar);
77: static PetscErrorCode MatMult_SeqAIJCUSPARSE(Mat, Vec, Vec);
78: static PetscErrorCode MatMultAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
79: static PetscErrorCode MatMultTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
80: static PetscErrorCode MatMultTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
81: static PetscErrorCode MatMultHermitianTranspose_SeqAIJCUSPARSE(Mat, Vec, Vec);
82: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec);
83: static PetscErrorCode MatMultAddKernel_SeqAIJCUSPARSE(Mat, Vec, Vec, Vec, PetscBool, PetscBool);
85: static PetscErrorCode CsrMatrix_Destroy(CsrMatrix **);
86: static PetscErrorCode MatSeqAIJCUSPARSEMultStruct_Destroy(Mat_SeqAIJCUSPARSEMultStruct **, MatCUSPARSEStorageFormat);
87: static PetscErrorCode MatSeqAIJCUSPARSETriFactors_Destroy(Mat_SeqAIJCUSPARSETriFactors **);
88: static PetscErrorCode MatSeqAIJCUSPARSE_Destroy(Mat);
90: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat);
91: static PetscErrorCode MatSeqAIJCUSPARSEInvalidateTranspose(Mat, PetscBool);
93: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat, PetscInt, const PetscInt[], PetscScalar[]);
94: static PetscErrorCode MatSetPreallocationCOO_SeqAIJCUSPARSE(Mat, PetscCount, PetscInt[], PetscInt[]);
95: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat, const PetscScalar[], InsertMode);
97: PETSC_INTERN PetscErrorCode MatCUSPARSESetFormat_SeqAIJCUSPARSE(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
98: {
99: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
101: PetscFunctionBegin;
102: switch (op) {
103: case MAT_CUSPARSE_MULT:
104: cusparsestruct->format = format;
105: break;
106: case MAT_CUSPARSE_ALL:
107: cusparsestruct->format = format;
108: break;
109: default:
110: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported operation %d for MatCUSPARSEFormatOperation. MAT_CUSPARSE_MULT and MAT_CUSPARSE_ALL are currently supported.", op);
111: }
112: PetscFunctionReturn(PETSC_SUCCESS);
113: }
115: /*@
116: MatCUSPARSESetFormat - Sets the storage format of `MATSEQCUSPARSE` matrices for a particular
117: operation. Only the `MatMult()` operation can use different GPU storage formats
119: Not Collective
121: Input Parameters:
122: + A - Matrix of type `MATSEQAIJCUSPARSE`
123: . op - `MatCUSPARSEFormatOperation`. `MATSEQAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT` and `MAT_CUSPARSE_ALL`.
124: `MATMPIAIJCUSPARSE` matrices support `MAT_CUSPARSE_MULT_DIAG`,`MAT_CUSPARSE_MULT_OFFDIAG`, and `MAT_CUSPARSE_ALL`.
125: - format - `MatCUSPARSEStorageFormat` (one of `MAT_CUSPARSE_CSR`, `MAT_CUSPARSE_ELL`, `MAT_CUSPARSE_HYB`.)
127: Level: intermediate
129: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
130: @*/
131: PetscErrorCode MatCUSPARSESetFormat(Mat A, MatCUSPARSEFormatOperation op, MatCUSPARSEStorageFormat format)
132: {
133: PetscFunctionBegin;
135: PetscTryMethod(A, "MatCUSPARSESetFormat_C", (Mat, MatCUSPARSEFormatOperation, MatCUSPARSEStorageFormat), (A, op, format));
136: PetscFunctionReturn(PETSC_SUCCESS);
137: }
139: PETSC_INTERN PetscErrorCode MatCUSPARSESetUseCPUSolve_SeqAIJCUSPARSE(Mat A, PetscBool use_cpu)
140: {
141: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
143: PetscFunctionBegin;
144: cusparsestruct->use_cpu_solve = use_cpu;
145: PetscFunctionReturn(PETSC_SUCCESS);
146: }
148: /*@
149: MatCUSPARSESetUseCPUSolve - Sets to use CPU `MatSolve()`.
151: Input Parameters:
152: + A - Matrix of type `MATSEQAIJCUSPARSE`
153: - use_cpu - set flag for using the built-in CPU `MatSolve()`
155: Level: intermediate
157: Note:
158: The cuSparse LU solver currently computes the factors with the built-in CPU method
159: 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.
160: This method to specify if the solve is done on the CPU or GPU (GPU is the default).
162: .seealso: [](ch_matrices), `Mat`, `MatSolve()`, `MATSEQAIJCUSPARSE`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
163: @*/
164: PetscErrorCode MatCUSPARSESetUseCPUSolve(Mat A, PetscBool use_cpu)
165: {
166: PetscFunctionBegin;
168: PetscTryMethod(A, "MatCUSPARSESetUseCPUSolve_C", (Mat, PetscBool), (A, use_cpu));
169: PetscFunctionReturn(PETSC_SUCCESS);
170: }
172: static PetscErrorCode MatSetOption_SeqAIJCUSPARSE(Mat A, MatOption op, PetscBool flg)
173: {
174: PetscFunctionBegin;
175: switch (op) {
176: case MAT_FORM_EXPLICIT_TRANSPOSE:
177: /* need to destroy the transpose matrix if present to prevent from logic errors if flg is set to true later */
178: if (A->form_explicit_transpose && !flg) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
179: A->form_explicit_transpose = flg;
180: break;
181: default:
182: PetscCall(MatSetOption_SeqAIJ(A, op, flg));
183: break;
184: }
185: PetscFunctionReturn(PETSC_SUCCESS);
186: }
188: static PetscErrorCode MatSetFromOptions_SeqAIJCUSPARSE(Mat A, PetscOptionItems *PetscOptionsObject)
189: {
190: MatCUSPARSEStorageFormat format;
191: PetscBool flg;
192: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
194: PetscFunctionBegin;
195: PetscOptionsHeadBegin(PetscOptionsObject, "SeqAIJCUSPARSE options");
196: if (A->factortype == MAT_FACTOR_NONE) {
197: 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));
198: if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_MULT, format));
200: 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));
201: if (flg) PetscCall(MatCUSPARSESetFormat(A, MAT_CUSPARSE_ALL, format));
202: PetscCall(PetscOptionsBool("-mat_cusparse_use_cpu_solve", "Use CPU (I)LU solve", "MatCUSPARSESetUseCPUSolve", cusparsestruct->use_cpu_solve, &cusparsestruct->use_cpu_solve, &flg));
203: if (flg) PetscCall(MatCUSPARSESetUseCPUSolve(A, cusparsestruct->use_cpu_solve));
204: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
205: 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));
206: /* If user did use this option, check its consistency with cuSPARSE, since PetscOptionsEnum() sets enum values based on their position in MatCUSPARSESpMVAlgorithms[] */
207: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
208: 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");
209: #else
210: 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");
211: #endif
212: 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));
213: 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");
215: PetscCall(
216: 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));
217: 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");
218: #endif
219: }
220: PetscOptionsHeadEnd();
221: PetscFunctionReturn(PETSC_SUCCESS);
222: }
224: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
225: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(Mat A)
226: {
227: Mat_SeqAIJ *a = static_cast<Mat_SeqAIJ *>(A->data);
228: PetscInt m = A->rmap->n;
229: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
230: const PetscInt *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
231: const MatScalar *Aa = a->a;
232: PetscInt *Mi, *Mj, Mnz;
233: PetscScalar *Ma;
235: PetscFunctionBegin;
236: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
237: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even when m=0
238: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host
239: Mnz = (Ai[m] - Ai[0]) + (Adiag[0] - Adiag[m]); // Lnz (without the unit diagonal) + Unz (with the non-unit diagonal)
240: PetscCall(PetscMalloc1(m + 1, &Mi));
241: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj is temp
242: PetscCall(PetscMalloc1(Mnz, &Ma));
243: Mi[0] = 0;
244: for (PetscInt i = 0; i < m; i++) {
245: PetscInt llen = Ai[i + 1] - Ai[i];
246: PetscInt ulen = Adiag[i] - Adiag[i + 1];
247: PetscCall(PetscArraycpy(Mj + Mi[i], Aj + Ai[i], llen)); // entries of L
248: Mj[Mi[i] + llen] = i; // diagonal entry
249: PetscCall(PetscArraycpy(Mj + Mi[i] + llen + 1, Aj + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
250: Mi[i + 1] = Mi[i] + llen + ulen;
251: }
252: // Copy M (L,U) from host to device
253: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
254: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
255: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
256: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Mi, sizeof(*fs->csrRowPtr) * (m + 1), cudaMemcpyHostToDevice));
257: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*fs->csrColIdx) * Mnz, cudaMemcpyHostToDevice));
259: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
260: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
261: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
262: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
263: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
264: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_LOWER;
265: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT;
266: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
268: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_L, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
269: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
270: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
272: fillMode = CUSPARSE_FILL_MODE_UPPER;
273: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
274: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
275: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
276: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
278: // Allocate work vectors in SpSv
279: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
280: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
282: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
283: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
285: // Query buffer sizes for SpSV and then allocate buffers, temporarily assuming opA = CUSPARSE_OPERATION_NON_TRANSPOSE
286: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
287: 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));
288: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
289: 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));
290: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
291: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
293: // Record for reuse
294: fs->csrRowPtr_h = Mi;
295: fs->csrVal_h = Ma;
296: PetscCall(PetscFree(Mj));
297: }
298: // Copy the value
299: Mi = fs->csrRowPtr_h;
300: Ma = fs->csrVal_h;
301: Mnz = Mi[m];
302: for (PetscInt i = 0; i < m; i++) {
303: PetscInt llen = Ai[i + 1] - Ai[i];
304: PetscInt ulen = Adiag[i] - Adiag[i + 1];
305: PetscCall(PetscArraycpy(Ma + Mi[i], Aa + Ai[i], llen)); // entries of L
306: Ma[Mi[i] + llen] = (MatScalar)1.0 / Aa[Adiag[i]]; // recover the diagonal entry
307: PetscCall(PetscArraycpy(Ma + Mi[i] + llen + 1, Aa + Adiag[i + 1] + 1, ulen - 1)); // entries of U on the right of the diagonal
308: }
309: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
311: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
312: 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));
314: 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));
316: // L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve
317: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
318: }
319: PetscFunctionReturn(PETSC_SUCCESS);
320: }
321: #else
322: static PetscErrorCode MatSeqAIJCUSPARSEBuildILULowerTriMatrix(Mat A)
323: {
324: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
325: PetscInt n = A->rmap->n;
326: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
327: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
328: const PetscInt *ai = a->i, *aj = a->j, *vi;
329: const MatScalar *aa = a->a, *v;
330: PetscInt *AiLo, *AjLo;
331: PetscInt i, nz, nzLower, offset, rowOffset;
333: PetscFunctionBegin;
334: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
335: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
336: try {
337: /* first figure out the number of nonzeros in the lower triangular matrix including 1's on the diagonal. */
338: nzLower = n + ai[n] - ai[1];
339: if (!loTriFactor) {
340: PetscScalar *AALo;
342: PetscCallCUDA(cudaMallocHost((void **)&AALo, nzLower * sizeof(PetscScalar)));
344: /* Allocate Space for the lower triangular matrix */
345: PetscCallCUDA(cudaMallocHost((void **)&AiLo, (n + 1) * sizeof(PetscInt)));
346: PetscCallCUDA(cudaMallocHost((void **)&AjLo, nzLower * sizeof(PetscInt)));
348: /* Fill the lower triangular matrix */
349: AiLo[0] = (PetscInt)0;
350: AiLo[n] = nzLower;
351: AjLo[0] = (PetscInt)0;
352: AALo[0] = (MatScalar)1.0;
353: v = aa;
354: vi = aj;
355: offset = 1;
356: rowOffset = 1;
357: for (i = 1; i < n; i++) {
358: nz = ai[i + 1] - ai[i];
359: /* additional 1 for the term on the diagonal */
360: AiLo[i] = rowOffset;
361: rowOffset += nz + 1;
363: PetscCall(PetscArraycpy(&AjLo[offset], vi, nz));
364: PetscCall(PetscArraycpy(&AALo[offset], v, nz));
366: offset += nz;
367: AjLo[offset] = (PetscInt)i;
368: AALo[offset] = (MatScalar)1.0;
369: offset += 1;
371: v += nz;
372: vi += nz;
373: }
375: /* allocate space for the triangular factor information */
376: PetscCall(PetscNew(&loTriFactor));
377: loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
378: /* Create the matrix description */
379: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
380: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
381: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
382: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
383: #else
384: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
385: #endif
386: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_LOWER));
387: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));
389: /* set the operation */
390: loTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
392: /* set the matrix */
393: loTriFactor->csrMat = new CsrMatrix;
394: loTriFactor->csrMat->num_rows = n;
395: loTriFactor->csrMat->num_cols = n;
396: loTriFactor->csrMat->num_entries = nzLower;
398: loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
399: loTriFactor->csrMat->row_offsets->assign(AiLo, AiLo + n + 1);
401: loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzLower);
402: loTriFactor->csrMat->column_indices->assign(AjLo, AjLo + nzLower);
404: loTriFactor->csrMat->values = new THRUSTARRAY(nzLower);
405: loTriFactor->csrMat->values->assign(AALo, AALo + nzLower);
407: /* Create the solve analysis information */
408: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
409: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
410: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
411: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
412: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
413: PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
414: #endif
416: /* perform the solve analysis */
417: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
418: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
419: PetscCallCUDA(WaitForCUDA());
420: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
422: /* assign the pointer */
423: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
424: loTriFactor->AA_h = AALo;
425: PetscCallCUDA(cudaFreeHost(AiLo));
426: PetscCallCUDA(cudaFreeHost(AjLo));
427: PetscCall(PetscLogCpuToGpu((n + 1 + nzLower) * sizeof(int) + nzLower * sizeof(PetscScalar)));
428: } else { /* update values only */
429: if (!loTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&loTriFactor->AA_h, nzLower * sizeof(PetscScalar)));
430: /* Fill the lower triangular matrix */
431: loTriFactor->AA_h[0] = 1.0;
432: v = aa;
433: vi = aj;
434: offset = 1;
435: for (i = 1; i < n; i++) {
436: nz = ai[i + 1] - ai[i];
437: PetscCall(PetscArraycpy(&loTriFactor->AA_h[offset], v, nz));
438: offset += nz;
439: loTriFactor->AA_h[offset] = 1.0;
440: offset += 1;
441: v += nz;
442: }
443: loTriFactor->csrMat->values->assign(loTriFactor->AA_h, loTriFactor->AA_h + nzLower);
444: PetscCall(PetscLogCpuToGpu(nzLower * sizeof(PetscScalar)));
445: }
446: } catch (char *ex) {
447: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
448: }
449: }
450: PetscFunctionReturn(PETSC_SUCCESS);
451: }
453: static PetscErrorCode MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(Mat A)
454: {
455: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
456: PetscInt n = A->rmap->n;
457: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
458: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
459: const PetscInt *aj = a->j, *adiag = a->diag, *vi;
460: const MatScalar *aa = a->a, *v;
461: PetscInt *AiUp, *AjUp;
462: PetscInt i, nz, nzUpper, offset;
464: PetscFunctionBegin;
465: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
466: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
467: try {
468: /* next, figure out the number of nonzeros in the upper triangular matrix. */
469: nzUpper = adiag[0] - adiag[n];
470: if (!upTriFactor) {
471: PetscScalar *AAUp;
473: PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
475: /* Allocate Space for the upper triangular matrix */
476: PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
477: PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));
479: /* Fill the upper triangular matrix */
480: AiUp[0] = (PetscInt)0;
481: AiUp[n] = nzUpper;
482: offset = nzUpper;
483: for (i = n - 1; i >= 0; i--) {
484: v = aa + adiag[i + 1] + 1;
485: vi = aj + adiag[i + 1] + 1;
487: /* number of elements NOT on the diagonal */
488: nz = adiag[i] - adiag[i + 1] - 1;
490: /* decrement the offset */
491: offset -= (nz + 1);
493: /* first, set the diagonal elements */
494: AjUp[offset] = (PetscInt)i;
495: AAUp[offset] = (MatScalar)1. / v[nz];
496: AiUp[i] = AiUp[i + 1] - (nz + 1);
498: PetscCall(PetscArraycpy(&AjUp[offset + 1], vi, nz));
499: PetscCall(PetscArraycpy(&AAUp[offset + 1], v, nz));
500: }
502: /* allocate space for the triangular factor information */
503: PetscCall(PetscNew(&upTriFactor));
504: upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
506: /* Create the matrix description */
507: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
508: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
509: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
510: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
511: #else
512: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
513: #endif
514: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
515: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));
517: /* set the operation */
518: upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
520: /* set the matrix */
521: upTriFactor->csrMat = new CsrMatrix;
522: upTriFactor->csrMat->num_rows = n;
523: upTriFactor->csrMat->num_cols = n;
524: upTriFactor->csrMat->num_entries = nzUpper;
526: upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(n + 1);
527: upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + n + 1);
529: upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(nzUpper);
530: upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + nzUpper);
532: upTriFactor->csrMat->values = new THRUSTARRAY(nzUpper);
533: upTriFactor->csrMat->values->assign(AAUp, AAUp + nzUpper);
535: /* Create the solve analysis information */
536: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
537: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
538: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
539: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
540: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
541: PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
542: #endif
544: /* perform the solve analysis */
545: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
546: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
548: PetscCallCUDA(WaitForCUDA());
549: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
551: /* assign the pointer */
552: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
553: upTriFactor->AA_h = AAUp;
554: PetscCallCUDA(cudaFreeHost(AiUp));
555: PetscCallCUDA(cudaFreeHost(AjUp));
556: PetscCall(PetscLogCpuToGpu((n + 1 + nzUpper) * sizeof(int) + nzUpper * sizeof(PetscScalar)));
557: } else {
558: if (!upTriFactor->AA_h) PetscCallCUDA(cudaMallocHost((void **)&upTriFactor->AA_h, nzUpper * sizeof(PetscScalar)));
559: /* Fill the upper triangular matrix */
560: offset = nzUpper;
561: for (i = n - 1; i >= 0; i--) {
562: v = aa + adiag[i + 1] + 1;
564: /* number of elements NOT on the diagonal */
565: nz = adiag[i] - adiag[i + 1] - 1;
567: /* decrement the offset */
568: offset -= (nz + 1);
570: /* first, set the diagonal elements */
571: upTriFactor->AA_h[offset] = 1. / v[nz];
572: PetscCall(PetscArraycpy(&upTriFactor->AA_h[offset + 1], v, nz));
573: }
574: upTriFactor->csrMat->values->assign(upTriFactor->AA_h, upTriFactor->AA_h + nzUpper);
575: PetscCall(PetscLogCpuToGpu(nzUpper * sizeof(PetscScalar)));
576: }
577: } catch (char *ex) {
578: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
579: }
580: }
581: PetscFunctionReturn(PETSC_SUCCESS);
582: }
583: #endif
585: static PetscErrorCode MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(Mat A)
586: {
587: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
588: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
589: IS isrow = a->row, iscol = a->icol;
590: PetscBool row_identity, col_identity;
591: PetscInt n = A->rmap->n;
593: PetscFunctionBegin;
594: PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
595: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
596: PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_LU(A));
597: #else
598: PetscCall(MatSeqAIJCUSPARSEBuildILULowerTriMatrix(A));
599: PetscCall(MatSeqAIJCUSPARSEBuildILUUpperTriMatrix(A));
600: if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
601: #endif
603: cusparseTriFactors->nnz = a->nz;
605: A->offloadmask = PETSC_OFFLOAD_BOTH; // factored matrix is sync'ed to GPU
606: /* lower triangular indices */
607: PetscCall(ISIdentity(isrow, &row_identity));
608: if (!row_identity && !cusparseTriFactors->rpermIndices) {
609: const PetscInt *r;
611: PetscCall(ISGetIndices(isrow, &r));
612: cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
613: cusparseTriFactors->rpermIndices->assign(r, r + n);
614: PetscCall(ISRestoreIndices(isrow, &r));
615: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
616: }
618: /* upper triangular indices */
619: PetscCall(ISIdentity(iscol, &col_identity));
620: if (!col_identity && !cusparseTriFactors->cpermIndices) {
621: const PetscInt *c;
623: PetscCall(ISGetIndices(iscol, &c));
624: cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
625: cusparseTriFactors->cpermIndices->assign(c, c + n);
626: PetscCall(ISRestoreIndices(iscol, &c));
627: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
628: }
629: PetscFunctionReturn(PETSC_SUCCESS);
630: }
632: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
633: static PetscErrorCode MatSeqAIJCUSPARSEBuildFactoredMatrix_Cheolesky(Mat A)
634: {
635: Mat_SeqAIJ *a = static_cast<Mat_SeqAIJ *>(A->data);
636: PetscInt m = A->rmap->n;
637: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
638: const PetscInt *Ai = a->i, *Aj = a->j, *Adiag = a->diag;
639: const MatScalar *Aa = a->a;
640: PetscInt *Mj, Mnz;
641: PetscScalar *Ma, *D;
643: PetscFunctionBegin;
644: if (A->offloadmask == PETSC_OFFLOAD_CPU) { // A's latest factors are on CPU
645: if (!fs->csrRowPtr) { // Is't the first time to do the setup? Use csrRowPtr since it is not null even m=0
646: // Re-arrange the (skewed) factored matrix and put the result into M, a regular csr matrix on host.
647: // See comments at MatICCFactorSymbolic_SeqAIJ() on the layout of the factored matrix (U) on host.
648: Mnz = Ai[m]; // Unz (with the unit diagonal)
649: PetscCall(PetscMalloc1(Mnz, &Ma));
650: PetscCall(PetscMalloc1(Mnz, &Mj)); // Mj[] is temp
651: PetscCall(PetscMalloc1(m, &D)); // the diagonal
652: for (PetscInt i = 0; i < m; i++) {
653: PetscInt ulen = Ai[i + 1] - Ai[i];
654: Mj[Ai[i]] = i; // diagonal entry
655: PetscCall(PetscArraycpy(Mj + Ai[i] + 1, Aj + Ai[i], ulen - 1)); // entries of U on the right of the diagonal
656: }
657: // Copy M (U) from host to device
658: PetscCallCUDA(cudaMalloc(&fs->csrRowPtr, sizeof(*fs->csrRowPtr) * (m + 1)));
659: PetscCallCUDA(cudaMalloc(&fs->csrColIdx, sizeof(*fs->csrColIdx) * Mnz));
660: PetscCallCUDA(cudaMalloc(&fs->csrVal, sizeof(*fs->csrVal) * Mnz));
661: PetscCallCUDA(cudaMalloc(&fs->diag, sizeof(*fs->diag) * m));
662: PetscCallCUDA(cudaMemcpy(fs->csrRowPtr, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyHostToDevice));
663: PetscCallCUDA(cudaMemcpy(fs->csrColIdx, Mj, sizeof(*Mj) * Mnz, cudaMemcpyHostToDevice));
665: // Create descriptors for L, U. See https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
666: // cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
667: // assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
668: // all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
669: // assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
670: cusparseFillMode_t fillMode = CUSPARSE_FILL_MODE_UPPER;
671: cusparseDiagType_t diagType = CUSPARSE_DIAG_TYPE_UNIT; // U is unit diagonal
672: const cusparseIndexType_t indexType = PetscDefined(USE_64BIT_INDICES) ? CUSPARSE_INDEX_64I : CUSPARSE_INDEX_32I;
674: PetscCallCUSPARSE(cusparseCreateCsr(&fs->spMatDescr_U, m, m, Mnz, fs->csrRowPtr, fs->csrColIdx, fs->csrVal, indexType, indexType, CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype));
675: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
676: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
678: // Allocate work vectors in SpSv
679: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(*fs->X) * m));
680: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(*fs->Y) * m));
682: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
683: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
685: // Query buffer sizes for SpSV and then allocate buffers
686: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
687: 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));
688: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
690: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut)); // Ut solve uses the same matrix (spMatDescr_U), but different descr and buffer
691: 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));
692: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
694: // Record for reuse
695: fs->csrVal_h = Ma;
696: fs->diag_h = D;
697: PetscCall(PetscFree(Mj));
698: }
699: // Copy the value
700: Ma = fs->csrVal_h;
701: D = fs->diag_h;
702: Mnz = Ai[m];
703: for (PetscInt i = 0; i < m; i++) {
704: D[i] = Aa[Adiag[i]]; // actually Aa[Adiag[i]] is the inverse of the diagonal
705: 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
706: for (PetscInt k = 0; k < Ai[i + 1] - Ai[i] - 1; k++) Ma[Ai[i] + 1 + k] = -Aa[Ai[i] + k];
707: }
708: PetscCallCUDA(cudaMemcpy(fs->csrVal, Ma, sizeof(*Ma) * Mnz, cudaMemcpyHostToDevice));
709: PetscCallCUDA(cudaMemcpy(fs->diag, D, sizeof(*D) * m, cudaMemcpyHostToDevice));
711: // Do cusparseSpSV_analysis(), which is numeric and requires valid and up-to-date matrix values
712: 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));
713: 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));
714: }
715: PetscFunctionReturn(PETSC_SUCCESS);
716: }
718: // Solve Ut D U x = b
719: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_Cholesky(Mat A, Vec b, Vec x)
720: {
721: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
722: Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
723: const PetscScalar *barray;
724: PetscScalar *xarray;
725: thrust::device_ptr<const PetscScalar> bGPU;
726: thrust::device_ptr<PetscScalar> xGPU;
727: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
728: PetscInt m = A->rmap->n;
730: PetscFunctionBegin;
731: PetscCall(PetscLogGpuTimeBegin());
732: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
733: PetscCall(VecCUDAGetArrayRead(b, &barray));
734: xGPU = thrust::device_pointer_cast(xarray);
735: bGPU = thrust::device_pointer_cast(barray);
737: // Reorder b with the row permutation if needed, and wrap the result in fs->X
738: if (fs->rpermIndices) {
739: 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)));
740: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
741: } else {
742: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
743: }
745: // Solve Ut Y = X
746: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
747: 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));
749: // Solve diag(D) Z = Y. Actually just do Y = Y*D since D is already inverted in MatCholeskyFactorNumeric_SeqAIJ().
750: // It is basically a vector element-wise multiplication, but cublas does not have it!
751: 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>()));
753: // Solve U X = Y
754: if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
755: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
756: } else {
757: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
758: }
759: 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));
761: // Reorder X with the column permutation if needed, and put the result back to x
762: if (fs->cpermIndices) {
763: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
764: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
765: }
767: PetscCall(VecCUDARestoreArrayRead(b, &barray));
768: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
769: PetscCall(PetscLogGpuTimeEnd());
770: PetscCall(PetscLogGpuFlops(4.0 * aij->nz - A->rmap->n));
771: PetscFunctionReturn(PETSC_SUCCESS);
772: }
773: #else
774: static PetscErrorCode MatSeqAIJCUSPARSEBuildICCTriMatrices(Mat A)
775: {
776: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
777: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
778: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
779: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
780: PetscInt *AiUp, *AjUp;
781: PetscScalar *AAUp;
782: PetscScalar *AALo;
783: PetscInt nzUpper = a->nz, n = A->rmap->n, i, offset, nz, j;
784: Mat_SeqSBAIJ *b = (Mat_SeqSBAIJ *)A->data;
785: const PetscInt *ai = b->i, *aj = b->j, *vj;
786: const MatScalar *aa = b->a, *v;
788: PetscFunctionBegin;
789: if (!n) PetscFunctionReturn(PETSC_SUCCESS);
790: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
791: try {
792: PetscCallCUDA(cudaMallocHost((void **)&AAUp, nzUpper * sizeof(PetscScalar)));
793: PetscCallCUDA(cudaMallocHost((void **)&AALo, nzUpper * sizeof(PetscScalar)));
794: if (!upTriFactor && !loTriFactor) {
795: /* Allocate Space for the upper triangular matrix */
796: PetscCallCUDA(cudaMallocHost((void **)&AiUp, (n + 1) * sizeof(PetscInt)));
797: PetscCallCUDA(cudaMallocHost((void **)&AjUp, nzUpper * sizeof(PetscInt)));
799: /* Fill the upper triangular matrix */
800: AiUp[0] = (PetscInt)0;
801: AiUp[n] = nzUpper;
802: offset = 0;
803: for (i = 0; i < n; i++) {
804: /* set the pointers */
805: v = aa + ai[i];
806: vj = aj + ai[i];
807: nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */
809: /* first, set the diagonal elements */
810: AjUp[offset] = (PetscInt)i;
811: AAUp[offset] = (MatScalar)1.0 / v[nz];
812: AiUp[i] = offset;
813: AALo[offset] = (MatScalar)1.0 / v[nz];
815: offset += 1;
816: if (nz > 0) {
817: PetscCall(PetscArraycpy(&AjUp[offset], vj, nz));
818: PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
819: for (j = offset; j < offset + nz; j++) {
820: AAUp[j] = -AAUp[j];
821: AALo[j] = AAUp[j] / v[nz];
822: }
823: offset += nz;
824: }
825: }
827: /* allocate space for the triangular factor information */
828: PetscCall(PetscNew(&upTriFactor));
829: upTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
831: /* Create the matrix description */
832: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactor->descr));
833: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
834: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
835: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
836: #else
837: PetscCallCUSPARSE(cusparseSetMatType(upTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
838: #endif
839: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
840: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactor->descr, CUSPARSE_DIAG_TYPE_UNIT));
842: /* set the matrix */
843: upTriFactor->csrMat = new CsrMatrix;
844: upTriFactor->csrMat->num_rows = A->rmap->n;
845: upTriFactor->csrMat->num_cols = A->cmap->n;
846: upTriFactor->csrMat->num_entries = a->nz;
848: upTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
849: upTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);
851: upTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
852: upTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);
854: upTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
855: upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);
857: /* set the operation */
858: upTriFactor->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
860: /* Create the solve analysis information */
861: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
862: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactor->solveInfo));
863: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
864: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
865: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, &upTriFactor->solveBufferSize));
866: PetscCallCUDA(cudaMalloc(&upTriFactor->solveBuffer, upTriFactor->solveBufferSize));
867: #endif
869: /* perform the solve analysis */
870: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactor->solveOp, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_entries, upTriFactor->descr, upTriFactor->csrMat->values->data().get(),
871: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
873: PetscCallCUDA(WaitForCUDA());
874: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
876: /* assign the pointer */
877: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtr = upTriFactor;
879: /* allocate space for the triangular factor information */
880: PetscCall(PetscNew(&loTriFactor));
881: loTriFactor->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
883: /* Create the matrix description */
884: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactor->descr));
885: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactor->descr, CUSPARSE_INDEX_BASE_ZERO));
886: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
887: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
888: #else
889: PetscCallCUSPARSE(cusparseSetMatType(loTriFactor->descr, CUSPARSE_MATRIX_TYPE_TRIANGULAR));
890: #endif
891: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactor->descr, CUSPARSE_FILL_MODE_UPPER));
892: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactor->descr, CUSPARSE_DIAG_TYPE_NON_UNIT));
894: /* set the operation */
895: loTriFactor->solveOp = CUSPARSE_OPERATION_TRANSPOSE;
897: /* set the matrix */
898: loTriFactor->csrMat = new CsrMatrix;
899: loTriFactor->csrMat->num_rows = A->rmap->n;
900: loTriFactor->csrMat->num_cols = A->cmap->n;
901: loTriFactor->csrMat->num_entries = a->nz;
903: loTriFactor->csrMat->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
904: loTriFactor->csrMat->row_offsets->assign(AiUp, AiUp + A->rmap->n + 1);
906: loTriFactor->csrMat->column_indices = new THRUSTINTARRAY32(a->nz);
907: loTriFactor->csrMat->column_indices->assign(AjUp, AjUp + a->nz);
909: loTriFactor->csrMat->values = new THRUSTARRAY(a->nz);
910: loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);
912: /* Create the solve analysis information */
913: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
914: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactor->solveInfo));
915: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
916: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
917: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, &loTriFactor->solveBufferSize));
918: PetscCallCUDA(cudaMalloc(&loTriFactor->solveBuffer, loTriFactor->solveBufferSize));
919: #endif
921: /* perform the solve analysis */
922: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactor->solveOp, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_entries, loTriFactor->descr, loTriFactor->csrMat->values->data().get(),
923: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
925: PetscCallCUDA(WaitForCUDA());
926: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
928: /* assign the pointer */
929: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtr = loTriFactor;
931: PetscCall(PetscLogCpuToGpu(2 * (((A->rmap->n + 1) + (a->nz)) * sizeof(int) + (a->nz) * sizeof(PetscScalar))));
932: PetscCallCUDA(cudaFreeHost(AiUp));
933: PetscCallCUDA(cudaFreeHost(AjUp));
934: } else {
935: /* Fill the upper triangular matrix */
936: offset = 0;
937: for (i = 0; i < n; i++) {
938: /* set the pointers */
939: v = aa + ai[i];
940: nz = ai[i + 1] - ai[i] - 1; /* exclude diag[i] */
942: /* first, set the diagonal elements */
943: AAUp[offset] = 1.0 / v[nz];
944: AALo[offset] = 1.0 / v[nz];
946: offset += 1;
947: if (nz > 0) {
948: PetscCall(PetscArraycpy(&AAUp[offset], v, nz));
949: for (j = offset; j < offset + nz; j++) {
950: AAUp[j] = -AAUp[j];
951: AALo[j] = AAUp[j] / v[nz];
952: }
953: offset += nz;
954: }
955: }
956: PetscCheck(upTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
957: PetscCheck(loTriFactor, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
958: upTriFactor->csrMat->values->assign(AAUp, AAUp + a->nz);
959: loTriFactor->csrMat->values->assign(AALo, AALo + a->nz);
960: PetscCall(PetscLogCpuToGpu(2 * (a->nz) * sizeof(PetscScalar)));
961: }
962: PetscCallCUDA(cudaFreeHost(AAUp));
963: PetscCallCUDA(cudaFreeHost(AALo));
964: } catch (char *ex) {
965: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
966: }
967: }
968: PetscFunctionReturn(PETSC_SUCCESS);
969: }
970: #endif
972: static PetscErrorCode MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(Mat A)
973: {
974: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
975: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
976: IS ip = a->row;
977: PetscBool perm_identity;
978: PetscInt n = A->rmap->n;
980: PetscFunctionBegin;
981: PetscCheck(cusparseTriFactors, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing cusparseTriFactors");
983: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
984: PetscCall(MatSeqAIJCUSPARSEBuildFactoredMatrix_Cheolesky(A));
985: #else
986: PetscCall(MatSeqAIJCUSPARSEBuildICCTriMatrices(A));
987: if (!cusparseTriFactors->workVector) cusparseTriFactors->workVector = new THRUSTARRAY(n);
988: #endif
989: cusparseTriFactors->nnz = (a->nz - n) * 2 + n;
991: A->offloadmask = PETSC_OFFLOAD_BOTH;
993: /* lower triangular indices */
994: PetscCall(ISIdentity(ip, &perm_identity));
995: if (!perm_identity) {
996: IS iip;
997: const PetscInt *irip, *rip;
999: PetscCall(ISInvertPermutation(ip, PETSC_DECIDE, &iip));
1000: PetscCall(ISGetIndices(iip, &irip));
1001: PetscCall(ISGetIndices(ip, &rip));
1002: cusparseTriFactors->rpermIndices = new THRUSTINTARRAY(n);
1003: cusparseTriFactors->rpermIndices->assign(rip, rip + n);
1004: cusparseTriFactors->cpermIndices = new THRUSTINTARRAY(n);
1005: cusparseTriFactors->cpermIndices->assign(irip, irip + n);
1006: PetscCall(ISRestoreIndices(iip, &irip));
1007: PetscCall(ISDestroy(&iip));
1008: PetscCall(ISRestoreIndices(ip, &rip));
1009: PetscCall(PetscLogCpuToGpu(2. * n * sizeof(PetscInt)));
1010: }
1011: PetscFunctionReturn(PETSC_SUCCESS);
1012: }
1014: static PetscErrorCode MatCholeskyFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
1015: {
1016: PetscFunctionBegin;
1017: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
1018: PetscCall(MatCholeskyFactorNumeric_SeqAIJ(B, A, info));
1019: B->offloadmask = PETSC_OFFLOAD_CPU;
1021: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1022: B->ops->solve = MatSolve_SeqAIJCUSPARSE_Cholesky;
1023: B->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_Cholesky;
1024: #else
1025: /* determine which version of MatSolve needs to be used. */
1026: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
1027: IS ip = b->row;
1028: PetscBool perm_identity;
1030: PetscCall(ISIdentity(ip, &perm_identity));
1031: if (perm_identity) {
1032: B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
1033: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
1034: } else {
1035: B->ops->solve = MatSolve_SeqAIJCUSPARSE;
1036: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
1037: }
1038: #endif
1039: B->ops->matsolve = NULL;
1040: B->ops->matsolvetranspose = NULL;
1042: /* get the triangular factors */
1043: PetscCall(MatSeqAIJCUSPARSEICCAnalysisAndCopyToGPU(B));
1044: PetscFunctionReturn(PETSC_SUCCESS);
1045: }
1047: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1048: static PetscErrorCode MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(Mat A)
1049: {
1050: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1051: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1052: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1053: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT;
1054: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT;
1055: cusparseIndexBase_t indexBase;
1056: cusparseMatrixType_t matrixType;
1057: cusparseFillMode_t fillMode;
1058: cusparseDiagType_t diagType;
1060: PetscFunctionBegin;
1061: /* allocate space for the transpose of the lower triangular factor */
1062: PetscCall(PetscNew(&loTriFactorT));
1063: loTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1065: /* set the matrix descriptors of the lower triangular factor */
1066: matrixType = cusparseGetMatType(loTriFactor->descr);
1067: indexBase = cusparseGetMatIndexBase(loTriFactor->descr);
1068: fillMode = cusparseGetMatFillMode(loTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1069: diagType = cusparseGetMatDiagType(loTriFactor->descr);
1071: /* Create the matrix description */
1072: PetscCallCUSPARSE(cusparseCreateMatDescr(&loTriFactorT->descr));
1073: PetscCallCUSPARSE(cusparseSetMatIndexBase(loTriFactorT->descr, indexBase));
1074: PetscCallCUSPARSE(cusparseSetMatType(loTriFactorT->descr, matrixType));
1075: PetscCallCUSPARSE(cusparseSetMatFillMode(loTriFactorT->descr, fillMode));
1076: PetscCallCUSPARSE(cusparseSetMatDiagType(loTriFactorT->descr, diagType));
1078: /* set the operation */
1079: loTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
1081: /* allocate GPU space for the CSC of the lower triangular factor*/
1082: loTriFactorT->csrMat = new CsrMatrix;
1083: loTriFactorT->csrMat->num_rows = loTriFactor->csrMat->num_cols;
1084: loTriFactorT->csrMat->num_cols = loTriFactor->csrMat->num_rows;
1085: loTriFactorT->csrMat->num_entries = loTriFactor->csrMat->num_entries;
1086: loTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_rows + 1);
1087: loTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(loTriFactorT->csrMat->num_entries);
1088: loTriFactorT->csrMat->values = new THRUSTARRAY(loTriFactorT->csrMat->num_entries);
1090: /* compute the transpose of the lower triangular factor, i.e. the CSC */
1091: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1092: PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, loTriFactor->csrMat->num_rows, loTriFactor->csrMat->num_cols, loTriFactor->csrMat->num_entries, loTriFactor->csrMat->values->data().get(),
1093: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(), loTriFactorT->csrMat->row_offsets->data().get(),
1094: loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &loTriFactor->csr2cscBufferSize));
1095: PetscCallCUDA(cudaMalloc(&loTriFactor->csr2cscBuffer, loTriFactor->csr2cscBufferSize));
1096: #endif
1098: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1099: {
1100: // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1101: 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(),
1102: loTriFactor->csrMat->column_indices->data().get(), loTriFactorT->csrMat->values->data().get(),
1103: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1104: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, loTriFactor->csr2cscBuffer);
1105: #else
1106: loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1107: #endif
1108: PetscCallCUSPARSE(stat);
1109: }
1111: PetscCallCUDA(WaitForCUDA());
1112: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1114: /* Create the solve analysis information */
1115: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1116: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&loTriFactorT->solveInfo));
1117: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1118: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1119: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, &loTriFactorT->solveBufferSize));
1120: PetscCallCUDA(cudaMalloc(&loTriFactorT->solveBuffer, loTriFactorT->solveBufferSize));
1121: #endif
1123: /* perform the solve analysis */
1124: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, loTriFactorT->solveOp, loTriFactorT->csrMat->num_rows, loTriFactorT->csrMat->num_entries, loTriFactorT->descr, loTriFactorT->csrMat->values->data().get(),
1125: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1127: PetscCallCUDA(WaitForCUDA());
1128: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1130: /* assign the pointer */
1131: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->loTriFactorPtrTranspose = loTriFactorT;
1133: /*********************************************/
1134: /* Now the Transpose of the Upper Tri Factor */
1135: /*********************************************/
1137: /* allocate space for the transpose of the upper triangular factor */
1138: PetscCall(PetscNew(&upTriFactorT));
1139: upTriFactorT->solvePolicy = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1141: /* set the matrix descriptors of the upper triangular factor */
1142: matrixType = cusparseGetMatType(upTriFactor->descr);
1143: indexBase = cusparseGetMatIndexBase(upTriFactor->descr);
1144: fillMode = cusparseGetMatFillMode(upTriFactor->descr) == CUSPARSE_FILL_MODE_UPPER ? CUSPARSE_FILL_MODE_LOWER : CUSPARSE_FILL_MODE_UPPER;
1145: diagType = cusparseGetMatDiagType(upTriFactor->descr);
1147: /* Create the matrix description */
1148: PetscCallCUSPARSE(cusparseCreateMatDescr(&upTriFactorT->descr));
1149: PetscCallCUSPARSE(cusparseSetMatIndexBase(upTriFactorT->descr, indexBase));
1150: PetscCallCUSPARSE(cusparseSetMatType(upTriFactorT->descr, matrixType));
1151: PetscCallCUSPARSE(cusparseSetMatFillMode(upTriFactorT->descr, fillMode));
1152: PetscCallCUSPARSE(cusparseSetMatDiagType(upTriFactorT->descr, diagType));
1154: /* set the operation */
1155: upTriFactorT->solveOp = CUSPARSE_OPERATION_NON_TRANSPOSE;
1157: /* allocate GPU space for the CSC of the upper triangular factor*/
1158: upTriFactorT->csrMat = new CsrMatrix;
1159: upTriFactorT->csrMat->num_rows = upTriFactor->csrMat->num_cols;
1160: upTriFactorT->csrMat->num_cols = upTriFactor->csrMat->num_rows;
1161: upTriFactorT->csrMat->num_entries = upTriFactor->csrMat->num_entries;
1162: upTriFactorT->csrMat->row_offsets = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_rows + 1);
1163: upTriFactorT->csrMat->column_indices = new THRUSTINTARRAY32(upTriFactorT->csrMat->num_entries);
1164: upTriFactorT->csrMat->values = new THRUSTARRAY(upTriFactorT->csrMat->num_entries);
1166: /* compute the transpose of the upper triangular factor, i.e. the CSC */
1167: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1168: PetscCallCUSPARSE(cusparseCsr2cscEx2_bufferSize(cusparseTriFactors->handle, upTriFactor->csrMat->num_rows, upTriFactor->csrMat->num_cols, upTriFactor->csrMat->num_entries, upTriFactor->csrMat->values->data().get(),
1169: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(), upTriFactorT->csrMat->row_offsets->data().get(),
1170: upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, &upTriFactor->csr2cscBufferSize));
1171: PetscCallCUDA(cudaMalloc(&upTriFactor->csr2cscBuffer, upTriFactor->csr2cscBufferSize));
1172: #endif
1174: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1175: {
1176: // there is no clean way to have PetscCallCUSPARSE wrapping this function...
1177: 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(),
1178: upTriFactor->csrMat->column_indices->data().get(), upTriFactorT->csrMat->values->data().get(),
1179: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1180: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, CUSPARSE_CSR2CSC_ALG1, upTriFactor->csr2cscBuffer);
1181: #else
1182: upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->csrMat->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1183: #endif
1184: PetscCallCUSPARSE(stat);
1185: }
1187: PetscCallCUDA(WaitForCUDA());
1188: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1190: /* Create the solve analysis information */
1191: PetscCall(PetscLogEventBegin(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1192: PetscCallCUSPARSE(cusparseCreateCsrsvInfo(&upTriFactorT->solveInfo));
1193: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
1194: PetscCallCUSPARSE(cusparseXcsrsv_buffsize(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1195: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, &upTriFactorT->solveBufferSize));
1196: PetscCallCUDA(cudaMalloc(&upTriFactorT->solveBuffer, upTriFactorT->solveBufferSize));
1197: #endif
1199: /* perform the solve analysis */
1200: /* christ, would it have killed you to put this stuff in a function????????? */
1201: PetscCallCUSPARSE(cusparseXcsrsv_analysis(cusparseTriFactors->handle, upTriFactorT->solveOp, upTriFactorT->csrMat->num_rows, upTriFactorT->csrMat->num_entries, upTriFactorT->descr, upTriFactorT->csrMat->values->data().get(),
1202: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1204: PetscCallCUDA(WaitForCUDA());
1205: PetscCall(PetscLogEventEnd(MAT_CUSPARSESolveAnalysis, A, 0, 0, 0));
1207: /* assign the pointer */
1208: ((Mat_SeqAIJCUSPARSETriFactors *)A->spptr)->upTriFactorPtrTranspose = upTriFactorT;
1209: PetscFunctionReturn(PETSC_SUCCESS);
1210: }
1211: #endif
1213: struct PetscScalarToPetscInt {
1214: __host__ __device__ PetscInt operator()(PetscScalar s) { return (PetscInt)PetscRealPart(s); }
1215: };
1217: static PetscErrorCode MatSeqAIJCUSPARSEFormExplicitTranspose(Mat A)
1218: {
1219: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
1220: Mat_SeqAIJCUSPARSEMultStruct *matstruct, *matstructT;
1221: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1222: cusparseStatus_t stat;
1223: cusparseIndexBase_t indexBase;
1225: PetscFunctionBegin;
1226: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1227: matstruct = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->mat;
1228: PetscCheck(matstruct, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing mat struct");
1229: matstructT = (Mat_SeqAIJCUSPARSEMultStruct *)cusparsestruct->matTranspose;
1230: PetscCheck(!A->transupdated || matstructT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing matTranspose struct");
1231: if (A->transupdated) PetscFunctionReturn(PETSC_SUCCESS);
1232: PetscCall(PetscLogEventBegin(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1233: PetscCall(PetscLogGpuTimeBegin());
1234: if (cusparsestruct->format != MAT_CUSPARSE_CSR) PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
1235: if (!cusparsestruct->matTranspose) { /* create cusparse matrix */
1236: matstructT = new Mat_SeqAIJCUSPARSEMultStruct;
1237: PetscCallCUSPARSE(cusparseCreateMatDescr(&matstructT->descr));
1238: indexBase = cusparseGetMatIndexBase(matstruct->descr);
1239: PetscCallCUSPARSE(cusparseSetMatIndexBase(matstructT->descr, indexBase));
1240: PetscCallCUSPARSE(cusparseSetMatType(matstructT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
1242: /* set alpha and beta */
1243: PetscCallCUDA(cudaMalloc((void **)&matstructT->alpha_one, sizeof(PetscScalar)));
1244: PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_zero, sizeof(PetscScalar)));
1245: PetscCallCUDA(cudaMalloc((void **)&matstructT->beta_one, sizeof(PetscScalar)));
1246: PetscCallCUDA(cudaMemcpy(matstructT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1247: PetscCallCUDA(cudaMemcpy(matstructT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1248: PetscCallCUDA(cudaMemcpy(matstructT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
1250: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
1251: CsrMatrix *matrixT = new CsrMatrix;
1252: matstructT->mat = matrixT;
1253: matrixT->num_rows = A->cmap->n;
1254: matrixT->num_cols = A->rmap->n;
1255: matrixT->num_entries = a->nz;
1256: matrixT->row_offsets = new THRUSTINTARRAY32(matrixT->num_rows + 1);
1257: matrixT->column_indices = new THRUSTINTARRAY32(a->nz);
1258: matrixT->values = new THRUSTARRAY(a->nz);
1260: if (!cusparsestruct->rowoffsets_gpu) cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1261: cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
1263: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1264: #if PETSC_PKG_CUDA_VERSION_GE(11, 2, 1)
1265: 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 */
1266: indexBase, cusparse_scalartype);
1267: PetscCallCUSPARSE(stat);
1268: #else
1269: /* cusparse-11.x returns errors with zero-sized matrices until 11.2.1,
1270: see https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cusparse-11.2.1
1272: I don't know what a proper value should be for matstructT->matDescr with empty matrices, so I just set
1273: it to NULL to blow it up if one relies on it. Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2,
1274: when nnz = 0, matrixT->row_offsets[] should be filled with indexBase. So I also set it accordingly.
1275: */
1276: if (matrixT->num_entries) {
1277: 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);
1278: PetscCallCUSPARSE(stat);
1280: } else {
1281: matstructT->matDescr = NULL;
1282: matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1283: }
1284: #endif
1285: #endif
1286: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
1287: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1288: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
1289: #else
1290: CsrMatrix *temp = new CsrMatrix;
1291: CsrMatrix *tempT = new CsrMatrix;
1292: /* First convert HYB to CSR */
1293: temp->num_rows = A->rmap->n;
1294: temp->num_cols = A->cmap->n;
1295: temp->num_entries = a->nz;
1296: temp->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
1297: temp->column_indices = new THRUSTINTARRAY32(a->nz);
1298: temp->values = new THRUSTARRAY(a->nz);
1300: 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());
1301: PetscCallCUSPARSE(stat);
1303: /* Next, convert CSR to CSC (i.e. the matrix transpose) */
1304: tempT->num_rows = A->rmap->n;
1305: tempT->num_cols = A->cmap->n;
1306: tempT->num_entries = a->nz;
1307: tempT->row_offsets = new THRUSTINTARRAY32(A->rmap->n + 1);
1308: tempT->column_indices = new THRUSTINTARRAY32(a->nz);
1309: tempT->values = new THRUSTARRAY(a->nz);
1311: 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(),
1312: tempT->column_indices->data().get(), tempT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1313: PetscCallCUSPARSE(stat);
1315: /* Last, convert CSC to HYB */
1316: cusparseHybMat_t hybMat;
1317: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
1318: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
1319: 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);
1320: PetscCallCUSPARSE(stat);
1322: /* assign the pointer */
1323: matstructT->mat = hybMat;
1324: A->transupdated = PETSC_TRUE;
1325: /* delete temporaries */
1326: if (tempT) {
1327: if (tempT->values) delete (THRUSTARRAY *)tempT->values;
1328: if (tempT->column_indices) delete (THRUSTINTARRAY32 *)tempT->column_indices;
1329: if (tempT->row_offsets) delete (THRUSTINTARRAY32 *)tempT->row_offsets;
1330: delete (CsrMatrix *)tempT;
1331: }
1332: if (temp) {
1333: if (temp->values) delete (THRUSTARRAY *)temp->values;
1334: if (temp->column_indices) delete (THRUSTINTARRAY32 *)temp->column_indices;
1335: if (temp->row_offsets) delete (THRUSTINTARRAY32 *)temp->row_offsets;
1336: delete (CsrMatrix *)temp;
1337: }
1338: #endif
1339: }
1340: }
1341: if (cusparsestruct->format == MAT_CUSPARSE_CSR) { /* transpose mat struct may be already present, update data */
1342: CsrMatrix *matrix = (CsrMatrix *)matstruct->mat;
1343: CsrMatrix *matrixT = (CsrMatrix *)matstructT->mat;
1344: PetscCheck(matrix, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix");
1345: PetscCheck(matrix->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix rows");
1346: PetscCheck(matrix->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix cols");
1347: PetscCheck(matrix->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrix values");
1348: PetscCheck(matrixT, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT");
1349: PetscCheck(matrixT->row_offsets, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT rows");
1350: PetscCheck(matrixT->column_indices, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT cols");
1351: PetscCheck(matrixT->values, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CsrMatrixT values");
1352: if (!cusparsestruct->rowoffsets_gpu) { /* this may be absent when we did not construct the transpose with csr2csc */
1353: cusparsestruct->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
1354: cusparsestruct->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
1355: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
1356: }
1357: if (!cusparsestruct->csr2csc_i) {
1358: THRUSTARRAY csr2csc_a(matrix->num_entries);
1359: PetscCallThrust(thrust::sequence(thrust::device, csr2csc_a.begin(), csr2csc_a.end(), 0.0));
1361: indexBase = cusparseGetMatIndexBase(matstruct->descr);
1362: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1363: void *csr2cscBuffer;
1364: size_t csr2cscBufferSize;
1365: 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(),
1366: matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, &csr2cscBufferSize);
1367: PetscCallCUSPARSE(stat);
1368: PetscCallCUDA(cudaMalloc(&csr2cscBuffer, csr2cscBufferSize));
1369: #endif
1371: if (matrix->num_entries) {
1372: /* When there are no nonzeros, this routine mistakenly returns CUSPARSE_STATUS_INVALID_VALUE in
1373: mat_tests-ex62_15_mpiaijcusparse on ranks 0 and 2 with CUDA-11. But CUDA-10 is OK.
1374: I checked every parameters and they were just fine. I have no clue why cusparse complains.
1376: Per https://docs.nvidia.com/cuda/cusparse/index.html#csr2cscEx2, when nnz = 0, matrixT->row_offsets[]
1377: should be filled with indexBase. So I just take a shortcut here.
1378: */
1379: 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(),
1380: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1381: matrixT->row_offsets->data().get(), matrixT->column_indices->data().get(), cusparse_scalartype, CUSPARSE_ACTION_NUMERIC, indexBase, cusparsestruct->csr2cscAlg, csr2cscBuffer);
1382: PetscCallCUSPARSE(stat);
1383: #else
1384: matrixT->column_indices->data().get(), matrixT->row_offsets->data().get(), CUSPARSE_ACTION_NUMERIC, indexBase);
1385: PetscCallCUSPARSE(stat);
1386: #endif
1387: } else {
1388: matrixT->row_offsets->assign(matrixT->row_offsets->size(), indexBase);
1389: }
1391: cusparsestruct->csr2csc_i = new THRUSTINTARRAY(matrix->num_entries);
1392: PetscCallThrust(thrust::transform(thrust::device, matrixT->values->begin(), matrixT->values->end(), cusparsestruct->csr2csc_i->begin(), PetscScalarToPetscInt()));
1393: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
1394: PetscCallCUDA(cudaFree(csr2cscBuffer));
1395: #endif
1396: }
1397: PetscCallThrust(
1398: 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()));
1399: }
1400: PetscCall(PetscLogGpuTimeEnd());
1401: PetscCall(PetscLogEventEnd(MAT_CUSPARSEGenerateTranspose, A, 0, 0, 0));
1402: /* the compressed row indices is not used for matTranspose */
1403: matstructT->cprowIndices = NULL;
1404: /* assign the pointer */
1405: ((Mat_SeqAIJCUSPARSE *)A->spptr)->matTranspose = matstructT;
1406: A->transupdated = PETSC_TRUE;
1407: PetscFunctionReturn(PETSC_SUCCESS);
1408: }
1410: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1411: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1412: {
1413: const PetscScalar *barray;
1414: PetscScalar *xarray;
1415: thrust::device_ptr<const PetscScalar> bGPU;
1416: thrust::device_ptr<PetscScalar> xGPU;
1417: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1418: const Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
1419: const cusparseOperation_t op = CUSPARSE_OPERATION_NON_TRANSPOSE;
1420: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
1421: PetscInt m = A->rmap->n;
1423: PetscFunctionBegin;
1424: PetscCall(PetscLogGpuTimeBegin());
1425: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1426: PetscCall(VecCUDAGetArrayRead(b, &barray));
1427: xGPU = thrust::device_pointer_cast(xarray);
1428: bGPU = thrust::device_pointer_cast(barray);
1430: // Reorder b with the row permutation if needed, and wrap the result in fs->X
1431: if (fs->rpermIndices) {
1432: 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)));
1433: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1434: } else {
1435: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1436: }
1438: // Solve L Y = X
1439: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1440: // Note that cusparseSpSV_solve() secretly uses the external buffer used in cusparseSpSV_analysis()!
1441: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_L));
1443: // Solve U X = Y
1444: if (fs->cpermIndices) {
1445: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1446: } else {
1447: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1448: }
1449: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, op, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_U));
1451: // Reorder X with the column permutation if needed, and put the result back to x
1452: if (fs->cpermIndices) {
1453: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1454: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1455: }
1456: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1457: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1458: PetscCall(PetscLogGpuTimeEnd());
1459: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - m));
1460: PetscFunctionReturn(PETSC_SUCCESS);
1461: }
1463: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_LU(Mat A, Vec b, Vec x)
1464: {
1465: Mat_SeqAIJCUSPARSETriFactors *fs = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(A->spptr);
1466: Mat_SeqAIJ *aij = static_cast<Mat_SeqAIJ *>(A->data);
1467: const PetscScalar *barray;
1468: PetscScalar *xarray;
1469: thrust::device_ptr<const PetscScalar> bGPU;
1470: thrust::device_ptr<PetscScalar> xGPU;
1471: const cusparseOperation_t opA = CUSPARSE_OPERATION_TRANSPOSE;
1472: const cusparseSpSVAlg_t alg = CUSPARSE_SPSV_ALG_DEFAULT;
1473: PetscInt m = A->rmap->n;
1475: PetscFunctionBegin;
1476: PetscCall(PetscLogGpuTimeBegin());
1477: if (!fs->createdTransposeSpSVDescr) { // Call MatSolveTranspose() for the first time
1478: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
1479: PetscCallCUSPARSE(cusparseSpSV_bufferSize(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* The matrix is still L. We only do transpose solve with it */
1480: fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Lt, &fs->spsvBufferSize_Lt));
1482: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Ut));
1483: 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));
1484: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
1485: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Ut, fs->spsvBufferSize_Ut));
1486: fs->createdTransposeSpSVDescr = PETSC_TRUE;
1487: }
1489: if (!fs->updatedTransposeSpSVAnalysis) {
1490: 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));
1492: 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));
1493: fs->updatedTransposeSpSVAnalysis = PETSC_TRUE;
1494: }
1496: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1497: PetscCall(VecCUDAGetArrayRead(b, &barray));
1498: xGPU = thrust::device_pointer_cast(xarray);
1499: bGPU = thrust::device_pointer_cast(barray);
1501: // Reorder b with the row permutation if needed, and wrap the result in fs->X
1502: if (fs->rpermIndices) {
1503: 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)));
1504: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1505: } else {
1506: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1507: }
1509: // Solve Ut Y = X
1510: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1511: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_U, fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, alg, fs->spsvDescr_Ut));
1513: // Solve Lt X = Y
1514: if (fs->cpermIndices) { // if need to permute, we need to use the intermediate buffer X
1515: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, fs->X));
1516: } else {
1517: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1518: }
1519: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, opA, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, alg, fs->spsvDescr_Lt));
1521: // Reorder X with the column permutation if needed, and put the result back to x
1522: if (fs->cpermIndices) {
1523: PetscCallThrust(thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X), fs->cpermIndices->begin()),
1524: thrust::make_permutation_iterator(thrust::device_pointer_cast(fs->X + m), fs->cpermIndices->end()), xGPU));
1525: }
1527: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1528: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1529: PetscCall(PetscLogGpuTimeEnd());
1530: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - A->rmap->n));
1531: PetscFunctionReturn(PETSC_SUCCESS);
1532: }
1533: #else
1534: /* Why do we need to analyze the transposed matrix again? Can't we just use op(A) = CUSPARSE_OPERATION_TRANSPOSE in MatSolve_SeqAIJCUSPARSE? */
1535: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1536: {
1537: PetscInt n = xx->map->n;
1538: const PetscScalar *barray;
1539: PetscScalar *xarray;
1540: thrust::device_ptr<const PetscScalar> bGPU;
1541: thrust::device_ptr<PetscScalar> xGPU;
1542: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1543: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1544: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1545: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1547: PetscFunctionBegin;
1548: /* Analyze the matrix and create the transpose ... on the fly */
1549: if (!loTriFactorT && !upTriFactorT) {
1550: PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1551: loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1552: upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1553: }
1555: /* Get the GPU pointers */
1556: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1557: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1558: xGPU = thrust::device_pointer_cast(xarray);
1559: bGPU = thrust::device_pointer_cast(barray);
1561: PetscCall(PetscLogGpuTimeBegin());
1562: /* First, reorder with the row permutation */
1563: 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);
1565: /* First, solve U */
1566: 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(),
1567: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, xarray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1569: /* Then, solve L */
1570: 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(),
1571: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1573: /* Last, copy the solution, xGPU, into a temporary with the column permutation ... can't be done in place. */
1574: 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());
1576: /* Copy the temporary to the full solution. */
1577: thrust::copy(thrust::cuda::par.on(PetscDefaultCudaStream), tempGPU->begin(), tempGPU->end(), xGPU);
1579: /* restore */
1580: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1581: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1582: PetscCall(PetscLogGpuTimeEnd());
1583: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1584: PetscFunctionReturn(PETSC_SUCCESS);
1585: }
1587: static PetscErrorCode MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1588: {
1589: const PetscScalar *barray;
1590: PetscScalar *xarray;
1591: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1592: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1593: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1594: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1596: PetscFunctionBegin;
1597: /* Analyze the matrix and create the transpose ... on the fly */
1598: if (!loTriFactorT && !upTriFactorT) {
1599: PetscCall(MatSeqAIJCUSPARSEAnalyzeTransposeForSolve(A));
1600: loTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtrTranspose;
1601: upTriFactorT = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtrTranspose;
1602: }
1604: /* Get the GPU pointers */
1605: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1606: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1608: PetscCall(PetscLogGpuTimeBegin());
1609: /* First, solve U */
1610: 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(),
1611: upTriFactorT->csrMat->row_offsets->data().get(), upTriFactorT->csrMat->column_indices->data().get(), upTriFactorT->solveInfo, barray, tempGPU->data().get(), upTriFactorT->solvePolicy, upTriFactorT->solveBuffer));
1613: /* Then, solve L */
1614: 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(),
1615: loTriFactorT->csrMat->row_offsets->data().get(), loTriFactorT->csrMat->column_indices->data().get(), loTriFactorT->solveInfo, tempGPU->data().get(), xarray, loTriFactorT->solvePolicy, loTriFactorT->solveBuffer));
1617: /* restore */
1618: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1619: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1620: PetscCall(PetscLogGpuTimeEnd());
1621: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1622: PetscFunctionReturn(PETSC_SUCCESS);
1623: }
1625: static PetscErrorCode MatSolve_SeqAIJCUSPARSE(Mat A, Vec bb, Vec xx)
1626: {
1627: const PetscScalar *barray;
1628: PetscScalar *xarray;
1629: thrust::device_ptr<const PetscScalar> bGPU;
1630: thrust::device_ptr<PetscScalar> xGPU;
1631: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1632: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1633: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1634: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1636: PetscFunctionBegin;
1637: /* Get the GPU pointers */
1638: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1639: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1640: xGPU = thrust::device_pointer_cast(xarray);
1641: bGPU = thrust::device_pointer_cast(barray);
1643: PetscCall(PetscLogGpuTimeBegin());
1644: /* First, reorder with the row permutation */
1645: 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());
1647: /* Next, solve L */
1648: 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(),
1649: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, tempGPU->data().get(), xarray, loTriFactor->solvePolicy, loTriFactor->solveBuffer));
1651: /* Then, solve U */
1652: 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(),
1653: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, xarray, tempGPU->data().get(), upTriFactor->solvePolicy, upTriFactor->solveBuffer));
1655: /* Last, reorder with the column permutation */
1656: 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);
1658: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1659: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1660: PetscCall(PetscLogGpuTimeEnd());
1661: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1662: PetscFunctionReturn(PETSC_SUCCESS);
1663: }
1665: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_NaturalOrdering(Mat A, Vec bb, Vec xx)
1666: {
1667: const PetscScalar *barray;
1668: PetscScalar *xarray;
1669: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
1670: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->loTriFactorPtr;
1671: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactor = (Mat_SeqAIJCUSPARSETriFactorStruct *)cusparseTriFactors->upTriFactorPtr;
1672: THRUSTARRAY *tempGPU = (THRUSTARRAY *)cusparseTriFactors->workVector;
1674: PetscFunctionBegin;
1675: /* Get the GPU pointers */
1676: PetscCall(VecCUDAGetArrayWrite(xx, &xarray));
1677: PetscCall(VecCUDAGetArrayRead(bb, &barray));
1679: PetscCall(PetscLogGpuTimeBegin());
1680: /* First, solve L */
1681: 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(),
1682: loTriFactor->csrMat->row_offsets->data().get(), loTriFactor->csrMat->column_indices->data().get(), loTriFactor->solveInfo, barray, tempGPU->data().get(), loTriFactor->solvePolicy, loTriFactor->solveBuffer));
1684: /* Next, solve U */
1685: 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(),
1686: upTriFactor->csrMat->row_offsets->data().get(), upTriFactor->csrMat->column_indices->data().get(), upTriFactor->solveInfo, tempGPU->data().get(), xarray, upTriFactor->solvePolicy, upTriFactor->solveBuffer));
1688: PetscCall(VecCUDARestoreArrayRead(bb, &barray));
1689: PetscCall(VecCUDARestoreArrayWrite(xx, &xarray));
1690: PetscCall(PetscLogGpuTimeEnd());
1691: PetscCall(PetscLogGpuFlops(2.0 * cusparseTriFactors->nnz - A->cmap->n));
1692: PetscFunctionReturn(PETSC_SUCCESS);
1693: }
1694: #endif
1696: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
1697: static PetscErrorCode MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, const MatFactorInfo *)
1698: {
1699: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1700: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1701: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1702: CsrMatrix *Acsr;
1703: PetscInt m, nz;
1704: PetscBool flg;
1706: PetscFunctionBegin;
1707: if (PetscDefined(USE_DEBUG)) {
1708: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1709: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1710: }
1712: /* Copy A's value to fact */
1713: m = fact->rmap->n;
1714: nz = aij->nz;
1715: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1716: Acsr = (CsrMatrix *)Acusp->mat->mat;
1717: PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1719: PetscCall(PetscLogGpuTimeBegin());
1720: /* Factorize fact inplace */
1721: if (m)
1722: PetscCallCUSPARSE(cusparseXcsrilu02(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1723: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1724: if (PetscDefined(USE_DEBUG)) {
1725: int numerical_zero;
1726: cusparseStatus_t status;
1727: status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &numerical_zero);
1728: 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);
1729: }
1731: /* cusparseSpSV_analysis() is numeric, i.e., it requires valid matrix values, therefore, we do it after cusparseXcsrilu02()
1732: See discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/78
1733: */
1734: 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));
1736: 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));
1738: /* L, U values have changed, reset the flag to indicate we need to redo cusparseSpSV_analysis() for transpose solve */
1739: fs->updatedTransposeSpSVAnalysis = PETSC_FALSE;
1741: fact->offloadmask = PETSC_OFFLOAD_GPU;
1742: 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.
1743: fact->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
1744: fact->ops->matsolve = NULL;
1745: fact->ops->matsolvetranspose = NULL;
1746: PetscCall(PetscLogGpuTimeEnd());
1747: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1748: PetscFunctionReturn(PETSC_SUCCESS);
1749: }
1751: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(Mat fact, Mat A, IS, IS, const MatFactorInfo *info)
1752: {
1753: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1754: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1755: PetscInt m, nz;
1757: PetscFunctionBegin;
1758: if (PetscDefined(USE_DEBUG)) {
1759: PetscInt i;
1760: PetscBool flg, missing;
1762: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1763: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1764: 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);
1765: PetscCall(MatMissingDiagonal(A, &missing, &i));
1766: PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
1767: }
1769: /* Free the old stale stuff */
1770: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
1772: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
1773: but they will not be used. Allocate them just for easy debugging.
1774: */
1775: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
1777: fact->offloadmask = PETSC_OFFLOAD_BOTH;
1778: fact->factortype = MAT_FACTOR_ILU;
1779: fact->info.factor_mallocs = 0;
1780: fact->info.fill_ratio_given = info->fill;
1781: fact->info.fill_ratio_needed = 1.0;
1783: aij->row = NULL;
1784: aij->col = NULL;
1786: /* ====================================================================== */
1787: /* Copy A's i, j to fact and also allocate the value array of fact. */
1788: /* We'll do in-place factorization on fact */
1789: /* ====================================================================== */
1790: const int *Ai, *Aj;
1792: m = fact->rmap->n;
1793: nz = aij->nz;
1795: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
1796: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
1797: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(*fs->csrVal) * nz));
1798: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai. The returned Ai, Aj are 32-bit */
1799: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1800: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1802: /* ====================================================================== */
1803: /* Create descriptors for M, L, U */
1804: /* ====================================================================== */
1805: cusparseFillMode_t fillMode;
1806: cusparseDiagType_t diagType;
1808: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
1809: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
1810: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
1812: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
1813: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
1814: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
1815: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
1816: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
1817: */
1818: fillMode = CUSPARSE_FILL_MODE_LOWER;
1819: diagType = CUSPARSE_DIAG_TYPE_UNIT;
1820: 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));
1821: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1822: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1824: fillMode = CUSPARSE_FILL_MODE_UPPER;
1825: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
1826: 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));
1827: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
1828: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_U, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
1830: /* ========================================================================= */
1831: /* Query buffer sizes for csrilu0, SpSV and allocate buffers */
1832: /* ========================================================================= */
1833: PetscCallCUSPARSE(cusparseCreateCsrilu02Info(&fs->ilu0Info_M));
1834: if (m)
1835: PetscCallCUSPARSE(cusparseXcsrilu02_bufferSize(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1836: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, &fs->factBufferSize_M));
1838: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
1839: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
1841: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
1842: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
1844: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
1845: 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));
1847: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_U));
1848: 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));
1850: /* From my experiment with the example at https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSE/bicgstab,
1851: and discussion at https://github.com/NVIDIA/CUDALibrarySamples/issues/77,
1852: 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.
1853: To save memory, we make factBuffer_M share with the bigger of spsvBuffer_L/U.
1854: */
1855: if (fs->spsvBufferSize_L > fs->spsvBufferSize_U) {
1856: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
1857: fs->spsvBuffer_L = fs->factBuffer_M;
1858: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_U, fs->spsvBufferSize_U));
1859: } else {
1860: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_U, (size_t)fs->factBufferSize_M)));
1861: fs->spsvBuffer_U = fs->factBuffer_M;
1862: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
1863: }
1865: /* ========================================================================== */
1866: /* Perform analysis of ilu0 on M, SpSv on L and U */
1867: /* The lower(upper) triangular part of M has the same sparsity pattern as L(U)*/
1868: /* ========================================================================== */
1869: int structural_zero;
1870: cusparseStatus_t status;
1872: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
1873: if (m)
1874: PetscCallCUSPARSE(cusparseXcsrilu02_analysis(fs->handle, m, nz, /* cusparseXcsrilu02 errors out with empty matrices (m=0) */
1875: fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ilu0Info_M, fs->policy_M, fs->factBuffer_M));
1876: if (PetscDefined(USE_DEBUG)) {
1877: /* Function cusparseXcsrilu02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
1878: status = cusparseXcsrilu02_zeroPivot(fs->handle, fs->ilu0Info_M, &structural_zero);
1879: 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);
1880: }
1882: /* Estimate FLOPs of the numeric factorization */
1883: {
1884: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
1885: PetscInt *Ai, *Adiag, nzRow, nzLeft;
1886: PetscLogDouble flops = 0.0;
1888: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1889: Ai = Aseq->i;
1890: Adiag = Aseq->diag;
1891: for (PetscInt i = 0; i < m; i++) {
1892: if (Ai[i] < Adiag[i] && Adiag[i] < Ai[i + 1]) { /* There are nonzeros left to the diagonal of row i */
1893: nzRow = Ai[i + 1] - Ai[i];
1894: nzLeft = Adiag[i] - Ai[i];
1895: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
1896: and include the eliminated one will be updated, which incurs a multiplication and an addition.
1897: */
1898: nzLeft = (nzRow - 1) / 2;
1899: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
1900: }
1901: }
1902: fs->numericFactFlops = flops;
1903: }
1904: fact->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJCUSPARSE_ILU0;
1905: PetscFunctionReturn(PETSC_SUCCESS);
1906: }
1908: static PetscErrorCode MatSolve_SeqAIJCUSPARSE_ICC0(Mat fact, Vec b, Vec x)
1909: {
1910: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1911: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1912: const PetscScalar *barray;
1913: PetscScalar *xarray;
1915: PetscFunctionBegin;
1916: PetscCall(VecCUDAGetArrayWrite(x, &xarray));
1917: PetscCall(VecCUDAGetArrayRead(b, &barray));
1918: PetscCall(PetscLogGpuTimeBegin());
1920: /* Solve L*y = b */
1921: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, (void *)barray));
1922: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_Y, fs->Y));
1923: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_NON_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* L Y = X */
1924: fs->dnVecDescr_X, fs->dnVecDescr_Y, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_L));
1926: /* Solve Lt*x = y */
1927: PetscCallCUSPARSE(cusparseDnVecSetValues(fs->dnVecDescr_X, xarray));
1928: PetscCallCUSPARSE(cusparseSpSV_solve(fs->handle, CUSPARSE_OPERATION_TRANSPOSE, &PETSC_CUSPARSE_ONE, fs->spMatDescr_L, /* Lt X = Y */
1929: fs->dnVecDescr_Y, fs->dnVecDescr_X, cusparse_scalartype, CUSPARSE_SPSV_ALG_DEFAULT, fs->spsvDescr_Lt));
1931: PetscCall(VecCUDARestoreArrayRead(b, &barray));
1932: PetscCall(VecCUDARestoreArrayWrite(x, &xarray));
1934: PetscCall(PetscLogGpuTimeEnd());
1935: PetscCall(PetscLogGpuFlops(2.0 * aij->nz - fact->rmap->n));
1936: PetscFunctionReturn(PETSC_SUCCESS);
1937: }
1939: static PetscErrorCode MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, const MatFactorInfo *)
1940: {
1941: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1942: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1943: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
1944: CsrMatrix *Acsr;
1945: PetscInt m, nz;
1946: PetscBool flg;
1948: PetscFunctionBegin;
1949: if (PetscDefined(USE_DEBUG)) {
1950: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
1951: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
1952: }
1954: /* Copy A's value to fact */
1955: m = fact->rmap->n;
1956: nz = aij->nz;
1957: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
1958: Acsr = (CsrMatrix *)Acusp->mat->mat;
1959: PetscCallCUDA(cudaMemcpyAsync(fs->csrVal, Acsr->values->data().get(), sizeof(PetscScalar) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
1961: /* Factorize fact inplace */
1962: /* https://docs.nvidia.com/cuda/cusparse/index.html#csric02_solve
1963: Function csric02() only takes the lower triangular part of matrix A to perform factorization.
1964: The matrix type must be CUSPARSE_MATRIX_TYPE_GENERAL, the fill mode and diagonal type are ignored,
1965: and the strictly upper triangular part is ignored and never touched. It does not matter if A is Hermitian or not.
1966: In other words, from the point of view of csric02() A is Hermitian and only the lower triangular part is provided.
1967: */
1968: 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));
1969: if (PetscDefined(USE_DEBUG)) {
1970: int numerical_zero;
1971: cusparseStatus_t status;
1972: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &numerical_zero);
1973: 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);
1974: }
1976: 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));
1978: /* Note that cusparse reports this error if we use double and CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE
1979: ** On entry to cusparseSpSV_analysis(): conjugate transpose (opA) is not supported for matA data type, current -> CUDA_R_64F
1980: */
1981: 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));
1983: fact->offloadmask = PETSC_OFFLOAD_GPU;
1984: fact->ops->solve = MatSolve_SeqAIJCUSPARSE_ICC0;
1985: fact->ops->solvetranspose = MatSolve_SeqAIJCUSPARSE_ICC0;
1986: fact->ops->matsolve = NULL;
1987: fact->ops->matsolvetranspose = NULL;
1988: PetscCall(PetscLogGpuFlops(fs->numericFactFlops));
1989: PetscFunctionReturn(PETSC_SUCCESS);
1990: }
1992: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(Mat fact, Mat A, IS, const MatFactorInfo *info)
1993: {
1994: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)fact->spptr;
1995: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)fact->data;
1996: PetscInt m, nz;
1998: PetscFunctionBegin;
1999: if (PetscDefined(USE_DEBUG)) {
2000: PetscInt i;
2001: PetscBool flg, missing;
2003: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2004: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Expected MATSEQAIJCUSPARSE, but input is %s", ((PetscObject)A)->type_name);
2005: 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);
2006: PetscCall(MatMissingDiagonal(A, &missing, &i));
2007: PetscCheck(!missing, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry %" PetscInt_FMT, i);
2008: }
2010: /* Free the old stale stuff */
2011: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&fs));
2013: /* Copy over A's meta data to fact. Note that we also allocated fact's i,j,a on host,
2014: but they will not be used. Allocate them just for easy debugging.
2015: */
2016: PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_TRUE /*malloc*/));
2018: fact->offloadmask = PETSC_OFFLOAD_BOTH;
2019: fact->factortype = MAT_FACTOR_ICC;
2020: fact->info.factor_mallocs = 0;
2021: fact->info.fill_ratio_given = info->fill;
2022: fact->info.fill_ratio_needed = 1.0;
2024: aij->row = NULL;
2025: aij->col = NULL;
2027: /* ====================================================================== */
2028: /* Copy A's i, j to fact and also allocate the value array of fact. */
2029: /* We'll do in-place factorization on fact */
2030: /* ====================================================================== */
2031: const int *Ai, *Aj;
2033: m = fact->rmap->n;
2034: nz = aij->nz;
2036: PetscCallCUDA(cudaMalloc((void **)&fs->csrRowPtr32, sizeof(*fs->csrRowPtr32) * (m + 1)));
2037: PetscCallCUDA(cudaMalloc((void **)&fs->csrColIdx32, sizeof(*fs->csrColIdx32) * nz));
2038: PetscCallCUDA(cudaMalloc((void **)&fs->csrVal, sizeof(PetscScalar) * nz));
2039: PetscCall(MatSeqAIJCUSPARSEGetIJ(A, PETSC_FALSE, &Ai, &Aj)); /* Do not use compressed Ai */
2040: PetscCallCUDA(cudaMemcpyAsync(fs->csrRowPtr32, Ai, sizeof(*Ai) * (m + 1), cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2041: PetscCallCUDA(cudaMemcpyAsync(fs->csrColIdx32, Aj, sizeof(*Aj) * nz, cudaMemcpyDeviceToDevice, PetscDefaultCudaStream));
2043: /* ====================================================================== */
2044: /* Create mat descriptors for M, L */
2045: /* ====================================================================== */
2046: cusparseFillMode_t fillMode;
2047: cusparseDiagType_t diagType;
2049: PetscCallCUSPARSE(cusparseCreateMatDescr(&fs->matDescr_M));
2050: PetscCallCUSPARSE(cusparseSetMatIndexBase(fs->matDescr_M, CUSPARSE_INDEX_BASE_ZERO));
2051: PetscCallCUSPARSE(cusparseSetMatType(fs->matDescr_M, CUSPARSE_MATRIX_TYPE_GENERAL));
2053: /* https://docs.nvidia.com/cuda/cusparse/index.html#cusparseDiagType_t
2054: cusparseDiagType_t: This type indicates if the matrix diagonal entries are unity. The diagonal elements are always
2055: assumed to be present, but if CUSPARSE_DIAG_TYPE_UNIT is passed to an API routine, then the routine assumes that
2056: all diagonal entries are unity and will not read or modify those entries. Note that in this case the routine
2057: assumes the diagonal entries are equal to one, regardless of what those entries are actually set to in memory.
2058: */
2059: fillMode = CUSPARSE_FILL_MODE_LOWER;
2060: diagType = CUSPARSE_DIAG_TYPE_NON_UNIT;
2061: 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));
2062: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_FILL_MODE, &fillMode, sizeof(fillMode)));
2063: PetscCallCUSPARSE(cusparseSpMatSetAttribute(fs->spMatDescr_L, CUSPARSE_SPMAT_DIAG_TYPE, &diagType, sizeof(diagType)));
2065: /* ========================================================================= */
2066: /* Query buffer sizes for csric0, SpSV of L and Lt, and allocate buffers */
2067: /* ========================================================================= */
2068: PetscCallCUSPARSE(cusparseCreateCsric02Info(&fs->ic0Info_M));
2069: if (m) PetscCallCUSPARSE(cusparseXcsric02_bufferSize(fs->handle, m, nz, fs->matDescr_M, fs->csrVal, fs->csrRowPtr32, fs->csrColIdx32, fs->ic0Info_M, &fs->factBufferSize_M));
2071: PetscCallCUDA(cudaMalloc((void **)&fs->X, sizeof(PetscScalar) * m));
2072: PetscCallCUDA(cudaMalloc((void **)&fs->Y, sizeof(PetscScalar) * m));
2074: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_X, m, fs->X, cusparse_scalartype));
2075: PetscCallCUSPARSE(cusparseCreateDnVec(&fs->dnVecDescr_Y, m, fs->Y, cusparse_scalartype));
2077: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_L));
2078: 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));
2080: PetscCallCUSPARSE(cusparseSpSV_createDescr(&fs->spsvDescr_Lt));
2081: 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));
2083: /* To save device memory, we make the factorization buffer share with one of the solver buffer.
2084: See also comments in MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0().
2085: */
2086: if (fs->spsvBufferSize_L > fs->spsvBufferSize_Lt) {
2087: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_L, (size_t)fs->factBufferSize_M)));
2088: fs->spsvBuffer_L = fs->factBuffer_M;
2089: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_Lt, fs->spsvBufferSize_Lt));
2090: } else {
2091: PetscCallCUDA(cudaMalloc((void **)&fs->factBuffer_M, PetscMax(fs->spsvBufferSize_Lt, (size_t)fs->factBufferSize_M)));
2092: fs->spsvBuffer_Lt = fs->factBuffer_M;
2093: PetscCallCUDA(cudaMalloc((void **)&fs->spsvBuffer_L, fs->spsvBufferSize_L));
2094: }
2096: /* ========================================================================== */
2097: /* Perform analysis of ic0 on M */
2098: /* The lower triangular part of M has the same sparsity pattern as L */
2099: /* ========================================================================== */
2100: int structural_zero;
2101: cusparseStatus_t status;
2103: fs->policy_M = CUSPARSE_SOLVE_POLICY_USE_LEVEL;
2104: 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));
2105: if (PetscDefined(USE_DEBUG)) {
2106: /* Function cusparseXcsric02_zeroPivot() is a blocking call. It calls cudaDeviceSynchronize() to make sure all previous kernels are done. */
2107: status = cusparseXcsric02_zeroPivot(fs->handle, fs->ic0Info_M, &structural_zero);
2108: 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);
2109: }
2111: /* Estimate FLOPs of the numeric factorization */
2112: {
2113: Mat_SeqAIJ *Aseq = (Mat_SeqAIJ *)A->data;
2114: PetscInt *Ai, nzRow, nzLeft;
2115: PetscLogDouble flops = 0.0;
2117: Ai = Aseq->i;
2118: for (PetscInt i = 0; i < m; i++) {
2119: nzRow = Ai[i + 1] - Ai[i];
2120: if (nzRow > 1) {
2121: /* We want to eliminate nonzeros left to the diagonal one by one. Assume each time, nonzeros right
2122: and include the eliminated one will be updated, which incurs a multiplication and an addition.
2123: */
2124: nzLeft = (nzRow - 1) / 2;
2125: flops += nzLeft * (2.0 * nzRow - nzLeft + 1);
2126: }
2127: }
2128: fs->numericFactFlops = flops;
2129: }
2130: fact->ops->choleskyfactornumeric = MatICCFactorNumeric_SeqAIJCUSPARSE_ICC0;
2131: PetscFunctionReturn(PETSC_SUCCESS);
2132: }
2133: #endif
2135: static PetscErrorCode MatLUFactorNumeric_SeqAIJCUSPARSE(Mat B, Mat A, const MatFactorInfo *info)
2136: {
2137: // use_cpu_solve is a field in Mat_SeqAIJCUSPARSE. B, a factored matrix, uses Mat_SeqAIJCUSPARSETriFactors.
2138: Mat_SeqAIJCUSPARSE *cusparsestruct = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2140: PetscFunctionBegin;
2141: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2142: PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
2143: B->offloadmask = PETSC_OFFLOAD_CPU;
2145: if (!cusparsestruct->use_cpu_solve) {
2146: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2147: B->ops->solve = MatSolve_SeqAIJCUSPARSE_LU;
2148: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_LU;
2149: #else
2150: /* determine which version of MatSolve needs to be used. */
2151: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2152: IS isrow = b->row, iscol = b->col;
2153: PetscBool row_identity, col_identity;
2155: PetscCall(ISIdentity(isrow, &row_identity));
2156: PetscCall(ISIdentity(iscol, &col_identity));
2157: if (row_identity && col_identity) {
2158: B->ops->solve = MatSolve_SeqAIJCUSPARSE_NaturalOrdering;
2159: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE_NaturalOrdering;
2160: } else {
2161: B->ops->solve = MatSolve_SeqAIJCUSPARSE;
2162: B->ops->solvetranspose = MatSolveTranspose_SeqAIJCUSPARSE;
2163: }
2164: #endif
2165: }
2166: B->ops->matsolve = NULL;
2167: B->ops->matsolvetranspose = NULL;
2169: /* get the triangular factors */
2170: if (!cusparsestruct->use_cpu_solve) PetscCall(MatSeqAIJCUSPARSEILUAnalysisAndCopyToGPU(B));
2171: PetscFunctionReturn(PETSC_SUCCESS);
2172: }
2174: static PetscErrorCode MatLUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2175: {
2176: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = static_cast<Mat_SeqAIJCUSPARSETriFactors *>(B->spptr);
2178: PetscFunctionBegin;
2179: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2180: PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2181: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2182: PetscFunctionReturn(PETSC_SUCCESS);
2183: }
2185: static PetscErrorCode MatILUFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2186: {
2187: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2189: PetscFunctionBegin;
2190: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2191: PetscBool row_identity = PETSC_FALSE, col_identity = PETSC_FALSE;
2192: if (cusparseTriFactors->factorizeOnDevice) {
2193: PetscCall(ISIdentity(isrow, &row_identity));
2194: PetscCall(ISIdentity(iscol, &col_identity));
2195: }
2196: if (!info->levels && row_identity && col_identity) {
2197: PetscCall(MatILUFactorSymbolic_SeqAIJCUSPARSE_ILU0(B, A, isrow, iscol, info));
2198: } else
2199: #endif
2200: {
2201: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2202: PetscCall(MatILUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
2203: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJCUSPARSE;
2204: }
2205: PetscFunctionReturn(PETSC_SUCCESS);
2206: }
2208: static PetscErrorCode MatICCFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2209: {
2210: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2212: PetscFunctionBegin;
2213: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2214: PetscBool perm_identity = PETSC_FALSE;
2215: if (cusparseTriFactors->factorizeOnDevice) PetscCall(ISIdentity(perm, &perm_identity));
2216: if (!info->levels && perm_identity) {
2217: PetscCall(MatICCFactorSymbolic_SeqAIJCUSPARSE_ICC0(B, A, perm, info));
2218: } else
2219: #endif
2220: {
2221: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2222: PetscCall(MatICCFactorSymbolic_SeqAIJ(B, A, perm, info));
2223: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2224: }
2225: PetscFunctionReturn(PETSC_SUCCESS);
2226: }
2228: static PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJCUSPARSE(Mat B, Mat A, IS perm, const MatFactorInfo *info)
2229: {
2230: Mat_SeqAIJCUSPARSETriFactors *cusparseTriFactors = (Mat_SeqAIJCUSPARSETriFactors *)B->spptr;
2232: PetscFunctionBegin;
2233: PetscCall(MatSeqAIJCUSPARSETriFactors_Reset(&cusparseTriFactors));
2234: PetscCall(MatCholeskyFactorSymbolic_SeqAIJ(B, A, perm, info));
2235: B->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJCUSPARSE;
2236: PetscFunctionReturn(PETSC_SUCCESS);
2237: }
2239: static PetscErrorCode MatFactorGetSolverType_seqaij_cusparse(Mat, MatSolverType *type)
2240: {
2241: PetscFunctionBegin;
2242: *type = MATSOLVERCUSPARSE;
2243: PetscFunctionReturn(PETSC_SUCCESS);
2244: }
2246: /*MC
2247: MATSOLVERCUSPARSE = "cusparse" - A matrix type providing triangular solvers for seq matrices
2248: on a single GPU of type, `MATSEQAIJCUSPARSE`. Currently supported
2249: algorithms are ILU(k) and ICC(k). Typically, deeper factorizations (larger k) results in poorer
2250: performance in the triangular solves. Full LU, and Cholesky decompositions can be solved through the
2251: CuSPARSE triangular solve algorithm. However, the performance can be quite poor and thus these
2252: algorithms are not recommended. This class does NOT support direct solver operations.
2254: Level: beginner
2256: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJCUSPARSE`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJCUSPARSE()`,
2257: `MATAIJCUSPARSE`, `MatCreateAIJCUSPARSE()`, `MatCUSPARSESetFormat()`, `MatCUSPARSEStorageFormat`, `MatCUSPARSEFormatOperation`
2258: M*/
2260: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijcusparse_cusparse(Mat A, MatFactorType ftype, Mat *B)
2261: {
2262: PetscInt n = A->rmap->n;
2263: PetscBool factOnDevice, factOnHost;
2264: char *prefix;
2265: char factPlace[32] = "device"; /* the default */
2267: PetscFunctionBegin;
2268: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2269: PetscCall(MatSetSizes(*B, n, n, n, n));
2270: (*B)->factortype = ftype; // factortype makes MatSetType() allocate spptr of type Mat_SeqAIJCUSPARSETriFactors
2271: PetscCall(MatSetType(*B, MATSEQAIJCUSPARSE));
2273: prefix = (*B)->factorprefix ? (*B)->factorprefix : ((PetscObject)A)->prefix;
2274: PetscOptionsBegin(PetscObjectComm((PetscObject)*B), prefix, "MatGetFactor", "Mat");
2275: PetscCall(PetscOptionsString("-mat_factor_bind_factorization", "Do matrix factorization on host or device when possible", "MatGetFactor", NULL, factPlace, sizeof(factPlace), NULL));
2276: PetscOptionsEnd();
2277: PetscCall(PetscStrcasecmp("device", factPlace, &factOnDevice));
2278: PetscCall(PetscStrcasecmp("host", factPlace, &factOnHost));
2279: 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);
2280: ((Mat_SeqAIJCUSPARSETriFactors *)(*B)->spptr)->factorizeOnDevice = factOnDevice;
2282: if (A->boundtocpu && A->bindingpropagates) PetscCall(MatBindToCPU(*B, PETSC_TRUE));
2283: if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
2284: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2285: if (!A->boundtocpu) {
2286: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJCUSPARSE;
2287: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJCUSPARSE;
2288: } else {
2289: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJ;
2290: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJ;
2291: }
2292: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
2293: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILU]));
2294: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILUDT]));
2295: } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
2296: if (!A->boundtocpu) {
2297: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJCUSPARSE;
2298: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJCUSPARSE;
2299: } else {
2300: (*B)->ops->iccfactorsymbolic = MatICCFactorSymbolic_SeqAIJ;
2301: (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJ;
2302: }
2303: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
2304: PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
2305: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported for CUSPARSE Matrix Types");
2307: PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
2308: (*B)->canuseordering = PETSC_TRUE;
2309: PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_cusparse));
2310: PetscFunctionReturn(PETSC_SUCCESS);
2311: }
2313: static PetscErrorCode MatSeqAIJCUSPARSECopyFromGPU(Mat A)
2314: {
2315: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2316: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2317: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2318: Mat_SeqAIJCUSPARSETriFactors *fs = (Mat_SeqAIJCUSPARSETriFactors *)A->spptr;
2319: #endif
2321: PetscFunctionBegin;
2322: if (A->offloadmask == PETSC_OFFLOAD_GPU) {
2323: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2324: if (A->factortype == MAT_FACTOR_NONE) {
2325: CsrMatrix *matrix = (CsrMatrix *)cusp->mat->mat;
2326: PetscCallCUDA(cudaMemcpy(a->a, matrix->values->data().get(), a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2327: }
2328: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2329: else if (fs->csrVal) {
2330: /* We have a factorized matrix on device and are able to copy it to host */
2331: PetscCallCUDA(cudaMemcpy(a->a, fs->csrVal, a->nz * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
2332: }
2333: #endif
2334: else
2335: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for copying this type of factorized matrix from device to host");
2336: PetscCall(PetscLogGpuToCpu(a->nz * sizeof(PetscScalar)));
2337: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyFromGPU, A, 0, 0, 0));
2338: A->offloadmask = PETSC_OFFLOAD_BOTH;
2339: }
2340: PetscFunctionReturn(PETSC_SUCCESS);
2341: }
2343: static PetscErrorCode MatSeqAIJGetArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2344: {
2345: PetscFunctionBegin;
2346: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2347: *array = ((Mat_SeqAIJ *)A->data)->a;
2348: PetscFunctionReturn(PETSC_SUCCESS);
2349: }
2351: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2352: {
2353: PetscFunctionBegin;
2354: A->offloadmask = PETSC_OFFLOAD_CPU;
2355: *array = NULL;
2356: PetscFunctionReturn(PETSC_SUCCESS);
2357: }
2359: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJCUSPARSE(Mat A, const PetscScalar *array[])
2360: {
2361: PetscFunctionBegin;
2362: PetscCall(MatSeqAIJCUSPARSECopyFromGPU(A));
2363: *array = ((Mat_SeqAIJ *)A->data)->a;
2364: PetscFunctionReturn(PETSC_SUCCESS);
2365: }
2367: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJCUSPARSE(Mat, const PetscScalar *array[])
2368: {
2369: PetscFunctionBegin;
2370: *array = NULL;
2371: PetscFunctionReturn(PETSC_SUCCESS);
2372: }
2374: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2375: {
2376: PetscFunctionBegin;
2377: *array = ((Mat_SeqAIJ *)A->data)->a;
2378: PetscFunctionReturn(PETSC_SUCCESS);
2379: }
2381: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJCUSPARSE(Mat A, PetscScalar *array[])
2382: {
2383: PetscFunctionBegin;
2384: A->offloadmask = PETSC_OFFLOAD_CPU;
2385: *array = NULL;
2386: PetscFunctionReturn(PETSC_SUCCESS);
2387: }
2389: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJCUSPARSE(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
2390: {
2391: Mat_SeqAIJCUSPARSE *cusp;
2392: CsrMatrix *matrix;
2394: PetscFunctionBegin;
2395: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2396: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2397: cusp = static_cast<Mat_SeqAIJCUSPARSE *>(A->spptr);
2398: PetscCheck(cusp != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "cusp is NULL");
2399: matrix = (CsrMatrix *)cusp->mat->mat;
2401: if (i) {
2402: #if !defined(PETSC_USE_64BIT_INDICES)
2403: *i = matrix->row_offsets->data().get();
2404: #else
2405: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2406: #endif
2407: }
2408: if (j) {
2409: #if !defined(PETSC_USE_64BIT_INDICES)
2410: *j = matrix->column_indices->data().get();
2411: #else
2412: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "cuSparse does not supported 64-bit indices");
2413: #endif
2414: }
2415: if (a) *a = matrix->values->data().get();
2416: if (mtype) *mtype = PETSC_MEMTYPE_CUDA;
2417: PetscFunctionReturn(PETSC_SUCCESS);
2418: }
2420: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat A)
2421: {
2422: Mat_SeqAIJCUSPARSE *cusparsestruct = (Mat_SeqAIJCUSPARSE *)A->spptr;
2423: Mat_SeqAIJCUSPARSEMultStruct *matstruct = cusparsestruct->mat;
2424: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2425: PetscInt m = A->rmap->n, *ii, *ridx, tmp;
2426: cusparseStatus_t stat;
2427: PetscBool both = PETSC_TRUE;
2429: PetscFunctionBegin;
2430: PetscCheck(!A->boundtocpu, PETSC_COMM_SELF, PETSC_ERR_GPU, "Cannot copy to GPU");
2431: if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
2432: if (A->nonzerostate == cusparsestruct->nonzerostate && cusparsestruct->format == MAT_CUSPARSE_CSR) { /* Copy values only */
2433: CsrMatrix *matrix;
2434: matrix = (CsrMatrix *)cusparsestruct->mat->mat;
2436: PetscCheck(!a->nz || a->a, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR values");
2437: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2438: matrix->values->assign(a->a, a->a + a->nz);
2439: PetscCallCUDA(WaitForCUDA());
2440: PetscCall(PetscLogCpuToGpu(a->nz * sizeof(PetscScalar)));
2441: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2442: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
2443: } else {
2444: PetscInt nnz;
2445: PetscCall(PetscLogEventBegin(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2446: PetscCall(MatSeqAIJCUSPARSEMultStruct_Destroy(&cusparsestruct->mat, cusparsestruct->format));
2447: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_TRUE));
2448: delete cusparsestruct->workVector;
2449: delete cusparsestruct->rowoffsets_gpu;
2450: cusparsestruct->workVector = NULL;
2451: cusparsestruct->rowoffsets_gpu = NULL;
2452: try {
2453: if (a->compressedrow.use) {
2454: m = a->compressedrow.nrows;
2455: ii = a->compressedrow.i;
2456: ridx = a->compressedrow.rindex;
2457: } else {
2458: m = A->rmap->n;
2459: ii = a->i;
2460: ridx = NULL;
2461: }
2462: PetscCheck(ii, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR row data");
2463: if (!a->a) {
2464: nnz = ii[m];
2465: both = PETSC_FALSE;
2466: } else nnz = a->nz;
2467: PetscCheck(!nnz || a->j, PETSC_COMM_SELF, PETSC_ERR_GPU, "Missing CSR column data");
2469: /* create cusparse matrix */
2470: cusparsestruct->nrows = m;
2471: matstruct = new Mat_SeqAIJCUSPARSEMultStruct;
2472: PetscCallCUSPARSE(cusparseCreateMatDescr(&matstruct->descr));
2473: PetscCallCUSPARSE(cusparseSetMatIndexBase(matstruct->descr, CUSPARSE_INDEX_BASE_ZERO));
2474: PetscCallCUSPARSE(cusparseSetMatType(matstruct->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
2476: PetscCallCUDA(cudaMalloc((void **)&matstruct->alpha_one, sizeof(PetscScalar)));
2477: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_zero, sizeof(PetscScalar)));
2478: PetscCallCUDA(cudaMalloc((void **)&matstruct->beta_one, sizeof(PetscScalar)));
2479: PetscCallCUDA(cudaMemcpy(matstruct->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2480: PetscCallCUDA(cudaMemcpy(matstruct->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2481: PetscCallCUDA(cudaMemcpy(matstruct->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
2482: PetscCallCUSPARSE(cusparseSetPointerMode(cusparsestruct->handle, CUSPARSE_POINTER_MODE_DEVICE));
2484: /* Build a hybrid/ellpack matrix if this option is chosen for the storage */
2485: if (cusparsestruct->format == MAT_CUSPARSE_CSR) {
2486: /* set the matrix */
2487: CsrMatrix *mat = new CsrMatrix;
2488: mat->num_rows = m;
2489: mat->num_cols = A->cmap->n;
2490: mat->num_entries = nnz;
2491: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2492: mat->row_offsets->assign(ii, ii + m + 1);
2494: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2495: mat->column_indices->assign(a->j, a->j + nnz);
2497: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2498: if (a->a) mat->values->assign(a->a, a->a + nnz);
2500: /* assign the pointer */
2501: matstruct->mat = mat;
2502: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2503: if (mat->num_rows) { /* cusparse errors on empty matrices! */
2504: 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 */
2505: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2506: PetscCallCUSPARSE(stat);
2507: }
2508: #endif
2509: } else if (cusparsestruct->format == MAT_CUSPARSE_ELL || cusparsestruct->format == MAT_CUSPARSE_HYB) {
2510: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2511: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_CUSPARSE_ELL and MAT_CUSPARSE_HYB are not supported since CUDA-11.0");
2512: #else
2513: CsrMatrix *mat = new CsrMatrix;
2514: mat->num_rows = m;
2515: mat->num_cols = A->cmap->n;
2516: mat->num_entries = nnz;
2517: PetscCallCXX(mat->row_offsets = new THRUSTINTARRAY32(m + 1));
2518: mat->row_offsets->assign(ii, ii + m + 1);
2520: PetscCallCXX(mat->column_indices = new THRUSTINTARRAY32(nnz));
2521: mat->column_indices->assign(a->j, a->j + nnz);
2523: PetscCallCXX(mat->values = new THRUSTARRAY(nnz));
2524: if (a->a) mat->values->assign(a->a, a->a + nnz);
2526: cusparseHybMat_t hybMat;
2527: PetscCallCUSPARSE(cusparseCreateHybMat(&hybMat));
2528: cusparseHybPartition_t partition = cusparsestruct->format == MAT_CUSPARSE_ELL ? CUSPARSE_HYB_PARTITION_MAX : CUSPARSE_HYB_PARTITION_AUTO;
2529: 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);
2530: PetscCallCUSPARSE(stat);
2531: /* assign the pointer */
2532: matstruct->mat = hybMat;
2534: if (mat) {
2535: if (mat->values) delete (THRUSTARRAY *)mat->values;
2536: if (mat->column_indices) delete (THRUSTINTARRAY32 *)mat->column_indices;
2537: if (mat->row_offsets) delete (THRUSTINTARRAY32 *)mat->row_offsets;
2538: delete (CsrMatrix *)mat;
2539: }
2540: #endif
2541: }
2543: /* assign the compressed row indices */
2544: if (a->compressedrow.use) {
2545: PetscCallCXX(cusparsestruct->workVector = new THRUSTARRAY(m));
2546: PetscCallCXX(matstruct->cprowIndices = new THRUSTINTARRAY(m));
2547: matstruct->cprowIndices->assign(ridx, ridx + m);
2548: tmp = m;
2549: } else {
2550: cusparsestruct->workVector = NULL;
2551: matstruct->cprowIndices = NULL;
2552: tmp = 0;
2553: }
2554: PetscCall(PetscLogCpuToGpu(((m + 1) + (a->nz)) * sizeof(int) + tmp * sizeof(PetscInt) + (3 + (a->nz)) * sizeof(PetscScalar)));
2556: /* assign the pointer */
2557: cusparsestruct->mat = matstruct;
2558: } catch (char *ex) {
2559: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "CUSPARSE error: %s", ex);
2560: }
2561: PetscCallCUDA(WaitForCUDA());
2562: PetscCall(PetscLogEventEnd(MAT_CUSPARSECopyToGPU, A, 0, 0, 0));
2563: cusparsestruct->nonzerostate = A->nonzerostate;
2564: }
2565: if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
2566: }
2567: PetscFunctionReturn(PETSC_SUCCESS);
2568: }
2570: struct VecCUDAPlusEquals {
2571: template <typename Tuple>
2572: __host__ __device__ void operator()(Tuple t)
2573: {
2574: thrust::get<1>(t) = thrust::get<1>(t) + thrust::get<0>(t);
2575: }
2576: };
2578: struct VecCUDAEquals {
2579: template <typename Tuple>
2580: __host__ __device__ void operator()(Tuple t)
2581: {
2582: thrust::get<1>(t) = thrust::get<0>(t);
2583: }
2584: };
2586: struct VecCUDAEqualsReverse {
2587: template <typename Tuple>
2588: __host__ __device__ void operator()(Tuple t)
2589: {
2590: thrust::get<0>(t) = thrust::get<1>(t);
2591: }
2592: };
2594: struct MatMatCusparse {
2595: PetscBool cisdense;
2596: PetscScalar *Bt;
2597: Mat X;
2598: PetscBool reusesym; /* Cusparse does not have split symbolic and numeric phases for sparse matmat operations */
2599: PetscLogDouble flops;
2600: CsrMatrix *Bcsr;
2602: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2603: cusparseSpMatDescr_t matSpBDescr;
2604: PetscBool initialized; /* C = alpha op(A) op(B) + beta C */
2605: cusparseDnMatDescr_t matBDescr;
2606: cusparseDnMatDescr_t matCDescr;
2607: PetscInt Blda, Clda; /* Record leading dimensions of B and C here to detect changes*/
2608: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2609: void *dBuffer4;
2610: void *dBuffer5;
2611: #endif
2612: size_t mmBufferSize;
2613: void *mmBuffer;
2614: void *mmBuffer2; /* SpGEMM WorkEstimation buffer */
2615: cusparseSpGEMMDescr_t spgemmDesc;
2616: #endif
2617: };
2619: static PetscErrorCode MatDestroy_MatMatCusparse(void *data)
2620: {
2621: MatMatCusparse *mmdata = (MatMatCusparse *)data;
2623: PetscFunctionBegin;
2624: PetscCallCUDA(cudaFree(mmdata->Bt));
2625: delete mmdata->Bcsr;
2626: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2627: if (mmdata->matSpBDescr) PetscCallCUSPARSE(cusparseDestroySpMat(mmdata->matSpBDescr));
2628: if (mmdata->matBDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2629: if (mmdata->matCDescr) PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2630: if (mmdata->spgemmDesc) PetscCallCUSPARSE(cusparseSpGEMM_destroyDescr(mmdata->spgemmDesc));
2631: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2632: if (mmdata->dBuffer4) PetscCallCUDA(cudaFree(mmdata->dBuffer4));
2633: if (mmdata->dBuffer5) PetscCallCUDA(cudaFree(mmdata->dBuffer5));
2634: #endif
2635: if (mmdata->mmBuffer) PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2636: if (mmdata->mmBuffer2) PetscCallCUDA(cudaFree(mmdata->mmBuffer2));
2637: #endif
2638: PetscCall(MatDestroy(&mmdata->X));
2639: PetscCall(PetscFree(data));
2640: PetscFunctionReturn(PETSC_SUCCESS);
2641: }
2643: #include <../src/mat/impls/dense/seq/dense.h>
2645: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2646: {
2647: Mat_Product *product = C->product;
2648: Mat A, B;
2649: PetscInt m, n, blda, clda;
2650: PetscBool flg, biscuda;
2651: Mat_SeqAIJCUSPARSE *cusp;
2652: cusparseStatus_t stat;
2653: cusparseOperation_t opA;
2654: const PetscScalar *barray;
2655: PetscScalar *carray;
2656: MatMatCusparse *mmdata;
2657: Mat_SeqAIJCUSPARSEMultStruct *mat;
2658: CsrMatrix *csrmat;
2660: PetscFunctionBegin;
2661: MatCheckProduct(C, 1);
2662: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2663: mmdata = (MatMatCusparse *)product->data;
2664: A = product->A;
2665: B = product->B;
2666: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2667: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2668: /* currently CopyToGpu does not copy if the matrix is bound to CPU
2669: Instead of silently accepting the wrong answer, I prefer to raise the error */
2670: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2671: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2672: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2673: switch (product->type) {
2674: case MATPRODUCT_AB:
2675: case MATPRODUCT_PtAP:
2676: mat = cusp->mat;
2677: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2678: m = A->rmap->n;
2679: n = B->cmap->n;
2680: break;
2681: case MATPRODUCT_AtB:
2682: if (!A->form_explicit_transpose) {
2683: mat = cusp->mat;
2684: opA = CUSPARSE_OPERATION_TRANSPOSE;
2685: } else {
2686: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
2687: mat = cusp->matTranspose;
2688: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2689: }
2690: m = A->cmap->n;
2691: n = B->cmap->n;
2692: break;
2693: case MATPRODUCT_ABt:
2694: case MATPRODUCT_RARt:
2695: mat = cusp->mat;
2696: opA = CUSPARSE_OPERATION_NON_TRANSPOSE;
2697: m = A->rmap->n;
2698: n = B->rmap->n;
2699: break;
2700: default:
2701: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2702: }
2703: PetscCheck(mat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing Mat_SeqAIJCUSPARSEMultStruct");
2704: csrmat = (CsrMatrix *)mat->mat;
2705: /* if the user passed a CPU matrix, copy the data to the GPU */
2706: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSECUDA, &biscuda));
2707: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSECUDA, MAT_INPLACE_MATRIX, &B));
2708: PetscCall(MatDenseGetArrayReadAndMemType(B, &barray, nullptr));
2710: PetscCall(MatDenseGetLDA(B, &blda));
2711: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2712: PetscCall(MatDenseGetArrayWriteAndMemType(mmdata->X, &carray, nullptr));
2713: PetscCall(MatDenseGetLDA(mmdata->X, &clda));
2714: } else {
2715: PetscCall(MatDenseGetArrayWriteAndMemType(C, &carray, nullptr));
2716: PetscCall(MatDenseGetLDA(C, &clda));
2717: }
2719: PetscCall(PetscLogGpuTimeBegin());
2720: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2721: cusparseOperation_t opB = (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE;
2722: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
2723: cusparseSpMatDescr_t &matADescr = mat->matDescr_SpMM[opA];
2724: #else
2725: cusparseSpMatDescr_t &matADescr = mat->matDescr;
2726: #endif
2728: /* (re)allocate mmBuffer if not initialized or LDAs are different */
2729: if (!mmdata->initialized || mmdata->Blda != blda || mmdata->Clda != clda) {
2730: size_t mmBufferSize;
2731: if (mmdata->initialized && mmdata->Blda != blda) {
2732: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matBDescr));
2733: mmdata->matBDescr = NULL;
2734: }
2735: if (!mmdata->matBDescr) {
2736: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matBDescr, B->rmap->n, B->cmap->n, blda, (void *)barray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2737: mmdata->Blda = blda;
2738: }
2740: if (mmdata->initialized && mmdata->Clda != clda) {
2741: PetscCallCUSPARSE(cusparseDestroyDnMat(mmdata->matCDescr));
2742: mmdata->matCDescr = NULL;
2743: }
2744: if (!mmdata->matCDescr) { /* matCDescr is for C or mmdata->X */
2745: PetscCallCUSPARSE(cusparseCreateDnMat(&mmdata->matCDescr, m, n, clda, (void *)carray, cusparse_scalartype, CUSPARSE_ORDER_COL));
2746: mmdata->Clda = clda;
2747: }
2749: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
2750: if (matADescr) {
2751: PetscCallCUSPARSE(cusparseDestroySpMat(matADescr)); // Because I find I could not reuse matADescr. It could be a cusparse bug
2752: matADescr = NULL;
2753: }
2754: #endif
2756: if (!matADescr) {
2757: 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 */
2758: CUSPARSE_INDEX_BASE_ZERO, cusparse_scalartype);
2759: PetscCallCUSPARSE(stat);
2760: }
2762: PetscCallCUSPARSE(cusparseSpMM_bufferSize(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, &mmBufferSize));
2764: if ((mmdata->mmBuffer && mmdata->mmBufferSize < mmBufferSize) || !mmdata->mmBuffer) {
2765: PetscCallCUDA(cudaFree(mmdata->mmBuffer));
2766: PetscCallCUDA(cudaMalloc(&mmdata->mmBuffer, mmBufferSize));
2767: mmdata->mmBufferSize = mmBufferSize;
2768: }
2770: #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
2771: PetscCallCUSPARSE(cusparseSpMM_preprocess(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2772: #endif
2774: mmdata->initialized = PETSC_TRUE;
2775: } else {
2776: /* to be safe, always update pointers of the mats */
2777: PetscCallCUSPARSE(cusparseSpMatSetValues(matADescr, csrmat->values->data().get()));
2778: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matBDescr, (void *)barray));
2779: PetscCallCUSPARSE(cusparseDnMatSetValues(mmdata->matCDescr, (void *)carray));
2780: }
2782: /* do cusparseSpMM, which supports transpose on B */
2783: PetscCallCUSPARSE(cusparseSpMM(cusp->handle, opA, opB, mat->alpha_one, matADescr, mmdata->matBDescr, mat->beta_zero, mmdata->matCDescr, cusparse_scalartype, cusp->spmmAlg, mmdata->mmBuffer));
2784: #else
2785: PetscInt k;
2786: /* cusparseXcsrmm does not support transpose on B */
2787: if (product->type == MATPRODUCT_ABt || product->type == MATPRODUCT_RARt) {
2788: cublasHandle_t cublasv2handle;
2789: cublasStatus_t cerr;
2791: PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
2792: 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);
2793: PetscCallCUBLAS(cerr);
2794: blda = B->cmap->n;
2795: k = B->cmap->n;
2796: } else {
2797: k = B->rmap->n;
2798: }
2800: /* perform the MatMat operation, op(A) is m x k, op(B) is k x n */
2801: 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);
2802: PetscCallCUSPARSE(stat);
2803: #endif
2804: PetscCall(PetscLogGpuTimeEnd());
2805: PetscCall(PetscLogGpuFlops(n * 2.0 * csrmat->num_entries));
2806: PetscCall(MatDenseRestoreArrayReadAndMemType(B, &barray));
2807: if (product->type == MATPRODUCT_RARt) {
2808: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2809: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_FALSE, PETSC_FALSE));
2810: } else if (product->type == MATPRODUCT_PtAP) {
2811: PetscCall(MatDenseRestoreArrayWriteAndMemType(mmdata->X, &carray));
2812: PetscCall(MatMatMultNumeric_SeqDenseCUDA_SeqDenseCUDA_Internal(B, mmdata->X, C, PETSC_TRUE, PETSC_FALSE));
2813: } else {
2814: PetscCall(MatDenseRestoreArrayWriteAndMemType(C, &carray));
2815: }
2816: if (mmdata->cisdense) PetscCall(MatConvert(C, MATSEQDENSE, MAT_INPLACE_MATRIX, &C));
2817: if (!biscuda) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
2818: PetscFunctionReturn(PETSC_SUCCESS);
2819: }
2821: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqDENSECUDA(Mat C)
2822: {
2823: Mat_Product *product = C->product;
2824: Mat A, B;
2825: PetscInt m, n;
2826: PetscBool cisdense, flg;
2827: MatMatCusparse *mmdata;
2828: Mat_SeqAIJCUSPARSE *cusp;
2830: PetscFunctionBegin;
2831: MatCheckProduct(C, 1);
2832: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
2833: A = product->A;
2834: B = product->B;
2835: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2836: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2837: cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2838: PetscCheck(cusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2839: switch (product->type) {
2840: case MATPRODUCT_AB:
2841: m = A->rmap->n;
2842: n = B->cmap->n;
2843: break;
2844: case MATPRODUCT_AtB:
2845: m = A->cmap->n;
2846: n = B->cmap->n;
2847: break;
2848: case MATPRODUCT_ABt:
2849: m = A->rmap->n;
2850: n = B->rmap->n;
2851: break;
2852: case MATPRODUCT_PtAP:
2853: m = B->cmap->n;
2854: n = B->cmap->n;
2855: break;
2856: case MATPRODUCT_RARt:
2857: m = B->rmap->n;
2858: n = B->rmap->n;
2859: break;
2860: default:
2861: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2862: }
2863: PetscCall(MatSetSizes(C, m, n, m, n));
2864: /* if C is of type MATSEQDENSE (CPU), perform the operation on the GPU and then copy on the CPU */
2865: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQDENSE, &cisdense));
2866: PetscCall(MatSetType(C, MATSEQDENSECUDA));
2868: /* product data */
2869: PetscCall(PetscNew(&mmdata));
2870: mmdata->cisdense = cisdense;
2871: #if PETSC_PKG_CUDA_VERSION_LT(11, 0, 0)
2872: /* cusparseXcsrmm does not support transpose on B, so we allocate buffer to store B^T */
2873: 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)));
2874: #endif
2875: /* for these products we need intermediate storage */
2876: if (product->type == MATPRODUCT_RARt || product->type == MATPRODUCT_PtAP) {
2877: PetscCall(MatCreate(PetscObjectComm((PetscObject)C), &mmdata->X));
2878: PetscCall(MatSetType(mmdata->X, MATSEQDENSECUDA));
2879: if (product->type == MATPRODUCT_RARt) { /* do not preallocate, since the first call to MatDenseCUDAGetArray will preallocate on the GPU for us */
2880: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->rmap->n, A->rmap->n, B->rmap->n));
2881: } else {
2882: PetscCall(MatSetSizes(mmdata->X, A->rmap->n, B->cmap->n, A->rmap->n, B->cmap->n));
2883: }
2884: }
2885: C->product->data = mmdata;
2886: C->product->destroy = MatDestroy_MatMatCusparse;
2888: C->ops->productnumeric = MatProductNumeric_SeqAIJCUSPARSE_SeqDENSECUDA;
2889: PetscFunctionReturn(PETSC_SUCCESS);
2890: }
2892: static PetscErrorCode MatProductNumeric_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
2893: {
2894: Mat_Product *product = C->product;
2895: Mat A, B;
2896: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
2897: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2898: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
2899: CsrMatrix *Acsr, *Bcsr, *Ccsr;
2900: PetscBool flg;
2901: cusparseStatus_t stat;
2902: MatProductType ptype;
2903: MatMatCusparse *mmdata;
2904: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2905: cusparseSpMatDescr_t BmatSpDescr;
2906: #endif
2907: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
2909: PetscFunctionBegin;
2910: MatCheckProduct(C, 1);
2911: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data empty");
2912: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATSEQAIJCUSPARSE, &flg));
2913: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for C of type %s", ((PetscObject)C)->type_name);
2914: mmdata = (MatMatCusparse *)C->product->data;
2915: A = product->A;
2916: B = product->B;
2917: if (mmdata->reusesym) { /* this happens when api_user is true, meaning that the matrix values have been already computed in the MatProductSymbolic phase */
2918: mmdata->reusesym = PETSC_FALSE;
2919: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2920: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2921: Cmat = Ccusp->mat;
2922: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[C->product->type]);
2923: Ccsr = (CsrMatrix *)Cmat->mat;
2924: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2925: goto finalize;
2926: }
2927: if (!c->nz) goto finalize;
2928: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
2929: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
2930: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
2931: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
2932: PetscCheck(!A->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2933: PetscCheck(!B->boundtocpu, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONG, "Cannot bind to CPU a CUSPARSE matrix between MatProductSymbolic and MatProductNumeric phases");
2934: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
2935: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
2936: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
2937: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2938: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2939: PetscCheck(Ccusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
2940: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
2941: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
2943: ptype = product->type;
2944: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
2945: ptype = MATPRODUCT_AB;
2946: 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");
2947: }
2948: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
2949: ptype = MATPRODUCT_AB;
2950: 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");
2951: }
2952: switch (ptype) {
2953: case MATPRODUCT_AB:
2954: Amat = Acusp->mat;
2955: Bmat = Bcusp->mat;
2956: break;
2957: case MATPRODUCT_AtB:
2958: Amat = Acusp->matTranspose;
2959: Bmat = Bcusp->mat;
2960: break;
2961: case MATPRODUCT_ABt:
2962: Amat = Acusp->mat;
2963: Bmat = Bcusp->matTranspose;
2964: break;
2965: default:
2966: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
2967: }
2968: Cmat = Ccusp->mat;
2969: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
2970: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
2971: PetscCheck(Cmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C mult struct for product type %s", MatProductTypes[ptype]);
2972: Acsr = (CsrMatrix *)Amat->mat;
2973: Bcsr = mmdata->Bcsr ? mmdata->Bcsr : (CsrMatrix *)Bmat->mat; /* B may be in compressed row storage */
2974: Ccsr = (CsrMatrix *)Cmat->mat;
2975: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
2976: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
2977: PetscCheck(Ccsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing C CSR struct");
2978: PetscCall(PetscLogGpuTimeBegin());
2979: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
2980: BmatSpDescr = mmdata->Bcsr ? mmdata->matSpBDescr : Bmat->matDescr; /* B may be in compressed row storage */
2981: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
2982: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
2983: 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);
2984: PetscCallCUSPARSE(stat);
2985: #else
2986: 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);
2987: PetscCallCUSPARSE(stat);
2988: 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);
2989: PetscCallCUSPARSE(stat);
2990: #endif
2991: #else
2992: 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,
2993: 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());
2994: PetscCallCUSPARSE(stat);
2995: #endif
2996: PetscCall(PetscLogGpuFlops(mmdata->flops));
2997: PetscCallCUDA(WaitForCUDA());
2998: PetscCall(PetscLogGpuTimeEnd());
2999: C->offloadmask = PETSC_OFFLOAD_GPU;
3000: finalize:
3001: /* shorter version of MatAssemblyEnd_SeqAIJ */
3002: 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));
3003: PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
3004: PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
3005: c->reallocs = 0;
3006: C->info.mallocs += 0;
3007: C->info.nz_unneeded = 0;
3008: C->assembled = C->was_assembled = PETSC_TRUE;
3009: C->num_ass++;
3010: PetscFunctionReturn(PETSC_SUCCESS);
3011: }
3013: static PetscErrorCode MatProductSymbolic_SeqAIJCUSPARSE_SeqAIJCUSPARSE(Mat C)
3014: {
3015: Mat_Product *product = C->product;
3016: Mat A, B;
3017: Mat_SeqAIJCUSPARSE *Acusp, *Bcusp, *Ccusp;
3018: Mat_SeqAIJ *a, *b, *c;
3019: Mat_SeqAIJCUSPARSEMultStruct *Amat, *Bmat, *Cmat;
3020: CsrMatrix *Acsr, *Bcsr, *Ccsr;
3021: PetscInt i, j, m, n, k;
3022: PetscBool flg;
3023: cusparseStatus_t stat;
3024: MatProductType ptype;
3025: MatMatCusparse *mmdata;
3026: PetscLogDouble flops;
3027: PetscBool biscompressed, ciscompressed;
3028: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3029: int64_t C_num_rows1, C_num_cols1, C_nnz1;
3030: cusparseSpMatDescr_t BmatSpDescr;
3031: #else
3032: int cnz;
3033: #endif
3034: cusparseOperation_t opA = CUSPARSE_OPERATION_NON_TRANSPOSE, opB = CUSPARSE_OPERATION_NON_TRANSPOSE; /* cuSPARSE spgemm doesn't support transpose yet */
3036: PetscFunctionBegin;
3037: MatCheckProduct(C, 1);
3038: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Product data not empty");
3039: A = product->A;
3040: B = product->B;
3041: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQAIJCUSPARSE, &flg));
3042: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for type %s", ((PetscObject)A)->type_name);
3043: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJCUSPARSE, &flg));
3044: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Not for B of type %s", ((PetscObject)B)->type_name);
3045: a = (Mat_SeqAIJ *)A->data;
3046: b = (Mat_SeqAIJ *)B->data;
3047: /* product data */
3048: PetscCall(PetscNew(&mmdata));
3049: C->product->data = mmdata;
3050: C->product->destroy = MatDestroy_MatMatCusparse;
3052: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
3053: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
3054: Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr; /* Access spptr after MatSeqAIJCUSPARSECopyToGPU, not before */
3055: Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr;
3056: PetscCheck(Acusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3057: PetscCheck(Bcusp->format == MAT_CUSPARSE_CSR, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Only for MAT_CUSPARSE_CSR format");
3059: ptype = product->type;
3060: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
3061: ptype = MATPRODUCT_AB;
3062: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
3063: }
3064: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
3065: ptype = MATPRODUCT_AB;
3066: product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
3067: }
3068: biscompressed = PETSC_FALSE;
3069: ciscompressed = PETSC_FALSE;
3070: switch (ptype) {
3071: case MATPRODUCT_AB:
3072: m = A->rmap->n;
3073: n = B->cmap->n;
3074: k = A->cmap->n;
3075: Amat = Acusp->mat;
3076: Bmat = Bcusp->mat;
3077: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3078: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3079: break;
3080: case MATPRODUCT_AtB:
3081: m = A->cmap->n;
3082: n = B->cmap->n;
3083: k = A->rmap->n;
3084: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
3085: Amat = Acusp->matTranspose;
3086: Bmat = Bcusp->mat;
3087: if (b->compressedrow.use) biscompressed = PETSC_TRUE;
3088: break;
3089: case MATPRODUCT_ABt:
3090: m = A->rmap->n;
3091: n = B->rmap->n;
3092: k = A->cmap->n;
3093: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
3094: Amat = Acusp->mat;
3095: Bmat = Bcusp->matTranspose;
3096: if (a->compressedrow.use) ciscompressed = PETSC_TRUE;
3097: break;
3098: default:
3099: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Unsupported product type %s", MatProductTypes[product->type]);
3100: }
3102: /* create cusparse matrix */
3103: PetscCall(MatSetSizes(C, m, n, m, n));
3104: PetscCall(MatSetType(C, MATSEQAIJCUSPARSE));
3105: c = (Mat_SeqAIJ *)C->data;
3106: Ccusp = (Mat_SeqAIJCUSPARSE *)C->spptr;
3107: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
3108: Ccsr = new CsrMatrix;
3110: c->compressedrow.use = ciscompressed;
3111: if (c->compressedrow.use) { /* if a is in compressed row, than c will be in compressed row format */
3112: c->compressedrow.nrows = a->compressedrow.nrows;
3113: PetscCall(PetscMalloc2(c->compressedrow.nrows + 1, &c->compressedrow.i, c->compressedrow.nrows, &c->compressedrow.rindex));
3114: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, c->compressedrow.nrows));
3115: Ccusp->workVector = new THRUSTARRAY(c->compressedrow.nrows);
3116: Cmat->cprowIndices = new THRUSTINTARRAY(c->compressedrow.nrows);
3117: Cmat->cprowIndices->assign(c->compressedrow.rindex, c->compressedrow.rindex + c->compressedrow.nrows);
3118: } else {
3119: c->compressedrow.nrows = 0;
3120: c->compressedrow.i = NULL;
3121: c->compressedrow.rindex = NULL;
3122: Ccusp->workVector = NULL;
3123: Cmat->cprowIndices = NULL;
3124: }
3125: Ccusp->nrows = ciscompressed ? c->compressedrow.nrows : m;
3126: Ccusp->mat = Cmat;
3127: Ccusp->mat->mat = Ccsr;
3128: Ccsr->num_rows = Ccusp->nrows;
3129: Ccsr->num_cols = n;
3130: Ccsr->row_offsets = new THRUSTINTARRAY32(Ccusp->nrows + 1);
3131: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
3132: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
3133: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
3134: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
3135: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
3136: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
3137: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3138: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3139: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
3140: if (!Ccsr->num_rows || !Ccsr->num_cols || !a->nz || !b->nz) { /* cusparse raise errors in different calls when matrices have zero rows/columns! */
3141: PetscCallThrust(thrust::fill(thrust::device, Ccsr->row_offsets->begin(), Ccsr->row_offsets->end(), 0));
3142: c->nz = 0;
3143: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3144: Ccsr->values = new THRUSTARRAY(c->nz);
3145: goto finalizesym;
3146: }
3148: PetscCheck(Amat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A mult struct for product type %s", MatProductTypes[ptype]);
3149: PetscCheck(Bmat, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B mult struct for product type %s", MatProductTypes[ptype]);
3150: Acsr = (CsrMatrix *)Amat->mat;
3151: if (!biscompressed) {
3152: Bcsr = (CsrMatrix *)Bmat->mat;
3153: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3154: BmatSpDescr = Bmat->matDescr;
3155: #endif
3156: } else { /* we need to use row offsets for the full matrix */
3157: CsrMatrix *cBcsr = (CsrMatrix *)Bmat->mat;
3158: Bcsr = new CsrMatrix;
3159: Bcsr->num_rows = B->rmap->n;
3160: Bcsr->num_cols = cBcsr->num_cols;
3161: Bcsr->num_entries = cBcsr->num_entries;
3162: Bcsr->column_indices = cBcsr->column_indices;
3163: Bcsr->values = cBcsr->values;
3164: if (!Bcusp->rowoffsets_gpu) {
3165: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
3166: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
3167: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
3168: }
3169: Bcsr->row_offsets = Bcusp->rowoffsets_gpu;
3170: mmdata->Bcsr = Bcsr;
3171: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3172: if (Bcsr->num_rows && Bcsr->num_cols) {
3173: 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);
3174: PetscCallCUSPARSE(stat);
3175: }
3176: BmatSpDescr = mmdata->matSpBDescr;
3177: #endif
3178: }
3179: PetscCheck(Acsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing A CSR struct");
3180: PetscCheck(Bcsr, PetscObjectComm((PetscObject)C), PETSC_ERR_GPU, "Missing B CSR struct");
3181: /* precompute flops count */
3182: if (ptype == MATPRODUCT_AB) {
3183: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3184: const PetscInt st = a->i[i];
3185: const PetscInt en = a->i[i + 1];
3186: for (j = st; j < en; j++) {
3187: const PetscInt brow = a->j[j];
3188: flops += 2. * (b->i[brow + 1] - b->i[brow]);
3189: }
3190: }
3191: } else if (ptype == MATPRODUCT_AtB) {
3192: for (i = 0, flops = 0; i < A->rmap->n; i++) {
3193: const PetscInt anzi = a->i[i + 1] - a->i[i];
3194: const PetscInt bnzi = b->i[i + 1] - b->i[i];
3195: flops += (2. * anzi) * bnzi;
3196: }
3197: } else { /* TODO */
3198: flops = 0.;
3199: }
3201: mmdata->flops = flops;
3202: PetscCall(PetscLogGpuTimeBegin());
3204: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
3205: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3206: // cuda-12.2 requires non-null csrRowOffsets
3207: 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);
3208: PetscCallCUSPARSE(stat);
3209: PetscCallCUSPARSE(cusparseSpGEMM_createDescr(&mmdata->spgemmDesc));
3210: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
3211: {
3212: /* cusparseSpGEMMreuse has more reasonable APIs than cusparseSpGEMM, so we prefer to use it.
3213: We follow the sample code at https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSPARSE/spgemm_reuse
3214: */
3215: void *dBuffer1 = NULL;
3216: void *dBuffer2 = NULL;
3217: void *dBuffer3 = NULL;
3218: /* dBuffer4, dBuffer5 are needed by cusparseSpGEMMreuse_compute, and therefore are stored in mmdata */
3219: size_t bufferSize1 = 0;
3220: size_t bufferSize2 = 0;
3221: size_t bufferSize3 = 0;
3222: size_t bufferSize4 = 0;
3223: size_t bufferSize5 = 0;
3225: /* ask bufferSize1 bytes for external memory */
3226: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, NULL);
3227: PetscCallCUSPARSE(stat);
3228: PetscCallCUDA(cudaMalloc((void **)&dBuffer1, bufferSize1));
3229: /* inspect the matrices A and B to understand the memory requirement for the next step */
3230: stat = cusparseSpGEMMreuse_workEstimation(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize1, dBuffer1);
3231: PetscCallCUSPARSE(stat);
3233: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, NULL, &bufferSize3, NULL, &bufferSize4, NULL);
3234: PetscCallCUSPARSE(stat);
3235: PetscCallCUDA(cudaMalloc((void **)&dBuffer2, bufferSize2));
3236: PetscCallCUDA(cudaMalloc((void **)&dBuffer3, bufferSize3));
3237: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer4, bufferSize4));
3238: stat = cusparseSpGEMMreuse_nnz(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize2, dBuffer2, &bufferSize3, dBuffer3, &bufferSize4, mmdata->dBuffer4);
3239: PetscCallCUSPARSE(stat);
3240: PetscCallCUDA(cudaFree(dBuffer1));
3241: PetscCallCUDA(cudaFree(dBuffer2));
3243: /* get matrix C non-zero entries C_nnz1 */
3244: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3245: c->nz = (PetscInt)C_nnz1;
3246: /* allocate matrix C */
3247: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3248: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3249: Ccsr->values = new THRUSTARRAY(c->nz);
3250: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3251: /* update matC with the new pointers */
3252: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3253: PetscCallCUSPARSE(stat);
3255: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, NULL);
3256: PetscCallCUSPARSE(stat);
3257: PetscCallCUDA(cudaMalloc((void **)&mmdata->dBuffer5, bufferSize5));
3258: stat = cusparseSpGEMMreuse_copy(Ccusp->handle, opA, opB, Amat->matDescr, BmatSpDescr, Cmat->matDescr, CUSPARSE_SPGEMM_DEFAULT, mmdata->spgemmDesc, &bufferSize5, mmdata->dBuffer5);
3259: PetscCallCUSPARSE(stat);
3260: PetscCallCUDA(cudaFree(dBuffer3));
3261: 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);
3262: PetscCallCUSPARSE(stat);
3263: 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));
3264: }
3265: #else
3266: size_t bufSize2;
3267: /* ask bufferSize bytes for external memory */
3268: 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);
3269: PetscCallCUSPARSE(stat);
3270: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer2, bufSize2));
3271: /* inspect the matrices A and B to understand the memory requirement for the next step */
3272: 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);
3273: PetscCallCUSPARSE(stat);
3274: /* ask bufferSize again bytes for external memory */
3275: 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);
3276: PetscCallCUSPARSE(stat);
3277: /* The CUSPARSE documentation is not clear, nor the API
3278: We need both buffers to perform the operations properly!
3279: mmdata->mmBuffer2 does not appear anywhere in the compute/copy API
3280: it only appears for the workEstimation stuff, but it seems it is needed in compute, so probably the address
3281: is stored in the descriptor! What a messy API... */
3282: PetscCallCUDA(cudaMalloc((void **)&mmdata->mmBuffer, mmdata->mmBufferSize));
3283: /* compute the intermediate product of A * B */
3284: 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);
3285: PetscCallCUSPARSE(stat);
3286: /* get matrix C non-zero entries C_nnz1 */
3287: PetscCallCUSPARSE(cusparseSpMatGetSize(Cmat->matDescr, &C_num_rows1, &C_num_cols1, &C_nnz1));
3288: c->nz = (PetscInt)C_nnz1;
3289: 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,
3290: mmdata->mmBufferSize / 1024));
3291: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3292: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3293: Ccsr->values = new THRUSTARRAY(c->nz);
3294: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3295: stat = cusparseCsrSetPointers(Cmat->matDescr, Ccsr->row_offsets->data().get(), Ccsr->column_indices->data().get(), Ccsr->values->data().get());
3296: PetscCallCUSPARSE(stat);
3297: 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);
3298: PetscCallCUSPARSE(stat);
3299: #endif // PETSC_PKG_CUDA_VERSION_GE(11,4,0)
3300: #else
3301: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_HOST));
3302: 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,
3303: Bcsr->row_offsets->data().get(), Bcsr->column_indices->data().get(), Cmat->descr, Ccsr->row_offsets->data().get(), &cnz);
3304: PetscCallCUSPARSE(stat);
3305: c->nz = cnz;
3306: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
3307: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3308: Ccsr->values = new THRUSTARRAY(c->nz);
3309: PetscCallCUDA(cudaPeekAtLastError()); /* catch out of memory errors */
3311: PetscCallCUSPARSE(cusparseSetPointerMode(Ccusp->handle, CUSPARSE_POINTER_MODE_DEVICE));
3312: /* with the old gemm interface (removed from 11.0 on) we cannot compute the symbolic factorization only.
3313: 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
3314: D is NULL, despite the fact that CUSPARSE documentation claims it is supported! */
3315: 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,
3316: 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());
3317: PetscCallCUSPARSE(stat);
3318: #endif
3319: PetscCall(PetscLogGpuFlops(mmdata->flops));
3320: PetscCall(PetscLogGpuTimeEnd());
3321: finalizesym:
3322: c->free_a = PETSC_TRUE;
3323: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
3324: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
3325: c->free_ij = PETSC_TRUE;
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 = (PetscInt)matstruct->cprowIndices->size();
3709: ScatterAdd<<<(int)((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_INTERN 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;
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(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", coo_d, MatCOOStructDestroy_SeqAIJCUSPARSE));
4355: PetscFunctionReturn(PETSC_SUCCESS);
4356: }
4358: __global__ static void MatAddCOOValues(const PetscScalar kv[], PetscCount nnz, const PetscCount jmap[], const PetscCount perm[], InsertMode imode, PetscScalar a[])
4359: {
4360: PetscCount i = blockIdx.x * blockDim.x + threadIdx.x;
4361: const PetscCount grid_size = gridDim.x * blockDim.x;
4362: for (; i < nnz; i += grid_size) {
4363: PetscScalar sum = 0.0;
4364: for (PetscCount k = jmap[i]; k < jmap[i + 1]; k++) sum += kv[perm[k]];
4365: a[i] = (imode == INSERT_VALUES ? 0.0 : a[i]) + sum;
4366: }
4367: }
4369: static PetscErrorCode MatSetValuesCOO_SeqAIJCUSPARSE(Mat A, const PetscScalar v[], InsertMode imode)
4370: {
4371: Mat_SeqAIJ *seq = (Mat_SeqAIJ *)A->data;
4372: Mat_SeqAIJCUSPARSE *dev = (Mat_SeqAIJCUSPARSE *)A->spptr;
4373: PetscCount Annz = seq->nz;
4374: PetscMemType memtype;
4375: const PetscScalar *v1 = v;
4376: PetscScalar *Aa;
4377: PetscContainer container;
4378: MatCOOStruct_SeqAIJ *coo;
4380: PetscFunctionBegin;
4381: if (!dev->mat) PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4383: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
4384: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4386: PetscCall(PetscGetMemType(v, &memtype));
4387: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
4388: PetscCallCUDA(cudaMalloc((void **)&v1, coo->n * sizeof(PetscScalar)));
4389: PetscCallCUDA(cudaMemcpy((void *)v1, v, coo->n * sizeof(PetscScalar), cudaMemcpyHostToDevice));
4390: }
4392: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSEGetArrayWrite(A, &Aa));
4393: else PetscCall(MatSeqAIJCUSPARSEGetArray(A, &Aa));
4395: PetscCall(PetscLogGpuTimeBegin());
4396: if (Annz) {
4397: MatAddCOOValues<<<((int)(Annz + 255) / 256), 256>>>(v1, Annz, coo->jmap, coo->perm, imode, Aa);
4398: PetscCallCUDA(cudaPeekAtLastError());
4399: }
4400: PetscCall(PetscLogGpuTimeEnd());
4402: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJCUSPARSERestoreArrayWrite(A, &Aa));
4403: else PetscCall(MatSeqAIJCUSPARSERestoreArray(A, &Aa));
4405: if (PetscMemTypeHost(memtype)) PetscCallCUDA(cudaFree((void *)v1));
4406: PetscFunctionReturn(PETSC_SUCCESS);
4407: }
4409: /*@C
4410: MatSeqAIJCUSPARSEGetIJ - returns the device row storage `i` and `j` indices for `MATSEQAIJCUSPARSE` matrices.
4412: Not Collective
4414: Input Parameters:
4415: + A - the matrix
4416: - compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4418: Output Parameters:
4419: + i - the CSR row pointers
4420: - j - the CSR column indices
4422: Level: developer
4424: Note:
4425: When compressed is true, the CSR structure does not contain empty rows
4427: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSERestoreIJ()`, `MatSeqAIJCUSPARSEGetArrayRead()`
4428: @*/
4429: PetscErrorCode MatSeqAIJCUSPARSEGetIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4430: {
4431: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4432: CsrMatrix *csr;
4433: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4435: PetscFunctionBegin;
4437: if (!i || !j) PetscFunctionReturn(PETSC_SUCCESS);
4438: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4439: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4440: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4441: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4442: csr = (CsrMatrix *)cusp->mat->mat;
4443: if (i) {
4444: if (!compressed && a->compressedrow.use) { /* need full row offset */
4445: if (!cusp->rowoffsets_gpu) {
4446: cusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4447: cusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4448: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4449: }
4450: *i = cusp->rowoffsets_gpu->data().get();
4451: } else *i = csr->row_offsets->data().get();
4452: }
4453: if (j) *j = csr->column_indices->data().get();
4454: PetscFunctionReturn(PETSC_SUCCESS);
4455: }
4457: /*@C
4458: MatSeqAIJCUSPARSERestoreIJ - restore the device row storage `i` and `j` indices obtained with `MatSeqAIJCUSPARSEGetIJ()`
4460: Not Collective
4462: Input Parameters:
4463: + A - the matrix
4464: . compressed - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be always returned in compressed form
4465: . i - the CSR row pointers
4466: - j - the CSR column indices
4468: Level: developer
4470: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetIJ()`
4471: @*/
4472: PetscErrorCode MatSeqAIJCUSPARSERestoreIJ(Mat A, PetscBool compressed, const int **i, const int **j)
4473: {
4474: PetscFunctionBegin;
4476: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4477: if (i) *i = NULL;
4478: if (j) *j = NULL;
4479: (void)compressed;
4480: PetscFunctionReturn(PETSC_SUCCESS);
4481: }
4483: /*@C
4484: MatSeqAIJCUSPARSEGetArrayRead - gives read-only access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4486: Not Collective
4488: Input Parameter:
4489: . A - a `MATSEQAIJCUSPARSE` matrix
4491: Output Parameter:
4492: . a - pointer to the device data
4494: Level: developer
4496: Note:
4497: May trigger host-device copies if up-to-date matrix data is on host
4499: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArrayRead()`
4500: @*/
4501: PetscErrorCode MatSeqAIJCUSPARSEGetArrayRead(Mat A, const PetscScalar **a)
4502: {
4503: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4504: CsrMatrix *csr;
4506: PetscFunctionBegin;
4508: PetscAssertPointer(a, 2);
4509: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4510: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4511: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4512: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4513: csr = (CsrMatrix *)cusp->mat->mat;
4514: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4515: *a = csr->values->data().get();
4516: PetscFunctionReturn(PETSC_SUCCESS);
4517: }
4519: /*@C
4520: MatSeqAIJCUSPARSERestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJCUSPARSEGetArrayRead()`
4522: Not Collective
4524: Input Parameters:
4525: + A - a `MATSEQAIJCUSPARSE` matrix
4526: - a - pointer to the device data
4528: Level: developer
4530: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`
4531: @*/
4532: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayRead(Mat A, const PetscScalar **a)
4533: {
4534: PetscFunctionBegin;
4536: PetscAssertPointer(a, 2);
4537: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4538: *a = NULL;
4539: PetscFunctionReturn(PETSC_SUCCESS);
4540: }
4542: /*@C
4543: MatSeqAIJCUSPARSEGetArray - gives read-write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4545: Not Collective
4547: Input Parameter:
4548: . A - a `MATSEQAIJCUSPARSE` matrix
4550: Output Parameter:
4551: . a - pointer to the device data
4553: Level: developer
4555: Note:
4556: May trigger host-device copies if up-to-date matrix data is on host
4558: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSEGetArrayWrite()`, `MatSeqAIJCUSPARSERestoreArray()`
4559: @*/
4560: PetscErrorCode MatSeqAIJCUSPARSEGetArray(Mat A, PetscScalar **a)
4561: {
4562: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4563: CsrMatrix *csr;
4565: PetscFunctionBegin;
4567: PetscAssertPointer(a, 2);
4568: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4569: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4570: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4571: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4572: csr = (CsrMatrix *)cusp->mat->mat;
4573: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4574: *a = csr->values->data().get();
4575: A->offloadmask = PETSC_OFFLOAD_GPU;
4576: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4577: PetscFunctionReturn(PETSC_SUCCESS);
4578: }
4579: /*@C
4580: MatSeqAIJCUSPARSERestoreArray - restore the read-write access array obtained from `MatSeqAIJCUSPARSEGetArray()`
4582: Not Collective
4584: Input Parameters:
4585: + A - a `MATSEQAIJCUSPARSE` matrix
4586: - a - pointer to the device data
4588: Level: developer
4590: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`
4591: @*/
4592: PetscErrorCode MatSeqAIJCUSPARSERestoreArray(Mat A, PetscScalar **a)
4593: {
4594: PetscFunctionBegin;
4596: PetscAssertPointer(a, 2);
4597: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4598: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4599: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4600: *a = NULL;
4601: PetscFunctionReturn(PETSC_SUCCESS);
4602: }
4604: /*@C
4605: MatSeqAIJCUSPARSEGetArrayWrite - gives write access to the array where the device data for a `MATSEQAIJCUSPARSE` matrix is stored
4607: Not Collective
4609: Input Parameter:
4610: . A - a `MATSEQAIJCUSPARSE` matrix
4612: Output Parameter:
4613: . a - pointer to the device data
4615: Level: developer
4617: Note:
4618: Does not trigger host-device copies and flags data validity on the GPU
4620: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArray()`, `MatSeqAIJCUSPARSEGetArrayRead()`, `MatSeqAIJCUSPARSERestoreArrayWrite()`
4621: @*/
4622: PetscErrorCode MatSeqAIJCUSPARSEGetArrayWrite(Mat A, PetscScalar **a)
4623: {
4624: Mat_SeqAIJCUSPARSE *cusp = (Mat_SeqAIJCUSPARSE *)A->spptr;
4625: CsrMatrix *csr;
4627: PetscFunctionBegin;
4629: PetscAssertPointer(a, 2);
4630: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4631: PetscCheck(cusp->format != MAT_CUSPARSE_ELL && cusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4632: PetscCheck(cusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4633: csr = (CsrMatrix *)cusp->mat->mat;
4634: PetscCheck(csr->values, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing CUDA memory");
4635: *a = csr->values->data().get();
4636: A->offloadmask = PETSC_OFFLOAD_GPU;
4637: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(A, PETSC_FALSE));
4638: PetscFunctionReturn(PETSC_SUCCESS);
4639: }
4641: /*@C
4642: MatSeqAIJCUSPARSERestoreArrayWrite - restore the write-only access array obtained from `MatSeqAIJCUSPARSEGetArrayWrite()`
4644: Not Collective
4646: Input Parameters:
4647: + A - a `MATSEQAIJCUSPARSE` matrix
4648: - a - pointer to the device data
4650: Level: developer
4652: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJCUSPARSEGetArrayWrite()`
4653: @*/
4654: PetscErrorCode MatSeqAIJCUSPARSERestoreArrayWrite(Mat A, PetscScalar **a)
4655: {
4656: PetscFunctionBegin;
4658: PetscAssertPointer(a, 2);
4659: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4660: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4661: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4662: *a = NULL;
4663: PetscFunctionReturn(PETSC_SUCCESS);
4664: }
4666: struct IJCompare4 {
4667: __host__ __device__ inline bool operator()(const thrust::tuple<int, int, PetscScalar, int> &t1, const thrust::tuple<int, int, PetscScalar, int> &t2)
4668: {
4669: if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
4670: if (thrust::get<0>(t1) == thrust::get<0>(t2)) return thrust::get<1>(t1) < thrust::get<1>(t2);
4671: return false;
4672: }
4673: };
4675: struct Shift {
4676: int _shift;
4678: Shift(int shift) : _shift(shift) { }
4679: __host__ __device__ inline int operator()(const int &c) { return c + _shift; }
4680: };
4682: /* merges two SeqAIJCUSPARSE matrices A, B by concatenating their rows. [A';B']' operation in MATLAB notation */
4683: PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
4684: {
4685: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data, *c;
4686: Mat_SeqAIJCUSPARSE *Acusp = (Mat_SeqAIJCUSPARSE *)A->spptr, *Bcusp = (Mat_SeqAIJCUSPARSE *)B->spptr, *Ccusp;
4687: Mat_SeqAIJCUSPARSEMultStruct *Cmat;
4688: CsrMatrix *Acsr, *Bcsr, *Ccsr;
4689: PetscInt Annz, Bnnz;
4690: cusparseStatus_t stat;
4691: PetscInt i, m, n, zero = 0;
4693: PetscFunctionBegin;
4696: PetscAssertPointer(C, 4);
4697: PetscCheckTypeName(A, MATSEQAIJCUSPARSE);
4698: PetscCheckTypeName(B, MATSEQAIJCUSPARSE);
4699: 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);
4700: PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
4701: PetscCheck(Acusp->format != MAT_CUSPARSE_ELL && Acusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4702: PetscCheck(Bcusp->format != MAT_CUSPARSE_ELL && Bcusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4703: if (reuse == MAT_INITIAL_MATRIX) {
4704: m = A->rmap->n;
4705: n = A->cmap->n + B->cmap->n;
4706: PetscCall(MatCreate(PETSC_COMM_SELF, C));
4707: PetscCall(MatSetSizes(*C, m, n, m, n));
4708: PetscCall(MatSetType(*C, MATSEQAIJCUSPARSE));
4709: c = (Mat_SeqAIJ *)(*C)->data;
4710: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4711: Cmat = new Mat_SeqAIJCUSPARSEMultStruct;
4712: Ccsr = new CsrMatrix;
4713: Cmat->cprowIndices = NULL;
4714: c->compressedrow.use = PETSC_FALSE;
4715: c->compressedrow.nrows = 0;
4716: c->compressedrow.i = NULL;
4717: c->compressedrow.rindex = NULL;
4718: Ccusp->workVector = NULL;
4719: Ccusp->nrows = m;
4720: Ccusp->mat = Cmat;
4721: Ccusp->mat->mat = Ccsr;
4722: Ccsr->num_rows = m;
4723: Ccsr->num_cols = n;
4724: PetscCallCUSPARSE(cusparseCreateMatDescr(&Cmat->descr));
4725: PetscCallCUSPARSE(cusparseSetMatIndexBase(Cmat->descr, CUSPARSE_INDEX_BASE_ZERO));
4726: PetscCallCUSPARSE(cusparseSetMatType(Cmat->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4727: PetscCallCUDA(cudaMalloc((void **)&Cmat->alpha_one, sizeof(PetscScalar)));
4728: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_zero, sizeof(PetscScalar)));
4729: PetscCallCUDA(cudaMalloc((void **)&Cmat->beta_one, sizeof(PetscScalar)));
4730: PetscCallCUDA(cudaMemcpy(Cmat->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4731: PetscCallCUDA(cudaMemcpy(Cmat->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4732: PetscCallCUDA(cudaMemcpy(Cmat->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4733: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4734: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4735: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4736: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4738: Acsr = (CsrMatrix *)Acusp->mat->mat;
4739: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4740: Annz = (PetscInt)Acsr->column_indices->size();
4741: Bnnz = (PetscInt)Bcsr->column_indices->size();
4742: c->nz = Annz + Bnnz;
4743: Ccsr->row_offsets = new THRUSTINTARRAY32(m + 1);
4744: Ccsr->column_indices = new THRUSTINTARRAY32(c->nz);
4745: Ccsr->values = new THRUSTARRAY(c->nz);
4746: Ccsr->num_entries = c->nz;
4747: Ccusp->coords = new THRUSTINTARRAY(c->nz);
4748: if (c->nz) {
4749: auto Acoo = new THRUSTINTARRAY32(Annz);
4750: auto Bcoo = new THRUSTINTARRAY32(Bnnz);
4751: auto Ccoo = new THRUSTINTARRAY32(c->nz);
4752: THRUSTINTARRAY32 *Aroff, *Broff;
4754: if (a->compressedrow.use) { /* need full row offset */
4755: if (!Acusp->rowoffsets_gpu) {
4756: Acusp->rowoffsets_gpu = new THRUSTINTARRAY32(A->rmap->n + 1);
4757: Acusp->rowoffsets_gpu->assign(a->i, a->i + A->rmap->n + 1);
4758: PetscCall(PetscLogCpuToGpu((A->rmap->n + 1) * sizeof(PetscInt)));
4759: }
4760: Aroff = Acusp->rowoffsets_gpu;
4761: } else Aroff = Acsr->row_offsets;
4762: if (b->compressedrow.use) { /* need full row offset */
4763: if (!Bcusp->rowoffsets_gpu) {
4764: Bcusp->rowoffsets_gpu = new THRUSTINTARRAY32(B->rmap->n + 1);
4765: Bcusp->rowoffsets_gpu->assign(b->i, b->i + B->rmap->n + 1);
4766: PetscCall(PetscLogCpuToGpu((B->rmap->n + 1) * sizeof(PetscInt)));
4767: }
4768: Broff = Bcusp->rowoffsets_gpu;
4769: } else Broff = Bcsr->row_offsets;
4770: PetscCall(PetscLogGpuTimeBegin());
4771: stat = cusparseXcsr2coo(Acusp->handle, Aroff->data().get(), Annz, m, Acoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4772: PetscCallCUSPARSE(stat);
4773: stat = cusparseXcsr2coo(Bcusp->handle, Broff->data().get(), Bnnz, m, Bcoo->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4774: PetscCallCUSPARSE(stat);
4775: /* Issues when using bool with large matrices on SUMMIT 10.2.89 */
4776: auto Aperm = thrust::make_constant_iterator(1);
4777: auto Bperm = thrust::make_constant_iterator(0);
4778: #if PETSC_PKG_CUDA_VERSION_GE(10, 0, 0)
4779: auto Bcib = thrust::make_transform_iterator(Bcsr->column_indices->begin(), Shift(A->cmap->n));
4780: auto Bcie = thrust::make_transform_iterator(Bcsr->column_indices->end(), Shift(A->cmap->n));
4781: #else
4782: /* there are issues instantiating the merge operation using a transform iterator for the columns of B */
4783: auto Bcib = Bcsr->column_indices->begin();
4784: auto Bcie = Bcsr->column_indices->end();
4785: thrust::transform(Bcib, Bcie, Bcib, Shift(A->cmap->n));
4786: #endif
4787: auto wPerm = new THRUSTINTARRAY32(Annz + Bnnz);
4788: auto Azb = thrust::make_zip_iterator(thrust::make_tuple(Acoo->begin(), Acsr->column_indices->begin(), Acsr->values->begin(), Aperm));
4789: auto Aze = thrust::make_zip_iterator(thrust::make_tuple(Acoo->end(), Acsr->column_indices->end(), Acsr->values->end(), Aperm));
4790: auto Bzb = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->begin(), Bcib, Bcsr->values->begin(), Bperm));
4791: auto Bze = thrust::make_zip_iterator(thrust::make_tuple(Bcoo->end(), Bcie, Bcsr->values->end(), Bperm));
4792: auto Czb = thrust::make_zip_iterator(thrust::make_tuple(Ccoo->begin(), Ccsr->column_indices->begin(), Ccsr->values->begin(), wPerm->begin()));
4793: auto p1 = Ccusp->coords->begin();
4794: auto p2 = Ccusp->coords->begin();
4795: thrust::advance(p2, Annz);
4796: PetscCallThrust(thrust::merge(thrust::device, Azb, Aze, Bzb, Bze, Czb, IJCompare4()));
4797: #if PETSC_PKG_CUDA_VERSION_LT(10, 0, 0)
4798: thrust::transform(Bcib, Bcie, Bcib, Shift(-A->cmap->n));
4799: #endif
4800: auto cci = thrust::make_counting_iterator(zero);
4801: auto cce = thrust::make_counting_iterator(c->nz);
4802: #if 0 //Errors on SUMMIT cuda 11.1.0
4803: PetscCallThrust(thrust::partition_copy(thrust::device,cci,cce,wPerm->begin(),p1,p2,thrust::identity<int>()));
4804: #else
4805: auto pred = thrust::identity<int>();
4806: PetscCallThrust(thrust::copy_if(thrust::device, cci, cce, wPerm->begin(), p1, pred));
4807: PetscCallThrust(thrust::remove_copy_if(thrust::device, cci, cce, wPerm->begin(), p2, pred));
4808: #endif
4809: stat = cusparseXcoo2csr(Ccusp->handle, Ccoo->data().get(), c->nz, m, Ccsr->row_offsets->data().get(), CUSPARSE_INDEX_BASE_ZERO);
4810: PetscCallCUSPARSE(stat);
4811: PetscCall(PetscLogGpuTimeEnd());
4812: delete wPerm;
4813: delete Acoo;
4814: delete Bcoo;
4815: delete Ccoo;
4816: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4817: 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);
4818: PetscCallCUSPARSE(stat);
4819: #endif
4820: if (A->form_explicit_transpose && B->form_explicit_transpose) { /* if A and B have the transpose, generate C transpose too */
4821: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(A));
4822: PetscCall(MatSeqAIJCUSPARSEFormExplicitTranspose(B));
4823: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4824: Mat_SeqAIJCUSPARSEMultStruct *CmatT = new Mat_SeqAIJCUSPARSEMultStruct;
4825: CsrMatrix *CcsrT = new CsrMatrix;
4826: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4827: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4829: (*C)->form_explicit_transpose = PETSC_TRUE;
4830: (*C)->transupdated = PETSC_TRUE;
4831: Ccusp->rowoffsets_gpu = NULL;
4832: CmatT->cprowIndices = NULL;
4833: CmatT->mat = CcsrT;
4834: CcsrT->num_rows = n;
4835: CcsrT->num_cols = m;
4836: CcsrT->num_entries = c->nz;
4838: CcsrT->row_offsets = new THRUSTINTARRAY32(n + 1);
4839: CcsrT->column_indices = new THRUSTINTARRAY32(c->nz);
4840: CcsrT->values = new THRUSTARRAY(c->nz);
4842: PetscCall(PetscLogGpuTimeBegin());
4843: auto rT = CcsrT->row_offsets->begin();
4844: if (AT) {
4845: rT = thrust::copy(AcsrT->row_offsets->begin(), AcsrT->row_offsets->end(), rT);
4846: thrust::advance(rT, -1);
4847: }
4848: if (BT) {
4849: auto titb = thrust::make_transform_iterator(BcsrT->row_offsets->begin(), Shift(a->nz));
4850: auto tite = thrust::make_transform_iterator(BcsrT->row_offsets->end(), Shift(a->nz));
4851: thrust::copy(titb, tite, rT);
4852: }
4853: auto cT = CcsrT->column_indices->begin();
4854: if (AT) cT = thrust::copy(AcsrT->column_indices->begin(), AcsrT->column_indices->end(), cT);
4855: if (BT) thrust::copy(BcsrT->column_indices->begin(), BcsrT->column_indices->end(), cT);
4856: auto vT = CcsrT->values->begin();
4857: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4858: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4859: PetscCall(PetscLogGpuTimeEnd());
4861: PetscCallCUSPARSE(cusparseCreateMatDescr(&CmatT->descr));
4862: PetscCallCUSPARSE(cusparseSetMatIndexBase(CmatT->descr, CUSPARSE_INDEX_BASE_ZERO));
4863: PetscCallCUSPARSE(cusparseSetMatType(CmatT->descr, CUSPARSE_MATRIX_TYPE_GENERAL));
4864: PetscCallCUDA(cudaMalloc((void **)&CmatT->alpha_one, sizeof(PetscScalar)));
4865: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_zero, sizeof(PetscScalar)));
4866: PetscCallCUDA(cudaMalloc((void **)&CmatT->beta_one, sizeof(PetscScalar)));
4867: PetscCallCUDA(cudaMemcpy(CmatT->alpha_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4868: PetscCallCUDA(cudaMemcpy(CmatT->beta_zero, &PETSC_CUSPARSE_ZERO, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4869: PetscCallCUDA(cudaMemcpy(CmatT->beta_one, &PETSC_CUSPARSE_ONE, sizeof(PetscScalar), cudaMemcpyHostToDevice));
4870: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
4871: 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);
4872: PetscCallCUSPARSE(stat);
4873: #endif
4874: Ccusp->matTranspose = CmatT;
4875: }
4876: }
4878: c->free_a = PETSC_TRUE;
4879: PetscCall(PetscShmgetAllocateArray(c->nz, sizeof(PetscInt), (void **)&c->j));
4880: PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
4881: c->free_ij = PETSC_TRUE;
4882: if (PetscDefined(USE_64BIT_INDICES)) { /* 32 to 64-bit conversion on the GPU and then copy to host (lazy) */
4883: THRUSTINTARRAY ii(Ccsr->row_offsets->size());
4884: THRUSTINTARRAY jj(Ccsr->column_indices->size());
4885: ii = *Ccsr->row_offsets;
4886: jj = *Ccsr->column_indices;
4887: PetscCallCUDA(cudaMemcpy(c->i, ii.data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4888: PetscCallCUDA(cudaMemcpy(c->j, jj.data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4889: } else {
4890: PetscCallCUDA(cudaMemcpy(c->i, Ccsr->row_offsets->data().get(), Ccsr->row_offsets->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4891: PetscCallCUDA(cudaMemcpy(c->j, Ccsr->column_indices->data().get(), Ccsr->column_indices->size() * sizeof(PetscInt), cudaMemcpyDeviceToHost));
4892: }
4893: PetscCall(PetscLogGpuToCpu((Ccsr->column_indices->size() + Ccsr->row_offsets->size()) * sizeof(PetscInt)));
4894: PetscCall(PetscMalloc1(m, &c->ilen));
4895: PetscCall(PetscMalloc1(m, &c->imax));
4896: c->maxnz = c->nz;
4897: c->nonzerorowcnt = 0;
4898: c->rmax = 0;
4899: for (i = 0; i < m; i++) {
4900: const PetscInt nn = c->i[i + 1] - c->i[i];
4901: c->ilen[i] = c->imax[i] = nn;
4902: c->nonzerorowcnt += (PetscInt)!!nn;
4903: c->rmax = PetscMax(c->rmax, nn);
4904: }
4905: PetscCall(MatMarkDiagonal_SeqAIJ(*C));
4906: PetscCall(PetscMalloc1(c->nz, &c->a));
4907: (*C)->nonzerostate++;
4908: PetscCall(PetscLayoutSetUp((*C)->rmap));
4909: PetscCall(PetscLayoutSetUp((*C)->cmap));
4910: Ccusp->nonzerostate = (*C)->nonzerostate;
4911: (*C)->preallocated = PETSC_TRUE;
4912: } else {
4913: 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);
4914: c = (Mat_SeqAIJ *)(*C)->data;
4915: if (c->nz) {
4916: Ccusp = (Mat_SeqAIJCUSPARSE *)(*C)->spptr;
4917: PetscCheck(Ccusp->coords, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing coords");
4918: PetscCheck(Ccusp->format != MAT_CUSPARSE_ELL && Ccusp->format != MAT_CUSPARSE_HYB, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not implemented");
4919: PetscCheck(Ccusp->nonzerostate == (*C)->nonzerostate, PETSC_COMM_SELF, PETSC_ERR_COR, "Wrong nonzerostate");
4920: PetscCall(MatSeqAIJCUSPARSECopyToGPU(A));
4921: PetscCall(MatSeqAIJCUSPARSECopyToGPU(B));
4922: PetscCheck(Acusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4923: PetscCheck(Bcusp->mat, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing Mat_SeqAIJCUSPARSEMultStruct");
4924: Acsr = (CsrMatrix *)Acusp->mat->mat;
4925: Bcsr = (CsrMatrix *)Bcusp->mat->mat;
4926: Ccsr = (CsrMatrix *)Ccusp->mat->mat;
4927: 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());
4928: 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());
4929: 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());
4930: 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);
4931: 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());
4932: auto pmid = Ccusp->coords->begin();
4933: thrust::advance(pmid, Acsr->num_entries);
4934: PetscCall(PetscLogGpuTimeBegin());
4935: auto zibait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->begin())));
4936: auto zieait = thrust::make_zip_iterator(thrust::make_tuple(Acsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4937: thrust::for_each(zibait, zieait, VecCUDAEquals());
4938: auto zibbit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->begin(), thrust::make_permutation_iterator(Ccsr->values->begin(), pmid)));
4939: auto ziebit = thrust::make_zip_iterator(thrust::make_tuple(Bcsr->values->end(), thrust::make_permutation_iterator(Ccsr->values->begin(), Ccusp->coords->end())));
4940: thrust::for_each(zibbit, ziebit, VecCUDAEquals());
4941: PetscCall(MatSeqAIJCUSPARSEInvalidateTranspose(*C, PETSC_FALSE));
4942: if (A->form_explicit_transpose && B->form_explicit_transpose && (*C)->form_explicit_transpose) {
4943: PetscCheck(Ccusp->matTranspose, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing transpose Mat_SeqAIJCUSPARSEMultStruct");
4944: PetscBool AT = Acusp->matTranspose ? PETSC_TRUE : PETSC_FALSE, BT = Bcusp->matTranspose ? PETSC_TRUE : PETSC_FALSE;
4945: CsrMatrix *AcsrT = AT ? (CsrMatrix *)Acusp->matTranspose->mat : NULL;
4946: CsrMatrix *BcsrT = BT ? (CsrMatrix *)Bcusp->matTranspose->mat : NULL;
4947: CsrMatrix *CcsrT = (CsrMatrix *)Ccusp->matTranspose->mat;
4948: auto vT = CcsrT->values->begin();
4949: if (AT) vT = thrust::copy(AcsrT->values->begin(), AcsrT->values->end(), vT);
4950: if (BT) thrust::copy(BcsrT->values->begin(), BcsrT->values->end(), vT);
4951: (*C)->transupdated = PETSC_TRUE;
4952: }
4953: PetscCall(PetscLogGpuTimeEnd());
4954: }
4955: }
4956: PetscCall(PetscObjectStateIncrease((PetscObject)*C));
4957: (*C)->assembled = PETSC_TRUE;
4958: (*C)->was_assembled = PETSC_FALSE;
4959: (*C)->offloadmask = PETSC_OFFLOAD_GPU;
4960: PetscFunctionReturn(PETSC_SUCCESS);
4961: }
4963: static PetscErrorCode MatSeqAIJCopySubArray_SeqAIJCUSPARSE(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
4964: {
4965: bool dmem;
4966: const PetscScalar *av;
4968: PetscFunctionBegin;
4969: dmem = isCudaMem(v);
4970: PetscCall(MatSeqAIJCUSPARSEGetArrayRead(A, &av));
4971: if (n && idx) {
4972: THRUSTINTARRAY widx(n);
4973: widx.assign(idx, idx + n);
4974: PetscCall(PetscLogCpuToGpu(n * sizeof(PetscInt)));
4976: THRUSTARRAY *w = NULL;
4977: thrust::device_ptr<PetscScalar> dv;
4978: if (dmem) {
4979: dv = thrust::device_pointer_cast(v);
4980: } else {
4981: w = new THRUSTARRAY(n);
4982: dv = w->data();
4983: }
4984: thrust::device_ptr<const PetscScalar> dav = thrust::device_pointer_cast(av);
4986: auto zibit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.begin()), dv));
4987: auto zieit = thrust::make_zip_iterator(thrust::make_tuple(thrust::make_permutation_iterator(dav, widx.end()), dv + n));
4988: thrust::for_each(zibit, zieit, VecCUDAEquals());
4989: if (w) PetscCallCUDA(cudaMemcpy(v, w->data().get(), n * sizeof(PetscScalar), cudaMemcpyDeviceToHost));
4990: delete w;
4991: } else {
4992: PetscCallCUDA(cudaMemcpy(v, av, n * sizeof(PetscScalar), dmem ? cudaMemcpyDeviceToDevice : cudaMemcpyDeviceToHost));
4993: }
4994: if (!dmem) PetscCall(PetscLogCpuToGpu(n * sizeof(PetscScalar)));
4995: PetscCall(MatSeqAIJCUSPARSERestoreArrayRead(A, &av));
4996: PetscFunctionReturn(PETSC_SUCCESS);
4997: }
4998: PETSC_PRAGMA_DIAGNOSTIC_IGNORED_END()