Actual source code: cusparsematimpl.h
1: #pragma once
3: #include <petscpkg_version.h>
4: #include <../src/vec/vec/impls/seq/cupm/vecseqcupm.hpp>
5: #include <../src/sys/objects/device/impls/cupm/cupmthrustutility.hpp>
6: #include <petsc/private/petsclegacycupmblas.h>
8: #include <algorithm>
9: #include <vector>
11: #include <thrust/device_vector.h>
12: #include <thrust/device_ptr.h>
13: #include <thrust/device_malloc_allocator.h>
14: #include <thrust/transform.h>
15: #include <thrust/functional.h>
16: #include <thrust/sequence.h>
17: #include <thrust/system/system_error.h>
19: #if defined(PETSC_USE_COMPLEX)
20: #if defined(PETSC_USE_REAL_SINGLE)
21: const cuComplex PETSC_CUSPARSE_ONE = {1.0f, 0.0f};
22: const cuComplex PETSC_CUSPARSE_ZERO = {0.0f, 0.0f};
23: #define cusparseXcsrilu02_bufferSize(a, b, c, d, e, f, g, h, i) cusparseCcsrilu02_bufferSize(a, b, c, d, (cuComplex *)e, f, g, h, i)
24: #define cusparseXcsrilu02_analysis(a, b, c, d, e, f, g, h, i, j) cusparseCcsrilu02_analysis(a, b, c, d, (cuComplex *)e, f, g, h, i, j)
25: #define cusparseXcsrilu02(a, b, c, d, e, f, g, h, i, j) cusparseCcsrilu02(a, b, c, d, (cuComplex *)e, f, g, h, i, j)
26: #define cusparseXcsric02_bufferSize(a, b, c, d, e, f, g, h, i) cusparseCcsric02_bufferSize(a, b, c, d, (cuComplex *)e, f, g, h, i)
27: #define cusparseXcsric02_analysis(a, b, c, d, e, f, g, h, i, j) cusparseCcsric02_analysis(a, b, c, d, (cuComplex *)e, f, g, h, i, j)
28: #define cusparseXcsric02(a, b, c, d, e, f, g, h, i, j) cusparseCcsric02(a, b, c, d, (cuComplex *)e, f, g, h, i, j)
29: #elif defined(PETSC_USE_REAL_DOUBLE)
30: const cuDoubleComplex PETSC_CUSPARSE_ONE = {1.0, 0.0};
31: const cuDoubleComplex PETSC_CUSPARSE_ZERO = {0.0, 0.0};
32: #define cusparseXcsrilu02_bufferSize(a, b, c, d, e, f, g, h, i) cusparseZcsrilu02_bufferSize(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i)
33: #define cusparseXcsrilu02_analysis(a, b, c, d, e, f, g, h, i, j) cusparseZcsrilu02_analysis(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i, j)
34: #define cusparseXcsrilu02(a, b, c, d, e, f, g, h, i, j) cusparseZcsrilu02(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i, j)
35: #define cusparseXcsric02_bufferSize(a, b, c, d, e, f, g, h, i) cusparseZcsric02_bufferSize(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i)
36: #define cusparseXcsric02_analysis(a, b, c, d, e, f, g, h, i, j) cusparseZcsric02_analysis(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i, j)
37: #define cusparseXcsric02(a, b, c, d, e, f, g, h, i, j) cusparseZcsric02(a, b, c, d, (cuDoubleComplex *)e, f, g, h, i, j)
38: #endif
39: #else
40: const PetscScalar PETSC_CUSPARSE_ONE = 1.0;
41: const PetscScalar PETSC_CUSPARSE_ZERO = 0.0;
42: #if defined(PETSC_USE_REAL_SINGLE)
43: #define cusparseXcsrilu02_bufferSize cusparseScsrilu02_bufferSize
44: #define cusparseXcsrilu02_analysis cusparseScsrilu02_analysis
45: #define cusparseXcsrilu02 cusparseScsrilu02
46: #define cusparseXcsric02_bufferSize cusparseScsric02_bufferSize
47: #define cusparseXcsric02_analysis cusparseScsric02_analysis
48: #define cusparseXcsric02 cusparseScsric02
49: #elif defined(PETSC_USE_REAL_DOUBLE)
50: #define cusparseXcsrilu02_bufferSize cusparseDcsrilu02_bufferSize
51: #define cusparseXcsrilu02_analysis cusparseDcsrilu02_analysis
52: #define cusparseXcsrilu02 cusparseDcsrilu02
53: #define cusparseXcsric02_bufferSize cusparseDcsric02_bufferSize
54: #define cusparseXcsric02_analysis cusparseDcsric02_analysis
55: #define cusparseXcsric02 cusparseDcsric02
56: #endif
57: #endif
59: #if PETSC_PKG_CUDA_VERSION_GE(9, 0, 0)
60: #define csrsvInfo_t csrsv2Info_t
61: #define cusparseCreateCsrsvInfo cusparseCreateCsrsv2Info
62: #define cusparseDestroyCsrsvInfo cusparseDestroyCsrsv2Info
63: #if defined(PETSC_USE_COMPLEX)
64: #if defined(PETSC_USE_REAL_SINGLE)
65: #define cusparseXcsrsv_buffsize(a, b, c, d, e, f, g, h, i, j) cusparseCcsrsv2_bufferSize(a, b, c, d, e, (cuComplex *)(f), g, h, i, j)
66: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j, k) cusparseCcsrsv2_analysis(a, b, c, d, e, (const cuComplex *)(f), g, h, i, j, k)
67: #define cusparseXcsrsv_solve(a, b, c, d, e, f, g, h, i, j, k, l, m, n) cusparseCcsrsv2_solve(a, b, c, d, (const cuComplex *)(e), f, (const cuComplex *)(g), h, i, j, (const cuComplex *)(k), (cuComplex *)(l), m, n)
68: #elif defined(PETSC_USE_REAL_DOUBLE)
69: #define cusparseXcsrsv_buffsize(a, b, c, d, e, f, g, h, i, j) cusparseZcsrsv2_bufferSize(a, b, c, d, e, (cuDoubleComplex *)(f), g, h, i, j)
70: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j, k) cusparseZcsrsv2_analysis(a, b, c, d, e, (const cuDoubleComplex *)(f), g, h, i, j, k)
71: #define cusparseXcsrsv_solve(a, b, c, d, e, f, g, h, i, j, k, l, m, n) cusparseZcsrsv2_solve(a, b, c, d, (const cuDoubleComplex *)(e), f, (const cuDoubleComplex *)(g), h, i, j, (const cuDoubleComplex *)(k), (cuDoubleComplex *)(l), m, n)
72: #endif
73: #else /* not complex */
74: #if defined(PETSC_USE_REAL_SINGLE)
75: #define cusparseXcsrsv_buffsize cusparseScsrsv2_bufferSize
76: #define cusparseXcsrsv_analysis cusparseScsrsv2_analysis
77: #define cusparseXcsrsv_solve cusparseScsrsv2_solve
78: #elif defined(PETSC_USE_REAL_DOUBLE)
79: #define cusparseXcsrsv_buffsize cusparseDcsrsv2_bufferSize
80: #define cusparseXcsrsv_analysis cusparseDcsrsv2_analysis
81: #define cusparseXcsrsv_solve cusparseDcsrsv2_solve
82: #endif
83: #endif
84: #else /* PETSC_PKG_CUDA_VERSION_GE(9, 0, 0) */
85: #define csrsvInfo_t cusparseSolveAnalysisInfo_t
86: #define cusparseCreateCsrsvInfo cusparseCreateSolveAnalysisInfo
87: #define cusparseDestroyCsrsvInfo cusparseDestroySolveAnalysisInfo
88: #if defined(PETSC_USE_COMPLEX)
89: #if defined(PETSC_USE_REAL_SINGLE)
90: #define cusparseXcsrsv_solve(a, b, c, d_IGNORED, e, f, g, h, i, j, k, l, m_IGNORED, n_IGNORED) cusparseCcsrsv_solve((a), (b), (c), (cuComplex *)(e), (f), (cuComplex *)(g), (h), (i), (j), (cuComplex *)(k), (cuComplex *)(l))
91: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j_IGNORED, k_IGNORED) cusparseCcsrsv_analysis((a), (b), (c), (d), (e), (cuComplex *)(f), (g), (h), (i))
92: #elif defined(PETSC_USE_REAL_DOUBLE)
93: #define cusparseXcsrsv_solve(a, b, c, d_IGNORED, e, f, g, h, i, j, k, l, m_IGNORED, n_IGNORED) \
94: cusparseZcsrsv_solve((a), (b), (c), (cuDoubleComplex *)(e), (f), (cuDoubleComplex *)(g), (h), (i), (j), (cuDoubleComplex *)(k), (cuDoubleComplex *)(l))
95: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j_IGNORED, k_IGNORED) cusparseZcsrsv_analysis((a), (b), (c), (d), (e), (cuDoubleComplex *)(f), (g), (h), (i))
96: #endif
97: #else /* not complex */
98: #if defined(PETSC_USE_REAL_SINGLE)
99: #define cusparseXcsrsv_solve cusparseScsrsv_solve
100: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j, k) cusparseScsrsv_analysis(a, b, c, d, e, f, g, h, i)
101: #elif defined(PETSC_USE_REAL_DOUBLE)
102: #define cusparseXcsrsv_solve cusparseDcsrsv_solve
103: #define cusparseXcsrsv_analysis(a, b, c, d, e, f, g, h, i, j, k) cusparseDcsrsv_analysis(a, b, c, d, e, f, g, h, i)
104: #endif
105: #endif
106: #endif /* PETSC_PKG_CUDA_VERSION_GE(9, 0, 0) */
108: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
109: #define cusparse_csr2csc cusparseCsr2cscEx2
110: #if defined(PETSC_USE_COMPLEX)
111: #if defined(PETSC_USE_REAL_SINGLE)
112: #define cusparse_scalartype CUDA_C_32F
113: #define cusparse_csr_spgeam(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) cusparseCcsrgeam2(a, b, c, (cuComplex *)d, e, f, (cuComplex *)g, h, i, (cuComplex *)j, k, l, (cuComplex *)m, n, o, p, (cuComplex *)q, r, s, t)
114: #define cusparse_csr_spgeam_bufferSize(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) \
115: cusparseCcsrgeam2_bufferSizeExt(a, b, c, (cuComplex *)d, e, f, (cuComplex *)g, h, i, (cuComplex *)j, k, l, (cuComplex *)m, n, o, p, (cuComplex *)q, r, s, t)
116: #elif defined(PETSC_USE_REAL_DOUBLE)
117: #define cusparse_scalartype CUDA_C_64F
118: #define cusparse_csr_spgeam(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) \
119: cusparseZcsrgeam2(a, b, c, (cuDoubleComplex *)d, e, f, (cuDoubleComplex *)g, h, i, (cuDoubleComplex *)j, k, l, (cuDoubleComplex *)m, n, o, p, (cuDoubleComplex *)q, r, s, t)
120: #define cusparse_csr_spgeam_bufferSize(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) \
121: cusparseZcsrgeam2_bufferSizeExt(a, b, c, (cuDoubleComplex *)d, e, f, (cuDoubleComplex *)g, h, i, (cuDoubleComplex *)j, k, l, (cuDoubleComplex *)m, n, o, p, (cuDoubleComplex *)q, r, s, t)
122: #endif
123: #else /* not complex */
124: #if defined(PETSC_USE_REAL_SINGLE)
125: #define cusparse_scalartype CUDA_R_32F
126: #define cusparse_csr_spgeam cusparseScsrgeam2
127: #define cusparse_csr_spgeam_bufferSize cusparseScsrgeam2_bufferSizeExt
128: #elif defined(PETSC_USE_REAL_DOUBLE)
129: #define cusparse_scalartype CUDA_R_64F
130: #define cusparse_csr_spgeam cusparseDcsrgeam2
131: #define cusparse_csr_spgeam_bufferSize cusparseDcsrgeam2_bufferSizeExt
132: #endif
133: #endif
134: #else /* PETSC_PKG_CUDA_VERSION_GE(11, 0, 0) */
135: #if defined(PETSC_USE_COMPLEX)
136: #if defined(PETSC_USE_REAL_SINGLE)
137: #define cusparse_csr_spmv(a, b, c, d, e, f, g, h, i, j, k, l, m) cusparseCcsrmv((a), (b), (c), (d), (e), (cuComplex *)(f), (g), (cuComplex *)(h), (i), (j), (cuComplex *)(k), (cuComplex *)(l), (cuComplex *)(m))
138: #define cusparse_csr_spmm(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p) cusparseCcsrmm((a), (b), (c), (d), (e), (f), (cuComplex *)(g), (h), (cuComplex *)(i), (j), (k), (cuComplex *)(l), (m), (cuComplex *)(n), (cuComplex *)(o), (p))
139: #define cusparse_csr2csc(a, b, c, d, e, f, g, h, i, j, k, l) cusparseCcsr2csc((a), (b), (c), (d), (cuComplex *)(e), (f), (g), (cuComplex *)(h), (i), (j), (k), (l))
140: #define cusparse_hyb_spmv(a, b, c, d, e, f, g, h) cusparseChybmv((a), (b), (cuComplex *)(c), (d), (e), (cuComplex *)(f), (cuComplex *)(g), (cuComplex *)(h))
141: #define cusparse_csr2hyb(a, b, c, d, e, f, g, h, i, j) cusparseCcsr2hyb((a), (b), (c), (d), (cuComplex *)(e), (f), (g), (h), (i), (j))
142: #define cusparse_hyb2csr(a, b, c, d, e, f) cusparseChyb2csr((a), (b), (c), (cuComplex *)(d), (e), (f))
143: #define cusparse_csr_spgemm(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) cusparseCcsrgemm(a, b, c, d, e, f, g, h, (cuComplex *)i, j, k, l, m, (cuComplex *)n, o, p, q, (cuComplex *)r, s, t)
144: #define cusparse_csr_spgeam(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s) cusparseCcsrgeam(a, b, c, (cuComplex *)d, e, f, (cuComplex *)g, h, i, (cuComplex *)j, k, l, (cuComplex *)m, n, o, p, (cuComplex *)q, r, s)
145: #elif defined(PETSC_USE_REAL_DOUBLE)
146: #define cusparse_csr_spmv(a, b, c, d, e, f, g, h, i, j, k, l, m) cusparseZcsrmv((a), (b), (c), (d), (e), (cuDoubleComplex *)(f), (g), (cuDoubleComplex *)(h), (i), (j), (cuDoubleComplex *)(k), (cuDoubleComplex *)(l), (cuDoubleComplex *)(m))
147: #define cusparse_csr_spmm(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p) \
148: cusparseZcsrmm((a), (b), (c), (d), (e), (f), (cuDoubleComplex *)(g), (h), (cuDoubleComplex *)(i), (j), (k), (cuDoubleComplex *)(l), (m), (cuDoubleComplex *)(n), (cuDoubleComplex *)(o), (p))
149: #define cusparse_csr2csc(a, b, c, d, e, f, g, h, i, j, k, l) cusparseZcsr2csc((a), (b), (c), (d), (cuDoubleComplex *)(e), (f), (g), (cuDoubleComplex *)(h), (i), (j), (k), (l))
150: #define cusparse_hyb_spmv(a, b, c, d, e, f, g, h) cusparseZhybmv((a), (b), (cuDoubleComplex *)(c), (d), (e), (cuDoubleComplex *)(f), (cuDoubleComplex *)(g), (cuDoubleComplex *)(h))
151: #define cusparse_csr2hyb(a, b, c, d, e, f, g, h, i, j) cusparseZcsr2hyb((a), (b), (c), (d), (cuDoubleComplex *)(e), (f), (g), (h), (i), (j))
152: #define cusparse_hyb2csr(a, b, c, d, e, f) cusparseZhyb2csr((a), (b), (c), (cuDoubleComplex *)(d), (e), (f))
153: #define cusparse_csr_spgemm(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) cusparseZcsrgemm(a, b, c, d, e, f, g, h, (cuDoubleComplex *)i, j, k, l, m, (cuDoubleComplex *)n, o, p, q, (cuDoubleComplex *)r, s, t)
154: #define cusparse_csr_spgeam(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s) \
155: cusparseZcsrgeam(a, b, c, (cuDoubleComplex *)d, e, f, (cuDoubleComplex *)g, h, i, (cuDoubleComplex *)j, k, l, (cuDoubleComplex *)m, n, o, p, (cuDoubleComplex *)q, r, s)
156: #endif
157: #else
158: #if defined(PETSC_USE_REAL_SINGLE)
159: #define cusparse_csr_spmv cusparseScsrmv
160: #define cusparse_csr_spmm cusparseScsrmm
161: #define cusparse_csr2csc cusparseScsr2csc
162: #define cusparse_hyb_spmv cusparseShybmv
163: #define cusparse_csr2hyb cusparseScsr2hyb
164: #define cusparse_hyb2csr cusparseShyb2csr
165: #define cusparse_csr_spgemm cusparseScsrgemm
166: #define cusparse_csr_spgeam cusparseScsrgeam
167: #elif defined(PETSC_USE_REAL_DOUBLE)
168: #define cusparse_csr_spmv cusparseDcsrmv
169: #define cusparse_csr_spmm cusparseDcsrmm
170: #define cusparse_csr2csc cusparseDcsr2csc
171: #define cusparse_hyb_spmv cusparseDhybmv
172: #define cusparse_csr2hyb cusparseDcsr2hyb
173: #define cusparse_hyb2csr cusparseDhyb2csr
174: #define cusparse_csr_spgemm cusparseDcsrgemm
175: #define cusparse_csr_spgeam cusparseDcsrgeam
176: #endif
177: #endif
178: #endif /* PETSC_PKG_CUDA_VERSION_GE(11, 0, 0) */
180: #define THRUSTINTARRAY32 thrust::device_vector<int>
181: #define THRUSTINTARRAY thrust::device_vector<PetscInt>
182: #define THRUSTARRAY thrust::device_vector<PetscScalar>
184: /* A CSR matrix nonzero structure */
185: struct CsrMatrix {
186: PetscInt num_rows;
187: PetscInt num_cols;
188: PetscInt num_entries;
189: THRUSTINTARRAY32 *row_offsets;
190: THRUSTINTARRAY32 *column_indices;
191: THRUSTARRAY *values;
192: };
194: /* This is struct holding the relevant data needed to a MatSolve */
195: struct Mat_SeqAIJCUSPARSETriFactorStruct {
196: /* Data needed for triangular solve */
197: cusparseMatDescr_t descr;
198: cusparseOperation_t solveOp;
199: CsrMatrix *csrMat;
200: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
201: csrsvInfo_t solveInfo;
202: cusparseSolvePolicy_t solvePolicy; /* whether level information is generated and used */
203: #endif
204: int solveBufferSize;
205: void *solveBuffer;
206: size_t csr2cscBufferSize; /* to transpose the triangular factor (only used for CUDA >= 11.0) */
207: void *csr2cscBuffer;
208: PetscScalar *AA_h; /* managed host buffer for moving values to the GPU */
209: };
211: /* This is a larger struct holding all the triangular factors for a solve, transpose solve, and any indices used in a reordering */
212: struct Mat_SeqAIJCUSPARSETriFactors {
213: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
214: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorPtr; /* pointer for lower triangular (factored matrix) on GPU */
215: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorPtr; /* pointer for upper triangular (factored matrix) on GPU */
216: Mat_SeqAIJCUSPARSETriFactorStruct *loTriFactorPtrTranspose; /* pointer for lower triangular (factored matrix) on GPU for the transpose (useful for BiCG) */
217: Mat_SeqAIJCUSPARSETriFactorStruct *upTriFactorPtrTranspose; /* pointer for upper triangular (factored matrix) on GPU for the transpose (useful for BiCG)*/
218: #endif
220: THRUSTINTARRAY *rpermIndices; /* indices used for any reordering */
221: THRUSTINTARRAY *cpermIndices; /* indices used for any reordering */
222: THRUSTARRAY *workVector;
223: cusparseHandle_t handle; /* a handle to the cusparse library */
224: PetscInt nnz; /* number of nonzeros ... need this for accurate logging between ICC and ILU */
225: cudaDeviceProp dev_prop;
226: PetscBool init_dev_prop;
228: #if PETSC_PKG_CUDA_VERSION_GE(11, 4, 0)
229: /* csrilu0/csric0 appeared in cusparse-8.0, but we use it along with cusparseSpSV,
230: which first appeared in cusparse-11.5 with cuda-11.3.
231: */
232: PetscScalar *csrVal, *diag; // the diagonal D in UtDU of Cholesky
233: int *csrRowPtr32, *csrColIdx32; // i,j of M. cusparseScsrilu02/ic02() etc require 32-bit indices
235: PetscInt *csrRowPtr, *csrColIdx; // i, j of M on device for CUDA APIs that support 64-bit indices
236: PetscScalar *csrVal_h, *diag_h; // Since LU is done on host, we prepare a factored matrix in regular csr format on host and then copy it to device
237: PetscInt *csrRowPtr_h; // csrColIdx_h is temporary, so it is not here
239: /* Mixed mat descriptor types? yes, different cusparse APIs use different types */
240: cusparseMatDescr_t matDescr_M;
241: cusparseSpMatDescr_t spMatDescr_L, spMatDescr_U;
242: cusparseSpSVDescr_t spsvDescr_L, spsvDescr_Lt, spsvDescr_U, spsvDescr_Ut;
244: cusparseDnVecDescr_t dnVecDescr_X, dnVecDescr_Y;
245: PetscScalar *X, *Y; /* data array of dnVec X and Y */
247: /* Mixed size types? yes, CUDA-11.7.0 declared cusparseDcsrilu02_bufferSizeExt() that returns size_t but did not implement it! */
248: int factBufferSize_M; /* M ~= LU or LLt */
249: size_t spsvBufferSize_L, spsvBufferSize_Lt, spsvBufferSize_U, spsvBufferSize_Ut;
250: /* cusparse needs various buffers for factorization and solve of L, U, Lt, or Ut.
251: So save memory, we share the factorization buffer with one of spsvBuffer_L/U.
252: */
253: void *factBuffer_M, *spsvBuffer_L, *spsvBuffer_U, *spsvBuffer_Lt, *spsvBuffer_Ut;
255: csrilu02Info_t ilu0Info_M;
256: csric02Info_t ic0Info_M;
257: int structural_zero, numerical_zero;
258: cusparseSolvePolicy_t policy_M;
260: /* In MatSolveTranspose() for ILU0, we use the two flags to do on-demand solve */
261: PetscBool createdTransposeSpSVDescr; /* Have we created SpSV descriptors for Lt, Ut? */
262: PetscBool updatedTransposeSpSVAnalysis; /* Have we ever updated (done) SpSV analysis for Lt, Ut */
263: PetscBool updatedSpSVAnalysis; /* Have we ever updated (done) SpSV Analysis for L, U? */
265: PetscLogDouble numericFactFlops; /* Estimated FLOPs in ILU0/ICC0 numeric factorization */
266: #endif
267: };
269: struct Mat_CusparseSpMV {
270: PetscBool initialized; /* Don't rely on spmvBuffer != NULL to test if the struct is initialized, */
271: size_t spmvBufferSize; /* since I'm not sure if smvBuffer can be NULL even after cusparseSpMV_bufferSize() */
272: void *spmvBuffer;
273: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0) /* these are present from CUDA 10.1, but PETSc code makes use of them from CUDA 11 on */
274: cusparseDnVecDescr_t vecXDescr, vecYDescr; /* descriptor for the dense vectors in y=op(A)x */
275: #endif
276: };
278: /* This is struct holding the relevant data needed to a MatMult */
279: struct Mat_SeqAIJCUSPARSEMultStruct {
280: void *mat; /* opaque pointer to a matrix. This could be either a cusparseHybMat_t or a CsrMatrix */
281: cusparseMatDescr_t descr; /* Data needed to describe the matrix for a multiply */
282: THRUSTINTARRAY *cprowIndices; /* compressed row indices used in the parallel SpMV */
283: PetscScalar *alpha_one; /* pointer to a device "scalar" storing the alpha parameter in the SpMV */
284: PetscScalar *beta_zero; /* pointer to a device "scalar" storing the beta parameter in the SpMV as zero*/
285: PetscScalar *beta_one; /* pointer to a device "scalar" storing the beta parameter in the SpMV as one */
286: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
287: cusparseSpMatDescr_t matDescr; /* descriptor for the matrix, used by SpMV and SpMM */
288: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0) // tested up to 12.6.0
289: cusparseSpMatDescr_t matDescr_SpMV[3]; // Use separate MatDescr for opA's, to workaround cusparse bugs after 12.4, see https://github.com/NVIDIA/CUDALibrarySamples/issues/212,
290: cusparseSpMatDescr_t matDescr_SpMM[3]; // and known issues https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cusparse-release-12-6
291: #endif
292: Mat_CusparseSpMV cuSpMV[3]; /* different Mat_CusparseSpMV structs for non-transpose, transpose, conj-transpose */
293: Mat_SeqAIJCUSPARSEMultStruct() : matDescr(NULL)
294: {
295: for (int i = 0; i < 3; i++) {
296: cuSpMV[i].initialized = PETSC_FALSE;
297: #if PETSC_PKG_CUDA_VERSION_GE(12, 4, 0)
298: matDescr_SpMV[i] = NULL;
299: matDescr_SpMM[i] = NULL;
300: #endif
301: }
302: }
303: #endif
304: };
306: /* This is a larger struct holding all the matrices for a SpMV, and SpMV Transpose */
307: struct Mat_SeqAIJCUSPARSE {
308: Mat_SeqAIJCUSPARSEMultStruct *mat; /* pointer to the matrix on the GPU */
309: Mat_SeqAIJCUSPARSEMultStruct *matTranspose; /* pointer to the matrix on the GPU (for the transpose ... useful for BiCG) */
310: THRUSTARRAY *workVector; /* pointer to a workvector to which we can copy the relevant indices of a vector we want to multiply */
311: THRUSTINTARRAY32 *rowoffsets_gpu; /* rowoffsets on GPU in non-compressed-row format. It is used to convert CSR to CSC */
312: PetscInt nrows; /* number of rows of the matrix seen by GPU */
313: MatCUSPARSEStorageFormat format; /* the storage format for the matrix on the device */
314: PetscBool use_cpu_solve; /* Use AIJ_Seq (I)LU solve */
315: cudaStream_t stream; /* a stream for the parallel SpMV ... this is not owned and should not be deleted */
316: cusparseHandle_t handle; /* a handle to the cusparse library ... this may not be owned (if we're working in parallel i.e. multiGPUs) */
317: PetscObjectState nonzerostate; /* track nonzero state to possibly recreate the GPU matrix */
318: #if PETSC_PKG_CUDA_VERSION_GE(11, 0, 0)
319: size_t csr2cscBufferSize; /* stuff used to compute the matTranspose above */
320: void *csr2cscBuffer; /* This is used as a C struct and is calloc'ed by PetscNew() */
321: cusparseCsr2CscAlg_t csr2cscAlg; /* algorithms can be selected from command line options */
322: cusparseSpMVAlg_t spmvAlg;
323: cusparseSpMMAlg_t spmmAlg;
324: #endif
325: THRUSTINTARRAY *csr2csc_i;
326: THRUSTINTARRAY *coords; /* permutation array used in MatSeqAIJCUSPARSEMergeMats */
327: };
329: typedef struct Mat_SeqAIJCUSPARSETriFactors *Mat_SeqAIJCUSPARSETriFactors_p;
331: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSECopyToGPU(Mat);
332: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSEMergeMats(Mat, Mat, MatReuse, Mat *);
333: PETSC_INTERN PetscErrorCode MatSeqAIJCUSPARSETriFactors_Reset(Mat_SeqAIJCUSPARSETriFactors_p *);
335: using VecSeq_CUDA = Petsc::vec::cupm::impl::VecSeq_CUPM<Petsc::device::cupm::DeviceType::CUDA>;
337: static inline bool isCudaMem(const void *data)
338: {
339: using namespace Petsc::device::cupm;
340: auto mtype = PETSC_MEMTYPE_HOST;
342: PetscFunctionBegin;
343: PetscCallAbort(PETSC_COMM_SELF, impl::Interface<DeviceType::CUDA>::PetscCUPMGetMemType(data, &mtype));
344: PetscFunctionReturn(PetscMemTypeDevice(mtype));
345: }