Actual source code: aijkok.kokkos.cxx
1: #include <petsc_kokkos.hpp>
2: #include <petscvec_kokkos.hpp>
3: #include <petscpkg_version.h>
4: #include <petsc/private/petscimpl.h>
5: #include <petsc/private/sfimpl.h>
6: #include <petscsystypes.h>
7: #include <petscerror.h>
9: #include <Kokkos_Core.hpp>
10: #include <KokkosBlas.hpp>
11: #include <KokkosSparse_CrsMatrix.hpp>
12: #include <KokkosSparse_spmv.hpp>
13: #include <KokkosSparse_spiluk.hpp>
14: #include <KokkosSparse_sptrsv.hpp>
15: #include <KokkosSparse_spgemm.hpp>
16: #include <KokkosSparse_spadd.hpp>
17: #include <KokkosBatched_LU_Decl.hpp>
18: #include <KokkosBatched_InverseLU_Decl.hpp>
20: #include <../src/mat/impls/aij/seq/kokkos/aijkok.hpp>
22: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_GE(3, 7, 0)
23: #include <KokkosSparse_Utils.hpp>
24: using KokkosSparse::sort_crs_matrix;
25: using KokkosSparse::Impl::transpose_matrix;
26: #else
27: #include <KokkosKernels_Sorting.hpp>
28: using KokkosKernels::sort_crs_matrix;
29: using KokkosKernels::Impl::transpose_matrix;
30: #endif
32: static PetscErrorCode MatSetOps_SeqAIJKokkos(Mat); /* Forward declaration */
34: /* MatAssemblyEnd_SeqAIJKokkos() happens when we finalized nonzeros of the matrix, either after
35: we assembled the matrix on host, or after we directly produced the matrix data on device (ex., through MatMatMult).
36: In the latter case, it is important to set a_dual's sync state correctly.
37: */
38: static PetscErrorCode MatAssemblyEnd_SeqAIJKokkos(Mat A, MatAssemblyType mode)
39: {
40: Mat_SeqAIJ *aijseq;
41: Mat_SeqAIJKokkos *aijkok;
43: PetscFunctionBegin;
44: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
45: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
47: aijseq = static_cast<Mat_SeqAIJ *>(A->data);
48: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
50: /* If aijkok does not exist, we just copy i, j to device.
51: If aijkok already exists, but the device's nonzero pattern does not match with the host's, we assume the latest data is on host.
52: In both cases, we build a new aijkok structure.
53: */
54: if (!aijkok || aijkok->nonzerostate != A->nonzerostate) { /* aijkok might not exist yet or nonzero pattern has changed */
55: delete aijkok;
56: aijkok = new Mat_SeqAIJKokkos(A->rmap->n, A->cmap->n, aijseq, A->nonzerostate, PETSC_FALSE /*don't copy mat values to device*/);
57: A->spptr = aijkok;
58: } else if (A->rmap->n && aijkok->diag_dual.extent(0) == 0) { // MatProduct might directly produce AIJ on device, but not the diag.
59: MatRowMapKokkosViewHost diag_h(aijseq->diag, A->rmap->n);
60: auto diag_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), diag_h);
61: aijkok->diag_dual = MatRowMapKokkosDualView(diag_d, diag_h);
62: }
63: PetscFunctionReturn(PETSC_SUCCESS);
64: }
66: /* Sync CSR data to device if not yet */
67: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosSyncDevice(Mat A)
68: {
69: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
71: PetscFunctionBegin;
72: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Can't sync factorized matrix from host to device");
73: PetscCheck(aijkok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
74: if (aijkok->a_dual.need_sync_device()) {
75: aijkok->a_dual.sync_device();
76: aijkok->transpose_updated = PETSC_FALSE; /* values of the transpose is out-of-date */
77: aijkok->hermitian_updated = PETSC_FALSE;
78: }
79: PetscFunctionReturn(PETSC_SUCCESS);
80: }
82: /* Mark the CSR data on device as modified */
83: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosModifyDevice(Mat A)
84: {
85: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
87: PetscFunctionBegin;
88: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Not supported for factorized matries");
89: aijkok->a_dual.clear_sync_state();
90: aijkok->a_dual.modify_device();
91: aijkok->transpose_updated = PETSC_FALSE;
92: aijkok->hermitian_updated = PETSC_FALSE;
93: PetscCall(MatSeqAIJInvalidateDiagonal(A));
94: PetscCall(PetscObjectStateIncrease((PetscObject)A));
95: PetscFunctionReturn(PETSC_SUCCESS);
96: }
98: static PetscErrorCode MatSeqAIJKokkosSyncHost(Mat A)
99: {
100: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
101: auto &exec = PetscGetKokkosExecutionSpace();
103: PetscFunctionBegin;
104: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
105: /* We do not expect one needs factors on host */
106: PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Can't sync factorized matrix from device to host");
107: PetscCheck(aijkok, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Missing AIJKOK");
108: PetscCallCXX(aijkok->a_dual.sync_host(exec));
109: PetscCallCXX(exec.fence());
110: PetscFunctionReturn(PETSC_SUCCESS);
111: }
113: static PetscErrorCode MatSeqAIJGetArray_SeqAIJKokkos(Mat A, PetscScalar *array[])
114: {
115: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
117: PetscFunctionBegin;
118: /* aijkok contains valid pointers only if the host's nonzerostate matches with the device's.
119: Calling MatSeqAIJSetPreallocation() or MatSetValues() on host, where aijseq->{i,j,a} might be
120: reallocated, will lead to stale {i,j,a}_dual in aijkok. In both operations, the hosts's nonzerostate
121: must have been updated. The stale aijkok will be rebuilt during MatAssemblyEnd.
122: */
123: if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
124: auto &exec = PetscGetKokkosExecutionSpace();
125: PetscCallCXX(aijkok->a_dual.sync_host(exec));
126: PetscCallCXX(exec.fence());
127: *array = aijkok->a_dual.view_host().data();
128: } else { /* Happens when calling MatSetValues on a newly created matrix */
129: *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
130: }
131: PetscFunctionReturn(PETSC_SUCCESS);
132: }
134: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJKokkos(Mat A, PetscScalar *array[])
135: {
136: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
138: PetscFunctionBegin;
139: if (aijkok && A->nonzerostate == aijkok->nonzerostate) aijkok->a_dual.modify_host();
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJKokkos(Mat A, const PetscScalar *array[])
144: {
145: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
147: PetscFunctionBegin;
148: if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
149: auto &exec = PetscGetKokkosExecutionSpace();
150: PetscCallCXX(aijkok->a_dual.sync_host(exec));
151: PetscCallCXX(exec.fence());
152: *array = aijkok->a_dual.view_host().data();
153: } else {
154: *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
155: }
156: PetscFunctionReturn(PETSC_SUCCESS);
157: }
159: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJKokkos(Mat A, const PetscScalar *array[])
160: {
161: PetscFunctionBegin;
162: *array = NULL;
163: PetscFunctionReturn(PETSC_SUCCESS);
164: }
166: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJKokkos(Mat A, PetscScalar *array[])
167: {
168: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
170: PetscFunctionBegin;
171: if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
172: *array = aijkok->a_dual.view_host().data();
173: } else { /* Ex. happens with MatZeroEntries on a preallocated but not assembled matrix */
174: *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
175: }
176: PetscFunctionReturn(PETSC_SUCCESS);
177: }
179: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJKokkos(Mat A, PetscScalar *array[])
180: {
181: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
183: PetscFunctionBegin;
184: if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
185: aijkok->a_dual.clear_sync_state();
186: aijkok->a_dual.modify_host();
187: }
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
191: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJKokkos(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
192: {
193: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
195: PetscFunctionBegin;
196: PetscCheck(aijkok != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "aijkok is NULL");
198: if (i) *i = aijkok->i_device_data();
199: if (j) *j = aijkok->j_device_data();
200: if (a) {
201: aijkok->a_dual.sync_device();
202: *a = aijkok->a_device_data();
203: }
204: if (mtype) *mtype = PETSC_MEMTYPE_KOKKOS;
205: PetscFunctionReturn(PETSC_SUCCESS);
206: }
208: /*
209: Generate the sparsity pattern of a MatSeqAIJKokkos matrix's transpose on device.
211: Input Parameter:
212: . A - the MATSEQAIJKOKKOS matrix
214: Output Parameters:
215: + perm_d - the permutation array on device, which connects Ta(i) = Aa(perm(i))
216: - T_d - the transpose on device, whose value array is allocated but not initialized
217: */
218: static PetscErrorCode MatSeqAIJKokkosGenerateTransposeStructure(Mat A, MatRowMapKokkosView &perm_d, KokkosCsrMatrix &T_d)
219: {
220: Mat_SeqAIJ *aseq = static_cast<Mat_SeqAIJ *>(A->data);
221: PetscInt nz = aseq->nz, m = A->rmap->N, n = A->cmap->n;
222: const PetscInt *Ai = aseq->i, *Aj = aseq->j;
223: MatRowMapKokkosViewHost Ti_h(NoInit("Ti"), n + 1);
224: MatRowMapType *Ti = Ti_h.data();
225: MatColIdxKokkosViewHost Tj_h(NoInit("Tj"), nz);
226: MatRowMapKokkosViewHost perm_h(NoInit("permutation"), nz);
227: PetscInt *Tj = Tj_h.data();
228: PetscInt *perm = perm_h.data();
229: PetscInt *offset;
231: PetscFunctionBegin;
232: // Populate Ti
233: PetscCallCXX(Kokkos::deep_copy(Ti_h, 0));
234: Ti++;
235: for (PetscInt i = 0; i < nz; i++) Ti[Aj[i]]++;
236: Ti--;
237: for (PetscInt i = 0; i < n; i++) Ti[i + 1] += Ti[i];
239: // Populate Tj and the permutation array
240: PetscCall(PetscCalloc1(n, &offset)); // offset in each T row to fill in its column indices
241: for (PetscInt i = 0; i < m; i++) {
242: for (PetscInt j = Ai[i]; j < Ai[i + 1]; j++) { // A's (i,j) is T's (j,i)
243: PetscInt r = Aj[j]; // row r of T
244: PetscInt disp = Ti[r] + offset[r];
246: Tj[disp] = i; // col i of T
247: perm[disp] = j;
248: offset[r]++;
249: }
250: }
251: PetscCall(PetscFree(offset));
253: // Sort each row of T, along with the permutation array
254: for (PetscInt i = 0; i < n; i++) PetscCall(PetscSortIntWithArray(Ti[i + 1] - Ti[i], Tj + Ti[i], perm + Ti[i]));
256: // Output perm and T on device
257: auto Ti_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Ti_h);
258: auto Tj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Tj_h);
259: PetscCallCXX(T_d = KokkosCsrMatrix("csrmatT", n, m, nz, MatScalarKokkosView("Ta", nz), Ti_d, Tj_d));
260: PetscCallCXX(perm_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), perm_h));
261: PetscFunctionReturn(PETSC_SUCCESS);
262: }
264: // Generate the transpose on device and cache it internally
265: // Note: KK transpose_matrix() does not have support symbolic/numeric transpose, so we do it on our own
266: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosGenerateTranspose_Private(Mat A, KokkosCsrMatrix *csrmatT)
267: {
268: Mat_SeqAIJ *aseq = static_cast<Mat_SeqAIJ *>(A->data);
269: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
270: PetscInt nz = aseq->nz, m = A->rmap->N, n = A->cmap->n;
271: KokkosCsrMatrix &T = akok->csrmatT;
273: PetscFunctionBegin;
274: PetscCheck(akok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
275: PetscCallCXX(akok->a_dual.sync_device()); // Sync A's values since we are going to access them on device
277: const auto &Aa = akok->a_dual.view_device();
279: if (A->symmetric == PETSC_BOOL3_TRUE) {
280: *csrmatT = akok->csrmat;
281: } else {
282: // See if we already have a cached transpose and its value is up to date
283: if (T.numRows() == n && T.numCols() == m) { // this indicates csrmatT had been generated before, otherwise T has 0 rows/cols after construction
284: if (!akok->transpose_updated) { // if the value is out of date, update the cached version
285: const auto &perm = akok->transpose_perm; // get the permutation array
286: auto &Ta = T.values;
288: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, nz), KOKKOS_LAMBDA(const PetscInt i) { Ta(i) = Aa(perm(i)); }));
289: }
290: } else { // Generate T of size n x m for the first time
291: MatRowMapKokkosView perm;
293: PetscCall(MatSeqAIJKokkosGenerateTransposeStructure(A, perm, T));
294: akok->transpose_perm = perm; // cache the perm in this matrix for reuse
295: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, nz), KOKKOS_LAMBDA(const PetscInt i) { T.values(i) = Aa(perm(i)); }));
296: }
297: akok->transpose_updated = PETSC_TRUE;
298: *csrmatT = akok->csrmatT;
299: }
300: PetscFunctionReturn(PETSC_SUCCESS);
301: }
303: // Generate the Hermitian on device and cache it internally
304: static PetscErrorCode MatSeqAIJKokkosGenerateHermitian_Private(Mat A, KokkosCsrMatrix *csrmatH)
305: {
306: Mat_SeqAIJ *aseq = static_cast<Mat_SeqAIJ *>(A->data);
307: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
308: PetscInt nz = aseq->nz, m = A->rmap->N, n = A->cmap->n;
309: KokkosCsrMatrix &T = akok->csrmatH;
311: PetscFunctionBegin;
312: PetscCheck(akok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
313: PetscCallCXX(akok->a_dual.sync_device()); // Sync A's values since we are going to access them on device
315: const auto &Aa = akok->a_dual.view_device();
317: if (A->hermitian == PETSC_BOOL3_TRUE) {
318: *csrmatH = akok->csrmat;
319: } else {
320: // See if we already have a cached hermitian and its value is up to date
321: if (T.numRows() == n && T.numCols() == m) { // this indicates csrmatT had been generated before, otherwise T has 0 rows/cols after construction
322: if (!akok->hermitian_updated) { // if the value is out of date, update the cached version
323: const auto &perm = akok->transpose_perm; // get the permutation array
324: auto &Ta = T.values;
326: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, nz), KOKKOS_LAMBDA(const PetscInt i) { Ta(i) = PetscConj(Aa(perm(i))); }));
327: }
328: } else { // Generate T of size n x m for the first time
329: MatRowMapKokkosView perm;
331: PetscCall(MatSeqAIJKokkosGenerateTransposeStructure(A, perm, T));
332: akok->transpose_perm = perm; // cache the perm in this matrix for reuse
333: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, nz), KOKKOS_LAMBDA(const PetscInt i) { T.values(i) = PetscConj(Aa(perm(i))); }));
334: }
335: akok->hermitian_updated = PETSC_TRUE;
336: *csrmatH = akok->csrmatH;
337: }
338: PetscFunctionReturn(PETSC_SUCCESS);
339: }
341: /* y = A x */
342: static PetscErrorCode MatMult_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
343: {
344: Mat_SeqAIJKokkos *aijkok;
345: ConstPetscScalarKokkosView xv;
346: PetscScalarKokkosView yv;
348: PetscFunctionBegin;
349: PetscCall(PetscLogGpuTimeBegin());
350: PetscCall(MatSeqAIJKokkosSyncDevice(A));
351: PetscCall(VecGetKokkosView(xx, &xv));
352: PetscCall(VecGetKokkosViewWrite(yy, &yv));
353: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
354: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), "N", 1.0 /*alpha*/, aijkok->csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A x + beta y */
355: PetscCall(VecRestoreKokkosView(xx, &xv));
356: PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
357: /* 2.0*nnz - numRows seems more accurate here but assumes there are no zero-rows. So a little sloppy here. */
358: PetscCall(PetscLogGpuFlops(2.0 * aijkok->csrmat.nnz()));
359: PetscCall(PetscLogGpuTimeEnd());
360: PetscFunctionReturn(PETSC_SUCCESS);
361: }
363: /* y = A^T x */
364: static PetscErrorCode MatMultTranspose_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
365: {
366: Mat_SeqAIJKokkos *aijkok;
367: const char *mode;
368: ConstPetscScalarKokkosView xv;
369: PetscScalarKokkosView yv;
370: KokkosCsrMatrix csrmat;
372: PetscFunctionBegin;
373: PetscCall(PetscLogGpuTimeBegin());
374: PetscCall(MatSeqAIJKokkosSyncDevice(A));
375: PetscCall(VecGetKokkosView(xx, &xv));
376: PetscCall(VecGetKokkosViewWrite(yy, &yv));
377: if (A->form_explicit_transpose) {
378: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmat));
379: mode = "N";
380: } else {
381: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
382: csrmat = aijkok->csrmat;
383: mode = "T";
384: }
385: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), mode, 1.0 /*alpha*/, csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A^T x + beta y */
386: PetscCall(VecRestoreKokkosView(xx, &xv));
387: PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
388: PetscCall(PetscLogGpuFlops(2.0 * csrmat.nnz()));
389: PetscCall(PetscLogGpuTimeEnd());
390: PetscFunctionReturn(PETSC_SUCCESS);
391: }
393: /* y = A^H x */
394: static PetscErrorCode MatMultHermitianTranspose_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
395: {
396: Mat_SeqAIJKokkos *aijkok;
397: const char *mode;
398: ConstPetscScalarKokkosView xv;
399: PetscScalarKokkosView yv;
400: KokkosCsrMatrix csrmat;
402: PetscFunctionBegin;
403: PetscCall(PetscLogGpuTimeBegin());
404: PetscCall(MatSeqAIJKokkosSyncDevice(A));
405: PetscCall(VecGetKokkosView(xx, &xv));
406: PetscCall(VecGetKokkosViewWrite(yy, &yv));
407: if (A->form_explicit_transpose) {
408: PetscCall(MatSeqAIJKokkosGenerateHermitian_Private(A, &csrmat));
409: mode = "N";
410: } else {
411: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
412: csrmat = aijkok->csrmat;
413: mode = "C";
414: }
415: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), mode, 1.0 /*alpha*/, csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A^H x + beta y */
416: PetscCall(VecRestoreKokkosView(xx, &xv));
417: PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
418: PetscCall(PetscLogGpuFlops(2.0 * csrmat.nnz()));
419: PetscCall(PetscLogGpuTimeEnd());
420: PetscFunctionReturn(PETSC_SUCCESS);
421: }
423: /* z = A x + y */
424: static PetscErrorCode MatMultAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
425: {
426: Mat_SeqAIJKokkos *aijkok;
427: ConstPetscScalarKokkosView xv;
428: PetscScalarKokkosView zv;
430: PetscFunctionBegin;
431: PetscCall(PetscLogGpuTimeBegin());
432: PetscCall(MatSeqAIJKokkosSyncDevice(A));
433: if (zz != yy) PetscCall(VecCopy(yy, zz)); // depending on yy's sync flags, zz might get its latest data on host
434: PetscCall(VecGetKokkosView(xx, &xv));
435: PetscCall(VecGetKokkosView(zz, &zv)); // do after VecCopy(yy, zz) to get the latest data on device
436: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
437: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), "N", 1.0 /*alpha*/, aijkok->csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A x + beta z */
438: PetscCall(VecRestoreKokkosView(xx, &xv));
439: PetscCall(VecRestoreKokkosView(zz, &zv));
440: PetscCall(PetscLogGpuFlops(2.0 * aijkok->csrmat.nnz()));
441: PetscCall(PetscLogGpuTimeEnd());
442: PetscFunctionReturn(PETSC_SUCCESS);
443: }
445: /* z = A^T x + y */
446: static PetscErrorCode MatMultTransposeAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
447: {
448: Mat_SeqAIJKokkos *aijkok;
449: const char *mode;
450: ConstPetscScalarKokkosView xv;
451: PetscScalarKokkosView zv;
452: KokkosCsrMatrix csrmat;
454: PetscFunctionBegin;
455: PetscCall(PetscLogGpuTimeBegin());
456: PetscCall(MatSeqAIJKokkosSyncDevice(A));
457: if (zz != yy) PetscCall(VecCopy(yy, zz));
458: PetscCall(VecGetKokkosView(xx, &xv));
459: PetscCall(VecGetKokkosView(zz, &zv));
460: if (A->form_explicit_transpose) {
461: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmat));
462: mode = "N";
463: } else {
464: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
465: csrmat = aijkok->csrmat;
466: mode = "T";
467: }
468: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), mode, 1.0 /*alpha*/, csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A^T x + beta z */
469: PetscCall(VecRestoreKokkosView(xx, &xv));
470: PetscCall(VecRestoreKokkosView(zz, &zv));
471: PetscCall(PetscLogGpuFlops(2.0 * csrmat.nnz()));
472: PetscCall(PetscLogGpuTimeEnd());
473: PetscFunctionReturn(PETSC_SUCCESS);
474: }
476: /* z = A^H x + y */
477: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
478: {
479: Mat_SeqAIJKokkos *aijkok;
480: const char *mode;
481: ConstPetscScalarKokkosView xv;
482: PetscScalarKokkosView zv;
483: KokkosCsrMatrix csrmat;
485: PetscFunctionBegin;
486: PetscCall(PetscLogGpuTimeBegin());
487: PetscCall(MatSeqAIJKokkosSyncDevice(A));
488: if (zz != yy) PetscCall(VecCopy(yy, zz));
489: PetscCall(VecGetKokkosView(xx, &xv));
490: PetscCall(VecGetKokkosView(zz, &zv));
491: if (A->form_explicit_transpose) {
492: PetscCall(MatSeqAIJKokkosGenerateHermitian_Private(A, &csrmat));
493: mode = "N";
494: } else {
495: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
496: csrmat = aijkok->csrmat;
497: mode = "C";
498: }
499: PetscCallCXX(KokkosSparse::spmv(PetscGetKokkosExecutionSpace(), mode, 1.0 /*alpha*/, csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A^H x + beta z */
500: PetscCall(VecRestoreKokkosView(xx, &xv));
501: PetscCall(VecRestoreKokkosView(zz, &zv));
502: PetscCall(PetscLogGpuFlops(2.0 * csrmat.nnz()));
503: PetscCall(PetscLogGpuTimeEnd());
504: PetscFunctionReturn(PETSC_SUCCESS);
505: }
507: static PetscErrorCode MatSetOption_SeqAIJKokkos(Mat A, MatOption op, PetscBool flg)
508: {
509: Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
511: PetscFunctionBegin;
512: switch (op) {
513: case MAT_FORM_EXPLICIT_TRANSPOSE:
514: /* need to destroy the transpose matrix if present to prevent from logic errors if flg is set to true later */
515: if (A->form_explicit_transpose && !flg && aijkok) PetscCall(aijkok->DestroyMatTranspose());
516: A->form_explicit_transpose = flg;
517: break;
518: default:
519: PetscCall(MatSetOption_SeqAIJ(A, op, flg));
520: break;
521: }
522: PetscFunctionReturn(PETSC_SUCCESS);
523: }
525: /* Depending on reuse, either build a new mat, or use the existing mat */
526: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat A, MatType mtype, MatReuse reuse, Mat *newmat)
527: {
528: Mat_SeqAIJ *aseq;
530: PetscFunctionBegin;
531: PetscCall(PetscKokkosInitializeCheck());
532: if (reuse == MAT_INITIAL_MATRIX) { /* Build a brand new mat */
533: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat)); /* the returned newmat is a SeqAIJKokkos */
534: } else if (reuse == MAT_REUSE_MATRIX) { /* Reuse the mat created before */
535: PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN)); /* newmat is already a SeqAIJKokkos */
536: } else if (reuse == MAT_INPLACE_MATRIX) { /* newmat is A */
537: PetscCheck(A == *newmat, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "A != *newmat with MAT_INPLACE_MATRIX");
538: PetscCall(PetscFree(A->defaultvectype));
539: PetscCall(PetscStrallocpy(VECKOKKOS, &A->defaultvectype)); /* Allocate and copy the string */
540: PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATSEQAIJKOKKOS));
541: PetscCall(MatSetOps_SeqAIJKokkos(A));
542: aseq = static_cast<Mat_SeqAIJ *>(A->data);
543: if (A->assembled) { /* Copy i, j (but not values) to device for an assembled matrix if not yet */
544: PetscCheck(!A->spptr, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Expect NULL (Mat_SeqAIJKokkos*)A->spptr");
545: A->spptr = new Mat_SeqAIJKokkos(A->rmap->n, A->cmap->n, aseq, A->nonzerostate, PETSC_FALSE);
546: }
547: }
548: PetscFunctionReturn(PETSC_SUCCESS);
549: }
551: /* MatDuplicate always creates a new matrix. MatDuplicate can be called either on an assembled matrix or
552: an unassembled matrix, even though MAT_COPY_VALUES is not allowed for unassembled matrix.
553: */
554: static PetscErrorCode MatDuplicate_SeqAIJKokkos(Mat A, MatDuplicateOption dupOption, Mat *B)
555: {
556: Mat_SeqAIJ *bseq;
557: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr), *bkok;
558: Mat mat;
560: PetscFunctionBegin;
561: /* Do not copy values on host as A's latest values might be on device. We don't want to do sync blindly */
562: PetscCall(MatDuplicate_SeqAIJ(A, MAT_DO_NOT_COPY_VALUES, B));
563: mat = *B;
564: if (A->assembled) {
565: bseq = static_cast<Mat_SeqAIJ *>(mat->data);
566: bkok = new Mat_SeqAIJKokkos(mat->rmap->n, mat->cmap->n, bseq, mat->nonzerostate, PETSC_FALSE);
567: bkok->a_dual.clear_sync_state(); /* Clear B's sync state as it will be decided below */
568: /* Now copy values to B if needed */
569: if (dupOption == MAT_COPY_VALUES) {
570: if (akok->a_dual.need_sync_device()) {
571: Kokkos::deep_copy(bkok->a_dual.view_host(), akok->a_dual.view_host());
572: bkok->a_dual.modify_host();
573: } else { /* If device has the latest data, we only copy data on device */
574: Kokkos::deep_copy(bkok->a_dual.view_device(), akok->a_dual.view_device());
575: bkok->a_dual.modify_device();
576: }
577: } else { /* MAT_DO_NOT_COPY_VALUES or MAT_SHARE_NONZERO_PATTERN. B's values should be zeroed */
578: /* B's values on host should be already zeroed by MatDuplicate_SeqAIJ() */
579: bkok->a_dual.modify_host();
580: }
581: mat->spptr = bkok;
582: }
584: PetscCall(PetscFree(mat->defaultvectype));
585: PetscCall(PetscStrallocpy(VECKOKKOS, &mat->defaultvectype)); /* Allocate and copy the string */
586: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, MATSEQAIJKOKKOS));
587: PetscCall(MatSetOps_SeqAIJKokkos(mat));
588: PetscFunctionReturn(PETSC_SUCCESS);
589: }
591: static PetscErrorCode MatTranspose_SeqAIJKokkos(Mat A, MatReuse reuse, Mat *B)
592: {
593: Mat At;
594: KokkosCsrMatrix internT;
595: Mat_SeqAIJKokkos *atkok, *bkok;
597: PetscFunctionBegin;
598: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B));
599: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &internT)); /* Generate a transpose internally */
600: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
601: /* Deep copy internT, as we want to isolate the internal transpose */
602: PetscCallCXX(atkok = new Mat_SeqAIJKokkos(KokkosCsrMatrix("csrmat", internT)));
603: PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PetscObjectComm((PetscObject)A), atkok, &At));
604: if (reuse == MAT_INITIAL_MATRIX) *B = At;
605: else PetscCall(MatHeaderReplace(A, &At)); /* Replace A with At inplace */
606: } else { /* MAT_REUSE_MATRIX, just need to copy values to B on device */
607: if ((*B)->assembled) {
608: bkok = static_cast<Mat_SeqAIJKokkos *>((*B)->spptr);
609: PetscCallCXX(Kokkos::deep_copy(bkok->a_dual.view_device(), internT.values));
610: PetscCall(MatSeqAIJKokkosModifyDevice(*B));
611: } else if ((*B)->preallocated) { /* It is ok for B to be only preallocated, as needed in MatTranspose_MPIAIJ */
612: Mat_SeqAIJ *bseq = static_cast<Mat_SeqAIJ *>((*B)->data);
613: MatScalarKokkosViewHost a_h(bseq->a, internT.nnz()); /* bseq->nz = 0 if unassembled */
614: MatColIdxKokkosViewHost j_h(bseq->j, internT.nnz());
615: PetscCallCXX(Kokkos::deep_copy(a_h, internT.values));
616: PetscCallCXX(Kokkos::deep_copy(j_h, internT.graph.entries));
617: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "B must be assembled or preallocated");
618: }
619: PetscFunctionReturn(PETSC_SUCCESS);
620: }
622: static PetscErrorCode MatDestroy_SeqAIJKokkos(Mat A)
623: {
624: Mat_SeqAIJKokkos *aijkok;
626: PetscFunctionBegin;
627: if (A->factortype == MAT_FACTOR_NONE) {
628: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
629: delete aijkok;
630: } else {
631: delete static_cast<Mat_SeqAIJKokkosTriFactors *>(A->spptr);
632: }
633: A->spptr = NULL;
634: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
635: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
636: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
637: PetscCall(MatDestroy_SeqAIJ(A));
638: PetscFunctionReturn(PETSC_SUCCESS);
639: }
641: /*MC
642: MATSEQAIJKOKKOS - MATAIJKOKKOS = "(seq)aijkokkos" - A matrix type to be used for sparse matrices with Kokkos
644: A matrix type using Kokkos-Kernels CrsMatrix type for portability across different device types
646: Options Database Key:
647: . -mat_type aijkokkos - sets the matrix type to `MATSEQAIJKOKKOS` during a call to `MatSetFromOptions()`
649: Level: beginner
651: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJKokkos()`, `MATMPIAIJKOKKOS`
652: M*/
653: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJKokkos(Mat A)
654: {
655: PetscFunctionBegin;
656: PetscCall(PetscKokkosInitializeCheck());
657: PetscCall(MatCreate_SeqAIJ(A));
658: PetscCall(MatConvert_SeqAIJ_SeqAIJKokkos(A, MATSEQAIJKOKKOS, MAT_INPLACE_MATRIX, &A));
659: PetscFunctionReturn(PETSC_SUCCESS);
660: }
662: /* Merge A, B into a matrix C. A is put before B. C's size would be A->rmap->n by (A->cmap->n + B->cmap->n) */
663: PetscErrorCode MatSeqAIJKokkosMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
664: {
665: Mat_SeqAIJ *a, *b;
666: Mat_SeqAIJKokkos *akok, *bkok, *ckok;
667: MatScalarKokkosView aa, ba, ca;
668: MatRowMapKokkosView ai, bi, ci;
669: MatColIdxKokkosView aj, bj, cj;
670: PetscInt m, n, nnz, aN;
672: PetscFunctionBegin;
675: PetscAssertPointer(C, 4);
676: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
677: PetscCheckTypeName(B, MATSEQAIJKOKKOS);
678: 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);
679: PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");
681: PetscCall(MatSeqAIJKokkosSyncDevice(A));
682: PetscCall(MatSeqAIJKokkosSyncDevice(B));
683: a = static_cast<Mat_SeqAIJ *>(A->data);
684: b = static_cast<Mat_SeqAIJ *>(B->data);
685: akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
686: bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
687: aa = akok->a_dual.view_device();
688: ai = akok->i_dual.view_device();
689: ba = bkok->a_dual.view_device();
690: bi = bkok->i_dual.view_device();
691: m = A->rmap->n; /* M, N and nnz of C */
692: n = A->cmap->n + B->cmap->n;
693: nnz = a->nz + b->nz;
694: aN = A->cmap->n; /* N of A */
695: if (reuse == MAT_INITIAL_MATRIX) {
696: aj = akok->j_dual.view_device();
697: bj = bkok->j_dual.view_device();
698: auto ca_dual = MatScalarKokkosDualView("a", aa.extent(0) + ba.extent(0));
699: auto ci_dual = MatRowMapKokkosDualView("i", ai.extent(0));
700: auto cj_dual = MatColIdxKokkosDualView("j", aj.extent(0) + bj.extent(0));
701: ca = ca_dual.view_device();
702: ci = ci_dual.view_device();
703: cj = cj_dual.view_device();
705: /* Concatenate A and B in parallel using Kokkos hierarchical parallelism */
706: Kokkos::parallel_for(
707: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), m, Kokkos::AUTO()), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
708: PetscInt i = t.league_rank(); /* row i */
709: PetscInt coffset = ai(i) + bi(i), alen = ai(i + 1) - ai(i), blen = bi(i + 1) - bi(i);
711: Kokkos::single(Kokkos::PerTeam(t), [=]() { /* this side effect only happens once per whole team */
712: ci(i) = coffset;
713: if (i == m - 1) ci(m) = ai(m) + bi(m);
714: });
716: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, alen + blen), [&](PetscInt k) {
717: if (k < alen) {
718: ca(coffset + k) = aa(ai(i) + k);
719: cj(coffset + k) = aj(ai(i) + k);
720: } else {
721: ca(coffset + k) = ba(bi(i) + k - alen);
722: cj(coffset + k) = bj(bi(i) + k - alen) + aN; /* Entries in B get new column indices in C */
723: }
724: });
725: });
726: ca_dual.modify_device();
727: ci_dual.modify_device();
728: cj_dual.modify_device();
729: PetscCallCXX(ckok = new Mat_SeqAIJKokkos(m, n, nnz, ci_dual, cj_dual, ca_dual));
730: PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PETSC_COMM_SELF, ckok, C));
731: } else if (reuse == MAT_REUSE_MATRIX) {
733: PetscCheckTypeName(*C, MATSEQAIJKOKKOS);
734: ckok = static_cast<Mat_SeqAIJKokkos *>((*C)->spptr);
735: ca = ckok->a_dual.view_device();
736: ci = ckok->i_dual.view_device();
738: Kokkos::parallel_for(
739: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), m, Kokkos::AUTO()), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
740: PetscInt i = t.league_rank(); /* row i */
741: PetscInt alen = ai(i + 1) - ai(i), blen = bi(i + 1) - bi(i);
742: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, alen + blen), [&](PetscInt k) {
743: if (k < alen) ca(ci(i) + k) = aa(ai(i) + k);
744: else ca(ci(i) + k) = ba(bi(i) + k - alen);
745: });
746: });
747: PetscCall(MatSeqAIJKokkosModifyDevice(*C));
748: }
749: PetscFunctionReturn(PETSC_SUCCESS);
750: }
752: static PetscErrorCode MatProductDataDestroy_SeqAIJKokkos(void *pdata)
753: {
754: PetscFunctionBegin;
755: delete static_cast<MatProductData_SeqAIJKokkos *>(pdata);
756: PetscFunctionReturn(PETSC_SUCCESS);
757: }
759: static PetscErrorCode MatProductNumeric_SeqAIJKokkos_SeqAIJKokkos(Mat C)
760: {
761: Mat_Product *product = C->product;
762: Mat A, B;
763: bool transA, transB; /* use bool, since KK needs this type */
764: Mat_SeqAIJKokkos *akok, *bkok, *ckok;
765: Mat_SeqAIJ *c;
766: MatProductData_SeqAIJKokkos *pdata;
767: KokkosCsrMatrix csrmatA, csrmatB;
769: PetscFunctionBegin;
770: MatCheckProduct(C, 1);
771: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
772: pdata = static_cast<MatProductData_SeqAIJKokkos *>(C->product->data);
774: // See if numeric has already been done in symbolic (e.g., user calls MatMatMult(A,B,MAT_INITIAL_MATRIX,..,C)).
775: // If yes, skip the numeric, but reset the flag so that next time when user calls MatMatMult(E,F,MAT_REUSE_MATRIX,..,C),
776: // we still do numeric.
777: if (pdata->reusesym) { // numeric reuses results from symbolic
778: pdata->reusesym = PETSC_FALSE;
779: PetscFunctionReturn(PETSC_SUCCESS);
780: }
782: switch (product->type) {
783: case MATPRODUCT_AB:
784: transA = false;
785: transB = false;
786: break;
787: case MATPRODUCT_AtB:
788: transA = true;
789: transB = false;
790: break;
791: case MATPRODUCT_ABt:
792: transA = false;
793: transB = true;
794: break;
795: default:
796: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Unsupported product type %s", MatProductTypes[product->type]);
797: }
799: A = product->A;
800: B = product->B;
801: PetscCall(MatSeqAIJKokkosSyncDevice(A));
802: PetscCall(MatSeqAIJKokkosSyncDevice(B));
803: akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
804: bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
805: ckok = static_cast<Mat_SeqAIJKokkos *>(C->spptr);
807: PetscCheck(ckok, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Device data structure spptr is empty");
809: csrmatA = akok->csrmat;
810: csrmatB = bkok->csrmat;
812: /* TODO: Once KK spgemm implements transpose, we can get rid of the explicit transpose here */
813: if (transA) {
814: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmatA));
815: transA = false;
816: }
818: if (transB) {
819: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(B, &csrmatB));
820: transB = false;
821: }
822: PetscCall(PetscLogGpuTimeBegin());
823: PetscCallCXX(KokkosSparse::spgemm_numeric(pdata->kh, csrmatA, transA, csrmatB, transB, ckok->csrmat));
824: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
825: auto spgemmHandle = pdata->kh.get_spgemm_handle();
826: if (spgemmHandle->get_sort_option() != 1) PetscCallCXX(sort_crs_matrix(ckok->csrmat)); /* without sort, mat_tests-ex62_14_seqaijkokkos fails */
827: #endif
829: PetscCall(PetscLogGpuTimeEnd());
830: PetscCall(MatSeqAIJKokkosModifyDevice(C));
831: /* shorter version of MatAssemblyEnd_SeqAIJ */
832: c = (Mat_SeqAIJ *)C->data;
833: 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));
834: PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
835: PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
836: c->reallocs = 0;
837: C->info.mallocs = 0;
838: C->info.nz_unneeded = 0;
839: C->assembled = C->was_assembled = PETSC_TRUE;
840: C->num_ass++;
841: PetscFunctionReturn(PETSC_SUCCESS);
842: }
844: static PetscErrorCode MatProductSymbolic_SeqAIJKokkos_SeqAIJKokkos(Mat C)
845: {
846: Mat_Product *product = C->product;
847: MatProductType ptype;
848: Mat A, B;
849: bool transA, transB;
850: Mat_SeqAIJKokkos *akok, *bkok, *ckok;
851: MatProductData_SeqAIJKokkos *pdata;
852: MPI_Comm comm;
853: KokkosCsrMatrix csrmatA, csrmatB, csrmatC;
855: PetscFunctionBegin;
856: MatCheckProduct(C, 1);
857: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
858: PetscCheck(!product->data, comm, PETSC_ERR_PLIB, "Product data not empty");
859: A = product->A;
860: B = product->B;
861: PetscCall(MatSeqAIJKokkosSyncDevice(A));
862: PetscCall(MatSeqAIJKokkosSyncDevice(B));
863: akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
864: bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
865: csrmatA = akok->csrmat;
866: csrmatB = bkok->csrmat;
868: ptype = product->type;
869: // Take advantage of the symmetry if true
870: if (A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
871: ptype = MATPRODUCT_AB;
872: product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
873: }
874: if (B->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_ABt) {
875: ptype = MATPRODUCT_AB;
876: product->symbolic_used_the_fact_B_is_symmetric = PETSC_TRUE;
877: }
879: switch (ptype) {
880: case MATPRODUCT_AB:
881: transA = false;
882: transB = false;
883: break;
884: case MATPRODUCT_AtB:
885: transA = true;
886: transB = false;
887: break;
888: case MATPRODUCT_ABt:
889: transA = false;
890: transB = true;
891: break;
892: default:
893: SETERRQ(comm, PETSC_ERR_PLIB, "Unsupported product type %s", MatProductTypes[product->type]);
894: }
895: PetscCallCXX(product->data = pdata = new MatProductData_SeqAIJKokkos());
896: pdata->reusesym = product->api_user;
898: /* TODO: add command line options to select spgemm algorithms */
899: auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; /* default alg is TPL if enabled, otherwise KK */
901: /* CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4 */
902: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
903: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
904: spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
905: #endif
906: #endif
907: PetscCallCXX(pdata->kh.create_spgemm_handle(spgemm_alg));
909: PetscCall(PetscLogGpuTimeBegin());
910: /* TODO: Get rid of the explicit transpose once KK-spgemm implements the transpose option */
911: if (transA) {
912: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmatA));
913: transA = false;
914: }
916: if (transB) {
917: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(B, &csrmatB));
918: transB = false;
919: }
921: PetscCallCXX(KokkosSparse::spgemm_symbolic(pdata->kh, csrmatA, transA, csrmatB, transB, csrmatC));
922: /* spgemm_symbolic() only populates C's rowmap, but not C's column indices.
923: So we have to do a fake spgemm_numeric() here to get csrmatC.j_d setup, before
924: calling new Mat_SeqAIJKokkos().
925: TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
926: */
927: PetscCallCXX(KokkosSparse::spgemm_numeric(pdata->kh, csrmatA, transA, csrmatB, transB, csrmatC));
928: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
929: /* Query if KK outputs a sorted matrix. If not, we need to sort it */
930: auto spgemmHandle = pdata->kh.get_spgemm_handle();
931: if (spgemmHandle->get_sort_option() != 1) PetscCallCXX(sort_crs_matrix(csrmatC)); /* sort_option defaults to -1 in KK!*/
932: #endif
933: PetscCall(PetscLogGpuTimeEnd());
935: PetscCallCXX(ckok = new Mat_SeqAIJKokkos(csrmatC));
936: PetscCall(MatSetSeqAIJKokkosWithCSRMatrix(C, ckok));
937: C->product->destroy = MatProductDataDestroy_SeqAIJKokkos;
938: PetscFunctionReturn(PETSC_SUCCESS);
939: }
941: /* handles sparse matrix matrix ops */
942: static PetscErrorCode MatProductSetFromOptions_SeqAIJKokkos(Mat mat)
943: {
944: Mat_Product *product = mat->product;
945: PetscBool Biskok = PETSC_FALSE, Ciskok = PETSC_TRUE;
947: PetscFunctionBegin;
948: MatCheckProduct(mat, 1);
949: PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJKOKKOS, &Biskok));
950: if (product->type == MATPRODUCT_ABC) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJKOKKOS, &Ciskok));
951: if (Biskok && Ciskok) {
952: switch (product->type) {
953: case MATPRODUCT_AB:
954: case MATPRODUCT_AtB:
955: case MATPRODUCT_ABt:
956: mat->ops->productsymbolic = MatProductSymbolic_SeqAIJKokkos_SeqAIJKokkos;
957: break;
958: case MATPRODUCT_PtAP:
959: case MATPRODUCT_RARt:
960: case MATPRODUCT_ABC:
961: mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
962: break;
963: default:
964: break;
965: }
966: } else { /* fallback for AIJ */
967: PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
968: }
969: PetscFunctionReturn(PETSC_SUCCESS);
970: }
972: static PetscErrorCode MatScale_SeqAIJKokkos(Mat A, PetscScalar a)
973: {
974: Mat_SeqAIJKokkos *aijkok;
976: PetscFunctionBegin;
977: PetscCall(PetscLogGpuTimeBegin());
978: PetscCall(MatSeqAIJKokkosSyncDevice(A));
979: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
980: KokkosBlas::scal(PetscGetKokkosExecutionSpace(), aijkok->a_dual.view_device(), a, aijkok->a_dual.view_device());
981: PetscCall(MatSeqAIJKokkosModifyDevice(A));
982: PetscCall(PetscLogGpuFlops(aijkok->a_dual.extent(0)));
983: PetscCall(PetscLogGpuTimeEnd());
984: PetscFunctionReturn(PETSC_SUCCESS);
985: }
987: // add a to A's diagonal (if A is square) or main diagonal (if A is rectangular)
988: static PetscErrorCode MatShift_SeqAIJKokkos(Mat A, PetscScalar a)
989: {
990: Mat_SeqAIJ *aijseq = static_cast<Mat_SeqAIJ *>(A->data);
992: PetscFunctionBegin;
993: if (A->assembled && aijseq->diagonaldense) { // no missing diagonals
994: PetscInt n = PetscMin(A->rmap->n, A->cmap->n);
996: PetscCall(PetscLogGpuTimeBegin());
997: PetscCall(MatSeqAIJKokkosSyncDevice(A));
998: const auto aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
999: const auto &Aa = aijkok->a_dual.view_device();
1000: const auto &Adiag = aijkok->diag_dual.view_device();
1001: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, n), KOKKOS_LAMBDA(const PetscInt i) { Aa(Adiag(i)) += a; }));
1002: PetscCall(MatSeqAIJKokkosModifyDevice(A));
1003: PetscCall(PetscLogGpuFlops(n));
1004: PetscCall(PetscLogGpuTimeEnd());
1005: } else { // need reassembly, very slow!
1006: PetscCall(MatShift_Basic(A, a));
1007: }
1008: PetscFunctionReturn(PETSC_SUCCESS);
1009: }
1011: static PetscErrorCode MatDiagonalSet_SeqAIJKokkos(Mat Y, Vec D, InsertMode is)
1012: {
1013: Mat_SeqAIJ *aijseq = static_cast<Mat_SeqAIJ *>(Y->data);
1015: PetscFunctionBegin;
1016: if (Y->assembled && aijseq->diagonaldense) { // no missing diagonals
1017: ConstPetscScalarKokkosView dv;
1018: PetscInt n, nv;
1020: PetscCall(PetscLogGpuTimeBegin());
1021: PetscCall(MatSeqAIJKokkosSyncDevice(Y));
1022: PetscCall(VecGetKokkosView(D, &dv));
1023: PetscCall(VecGetLocalSize(D, &nv));
1024: n = PetscMin(Y->rmap->n, Y->cmap->n);
1025: PetscCheck(n == nv, PetscObjectComm((PetscObject)Y), PETSC_ERR_ARG_SIZ, "Matrix size and vector size do not match");
1027: const auto aijkok = static_cast<Mat_SeqAIJKokkos *>(Y->spptr);
1028: const auto &Aa = aijkok->a_dual.view_device();
1029: const auto &Adiag = aijkok->diag_dual.view_device();
1030: PetscCallCXX(Kokkos::parallel_for(
1031: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, n), KOKKOS_LAMBDA(const PetscInt i) {
1032: if (is == INSERT_VALUES) Aa(Adiag(i)) = dv(i);
1033: else Aa(Adiag(i)) += dv(i);
1034: }));
1035: PetscCall(VecRestoreKokkosView(D, &dv));
1036: PetscCall(MatSeqAIJKokkosModifyDevice(Y));
1037: PetscCall(PetscLogGpuFlops(n));
1038: PetscCall(PetscLogGpuTimeEnd());
1039: } else { // need reassembly, very slow!
1040: PetscCall(MatDiagonalSet_Default(Y, D, is));
1041: }
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: }
1045: static PetscErrorCode MatDiagonalScale_SeqAIJKokkos(Mat A, Vec ll, Vec rr)
1046: {
1047: Mat_SeqAIJ *aijseq = static_cast<Mat_SeqAIJ *>(A->data);
1048: PetscInt m = A->rmap->n, n = A->cmap->n, nz = aijseq->nz;
1049: ConstPetscScalarKokkosView lv, rv;
1051: PetscFunctionBegin;
1052: PetscCall(PetscLogGpuTimeBegin());
1053: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1054: const auto aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1055: const auto &Aa = aijkok->a_dual.view_device();
1056: const auto &Ai = aijkok->i_dual.view_device();
1057: const auto &Aj = aijkok->j_dual.view_device();
1058: if (ll) {
1059: PetscCall(VecGetLocalSize(ll, &m));
1060: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
1061: PetscCall(VecGetKokkosView(ll, &lv));
1062: PetscCallCXX(Kokkos::parallel_for( // for each row
1063: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), m, Kokkos::AUTO()), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1064: PetscInt i = t.league_rank(); // row i
1065: PetscInt len = Ai(i + 1) - Ai(i);
1066: // scale entries on the row
1067: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, len), [&](PetscInt j) { Aa(Ai(i) + j) *= lv(i); });
1068: }));
1069: PetscCall(VecRestoreKokkosView(ll, &lv));
1070: PetscCall(PetscLogGpuFlops(nz));
1071: }
1072: if (rr) {
1073: PetscCall(VecGetLocalSize(rr, &n));
1074: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
1075: PetscCall(VecGetKokkosView(rr, &rv));
1076: PetscCallCXX(Kokkos::parallel_for( // for each nonzero
1077: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, nz), KOKKOS_LAMBDA(const PetscInt k) { Aa(k) *= rv(Aj(k)); }));
1078: PetscCall(VecRestoreKokkosView(rr, &lv));
1079: PetscCall(PetscLogGpuFlops(nz));
1080: }
1081: PetscCall(MatSeqAIJKokkosModifyDevice(A));
1082: PetscCall(PetscLogGpuTimeEnd());
1083: PetscFunctionReturn(PETSC_SUCCESS);
1084: }
1086: static PetscErrorCode MatZeroEntries_SeqAIJKokkos(Mat A)
1087: {
1088: Mat_SeqAIJKokkos *aijkok;
1090: PetscFunctionBegin;
1091: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1092: if (aijkok) { /* Only zero the device if data is already there */
1093: KokkosBlas::fill(PetscGetKokkosExecutionSpace(), aijkok->a_dual.view_device(), 0.0);
1094: PetscCall(MatSeqAIJKokkosModifyDevice(A));
1095: } else { /* Might be preallocated but not assembled */
1096: PetscCall(MatZeroEntries_SeqAIJ(A));
1097: }
1098: PetscFunctionReturn(PETSC_SUCCESS);
1099: }
1101: static PetscErrorCode MatGetDiagonal_SeqAIJKokkos(Mat A, Vec x)
1102: {
1103: Mat_SeqAIJKokkos *aijkok;
1104: PetscInt n;
1105: PetscScalarKokkosView xv;
1107: PetscFunctionBegin;
1108: PetscCall(VecGetLocalSize(x, &n));
1109: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1110: PetscCheck(A->factortype == MAT_FACTOR_NONE, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatGetDiagonal_SeqAIJKokkos not supported on factored matrices");
1112: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1113: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1115: const auto &Aa = aijkok->a_dual.view_device();
1116: const auto &Ai = aijkok->i_dual.view_device();
1117: const auto &Adiag = aijkok->diag_dual.view_device();
1119: PetscCall(VecGetKokkosViewWrite(x, &xv));
1120: Kokkos::parallel_for(
1121: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, n), KOKKOS_LAMBDA(const PetscInt i) {
1122: if (Adiag(i) < Ai(i + 1)) xv(i) = Aa(Adiag(i));
1123: else xv(i) = 0;
1124: });
1125: PetscCall(VecRestoreKokkosViewWrite(x, &xv));
1126: PetscFunctionReturn(PETSC_SUCCESS);
1127: }
1129: /* Get a Kokkos View from a mat of type MatSeqAIJKokkos */
1130: PetscErrorCode MatSeqAIJGetKokkosView(Mat A, ConstMatScalarKokkosView *kv)
1131: {
1132: Mat_SeqAIJKokkos *aijkok;
1134: PetscFunctionBegin;
1136: PetscAssertPointer(kv, 2);
1137: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1138: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1139: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1140: *kv = aijkok->a_dual.view_device();
1141: PetscFunctionReturn(PETSC_SUCCESS);
1142: }
1144: PetscErrorCode MatSeqAIJRestoreKokkosView(Mat A, ConstMatScalarKokkosView *kv)
1145: {
1146: PetscFunctionBegin;
1148: PetscAssertPointer(kv, 2);
1149: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1150: PetscFunctionReturn(PETSC_SUCCESS);
1151: }
1153: PetscErrorCode MatSeqAIJGetKokkosView(Mat A, MatScalarKokkosView *kv)
1154: {
1155: Mat_SeqAIJKokkos *aijkok;
1157: PetscFunctionBegin;
1159: PetscAssertPointer(kv, 2);
1160: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1161: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1162: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1163: *kv = aijkok->a_dual.view_device();
1164: PetscFunctionReturn(PETSC_SUCCESS);
1165: }
1167: PetscErrorCode MatSeqAIJRestoreKokkosView(Mat A, MatScalarKokkosView *kv)
1168: {
1169: PetscFunctionBegin;
1171: PetscAssertPointer(kv, 2);
1172: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1173: PetscCall(MatSeqAIJKokkosModifyDevice(A));
1174: PetscFunctionReturn(PETSC_SUCCESS);
1175: }
1177: PetscErrorCode MatSeqAIJGetKokkosViewWrite(Mat A, MatScalarKokkosView *kv)
1178: {
1179: Mat_SeqAIJKokkos *aijkok;
1181: PetscFunctionBegin;
1183: PetscAssertPointer(kv, 2);
1184: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1185: aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1186: *kv = aijkok->a_dual.view_device();
1187: PetscFunctionReturn(PETSC_SUCCESS);
1188: }
1190: PetscErrorCode MatSeqAIJRestoreKokkosViewWrite(Mat A, MatScalarKokkosView *kv)
1191: {
1192: PetscFunctionBegin;
1194: PetscAssertPointer(kv, 2);
1195: PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1196: PetscCall(MatSeqAIJKokkosModifyDevice(A));
1197: PetscFunctionReturn(PETSC_SUCCESS);
1198: }
1200: /* Computes Y += alpha X */
1201: static PetscErrorCode MatAXPY_SeqAIJKokkos(Mat Y, PetscScalar alpha, Mat X, MatStructure pattern)
1202: {
1203: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
1204: Mat_SeqAIJKokkos *xkok, *ykok, *zkok;
1205: ConstMatScalarKokkosView Xa;
1206: MatScalarKokkosView Ya;
1207: auto &exec = PetscGetKokkosExecutionSpace();
1209: PetscFunctionBegin;
1210: PetscCheckTypeName(Y, MATSEQAIJKOKKOS);
1211: PetscCheckTypeName(X, MATSEQAIJKOKKOS);
1212: PetscCall(MatSeqAIJKokkosSyncDevice(Y));
1213: PetscCall(MatSeqAIJKokkosSyncDevice(X));
1214: PetscCall(PetscLogGpuTimeBegin());
1216: if (pattern != SAME_NONZERO_PATTERN && x->nz == y->nz) {
1217: PetscBool e;
1218: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
1219: if (e) {
1220: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
1221: if (e) pattern = SAME_NONZERO_PATTERN;
1222: }
1223: }
1225: /* cusparseDcsrgeam2() computes C = alpha A + beta B. If one knew sparsity pattern of C, one can skip
1226: cusparseScsrgeam2_bufferSizeExt() / cusparseXcsrgeam2Nnz(), and directly call cusparseScsrgeam2().
1227: If X is SUBSET_NONZERO_PATTERN of Y, we could take advantage of this cusparse feature. However,
1228: KokkosSparse::spadd(alpha,A,beta,B,C) has symbolic and numeric phases, MatAXPY does not.
1229: */
1230: ykok = static_cast<Mat_SeqAIJKokkos *>(Y->spptr);
1231: xkok = static_cast<Mat_SeqAIJKokkos *>(X->spptr);
1232: Xa = xkok->a_dual.view_device();
1233: Ya = ykok->a_dual.view_device();
1235: if (pattern == SAME_NONZERO_PATTERN) {
1236: KokkosBlas::axpy(exec, alpha, Xa, Ya);
1237: PetscCall(MatSeqAIJKokkosModifyDevice(Y));
1238: } else if (pattern == SUBSET_NONZERO_PATTERN) {
1239: MatRowMapKokkosView Xi = xkok->i_dual.view_device(), Yi = ykok->i_dual.view_device();
1240: MatColIdxKokkosView Xj = xkok->j_dual.view_device(), Yj = ykok->j_dual.view_device();
1242: Kokkos::parallel_for(
1243: Kokkos::TeamPolicy<>(exec, Y->rmap->n, 1), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1244: PetscInt i = t.league_rank(); // row i
1245: Kokkos::single(Kokkos::PerTeam(t), [=]() {
1246: // Only one thread works in a team
1247: PetscInt p, q = Yi(i);
1248: for (p = Xi(i); p < Xi(i + 1); p++) { // For each nonzero on row i of X,
1249: while (Xj(p) != Yj(q) && q < Yi(i + 1)) q++; // find the matching nonzero on row i of Y.
1250: if (Xj(p) == Yj(q)) { // Found it
1251: Ya(q) += alpha * Xa(p);
1252: q++;
1253: } else {
1254: // If not found, it indicates the input is wrong (X is not a SUBSET_NONZERO_PATTERN of Y).
1255: // Just insert a NaN at the beginning of row i if it is not empty, to make the result wrong.
1256: #if PETSC_PKG_KOKKOS_VERSION_GE(3, 7, 0)
1257: if (Yi(i) != Yi(i + 1)) Ya(Yi(i)) = Kokkos::ArithTraits<PetscScalar>::nan();
1258: #else
1259: if (Yi(i) != Yi(i + 1)) Ya(Yi(i)) = Kokkos::Experimental::nan("1");
1260: #endif
1261: }
1262: }
1263: });
1264: });
1265: PetscCall(MatSeqAIJKokkosModifyDevice(Y));
1266: } else { // different nonzero patterns
1267: Mat Z;
1268: KokkosCsrMatrix zcsr;
1269: KernelHandle kh;
1270: kh.create_spadd_handle(true); // X, Y are sorted
1271: KokkosSparse::spadd_symbolic(&kh, xkok->csrmat, ykok->csrmat, zcsr);
1272: KokkosSparse::spadd_numeric(&kh, alpha, xkok->csrmat, (PetscScalar)1.0, ykok->csrmat, zcsr);
1273: zkok = new Mat_SeqAIJKokkos(zcsr);
1274: PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PETSC_COMM_SELF, zkok, &Z));
1275: PetscCall(MatHeaderReplace(Y, &Z));
1276: kh.destroy_spadd_handle();
1277: }
1278: PetscCall(PetscLogGpuTimeEnd());
1279: PetscCall(PetscLogGpuFlops(xkok->a_dual.extent(0) * 2)); // Because we scaled X and then added it to Y
1280: PetscFunctionReturn(PETSC_SUCCESS);
1281: }
1283: struct MatCOOStruct_SeqAIJKokkos {
1284: PetscCount n;
1285: PetscCount Atot;
1286: PetscInt nz;
1287: PetscCountKokkosView jmap;
1288: PetscCountKokkosView perm;
1290: MatCOOStruct_SeqAIJKokkos(const MatCOOStruct_SeqAIJ *coo_h)
1291: {
1292: nz = coo_h->nz;
1293: n = coo_h->n;
1294: Atot = coo_h->Atot;
1295: jmap = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->jmap, nz + 1));
1296: perm = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->perm, Atot));
1297: }
1298: };
1300: static PetscErrorCode MatCOOStructDestroy_SeqAIJKokkos(void *data)
1301: {
1302: PetscFunctionBegin;
1303: PetscCallCXX(delete static_cast<MatCOOStruct_SeqAIJKokkos *>(data));
1304: PetscFunctionReturn(PETSC_SUCCESS);
1305: }
1307: static PetscErrorCode MatSetPreallocationCOO_SeqAIJKokkos(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
1308: {
1309: Mat_SeqAIJKokkos *akok;
1310: Mat_SeqAIJ *aseq;
1311: PetscContainer container_h, container_d;
1312: MatCOOStruct_SeqAIJ *coo_h;
1313: MatCOOStruct_SeqAIJKokkos *coo_d;
1315: PetscFunctionBegin;
1316: PetscCall(MatSetPreallocationCOO_SeqAIJ(mat, coo_n, coo_i, coo_j));
1317: aseq = static_cast<Mat_SeqAIJ *>(mat->data);
1318: akok = static_cast<Mat_SeqAIJKokkos *>(mat->spptr);
1319: delete akok;
1320: mat->spptr = akok = new Mat_SeqAIJKokkos(mat->rmap->n, mat->cmap->n, aseq, mat->nonzerostate + 1, PETSC_FALSE);
1321: PetscCall(MatZeroEntries_SeqAIJKokkos(mat));
1323: // Copy the COO struct to device
1324: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
1325: PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
1326: PetscCallCXX(coo_d = new MatCOOStruct_SeqAIJKokkos(coo_h));
1328: // Put the COO struct in a container and then attach that to the matrix
1329: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container_d));
1330: PetscCall(PetscContainerSetPointer(container_d, coo_d));
1331: PetscCall(PetscContainerSetUserDestroy(container_d, MatCOOStructDestroy_SeqAIJKokkos));
1332: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject)container_d));
1333: PetscCall(PetscContainerDestroy(&container_d));
1334: PetscFunctionReturn(PETSC_SUCCESS);
1335: }
1337: static PetscErrorCode MatSetValuesCOO_SeqAIJKokkos(Mat A, const PetscScalar v[], InsertMode imode)
1338: {
1339: MatScalarKokkosView Aa;
1340: ConstMatScalarKokkosView kv;
1341: PetscMemType memtype;
1342: PetscContainer container;
1343: MatCOOStruct_SeqAIJKokkos *coo;
1345: PetscFunctionBegin;
1346: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
1347: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
1349: const auto &n = coo->n;
1350: const auto &Annz = coo->nz;
1351: const auto &jmap = coo->jmap;
1352: const auto &perm = coo->perm;
1354: PetscCall(PetscGetMemType(v, &memtype));
1355: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
1356: kv = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), ConstMatScalarKokkosViewHost(v, n));
1357: } else {
1358: kv = ConstMatScalarKokkosView(v, n); /* Directly use v[]'s memory */
1359: }
1361: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJGetKokkosViewWrite(A, &Aa)); /* write matrix values */
1362: else PetscCall(MatSeqAIJGetKokkosView(A, &Aa)); /* read & write matrix values */
1364: PetscCall(PetscLogGpuTimeBegin());
1365: Kokkos::parallel_for(
1366: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, Annz), KOKKOS_LAMBDA(const PetscCount i) {
1367: PetscScalar sum = 0.0;
1368: for (PetscCount k = jmap(i); k < jmap(i + 1); k++) sum += kv(perm(k));
1369: Aa(i) = (imode == INSERT_VALUES ? 0.0 : Aa(i)) + sum;
1370: });
1371: PetscCall(PetscLogGpuTimeEnd());
1373: if (imode == INSERT_VALUES) PetscCall(MatSeqAIJRestoreKokkosViewWrite(A, &Aa));
1374: else PetscCall(MatSeqAIJRestoreKokkosView(A, &Aa));
1375: PetscFunctionReturn(PETSC_SUCCESS);
1376: }
1378: static PetscErrorCode MatLUFactorNumeric_SeqAIJKokkos(Mat B, Mat A, const MatFactorInfo *info)
1379: {
1380: PetscFunctionBegin;
1381: PetscCall(MatSeqAIJKokkosSyncHost(A));
1382: PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
1383: B->offloadmask = PETSC_OFFLOAD_CPU;
1384: PetscFunctionReturn(PETSC_SUCCESS);
1385: }
1387: static PetscErrorCode MatSetOps_SeqAIJKokkos(Mat A)
1388: {
1389: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1391: PetscFunctionBegin;
1392: A->offloadmask = PETSC_OFFLOAD_KOKKOS; /* We do not really use this flag */
1393: A->boundtocpu = PETSC_FALSE;
1395: A->ops->assemblyend = MatAssemblyEnd_SeqAIJKokkos;
1396: A->ops->destroy = MatDestroy_SeqAIJKokkos;
1397: A->ops->duplicate = MatDuplicate_SeqAIJKokkos;
1398: A->ops->axpy = MatAXPY_SeqAIJKokkos;
1399: A->ops->scale = MatScale_SeqAIJKokkos;
1400: A->ops->zeroentries = MatZeroEntries_SeqAIJKokkos;
1401: A->ops->productsetfromoptions = MatProductSetFromOptions_SeqAIJKokkos;
1402: A->ops->mult = MatMult_SeqAIJKokkos;
1403: A->ops->multadd = MatMultAdd_SeqAIJKokkos;
1404: A->ops->multtranspose = MatMultTranspose_SeqAIJKokkos;
1405: A->ops->multtransposeadd = MatMultTransposeAdd_SeqAIJKokkos;
1406: A->ops->multhermitiantranspose = MatMultHermitianTranspose_SeqAIJKokkos;
1407: A->ops->multhermitiantransposeadd = MatMultHermitianTransposeAdd_SeqAIJKokkos;
1408: A->ops->productnumeric = MatProductNumeric_SeqAIJKokkos_SeqAIJKokkos;
1409: A->ops->transpose = MatTranspose_SeqAIJKokkos;
1410: A->ops->setoption = MatSetOption_SeqAIJKokkos;
1411: A->ops->getdiagonal = MatGetDiagonal_SeqAIJKokkos;
1412: A->ops->shift = MatShift_SeqAIJKokkos;
1413: A->ops->diagonalset = MatDiagonalSet_SeqAIJKokkos;
1414: A->ops->diagonalscale = MatDiagonalScale_SeqAIJKokkos;
1415: a->ops->getarray = MatSeqAIJGetArray_SeqAIJKokkos;
1416: a->ops->restorearray = MatSeqAIJRestoreArray_SeqAIJKokkos;
1417: a->ops->getarrayread = MatSeqAIJGetArrayRead_SeqAIJKokkos;
1418: a->ops->restorearrayread = MatSeqAIJRestoreArrayRead_SeqAIJKokkos;
1419: a->ops->getarraywrite = MatSeqAIJGetArrayWrite_SeqAIJKokkos;
1420: a->ops->restorearraywrite = MatSeqAIJRestoreArrayWrite_SeqAIJKokkos;
1421: a->ops->getcsrandmemtype = MatSeqAIJGetCSRAndMemType_SeqAIJKokkos;
1423: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJKokkos));
1424: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJKokkos));
1425: PetscFunctionReturn(PETSC_SUCCESS);
1426: }
1428: /*
1429: Extract the (prescribled) diagonal blocks of the matrix and then invert them
1431: Input Parameters:
1432: + A - the MATSEQAIJKOKKOS matrix
1433: . bs - block sizes in 'csr' format, i.e., the i-th block has size bs(i+1) - bs(i)
1434: . bs2 - square of block sizes in 'csr' format, i.e., the i-th block should be stored at offset bs2(i) in diagVal[]
1435: . blkMap - map row ids to block ids, i.e., row i belongs to the block blkMap(i)
1436: - work - a pre-allocated work buffer (as big as diagVal) for use by this routine
1438: Output Parameter:
1439: . diagVal - the (pre-allocated) buffer to store the inverted blocks (each block is stored in column-major order)
1440: */
1441: PETSC_INTERN PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJKokkos(Mat A, const PetscIntKokkosView &bs, const PetscIntKokkosView &bs2, const PetscIntKokkosView &blkMap, PetscScalarKokkosView &work, PetscScalarKokkosView &diagVal)
1442: {
1443: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1444: PetscInt nblocks = bs.extent(0) - 1;
1446: PetscFunctionBegin;
1447: PetscCall(MatSeqAIJKokkosSyncDevice(A)); // Since we'll access A's value on device
1449: // Pull out the diagonal blocks of the matrix and then invert the blocks
1450: auto Aa = akok->a_dual.view_device();
1451: auto Ai = akok->i_dual.view_device();
1452: auto Aj = akok->j_dual.view_device();
1453: auto Adiag = akok->diag_dual.view_device();
1454: // TODO: how to tune the team size?
1455: #if defined(KOKKOS_ENABLE_DEFAULT_DEVICE_TYPE_HOST)
1456: auto ts = Kokkos::AUTO();
1457: #else
1458: auto ts = 16; // improved performance 30% over Kokkos::AUTO() with CUDA, but failed with "Kokkos::abort: Requested Team Size is too large!" on CPUs
1459: #endif
1460: PetscCallCXX(Kokkos::parallel_for(
1461: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), nblocks, ts), KOKKOS_LAMBDA(const KokkosTeamMemberType &teamMember) {
1462: const PetscInt bid = teamMember.league_rank(); // block id
1463: const PetscInt rstart = bs(bid); // this block starts from this row
1464: const PetscInt m = bs(bid + 1) - bs(bid); // size of this block
1465: const auto &B = Kokkos::View<PetscScalar **, Kokkos::LayoutLeft>(&diagVal(bs2(bid)), m, m); // column-major order
1466: const auto &W = PetscScalarKokkosView(&work(bs2(bid)), m * m);
1468: Kokkos::parallel_for(Kokkos::TeamThreadRange(teamMember, m), [=](const PetscInt &r) { // r-th row in B
1469: PetscInt i = rstart + r; // i-th row in A
1471: if (Ai(i) <= Adiag(i) && Adiag(i) < Ai(i + 1)) { // if the diagonal exists (common case)
1472: PetscInt first = Adiag(i) - r; // we start to check nonzeros from here along this row
1474: for (PetscInt c = 0; c < m; c++) { // walk n steps to see what column indices we will meet
1475: if (first + c < Ai(i) || first + c >= Ai(i + 1)) { // this entry (first+c) is out of range of this row, in other words, its value is zero
1476: B(r, c) = 0.0;
1477: } else if (Aj(first + c) == rstart + c) { // this entry is right on the (rstart+c) column
1478: B(r, c) = Aa(first + c);
1479: } else { // this entry does not show up in the CSR
1480: B(r, c) = 0.0;
1481: }
1482: }
1483: } else { // rare case that the diagonal does not exist
1484: const PetscInt begin = Ai(i);
1485: const PetscInt end = Ai(i + 1);
1486: for (PetscInt c = 0; c < m; c++) B(r, c) = 0.0;
1487: for (PetscInt j = begin; j < end; j++) { // scan the whole row; could use binary search but this is a rare case so we did not.
1488: if (rstart <= Aj(j) && Aj(j) < rstart + m) B(r, Aj(j) - rstart) = Aa(j);
1489: else if (Aj(j) >= rstart + m) break;
1490: }
1491: }
1492: });
1494: // LU-decompose B (w/o pivoting) and then invert B
1495: KokkosBatched::TeamLU<KokkosTeamMemberType, KokkosBatched::Algo::LU::Unblocked>::invoke(teamMember, B, 0.0);
1496: KokkosBatched::TeamInverseLU<KokkosTeamMemberType, KokkosBatched::Algo::InverseLU::Unblocked>::invoke(teamMember, B, W);
1497: }));
1498: // PetscLogGpuFlops() is done in the caller PCSetUp_VPBJacobi_Kokkos as we don't want to compute the flops in kernels
1499: PetscFunctionReturn(PETSC_SUCCESS);
1500: }
1502: PETSC_INTERN PetscErrorCode MatSetSeqAIJKokkosWithCSRMatrix(Mat A, Mat_SeqAIJKokkos *akok)
1503: {
1504: Mat_SeqAIJ *aseq;
1505: PetscInt i, m, n;
1506: auto &exec = PetscGetKokkosExecutionSpace();
1508: PetscFunctionBegin;
1509: PetscCheck(!A->spptr, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A->spptr is supposed to be empty");
1511: m = akok->nrows();
1512: n = akok->ncols();
1513: PetscCall(MatSetSizes(A, m, n, m, n));
1514: PetscCall(MatSetType(A, MATSEQAIJKOKKOS));
1516: /* Set up data structures of A as a MATSEQAIJ */
1517: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, MAT_SKIP_ALLOCATION, NULL));
1518: aseq = (Mat_SeqAIJ *)(A)->data;
1520: PetscCallCXX(akok->i_dual.sync_host(exec)); /* We always need sync'ed i, j on host */
1521: PetscCallCXX(akok->j_dual.sync_host(exec));
1522: PetscCallCXX(exec.fence());
1524: aseq->i = akok->i_host_data();
1525: aseq->j = akok->j_host_data();
1526: aseq->a = akok->a_host_data();
1527: aseq->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
1528: aseq->free_a = PETSC_FALSE;
1529: aseq->free_ij = PETSC_FALSE;
1530: aseq->nz = akok->nnz();
1531: aseq->maxnz = aseq->nz;
1533: PetscCall(PetscMalloc1(m, &aseq->imax));
1534: PetscCall(PetscMalloc1(m, &aseq->ilen));
1535: for (i = 0; i < m; i++) aseq->ilen[i] = aseq->imax[i] = aseq->i[i + 1] - aseq->i[i];
1537: /* It is critical to set the nonzerostate, as we use it to check if sparsity pattern (hence data) has changed on host in MatAssemblyEnd */
1538: akok->nonzerostate = A->nonzerostate;
1539: A->spptr = akok; /* Set A->spptr before MatAssembly so that A->spptr won't be allocated again there */
1540: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1541: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1542: PetscFunctionReturn(PETSC_SUCCESS);
1543: }
1545: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosGetKokkosCsrMatrix(Mat A, KokkosCsrMatrix *csr)
1546: {
1547: PetscFunctionBegin;
1548: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1549: *csr = static_cast<Mat_SeqAIJKokkos *>(A->spptr)->csrmat;
1550: PetscFunctionReturn(PETSC_SUCCESS);
1551: }
1553: PETSC_INTERN PetscErrorCode MatCreateSeqAIJKokkosWithKokkosCsrMatrix(MPI_Comm comm, KokkosCsrMatrix csr, Mat *A)
1554: {
1555: Mat_SeqAIJKokkos *akok;
1557: PetscFunctionBegin;
1558: PetscCallCXX(akok = new Mat_SeqAIJKokkos(csr));
1559: PetscCall(MatCreate(comm, A));
1560: PetscCall(MatSetSeqAIJKokkosWithCSRMatrix(*A, akok));
1561: PetscFunctionReturn(PETSC_SUCCESS);
1562: }
1564: /* Crete a SEQAIJKOKKOS matrix with a Mat_SeqAIJKokkos data structure
1566: Note we have names like MatSeqAIJSetPreallocationCSR, so I use capitalized CSR
1567: */
1568: PETSC_INTERN PetscErrorCode MatCreateSeqAIJKokkosWithCSRMatrix(MPI_Comm comm, Mat_SeqAIJKokkos *akok, Mat *A)
1569: {
1570: PetscFunctionBegin;
1571: PetscCall(MatCreate(comm, A));
1572: PetscCall(MatSetSeqAIJKokkosWithCSRMatrix(*A, akok));
1573: PetscFunctionReturn(PETSC_SUCCESS);
1574: }
1576: /*@C
1577: MatCreateSeqAIJKokkos - Creates a sparse matrix in `MATSEQAIJKOKKOS` (compressed row) format
1578: (the default parallel PETSc format). This matrix will ultimately be handled by
1579: Kokkos for calculations.
1581: Collective
1583: Input Parameters:
1584: + comm - MPI communicator, set to `PETSC_COMM_SELF`
1585: . m - number of rows
1586: . n - number of columns
1587: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is provided
1588: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
1590: Output Parameter:
1591: . A - the matrix
1593: Level: intermediate
1595: Notes:
1596: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
1597: MatXXXXSetPreallocation() paradgm instead of this routine directly.
1598: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
1600: The AIJ format, also called
1601: compressed row storage, is fully compatible with standard Fortran
1602: storage. That is, the stored row and column indices can begin at
1603: either one (as in Fortran) or zero.
1605: Specify the preallocated storage with either `nz` or `nnz` (not both).
1606: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
1607: allocation.
1609: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
1610: @*/
1611: PetscErrorCode MatCreateSeqAIJKokkos(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
1612: {
1613: PetscFunctionBegin;
1614: PetscCall(PetscKokkosInitializeCheck());
1615: PetscCall(MatCreate(comm, A));
1616: PetscCall(MatSetSizes(*A, m, n, m, n));
1617: PetscCall(MatSetType(*A, MATSEQAIJKOKKOS));
1618: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
1619: PetscFunctionReturn(PETSC_SUCCESS);
1620: }
1622: static PetscErrorCode MatLUFactorSymbolic_SeqAIJKokkos(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1623: {
1624: PetscFunctionBegin;
1625: PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
1626: B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJKokkos;
1627: PetscFunctionReturn(PETSC_SUCCESS);
1628: }
1630: static PetscErrorCode MatSeqAIJKokkosSymbolicSolveCheck(Mat A)
1631: {
1632: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;
1634: PetscFunctionBegin;
1635: if (!factors->sptrsv_symbolic_completed) {
1636: KokkosSparse::Experimental::sptrsv_symbolic(&factors->khU, factors->iU_d, factors->jU_d, factors->aU_d);
1637: KokkosSparse::Experimental::sptrsv_symbolic(&factors->khL, factors->iL_d, factors->jL_d, factors->aL_d);
1638: factors->sptrsv_symbolic_completed = PETSC_TRUE;
1639: }
1640: PetscFunctionReturn(PETSC_SUCCESS);
1641: }
1643: /* Check if we need to update factors etc for transpose solve */
1644: static PetscErrorCode MatSeqAIJKokkosTransposeSolveCheck(Mat A)
1645: {
1646: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;
1647: MatColIdxType n = A->rmap->n;
1649: PetscFunctionBegin;
1650: if (!factors->transpose_updated) { /* TODO: KK needs to provide functions to do numeric transpose only */
1651: /* Update L^T and do sptrsv symbolic */
1652: factors->iLt_d = MatRowMapKokkosView("factors->iLt_d", n + 1); // KK requires 0
1653: factors->jLt_d = MatColIdxKokkosView(NoInit("factors->jLt_d"), factors->jL_d.extent(0));
1654: factors->aLt_d = MatScalarKokkosView(NoInit("factors->aLt_d"), factors->aL_d.extent(0));
1656: transpose_matrix<ConstMatRowMapKokkosView, ConstMatColIdxKokkosView, ConstMatScalarKokkosView, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView, MatRowMapKokkosView, DefaultExecutionSpace>(n, n, factors->iL_d, factors->jL_d, factors->aL_d,
1657: factors->iLt_d, factors->jLt_d, factors->aLt_d);
1659: /* TODO: KK transpose_matrix() does not sort column indices, however cusparse requires sorted indices.
1660: We have to sort the indices, until KK provides finer control options.
1661: */
1662: sort_crs_matrix<DefaultExecutionSpace, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView>(factors->iLt_d, factors->jLt_d, factors->aLt_d);
1664: KokkosSparse::Experimental::sptrsv_symbolic(&factors->khLt, factors->iLt_d, factors->jLt_d, factors->aLt_d);
1666: /* Update U^T and do sptrsv symbolic */
1667: factors->iUt_d = MatRowMapKokkosView("factors->iUt_d", n + 1); // KK requires 0
1668: factors->jUt_d = MatColIdxKokkosView(NoInit("factors->jUt_d"), factors->jU_d.extent(0));
1669: factors->aUt_d = MatScalarKokkosView(NoInit("factors->aUt_d"), factors->aU_d.extent(0));
1671: transpose_matrix<ConstMatRowMapKokkosView, ConstMatColIdxKokkosView, ConstMatScalarKokkosView, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView, MatRowMapKokkosView, DefaultExecutionSpace>(n, n, factors->iU_d, factors->jU_d, factors->aU_d,
1672: factors->iUt_d, factors->jUt_d, factors->aUt_d);
1674: /* Sort indices. See comments above */
1675: sort_crs_matrix<DefaultExecutionSpace, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView>(factors->iUt_d, factors->jUt_d, factors->aUt_d);
1677: KokkosSparse::Experimental::sptrsv_symbolic(&factors->khUt, factors->iUt_d, factors->jUt_d, factors->aUt_d);
1678: factors->transpose_updated = PETSC_TRUE;
1679: }
1680: PetscFunctionReturn(PETSC_SUCCESS);
1681: }
1683: /* Solve Ax = b, with A = LU */
1684: static PetscErrorCode MatSolve_SeqAIJKokkos(Mat A, Vec b, Vec x)
1685: {
1686: ConstPetscScalarKokkosView bv;
1687: PetscScalarKokkosView xv;
1688: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;
1690: PetscFunctionBegin;
1691: PetscCall(PetscLogGpuTimeBegin());
1692: PetscCall(MatSeqAIJKokkosSymbolicSolveCheck(A));
1693: PetscCall(VecGetKokkosView(b, &bv));
1694: PetscCall(VecGetKokkosViewWrite(x, &xv));
1695: /* Solve L tmpv = b */
1696: PetscCallCXX(KokkosSparse::Experimental::sptrsv_solve(&factors->khL, factors->iL_d, factors->jL_d, factors->aL_d, bv, factors->workVector));
1697: /* Solve Ux = tmpv */
1698: PetscCallCXX(KokkosSparse::Experimental::sptrsv_solve(&factors->khU, factors->iU_d, factors->jU_d, factors->aU_d, factors->workVector, xv));
1699: PetscCall(VecRestoreKokkosView(b, &bv));
1700: PetscCall(VecRestoreKokkosViewWrite(x, &xv));
1701: PetscCall(PetscLogGpuTimeEnd());
1702: PetscFunctionReturn(PETSC_SUCCESS);
1703: }
1705: /* Solve A^T x = b, where A^T = U^T L^T */
1706: static PetscErrorCode MatSolveTranspose_SeqAIJKokkos(Mat A, Vec b, Vec x)
1707: {
1708: ConstPetscScalarKokkosView bv;
1709: PetscScalarKokkosView xv;
1710: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;
1712: PetscFunctionBegin;
1713: PetscCall(PetscLogGpuTimeBegin());
1714: PetscCall(MatSeqAIJKokkosTransposeSolveCheck(A));
1715: PetscCall(VecGetKokkosView(b, &bv));
1716: PetscCall(VecGetKokkosViewWrite(x, &xv));
1717: /* Solve U^T tmpv = b */
1718: KokkosSparse::Experimental::sptrsv_solve(&factors->khUt, factors->iUt_d, factors->jUt_d, factors->aUt_d, bv, factors->workVector);
1720: /* Solve L^T x = tmpv */
1721: KokkosSparse::Experimental::sptrsv_solve(&factors->khLt, factors->iLt_d, factors->jLt_d, factors->aLt_d, factors->workVector, xv);
1722: PetscCall(VecRestoreKokkosView(b, &bv));
1723: PetscCall(VecRestoreKokkosViewWrite(x, &xv));
1724: PetscCall(PetscLogGpuTimeEnd());
1725: PetscFunctionReturn(PETSC_SUCCESS);
1726: }
1728: static PetscErrorCode MatILUFactorNumeric_SeqAIJKokkos(Mat B, Mat A, const MatFactorInfo *info)
1729: {
1730: Mat_SeqAIJKokkos *aijkok = (Mat_SeqAIJKokkos *)A->spptr;
1731: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)B->spptr;
1732: PetscInt fill_lev = info->levels;
1734: PetscFunctionBegin;
1735: PetscCall(PetscLogGpuTimeBegin());
1736: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1738: auto a_d = aijkok->a_dual.view_device();
1739: auto i_d = aijkok->i_dual.view_device();
1740: auto j_d = aijkok->j_dual.view_device();
1742: KokkosSparse::Experimental::spiluk_numeric(&factors->kh, fill_lev, i_d, j_d, a_d, factors->iL_d, factors->jL_d, factors->aL_d, factors->iU_d, factors->jU_d, factors->aU_d);
1744: B->assembled = PETSC_TRUE;
1745: B->preallocated = PETSC_TRUE;
1746: B->ops->solve = MatSolve_SeqAIJKokkos;
1747: B->ops->solvetranspose = MatSolveTranspose_SeqAIJKokkos;
1748: B->ops->matsolve = NULL;
1749: B->ops->matsolvetranspose = NULL;
1750: B->offloadmask = PETSC_OFFLOAD_GPU;
1752: /* Once the factors' value changed, we need to update their transpose and sptrsv handle */
1753: factors->transpose_updated = PETSC_FALSE;
1754: factors->sptrsv_symbolic_completed = PETSC_FALSE;
1755: /* TODO: log flops, but how to know that? */
1756: PetscCall(PetscLogGpuTimeEnd());
1757: PetscFunctionReturn(PETSC_SUCCESS);
1758: }
1760: static PetscErrorCode MatILUFactorSymbolic_SeqAIJKokkos(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1761: {
1762: Mat_SeqAIJKokkos *aijkok;
1763: Mat_SeqAIJ *b;
1764: Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)B->spptr;
1765: PetscInt fill_lev = info->levels;
1766: PetscInt nnzA = ((Mat_SeqAIJ *)A->data)->nz, nnzL, nnzU;
1767: PetscInt n = A->rmap->n;
1769: PetscFunctionBegin;
1770: PetscCall(MatSeqAIJKokkosSyncDevice(A));
1771: /* Rebuild factors */
1772: if (factors) {
1773: factors->Destroy();
1774: } /* Destroy the old if it exists */
1775: else {
1776: B->spptr = factors = new Mat_SeqAIJKokkosTriFactors(n);
1777: }
1779: /* Create a spiluk handle and then do symbolic factorization */
1780: nnzL = nnzU = PetscRealIntMultTruncate(info->fill, nnzA);
1781: factors->kh.create_spiluk_handle(KokkosSparse::Experimental::SPILUKAlgorithm::SEQLVLSCHD_TP1, n, nnzL, nnzU);
1783: auto spiluk_handle = factors->kh.get_spiluk_handle();
1785: Kokkos::realloc(factors->iL_d, n + 1); /* Free old arrays and realloc */
1786: Kokkos::realloc(factors->jL_d, spiluk_handle->get_nnzL());
1787: Kokkos::realloc(factors->iU_d, n + 1);
1788: Kokkos::realloc(factors->jU_d, spiluk_handle->get_nnzU());
1790: aijkok = (Mat_SeqAIJKokkos *)A->spptr;
1791: auto i_d = aijkok->i_dual.view_device();
1792: auto j_d = aijkok->j_dual.view_device();
1793: KokkosSparse::Experimental::spiluk_symbolic(&factors->kh, fill_lev, i_d, j_d, factors->iL_d, factors->jL_d, factors->iU_d, factors->jU_d);
1794: /* TODO: if spiluk_symbolic is asynchronous, do we need to sync before calling get_nnzL()? */
1796: Kokkos::resize(factors->jL_d, spiluk_handle->get_nnzL()); /* Shrink or expand, and retain old value */
1797: Kokkos::resize(factors->jU_d, spiluk_handle->get_nnzU());
1798: Kokkos::realloc(factors->aL_d, spiluk_handle->get_nnzL()); /* No need to retain old value */
1799: Kokkos::realloc(factors->aU_d, spiluk_handle->get_nnzU());
1801: /* TODO: add options to select sptrsv algorithms */
1802: /* Create sptrsv handles for L, U and their transpose */
1803: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1804: auto sptrsv_alg = KokkosSparse::Experimental::SPTRSVAlgorithm::SPTRSV_CUSPARSE;
1805: #else
1806: auto sptrsv_alg = KokkosSparse::Experimental::SPTRSVAlgorithm::SEQLVLSCHD_TP1;
1807: #endif
1809: factors->khL.create_sptrsv_handle(sptrsv_alg, n, true /* L is lower tri */);
1810: factors->khU.create_sptrsv_handle(sptrsv_alg, n, false /* U is not lower tri */);
1811: factors->khLt.create_sptrsv_handle(sptrsv_alg, n, false /* L^T is not lower tri */);
1812: factors->khUt.create_sptrsv_handle(sptrsv_alg, n, true /* U^T is lower tri */);
1814: /* Fill fields of the factor matrix B */
1815: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(B, MAT_SKIP_ALLOCATION, NULL));
1816: b = (Mat_SeqAIJ *)B->data;
1817: b->nz = b->maxnz = spiluk_handle->get_nnzL() + spiluk_handle->get_nnzU();
1818: B->info.fill_ratio_given = info->fill;
1819: B->info.fill_ratio_needed = nnzA > 0 ? ((PetscReal)b->nz) / ((PetscReal)nnzA) : 1.0;
1821: B->offloadmask = PETSC_OFFLOAD_GPU;
1822: B->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJKokkos;
1823: PetscFunctionReturn(PETSC_SUCCESS);
1824: }
1826: static PetscErrorCode MatFactorGetSolverType_SeqAIJKokkos(Mat A, MatSolverType *type)
1827: {
1828: PetscFunctionBegin;
1829: *type = MATSOLVERKOKKOS;
1830: PetscFunctionReturn(PETSC_SUCCESS);
1831: }
1833: /*MC
1834: MATSOLVERKOKKOS = "Kokkos" - A matrix solver type providing triangular solvers for sequential matrices
1835: on a single GPU of type, `MATSEQAIJKOKKOS`, `MATAIJKOKKOS`.
1837: Level: beginner
1839: .seealso: [](ch_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJKokkos()`, `MATAIJKOKKOS`, `MatKokkosSetFormat()`, `MatKokkosStorageFormat`, `MatKokkosFormatOperation`
1840: M*/
1841: PETSC_EXTERN PetscErrorCode MatGetFactor_SeqAIJKokkos_Kokkos(Mat A, MatFactorType ftype, Mat *B) /* MatGetFactor_<MatType>_<MatSolverType> */
1842: {
1843: PetscInt n = A->rmap->n;
1845: PetscFunctionBegin;
1846: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
1847: PetscCall(MatSetSizes(*B, n, n, n, n));
1848: (*B)->factortype = ftype;
1849: PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
1850: PetscCall(MatSetType(*B, MATSEQAIJKOKKOS));
1852: if (ftype == MAT_FACTOR_LU) {
1853: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
1854: (*B)->canuseordering = PETSC_TRUE;
1855: (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJKokkos;
1856: } else if (ftype == MAT_FACTOR_ILU) {
1857: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
1858: (*B)->canuseordering = PETSC_FALSE;
1859: (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJKokkos;
1860: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatFactorType %s is not supported by MatType SeqAIJKokkos", MatFactorTypes[ftype]);
1862: PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
1863: PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_SeqAIJKokkos));
1864: PetscFunctionReturn(PETSC_SUCCESS);
1865: }
1867: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_KOKKOS(void)
1868: {
1869: PetscFunctionBegin;
1870: PetscCall(MatSolverTypeRegister(MATSOLVERKOKKOS, MATSEQAIJKOKKOS, MAT_FACTOR_LU, MatGetFactor_SeqAIJKokkos_Kokkos));
1871: PetscCall(MatSolverTypeRegister(MATSOLVERKOKKOS, MATSEQAIJKOKKOS, MAT_FACTOR_ILU, MatGetFactor_SeqAIJKokkos_Kokkos));
1872: PetscFunctionReturn(PETSC_SUCCESS);
1873: }
1875: /* Utility to print out a KokkosCsrMatrix for debugging */
1876: PETSC_INTERN PetscErrorCode PrintCsrMatrix(const KokkosCsrMatrix &csrmat)
1877: {
1878: const auto &iv = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.graph.row_map);
1879: const auto &jv = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.graph.entries);
1880: const auto &av = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.values);
1881: const PetscInt *i = iv.data();
1882: const PetscInt *j = jv.data();
1883: const PetscScalar *a = av.data();
1884: PetscInt m = csrmat.numRows(), n = csrmat.numCols(), nnz = csrmat.nnz();
1886: PetscFunctionBegin;
1887: PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT " x %" PetscInt_FMT " SeqAIJKokkos, with %" PetscInt_FMT " nonzeros\n", m, n, nnz));
1888: for (PetscInt k = 0; k < m; k++) {
1889: PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT ": ", k));
1890: for (PetscInt p = i[k]; p < i[k + 1]; p++) PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT "(%.1f), ", j[p], (double)PetscRealPart(a[p])));
1891: PetscCall(PetscPrintf(PETSC_COMM_SELF, "\n"));
1892: }
1893: PetscFunctionReturn(PETSC_SUCCESS);
1894: }