Actual source code: aijkok.kokkos.cxx

  1: #include <petscvec_kokkos.hpp>
  2: #include <petscpkg_version.h>
  3: #include <petsc/private/petscimpl.h>
  4: #include <petsc/private/sfimpl.h>
  5: #include <petscsystypes.h>
  6: #include <petscerror.h>

  8: #include <Kokkos_Core.hpp>
  9: #include <KokkosBlas.hpp>
 10: #include <KokkosSparse_CrsMatrix.hpp>
 11: #include <KokkosSparse_spmv.hpp>
 12: #include <KokkosSparse_spiluk.hpp>
 13: #include <KokkosSparse_sptrsv.hpp>
 14: #include <KokkosSparse_spgemm.hpp>
 15: #include <KokkosSparse_spadd.hpp>

 17: #include <../src/mat/impls/aij/seq/kokkos/aijkok.hpp>

 19: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_GE(3, 6, 99)
 20:   #include <KokkosSparse_Utils.hpp>
 21: using KokkosSparse::sort_crs_matrix;
 22: using KokkosSparse::Impl::transpose_matrix;
 23: #else
 24:   #include <KokkosKernels_Sorting.hpp>
 25: using KokkosKernels::sort_crs_matrix;
 26: using KokkosKernels::Impl::transpose_matrix;
 27: #endif

 29: static PetscErrorCode MatSetOps_SeqAIJKokkos(Mat); /* Forward declaration */

 31: /* MatAssemblyEnd_SeqAIJKokkos() happens when we finalized nonzeros of the matrix, either after
 32:    we assembled the matrix on host, or after we directly produced the matrix data on device (ex., through MatMatMult).
 33:    In the latter case, it is important to set a_dual's sync state correctly.
 34:  */
 35: static PetscErrorCode MatAssemblyEnd_SeqAIJKokkos(Mat A, MatAssemblyType mode)
 36: {
 37:   Mat_SeqAIJ       *aijseq;
 38:   Mat_SeqAIJKokkos *aijkok;

 40:   PetscFunctionBegin;
 41:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
 42:   PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));

 44:   aijseq = static_cast<Mat_SeqAIJ *>(A->data);
 45:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

 47:   /* If aijkok does not exist, we just copy i, j to device.
 48:      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.
 49:      In both cases, we build a new aijkok structure.
 50:   */
 51:   if (!aijkok || aijkok->nonzerostate != A->nonzerostate) { /* aijkok might not exist yet or nonzero pattern has changed */
 52:     delete aijkok;
 53:     aijkok   = new Mat_SeqAIJKokkos(A->rmap->n, A->cmap->n, aijseq->nz, aijseq->i, aijseq->j, aijseq->a, A->nonzerostate, PETSC_FALSE /*don't copy mat values to device*/);
 54:     A->spptr = aijkok;
 55:   }

 57:   if (aijkok->device_mat_d.data()) {
 58:     A->offloadmask = PETSC_OFFLOAD_GPU; // in GPU mode, no going back. MatSetValues checks this
 59:   }
 60:   PetscFunctionReturn(PETSC_SUCCESS);
 61: }

 63: /* Sync CSR data to device if not yet */
 64: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosSyncDevice(Mat A)
 65: {
 66:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

 68:   PetscFunctionBegin;
 69:   PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Cann't sync factorized matrix from host to device");
 70:   PetscCheck(aijkok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
 71:   if (aijkok->a_dual.need_sync_device()) {
 72:     aijkok->a_dual.sync_device();
 73:     aijkok->transpose_updated = PETSC_FALSE; /* values of the transpose is out-of-date */
 74:     aijkok->hermitian_updated = PETSC_FALSE;
 75:   }
 76:   PetscFunctionReturn(PETSC_SUCCESS);
 77: }

 79: /* Mark the CSR data on device as modified */
 80: PETSC_INTERN PetscErrorCode MatSeqAIJKokkosModifyDevice(Mat A)
 81: {
 82:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

 84:   PetscFunctionBegin;
 85:   PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Not supported for factorized matries");
 86:   aijkok->a_dual.clear_sync_state();
 87:   aijkok->a_dual.modify_device();
 88:   aijkok->transpose_updated = PETSC_FALSE;
 89:   aijkok->hermitian_updated = PETSC_FALSE;
 90:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
 91:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
 92:   PetscFunctionReturn(PETSC_SUCCESS);
 93: }

 95: static PetscErrorCode MatSeqAIJKokkosSyncHost(Mat A)
 96: {
 97:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

 99:   PetscFunctionBegin;
100:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
101:   /* We do not expect one needs factors on host  */
102:   PetscCheck(A->factortype == MAT_FACTOR_NONE, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Cann't sync factorized matrix from device to host");
103:   PetscCheck(aijkok, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "Missing AIJKOK");
104:   aijkok->a_dual.sync_host();
105:   PetscFunctionReturn(PETSC_SUCCESS);
106: }

108: static PetscErrorCode MatSeqAIJGetArray_SeqAIJKokkos(Mat A, PetscScalar *array[])
109: {
110:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

112:   PetscFunctionBegin;
113:   /* aijkok contains valid pointers only if the host's nonzerostate matches with the device's.
114:     Calling MatSeqAIJSetPreallocation() or MatSetValues() on host, where aijseq->{i,j,a} might be
115:     reallocated, will lead to stale {i,j,a}_dual in aijkok. In both operations, the hosts's nonzerostate
116:     must have been updated. The stale aijkok will be rebuilt during MatAssemblyEnd.
117:   */
118:   if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
119:     aijkok->a_dual.sync_host();
120:     *array = aijkok->a_dual.view_host().data();
121:   } else { /* Happens when calling MatSetValues on a newly created matrix */
122:     *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
123:   }
124:   PetscFunctionReturn(PETSC_SUCCESS);
125: }

127: static PetscErrorCode MatSeqAIJRestoreArray_SeqAIJKokkos(Mat A, PetscScalar *array[])
128: {
129:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

131:   PetscFunctionBegin;
132:   if (aijkok && A->nonzerostate == aijkok->nonzerostate) aijkok->a_dual.modify_host();
133:   PetscFunctionReturn(PETSC_SUCCESS);
134: }

136: static PetscErrorCode MatSeqAIJGetArrayRead_SeqAIJKokkos(Mat A, const PetscScalar *array[])
137: {
138:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

140:   PetscFunctionBegin;
141:   if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
142:     aijkok->a_dual.sync_host();
143:     *array = aijkok->a_dual.view_host().data();
144:   } else {
145:     *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
146:   }
147:   PetscFunctionReturn(PETSC_SUCCESS);
148: }

150: static PetscErrorCode MatSeqAIJRestoreArrayRead_SeqAIJKokkos(Mat A, const PetscScalar *array[])
151: {
152:   PetscFunctionBegin;
153:   *array = NULL;
154:   PetscFunctionReturn(PETSC_SUCCESS);
155: }

157: static PetscErrorCode MatSeqAIJGetArrayWrite_SeqAIJKokkos(Mat A, PetscScalar *array[])
158: {
159:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

161:   PetscFunctionBegin;
162:   if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
163:     *array = aijkok->a_dual.view_host().data();
164:   } else { /* Ex. happens with MatZeroEntries on a preallocated but not assembled matrix */
165:     *array = static_cast<Mat_SeqAIJ *>(A->data)->a;
166:   }
167:   PetscFunctionReturn(PETSC_SUCCESS);
168: }

170: static PetscErrorCode MatSeqAIJRestoreArrayWrite_SeqAIJKokkos(Mat A, PetscScalar *array[])
171: {
172:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

174:   PetscFunctionBegin;
175:   if (aijkok && A->nonzerostate == aijkok->nonzerostate) {
176:     aijkok->a_dual.clear_sync_state();
177:     aijkok->a_dual.modify_host();
178:   }
179:   PetscFunctionReturn(PETSC_SUCCESS);
180: }

182: static PetscErrorCode MatSeqAIJGetCSRAndMemType_SeqAIJKokkos(Mat A, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
183: {
184:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

186:   PetscFunctionBegin;
187:   PetscCheck(aijkok != NULL, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "aijkok is NULL");

189:   if (i) *i = aijkok->i_device_data();
190:   if (j) *j = aijkok->j_device_data();
191:   if (a) {
192:     aijkok->a_dual.sync_device();
193:     *a = aijkok->a_device_data();
194:   }
195:   if (mtype) *mtype = PETSC_MEMTYPE_KOKKOS;
196:   PetscFunctionReturn(PETSC_SUCCESS);
197: }

199: // MatSeqAIJKokkosSetDeviceMat takes a PetscSplitCSRDataStructure with device data and copies it to the device. Note, "deep_copy" here is really a shallow copy
200: PetscErrorCode MatSeqAIJKokkosSetDeviceMat(Mat A, PetscSplitCSRDataStructure h_mat)
201: {
202:   Mat_SeqAIJKokkos                            *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
203:   Kokkos::View<SplitCSRMat, Kokkos::HostSpace> h_mat_k(h_mat);

205:   PetscFunctionBegin;
206:   PetscCheck(aijkok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
207:   aijkok->device_mat_d = create_mirror(DefaultMemorySpace(), h_mat_k);
208:   Kokkos::deep_copy(aijkok->device_mat_d, h_mat_k);
209:   PetscFunctionReturn(PETSC_SUCCESS);
210: }

212: // MatSeqAIJKokkosGetDeviceMat gets the device if it is here, otherwise it creates a place for it and returns NULL
213: PetscErrorCode MatSeqAIJKokkosGetDeviceMat(Mat A, PetscSplitCSRDataStructure *d_mat)
214: {
215:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

217:   PetscFunctionBegin;
218:   if (aijkok && aijkok->device_mat_d.data()) {
219:     *d_mat = aijkok->device_mat_d.data();
220:   } else {
221:     PetscCall(MatSeqAIJKokkosSyncDevice(A)); // create aijkok (we are making d_mat now so make a place for it)
222:     *d_mat = NULL;
223:   }
224:   PetscFunctionReturn(PETSC_SUCCESS);
225: }

227: /* Generate the transpose on device and cache it internally */
228: static PetscErrorCode MatSeqAIJKokkosGenerateTranspose_Private(Mat A, KokkosCsrMatrix **csrmatT)
229: {
230:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

232:   PetscFunctionBegin;
233:   PetscCheck(aijkok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
234:   if (!aijkok->csrmatT.nnz() || !aijkok->transpose_updated) { /* Generate At for the first time OR just update its values */
235:     /* FIXME: KK does not separate symbolic/numeric transpose. We could have a permutation array to help value-only update */
236:     PetscCallCXX(aijkok->a_dual.sync_device());
237:     PetscCallCXX(aijkok->csrmatT = transpose_matrix(aijkok->csrmat));
238:     PetscCallCXX(sort_crs_matrix(aijkok->csrmatT));
239:     aijkok->transpose_updated = PETSC_TRUE;
240:   }
241:   *csrmatT = &aijkok->csrmatT;
242:   PetscFunctionReturn(PETSC_SUCCESS);
243: }

245: /* Generate the Hermitian on device and cache it internally */
246: static PetscErrorCode MatSeqAIJKokkosGenerateHermitian_Private(Mat A, KokkosCsrMatrix **csrmatH)
247: {
248:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

250:   PetscFunctionBegin;
251:   PetscCall(PetscLogGpuTimeBegin());
252:   PetscCheck(aijkok, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Unexpected NULL (Mat_SeqAIJKokkos*)A->spptr");
253:   if (!aijkok->csrmatH.nnz() || !aijkok->hermitian_updated) { /* Generate Ah for the first time OR just update its values */
254:     PetscCallCXX(aijkok->a_dual.sync_device());
255:     PetscCallCXX(aijkok->csrmatH = transpose_matrix(aijkok->csrmat));
256:     PetscCallCXX(sort_crs_matrix(aijkok->csrmatH));
257: #if defined(PETSC_USE_COMPLEX)
258:     const auto &a = aijkok->csrmatH.values;
259:     Kokkos::parallel_for(
260:       a.extent(0), KOKKOS_LAMBDA(MatRowMapType i) { a(i) = PetscConj(a(i)); });
261: #endif
262:     aijkok->hermitian_updated = PETSC_TRUE;
263:   }
264:   *csrmatH = &aijkok->csrmatH;
265:   PetscCall(PetscLogGpuTimeEnd());
266:   PetscFunctionReturn(PETSC_SUCCESS);
267: }

269: /* y = A x */
270: static PetscErrorCode MatMult_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
271: {
272:   Mat_SeqAIJKokkos          *aijkok;
273:   ConstPetscScalarKokkosView xv;
274:   PetscScalarKokkosView      yv;

276:   PetscFunctionBegin;
277:   PetscCall(PetscLogGpuTimeBegin());
278:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
279:   PetscCall(VecGetKokkosView(xx, &xv));
280:   PetscCall(VecGetKokkosViewWrite(yy, &yv));
281:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
282:   PetscCallCXX(KokkosSparse::spmv("N", 1.0 /*alpha*/, aijkok->csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A x + beta y */
283:   PetscCall(VecRestoreKokkosView(xx, &xv));
284:   PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
285:   /* 2.0*nnz - numRows seems more accurate here but assumes there are no zero-rows. So a little sloppy here. */
286:   PetscCall(PetscLogGpuFlops(2.0 * aijkok->csrmat.nnz()));
287:   PetscCall(PetscLogGpuTimeEnd());
288:   PetscFunctionReturn(PETSC_SUCCESS);
289: }

291: /* y = A^T x */
292: static PetscErrorCode MatMultTranspose_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
293: {
294:   Mat_SeqAIJKokkos          *aijkok;
295:   const char                *mode;
296:   ConstPetscScalarKokkosView xv;
297:   PetscScalarKokkosView      yv;
298:   KokkosCsrMatrix           *csrmat;

300:   PetscFunctionBegin;
301:   PetscCall(PetscLogGpuTimeBegin());
302:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
303:   PetscCall(VecGetKokkosView(xx, &xv));
304:   PetscCall(VecGetKokkosViewWrite(yy, &yv));
305:   if (A->form_explicit_transpose) {
306:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmat));
307:     mode = "N";
308:   } else {
309:     aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
310:     csrmat = &aijkok->csrmat;
311:     mode   = "T";
312:   }
313:   PetscCallCXX(KokkosSparse::spmv(mode, 1.0 /*alpha*/, *csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A^T x + beta y */
314:   PetscCall(VecRestoreKokkosView(xx, &xv));
315:   PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
316:   PetscCall(PetscLogGpuFlops(2.0 * csrmat->nnz()));
317:   PetscCall(PetscLogGpuTimeEnd());
318:   PetscFunctionReturn(PETSC_SUCCESS);
319: }

321: /* y = A^H x */
322: static PetscErrorCode MatMultHermitianTranspose_SeqAIJKokkos(Mat A, Vec xx, Vec yy)
323: {
324:   Mat_SeqAIJKokkos          *aijkok;
325:   const char                *mode;
326:   ConstPetscScalarKokkosView xv;
327:   PetscScalarKokkosView      yv;
328:   KokkosCsrMatrix           *csrmat;

330:   PetscFunctionBegin;
331:   PetscCall(PetscLogGpuTimeBegin());
332:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
333:   PetscCall(VecGetKokkosView(xx, &xv));
334:   PetscCall(VecGetKokkosViewWrite(yy, &yv));
335:   if (A->form_explicit_transpose) {
336:     PetscCall(MatSeqAIJKokkosGenerateHermitian_Private(A, &csrmat));
337:     mode = "N";
338:   } else {
339:     aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
340:     csrmat = &aijkok->csrmat;
341:     mode   = "C";
342:   }
343:   PetscCallCXX(KokkosSparse::spmv(mode, 1.0 /*alpha*/, *csrmat, xv, 0.0 /*beta*/, yv)); /* y = alpha A^H x + beta y */
344:   PetscCall(VecRestoreKokkosView(xx, &xv));
345:   PetscCall(VecRestoreKokkosViewWrite(yy, &yv));
346:   PetscCall(PetscLogGpuFlops(2.0 * csrmat->nnz()));
347:   PetscCall(PetscLogGpuTimeEnd());
348:   PetscFunctionReturn(PETSC_SUCCESS);
349: }

351: /* z = A x + y */
352: static PetscErrorCode MatMultAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
353: {
354:   Mat_SeqAIJKokkos          *aijkok;
355:   ConstPetscScalarKokkosView xv, yv;
356:   PetscScalarKokkosView      zv;

358:   PetscFunctionBegin;
359:   PetscCall(PetscLogGpuTimeBegin());
360:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
361:   PetscCall(VecGetKokkosView(xx, &xv));
362:   PetscCall(VecGetKokkosView(yy, &yv));
363:   PetscCall(VecGetKokkosViewWrite(zz, &zv));
364:   if (zz != yy) Kokkos::deep_copy(zv, yv);
365:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
366:   PetscCallCXX(KokkosSparse::spmv("N", 1.0 /*alpha*/, aijkok->csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A x + beta z */
367:   PetscCall(VecRestoreKokkosView(xx, &xv));
368:   PetscCall(VecRestoreKokkosView(yy, &yv));
369:   PetscCall(VecRestoreKokkosViewWrite(zz, &zv));
370:   PetscCall(PetscLogGpuFlops(2.0 * aijkok->csrmat.nnz()));
371:   PetscCall(PetscLogGpuTimeEnd());
372:   PetscFunctionReturn(PETSC_SUCCESS);
373: }

375: /* z = A^T x + y */
376: static PetscErrorCode MatMultTransposeAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
377: {
378:   Mat_SeqAIJKokkos          *aijkok;
379:   const char                *mode;
380:   ConstPetscScalarKokkosView xv, yv;
381:   PetscScalarKokkosView      zv;
382:   KokkosCsrMatrix           *csrmat;

384:   PetscFunctionBegin;
385:   PetscCall(PetscLogGpuTimeBegin());
386:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
387:   PetscCall(VecGetKokkosView(xx, &xv));
388:   PetscCall(VecGetKokkosView(yy, &yv));
389:   PetscCall(VecGetKokkosViewWrite(zz, &zv));
390:   if (zz != yy) Kokkos::deep_copy(zv, yv);
391:   if (A->form_explicit_transpose) {
392:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmat));
393:     mode = "N";
394:   } else {
395:     aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
396:     csrmat = &aijkok->csrmat;
397:     mode   = "T";
398:   }
399:   PetscCallCXX(KokkosSparse::spmv(mode, 1.0 /*alpha*/, *csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A^T x + beta z */
400:   PetscCall(VecRestoreKokkosView(xx, &xv));
401:   PetscCall(VecRestoreKokkosView(yy, &yv));
402:   PetscCall(VecRestoreKokkosViewWrite(zz, &zv));
403:   PetscCall(PetscLogGpuFlops(2.0 * csrmat->nnz()));
404:   PetscCall(PetscLogGpuTimeEnd());
405:   PetscFunctionReturn(PETSC_SUCCESS);
406: }

408: /* z = A^H x + y */
409: static PetscErrorCode MatMultHermitianTransposeAdd_SeqAIJKokkos(Mat A, Vec xx, Vec yy, Vec zz)
410: {
411:   Mat_SeqAIJKokkos          *aijkok;
412:   const char                *mode;
413:   ConstPetscScalarKokkosView xv, yv;
414:   PetscScalarKokkosView      zv;
415:   KokkosCsrMatrix           *csrmat;

417:   PetscFunctionBegin;
418:   PetscCall(PetscLogGpuTimeBegin());
419:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
420:   PetscCall(VecGetKokkosView(xx, &xv));
421:   PetscCall(VecGetKokkosView(yy, &yv));
422:   PetscCall(VecGetKokkosViewWrite(zz, &zv));
423:   if (zz != yy) Kokkos::deep_copy(zv, yv);
424:   if (A->form_explicit_transpose) {
425:     PetscCall(MatSeqAIJKokkosGenerateHermitian_Private(A, &csrmat));
426:     mode = "N";
427:   } else {
428:     aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
429:     csrmat = &aijkok->csrmat;
430:     mode   = "C";
431:   }
432:   PetscCallCXX(KokkosSparse::spmv(mode, 1.0 /*alpha*/, *csrmat, xv, 1.0 /*beta*/, zv)); /* z = alpha A^H x + beta z */
433:   PetscCall(VecRestoreKokkosView(xx, &xv));
434:   PetscCall(VecRestoreKokkosView(yy, &yv));
435:   PetscCall(VecRestoreKokkosViewWrite(zz, &zv));
436:   PetscCall(PetscLogGpuFlops(2.0 * csrmat->nnz()));
437:   PetscCall(PetscLogGpuTimeEnd());
438:   PetscFunctionReturn(PETSC_SUCCESS);
439: }

441: PetscErrorCode MatSetOption_SeqAIJKokkos(Mat A, MatOption op, PetscBool flg)
442: {
443:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

445:   PetscFunctionBegin;
446:   switch (op) {
447:   case MAT_FORM_EXPLICIT_TRANSPOSE:
448:     /* need to destroy the transpose matrix if present to prevent from logic errors if flg is set to true later */
449:     if (A->form_explicit_transpose && !flg && aijkok) PetscCall(aijkok->DestroyMatTranspose());
450:     A->form_explicit_transpose = flg;
451:     break;
452:   default:
453:     PetscCall(MatSetOption_SeqAIJ(A, op, flg));
454:     break;
455:   }
456:   PetscFunctionReturn(PETSC_SUCCESS);
457: }

459: /* Depending on reuse, either build a new mat, or use the existing mat */
460: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat A, MatType mtype, MatReuse reuse, Mat *newmat)
461: {
462:   Mat_SeqAIJ *aseq;

464:   PetscFunctionBegin;
465:   PetscCall(PetscKokkosInitializeCheck());
466:   if (reuse == MAT_INITIAL_MATRIX) {                      /* Build a brand new mat */
467:     PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));  /* the returned newmat is a SeqAIJKokkos */
468:   } else if (reuse == MAT_REUSE_MATRIX) {                 /* Reuse the mat created before */
469:     PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN)); /* newmat is already a SeqAIJKokkos */
470:   } else if (reuse == MAT_INPLACE_MATRIX) {               /* newmat is A */
471:     PetscCheck(A == *newmat, PetscObjectComm((PetscObject)A), PETSC_ERR_PLIB, "A != *newmat with MAT_INPLACE_MATRIX");
472:     PetscCall(PetscFree(A->defaultvectype));
473:     PetscCall(PetscStrallocpy(VECKOKKOS, &A->defaultvectype)); /* Allocate and copy the string */
474:     PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATSEQAIJKOKKOS));
475:     PetscCall(MatSetOps_SeqAIJKokkos(A));
476:     aseq = static_cast<Mat_SeqAIJ *>(A->data);
477:     if (A->assembled) { /* Copy i, j (but not values) to device for an assembled matrix if not yet */
478:       PetscCheck(!A->spptr, PETSC_COMM_WORLD, PETSC_ERR_PLIB, "Expect NULL (Mat_SeqAIJKokkos*)A->spptr");
479:       A->spptr = new Mat_SeqAIJKokkos(A->rmap->n, A->cmap->n, aseq->nz, aseq->i, aseq->j, aseq->a, A->nonzerostate, PETSC_FALSE);
480:     }
481:   }
482:   PetscFunctionReturn(PETSC_SUCCESS);
483: }

485: /* MatDuplicate always creates a new matrix. MatDuplicate can be called either on an assembled matrix or
486:    an unassembled matrix, even though MAT_COPY_VALUES is not allowed for unassembled matrix.
487:  */
488: static PetscErrorCode MatDuplicate_SeqAIJKokkos(Mat A, MatDuplicateOption dupOption, Mat *B)
489: {
490:   Mat_SeqAIJ       *bseq;
491:   Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr), *bkok;
492:   Mat               mat;

494:   PetscFunctionBegin;
495:   /* Do not copy values on host as A's latest values might be on device. We don't want to do sync blindly */
496:   PetscCall(MatDuplicate_SeqAIJ(A, MAT_DO_NOT_COPY_VALUES, B));
497:   mat = *B;
498:   if (A->assembled) {
499:     bseq = static_cast<Mat_SeqAIJ *>(mat->data);
500:     bkok = new Mat_SeqAIJKokkos(mat->rmap->n, mat->cmap->n, bseq->nz, bseq->i, bseq->j, bseq->a, mat->nonzerostate, PETSC_FALSE);
501:     bkok->a_dual.clear_sync_state(); /* Clear B's sync state as it will be decided below */
502:     /* Now copy values to B if needed */
503:     if (dupOption == MAT_COPY_VALUES) {
504:       if (akok->a_dual.need_sync_device()) {
505:         Kokkos::deep_copy(bkok->a_dual.view_host(), akok->a_dual.view_host());
506:         bkok->a_dual.modify_host();
507:       } else { /* If device has the latest data, we only copy data on device */
508:         Kokkos::deep_copy(bkok->a_dual.view_device(), akok->a_dual.view_device());
509:         bkok->a_dual.modify_device();
510:       }
511:     } else { /* MAT_DO_NOT_COPY_VALUES or MAT_SHARE_NONZERO_PATTERN. B's values should be zeroed */
512:       /* B's values on host should be already zeroed by MatDuplicate_SeqAIJ() */
513:       bkok->a_dual.modify_host();
514:     }
515:     mat->spptr = bkok;
516:   }

518:   PetscCall(PetscFree(mat->defaultvectype));
519:   PetscCall(PetscStrallocpy(VECKOKKOS, &mat->defaultvectype)); /* Allocate and copy the string */
520:   PetscCall(PetscObjectChangeTypeName((PetscObject)mat, MATSEQAIJKOKKOS));
521:   PetscCall(MatSetOps_SeqAIJKokkos(mat));
522:   PetscFunctionReturn(PETSC_SUCCESS);
523: }

525: static PetscErrorCode MatTranspose_SeqAIJKokkos(Mat A, MatReuse reuse, Mat *B)
526: {
527:   Mat               At;
528:   KokkosCsrMatrix  *internT;
529:   Mat_SeqAIJKokkos *atkok, *bkok;

531:   PetscFunctionBegin;
532:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B));
533:   PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &internT)); /* Generate a transpose internally */
534:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
535:     /* Deep copy internT, as we want to isolate the internal transpose */
536:     PetscCallCXX(atkok = new Mat_SeqAIJKokkos(KokkosCsrMatrix("csrmat", *internT)));
537:     PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PetscObjectComm((PetscObject)A), atkok, &At));
538:     if (reuse == MAT_INITIAL_MATRIX) *B = At;
539:     else PetscCall(MatHeaderReplace(A, &At)); /* Replace A with At inplace */
540:   } else {                                    /* MAT_REUSE_MATRIX, just need to copy values to B on device */
541:     if ((*B)->assembled) {
542:       bkok = static_cast<Mat_SeqAIJKokkos *>((*B)->spptr);
543:       PetscCallCXX(Kokkos::deep_copy(bkok->a_dual.view_device(), internT->values));
544:       PetscCall(MatSeqAIJKokkosModifyDevice(*B));
545:     } else if ((*B)->preallocated) { /* It is ok for B to be only preallocated, as needed in MatTranspose_MPIAIJ */
546:       Mat_SeqAIJ             *bseq = static_cast<Mat_SeqAIJ *>((*B)->data);
547:       MatScalarKokkosViewHost a_h(bseq->a, internT->nnz()); /* bseq->nz = 0 if unassembled */
548:       MatColIdxKokkosViewHost j_h(bseq->j, internT->nnz());
549:       PetscCallCXX(Kokkos::deep_copy(a_h, internT->values));
550:       PetscCallCXX(Kokkos::deep_copy(j_h, internT->graph.entries));
551:     } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "B must be assembled or preallocated");
552:   }
553:   PetscFunctionReturn(PETSC_SUCCESS);
554: }

556: static PetscErrorCode MatDestroy_SeqAIJKokkos(Mat A)
557: {
558:   Mat_SeqAIJKokkos *aijkok;

560:   PetscFunctionBegin;
561:   if (A->factortype == MAT_FACTOR_NONE) {
562:     aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
563:     delete aijkok;
564:   } else {
565:     delete static_cast<Mat_SeqAIJKokkosTriFactors *>(A->spptr);
566:   }
567:   A->spptr = NULL;
568:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
569:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
570:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
571:   PetscCall(MatDestroy_SeqAIJ(A));
572:   PetscFunctionReturn(PETSC_SUCCESS);
573: }

575: /*MC
576:    MATSEQAIJKOKKOS - MATAIJKOKKOS = "(seq)aijkokkos" - A matrix type to be used for sparse matrices with Kokkos

578:    A matrix type type using Kokkos-Kernels CrsMatrix type for portability across different device types

580:    Options Database Key:
581: .  -mat_type aijkokkos - sets the matrix type to `MATSEQAIJKOKKOS` during a call to `MatSetFromOptions()`

583:   Level: beginner

585: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqAIJKokkos()`, `MATMPIAIJKOKKOS`
586: M*/
587: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJKokkos(Mat A)
588: {
589:   PetscFunctionBegin;
590:   PetscCall(PetscKokkosInitializeCheck());
591:   PetscCall(MatCreate_SeqAIJ(A));
592:   PetscCall(MatConvert_SeqAIJ_SeqAIJKokkos(A, MATSEQAIJKOKKOS, MAT_INPLACE_MATRIX, &A));
593:   PetscFunctionReturn(PETSC_SUCCESS);
594: }

596: /* 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) */
597: PetscErrorCode MatSeqAIJKokkosMergeMats(Mat A, Mat B, MatReuse reuse, Mat *C)
598: {
599:   Mat_SeqAIJ         *a, *b;
600:   Mat_SeqAIJKokkos   *akok, *bkok, *ckok;
601:   MatScalarKokkosView aa, ba, ca;
602:   MatRowMapKokkosView ai, bi, ci;
603:   MatColIdxKokkosView aj, bj, cj;
604:   PetscInt            m, n, nnz, aN;

606:   PetscFunctionBegin;
610:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
611:   PetscCheckTypeName(B, MATSEQAIJKOKKOS);
612:   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);
613:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MAT_INPLACE_MATRIX not supported");

615:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
616:   PetscCall(MatSeqAIJKokkosSyncDevice(B));
617:   a    = static_cast<Mat_SeqAIJ *>(A->data);
618:   b    = static_cast<Mat_SeqAIJ *>(B->data);
619:   akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
620:   bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
621:   aa   = akok->a_dual.view_device();
622:   ai   = akok->i_dual.view_device();
623:   ba   = bkok->a_dual.view_device();
624:   bi   = bkok->i_dual.view_device();
625:   m    = A->rmap->n; /* M, N and nnz of C */
626:   n    = A->cmap->n + B->cmap->n;
627:   nnz  = a->nz + b->nz;
628:   aN   = A->cmap->n; /* N of A */
629:   if (reuse == MAT_INITIAL_MATRIX) {
630:     aj           = akok->j_dual.view_device();
631:     bj           = bkok->j_dual.view_device();
632:     auto ca_dual = MatScalarKokkosDualView("a", aa.extent(0) + ba.extent(0));
633:     auto ci_dual = MatRowMapKokkosDualView("i", ai.extent(0));
634:     auto cj_dual = MatColIdxKokkosDualView("j", aj.extent(0) + bj.extent(0));
635:     ca           = ca_dual.view_device();
636:     ci           = ci_dual.view_device();
637:     cj           = cj_dual.view_device();

639:     /* Concatenate A and B in parallel using Kokkos hierarchical parallelism */
640:     Kokkos::parallel_for(
641:       Kokkos::TeamPolicy<>(m, Kokkos::AUTO()), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
642:         PetscInt i       = t.league_rank(); /* row i */
643:         PetscInt coffset = ai(i) + bi(i), alen = ai(i + 1) - ai(i), blen = bi(i + 1) - bi(i);

645:         Kokkos::single(Kokkos::PerTeam(t), [=]() { /* this side effect only happens once per whole team */
646:                                                    ci(i) = coffset;
647:                                                    if (i == m - 1) ci(m) = ai(m) + bi(m);
648:         });

650:         Kokkos::parallel_for(Kokkos::TeamThreadRange(t, alen + blen), [&](PetscInt k) {
651:           if (k < alen) {
652:             ca(coffset + k) = aa(ai(i) + k);
653:             cj(coffset + k) = aj(ai(i) + k);
654:           } else {
655:             ca(coffset + k) = ba(bi(i) + k - alen);
656:             cj(coffset + k) = bj(bi(i) + k - alen) + aN; /* Entries in B get new column indices in C */
657:           }
658:         });
659:       });
660:     ca_dual.modify_device();
661:     ci_dual.modify_device();
662:     cj_dual.modify_device();
663:     PetscCallCXX(ckok = new Mat_SeqAIJKokkos(m, n, nnz, ci_dual, cj_dual, ca_dual));
664:     PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PETSC_COMM_SELF, ckok, C));
665:   } else if (reuse == MAT_REUSE_MATRIX) {
667:     PetscCheckTypeName(*C, MATSEQAIJKOKKOS);
668:     ckok = static_cast<Mat_SeqAIJKokkos *>((*C)->spptr);
669:     ca   = ckok->a_dual.view_device();
670:     ci   = ckok->i_dual.view_device();

672:     Kokkos::parallel_for(
673:       Kokkos::TeamPolicy<>(m, Kokkos::AUTO()), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
674:         PetscInt i    = t.league_rank(); /* row i */
675:         PetscInt alen = ai(i + 1) - ai(i), blen = bi(i + 1) - bi(i);
676:         Kokkos::parallel_for(Kokkos::TeamThreadRange(t, alen + blen), [&](PetscInt k) {
677:           if (k < alen) ca(ci(i) + k) = aa(ai(i) + k);
678:           else ca(ci(i) + k) = ba(bi(i) + k - alen);
679:         });
680:       });
681:     PetscCall(MatSeqAIJKokkosModifyDevice(*C));
682:   }
683:   PetscFunctionReturn(PETSC_SUCCESS);
684: }

686: static PetscErrorCode MatProductDataDestroy_SeqAIJKokkos(void *pdata)
687: {
688:   PetscFunctionBegin;
689:   delete static_cast<MatProductData_SeqAIJKokkos *>(pdata);
690:   PetscFunctionReturn(PETSC_SUCCESS);
691: }

693: static PetscErrorCode MatProductNumeric_SeqAIJKokkos_SeqAIJKokkos(Mat C)
694: {
695:   Mat_Product                 *product = C->product;
696:   Mat                          A, B;
697:   bool                         transA, transB; /* use bool, since KK needs this type */
698:   Mat_SeqAIJKokkos            *akok, *bkok, *ckok;
699:   Mat_SeqAIJ                  *c;
700:   MatProductData_SeqAIJKokkos *pdata;
701:   KokkosCsrMatrix             *csrmatA, *csrmatB;

703:   PetscFunctionBegin;
704:   MatCheckProduct(C, 1);
705:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
706:   pdata = static_cast<MatProductData_SeqAIJKokkos *>(C->product->data);

708:   if (pdata->reusesym) {           /* We reached here through e.g., MatMatMult(A,B,MAT_INITIAL_MATRIX,..,C), where symbolic/numeric are combined */
709:     pdata->reusesym = PETSC_FALSE; /* So that next time when user calls MatMatMult(E,F,MAT_REUSE_MATRIX,..,C), we still do numeric  */
710:     PetscFunctionReturn(PETSC_SUCCESS);
711:   }

713:   switch (product->type) {
714:   case MATPRODUCT_AB:
715:     transA = false;
716:     transB = false;
717:     break;
718:   case MATPRODUCT_AtB:
719:     transA = true;
720:     transB = false;
721:     break;
722:   case MATPRODUCT_ABt:
723:     transA = false;
724:     transB = true;
725:     break;
726:   default:
727:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Unsupported product type %s", MatProductTypes[product->type]);
728:   }

730:   A = product->A;
731:   B = product->B;
732:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
733:   PetscCall(MatSeqAIJKokkosSyncDevice(B));
734:   akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
735:   bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
736:   ckok = static_cast<Mat_SeqAIJKokkos *>(C->spptr);

738:   PetscCheck(ckok, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Device data structure spptr is empty");

740:   csrmatA = &akok->csrmat;
741:   csrmatB = &bkok->csrmat;

743:   /* TODO: Once KK spgemm implements transpose, we can get rid of the explicit transpose here */
744:   if (transA) {
745:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmatA));
746:     transA = false;
747:   }

749:   if (transB) {
750:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(B, &csrmatB));
751:     transB = false;
752:   }
753:   PetscCall(PetscLogGpuTimeBegin());
754:   PetscCallCXX(KokkosSparse::spgemm_numeric(pdata->kh, *csrmatA, transA, *csrmatB, transB, ckok->csrmat));
755: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(3, 7, 99)
756:   auto spgemmHandle = pdata->kh.get_spgemm_handle();
757:   if (spgemmHandle->get_sort_option() != 1) PetscCallCXX(sort_crs_matrix(ckok->csrmat)); /* without sort, mat_tests-ex62_14_seqaijkokkos fails */
758: #endif

760:   PetscCall(PetscLogGpuTimeEnd());
761:   PetscCall(MatSeqAIJKokkosModifyDevice(C));
762:   /* shorter version of MatAssemblyEnd_SeqAIJ */
763:   c = (Mat_SeqAIJ *)C->data;
764:   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));
765:   PetscCall(PetscInfo(C, "Number of mallocs during MatSetValues() is 0\n"));
766:   PetscCall(PetscInfo(C, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", c->rmax));
767:   c->reallocs         = 0;
768:   C->info.mallocs     = 0;
769:   C->info.nz_unneeded = 0;
770:   C->assembled = C->was_assembled = PETSC_TRUE;
771:   C->num_ass++;
772:   PetscFunctionReturn(PETSC_SUCCESS);
773: }

775: static PetscErrorCode MatProductSymbolic_SeqAIJKokkos_SeqAIJKokkos(Mat C)
776: {
777:   Mat_Product                 *product = C->product;
778:   MatProductType               ptype;
779:   Mat                          A, B;
780:   bool                         transA, transB;
781:   Mat_SeqAIJKokkos            *akok, *bkok, *ckok;
782:   MatProductData_SeqAIJKokkos *pdata;
783:   MPI_Comm                     comm;
784:   KokkosCsrMatrix             *csrmatA, *csrmatB, csrmatC;

786:   PetscFunctionBegin;
787:   MatCheckProduct(C, 1);
788:   PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
789:   PetscCheck(!product->data, comm, PETSC_ERR_PLIB, "Product data not empty");
790:   A = product->A;
791:   B = product->B;
792:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
793:   PetscCall(MatSeqAIJKokkosSyncDevice(B));
794:   akok    = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
795:   bkok    = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
796:   csrmatA = &akok->csrmat;
797:   csrmatB = &bkok->csrmat;

799:   ptype = product->type;
800:   switch (ptype) {
801:   case MATPRODUCT_AB:
802:     transA = false;
803:     transB = false;
804:     break;
805:   case MATPRODUCT_AtB:
806:     transA = true;
807:     transB = false;
808:     break;
809:   case MATPRODUCT_ABt:
810:     transA = false;
811:     transB = true;
812:     break;
813:   default:
814:     SETERRQ(comm, PETSC_ERR_PLIB, "Unsupported product type %s", MatProductTypes[product->type]);
815:   }

817:   product->data = pdata = new MatProductData_SeqAIJKokkos();
818:   pdata->kh.set_team_work_size(16);
819:   pdata->kh.set_dynamic_scheduling(true);
820:   pdata->reusesym = product->api_user;

822:   /* TODO: add command line options to select spgemm algorithms */
823:   auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; /* default alg is TPL if enabled, otherwise KK */

825:   /* CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4 */
826: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
827:   #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
828:   spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
829:   #endif
830: #endif

832:   pdata->kh.create_spgemm_handle(spgemm_alg);

834:   PetscCall(PetscLogGpuTimeBegin());
835:   /* TODO: Get rid of the explicit transpose once KK-spgemm implements the transpose option */
836:   if (transA) {
837:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(A, &csrmatA));
838:     transA = false;
839:   }

841:   if (transB) {
842:     PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(B, &csrmatB));
843:     transB = false;
844:   }

846:   PetscCallCXX(KokkosSparse::spgemm_symbolic(pdata->kh, *csrmatA, transA, *csrmatB, transB, csrmatC));

848:   /* spgemm_symbolic() only populates C's rowmap, but not C's column indices.
849:     So we have to do a fake spgemm_numeric() here to get csrmatC.j_d setup, before
850:     calling new Mat_SeqAIJKokkos().
851:     TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
852:   */
853:   PetscCallCXX(KokkosSparse::spgemm_numeric(pdata->kh, *csrmatA, transA, *csrmatB, transB, csrmatC));
854: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(3, 7, 99)
855:   /* Query if KK outputs a sorted matrix. If not, we need to sort it */
856:   auto spgemmHandle = pdata->kh.get_spgemm_handle();
857:   if (spgemmHandle->get_sort_option() != 1) PetscCallCXX(sort_crs_matrix(csrmatC)); /* sort_option defaults to -1 in KK!*/
858: #endif
859:   PetscCall(PetscLogGpuTimeEnd());

861:   PetscCallCXX(ckok = new Mat_SeqAIJKokkos(csrmatC));
862:   PetscCall(MatSetSeqAIJKokkosWithCSRMatrix(C, ckok));
863:   C->product->destroy = MatProductDataDestroy_SeqAIJKokkos;
864:   PetscFunctionReturn(PETSC_SUCCESS);
865: }

867: /* handles sparse matrix matrix ops */
868: static PetscErrorCode MatProductSetFromOptions_SeqAIJKokkos(Mat mat)
869: {
870:   Mat_Product *product = mat->product;
871:   PetscBool    Biskok = PETSC_FALSE, Ciskok = PETSC_TRUE;

873:   PetscFunctionBegin;
874:   MatCheckProduct(mat, 1);
875:   PetscCall(PetscObjectTypeCompare((PetscObject)product->B, MATSEQAIJKOKKOS, &Biskok));
876:   if (product->type == MATPRODUCT_ABC) PetscCall(PetscObjectTypeCompare((PetscObject)product->C, MATSEQAIJKOKKOS, &Ciskok));
877:   if (Biskok && Ciskok) {
878:     switch (product->type) {
879:     case MATPRODUCT_AB:
880:     case MATPRODUCT_AtB:
881:     case MATPRODUCT_ABt:
882:       mat->ops->productsymbolic = MatProductSymbolic_SeqAIJKokkos_SeqAIJKokkos;
883:       break;
884:     case MATPRODUCT_PtAP:
885:     case MATPRODUCT_RARt:
886:     case MATPRODUCT_ABC:
887:       mat->ops->productsymbolic = MatProductSymbolic_ABC_Basic;
888:       break;
889:     default:
890:       break;
891:     }
892:   } else { /* fallback for AIJ */
893:     PetscCall(MatProductSetFromOptions_SeqAIJ(mat));
894:   }
895:   PetscFunctionReturn(PETSC_SUCCESS);
896: }

898: static PetscErrorCode MatScale_SeqAIJKokkos(Mat A, PetscScalar a)
899: {
900:   Mat_SeqAIJKokkos *aijkok;

902:   PetscFunctionBegin;
903:   PetscCall(PetscLogGpuTimeBegin());
904:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
905:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
906:   KokkosBlas::scal(aijkok->a_dual.view_device(), a, aijkok->a_dual.view_device());
907:   PetscCall(MatSeqAIJKokkosModifyDevice(A));
908:   PetscCall(PetscLogGpuFlops(aijkok->a_dual.extent(0)));
909:   PetscCall(PetscLogGpuTimeEnd());
910:   PetscFunctionReturn(PETSC_SUCCESS);
911: }

913: static PetscErrorCode MatZeroEntries_SeqAIJKokkos(Mat A)
914: {
915:   Mat_SeqAIJKokkos *aijkok;

917:   PetscFunctionBegin;
918:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
919:   if (aijkok) { /* Only zero the device if data is already there */
920:     KokkosBlas::fill(aijkok->a_dual.view_device(), 0.0);
921:     PetscCall(MatSeqAIJKokkosModifyDevice(A));
922:   } else { /* Might be preallocated but not assembled */
923:     PetscCall(MatZeroEntries_SeqAIJ(A));
924:   }
925:   PetscFunctionReturn(PETSC_SUCCESS);
926: }

928: static PetscErrorCode MatGetDiagonal_SeqAIJKokkos(Mat A, Vec x)
929: {
930:   Mat_SeqAIJ           *aijseq;
931:   Mat_SeqAIJKokkos     *aijkok;
932:   PetscInt              n;
933:   PetscScalarKokkosView xv;

935:   PetscFunctionBegin;
936:   PetscCall(VecGetLocalSize(x, &n));
937:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
938:   PetscCheck(A->factortype == MAT_FACTOR_NONE, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatGetDiagonal_SeqAIJKokkos not supported on factored matrices");

940:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
941:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);

943:   if (A->rmap->n && aijkok->diag_dual.extent(0) == 0) { /* Set the diagonal pointer if not already */
944:     PetscCall(MatMarkDiagonal_SeqAIJ(A));
945:     aijseq = static_cast<Mat_SeqAIJ *>(A->data);
946:     aijkok->SetDiagonal(aijseq->diag);
947:   }

949:   const auto &Aa    = aijkok->a_dual.view_device();
950:   const auto &Ai    = aijkok->i_dual.view_device();
951:   const auto &Adiag = aijkok->diag_dual.view_device();

953:   PetscCall(VecGetKokkosViewWrite(x, &xv));
954:   Kokkos::parallel_for(
955:     n, KOKKOS_LAMBDA(const PetscInt i) {
956:       if (Adiag(i) < Ai(i + 1)) xv(i) = Aa(Adiag(i));
957:       else xv(i) = 0;
958:     });
959:   PetscCall(VecRestoreKokkosViewWrite(x, &xv));
960:   PetscFunctionReturn(PETSC_SUCCESS);
961: }

963: /* Get a Kokkos View from a mat of type MatSeqAIJKokkos */
964: PetscErrorCode MatSeqAIJGetKokkosView(Mat A, ConstMatScalarKokkosView *kv)
965: {
966:   Mat_SeqAIJKokkos *aijkok;

968:   PetscFunctionBegin;
971:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
972:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
973:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
974:   *kv    = aijkok->a_dual.view_device();
975:   PetscFunctionReturn(PETSC_SUCCESS);
976: }

978: PetscErrorCode MatSeqAIJRestoreKokkosView(Mat A, ConstMatScalarKokkosView *kv)
979: {
980:   PetscFunctionBegin;
983:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
984:   PetscFunctionReturn(PETSC_SUCCESS);
985: }

987: PetscErrorCode MatSeqAIJGetKokkosView(Mat A, MatScalarKokkosView *kv)
988: {
989:   Mat_SeqAIJKokkos *aijkok;

991:   PetscFunctionBegin;
994:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
995:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
996:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
997:   *kv    = aijkok->a_dual.view_device();
998:   PetscFunctionReturn(PETSC_SUCCESS);
999: }

1001: PetscErrorCode MatSeqAIJRestoreKokkosView(Mat A, MatScalarKokkosView *kv)
1002: {
1003:   PetscFunctionBegin;
1006:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1007:   PetscCall(MatSeqAIJKokkosModifyDevice(A));
1008:   PetscFunctionReturn(PETSC_SUCCESS);
1009: }

1011: PetscErrorCode MatSeqAIJGetKokkosViewWrite(Mat A, MatScalarKokkosView *kv)
1012: {
1013:   Mat_SeqAIJKokkos *aijkok;

1015:   PetscFunctionBegin;
1018:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1019:   aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1020:   *kv    = aijkok->a_dual.view_device();
1021:   PetscFunctionReturn(PETSC_SUCCESS);
1022: }

1024: PetscErrorCode MatSeqAIJRestoreKokkosViewWrite(Mat A, MatScalarKokkosView *kv)
1025: {
1026:   PetscFunctionBegin;
1029:   PetscCheckTypeName(A, MATSEQAIJKOKKOS);
1030:   PetscCall(MatSeqAIJKokkosModifyDevice(A));
1031:   PetscFunctionReturn(PETSC_SUCCESS);
1032: }

1034: /* Computes Y += alpha X */
1035: static PetscErrorCode MatAXPY_SeqAIJKokkos(Mat Y, PetscScalar alpha, Mat X, MatStructure pattern)
1036: {
1037:   Mat_SeqAIJ              *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
1038:   Mat_SeqAIJKokkos        *xkok, *ykok, *zkok;
1039:   ConstMatScalarKokkosView Xa;
1040:   MatScalarKokkosView      Ya;

1042:   PetscFunctionBegin;
1043:   PetscCheckTypeName(Y, MATSEQAIJKOKKOS);
1044:   PetscCheckTypeName(X, MATSEQAIJKOKKOS);
1045:   PetscCall(MatSeqAIJKokkosSyncDevice(Y));
1046:   PetscCall(MatSeqAIJKokkosSyncDevice(X));
1047:   PetscCall(PetscLogGpuTimeBegin());

1049:   if (pattern != SAME_NONZERO_PATTERN && x->nz == y->nz) {
1050:     /* We could compare on device, but have to get the comparison result on host. So compare on host instead. */
1051:     PetscBool e;
1052:     PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
1053:     if (e) {
1054:       PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
1055:       if (e) pattern = SAME_NONZERO_PATTERN;
1056:     }
1057:   }

1059:   /* cusparseDcsrgeam2() computes C = alpha A + beta B. If one knew sparsity pattern of C, one can skip
1060:     cusparseScsrgeam2_bufferSizeExt() / cusparseXcsrgeam2Nnz(), and directly call cusparseScsrgeam2().
1061:     If X is SUBSET_NONZERO_PATTERN of Y, we could take advantage of this cusparse feature. However,
1062:     KokkosSparse::spadd(alpha,A,beta,B,C) has symbolic and numeric phases, MatAXPY does not.
1063:   */
1064:   ykok = static_cast<Mat_SeqAIJKokkos *>(Y->spptr);
1065:   xkok = static_cast<Mat_SeqAIJKokkos *>(X->spptr);
1066:   Xa   = xkok->a_dual.view_device();
1067:   Ya   = ykok->a_dual.view_device();

1069:   if (pattern == SAME_NONZERO_PATTERN) {
1070:     KokkosBlas::axpy(alpha, Xa, Ya);
1071:     PetscCall(MatSeqAIJKokkosModifyDevice(Y));
1072:   } else if (pattern == SUBSET_NONZERO_PATTERN) {
1073:     MatRowMapKokkosView Xi = xkok->i_dual.view_device(), Yi = ykok->i_dual.view_device();
1074:     MatColIdxKokkosView Xj = xkok->j_dual.view_device(), Yj = ykok->j_dual.view_device();

1076:     Kokkos::parallel_for(
1077:       Kokkos::TeamPolicy<>(Y->rmap->n, 1), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1078:         PetscInt i = t.league_rank();              /* row i */
1079:         Kokkos::single(Kokkos::PerTeam(t), [=]() { /* Only one thread works in a team */
1080:                                                    PetscInt p, q = Yi(i);
1081:                                                    for (p = Xi(i); p < Xi(i + 1); p++) {          /* For each nonzero on row i of X */
1082:                                                      while (Xj(p) != Yj(q) && q < Yi(i + 1)) q++; /* find the matching nonzero on row i of Y */
1083:                                                      if (Xj(p) == Yj(q)) {                        /* Found it */
1084:                                                        Ya(q) += alpha * Xa(p);
1085:                                                        q++;
1086:                                                      } else {
1087:                                                        /* If not found, it indicates the input is wrong (X is not a SUBSET_NONZERO_PATTERN of Y).
1088:                Just insert a NaN at the beginning of row i if it is not empty, to make the result wrong.
1089:             */
1090:                                                        if (Yi(i) != Yi(i + 1))
1091:                                                          Ya(Yi(i)) =
1092: #if PETSC_PKG_KOKKOS_VERSION_GE(3, 6, 99)
1093:                                                            Kokkos::ArithTraits<PetscScalar>::nan();
1094: #else
1095:               Kokkos::Experimental::nan("1");
1096: #endif
1097:                                                      }
1098:                                                    }
1099:         });
1100:       });
1101:     PetscCall(MatSeqAIJKokkosModifyDevice(Y));
1102:   } else { /* different nonzero patterns */
1103:     Mat             Z;
1104:     KokkosCsrMatrix zcsr;
1105:     KernelHandle    kh;
1106:     kh.create_spadd_handle(false);
1107:     KokkosSparse::spadd_symbolic(&kh, xkok->csrmat, ykok->csrmat, zcsr);
1108:     KokkosSparse::spadd_numeric(&kh, alpha, xkok->csrmat, (PetscScalar)1.0, ykok->csrmat, zcsr);
1109:     zkok = new Mat_SeqAIJKokkos(zcsr);
1110:     PetscCall(MatCreateSeqAIJKokkosWithCSRMatrix(PETSC_COMM_SELF, zkok, &Z));
1111:     PetscCall(MatHeaderReplace(Y, &Z));
1112:     kh.destroy_spadd_handle();
1113:   }
1114:   PetscCall(PetscLogGpuTimeEnd());
1115:   PetscCall(PetscLogGpuFlops(xkok->a_dual.extent(0) * 2)); /* Because we scaled X and then added it to Y */
1116:   PetscFunctionReturn(PETSC_SUCCESS);
1117: }

1119: static PetscErrorCode MatSetPreallocationCOO_SeqAIJKokkos(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
1120: {
1121:   Mat_SeqAIJKokkos *akok;
1122:   Mat_SeqAIJ       *aseq;

1124:   PetscFunctionBegin;
1125:   PetscCall(MatSetPreallocationCOO_SeqAIJ(mat, coo_n, coo_i, coo_j));
1126:   aseq = static_cast<Mat_SeqAIJ *>(mat->data);
1127:   akok = static_cast<Mat_SeqAIJKokkos *>(mat->spptr);
1128:   delete akok;
1129:   mat->spptr = akok = new Mat_SeqAIJKokkos(mat->rmap->n, mat->cmap->n, aseq->nz, aseq->i, aseq->j, aseq->a, mat->nonzerostate + 1, PETSC_FALSE);
1130:   PetscCall(MatZeroEntries_SeqAIJKokkos(mat));
1131:   akok->SetUpCOO(aseq);
1132:   PetscFunctionReturn(PETSC_SUCCESS);
1133: }

1135: static PetscErrorCode MatSetValuesCOO_SeqAIJKokkos(Mat A, const PetscScalar v[], InsertMode imode)
1136: {
1137:   Mat_SeqAIJ                 *aseq = static_cast<Mat_SeqAIJ *>(A->data);
1138:   Mat_SeqAIJKokkos           *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1139:   PetscCount                  Annz = aseq->nz;
1140:   const PetscCountKokkosView &jmap = akok->jmap_d;
1141:   const PetscCountKokkosView &perm = akok->perm_d;
1142:   MatScalarKokkosView         Aa;
1143:   ConstMatScalarKokkosView    kv;
1144:   PetscMemType                memtype;

1146:   PetscFunctionBegin;
1147:   PetscCall(PetscGetMemType(v, &memtype));
1148:   if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we might need to copy it to device if any */
1149:     kv = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), ConstMatScalarKokkosViewHost(v, aseq->coo_n));
1150:   } else {
1151:     kv = ConstMatScalarKokkosView(v, aseq->coo_n); /* Directly use v[]'s memory */
1152:   }

1154:   if (imode == INSERT_VALUES) PetscCall(MatSeqAIJGetKokkosViewWrite(A, &Aa)); /* write matrix values */
1155:   else PetscCall(MatSeqAIJGetKokkosView(A, &Aa));                             /* read & write matrix values */

1157:   Kokkos::parallel_for(
1158:     Annz, KOKKOS_LAMBDA(const PetscCount i) {
1159:       PetscScalar sum = 0.0;
1160:       for (PetscCount k = jmap(i); k < jmap(i + 1); k++) sum += kv(perm(k));
1161:       Aa(i) = (imode == INSERT_VALUES ? 0.0 : Aa(i)) + sum;
1162:     });

1164:   if (imode == INSERT_VALUES) PetscCall(MatSeqAIJRestoreKokkosViewWrite(A, &Aa));
1165:   else PetscCall(MatSeqAIJRestoreKokkosView(A, &Aa));
1166:   PetscFunctionReturn(PETSC_SUCCESS);
1167: }

1169: PETSC_INTERN PetscErrorCode MatSeqAIJMoveDiagonalValuesFront_SeqAIJKokkos(Mat A, const PetscInt *diag)
1170: {
1171:   Mat_SeqAIJKokkos          *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr);
1172:   MatScalarKokkosView        Aa;
1173:   const MatRowMapKokkosView &Ai = akok->i_dual.view_device();
1174:   PetscInt                   m  = A->rmap->n;
1175:   ConstMatRowMapKokkosView   Adiag(diag, m); /* diag is a device pointer */

1177:   PetscFunctionBegin;
1178:   PetscCall(MatSeqAIJGetKokkosViewWrite(A, &Aa));
1179:   Kokkos::parallel_for(
1180:     m, KOKKOS_LAMBDA(const PetscInt i) {
1181:       PetscScalar tmp;
1182:       if (Adiag(i) >= Ai(i) && Adiag(i) < Ai(i + 1)) { /* The diagonal element exists */
1183:         tmp          = Aa(Ai(i));
1184:         Aa(Ai(i))    = Aa(Adiag(i));
1185:         Aa(Adiag(i)) = tmp;
1186:       }
1187:     });
1188:   PetscCall(MatSeqAIJRestoreKokkosViewWrite(A, &Aa));
1189:   PetscFunctionReturn(PETSC_SUCCESS);
1190: }

1192: static PetscErrorCode MatLUFactorNumeric_SeqAIJKokkos(Mat B, Mat A, const MatFactorInfo *info)
1193: {
1194:   PetscFunctionBegin;
1195:   PetscCall(MatSeqAIJKokkosSyncHost(A));
1196:   PetscCall(MatLUFactorNumeric_SeqAIJ(B, A, info));
1197:   B->offloadmask = PETSC_OFFLOAD_CPU;
1198:   PetscFunctionReturn(PETSC_SUCCESS);
1199: }

1201: static PetscErrorCode MatSetOps_SeqAIJKokkos(Mat A)
1202: {
1203:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1205:   PetscFunctionBegin;
1206:   A->offloadmask = PETSC_OFFLOAD_KOKKOS; /* We do not really use this flag */
1207:   A->boundtocpu  = PETSC_FALSE;

1209:   A->ops->assemblyend               = MatAssemblyEnd_SeqAIJKokkos;
1210:   A->ops->destroy                   = MatDestroy_SeqAIJKokkos;
1211:   A->ops->duplicate                 = MatDuplicate_SeqAIJKokkos;
1212:   A->ops->axpy                      = MatAXPY_SeqAIJKokkos;
1213:   A->ops->scale                     = MatScale_SeqAIJKokkos;
1214:   A->ops->zeroentries               = MatZeroEntries_SeqAIJKokkos;
1215:   A->ops->productsetfromoptions     = MatProductSetFromOptions_SeqAIJKokkos;
1216:   A->ops->mult                      = MatMult_SeqAIJKokkos;
1217:   A->ops->multadd                   = MatMultAdd_SeqAIJKokkos;
1218:   A->ops->multtranspose             = MatMultTranspose_SeqAIJKokkos;
1219:   A->ops->multtransposeadd          = MatMultTransposeAdd_SeqAIJKokkos;
1220:   A->ops->multhermitiantranspose    = MatMultHermitianTranspose_SeqAIJKokkos;
1221:   A->ops->multhermitiantransposeadd = MatMultHermitianTransposeAdd_SeqAIJKokkos;
1222:   A->ops->productnumeric            = MatProductNumeric_SeqAIJKokkos_SeqAIJKokkos;
1223:   A->ops->transpose                 = MatTranspose_SeqAIJKokkos;
1224:   A->ops->setoption                 = MatSetOption_SeqAIJKokkos;
1225:   A->ops->getdiagonal               = MatGetDiagonal_SeqAIJKokkos;
1226:   a->ops->getarray                  = MatSeqAIJGetArray_SeqAIJKokkos;
1227:   a->ops->restorearray              = MatSeqAIJRestoreArray_SeqAIJKokkos;
1228:   a->ops->getarrayread              = MatSeqAIJGetArrayRead_SeqAIJKokkos;
1229:   a->ops->restorearrayread          = MatSeqAIJRestoreArrayRead_SeqAIJKokkos;
1230:   a->ops->getarraywrite             = MatSeqAIJGetArrayWrite_SeqAIJKokkos;
1231:   a->ops->restorearraywrite         = MatSeqAIJRestoreArrayWrite_SeqAIJKokkos;
1232:   a->ops->getcsrandmemtype          = MatSeqAIJGetCSRAndMemType_SeqAIJKokkos;

1234:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJKokkos));
1235:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJKokkos));
1236:   PetscFunctionReturn(PETSC_SUCCESS);
1237: }

1239: PETSC_INTERN PetscErrorCode MatSetSeqAIJKokkosWithCSRMatrix(Mat A, Mat_SeqAIJKokkos *akok)
1240: {
1241:   Mat_SeqAIJ *aseq;
1242:   PetscInt    i, m, n;

1244:   PetscFunctionBegin;
1245:   PetscCheck(!A->spptr, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A->spptr is supposed to be empty");

1247:   m = akok->nrows();
1248:   n = akok->ncols();
1249:   PetscCall(MatSetSizes(A, m, n, m, n));
1250:   PetscCall(MatSetType(A, MATSEQAIJKOKKOS));

1252:   /* Set up data structures of A as a MATSEQAIJ */
1253:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, MAT_SKIP_ALLOCATION, NULL));
1254:   aseq = (Mat_SeqAIJ *)(A)->data;

1256:   akok->i_dual.sync_host(); /* We always need sync'ed i, j on host */
1257:   akok->j_dual.sync_host();

1259:   aseq->i            = akok->i_host_data();
1260:   aseq->j            = akok->j_host_data();
1261:   aseq->a            = akok->a_host_data();
1262:   aseq->nonew        = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
1263:   aseq->singlemalloc = PETSC_FALSE;
1264:   aseq->free_a       = PETSC_FALSE;
1265:   aseq->free_ij      = PETSC_FALSE;
1266:   aseq->nz           = akok->nnz();
1267:   aseq->maxnz        = aseq->nz;

1269:   PetscCall(PetscMalloc1(m, &aseq->imax));
1270:   PetscCall(PetscMalloc1(m, &aseq->ilen));
1271:   for (i = 0; i < m; i++) aseq->ilen[i] = aseq->imax[i] = aseq->i[i + 1] - aseq->i[i];

1273:   /* It is critical to set the nonzerostate, as we use it to check if sparsity pattern (hence data) has changed on host in MatAssemblyEnd */
1274:   akok->nonzerostate = A->nonzerostate;
1275:   A->spptr           = akok; /* Set A->spptr before MatAssembly so that A->spptr won't be allocated again there */
1276:   PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1277:   PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1278:   PetscFunctionReturn(PETSC_SUCCESS);
1279: }

1281: /* Crete a SEQAIJKOKKOS matrix with a Mat_SeqAIJKokkos data structure

1283:    Note we have names like MatSeqAIJSetPreallocationCSR, so I use capitalized CSR
1284:  */
1285: PETSC_INTERN PetscErrorCode MatCreateSeqAIJKokkosWithCSRMatrix(MPI_Comm comm, Mat_SeqAIJKokkos *akok, Mat *A)
1286: {
1287:   PetscFunctionBegin;
1288:   PetscCall(MatCreate(comm, A));
1289:   PetscCall(MatSetSeqAIJKokkosWithCSRMatrix(*A, akok));
1290:   PetscFunctionReturn(PETSC_SUCCESS);
1291: }

1293: /*@C
1294:    MatCreateSeqAIJKokkos - Creates a sparse matrix in `MATSEQAIJKOKKOS` (compressed row) format
1295:    (the default parallel PETSc format). This matrix will ultimately be handled by
1296:    Kokkos for calculations. For good matrix
1297:    assembly performance the user should preallocate the matrix storage by setting
1298:    the parameter nz (or the array nnz).  By setting these parameters accurately,
1299:    performance during matrix assembly can be increased by more than a factor of 50.

1301:    Collective

1303:    Input Parameters:
1304: +  comm - MPI communicator, set to `PETSC_COMM_SELF`
1305: .  m - number of rows
1306: .  n - number of columns
1307: .  nz - number of nonzeros per row (same for all rows)
1308: -  nnz - array containing the number of nonzeros in the various rows
1309:          (possibly different for each row) or `NULL`

1311:    Output Parameter:
1312: .  A - the matrix

1314:    Level: intermediate

1316:    Notes:
1317:    It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
1318:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
1319:    [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

1321:    If `nnz` is given then `nz` is ignored

1323:    The AIJ format, also called
1324:    compressed row storage, is fully compatible with standard Fortran
1325:    storage.  That is, the stored row and column indices can begin at
1326:    either one (as in Fortran) or zero.  See the users' manual for details.

1328:    Specify the preallocated storage with either `nz` or `nnz` (not both).
1329:    Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
1330:    allocation.

1332:    By default, this format uses inodes (identical nodes) when possible, to
1333:    improve numerical efficiency of matrix-vector products and solves. We
1334:    search for consecutive rows with the same nonzero structure, thereby
1335:    reusing matrix information to achieve increased efficiency.

1337: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatCreateAIJ()`
1338: @*/
1339: PetscErrorCode MatCreateSeqAIJKokkos(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
1340: {
1341:   PetscFunctionBegin;
1342:   PetscCall(PetscKokkosInitializeCheck());
1343:   PetscCall(MatCreate(comm, A));
1344:   PetscCall(MatSetSizes(*A, m, n, m, n));
1345:   PetscCall(MatSetType(*A, MATSEQAIJKOKKOS));
1346:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, (PetscInt *)nnz));
1347:   PetscFunctionReturn(PETSC_SUCCESS);
1348: }

1350: typedef Kokkos::TeamPolicy<>::member_type team_member;
1351: //
1352: // This factorization exploits block diagonal matrices with "Nf" (not used).
1353: // Use -pc_factor_mat_ordering_type rcm to order decouple blocks of size N/Nf for this optimization
1354: //
1355: static PetscErrorCode MatLUFactorNumeric_SeqAIJKOKKOSDEVICE(Mat B, Mat A, const MatFactorInfo *info)
1356: {
1357:   Mat_SeqAIJ       *b      = (Mat_SeqAIJ *)B->data;
1358:   Mat_SeqAIJKokkos *aijkok = static_cast<Mat_SeqAIJKokkos *>(A->spptr), *baijkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
1359:   IS                isrow = b->row, isicol = b->icol;
1360:   const PetscInt   *r_h, *ic_h;
1361:   const PetscInt n = A->rmap->n, *ai_d = aijkok->i_dual.view_device().data(), *aj_d = aijkok->j_dual.view_device().data(), *bi_d = baijkok->i_dual.view_device().data(), *bj_d = baijkok->j_dual.view_device().data(), *bdiag_d = baijkok->diag_d.data();
1362:   const PetscScalar *aa_d = aijkok->a_dual.view_device().data();
1363:   PetscScalar       *ba_d = baijkok->a_dual.view_device().data();
1364:   PetscBool          row_identity, col_identity;
1365:   PetscInt           nc, Nf = 1, nVec = 32; // should be a parameter, Nf is batch size - not used

1367:   PetscFunctionBegin;
1368:   PetscCheck(A->rmap->n == n, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "square matrices only supported %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->n, n);
1369:   PetscCall(MatIsStructurallySymmetric(A, &row_identity));
1370:   PetscCheck(row_identity, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "structurally symmetric matrices only supported");
1371:   PetscCall(ISGetIndices(isrow, &r_h));
1372:   PetscCall(ISGetIndices(isicol, &ic_h));
1373:   PetscCall(ISGetSize(isicol, &nc));
1374:   PetscCall(PetscLogGpuTimeBegin());
1375:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
1376:   {
1377: #define KOKKOS_SHARED_LEVEL 1
1378:     using scr_mem_t    = Kokkos::DefaultExecutionSpace::scratch_memory_space;
1379:     using sizet_scr_t  = Kokkos::View<size_t, scr_mem_t>;
1380:     using scalar_scr_t = Kokkos::View<PetscScalar, scr_mem_t>;
1381:     const Kokkos::View<const PetscInt *, Kokkos::LayoutLeft, Kokkos::HostSpace, Kokkos::MemoryTraits<Kokkos::Unmanaged>> h_r_k(r_h, n);
1382:     Kokkos::View<PetscInt *, Kokkos::LayoutLeft>                                                                         d_r_k("r", n);
1383:     const Kokkos::View<const PetscInt *, Kokkos::LayoutLeft, Kokkos::HostSpace, Kokkos::MemoryTraits<Kokkos::Unmanaged>> h_ic_k(ic_h, nc);
1384:     Kokkos::View<PetscInt *, Kokkos::LayoutLeft>                                                                         d_ic_k("ic", nc);
1385:     size_t                                                                                                               flops_h = 0.0;
1386:     Kokkos::View<size_t, Kokkos::HostSpace>                                                                              h_flops_k(&flops_h);
1387:     Kokkos::View<size_t>                                                                                                 d_flops_k("flops");
1388:     const int                                                                                                            conc = Kokkos::DefaultExecutionSpace().concurrency(), team_size = conc > 1 ? 16 : 1; // 8*32 = 256
1389:     const int                                                                                                            nloc = n / Nf, Ni = (conc > 8) ? 1 /* some intelligent number of SMs -- but need league_barrier */ : 1;
1390:     Kokkos::deep_copy(d_flops_k, h_flops_k);
1391:     Kokkos::deep_copy(d_r_k, h_r_k);
1392:     Kokkos::deep_copy(d_ic_k, h_ic_k);
1393:     // Fill A --> fact
1394:     Kokkos::parallel_for(
1395:       Kokkos::TeamPolicy<>(Nf * Ni, team_size, nVec), KOKKOS_LAMBDA(const team_member team) {
1396:         const PetscInt  field = team.league_rank() / Ni, field_block = team.league_rank() % Ni; // use grid.x/y in CUDA
1397:         const PetscInt  nloc_i = (nloc / Ni + !!(nloc % Ni)), start_i = field * nloc + field_block * nloc_i, end_i = (start_i + nloc_i) > (field + 1) * nloc ? (field + 1) * nloc : (start_i + nloc_i);
1398:         const PetscInt *ic = d_ic_k.data(), *r = d_r_k.data();
1399:         // zero rows of B
1400:         Kokkos::parallel_for(Kokkos::TeamVectorRange(team, start_i, end_i), [=](const int &rowb) {
1401:           PetscInt     nzbL = bi_d[rowb + 1] - bi_d[rowb], nzbU = bdiag_d[rowb] - bdiag_d[rowb + 1]; // with diag
1402:           PetscScalar *baL = ba_d + bi_d[rowb];
1403:           PetscScalar *baU = ba_d + bdiag_d[rowb + 1] + 1;
1404:           /* zero (unfactored row) */
1405:           for (int j = 0; j < nzbL; j++) baL[j] = 0;
1406:           for (int j = 0; j < nzbU; j++) baU[j] = 0;
1407:         });
1408:         // copy A into B
1409:         Kokkos::parallel_for(Kokkos::TeamVectorRange(team, start_i, end_i), [=](const int &rowb) {
1410:           PetscInt           rowa = r[rowb], nza = ai_d[rowa + 1] - ai_d[rowa];
1411:           const PetscScalar *av    = aa_d + ai_d[rowa];
1412:           const PetscInt    *ajtmp = aj_d + ai_d[rowa];
1413:           /* load in initial (unfactored row) */
1414:           for (int j = 0; j < nza; j++) {
1415:             PetscInt    colb = ic[ajtmp[j]];
1416:             PetscScalar vala = av[j];
1417:             if (colb == rowb) {
1418:               *(ba_d + bdiag_d[rowb]) = vala;
1419:             } else {
1420:               const PetscInt *pbj = bj_d + ((colb > rowb) ? bdiag_d[rowb + 1] + 1 : bi_d[rowb]);
1421:               PetscScalar    *pba = ba_d + ((colb > rowb) ? bdiag_d[rowb + 1] + 1 : bi_d[rowb]);
1422:               PetscInt        nz = (colb > rowb) ? bdiag_d[rowb] - (bdiag_d[rowb + 1] + 1) : bi_d[rowb + 1] - bi_d[rowb], set = 0;
1423:               for (int j = 0; j < nz; j++) {
1424:                 if (pbj[j] == colb) {
1425:                   pba[j] = vala;
1426:                   set++;
1427:                   break;
1428:                 }
1429:               }
1430: #if !defined(PETSC_HAVE_SYCL)
1431:               if (set != 1) printf("\t\t\t ERROR DID NOT SET ?????\n");
1432: #endif
1433:             }
1434:           }
1435:         });
1436:       });
1437:     Kokkos::fence();

1439:     Kokkos::parallel_for(
1440:       Kokkos::TeamPolicy<>(Nf * Ni, team_size, nVec).set_scratch_size(KOKKOS_SHARED_LEVEL, Kokkos::PerThread(sizet_scr_t::shmem_size() + scalar_scr_t::shmem_size()), Kokkos::PerTeam(sizet_scr_t::shmem_size())), KOKKOS_LAMBDA(const team_member team) {
1441:         sizet_scr_t    colkIdx(team.thread_scratch(KOKKOS_SHARED_LEVEL));
1442:         scalar_scr_t   L_ki(team.thread_scratch(KOKKOS_SHARED_LEVEL));
1443:         sizet_scr_t    flops(team.team_scratch(KOKKOS_SHARED_LEVEL));
1444:         const PetscInt field = team.league_rank() / Ni, field_block_idx = team.league_rank() % Ni; // use grid.x/y in CUDA
1445:         const PetscInt start = field * nloc, end = start + nloc;
1446:         Kokkos::single(Kokkos::PerTeam(team), [=]() { flops() = 0; });
1447:         // A22 panel update for each row A(1,:) and col A(:,1)
1448:         for (int ii = start; ii < end - 1; ii++) {
1449:           const PetscInt    *bjUi = bj_d + bdiag_d[ii + 1] + 1, nzUi = bdiag_d[ii] - (bdiag_d[ii + 1] + 1); // vector, and vector size, of column indices of U(i,(i+1):end)
1450:           const PetscScalar *baUi    = ba_d + bdiag_d[ii + 1] + 1;                                          // vector of data  U(i,i+1:end)
1451:           const PetscInt     nUi_its = nzUi / Ni + !!(nzUi % Ni);
1452:           const PetscScalar  Bii     = *(ba_d + bdiag_d[ii]); // diagonal in its special place
1453:           Kokkos::parallel_for(Kokkos::TeamThreadRange(team, nUi_its), [=](const int j) {
1454:             PetscInt kIdx = j * Ni + field_block_idx;
1455:             if (kIdx >= nzUi) /* void */
1456:               ;
1457:             else {
1458:               const PetscInt  myk = bjUi[kIdx];                // assume symmetric structure, need a transposed meta-data here in general
1459:               const PetscInt *pjL = bj_d + bi_d[myk];          // look for L(myk,ii) in start of row
1460:               const PetscInt  nzL = bi_d[myk + 1] - bi_d[myk]; // size of L_k(:)
1461:               size_t          st_idx;
1462:               // find and do L(k,i) = A(:k,i) / A(i,i)
1463:               Kokkos::single(Kokkos::PerThread(team), [&]() { colkIdx() = PETSC_MAX_INT; });
1464:               // get column, there has got to be a better way
1465:               Kokkos::parallel_reduce(
1466:                 Kokkos::ThreadVectorRange(team, nzL),
1467:                 [&](const int &j, size_t &idx) {
1468:                   if (pjL[j] == ii) {
1469:                     PetscScalar *pLki = ba_d + bi_d[myk] + j;
1470:                     idx               = j;           // output
1471:                     *pLki             = *pLki / Bii; // column scaling:  L(k,i) = A(:k,i) / A(i,i)
1472:                   }
1473:                 },
1474:                 st_idx);
1475:               Kokkos::single(Kokkos::PerThread(team), [=]() {
1476:                 colkIdx() = st_idx;
1477:                 L_ki()    = *(ba_d + bi_d[myk] + st_idx);
1478:               });
1479: #if defined(PETSC_USE_DEBUG) && !defined(PETSC_HAVE_SYCL)
1480:               if (colkIdx() == PETSC_MAX_INT) printf("\t\t\t\t\t\t\tERROR: failed to find L_ki(%d,%d)\n", (int)myk, ii); // uses a register
1481: #endif
1482:               // active row k, do  A_kj -= Lki * U_ij; j \in U(i,:) j != i
1483:               // U(i+1,:end)
1484:               Kokkos::parallel_for(Kokkos::ThreadVectorRange(team, nzUi), [=](const int &uiIdx) { // index into i (U)
1485:                 PetscScalar Uij = baUi[uiIdx];
1486:                 PetscInt    col = bjUi[uiIdx];
1487:                 if (col == myk) {
1488:                   // A_kk = A_kk - L_ki * U_ij(k)
1489:                   PetscScalar *Akkv = (ba_d + bdiag_d[myk]); // diagonal in its special place
1490:                   *Akkv             = *Akkv - L_ki() * Uij;  // UiK
1491:                 } else {
1492:                   PetscScalar    *start, *end, *pAkjv = NULL;
1493:                   PetscInt        high, low;
1494:                   const PetscInt *startj;
1495:                   if (col < myk) { // L
1496:                     PetscScalar *pLki = ba_d + bi_d[myk] + colkIdx();
1497:                     PetscInt     idx  = (pLki + 1) - (ba_d + bi_d[myk]); // index into row
1498:                     start             = pLki + 1;                        // start at pLki+1, A22(myk,1)
1499:                     startj            = bj_d + bi_d[myk] + idx;
1500:                     end               = ba_d + bi_d[myk + 1];
1501:                   } else {
1502:                     PetscInt idx = bdiag_d[myk + 1] + 1;
1503:                     start        = ba_d + idx;
1504:                     startj       = bj_d + idx;
1505:                     end          = ba_d + bdiag_d[myk];
1506:                   }
1507:                   // search for 'col', use bisection search - TODO
1508:                   low  = 0;
1509:                   high = (PetscInt)(end - start);
1510:                   while (high - low > 5) {
1511:                     int t = (low + high) / 2;
1512:                     if (startj[t] > col) high = t;
1513:                     else low = t;
1514:                   }
1515:                   for (pAkjv = start + low; pAkjv < start + high; pAkjv++) {
1516:                     if (startj[pAkjv - start] == col) break;
1517:                   }
1518: #if defined(PETSC_USE_DEBUG) && !defined(PETSC_HAVE_SYCL)
1519:                   if (pAkjv == start + high) printf("\t\t\t\t\t\t\t\t\t\t\tERROR: *** failed to find Akj(%d,%d)\n", (int)myk, (int)col); // uses a register
1520: #endif
1521:                   *pAkjv = *pAkjv - L_ki() * Uij; // A_kj = A_kj - L_ki * U_ij
1522:                 }
1523:               });
1524:             }
1525:           });
1526:           team.team_barrier(); // this needs to be a league barrier to use more that one SM per block
1527:           if (field_block_idx == 0) Kokkos::single(Kokkos::PerTeam(team), [&]() { Kokkos::atomic_add(flops.data(), (size_t)(2 * (nzUi * nzUi) + 2)); });
1528:         } /* endof for (i=0; i<n; i++) { */
1529:         Kokkos::single(Kokkos::PerTeam(team), [=]() {
1530:           Kokkos::atomic_add(&d_flops_k(), flops());
1531:           flops() = 0;
1532:         });
1533:       });
1534:     Kokkos::fence();
1535:     Kokkos::deep_copy(h_flops_k, d_flops_k);
1536:     PetscCall(PetscLogGpuFlops((PetscLogDouble)h_flops_k()));
1537:     Kokkos::parallel_for(
1538:       Kokkos::TeamPolicy<>(Nf * Ni, 1, 256), KOKKOS_LAMBDA(const team_member team) {
1539:         const PetscInt lg_rank = team.league_rank(), field = lg_rank / Ni;                            //, field_offset = lg_rank%Ni;
1540:         const PetscInt start = field * nloc, end = start + nloc, n_its = (nloc / Ni + !!(nloc % Ni)); // 1/Ni iters
1541:         /* Invert diagonal for simpler triangular solves */
1542:         Kokkos::parallel_for(Kokkos::TeamVectorRange(team, n_its), [=](int outer_index) {
1543:           int i = start + outer_index * Ni + lg_rank % Ni;
1544:           if (i < end) {
1545:             PetscScalar *pv = ba_d + bdiag_d[i];
1546:             *pv             = 1.0 / (*pv);
1547:           }
1548:         });
1549:       });
1550:   }
1551:   PetscCall(PetscLogGpuTimeEnd());
1552:   PetscCall(ISRestoreIndices(isicol, &ic_h));
1553:   PetscCall(ISRestoreIndices(isrow, &r_h));

1555:   PetscCall(ISIdentity(isrow, &row_identity));
1556:   PetscCall(ISIdentity(isicol, &col_identity));
1557:   if (b->inode.size) {
1558:     B->ops->solve = MatSolve_SeqAIJ_Inode;
1559:   } else if (row_identity && col_identity) {
1560:     B->ops->solve = MatSolve_SeqAIJ_NaturalOrdering;
1561:   } else {
1562:     B->ops->solve = MatSolve_SeqAIJ; // at least this needs to be in Kokkos
1563:   }
1564:   B->offloadmask = PETSC_OFFLOAD_GPU;
1565:   PetscCall(MatSeqAIJKokkosSyncHost(B));          // solve on CPU
1566:   B->ops->solveadd          = MatSolveAdd_SeqAIJ; // and this
1567:   B->ops->solvetranspose    = MatSolveTranspose_SeqAIJ;
1568:   B->ops->solvetransposeadd = MatSolveTransposeAdd_SeqAIJ;
1569:   B->ops->matsolve          = MatMatSolve_SeqAIJ;
1570:   B->assembled              = PETSC_TRUE;
1571:   B->preallocated           = PETSC_TRUE;

1573:   PetscFunctionReturn(PETSC_SUCCESS);
1574: }

1576: static PetscErrorCode MatLUFactorSymbolic_SeqAIJKokkos(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1577: {
1578:   PetscFunctionBegin;
1579:   PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
1580:   B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJKokkos;
1581:   PetscFunctionReturn(PETSC_SUCCESS);
1582: }

1584: static PetscErrorCode MatSeqAIJKokkosSymbolicSolveCheck(Mat A)
1585: {
1586:   Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;

1588:   PetscFunctionBegin;
1589:   if (!factors->sptrsv_symbolic_completed) {
1590:     KokkosSparse::Experimental::sptrsv_symbolic(&factors->khU, factors->iU_d, factors->jU_d, factors->aU_d);
1591:     KokkosSparse::Experimental::sptrsv_symbolic(&factors->khL, factors->iL_d, factors->jL_d, factors->aL_d);
1592:     factors->sptrsv_symbolic_completed = PETSC_TRUE;
1593:   }
1594:   PetscFunctionReturn(PETSC_SUCCESS);
1595: }

1597: /* Check if we need to update factors etc for transpose solve */
1598: static PetscErrorCode MatSeqAIJKokkosTransposeSolveCheck(Mat A)
1599: {
1600:   Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;
1601:   MatColIdxType               n       = A->rmap->n;

1603:   PetscFunctionBegin;
1604:   if (!factors->transpose_updated) { /* TODO: KK needs to provide functions to do numeric transpose only */
1605:     /* Update L^T and do sptrsv symbolic */
1606:     factors->iLt_d = MatRowMapKokkosView("factors->iLt_d", n + 1);
1607:     Kokkos::deep_copy(factors->iLt_d, 0); /* KK requires 0 */
1608:     factors->jLt_d = MatColIdxKokkosView("factors->jLt_d", factors->jL_d.extent(0));
1609:     factors->aLt_d = MatScalarKokkosView("factors->aLt_d", factors->aL_d.extent(0));

1611:     transpose_matrix<ConstMatRowMapKokkosView, ConstMatColIdxKokkosView, ConstMatScalarKokkosView, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView, MatRowMapKokkosView, DefaultExecutionSpace>(n, n, factors->iL_d, factors->jL_d, factors->aL_d,
1612:                                                                                                                                                                                                               factors->iLt_d, factors->jLt_d, factors->aLt_d);

1614:     /* TODO: KK transpose_matrix() does not sort column indices, however cusparse requires sorted indices.
1615:       We have to sort the indices, until KK provides finer control options.
1616:     */
1617:     sort_crs_matrix<DefaultExecutionSpace, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView>(factors->iLt_d, factors->jLt_d, factors->aLt_d);

1619:     KokkosSparse::Experimental::sptrsv_symbolic(&factors->khLt, factors->iLt_d, factors->jLt_d, factors->aLt_d);

1621:     /* Update U^T and do sptrsv symbolic */
1622:     factors->iUt_d = MatRowMapKokkosView("factors->iUt_d", n + 1);
1623:     Kokkos::deep_copy(factors->iUt_d, 0); /* KK requires 0 */
1624:     factors->jUt_d = MatColIdxKokkosView("factors->jUt_d", factors->jU_d.extent(0));
1625:     factors->aUt_d = MatScalarKokkosView("factors->aUt_d", factors->aU_d.extent(0));

1627:     transpose_matrix<ConstMatRowMapKokkosView, ConstMatColIdxKokkosView, ConstMatScalarKokkosView, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView, MatRowMapKokkosView, DefaultExecutionSpace>(n, n, factors->iU_d, factors->jU_d, factors->aU_d,
1628:                                                                                                                                                                                                               factors->iUt_d, factors->jUt_d, factors->aUt_d);

1630:     /* Sort indices. See comments above */
1631:     sort_crs_matrix<DefaultExecutionSpace, MatRowMapKokkosView, MatColIdxKokkosView, MatScalarKokkosView>(factors->iUt_d, factors->jUt_d, factors->aUt_d);

1633:     KokkosSparse::Experimental::sptrsv_symbolic(&factors->khUt, factors->iUt_d, factors->jUt_d, factors->aUt_d);
1634:     factors->transpose_updated = PETSC_TRUE;
1635:   }
1636:   PetscFunctionReturn(PETSC_SUCCESS);
1637: }

1639: /* Solve Ax = b, with A = LU */
1640: static PetscErrorCode MatSolve_SeqAIJKokkos(Mat A, Vec b, Vec x)
1641: {
1642:   ConstPetscScalarKokkosView  bv;
1643:   PetscScalarKokkosView       xv;
1644:   Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;

1646:   PetscFunctionBegin;
1647:   PetscCall(PetscLogGpuTimeBegin());
1648:   PetscCall(MatSeqAIJKokkosSymbolicSolveCheck(A));
1649:   PetscCall(VecGetKokkosView(b, &bv));
1650:   PetscCall(VecGetKokkosViewWrite(x, &xv));
1651:   /* Solve L tmpv = b */
1652:   PetscCallCXX(KokkosSparse::Experimental::sptrsv_solve(&factors->khL, factors->iL_d, factors->jL_d, factors->aL_d, bv, factors->workVector));
1653:   /* Solve Ux = tmpv */
1654:   PetscCallCXX(KokkosSparse::Experimental::sptrsv_solve(&factors->khU, factors->iU_d, factors->jU_d, factors->aU_d, factors->workVector, xv));
1655:   PetscCall(VecRestoreKokkosView(b, &bv));
1656:   PetscCall(VecRestoreKokkosViewWrite(x, &xv));
1657:   PetscCall(PetscLogGpuTimeEnd());
1658:   PetscFunctionReturn(PETSC_SUCCESS);
1659: }

1661: /* Solve A^T x = b, where A^T = U^T L^T */
1662: static PetscErrorCode MatSolveTranspose_SeqAIJKokkos(Mat A, Vec b, Vec x)
1663: {
1664:   ConstPetscScalarKokkosView  bv;
1665:   PetscScalarKokkosView       xv;
1666:   Mat_SeqAIJKokkosTriFactors *factors = (Mat_SeqAIJKokkosTriFactors *)A->spptr;

1668:   PetscFunctionBegin;
1669:   PetscCall(PetscLogGpuTimeBegin());
1670:   PetscCall(MatSeqAIJKokkosTransposeSolveCheck(A));
1671:   PetscCall(VecGetKokkosView(b, &bv));
1672:   PetscCall(VecGetKokkosViewWrite(x, &xv));
1673:   /* Solve U^T tmpv = b */
1674:   KokkosSparse::Experimental::sptrsv_solve(&factors->khUt, factors->iUt_d, factors->jUt_d, factors->aUt_d, bv, factors->workVector);

1676:   /* Solve L^T x = tmpv */
1677:   KokkosSparse::Experimental::sptrsv_solve(&factors->khLt, factors->iLt_d, factors->jLt_d, factors->aLt_d, factors->workVector, xv);
1678:   PetscCall(VecRestoreKokkosView(b, &bv));
1679:   PetscCall(VecRestoreKokkosViewWrite(x, &xv));
1680:   PetscCall(PetscLogGpuTimeEnd());
1681:   PetscFunctionReturn(PETSC_SUCCESS);
1682: }

1684: static PetscErrorCode MatILUFactorNumeric_SeqAIJKokkos(Mat B, Mat A, const MatFactorInfo *info)
1685: {
1686:   Mat_SeqAIJKokkos           *aijkok   = (Mat_SeqAIJKokkos *)A->spptr;
1687:   Mat_SeqAIJKokkosTriFactors *factors  = (Mat_SeqAIJKokkosTriFactors *)B->spptr;
1688:   PetscInt                    fill_lev = info->levels;

1690:   PetscFunctionBegin;
1691:   PetscCall(PetscLogGpuTimeBegin());
1692:   PetscCall(MatSeqAIJKokkosSyncDevice(A));

1694:   auto a_d = aijkok->a_dual.view_device();
1695:   auto i_d = aijkok->i_dual.view_device();
1696:   auto j_d = aijkok->j_dual.view_device();

1698:   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);

1700:   B->assembled              = PETSC_TRUE;
1701:   B->preallocated           = PETSC_TRUE;
1702:   B->ops->solve             = MatSolve_SeqAIJKokkos;
1703:   B->ops->solvetranspose    = MatSolveTranspose_SeqAIJKokkos;
1704:   B->ops->matsolve          = NULL;
1705:   B->ops->matsolvetranspose = NULL;
1706:   B->offloadmask            = PETSC_OFFLOAD_GPU;

1708:   /* Once the factors' value changed, we need to update their transpose and sptrsv handle */
1709:   factors->transpose_updated         = PETSC_FALSE;
1710:   factors->sptrsv_symbolic_completed = PETSC_FALSE;
1711:   /* TODO: log flops, but how to know that? */
1712:   PetscCall(PetscLogGpuTimeEnd());
1713:   PetscFunctionReturn(PETSC_SUCCESS);
1714: }

1716: static PetscErrorCode MatILUFactorSymbolic_SeqAIJKokkos(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1717: {
1718:   Mat_SeqAIJKokkos           *aijkok;
1719:   Mat_SeqAIJ                 *b;
1720:   Mat_SeqAIJKokkosTriFactors *factors  = (Mat_SeqAIJKokkosTriFactors *)B->spptr;
1721:   PetscInt                    fill_lev = info->levels;
1722:   PetscInt                    nnzA     = ((Mat_SeqAIJ *)A->data)->nz, nnzL, nnzU;
1723:   PetscInt                    n        = A->rmap->n;

1725:   PetscFunctionBegin;
1726:   PetscCall(MatSeqAIJKokkosSyncDevice(A));
1727:   /* Rebuild factors */
1728:   if (factors) {
1729:     factors->Destroy();
1730:   } /* Destroy the old if it exists */
1731:   else {
1732:     B->spptr = factors = new Mat_SeqAIJKokkosTriFactors(n);
1733:   }

1735:   /* Create a spiluk handle and then do symbolic factorization */
1736:   nnzL = nnzU = PetscRealIntMultTruncate(info->fill, nnzA);
1737:   factors->kh.create_spiluk_handle(KokkosSparse::Experimental::SPILUKAlgorithm::SEQLVLSCHD_TP1, n, nnzL, nnzU);

1739:   auto spiluk_handle = factors->kh.get_spiluk_handle();

1741:   Kokkos::realloc(factors->iL_d, n + 1); /* Free old arrays and realloc */
1742:   Kokkos::realloc(factors->jL_d, spiluk_handle->get_nnzL());
1743:   Kokkos::realloc(factors->iU_d, n + 1);
1744:   Kokkos::realloc(factors->jU_d, spiluk_handle->get_nnzU());

1746:   aijkok   = (Mat_SeqAIJKokkos *)A->spptr;
1747:   auto i_d = aijkok->i_dual.view_device();
1748:   auto j_d = aijkok->j_dual.view_device();
1749:   KokkosSparse::Experimental::spiluk_symbolic(&factors->kh, fill_lev, i_d, j_d, factors->iL_d, factors->jL_d, factors->iU_d, factors->jU_d);
1750:   /* TODO: if spiluk_symbolic is asynchronous, do we need to sync before calling get_nnzL()? */

1752:   Kokkos::resize(factors->jL_d, spiluk_handle->get_nnzL()); /* Shrink or expand, and retain old value */
1753:   Kokkos::resize(factors->jU_d, spiluk_handle->get_nnzU());
1754:   Kokkos::realloc(factors->aL_d, spiluk_handle->get_nnzL()); /* No need to retain old value */
1755:   Kokkos::realloc(factors->aU_d, spiluk_handle->get_nnzU());

1757:   /* TODO: add options to select sptrsv algorithms */
1758:   /* Create sptrsv handles for L, U and their transpose */
1759: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1760:   auto sptrsv_alg = KokkosSparse::Experimental::SPTRSVAlgorithm::SPTRSV_CUSPARSE;
1761: #else
1762:   auto sptrsv_alg = KokkosSparse::Experimental::SPTRSVAlgorithm::SEQLVLSCHD_TP1;
1763: #endif

1765:   factors->khL.create_sptrsv_handle(sptrsv_alg, n, true /* L is lower tri */);
1766:   factors->khU.create_sptrsv_handle(sptrsv_alg, n, false /* U is not lower tri */);
1767:   factors->khLt.create_sptrsv_handle(sptrsv_alg, n, false /* L^T is not lower tri */);
1768:   factors->khUt.create_sptrsv_handle(sptrsv_alg, n, true /* U^T is lower tri */);

1770:   /* Fill fields of the factor matrix B */
1771:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(B, MAT_SKIP_ALLOCATION, NULL));
1772:   b     = (Mat_SeqAIJ *)B->data;
1773:   b->nz = b->maxnz          = spiluk_handle->get_nnzL() + spiluk_handle->get_nnzU();
1774:   B->info.fill_ratio_given  = info->fill;
1775:   B->info.fill_ratio_needed = ((PetscReal)b->nz) / ((PetscReal)nnzA);

1777:   B->offloadmask          = PETSC_OFFLOAD_GPU;
1778:   B->ops->lufactornumeric = MatILUFactorNumeric_SeqAIJKokkos;
1779:   PetscFunctionReturn(PETSC_SUCCESS);
1780: }

1782: static PetscErrorCode MatLUFactorSymbolic_SeqAIJKOKKOSDEVICE(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1783: {
1784:   Mat_SeqAIJ    *b     = (Mat_SeqAIJ *)B->data;
1785:   const PetscInt nrows = A->rmap->n;

1787:   PetscFunctionBegin;
1788:   PetscCall(MatLUFactorSymbolic_SeqAIJ(B, A, isrow, iscol, info));
1789:   B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJKOKKOSDEVICE;
1790:   // move B data into Kokkos
1791:   PetscCall(MatSeqAIJKokkosSyncDevice(B)); // create aijkok
1792:   PetscCall(MatSeqAIJKokkosSyncDevice(A)); // create aijkok
1793:   {
1794:     Mat_SeqAIJKokkos *baijkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
1795:     if (!baijkok->diag_d.extent(0)) {
1796:       const Kokkos::View<PetscInt *, Kokkos::HostSpace, Kokkos::MemoryTraits<Kokkos::Unmanaged>> h_diag(b->diag, nrows + 1);
1797:       baijkok->diag_d = Kokkos::View<PetscInt *>(Kokkos::create_mirror(DefaultMemorySpace(), h_diag));
1798:       Kokkos::deep_copy(baijkok->diag_d, h_diag);
1799:     }
1800:   }
1801:   PetscFunctionReturn(PETSC_SUCCESS);
1802: }

1804: static PetscErrorCode MatFactorGetSolverType_SeqAIJKokkos(Mat A, MatSolverType *type)
1805: {
1806:   PetscFunctionBegin;
1807:   *type = MATSOLVERKOKKOS;
1808:   PetscFunctionReturn(PETSC_SUCCESS);
1809: }

1811: static PetscErrorCode MatFactorGetSolverType_seqaij_kokkos_device(Mat A, MatSolverType *type)
1812: {
1813:   PetscFunctionBegin;
1814:   *type = MATSOLVERKOKKOSDEVICE;
1815:   PetscFunctionReturn(PETSC_SUCCESS);
1816: }

1818: /*MC
1819:   MATSOLVERKOKKOS = "Kokkos" - A matrix solver type providing triangular solvers for sequential matrices
1820:   on a single GPU of type, `MATSEQAIJKOKKOS`, `MATAIJKOKKOS`.

1822:   Level: beginner

1824: .seealso: [](chapter_matrices), `Mat`, `PCFactorSetMatSolverType()`, `MatSolverType`, `MatCreateSeqAIJKokkos()`, `MATAIJKOKKOS`, `MatKokkosSetFormat()`, `MatKokkosStorageFormat`, `MatKokkosFormatOperation`
1825: M*/
1826: PETSC_EXTERN PetscErrorCode MatGetFactor_SeqAIJKokkos_Kokkos(Mat A, MatFactorType ftype, Mat *B) /* MatGetFactor_<MatType>_<MatSolverType> */
1827: {
1828:   PetscInt n = A->rmap->n;

1830:   PetscFunctionBegin;
1831:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
1832:   PetscCall(MatSetSizes(*B, n, n, n, n));
1833:   (*B)->factortype = ftype;
1834:   PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
1835:   PetscCall(MatSetType(*B, MATSEQAIJKOKKOS));

1837:   if (ftype == MAT_FACTOR_LU) {
1838:     PetscCall(MatSetBlockSizesFromMats(*B, A, A));
1839:     (*B)->canuseordering        = PETSC_TRUE;
1840:     (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJKokkos;
1841:   } else if (ftype == MAT_FACTOR_ILU) {
1842:     PetscCall(MatSetBlockSizesFromMats(*B, A, A));
1843:     (*B)->canuseordering         = PETSC_FALSE;
1844:     (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJKokkos;
1845:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatFactorType %s is not supported by MatType SeqAIJKokkos", MatFactorTypes[ftype]);

1847:   PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
1848:   PetscCall(PetscObjectComposeFunction((PetscObject)(*B), "MatFactorGetSolverType_C", MatFactorGetSolverType_SeqAIJKokkos));
1849:   PetscFunctionReturn(PETSC_SUCCESS);
1850: }

1852: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaijkokkos_kokkos_device(Mat A, MatFactorType ftype, Mat *B)
1853: {
1854:   PetscInt n = A->rmap->n;

1856:   PetscFunctionBegin;
1857:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
1858:   PetscCall(MatSetSizes(*B, n, n, n, n));
1859:   (*B)->factortype     = ftype;
1860:   (*B)->canuseordering = PETSC_TRUE;
1861:   PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
1862:   PetscCall(MatSetType(*B, MATSEQAIJKOKKOS));

1864:   if (ftype == MAT_FACTOR_LU) {
1865:     PetscCall(MatSetBlockSizesFromMats(*B, A, A));
1866:     (*B)->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJKOKKOSDEVICE;
1867:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported for KOKKOS Matrix Types");

1869:   PetscCall(MatSeqAIJSetPreallocation(*B, MAT_SKIP_ALLOCATION, NULL));
1870:   PetscCall(PetscObjectComposeFunction((PetscObject)(*B), "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_kokkos_device));
1871:   PetscFunctionReturn(PETSC_SUCCESS);
1872: }

1874: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_KOKKOS(void)
1875: {
1876:   PetscFunctionBegin;
1877:   PetscCall(MatSolverTypeRegister(MATSOLVERKOKKOS, MATSEQAIJKOKKOS, MAT_FACTOR_LU, MatGetFactor_SeqAIJKokkos_Kokkos));
1878:   PetscCall(MatSolverTypeRegister(MATSOLVERKOKKOS, MATSEQAIJKOKKOS, MAT_FACTOR_ILU, MatGetFactor_SeqAIJKokkos_Kokkos));
1879:   PetscCall(MatSolverTypeRegister(MATSOLVERKOKKOSDEVICE, MATSEQAIJKOKKOS, MAT_FACTOR_LU, MatGetFactor_seqaijkokkos_kokkos_device));
1880:   PetscFunctionReturn(PETSC_SUCCESS);
1881: }

1883: /* Utility to print out a KokkosCsrMatrix for debugging */
1884: PETSC_INTERN PetscErrorCode PrintCsrMatrix(const KokkosCsrMatrix &csrmat)
1885: {
1886:   const auto        &iv = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.graph.row_map);
1887:   const auto        &jv = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.graph.entries);
1888:   const auto        &av = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), csrmat.values);
1889:   const PetscInt    *i  = iv.data();
1890:   const PetscInt    *j  = jv.data();
1891:   const PetscScalar *a  = av.data();
1892:   PetscInt           m = csrmat.numRows(), n = csrmat.numCols(), nnz = csrmat.nnz();

1894:   PetscFunctionBegin;
1895:   PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT " x %" PetscInt_FMT " SeqAIJKokkos, with %" PetscInt_FMT " nonzeros\n", m, n, nnz));
1896:   for (PetscInt k = 0; k < m; k++) {
1897:     PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT ": ", k));
1898:     for (PetscInt p = i[k]; p < i[k + 1]; p++) PetscCall(PetscPrintf(PETSC_COMM_SELF, "%" PetscInt_FMT "(%.1f), ", j[p], (double)PetscRealPart(a[p])));
1899:     PetscCall(PetscPrintf(PETSC_COMM_SELF, "\n"));
1900:   }
1901:   PetscFunctionReturn(PETSC_SUCCESS);
1902: }