Actual source code: matseqdensecupm.hpp

  1: #ifndef PETSCMATSEQDENSECUPM_HPP
  2: #define PETSCMATSEQDENSECUPM_HPP

  4: #include <petsc/private/matdensecupmimpl.h>
  5: #include <../src/mat/impls/dense/seq/dense.h>

  7: #if defined(__cplusplus)
  8: #include <petsc/private/deviceimpl.h>
  9: #include <petsc/private/randomimpl.h>
 10: #include <petsc/private/vecimpl.h>
 11: #include <petsc/private/cupmobject.hpp>
 12: #include <petsc/private/cupmsolverinterface.hpp>

 14: #include <petsc/private/cpp/type_traits.hpp>
 15: #include <petsc/private/cpp/utility.hpp>

 17: #include <../src/vec/vec/impls/seq/cupm/vecseqcupm.hpp>

 19: namespace Petsc
 20: {

 22: namespace mat
 23: {

 25: namespace cupm
 26: {

 28: namespace impl
 29: {

 31: template <device::cupm::DeviceType T>
 32: class MatDense_Seq_CUPM : MatDense_CUPM<T, MatDense_Seq_CUPM<T>> {
 33: public:
 34:   MATDENSECUPM_HEADER(T, MatDense_Seq_CUPM<T>);

 36: private:
 37:   struct Mat_SeqDenseCUPM {
 38:     PetscScalar *d_v;           // pointer to the matrix on the GPU
 39:     PetscScalar *unplacedarray; // if one called MatCUPMDensePlaceArray(), this is where it stashed the original
 40:     bool         d_user_alloc;
 41:     bool         d_unplaced_user_alloc;
 42:     // factorization support
 43:     cupmBlasInt_t *d_fact_ipiv;  // device pivots
 44:     cupmScalar_t  *d_fact_tau;   // device QR tau vector
 45:     cupmBlasInt_t *d_fact_info;  // device info
 46:     cupmScalar_t  *d_fact_work;  // device workspace
 47:     cupmBlasInt_t  d_fact_lwork; // size of device workspace
 48:     // workspace
 49:     Vec workvec;
 50:   };

 52:   static PetscErrorCode SetPreallocation_(Mat, PetscDeviceContext, PetscScalar *) noexcept;

 54:   static PetscErrorCode HostToDevice_(Mat, PetscDeviceContext) noexcept;
 55:   static PetscErrorCode DeviceToHost_(Mat, PetscDeviceContext) noexcept;

 57:   static PetscErrorCode CheckCUPMSolverInfo_(const cupmBlasInt_t *, cupmStream_t) noexcept;

 59:   template <typename Derived>
 60:   struct SolveCommon;
 61:   struct SolveQR;
 62:   struct SolveCholesky;
 63:   struct SolveLU;

 65:   template <typename Solver, bool transpose>
 66:   static PetscErrorCode MatSolve_Factored_Dispatch_(Mat, Vec, Vec) noexcept;
 67:   template <typename Solver, bool transpose>
 68:   static PetscErrorCode MatMatSolve_Factored_Dispatch_(Mat, Mat, Mat) noexcept;
 69:   template <bool transpose>
 70:   static PetscErrorCode MatMultAdd_Dispatch_(Mat, Vec, Vec, Vec) noexcept;

 72:   template <bool to_host>
 73:   static PetscErrorCode Convert_Dispatch_(Mat, MatType, MatReuse, Mat *) noexcept;

 75:   PETSC_NODISCARD static constexpr MatType       MATIMPLCUPM_() noexcept;
 76:   PETSC_NODISCARD static constexpr Mat_SeqDense *MatIMPLCast_(Mat) noexcept;

 78: public:
 79:   PETSC_NODISCARD static constexpr Mat_SeqDenseCUPM *MatCUPMCast(Mat) noexcept;

 81:   // define these by hand since they don't fit the above mold
 82:   PETSC_NODISCARD static constexpr const char *MatConvert_seqdensecupm_seqdense_C() noexcept;
 83:   PETSC_NODISCARD static constexpr const char *MatProductSetFromOptions_seqaij_seqdensecupm_C() noexcept;

 85:   static PetscErrorCode Create(Mat) noexcept;
 86:   static PetscErrorCode Destroy(Mat) noexcept;
 87:   static PetscErrorCode SetUp(Mat) noexcept;
 88:   static PetscErrorCode Reset(Mat) noexcept;

 90:   static PetscErrorCode BindToCPU(Mat, PetscBool) noexcept;
 91:   static PetscErrorCode Convert_SeqDense_SeqDenseCUPM(Mat, MatType, MatReuse, Mat *) noexcept;
 92:   static PetscErrorCode Convert_SeqDenseCUPM_SeqDense(Mat, MatType, MatReuse, Mat *) noexcept;

 94:   template <PetscMemType, PetscMemoryAccessMode>
 95:   static PetscErrorCode GetArray(Mat, PetscScalar **, PetscDeviceContext) noexcept;
 96:   template <PetscMemType, PetscMemoryAccessMode>
 97:   static PetscErrorCode RestoreArray(Mat, PetscScalar **, PetscDeviceContext) noexcept;
 98:   template <PetscMemoryAccessMode>
 99:   static PetscErrorCode GetArrayAndMemType(Mat, PetscScalar **, PetscMemType *, PetscDeviceContext) noexcept;
100:   template <PetscMemoryAccessMode>
101:   static PetscErrorCode RestoreArrayAndMemType(Mat, PetscScalar **, PetscDeviceContext) noexcept;

103: private:
104:   template <PetscMemType mtype, PetscMemoryAccessMode mode>
105:   static PetscErrorCode GetArrayC_(Mat m, PetscScalar **p) noexcept
106:   {
107:     PetscDeviceContext dctx;

109:     PetscFunctionBegin;
110:     PetscCall(GetHandles_(&dctx));
111:     PetscCall(GetArray<mtype, mode>(m, p, dctx));
112:     PetscFunctionReturn(PETSC_SUCCESS);
113:   }

115:   template <PetscMemType mtype, PetscMemoryAccessMode mode>
116:   static PetscErrorCode RestoreArrayC_(Mat m, PetscScalar **p) noexcept
117:   {
118:     PetscDeviceContext dctx;

120:     PetscFunctionBegin;
121:     PetscCall(GetHandles_(&dctx));
122:     PetscCall(RestoreArray<mtype, mode>(m, p, dctx));
123:     PetscFunctionReturn(PETSC_SUCCESS);
124:   }

126:   template <PetscMemoryAccessMode mode>
127:   static PetscErrorCode GetArrayAndMemTypeC_(Mat m, PetscScalar **p, PetscMemType *tp) noexcept
128:   {
129:     PetscDeviceContext dctx;

131:     PetscFunctionBegin;
132:     PetscCall(GetHandles_(&dctx));
133:     PetscCall(GetArrayAndMemType<mode>(m, p, tp, dctx));
134:     PetscFunctionReturn(PETSC_SUCCESS);
135:   }

137:   template <PetscMemoryAccessMode mode>
138:   static PetscErrorCode RestoreArrayAndMemTypeC_(Mat m, PetscScalar **p) noexcept
139:   {
140:     PetscDeviceContext dctx;

142:     PetscFunctionBegin;
143:     PetscCall(GetHandles_(&dctx));
144:     PetscCall(RestoreArrayAndMemType<mode>(m, p, dctx));
145:     PetscFunctionReturn(PETSC_SUCCESS);
146:   }

148: public:
149:   static PetscErrorCode PlaceArray(Mat, const PetscScalar *) noexcept;
150:   static PetscErrorCode ReplaceArray(Mat, const PetscScalar *) noexcept;
151:   static PetscErrorCode ResetArray(Mat) noexcept;

153:   template <bool transpose_A, bool transpose_B>
154:   static PetscErrorCode MatMatMult_Numeric_Dispatch(Mat, Mat, Mat) noexcept;
155:   static PetscErrorCode Copy(Mat, Mat, MatStructure) noexcept;
156:   static PetscErrorCode ZeroEntries(Mat) noexcept;
157:   static PetscErrorCode Scale(Mat, PetscScalar) noexcept;
158:   static PetscErrorCode Shift(Mat, PetscScalar) noexcept;
159:   static PetscErrorCode AXPY(Mat, PetscScalar, Mat, MatStructure) noexcept;
160:   static PetscErrorCode Duplicate(Mat, MatDuplicateOption, Mat *) noexcept;
161:   static PetscErrorCode SetRandom(Mat, PetscRandom) noexcept;

163:   static PetscErrorCode GetColumnVector(Mat, Vec, PetscInt) noexcept;
164:   template <PetscMemoryAccessMode>
165:   static PetscErrorCode GetColumnVec(Mat, PetscInt, Vec *) noexcept;
166:   template <PetscMemoryAccessMode>
167:   static PetscErrorCode RestoreColumnVec(Mat, PetscInt, Vec *) noexcept;

169:   static PetscErrorCode GetFactor(Mat, MatFactorType, Mat *) noexcept;
170:   static PetscErrorCode InvertFactors(Mat) noexcept;

172:   static PetscErrorCode GetSubMatrix(Mat, PetscInt, PetscInt, PetscInt, PetscInt, Mat *) noexcept;
173:   static PetscErrorCode RestoreSubMatrix(Mat, Mat *) noexcept;
174: };

176: } // namespace impl

178: namespace
179: {

181: // Declare this here so that the functions below can make use of it
182: template <device::cupm::DeviceType T>
183: inline PetscErrorCode MatCreateSeqDenseCUPM(MPI_Comm comm, PetscInt m, PetscInt n, PetscScalar *data, Mat *A, PetscDeviceContext dctx = nullptr, bool preallocate = true) noexcept
184: {
185:   PetscFunctionBegin;
186:   PetscCall(impl::MatDense_Seq_CUPM<T>::CreateIMPLDenseCUPM(comm, m, n, m, n, data, A, dctx, preallocate));
187:   PetscFunctionReturn(PETSC_SUCCESS);
188: }

190: } // anonymous namespace

192: namespace impl
193: {

195: // ==========================================================================================
196: // MatDense_Seq_CUPM - Private API - Utility
197: // ==========================================================================================

199: template <device::cupm::DeviceType T>
200: inline PetscErrorCode MatDense_Seq_CUPM<T>::SetPreallocation_(Mat m, PetscDeviceContext dctx, PetscScalar *user_device_array) noexcept
201: {
202:   const auto   mcu   = MatCUPMCast(m);
203:   const auto   nrows = m->rmap->n;
204:   const auto   ncols = m->cmap->n;
205:   auto        &lda   = MatIMPLCast(m)->lda;
206:   cupmStream_t stream;

208:   PetscFunctionBegin;
209:   PetscCheckTypeName(m, MATSEQDENSECUPM());
211:   PetscCall(checkCupmBlasIntCast(nrows));
212:   PetscCall(checkCupmBlasIntCast(ncols));
213:   PetscCall(GetHandlesFrom_(dctx, &stream));
214:   if (lda <= 0) lda = nrows;
215:   if (!mcu->d_user_alloc) PetscCallCUPM(cupmFreeAsync(mcu->d_v, stream));
216:   if (user_device_array) {
217:     mcu->d_user_alloc = PETSC_TRUE;
218:     mcu->d_v          = user_device_array;
219:   } else {
220:     PetscInt size;

222:     mcu->d_user_alloc = PETSC_FALSE;
223:     PetscCall(PetscIntMultError(lda, ncols, &size));
224:     PetscCall(PetscCUPMMallocAsync(&mcu->d_v, size, stream));
225:     PetscCall(PetscCUPMMemsetAsync(mcu->d_v, 0, size, stream));
226:   }
227:   m->offloadmask = PETSC_OFFLOAD_GPU;
228:   PetscFunctionReturn(PETSC_SUCCESS);
229: }

231: template <device::cupm::DeviceType T>
232: inline PetscErrorCode MatDense_Seq_CUPM<T>::HostToDevice_(Mat m, PetscDeviceContext dctx) noexcept
233: {
234:   const auto nrows = m->rmap->n;
235:   const auto ncols = m->cmap->n;
236:   const auto copy  = m->offloadmask == PETSC_OFFLOAD_CPU || m->offloadmask == PETSC_OFFLOAD_UNALLOCATED;

238:   PetscFunctionBegin;
239:   PetscCheckTypeName(m, MATSEQDENSECUPM());
240:   if (m->boundtocpu) PetscFunctionReturn(PETSC_SUCCESS);
241:   PetscCall(PetscInfo(m, "%s matrix %" PetscInt_FMT " x %" PetscInt_FMT "\n", copy ? "Copy" : "Reusing", nrows, ncols));
242:   if (copy) {
243:     const auto   mcu = MatCUPMCast(m);
244:     cupmStream_t stream;

246:     // Allocate GPU memory if not present
247:     if (!mcu->d_v) PetscCall(SetPreallocation(m, dctx));
248:     PetscCall(GetHandlesFrom_(dctx, &stream));
249:     PetscCall(PetscLogEventBegin(MAT_DenseCopyToGPU, m, 0, 0, 0));
250:     {
251:       const auto mimpl = MatIMPLCast(m);
252:       const auto lda   = mimpl->lda;
253:       const auto src   = mimpl->v;
254:       const auto dest  = mcu->d_v;

256:       if (lda > nrows) {
257:         PetscCall(PetscCUPMMemcpy2DAsync(dest, lda, src, lda, nrows, ncols, cupmMemcpyHostToDevice, stream));
258:       } else {
259:         PetscCall(PetscCUPMMemcpyAsync(dest, src, lda * ncols, cupmMemcpyHostToDevice, stream));
260:       }
261:     }
262:     PetscCall(PetscLogEventEnd(MAT_DenseCopyToGPU, m, 0, 0, 0));
263:     // order important, ensure that offloadmask is PETSC_OFFLOAD_BOTH
264:     m->offloadmask = PETSC_OFFLOAD_BOTH;
265:   }
266:   PetscFunctionReturn(PETSC_SUCCESS);
267: }

269: template <device::cupm::DeviceType T>
270: inline PetscErrorCode MatDense_Seq_CUPM<T>::DeviceToHost_(Mat m, PetscDeviceContext dctx) noexcept
271: {
272:   const auto nrows = m->rmap->n;
273:   const auto ncols = m->cmap->n;
274:   const auto copy  = m->offloadmask == PETSC_OFFLOAD_GPU;

276:   PetscFunctionBegin;
277:   PetscCheckTypeName(m, MATSEQDENSECUPM());
278:   PetscCall(PetscInfo(m, "%s matrix %" PetscInt_FMT " x %" PetscInt_FMT "\n", copy ? "Copy" : "Reusing", nrows, ncols));
279:   if (copy) {
280:     const auto   mimpl = MatIMPLCast(m);
281:     cupmStream_t stream;

283:     // MatCreateSeqDenseCUPM may not allocate CPU memory. Allocate if needed
284:     if (!mimpl->v) PetscCall(MatSeqDenseSetPreallocation(m, nullptr));
285:     PetscCall(GetHandlesFrom_(dctx, &stream));
286:     PetscCall(PetscLogEventBegin(MAT_DenseCopyFromGPU, m, 0, 0, 0));
287:     {
288:       const auto lda  = mimpl->lda;
289:       const auto dest = mimpl->v;
290:       const auto src  = MatCUPMCast(m)->d_v;

292:       if (lda > nrows) {
293:         PetscCall(PetscCUPMMemcpy2DAsync(dest, lda, src, lda, nrows, ncols, cupmMemcpyDeviceToHost, stream));
294:       } else {
295:         PetscCall(PetscCUPMMemcpyAsync(dest, src, lda * ncols, cupmMemcpyDeviceToHost, stream));
296:       }
297:     }
298:     PetscCall(PetscLogEventEnd(MAT_DenseCopyFromGPU, m, 0, 0, 0));
299:     // order is important, MatSeqDenseSetPreallocation() might set offloadmask
300:     m->offloadmask = PETSC_OFFLOAD_BOTH;
301:   }
302:   PetscFunctionReturn(PETSC_SUCCESS);
303: }

305: template <device::cupm::DeviceType T>
306: inline PetscErrorCode MatDense_Seq_CUPM<T>::CheckCUPMSolverInfo_(const cupmBlasInt_t *fact_info, cupmStream_t stream) noexcept
307: {
308:   PetscFunctionBegin;
309:   if (PetscDefined(USE_DEBUG)) {
310:     cupmBlasInt_t info = 0;

312:     PetscCall(PetscCUPMMemcpyAsync(&info, fact_info, 1, cupmMemcpyDeviceToHost, stream));
313:     if (stream) PetscCallCUPM(cupmStreamSynchronize(stream));
314:     static_assert(std::is_same<decltype(info), int>::value, "");
315:     PetscCheck(info <= 0, PETSC_COMM_SELF, PETSC_ERR_MAT_CH_ZRPVT, "Bad factorization: zero pivot in row %d", info - 1);
316:     PetscCheck(info >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Wrong argument to cupmSolver %d", -info);
317:   }
318:   PetscFunctionReturn(PETSC_SUCCESS);
319: }

321: // ==========================================================================================
322: // MatDense_Seq_CUPM - Private API - Solver Dispatch
323: // ==========================================================================================

325: // specific solvers called through the dispatch_() family of functions
326: template <device::cupm::DeviceType T>
327: template <typename Derived>
328: struct MatDense_Seq_CUPM<T>::SolveCommon {
329:   using derived_type = Derived;

331:   template <typename F>
332:   static PetscErrorCode ResizeFactLwork(Mat_SeqDenseCUPM *mcu, cupmStream_t stream, F &&cupmSolverComputeFactLwork) noexcept
333:   {
334:     cupmBlasInt_t lwork;

336:     PetscFunctionBegin;
337:     PetscCallCUPMSOLVER(cupmSolverComputeFactLwork(&lwork));
338:     if (lwork > mcu->d_fact_lwork) {
339:       mcu->d_fact_lwork = lwork;
340:       PetscCallCUPM(cupmFreeAsync(mcu->d_fact_work, stream));
341:       PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_work, lwork, stream));
342:     }
343:     PetscFunctionReturn(PETSC_SUCCESS);
344:   }

346:   static PetscErrorCode FactorPrepare(Mat A, cupmStream_t stream) noexcept
347:   {
348:     const auto mcu = MatCUPMCast(A);

350:     PetscFunctionBegin;
351:     PetscCall(PetscInfo(A, "%s factor %" PetscInt_FMT " x %" PetscInt_FMT " on backend\n", derived_type::NAME(), A->rmap->n, A->cmap->n));
352:     A->factortype             = derived_type::MATFACTORTYPE();
353:     A->ops->solve             = MatSolve_Factored_Dispatch_<derived_type, false>;
354:     A->ops->solvetranspose    = MatSolve_Factored_Dispatch_<derived_type, true>;
355:     A->ops->matsolve          = MatMatSolve_Factored_Dispatch_<derived_type, false>;
356:     A->ops->matsolvetranspose = MatMatSolve_Factored_Dispatch_<derived_type, true>;

358:     PetscCall(PetscStrFreeAllocpy(MATSOLVERCUPM(), &A->solvertype));
359:     if (!mcu->d_fact_info) PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_info, 1, stream));
360:     PetscFunctionReturn(PETSC_SUCCESS);
361:   }
362: };

364: template <device::cupm::DeviceType T>
365: struct MatDense_Seq_CUPM<T>::SolveLU : SolveCommon<SolveLU> {
366:   using base_type = SolveCommon<SolveLU>;

368:   static constexpr const char   *NAME() noexcept { return "LU"; }
369:   static constexpr MatFactorType MATFACTORTYPE() noexcept { return MAT_FACTOR_LU; }

371:   static PetscErrorCode Factor(Mat A, IS, IS, const MatFactorInfo *) noexcept
372:   {
373:     const auto         m = static_cast<cupmBlasInt_t>(A->rmap->n);
374:     const auto         n = static_cast<cupmBlasInt_t>(A->cmap->n);
375:     cupmStream_t       stream;
376:     cupmSolverHandle_t handle;
377:     PetscDeviceContext dctx;

379:     PetscFunctionBegin;
380:     if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
381:     PetscCall(GetHandles_(&dctx, &handle, &stream));
382:     PetscCall(base_type::FactorPrepare(A, stream));
383:     {
384:       const auto mcu = MatCUPMCast(A);
385:       const auto da  = DeviceArrayReadWrite(dctx, A);
386:       const auto lda = static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda);

388:       // clang-format off
389:       PetscCall(
390:         base_type::ResizeFactLwork(
391:           mcu, stream,
392:           [&](cupmBlasInt_t *fact_lwork)
393:           {
394:             return cupmSolverXgetrf_bufferSize(handle, m, n, da.cupmdata(), lda, fact_lwork);
395:           }
396:         )
397:       );
398:       // clang-format on
399:       if (!mcu->d_fact_ipiv) PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_ipiv, n, stream));

401:       PetscCall(PetscLogGpuTimeBegin());
402:       PetscCallCUPMSOLVER(cupmSolverXgetrf(handle, m, n, da.cupmdata(), lda, mcu->d_fact_work, mcu->d_fact_lwork, mcu->d_fact_ipiv, mcu->d_fact_info));
403:       PetscCall(PetscLogGpuTimeEnd());
404:       PetscCall(CheckCUPMSolverInfo_(mcu->d_fact_info, stream));
405:     }
406:     PetscCall(PetscLogGpuFlops(2.0 * n * n * m / 3.0));
407:     PetscFunctionReturn(PETSC_SUCCESS);
408:   }

410:   template <bool transpose>
411:   static PetscErrorCode Solve(Mat A, cupmScalar_t *x, cupmBlasInt_t ldx, cupmBlasInt_t m, cupmBlasInt_t nrhs, cupmBlasInt_t k, PetscDeviceContext dctx, cupmStream_t stream) noexcept
412:   {
413:     const auto         mcu       = MatCUPMCast(A);
414:     const auto         fact_info = mcu->d_fact_info;
415:     const auto         fact_ipiv = mcu->d_fact_ipiv;
416:     const auto         lda       = static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda);
417:     cupmSolverHandle_t handle;

419:     PetscFunctionBegin;
420:     PetscCall(GetHandlesFrom_(dctx, &handle));
421:     PetscCall(PetscInfo(A, "%s solve %d x %d on backend\n", NAME(), m, k));
422:     PetscCall(PetscLogGpuTimeBegin());
423:     {
424:       constexpr auto op  = transpose ? CUPMSOLVER_OP_T : CUPMSOLVER_OP_N;
425:       const auto     da  = DeviceArrayRead(dctx, A);
426:       const auto     lda = static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda);

428:       // clang-format off
429:       PetscCall(
430:         base_type::ResizeFactLwork(
431:           mcu, stream,
432:           [&](cupmBlasInt_t *lwork)
433:           {
434:             return cupmSolverXgetrs_bufferSize(
435:               handle, op, m, nrhs, da.cupmdata(), lda, fact_ipiv, x, ldx, lwork
436:             );
437:           }
438:         )
439:       );
440:       // clang-format on
441:       PetscCallCUPMSOLVER(cupmSolverXgetrs(handle, op, m, nrhs, da.cupmdata(), lda, fact_ipiv, x, ldx, mcu->d_fact_work, mcu->d_fact_lwork, fact_info));
442:       PetscCall(CheckCUPMSolverInfo_(fact_info, stream));
443:     }
444:     PetscCall(PetscLogGpuTimeEnd());
445:     PetscCall(PetscLogGpuFlops(nrhs * (2.0 * m * m - m)));
446:     PetscFunctionReturn(PETSC_SUCCESS);
447:   }
448: };

450: template <device::cupm::DeviceType T>
451: struct MatDense_Seq_CUPM<T>::SolveCholesky : SolveCommon<SolveCholesky> {
452:   using base_type = SolveCommon<SolveCholesky>;

454:   static constexpr const char   *NAME() noexcept { return "Cholesky"; }
455:   static constexpr MatFactorType MATFACTORTYPE() noexcept { return MAT_FACTOR_CHOLESKY; }

457:   static PetscErrorCode Factor(Mat A, IS, const MatFactorInfo *) noexcept
458:   {
459:     const auto         n = static_cast<cupmBlasInt_t>(A->rmap->n);
460:     PetscDeviceContext dctx;
461:     cupmSolverHandle_t handle;
462:     cupmStream_t       stream;

464:     PetscFunctionBegin;
465:     if (!n || !A->cmap->n) PetscFunctionReturn(PETSC_SUCCESS);
466:     PetscCheck(A->spd == PETSC_BOOL3_TRUE, PETSC_COMM_SELF, PETSC_ERR_SUP, "%ssytrs unavailable. Use MAT_FACTOR_LU", cupmSolverName());
467:     PetscCall(GetHandles_(&dctx, &handle, &stream));
468:     PetscCall(base_type::FactorPrepare(A, stream));
469:     {
470:       const auto mcu = MatCUPMCast(A);
471:       const auto da  = DeviceArrayReadWrite(dctx, A);
472:       const auto lda = static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda);

474:       // clang-format off
475:       PetscCall(
476:         base_type::ResizeFactLwork(
477:           mcu, stream,
478:           [&](cupmBlasInt_t *fact_lwork)
479:           {
480:             return cupmSolverXpotrf_bufferSize(
481:               handle, CUPMSOLVER_FILL_MODE_LOWER, n, da.cupmdata(), lda, fact_lwork
482:             );
483:           }
484:         )
485:       );
486:       // clang-format on
487:       PetscCall(PetscLogGpuTimeBegin());
488:       PetscCallCUPMSOLVER(cupmSolverXpotrf(handle, CUPMSOLVER_FILL_MODE_LOWER, n, da.cupmdata(), lda, mcu->d_fact_work, mcu->d_fact_lwork, mcu->d_fact_info));
489:       PetscCall(PetscLogGpuTimeEnd());
490:       PetscCall(CheckCUPMSolverInfo_(mcu->d_fact_info, stream));
491:     }
492:     PetscCall(PetscLogGpuFlops(1.0 * n * n * n / 3.0));

494:   #if 0
495:     // At the time of writing this interface (cuda 10.0), cusolverDn does not implement *sytrs
496:     // and *hetr* routines. The code below should work, and it can be activated when *sytrs
497:     // routines will be available
498:     if (!mcu->d_fact_ipiv) PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_ipiv, n, stream));
499:     if (!mcu->d_fact_lwork) {
500:       PetscCallCUPMSOLVER(cupmSolverDnXsytrf_bufferSize(handle, n, da.cupmdata(), lda, &mcu->d_fact_lwork));
501:       PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_work, mcu->d_fact_lwork, stream));
502:     }
503:     if (mcu->d_fact_info) PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_info, 1, stream));
504:     PetscCall(PetscLogGpuTimeBegin());
505:     PetscCallCUPMSOLVER(cupmSolverXsytrf(handle, CUPMSOLVER_FILL_MODE_LOWER, n, da, lda, mcu->d_fact_ipiv, mcu->d_fact_work, mcu->d_fact_lwork, mcu->d_fact_info));
506:     PetscCall(PetscLogGpuTimeEnd());
507:   #endif
508:     PetscFunctionReturn(PETSC_SUCCESS);
509:   }

511:   template <bool transpose>
512:   static PetscErrorCode Solve(Mat A, cupmScalar_t *x, cupmBlasInt_t ldx, cupmBlasInt_t m, cupmBlasInt_t nrhs, cupmBlasInt_t k, PetscDeviceContext dctx, cupmStream_t stream) noexcept
513:   {
514:     const auto         mcu       = MatCUPMCast(A);
515:     const auto         fact_info = mcu->d_fact_info;
516:     cupmSolverHandle_t handle;

518:     PetscFunctionBegin;
519:     PetscAssert(!mcu->d_fact_ipiv, PETSC_COMM_SELF, PETSC_ERR_LIB, "%ssytrs not implemented", cupmSolverName());
520:     PetscCall(GetHandlesFrom_(dctx, &handle));
521:     PetscCall(PetscInfo(A, "%s solve %d x %d on backend\n", NAME(), m, k));
522:     PetscCall(PetscLogGpuTimeBegin());
523:     {
524:       const auto da  = DeviceArrayRead(dctx, A);
525:       const auto lda = static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda);

527:       // clang-format off
528:       PetscCall(
529:         base_type::ResizeFactLwork(
530:           mcu, stream,
531:           [&](cupmBlasInt_t *lwork)
532:           {
533:             return cupmSolverXpotrs_bufferSize(
534:               handle, CUPMSOLVER_FILL_MODE_LOWER, m, nrhs, da.cupmdata(), lda, x, ldx, lwork
535:             );
536:           }
537:         )
538:       );
539:       // clang-format on
540:       PetscCallCUPMSOLVER(cupmSolverXpotrs(handle, CUPMSOLVER_FILL_MODE_LOWER, m, nrhs, da.cupmdata(), lda, x, ldx, mcu->d_fact_work, mcu->d_fact_lwork, fact_info));
541:       PetscCall(CheckCUPMSolverInfo_(fact_info, stream));
542:     }
543:     PetscCall(PetscLogGpuTimeEnd());
544:     PetscCall(PetscLogGpuFlops(nrhs * (2.0 * m * m - m)));
545:     PetscFunctionReturn(PETSC_SUCCESS);
546:   }
547: };

549: template <device::cupm::DeviceType T>
550: struct MatDense_Seq_CUPM<T>::SolveQR : SolveCommon<SolveQR> {
551:   using base_type = SolveCommon<SolveQR>;

553:   static constexpr const char   *NAME() noexcept { return "QR"; }
554:   static constexpr MatFactorType MATFACTORTYPE() noexcept { return MAT_FACTOR_QR; }

556:   static PetscErrorCode Factor(Mat A, IS, const MatFactorInfo *) noexcept
557:   {
558:     const auto         m     = static_cast<cupmBlasInt_t>(A->rmap->n);
559:     const auto         n     = static_cast<cupmBlasInt_t>(A->cmap->n);
560:     const auto         min   = std::min(m, n);
561:     const auto         mimpl = MatIMPLCast(A);
562:     cupmStream_t       stream;
563:     cupmSolverHandle_t handle;
564:     PetscDeviceContext dctx;

566:     PetscFunctionBegin;
567:     if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
568:     PetscCall(GetHandles_(&dctx, &handle, &stream));
569:     PetscCall(base_type::FactorPrepare(A, stream));
570:     mimpl->rank = min;
571:     {
572:       const auto mcu = MatCUPMCast(A);
573:       const auto da  = DeviceArrayReadWrite(dctx, A);
574:       const auto lda = static_cast<cupmBlasInt_t>(mimpl->lda);

576:       if (!mcu->workvec) PetscCall(vec::cupm::VecCreateSeqCUPMAsync<T>(PetscObjectComm(PetscObjectCast(A)), m, &mcu->workvec));
577:       if (!mcu->d_fact_tau) PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_tau, min, stream));
578:       // clang-format off
579:       PetscCall(
580:         base_type::ResizeFactLwork(
581:           mcu, stream,
582:           [&](cupmBlasInt_t *fact_lwork)
583:           {
584:             return cupmSolverXgeqrf_bufferSize(handle, m, n, da.cupmdata(), lda, fact_lwork);
585:           }
586:         )
587:       );
588:       // clang-format on
589:       PetscCall(PetscLogGpuTimeBegin());
590:       PetscCallCUPMSOLVER(cupmSolverXgeqrf(handle, m, n, da.cupmdata(), lda, mcu->d_fact_tau, mcu->d_fact_work, mcu->d_fact_lwork, mcu->d_fact_info));
591:       PetscCall(PetscLogGpuTimeEnd());
592:       PetscCall(CheckCUPMSolverInfo_(mcu->d_fact_info, stream));
593:     }
594:     PetscCall(PetscLogGpuFlops(2.0 * min * min * (std::max(m, n) - min / 3.0)));
595:     PetscFunctionReturn(PETSC_SUCCESS);
596:   }

598:   template <bool transpose>
599:   static PetscErrorCode Solve(Mat A, cupmScalar_t *x, cupmBlasInt_t ldx, cupmBlasInt_t m, cupmBlasInt_t nrhs, cupmBlasInt_t k, PetscDeviceContext dctx, cupmStream_t stream) noexcept
600:   {
601:     const auto         mimpl      = MatIMPLCast(A);
602:     const auto         rank       = static_cast<cupmBlasInt_t>(mimpl->rank);
603:     const auto         mcu        = MatCUPMCast(A);
604:     const auto         fact_info  = mcu->d_fact_info;
605:     const auto         fact_tau   = mcu->d_fact_tau;
606:     const auto         fact_work  = mcu->d_fact_work;
607:     const auto         fact_lwork = mcu->d_fact_lwork;
608:     cupmSolverHandle_t solver_handle;
609:     cupmBlasHandle_t   blas_handle;

611:     PetscFunctionBegin;
612:     PetscCall(GetHandlesFrom_(dctx, &blas_handle, &solver_handle));
613:     PetscCall(PetscInfo(A, "%s solve %d x %d on backend\n", NAME(), m, k));
614:     PetscCall(PetscLogGpuTimeBegin());
615:     {
616:       const auto da  = DeviceArrayRead(dctx, A);
617:       const auto one = cupmScalarCast(1.0);
618:       const auto lda = static_cast<cupmBlasInt_t>(mimpl->lda);

620:       if (transpose) {
621:         PetscCallCUPMBLAS(cupmBlasXtrsm(blas_handle, CUPMBLAS_SIDE_LEFT, CUPMBLAS_FILL_MODE_UPPER, CUPMBLAS_OP_T, CUPMBLAS_DIAG_NON_UNIT, rank, nrhs, &one, da.cupmdata(), lda, x, ldx));
622:         PetscCallCUPMSOLVER(cupmSolverXormqr(solver_handle, CUPMSOLVER_SIDE_LEFT, CUPMSOLVER_OP_N, m, nrhs, rank, da.cupmdata(), lda, fact_tau, x, ldx, fact_work, fact_lwork, fact_info));
623:         PetscCall(CheckCUPMSolverInfo_(fact_info, stream));
624:       } else {
625:         constexpr auto op = PetscDefined(USE_COMPLEX) ? CUPMSOLVER_OP_C : CUPMSOLVER_OP_T;

627:         PetscCallCUPMSOLVER(cupmSolverXormqr(solver_handle, CUPMSOLVER_SIDE_LEFT, op, m, nrhs, rank, da.cupmdata(), lda, fact_tau, x, ldx, fact_work, fact_lwork, fact_info));
628:         PetscCall(CheckCUPMSolverInfo_(fact_info, stream));
629:         PetscCallCUPMBLAS(cupmBlasXtrsm(blas_handle, CUPMBLAS_SIDE_LEFT, CUPMBLAS_FILL_MODE_UPPER, CUPMBLAS_OP_N, CUPMBLAS_DIAG_NON_UNIT, rank, nrhs, &one, da.cupmdata(), lda, x, ldx));
630:       }
631:     }
632:     PetscCall(PetscLogGpuTimeEnd());
633:     PetscCall(PetscLogFlops(nrhs * (4.0 * m * rank - (rank * rank))));
634:     PetscFunctionReturn(PETSC_SUCCESS);
635:   }
636: };

638: template <device::cupm::DeviceType T>
639: template <typename Solver, bool transpose>
640: inline PetscErrorCode MatDense_Seq_CUPM<T>::MatSolve_Factored_Dispatch_(Mat A, Vec x, Vec y) noexcept
641: {
642:   using namespace vec::cupm;
643:   const auto         pobj_A  = PetscObjectCast(A);
644:   const auto         m       = static_cast<cupmBlasInt_t>(A->rmap->n);
645:   const auto         k       = static_cast<cupmBlasInt_t>(A->cmap->n);
646:   auto              &workvec = MatCUPMCast(A)->workvec;
647:   PetscScalar       *y_array = nullptr;
648:   PetscDeviceContext dctx;
649:   PetscBool          xiscupm, yiscupm, aiscupm;
650:   bool               use_y_array_directly;
651:   cupmStream_t       stream;

653:   PetscFunctionBegin;
654:   PetscCheck(A->factortype != MAT_FACTOR_NONE, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix must be factored to solve");
655:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(x), VecSeq_CUPM::VECSEQCUPM(), &xiscupm));
656:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(y), VecSeq_CUPM::VECSEQCUPM(), &yiscupm));
657:   PetscCall(PetscObjectTypeCompare(pobj_A, MATSEQDENSECUPM(), &aiscupm));
658:   PetscAssert(aiscupm, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Matrix A is somehow not CUPM?????????????????????????????");
659:   PetscCall(GetHandles_(&dctx, &stream));
660:   use_y_array_directly = yiscupm && (k >= m);
661:   {
662:     const PetscScalar *x_array;
663:     const auto         xisdevice = xiscupm && PetscOffloadDevice(x->offloadmask);
664:     const auto         copy_mode = xisdevice ? cupmMemcpyDeviceToDevice : cupmMemcpyHostToDevice;

666:     if (!use_y_array_directly && !workvec) PetscCall(VecCreateSeqCUPMAsync<T>(PetscObjectComm(pobj_A), m, &workvec));
667:     // The logic here is to try to minimize the amount of memory copying:
668:     //
669:     // If we call VecCUPMGetArrayRead(X, &x) every time xiscupm and the data is not offloaded
670:     // to the GPU yet, then the data is copied to the GPU. But we are only trying to get the
671:     // data in order to copy it into the y array. So the array x will be wherever the data
672:     // already is so that only one memcpy is performed
673:     if (xisdevice) {
674:       PetscCall(VecCUPMGetArrayReadAsync<T>(x, &x_array, dctx));
675:     } else {
676:       PetscCall(VecGetArrayRead(x, &x_array));
677:     }
678:     PetscCall(VecCUPMGetArrayWriteAsync<T>(use_y_array_directly ? y : workvec, &y_array, dctx));
679:     PetscCall(PetscCUPMMemcpyAsync(y_array, x_array, m, copy_mode, stream));
680:     if (xisdevice) {
681:       PetscCall(VecCUPMRestoreArrayReadAsync<T>(x, &x_array, dctx));
682:     } else {
683:       PetscCall(VecRestoreArrayRead(x, &x_array));
684:     }
685:   }

687:   if (!aiscupm) PetscCall(MatConvert(A, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &A));
688:   PetscCall(Solver{}.template Solve<transpose>(A, cupmScalarPtrCast(y_array), m, m, 1, k, dctx, stream));
689:   if (!aiscupm) PetscCall(MatConvert(A, MATSEQDENSE, MAT_INPLACE_MATRIX, &A));

691:   if (use_y_array_directly) {
692:     PetscCall(VecCUPMRestoreArrayWriteAsync<T>(y, &y_array, dctx));
693:   } else {
694:     const auto   copy_mode = yiscupm ? cupmMemcpyDeviceToDevice : cupmMemcpyDeviceToHost;
695:     PetscScalar *yv;

697:     // The logic here is that the data is not yet in either y's GPU array or its CPU array.
698:     // There is nothing in the interface to say where the user would like it to end up. So we
699:     // choose the GPU, because it is the faster option
700:     if (yiscupm) {
701:       PetscCall(VecCUPMGetArrayWriteAsync<T>(y, &yv, dctx));
702:     } else {
703:       PetscCall(VecGetArray(y, &yv));
704:     }
705:     PetscCall(PetscCUPMMemcpyAsync(yv, y_array, k, copy_mode, stream));
706:     if (yiscupm) {
707:       PetscCall(VecCUPMRestoreArrayWriteAsync<T>(y, &yv, dctx));
708:     } else {
709:       PetscCall(VecRestoreArray(y, &yv));
710:     }
711:     PetscCall(VecCUPMRestoreArrayWriteAsync<T>(workvec, &y_array));
712:   }
713:   PetscFunctionReturn(PETSC_SUCCESS);
714: }

716: template <device::cupm::DeviceType T>
717: template <typename Solver, bool transpose>
718: inline PetscErrorCode MatDense_Seq_CUPM<T>::MatMatSolve_Factored_Dispatch_(Mat A, Mat B, Mat X) noexcept
719: {
720:   const auto         m = static_cast<cupmBlasInt_t>(A->rmap->n);
721:   const auto         k = static_cast<cupmBlasInt_t>(A->cmap->n);
722:   cupmBlasInt_t      nrhs, ldb, ldx, ldy;
723:   PetscScalar       *y;
724:   PetscBool          biscupm, xiscupm, aiscupm;
725:   PetscDeviceContext dctx;
726:   cupmStream_t       stream;

728:   PetscFunctionBegin;
729:   PetscCheck(A->factortype != MAT_FACTOR_NONE, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix must be factored to solve");
730:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(B), MATSEQDENSECUPM(), &biscupm));
731:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(X), MATSEQDENSECUPM(), &xiscupm));
732:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(A), MATSEQDENSECUPM(), &aiscupm));
733:   PetscCall(GetHandles_(&dctx, &stream));
734:   {
735:     PetscInt n;

737:     PetscCall(MatGetSize(B, nullptr, &n));
738:     PetscCall(PetscCUPMBlasIntCast(n, &nrhs));
739:     PetscCall(MatDenseGetLDA(B, &n));
740:     PetscCall(PetscCUPMBlasIntCast(n, &ldb));
741:     PetscCall(MatDenseGetLDA(X, &n));
742:     PetscCall(PetscCUPMBlasIntCast(n, &ldx));
743:   }
744:   {
745:     // The logic here is to try to minimize the amount of memory copying:
746:     //
747:     // If we call MatDenseCUPMGetArrayRead(B, &b) every time biscupm and the data is not
748:     // offloaded to the GPU yet, then the data is copied to the GPU. But we are only trying to
749:     // get the data in order to copy it into the y array. So the array b will be wherever the
750:     // data already is so that only one memcpy is performed
751:     const auto         bisdevice = biscupm && PetscOffloadDevice(B->offloadmask);
752:     const auto         copy_mode = bisdevice ? cupmMemcpyDeviceToDevice : cupmMemcpyHostToDevice;
753:     const PetscScalar *b;

755:     if (bisdevice) {
756:       b = DeviceArrayRead(dctx, B);
757:     } else if (biscupm) {
758:       b = HostArrayRead(dctx, B);
759:     } else {
760:       PetscCall(MatDenseGetArrayRead(B, &b));
761:     }

763:     if (ldx < m || !xiscupm) {
764:       // X's array cannot serve as the array (too small or not on device), B's array cannot
765:       // serve as the array (const), so allocate a new array
766:       ldy = m;
767:       PetscCall(PetscCUPMMallocAsync(&y, nrhs * m));
768:     } else {
769:       // X's array should serve as the array
770:       ldy = ldx;
771:       y   = DeviceArrayWrite(dctx, X);
772:     }
773:     PetscCall(PetscCUPMMemcpy2DAsync(y, ldy, b, ldb, m, nrhs, copy_mode, stream));
774:     if (!bisdevice && !biscupm) PetscCall(MatDenseRestoreArrayRead(B, &b));
775:   }

777:   // convert to CUPM twice??????????????????????????????????
778:   // but A should already be CUPM??????????????????????????????????????
779:   if (!aiscupm) PetscCall(MatConvert(A, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &A));
780:   PetscCall(Solver{}.template Solve<transpose>(A, cupmScalarPtrCast(y), ldy, m, nrhs, k, dctx, stream));
781:   if (!aiscupm) PetscCall(MatConvert(A, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &A));

783:   if (ldx < m || !xiscupm) {
784:     const auto   copy_mode = xiscupm ? cupmMemcpyDeviceToDevice : cupmMemcpyDeviceToHost;
785:     PetscScalar *x;

787:     // The logic here is that the data is not yet in either X's GPU array or its CPU
788:     // array. There is nothing in the interface to say where the user would like it to end up.
789:     // So we choose the GPU, because it is the faster option
790:     if (xiscupm) {
791:       x = DeviceArrayWrite(dctx, X);
792:     } else {
793:       PetscCall(MatDenseGetArray(X, &x));
794:     }
795:     PetscCall(PetscCUPMMemcpy2DAsync(x, ldx, y, ldy, k, nrhs, copy_mode, stream));
796:     if (!xiscupm) PetscCall(MatDenseRestoreArray(X, &x));
797:     PetscCallCUPM(cupmFreeAsync(y, stream));
798:   }
799:   PetscFunctionReturn(PETSC_SUCCESS);
800: }

802: template <device::cupm::DeviceType T>
803: template <bool transpose>
804: inline PetscErrorCode MatDense_Seq_CUPM<T>::MatMultAdd_Dispatch_(Mat A, Vec xx, Vec yy, Vec zz) noexcept
805: {
806:   const auto         m = static_cast<cupmBlasInt_t>(A->rmap->n);
807:   const auto         n = static_cast<cupmBlasInt_t>(A->cmap->n);
808:   cupmBlasHandle_t   handle;
809:   PetscDeviceContext dctx;

811:   PetscFunctionBegin;
812:   if (yy && yy != zz) PetscCall(VecSeq_CUPM::Copy(yy, zz)); // mult add
813:   if (!m || !n) {
814:     // mult only
815:     if (!yy) PetscCall(VecSeq_CUPM::Set(zz, 0.0));
816:     PetscFunctionReturn(PETSC_SUCCESS);
817:   }
818:   PetscCall(PetscInfo(A, "Matrix-vector product %" PetscBLASInt_FMT " x %" PetscBLASInt_FMT " on backend\n", m, n));
819:   PetscCall(GetHandles_(&dctx, &handle));
820:   {
821:     constexpr auto op   = transpose ? CUPMBLAS_OP_T : CUPMBLAS_OP_N;
822:     const auto     one  = cupmScalarCast(1.0);
823:     const auto     zero = cupmScalarCast(0.0);
824:     const auto     da   = DeviceArrayRead(dctx, A);
825:     const auto     dxx  = VecSeq_CUPM::DeviceArrayRead(dctx, xx);
826:     const auto     dzz  = VecSeq_CUPM::DeviceArrayReadWrite(dctx, zz);

828:     PetscCall(PetscLogGpuTimeBegin());
829:     PetscCallCUPMBLAS(cupmBlasXgemv(handle, op, m, n, &one, da.cupmdata(), static_cast<cupmBlasInt_t>(MatIMPLCast(A)->lda), dxx.cupmdata(), 1, (yy ? &one : &zero), dzz.cupmdata(), 1));
830:     PetscCall(PetscLogGpuTimeEnd());
831:   }
832:   PetscCall(PetscLogGpuFlops(2.0 * m * n - (yy ? 0 : m)));
833:   PetscFunctionReturn(PETSC_SUCCESS);
834: }

836: // ==========================================================================================
837: // MatDense_Seq_CUPM - Private API - Conversion Dispatch
838: // ==========================================================================================

840: template <device::cupm::DeviceType T>
841: template <bool to_host>
842: inline PetscErrorCode MatDense_Seq_CUPM<T>::Convert_Dispatch_(Mat M, MatType type, MatReuse reuse, Mat *newmat) noexcept
843: {
844:   PetscFunctionBegin;
845:   if (reuse == MAT_REUSE_MATRIX || reuse == MAT_INITIAL_MATRIX) {
846:     // TODO these cases should be optimized
847:     PetscCall(MatConvert_Basic(M, type, reuse, newmat));
848:   } else {
849:     const auto B    = *newmat;
850:     const auto pobj = PetscObjectCast(B);

852:     if (to_host) {
853:       PetscCall(BindToCPU(B, PETSC_TRUE));
854:       PetscCall(Reset(B));
855:     } else {
856:       PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUPM()));
857:     }

859:     PetscCall(PetscStrFreeAllocpy(to_host ? VECSTANDARD : VecSeq_CUPM::VECCUPM(), &B->defaultvectype));
860:     PetscCall(PetscObjectChangeTypeName(pobj, to_host ? MATSEQDENSE : MATSEQDENSECUPM()));
861:     // cvec might be the wrong VecType, destroy and rebuild it if necessary
862:     // REVIEW ME: this is possibly very inefficient
863:     PetscCall(VecDestroy(&MatIMPLCast(B)->cvec));

865:     MatComposeOp_CUPM(to_host, pobj, MatConvert_seqdensecupm_seqdense_C(), nullptr, Convert_SeqDenseCUPM_SeqDense);
866:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMGetArray_C(), nullptr, GetArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_READ_WRITE>);
867:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMGetArrayRead_C(), nullptr, GetArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_READ>);
868:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMGetArrayWrite_C(), nullptr, GetArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_WRITE>);
869:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMRestoreArray_C(), nullptr, RestoreArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_READ_WRITE>);
870:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMRestoreArrayRead_C(), nullptr, RestoreArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_READ>);
871:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMRestoreArrayWrite_C(), nullptr, RestoreArrayC_<PETSC_MEMTYPE_DEVICE, PETSC_MEMORY_ACCESS_WRITE>);
872:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMPlaceArray_C(), nullptr, PlaceArray);
873:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMResetArray_C(), nullptr, ResetArray);
874:     MatComposeOp_CUPM(to_host, pobj, MatDenseCUPMReplaceArray_C(), nullptr, ReplaceArray);
875:     MatComposeOp_CUPM(to_host, pobj, MatProductSetFromOptions_seqaij_seqdensecupm_C(), nullptr, MatProductSetFromOptions_SeqAIJ_SeqDense);

877:     if (to_host) {
878:       B->offloadmask = PETSC_OFFLOAD_CPU;
879:     } else {
880:       Mat_SeqDenseCUPM *mcu;

882:       PetscCall(PetscNew(&mcu));
883:       B->spptr       = mcu;
884:       B->offloadmask = PETSC_OFFLOAD_UNALLOCATED; // REVIEW ME: why not offload host??
885:       PetscCall(BindToCPU(B, PETSC_FALSE));
886:     }

888:     MatSetOp_CUPM(to_host, B, bindtocpu, nullptr, BindToCPU);
889:     MatSetOp_CUPM(to_host, B, destroy, MatDestroy_SeqDense, Destroy);
890:   }
891:   PetscFunctionReturn(PETSC_SUCCESS);
892: }

894: // ==========================================================================================
895: // MatDense_Seq_CUPM - Public API
896: // ==========================================================================================

898: template <device::cupm::DeviceType T>
899: inline constexpr MatType MatDense_Seq_CUPM<T>::MATIMPLCUPM_() noexcept
900: {
901:   return MATSEQDENSECUPM();
902: }

904: template <device::cupm::DeviceType T>
905: inline constexpr typename MatDense_Seq_CUPM<T>::Mat_SeqDenseCUPM *MatDense_Seq_CUPM<T>::MatCUPMCast(Mat m) noexcept
906: {
907:   return static_cast<Mat_SeqDenseCUPM *>(m->spptr);
908: }

910: template <device::cupm::DeviceType T>
911: inline constexpr Mat_SeqDense *MatDense_Seq_CUPM<T>::MatIMPLCast_(Mat m) noexcept
912: {
913:   return static_cast<Mat_SeqDense *>(m->data);
914: }

916: template <device::cupm::DeviceType T>
917: inline constexpr const char *MatDense_Seq_CUPM<T>::MatConvert_seqdensecupm_seqdense_C() noexcept
918: {
919:   return T == device::cupm::DeviceType::CUDA ? "MatConvert_seqdensecuda_seqdense_C" : "MatConvert_seqdensehip_seqdense_C";
920: }

922: template <device::cupm::DeviceType T>
923: inline constexpr const char *MatDense_Seq_CUPM<T>::MatProductSetFromOptions_seqaij_seqdensecupm_C() noexcept
924: {
925:   return T == device::cupm::DeviceType::CUDA ? "MatProductSetFromOptions_seqaij_seqdensecuda_C" : "MatProductSetFromOptions_seqaij_seqdensehip_C";
926: }

928: // ==========================================================================================

930: // MatCreate_SeqDenseCUPM()
931: template <device::cupm::DeviceType T>
932: inline PetscErrorCode MatDense_Seq_CUPM<T>::Create(Mat A) noexcept
933: {
934:   PetscFunctionBegin;
935:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUPM()));
936:   PetscCall(MatCreate_SeqDense(A));
937:   PetscCall(Convert_SeqDense_SeqDenseCUPM(A, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &A));
938:   PetscFunctionReturn(PETSC_SUCCESS);
939: }

941: template <device::cupm::DeviceType T>
942: inline PetscErrorCode MatDense_Seq_CUPM<T>::Destroy(Mat A) noexcept
943: {
944:   PetscFunctionBegin;
945:   // prevent copying back data if we own the data pointer
946:   if (!MatIMPLCast(A)->user_alloc) A->offloadmask = PETSC_OFFLOAD_CPU;
947:   PetscCall(Convert_SeqDenseCUPM_SeqDense(A, MATSEQDENSE, MAT_INPLACE_MATRIX, &A));
948:   PetscCall(MatDestroy_SeqDense(A));
949:   PetscFunctionReturn(PETSC_SUCCESS);
950: }

952: // obj->ops->setup()
953: template <device::cupm::DeviceType T>
954: inline PetscErrorCode MatDense_Seq_CUPM<T>::SetUp(Mat A) noexcept
955: {
956:   PetscFunctionBegin;
957:   PetscCall(PetscLayoutSetUp(A->rmap));
958:   PetscCall(PetscLayoutSetUp(A->cmap));
959:   if (!A->preallocated) {
960:     PetscDeviceContext dctx;

962:     PetscCall(GetHandles_(&dctx));
963:     PetscCall(SetPreallocation(A, dctx));
964:   }
965:   PetscFunctionReturn(PETSC_SUCCESS);
966: }

968: template <device::cupm::DeviceType T>
969: inline PetscErrorCode MatDense_Seq_CUPM<T>::Reset(Mat A) noexcept
970: {
971:   PetscFunctionBegin;
972:   if (const auto mcu = MatCUPMCast(A)) {
973:     cupmStream_t stream;

975:     PetscCheck(!mcu->unplacedarray, PETSC_COMM_SELF, PETSC_ERR_ORDER, "MatDense%sResetArray() must be called first", cupmNAME());
976:     PetscCall(GetHandles_(&stream));
977:     if (!mcu->d_user_alloc) PetscCallCUPM(cupmFreeAsync(mcu->d_v, stream));
978:     PetscCallCUPM(cupmFreeAsync(mcu->d_fact_tau, stream));
979:     PetscCallCUPM(cupmFreeAsync(mcu->d_fact_ipiv, stream));
980:     PetscCallCUPM(cupmFreeAsync(mcu->d_fact_info, stream));
981:     PetscCallCUPM(cupmFreeAsync(mcu->d_fact_work, stream));
982:     PetscCall(VecDestroy(&mcu->workvec));
983:     PetscCall(PetscFree(A->spptr /* mcu */));
984:   }
985:   PetscFunctionReturn(PETSC_SUCCESS);
986: }

988: // ==========================================================================================

990: template <device::cupm::DeviceType T>
991: inline PetscErrorCode MatDense_Seq_CUPM<T>::BindToCPU(Mat A, PetscBool to_host) noexcept
992: {
993:   const auto mimpl = MatIMPLCast(A);
994:   const auto pobj  = PetscObjectCast(A);

996:   PetscFunctionBegin;
997:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
998:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
999:   A->boundtocpu = to_host;
1000:   PetscCall(PetscStrFreeAllocpy(to_host ? PETSCRANDER48 : PETSCDEVICERAND(), &A->defaultrandtype));
1001:   if (to_host) {
1002:     PetscDeviceContext dctx;

1004:     // make sure we have an up-to-date copy on the CPU
1005:     PetscCall(GetHandles_(&dctx));
1006:     PetscCall(DeviceToHost_(A, dctx));
1007:   } else {
1008:     PetscBool iscupm;

1010:     if (auto &cvec = mimpl->cvec) {
1011:       PetscCall(PetscObjectTypeCompare(PetscObjectCast(cvec), VecSeq_CUPM::VECSEQCUPM(), &iscupm));
1012:       if (!iscupm) PetscCall(VecDestroy(&cvec));
1013:     }
1014:     if (auto &cmat = mimpl->cmat) {
1015:       PetscCall(PetscObjectTypeCompare(PetscObjectCast(cmat), MATSEQDENSECUPM(), &iscupm));
1016:       if (!iscupm) PetscCall(MatDestroy(&cmat));
1017:     }
1018:   }

1020:   // ============================================================
1021:   // Composed ops
1022:   // ============================================================
1023:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArray_C", MatDenseGetArray_SeqDense, GetArrayC_<PETSC_MEMTYPE_HOST, PETSC_MEMORY_ACCESS_READ_WRITE>);
1024:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArrayRead_C", MatDenseGetArray_SeqDense, GetArrayC_<PETSC_MEMTYPE_HOST, PETSC_MEMORY_ACCESS_READ>);
1025:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArrayWrite_C", MatDenseGetArray_SeqDense, GetArrayC_<PETSC_MEMTYPE_HOST, PETSC_MEMORY_ACCESS_WRITE>);
1026:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArrayAndMemType_C", nullptr, GetArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_READ_WRITE>);
1027:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreArrayAndMemType_C", nullptr, RestoreArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_READ_WRITE>);
1028:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArrayReadAndMemType_C", nullptr, GetArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_READ>);
1029:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreArrayReadAndMemType_C", nullptr, RestoreArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_READ>);
1030:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetArrayWriteAndMemType_C", nullptr, GetArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_WRITE>);
1031:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreArrayWriteAndMemType_C", nullptr, RestoreArrayAndMemTypeC_<PETSC_MEMORY_ACCESS_WRITE>);
1032:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetColumnVec_C", MatDenseGetColumnVec_SeqDense, GetColumnVec<PETSC_MEMORY_ACCESS_READ_WRITE>);
1033:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreColumnVec_C", MatDenseRestoreColumnVec_SeqDense, RestoreColumnVec<PETSC_MEMORY_ACCESS_READ_WRITE>);
1034:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetColumnVecRead_C", MatDenseGetColumnVecRead_SeqDense, GetColumnVec<PETSC_MEMORY_ACCESS_READ>);
1035:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreColumnVecRead_C", MatDenseRestoreColumnVecRead_SeqDense, RestoreColumnVec<PETSC_MEMORY_ACCESS_READ>);
1036:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetColumnVecWrite_C", MatDenseGetColumnVecWrite_SeqDense, GetColumnVec<PETSC_MEMORY_ACCESS_WRITE>);
1037:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreColumnVecWrite_C", MatDenseRestoreColumnVecWrite_SeqDense, RestoreColumnVec<PETSC_MEMORY_ACCESS_WRITE>);
1038:   MatComposeOp_CUPM(to_host, pobj, "MatDenseGetSubMatrix_C", MatDenseGetSubMatrix_SeqDense, GetSubMatrix);
1039:   MatComposeOp_CUPM(to_host, pobj, "MatDenseRestoreSubMatrix_C", MatDenseRestoreSubMatrix_SeqDense, RestoreSubMatrix);
1040:   MatComposeOp_CUPM(to_host, pobj, "MatQRFactor_C", MatQRFactor_SeqDense, SolveQR::Factor);
1041:   // always the same
1042:   PetscCall(PetscObjectComposeFunction(pobj, "MatDenseSetLDA_C", MatDenseSetLDA_SeqDense));

1044:   // ============================================================
1045:   // Function pointer ops
1046:   // ============================================================
1047:   MatSetOp_CUPM(to_host, A, duplicate, MatDuplicate_SeqDense, Duplicate);
1048:   MatSetOp_CUPM(to_host, A, mult, MatMult_SeqDense, [](Mat A, Vec xx, Vec yy) { return MatMultAdd_Dispatch_</* transpose */ false>(A, xx, nullptr, yy); });
1049:   MatSetOp_CUPM(to_host, A, multtranspose, MatMultTranspose_SeqDense, [](Mat A, Vec xx, Vec yy) { return MatMultAdd_Dispatch_</* transpose */ true>(A, xx, nullptr, yy); });
1050:   MatSetOp_CUPM(to_host, A, multadd, MatMultAdd_SeqDense, MatMultAdd_Dispatch_</* transpose */ false>);
1051:   MatSetOp_CUPM(to_host, A, multtransposeadd, MatMultTransposeAdd_SeqDense, MatMultAdd_Dispatch_</* transpose */ true>);
1052:   MatSetOp_CUPM(to_host, A, matmultnumeric, MatMatMultNumeric_SeqDense_SeqDense, MatMatMult_Numeric_Dispatch</* transpose_A */ false, /* transpose_B */ false>);
1053:   MatSetOp_CUPM(to_host, A, mattransposemultnumeric, MatMatTransposeMultNumeric_SeqDense_SeqDense, MatMatMult_Numeric_Dispatch</* transpose_A */ false, /* transpose_B */ true>);
1054:   MatSetOp_CUPM(to_host, A, transposematmultnumeric, MatTransposeMatMultNumeric_SeqDense_SeqDense, MatMatMult_Numeric_Dispatch</* transpose_A */ true, /* transpose_B */ false>);
1055:   MatSetOp_CUPM(to_host, A, axpy, MatAXPY_SeqDense, AXPY);
1056:   MatSetOp_CUPM(to_host, A, choleskyfactor, MatCholeskyFactor_SeqDense, SolveCholesky::Factor);
1057:   MatSetOp_CUPM(to_host, A, lufactor, MatLUFactor_SeqDense, SolveLU::Factor);
1058:   MatSetOp_CUPM(to_host, A, getcolumnvector, MatGetColumnVector_SeqDense, GetColumnVector);
1059:   MatSetOp_CUPM(to_host, A, scale, MatScale_SeqDense, Scale);
1060:   MatSetOp_CUPM(to_host, A, shift, MatShift_SeqDense, Shift);
1061:   MatSetOp_CUPM(to_host, A, copy, MatCopy_SeqDense, Copy);
1062:   MatSetOp_CUPM(to_host, A, zeroentries, MatZeroEntries_SeqDense, ZeroEntries);
1063:   MatSetOp_CUPM(to_host, A, setup, MatSetUp_SeqDense, SetUp);
1064:   MatSetOp_CUPM(to_host, A, setrandom, MatSetRandom_SeqDense, SetRandom);
1065:   // seemingly always the same
1066:   A->ops->productsetfromoptions = MatProductSetFromOptions_SeqDense;

1068:   if (const auto cmat = mimpl->cmat) PetscCall(MatBindToCPU(cmat, to_host));
1069:   PetscFunctionReturn(PETSC_SUCCESS);
1070: }

1072: template <device::cupm::DeviceType T>
1073: inline PetscErrorCode MatDense_Seq_CUPM<T>::Convert_SeqDenseCUPM_SeqDense(Mat M, MatType type, MatReuse reuse, Mat *newmat) noexcept
1074: {
1075:   PetscFunctionBegin;
1076:   PetscCall(Convert_Dispatch_</* to host */ true>(M, type, reuse, newmat));
1077:   PetscFunctionReturn(PETSC_SUCCESS);
1078: }

1080: template <device::cupm::DeviceType T>
1081: inline PetscErrorCode MatDense_Seq_CUPM<T>::Convert_SeqDense_SeqDenseCUPM(Mat M, MatType type, MatReuse reuse, Mat *newmat) noexcept
1082: {
1083:   PetscFunctionBegin;
1084:   PetscCall(Convert_Dispatch_</* to host */ false>(M, type, reuse, newmat));
1085:   PetscFunctionReturn(PETSC_SUCCESS);
1086: }

1088: // ==========================================================================================

1090: template <device::cupm::DeviceType T>
1091: template <PetscMemType mtype, PetscMemoryAccessMode access>
1092: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetArray(Mat m, PetscScalar **array, PetscDeviceContext dctx) noexcept
1093: {
1094:   constexpr auto hostmem     = PetscMemTypeHost(mtype);
1095:   constexpr auto read_access = PetscMemoryAccessRead(access);

1097:   PetscFunctionBegin;
1098:   static_assert((mtype == PETSC_MEMTYPE_HOST) || (mtype == PETSC_MEMTYPE_DEVICE), "");
1099:   PetscCall(PetscDeviceContextGetOptionalNullContext_Internal(&dctx));
1100:   if (hostmem) {
1101:     if (read_access) {
1102:       PetscCall(DeviceToHost_(m, dctx));
1103:     } else if (!MatIMPLCast(m)->v) {
1104:       // MatCreateSeqDenseCUPM may not allocate CPU memory. Allocate if needed
1105:       PetscCall(MatSeqDenseSetPreallocation(m, nullptr));
1106:     }
1107:     *array = MatIMPLCast(m)->v;
1108:   } else {
1109:     if (read_access) {
1110:       PetscCall(HostToDevice_(m, dctx));
1111:     } else if (!MatCUPMCast(m)->d_v) {
1112:       // write-only
1113:       PetscCall(SetPreallocation(m, dctx, nullptr));
1114:     }
1115:     *array = MatCUPMCast(m)->d_v;
1116:   }
1117:   if (PetscMemoryAccessWrite(access)) {
1118:     m->offloadmask = hostmem ? PETSC_OFFLOAD_CPU : PETSC_OFFLOAD_GPU;
1119:     PetscCall(PetscObjectStateIncrease(PetscObjectCast(m)));
1120:   }
1121:   PetscFunctionReturn(PETSC_SUCCESS);
1122: }

1124: template <device::cupm::DeviceType T>
1125: template <PetscMemType mtype, PetscMemoryAccessMode access>
1126: inline PetscErrorCode MatDense_Seq_CUPM<T>::RestoreArray(Mat m, PetscScalar **array, PetscDeviceContext) noexcept
1127: {
1128:   PetscFunctionBegin;
1129:   static_assert((mtype == PETSC_MEMTYPE_HOST) || (mtype == PETSC_MEMTYPE_DEVICE), "");
1130:   if (PetscMemoryAccessWrite(access)) {
1131:     // WRITE or READ_WRITE
1132:     m->offloadmask = PetscMemTypeHost(mtype) ? PETSC_OFFLOAD_CPU : PETSC_OFFLOAD_GPU;
1133:     PetscCall(PetscObjectStateIncrease(PetscObjectCast(m)));
1134:   }
1135:   if (array) {
1136:     PetscCall(CheckPointerMatchesMemType_(*array, mtype));
1137:     *array = nullptr;
1138:   }
1139:   PetscFunctionReturn(PETSC_SUCCESS);
1140: }

1142: template <device::cupm::DeviceType T>
1143: template <PetscMemoryAccessMode access>
1144: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetArrayAndMemType(Mat m, PetscScalar **array, PetscMemType *mtype, PetscDeviceContext dctx) noexcept
1145: {
1146:   PetscFunctionBegin;
1147:   PetscCall(GetArray<PETSC_MEMTYPE_DEVICE, access>(m, array, dctx));
1148:   if (mtype) *mtype = PETSC_MEMTYPE_CUPM();
1149:   PetscFunctionReturn(PETSC_SUCCESS);
1150: }

1152: template <device::cupm::DeviceType T>
1153: template <PetscMemoryAccessMode access>
1154: inline PetscErrorCode MatDense_Seq_CUPM<T>::RestoreArrayAndMemType(Mat m, PetscScalar **array, PetscDeviceContext dctx) noexcept
1155: {
1156:   PetscFunctionBegin;
1157:   PetscCall(RestoreArray<PETSC_MEMTYPE_DEVICE, access>(m, array, dctx));
1158:   PetscFunctionReturn(PETSC_SUCCESS);
1159: }

1161: // ==========================================================================================

1163: template <device::cupm::DeviceType T>
1164: inline PetscErrorCode MatDense_Seq_CUPM<T>::PlaceArray(Mat A, const PetscScalar *array) noexcept
1165: {
1166:   const auto mimpl = MatIMPLCast(A);
1167:   const auto mcu   = MatCUPMCast(A);

1169:   PetscFunctionBegin;
1170:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
1171:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
1172:   PetscCheck(!mcu->unplacedarray, PETSC_COMM_SELF, PETSC_ERR_ORDER, "MatDense%sResetArray() must be called first", cupmNAME());
1173:   if (mimpl->v) {
1174:     PetscDeviceContext dctx;

1176:     PetscCall(GetHandles_(&dctx));
1177:     PetscCall(HostToDevice_(A, dctx));
1178:   }
1179:   mcu->unplacedarray         = util::exchange(mcu->d_v, const_cast<PetscScalar *>(array));
1180:   mcu->d_unplaced_user_alloc = util::exchange(mcu->d_user_alloc, PETSC_TRUE);
1181:   PetscFunctionReturn(PETSC_SUCCESS);
1182: }

1184: template <device::cupm::DeviceType T>
1185: inline PetscErrorCode MatDense_Seq_CUPM<T>::ReplaceArray(Mat A, const PetscScalar *array) noexcept
1186: {
1187:   const auto mimpl = MatIMPLCast(A);
1188:   const auto mcu   = MatCUPMCast(A);

1190:   PetscFunctionBegin;
1191:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
1192:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
1193:   PetscCheck(!mcu->unplacedarray, PETSC_COMM_SELF, PETSC_ERR_ORDER, "MatDense%sResetArray() must be called first", cupmNAME());
1194:   if (!mcu->d_user_alloc) {
1195:     cupmStream_t stream;

1197:     PetscCall(GetHandles_(&stream));
1198:     PetscCallCUPM(cupmFreeAsync(mcu->d_v, stream));
1199:   }
1200:   mcu->d_v          = const_cast<PetscScalar *>(array);
1201:   mcu->d_user_alloc = PETSC_FALSE;
1202:   PetscFunctionReturn(PETSC_SUCCESS);
1203: }

1205: template <device::cupm::DeviceType T>
1206: inline PetscErrorCode MatDense_Seq_CUPM<T>::ResetArray(Mat A) noexcept
1207: {
1208:   const auto mimpl = MatIMPLCast(A);
1209:   const auto mcu   = MatCUPMCast(A);

1211:   PetscFunctionBegin;
1212:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
1213:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
1214:   if (mimpl->v) {
1215:     PetscDeviceContext dctx;

1217:     PetscCall(GetHandles_(&dctx));
1218:     PetscCall(HostToDevice_(A, dctx));
1219:   }
1220:   mcu->d_v          = util::exchange(mcu->unplacedarray, nullptr);
1221:   mcu->d_user_alloc = mcu->d_unplaced_user_alloc;
1222:   PetscFunctionReturn(PETSC_SUCCESS);
1223: }

1225: // ==========================================================================================

1227: template <device::cupm::DeviceType T>
1228: template <bool transpose_A, bool transpose_B>
1229: inline PetscErrorCode MatDense_Seq_CUPM<T>::MatMatMult_Numeric_Dispatch(Mat A, Mat B, Mat C) noexcept
1230: {
1231:   cupmBlasInt_t      m, n, k;
1232:   PetscBool          Aiscupm, Biscupm;
1233:   PetscDeviceContext dctx;
1234:   cupmBlasHandle_t   handle;

1236:   PetscFunctionBegin;
1237:   PetscCall(PetscCUPMBlasIntCast(C->rmap->n, &m));
1238:   PetscCall(PetscCUPMBlasIntCast(C->cmap->n, &n));
1239:   PetscCall(PetscCUPMBlasIntCast(transpose_A ? A->rmap->n : A->cmap->n, &k));
1240:   if (!m || !n || !k) PetscFunctionReturn(PETSC_SUCCESS);

1242:   // we may end up with SEQDENSE as one of the arguments
1243:   // REVIEW ME: how? and why is it not B and C????????
1244:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(A), MATSEQDENSECUPM(), &Aiscupm));
1245:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(B), MATSEQDENSECUPM(), &Biscupm));
1246:   if (!Aiscupm) PetscCall(MatConvert(A, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &A));
1247:   if (!Biscupm) PetscCall(MatConvert(B, MATSEQDENSECUPM(), MAT_INPLACE_MATRIX, &B));
1248:   PetscCall(PetscInfo(C, "Matrix-Matrix product %" PetscBLASInt_FMT " x %" PetscBLASInt_FMT " x %" PetscBLASInt_FMT " on backend\n", m, k, n));
1249:   PetscCall(GetHandles_(&dctx, &handle));

1251:   PetscCall(PetscLogGpuTimeBegin());
1252:   {
1253:     const auto one  = cupmScalarCast(1.0);
1254:     const auto zero = cupmScalarCast(0.0);
1255:     const auto da   = DeviceArrayRead(dctx, A);
1256:     const auto db   = DeviceArrayRead(dctx, B);
1257:     const auto dc   = DeviceArrayWrite(dctx, C);
1258:     PetscInt   alda, blda, clda;

1260:     PetscCall(MatDenseGetLDA(A, &alda));
1261:     PetscCall(MatDenseGetLDA(B, &blda));
1262:     PetscCall(MatDenseGetLDA(C, &clda));
1263:     PetscCallCUPMBLAS(cupmBlasXgemm(handle, transpose_A ? CUPMBLAS_OP_T : CUPMBLAS_OP_N, transpose_B ? CUPMBLAS_OP_T : CUPMBLAS_OP_N, m, n, k, &one, da.cupmdata(), alda, db.cupmdata(), blda, &zero, dc.cupmdata(), clda));
1264:   }
1265:   PetscCall(PetscLogGpuTimeEnd());

1267:   PetscCall(PetscLogGpuFlops(1.0 * m * n * k + 1.0 * m * n * (k - 1)));
1268:   if (!Aiscupm) PetscCall(MatConvert(A, MATSEQDENSE, MAT_INPLACE_MATRIX, &A));
1269:   if (!Biscupm) PetscCall(MatConvert(B, MATSEQDENSE, MAT_INPLACE_MATRIX, &B));
1270:   PetscFunctionReturn(PETSC_SUCCESS);
1271: }

1273: template <device::cupm::DeviceType T>
1274: inline PetscErrorCode MatDense_Seq_CUPM<T>::Copy(Mat A, Mat B, MatStructure str) noexcept
1275: {
1276:   const auto m = A->rmap->n;
1277:   const auto n = A->cmap->n;

1279:   PetscFunctionBegin;
1280:   PetscAssert(m == B->rmap->n && n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "size(B) != size(A)");
1281:   // The two matrices must have the same copy implementation to be eligible for fast copy
1282:   if (A->ops->copy == B->ops->copy) {
1283:     PetscDeviceContext dctx;
1284:     cupmStream_t       stream;

1286:     PetscCall(GetHandles_(&dctx, &stream));
1287:     PetscCall(PetscLogGpuTimeBegin());
1288:     {
1289:       const auto va = DeviceArrayRead(dctx, A);
1290:       const auto vb = DeviceArrayWrite(dctx, B);
1291:       // order is important, DeviceArrayRead/Write() might call SetPreallocation() which sets
1292:       // lda!
1293:       const auto lda_a = MatIMPLCast(A)->lda;
1294:       const auto lda_b = MatIMPLCast(B)->lda;

1296:       if (lda_a > m || lda_b > m) {
1297:         PetscAssert(lda_b > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "B lda (%" PetscBLASInt_FMT ") must be > 0 at this point, this indicates Mat%sSetPreallocation() was not called when it should have been!", lda_b, cupmNAME());
1298:         PetscAssert(lda_a > 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A lda (%" PetscBLASInt_FMT ") must be > 0 at this point, this indicates Mat%sSetPreallocation() was not called when it should have been!", lda_a, cupmNAME());
1299:         PetscCall(PetscCUPMMemcpy2DAsync(vb.data(), lda_b, va.data(), lda_a, m, n, cupmMemcpyDeviceToDevice, stream));
1300:       } else {
1301:         PetscCall(PetscCUPMMemcpyAsync(vb.data(), va.data(), m * n, cupmMemcpyDeviceToDevice, stream));
1302:       }
1303:     }
1304:     PetscCall(PetscLogGpuTimeEnd());
1305:   } else {
1306:     PetscCall(MatCopy_Basic(A, B, str));
1307:   }
1308:   PetscFunctionReturn(PETSC_SUCCESS);
1309: }

1311: template <device::cupm::DeviceType T>
1312: inline PetscErrorCode MatDense_Seq_CUPM<T>::ZeroEntries(Mat m) noexcept
1313: {
1314:   PetscDeviceContext dctx;
1315:   cupmStream_t       stream;

1317:   PetscFunctionBegin;
1318:   PetscCall(GetHandles_(&dctx, &stream));
1319:   PetscCall(PetscLogGpuTimeBegin());
1320:   {
1321:     const auto va  = DeviceArrayWrite(dctx, m);
1322:     const auto lda = MatIMPLCast(m)->lda;
1323:     const auto ma  = m->rmap->n;
1324:     const auto na  = m->cmap->n;

1326:     if (lda > ma) {
1327:       PetscCall(PetscCUPMMemset2DAsync(va.data(), lda, 0, ma, na, stream));
1328:     } else {
1329:       PetscCall(PetscCUPMMemsetAsync(va.data(), 0, ma * na, stream));
1330:     }
1331:   }
1332:   PetscCall(PetscLogGpuTimeEnd());
1333:   PetscFunctionReturn(PETSC_SUCCESS);
1334: }

1336: namespace detail
1337: {

1339: // ==========================================================================================
1340: // SubMatIndexFunctor
1341: //
1342: // Iterator which permutes a linear index range into matrix indices for am nrows x ncols
1343: // submat with leading dimension lda. Essentially SubMatIndexFunctor(i) returns the index for
1344: // the i'th sequential entry in the matrix.
1345: // ==========================================================================================
1346: template <typename T>
1347: struct SubMatIndexFunctor {
1348:   PETSC_HOSTDEVICE_INLINE_DECL T operator()(T x) const noexcept { return ((x / nrows) * lda) + (x % nrows); }

1350:   PetscInt nrows;
1351:   PetscInt ncols;
1352:   PetscInt lda;
1353: };

1355: template <typename Iterator>
1356: struct SubMatrixIterator : MatrixIteratorBase<Iterator, SubMatIndexFunctor<typename thrust::iterator_difference<Iterator>::type>> {
1357:   using base_type = MatrixIteratorBase<Iterator, SubMatIndexFunctor<typename thrust::iterator_difference<Iterator>::type>>;

1359:   using iterator = typename base_type::iterator;

1361:   constexpr SubMatrixIterator(Iterator first, Iterator last, PetscInt nrows, PetscInt ncols, PetscInt lda) noexcept :
1362:     base_type{
1363:       std::move(first), std::move(last), {nrows, ncols, lda}
1364:   }
1365:   {
1366:   }

1368:   PETSC_NODISCARD iterator end() const noexcept { return this->begin() + (this->func.nrows * this->func.ncols); }
1369: };

1371: namespace
1372: {

1374: template <typename T>
1375: PETSC_NODISCARD inline SubMatrixIterator<typename thrust::device_vector<T>::iterator> make_submat_iterator(PetscInt rstart, PetscInt rend, PetscInt cstart, PetscInt cend, PetscInt lda, T *ptr) noexcept
1376: {
1377:   const auto nrows = rend - rstart;
1378:   const auto ncols = cend - cstart;
1379:   const auto dptr  = thrust::device_pointer_cast(ptr);

1381:   return {dptr + (rstart * lda) + cstart, dptr + ((rstart + nrows) * lda) + cstart, nrows, ncols, lda};
1382: }

1384: } // namespace

1386: } // namespace detail

1388: template <device::cupm::DeviceType T>
1389: inline PetscErrorCode MatDense_Seq_CUPM<T>::Scale(Mat A, PetscScalar alpha) noexcept
1390: {
1391:   const auto         m = A->rmap->n;
1392:   const auto         n = A->cmap->n;
1393:   const auto         N = m * n;
1394:   PetscDeviceContext dctx;

1396:   PetscFunctionBegin;
1397:   PetscCall(PetscInfo(A, "Performing Scale %" PetscInt_FMT " x %" PetscInt_FMT " on backend\n", m, n));
1398:   PetscCall(GetHandles_(&dctx));
1399:   {
1400:     const auto da  = DeviceArrayReadWrite(dctx, A);
1401:     const auto lda = MatIMPLCast(A)->lda;

1403:     if (lda > m) {
1404:       cupmStream_t stream;

1406:       PetscCall(GetHandlesFrom_(dctx, &stream));
1407:       // clang-format off
1408:       PetscCallThrust(
1409:         const auto sub_mat = detail::make_submat_iterator(0, m, 0, n, lda, da.data());

1411:         THRUST_CALL(
1412:           thrust::transform,
1413:           stream,
1414:           sub_mat.begin(), sub_mat.end(), sub_mat.begin(),
1415:           device::cupm::functors::make_times_equals(alpha)
1416:         )
1417:       );
1418:       // clang-format on
1419:     } else {
1420:       const auto       cu_alpha = cupmScalarCast(alpha);
1421:       cupmBlasHandle_t handle;

1423:       PetscCall(GetHandlesFrom_(dctx, &handle));
1424:       PetscCall(PetscLogGpuTimeBegin());
1425:       PetscCallCUPMBLAS(cupmBlasXscal(handle, N, &cu_alpha, da.cupmdata(), 1));
1426:       PetscCall(PetscLogGpuTimeEnd());
1427:     }
1428:   }
1429:   PetscCall(PetscLogGpuFlops(N));
1430:   PetscFunctionReturn(PETSC_SUCCESS);
1431: }

1433: template <device::cupm::DeviceType T>
1434: inline PetscErrorCode MatDense_Seq_CUPM<T>::Shift(Mat A, PetscScalar alpha) noexcept
1435: {
1436:   const auto         m = A->rmap->n;
1437:   const auto         n = A->cmap->n;
1438:   PetscDeviceContext dctx;

1440:   PetscFunctionBegin;
1441:   PetscCall(GetHandles_(&dctx));
1442:   PetscCall(PetscInfo(A, "Performing Shift %" PetscInt_FMT " x %" PetscInt_FMT " on backend\n", m, n));
1443:   PetscCall(DiagonalUnaryTransform(A, 0, m, n, dctx, device::cupm::functors::make_plus_equals(alpha)));
1444:   PetscFunctionReturn(PETSC_SUCCESS);
1445: }

1447: template <device::cupm::DeviceType T>
1448: inline PetscErrorCode MatDense_Seq_CUPM<T>::AXPY(Mat Y, PetscScalar alpha, Mat X, MatStructure) noexcept
1449: {
1450:   const auto         m_x = X->rmap->n, m_y = Y->rmap->n;
1451:   const auto         n_x = X->cmap->n, n_y = Y->cmap->n;
1452:   const auto         N = m_x * n_x;
1453:   PetscDeviceContext dctx;

1455:   PetscFunctionBegin;
1456:   if (!m_x || !n_x || alpha == (PetscScalar)0.0) PetscFunctionReturn(PETSC_SUCCESS);
1457:   PetscCall(PetscInfo(Y, "Performing AXPY %" PetscInt_FMT " x %" PetscInt_FMT " on backend\n", m_y, n_y));
1458:   PetscCall(GetHandles_(&dctx));
1459:   {
1460:     const auto dx    = DeviceArrayRead(dctx, X);
1461:     const auto dy    = DeviceArrayReadWrite(dctx, Y);
1462:     const auto lda_x = MatIMPLCast(X)->lda;
1463:     const auto lda_y = MatIMPLCast(Y)->lda;

1465:     if (lda_x > m_x || lda_y > m_x) {
1466:       cupmStream_t stream;

1468:       PetscCall(GetHandlesFrom_(dctx, &stream));
1469:       // clang-format off
1470:       PetscCallThrust(
1471:         const auto sub_mat_y = detail::make_submat_iterator(0, m_y, 0, n_y, lda_y, dy.data());
1472:         const auto sub_mat_x = detail::make_submat_iterator(0, m_x, 0, n_x, lda_x, dx.data());

1474:         THRUST_CALL(
1475:           thrust::transform,
1476:           stream,
1477:           sub_mat_x.begin(), sub_mat_x.end(), sub_mat_y.begin(), sub_mat_y.begin(),
1478:           device::cupm::functors::make_axpy(alpha)
1479:         );
1480:       );
1481:       // clang-format on
1482:     } else {
1483:       const auto       cu_alpha = cupmScalarCast(alpha);
1484:       cupmBlasHandle_t handle;

1486:       PetscCall(GetHandlesFrom_(dctx, &handle));
1487:       PetscCall(PetscLogGpuTimeBegin());
1488:       PetscCallCUPMBLAS(cupmBlasXaxpy(handle, N, &cu_alpha, dx.cupmdata(), 1, dy.cupmdata(), 1));
1489:       PetscCall(PetscLogGpuTimeEnd());
1490:     }
1491:   }
1492:   PetscCall(PetscLogGpuFlops(PetscMax(2 * N - 1, 0)));
1493:   PetscFunctionReturn(PETSC_SUCCESS);
1494: }

1496: template <device::cupm::DeviceType T>
1497: inline PetscErrorCode MatDense_Seq_CUPM<T>::Duplicate(Mat A, MatDuplicateOption opt, Mat *B) noexcept
1498: {
1499:   const auto         hopt = (opt == MAT_COPY_VALUES && A->offloadmask != PETSC_OFFLOAD_CPU) ? MAT_DO_NOT_COPY_VALUES : opt;
1500:   PetscDeviceContext dctx;

1502:   PetscFunctionBegin;
1503:   PetscCall(GetHandles_(&dctx));
1504:   // do not call SetPreallocation() yet, we call it afterwards??
1505:   PetscCall(MatCreateSeqDenseCUPM<T>(PetscObjectComm(PetscObjectCast(A)), A->rmap->n, A->cmap->n, nullptr, B, dctx, /* preallocate */ false));
1506:   PetscCall(MatDuplicateNoCreate_SeqDense(*B, A, hopt));
1507:   if (opt == MAT_COPY_VALUES && hopt != MAT_COPY_VALUES) PetscCall(Copy(A, *B, SAME_NONZERO_PATTERN));
1508:   // allocate memory if needed
1509:   if (opt != MAT_COPY_VALUES && !MatCUPMCast(*B)->d_v) PetscCall(SetPreallocation(*B, dctx));
1510:   PetscFunctionReturn(PETSC_SUCCESS);
1511: }

1513: template <device::cupm::DeviceType T>
1514: inline PetscErrorCode MatDense_Seq_CUPM<T>::SetRandom(Mat A, PetscRandom rng) noexcept
1515: {
1516:   PetscBool device;

1518:   PetscFunctionBegin;
1519:   PetscCall(PetscObjectTypeCompare(PetscObjectCast(rng), PETSCDEVICERAND(), &device));
1520:   if (device) {
1521:     const auto         m = A->rmap->n;
1522:     const auto         n = A->cmap->n;
1523:     PetscDeviceContext dctx;

1525:     PetscCall(GetHandles_(&dctx));
1526:     {
1527:       const auto a = DeviceArrayWrite(dctx, A);
1528:       PetscInt   lda;

1530:       PetscCall(MatDenseGetLDA(A, &lda));
1531:       if (lda > m) {
1532:         for (PetscInt i = 0; i < n; i++) PetscCall(PetscRandomGetValues(rng, m, a.data() + i * lda));
1533:       } else {
1534:         PetscInt mn;

1536:         PetscCall(PetscIntMultError(m, n, &mn));
1537:         PetscCall(PetscRandomGetValues(rng, mn, a));
1538:       }
1539:     }
1540:   } else {
1541:     PetscCall(MatSetRandom_SeqDense(A, rng));
1542:   }
1543:   PetscFunctionReturn(PETSC_SUCCESS);
1544: }

1546: // ==========================================================================================

1548: template <device::cupm::DeviceType T>
1549: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetColumnVector(Mat A, Vec v, PetscInt col) noexcept
1550: {
1551:   const auto         offloadmask = A->offloadmask;
1552:   const auto         n           = A->rmap->n;
1553:   const auto         col_offset  = [&](const PetscScalar *ptr) { return ptr + col * MatIMPLCast(A)->lda; };
1554:   PetscBool          viscupm;
1555:   PetscDeviceContext dctx;
1556:   cupmStream_t       stream;

1558:   PetscFunctionBegin;
1559:   PetscCall(PetscObjectTypeCompareAny(PetscObjectCast(v), &viscupm, VecSeq_CUPM::VECSEQCUPM(), VecSeq_CUPM::VECMPICUPM(), VecSeq_CUPM::VECCUPM(), ""));
1560:   PetscCall(GetHandles_(&dctx, &stream));
1561:   if (viscupm && !v->boundtocpu) {
1562:     const auto x = VecSeq_CUPM::DeviceArrayWrite(dctx, v);

1564:     // update device data
1565:     if (PetscOffloadDevice(offloadmask)) {
1566:       PetscCall(PetscCUPMMemcpyAsync(x.data(), col_offset(DeviceArrayRead(dctx, A)), n, cupmMemcpyDeviceToDevice, stream));
1567:     } else {
1568:       PetscCall(PetscCUPMMemcpyAsync(x.data(), col_offset(HostArrayRead(dctx, A)), n, cupmMemcpyHostToDevice, stream));
1569:     }
1570:   } else {
1571:     PetscScalar *x;

1573:     // update host data
1574:     PetscCall(VecGetArrayWrite(v, &x));
1575:     if (PetscOffloadUnallocated(offloadmask) || PetscOffloadHost(offloadmask)) {
1576:       PetscCall(PetscArraycpy(x, col_offset(HostArrayRead(dctx, A)), n));
1577:     } else if (PetscOffloadDevice(offloadmask)) {
1578:       PetscCall(PetscCUPMMemcpyAsync(x, col_offset(DeviceArrayRead(dctx, A)), n, cupmMemcpyDeviceToHost, stream));
1579:     }
1580:     PetscCall(VecRestoreArrayWrite(v, &x));
1581:   }
1582:   PetscFunctionReturn(PETSC_SUCCESS);
1583: }

1585: template <device::cupm::DeviceType T>
1586: template <PetscMemoryAccessMode access>
1587: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetColumnVec(Mat A, PetscInt col, Vec *v) noexcept
1588: {
1589:   using namespace vec::cupm;
1590:   const auto         mimpl = MatIMPLCast(A);
1591:   PetscDeviceContext dctx;

1593:   PetscFunctionBegin;
1594:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
1595:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
1596:   mimpl->vecinuse = col + 1;
1597:   PetscCall(GetHandles_(&dctx));
1598:   PetscCall(GetArray<PETSC_MEMTYPE_DEVICE, access>(A, const_cast<PetscScalar **>(&mimpl->ptrinuse), dctx));
1599:   if (!mimpl->cvec) {
1600:     // we pass the data of A, to prevent allocating needless GPU memory the first time
1601:     // VecCUPMPlaceArray is called
1602:     PetscCall(VecCreateSeqCUPMWithArraysAsync<T>(PetscObjectComm(PetscObjectCast(A)), A->rmap->bs, A->rmap->n, nullptr, mimpl->ptrinuse, &mimpl->cvec));
1603:   }
1604:   PetscCall(VecCUPMPlaceArrayAsync<T>(mimpl->cvec, mimpl->ptrinuse + static_cast<std::size_t>(col) * static_cast<std::size_t>(mimpl->lda)));
1605:   if (access == PETSC_MEMORY_ACCESS_READ) PetscCall(VecLockReadPush(mimpl->cvec));
1606:   *v = mimpl->cvec;
1607:   PetscFunctionReturn(PETSC_SUCCESS);
1608: }

1610: template <device::cupm::DeviceType T>
1611: template <PetscMemoryAccessMode access>
1612: inline PetscErrorCode MatDense_Seq_CUPM<T>::RestoreColumnVec(Mat A, PetscInt, Vec *v) noexcept
1613: {
1614:   using namespace vec::cupm;
1615:   const auto         mimpl = MatIMPLCast(A);
1616:   const auto         cvec  = mimpl->cvec;
1617:   PetscDeviceContext dctx;

1619:   PetscFunctionBegin;
1620:   PetscCheck(mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseGetColumnVec() first");
1621:   PetscCheck(cvec, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Missing internal column vector");
1622:   mimpl->vecinuse = 0;
1623:   if (access == PETSC_MEMORY_ACCESS_READ) PetscCall(VecLockReadPop(cvec));
1624:   PetscCall(VecCUPMResetArrayAsync<T>(cvec));
1625:   PetscCall(GetHandles_(&dctx));
1626:   PetscCall(RestoreArray<PETSC_MEMTYPE_DEVICE, access>(A, const_cast<PetscScalar **>(&mimpl->ptrinuse), dctx));
1627:   if (v) *v = nullptr;
1628:   PetscFunctionReturn(PETSC_SUCCESS);
1629: }

1631: // ==========================================================================================

1633: template <device::cupm::DeviceType T>
1634: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetFactor(Mat A, MatFactorType ftype, Mat *fact_out) noexcept
1635: {
1636:   Mat                fact = nullptr;
1637:   PetscDeviceContext dctx;

1639:   PetscFunctionBegin;
1640:   PetscCall(GetHandles_(&dctx));
1641:   PetscCall(MatCreateSeqDenseCUPM<T>(PetscObjectComm(PetscObjectCast(A)), A->rmap->n, A->cmap->n, nullptr, &fact, dctx, /* preallocate */ false));
1642:   fact->factortype = ftype;
1643:   switch (ftype) {
1644:   case MAT_FACTOR_LU:
1645:   case MAT_FACTOR_ILU: // fall-through
1646:     fact->ops->lufactorsymbolic  = MatLUFactorSymbolic_SeqDense;
1647:     fact->ops->ilufactorsymbolic = MatLUFactorSymbolic_SeqDense;
1648:     break;
1649:   case MAT_FACTOR_CHOLESKY:
1650:   case MAT_FACTOR_ICC: // fall-through
1651:     fact->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqDense;
1652:     break;
1653:   case MAT_FACTOR_QR: {
1654:     const auto pobj = PetscObjectCast(fact);

1656:     PetscCall(PetscObjectComposeFunction(pobj, "MatQRFactor_C", MatQRFactor_SeqDense));
1657:     PetscCall(PetscObjectComposeFunction(pobj, "MatQRFactorSymbolic_C", MatQRFactorSymbolic_SeqDense));
1658:   } break;
1659:   case MAT_FACTOR_NONE:
1660:   case MAT_FACTOR_ILUDT:     // fall-through
1661:   case MAT_FACTOR_NUM_TYPES: // fall-through
1662:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatFactorType %s not supported", MatFactorTypes[ftype]);
1663:   }
1664:   PetscCall(PetscStrFreeAllocpy(MATSOLVERCUPM(), &fact->solvertype));
1665:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, const_cast<char **>(fact->preferredordering) + MAT_FACTOR_LU));
1666:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, const_cast<char **>(fact->preferredordering) + MAT_FACTOR_ILU));
1667:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, const_cast<char **>(fact->preferredordering) + MAT_FACTOR_CHOLESKY));
1668:   PetscCall(PetscStrallocpy(MATORDERINGEXTERNAL, const_cast<char **>(fact->preferredordering) + MAT_FACTOR_ICC));
1669:   *fact_out = fact;
1670:   PetscFunctionReturn(PETSC_SUCCESS);
1671: }

1673: template <device::cupm::DeviceType T>
1674: inline PetscErrorCode MatDense_Seq_CUPM<T>::InvertFactors(Mat A) noexcept
1675: {
1676:   const auto         mimpl = MatIMPLCast(A);
1677:   const auto         mcu   = MatCUPMCast(A);
1678:   const auto         n     = static_cast<cupmBlasInt_t>(A->cmap->n);
1679:   cupmSolverHandle_t handle;
1680:   PetscDeviceContext dctx;
1681:   cupmStream_t       stream;

1683:   PetscFunctionBegin;
1684:   #if PetscDefined(HAVE_CUDA) && PetscDefined(USING_NVCC)
1685:   // HIP appears to have this by default??
1686:   PetscCheck(PETSC_PKG_CUDA_VERSION_GE(10, 1, 0), PETSC_COMM_SELF, PETSC_ERR_SUP, "Upgrade to CUDA version 10.1.0 or higher");
1687:   #endif
1688:   if (!n || !A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
1689:   PetscCheck(A->factortype == MAT_FACTOR_CHOLESKY, PETSC_COMM_SELF, PETSC_ERR_LIB, "Factor type %s not implemented", MatFactorTypes[A->factortype]);
1690:   // spd
1691:   PetscCheck(!mcu->d_fact_ipiv, PETSC_COMM_SELF, PETSC_ERR_LIB, "%sDnsytri not implemented", cupmSolverName());

1693:   PetscCall(GetHandles_(&dctx, &handle, &stream));
1694:   {
1695:     const auto    da  = DeviceArrayReadWrite(dctx, A);
1696:     const auto    lda = static_cast<cupmBlasInt_t>(mimpl->lda);
1697:     cupmBlasInt_t il;

1699:     PetscCallCUPMSOLVER(cupmSolverXpotri_bufferSize(handle, CUPMSOLVER_FILL_MODE_LOWER, n, da.cupmdata(), lda, &il));
1700:     if (il > mcu->d_fact_lwork) {
1701:       mcu->d_fact_lwork = il;
1702:       PetscCallCUPM(cupmFreeAsync(mcu->d_fact_work, stream));
1703:       PetscCall(PetscCUPMMallocAsync(&mcu->d_fact_work, il, stream));
1704:     }
1705:     PetscCall(PetscLogGpuTimeBegin());
1706:     PetscCallCUPMSOLVER(cupmSolverXpotri(handle, CUPMSOLVER_FILL_MODE_LOWER, n, da.cupmdata(), lda, mcu->d_fact_work, mcu->d_fact_lwork, mcu->d_fact_info));
1707:     PetscCall(PetscLogGpuTimeEnd());
1708:   }
1709:   PetscCall(CheckCUPMSolverInfo_(mcu->d_fact_info, stream));
1710:   // TODO (write cuda kernel)
1711:   PetscCall(MatSeqDenseSymmetrize_Private(A, PETSC_TRUE));
1712:   PetscCall(PetscLogGpuFlops(1.0 * n * n * n / 3.0));

1714:   A->ops->solve          = nullptr;
1715:   A->ops->solvetranspose = nullptr;
1716:   A->ops->matsolve       = nullptr;
1717:   A->factortype          = MAT_FACTOR_NONE;

1719:   PetscCall(PetscFree(A->solvertype));
1720:   PetscFunctionReturn(PETSC_SUCCESS);
1721: }

1723: // ==========================================================================================

1725: template <device::cupm::DeviceType T>
1726: inline PetscErrorCode MatDense_Seq_CUPM<T>::GetSubMatrix(Mat A, PetscInt rbegin, PetscInt rend, PetscInt cbegin, PetscInt cend, Mat *mat) noexcept
1727: {
1728:   const auto         mimpl        = MatIMPLCast(A);
1729:   const auto         array_offset = [&](PetscScalar *ptr) { return ptr + rbegin + static_cast<std::size_t>(cbegin) * mimpl->lda; };
1730:   const auto         n            = rend - rbegin;
1731:   const auto         m            = cend - cbegin;
1732:   auto              &cmat         = mimpl->cmat;
1733:   PetscDeviceContext dctx;

1735:   PetscFunctionBegin;
1736:   PetscCheck(!mimpl->vecinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreColumnVec() first");
1737:   PetscCheck(!mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseRestoreSubMatrix() first");
1738:   mimpl->matinuse = cbegin + 1;

1740:   PetscCall(GetHandles_(&dctx));
1741:   PetscCall(HostToDevice_(A, dctx));

1743:   if (cmat && ((m != cmat->cmap->N) || (n != cmat->rmap->N))) PetscCall(MatDestroy(&cmat));
1744:   {
1745:     const auto device_array = array_offset(MatCUPMCast(A)->d_v);

1747:     if (cmat) {
1748:       PetscCall(PlaceArray(cmat, device_array));
1749:     } else {
1750:       PetscCall(MatCreateSeqDenseCUPM<T>(PetscObjectComm(PetscObjectCast(A)), n, m, device_array, &cmat, dctx));
1751:     }
1752:   }
1753:   PetscCall(MatDenseSetLDA(cmat, mimpl->lda));
1754:   // place CPU array if present but do not copy any data
1755:   if (const auto host_array = mimpl->v) {
1756:     cmat->offloadmask = PETSC_OFFLOAD_GPU;
1757:     PetscCall(MatDensePlaceArray(cmat, array_offset(host_array)));
1758:   }

1760:   cmat->offloadmask = A->offloadmask;
1761:   *mat              = cmat;
1762:   PetscFunctionReturn(PETSC_SUCCESS);
1763: }

1765: template <device::cupm::DeviceType T>
1766: inline PetscErrorCode MatDense_Seq_CUPM<T>::RestoreSubMatrix(Mat A, Mat *m) noexcept
1767: {
1768:   const auto mimpl = MatIMPLCast(A);
1769:   const auto cmat  = mimpl->cmat;
1770:   const auto reset = static_cast<bool>(mimpl->v);
1771:   bool       copy, was_offload_host;

1773:   PetscFunctionBegin;
1774:   PetscCheck(mimpl->matinuse, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Need to call MatDenseGetSubMatrix() first");
1775:   PetscCheck(cmat, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Missing internal column matrix");
1776:   PetscCheck(*m == cmat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Not the matrix obtained from MatDenseGetSubMatrix()");
1777:   mimpl->matinuse = 0;

1779:   // calls to ResetArray may change it, so save it here
1780:   was_offload_host = cmat->offloadmask == PETSC_OFFLOAD_CPU;
1781:   if (was_offload_host && !reset) {
1782:     copy = true;
1783:     PetscCall(MatSeqDenseSetPreallocation(A, nullptr));
1784:   } else {
1785:     copy = false;
1786:   }

1788:   PetscCall(ResetArray(cmat));
1789:   if (reset) PetscCall(MatDenseResetArray(cmat));
1790:   if (copy) {
1791:     PetscDeviceContext dctx;

1793:     PetscCall(GetHandles_(&dctx));
1794:     PetscCall(DeviceToHost_(A, dctx));
1795:   } else {
1796:     A->offloadmask = was_offload_host ? PETSC_OFFLOAD_CPU : PETSC_OFFLOAD_GPU;
1797:   }

1799:   cmat->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
1800:   *m                = nullptr;
1801:   PetscFunctionReturn(PETSC_SUCCESS);
1802: }

1804: // ==========================================================================================

1806: namespace
1807: {

1809: template <device::cupm::DeviceType T>
1810: inline PetscErrorCode MatMatMultNumeric_SeqDenseCUPM_SeqDenseCUPM(Mat A, Mat B, Mat C, PetscBool TA, PetscBool TB) noexcept
1811: {
1812:   PetscFunctionBegin;
1813:   if (TA) {
1814:     if (TB) {
1815:       PetscCall(MatDense_Seq_CUPM<T>::template MatMatMult_Numeric_Dispatch<true, true>(A, B, C));
1816:     } else {
1817:       PetscCall(MatDense_Seq_CUPM<T>::template MatMatMult_Numeric_Dispatch<true, false>(A, B, C));
1818:     }
1819:   } else {
1820:     if (TB) {
1821:       PetscCall(MatDense_Seq_CUPM<T>::template MatMatMult_Numeric_Dispatch<false, true>(A, B, C));
1822:     } else {
1823:       PetscCall(MatDense_Seq_CUPM<T>::template MatMatMult_Numeric_Dispatch<false, false>(A, B, C));
1824:     }
1825:   }
1826:   PetscFunctionReturn(PETSC_SUCCESS);
1827: }

1829: template <device::cupm::DeviceType T>
1830: inline PetscErrorCode MatSolverTypeRegister_DENSECUPM() noexcept
1831: {
1832:   PetscFunctionBegin;
1833:   for (auto ftype : util::make_array(MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_QR)) {
1834:     PetscCall(MatSolverTypeRegister(MatDense_Seq_CUPM<T>::MATSOLVERCUPM(), MATSEQDENSE, ftype, MatDense_Seq_CUPM<T>::GetFactor));
1835:     PetscCall(MatSolverTypeRegister(MatDense_Seq_CUPM<T>::MATSOLVERCUPM(), MatDense_Seq_CUPM<T>::MATSEQDENSECUPM(), ftype, MatDense_Seq_CUPM<T>::GetFactor));
1836:   }
1837:   PetscFunctionReturn(PETSC_SUCCESS);
1838: }

1840: } // anonymous namespace

1842: } // namespace impl

1844: } // namespace cupm

1846: } // namespace mat

1848: } // namespace Petsc

1850: #endif // __cplusplus

1852: #endif // PETSCMATSEQDENSECUPM_HPP