Date: 2025-03-29T05:58:32+00:00

Actual source code: mpiaijhipsparse.hip.cpp

      1: /* Portions of this code are under:
      2:    Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
      3: */
      4: #include <../src/mat/impls/aij/mpi/mpiaij.h>
      5: #include <../src/mat/impls/aij/seq/seqhipsparse/hipsparsematimpl.h>
      6: #include <../src/mat/impls/aij/mpi/mpihipsparse/mpihipsparsematimpl.h>
      7: #include <thrust/advance.h>
      8: #include <thrust/partition.h>
      9: #include <thrust/sort.h>
     10: #include <thrust/unique.h>
     11: #include <petscsf.h>

     13: struct VecHIPEquals {
     14:   template <typename Tuple>
     15:   __host__ __device__ void operator()(Tuple t)
     16:   {
     17:     thrust::get<1>(t) = thrust::get<0>(t);
     18:   }
     19: };

     21: static PetscErrorCode MatCOOStructDestroy_MPIAIJHIPSPARSE(void **data)
     22: {
     23:   MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;

     25:   PetscFunctionBegin;
     26:   PetscCall(PetscSFDestroy(&coo->sf));
     27:   PetscCallHIP(hipFree(coo->Ajmap1));
     28:   PetscCallHIP(hipFree(coo->Aperm1));
     29:   PetscCallHIP(hipFree(coo->Bjmap1));
     30:   PetscCallHIP(hipFree(coo->Bperm1));
     31:   PetscCallHIP(hipFree(coo->Aimap2));
     32:   PetscCallHIP(hipFree(coo->Ajmap2));
     33:   PetscCallHIP(hipFree(coo->Aperm2));
     34:   PetscCallHIP(hipFree(coo->Bimap2));
     35:   PetscCallHIP(hipFree(coo->Bjmap2));
     36:   PetscCallHIP(hipFree(coo->Bperm2));
     37:   PetscCallHIP(hipFree(coo->Cperm1));
     38:   PetscCallHIP(hipFree(coo->sendbuf));
     39:   PetscCallHIP(hipFree(coo->recvbuf));
     40:   PetscCall(PetscFree(coo));
     41:   PetscFunctionReturn(PETSC_SUCCESS);
     42: }

     44: static PetscErrorCode MatSetPreallocationCOO_MPIAIJHIPSPARSE(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
     45: {
     46:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
     47:   PetscBool            dev_ij = PETSC_FALSE;
     48:   PetscMemType         mtype  = PETSC_MEMTYPE_HOST;
     49:   PetscInt            *i, *j;
     50:   PetscContainer       container_h;
     51:   MatCOOStruct_MPIAIJ *coo_h, *coo_d;

     53:   PetscFunctionBegin;
     54:   PetscCall(PetscFree(mpiaij->garray));
     55:   PetscCall(VecDestroy(&mpiaij->lvec));
     56: #if defined(PETSC_USE_CTABLE)
     57:   PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
     58: #else
     59:   PetscCall(PetscFree(mpiaij->colmap));
     60: #endif
     61:   PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
     62:   mat->assembled     = PETSC_FALSE;
     63:   mat->was_assembled = PETSC_FALSE;
     64:   PetscCall(PetscGetMemType(coo_i, &mtype));
     65:   if (PetscMemTypeDevice(mtype)) {
     66:     dev_ij = PETSC_TRUE;
     67:     PetscCall(PetscMalloc2(coo_n, &i, coo_n, &j));
     68:     PetscCallHIP(hipMemcpy(i, coo_i, coo_n * sizeof(PetscInt), hipMemcpyDeviceToHost));
     69:     PetscCallHIP(hipMemcpy(j, coo_j, coo_n * sizeof(PetscInt), hipMemcpyDeviceToHost));
     70:   } else {
     71:     i = coo_i;
     72:     j = coo_j;
     73:   }

     75:   PetscCall(MatSetPreallocationCOO_MPIAIJ(mat, coo_n, coo_i, coo_j));
     76:   if (dev_ij) PetscCall(PetscFree2(i, j));
     77:   mat->offloadmask = PETSC_OFFLOAD_CPU;
     78:   // Create the GPU memory
     79:   PetscCall(MatSeqAIJHIPSPARSECopyToGPU(mpiaij->A));
     80:   PetscCall(MatSeqAIJHIPSPARSECopyToGPU(mpiaij->B));

     82:   // Copy the COO struct to device
     83:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
     84:   PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
     85:   PetscCall(PetscMalloc1(1, &coo_d));
     86:   *coo_d = *coo_h; // do a shallow copy and then amend fields in coo_d

     88:   PetscCall(PetscObjectReference((PetscObject)coo_d->sf)); // Since we destroy the sf in both coo_h and coo_d
     89:   PetscCallHIP(hipMalloc((void **)&coo_d->Ajmap1, (coo_h->Annz + 1) * sizeof(PetscCount)));
     90:   PetscCallHIP(hipMalloc((void **)&coo_d->Aperm1, coo_h->Atot1 * sizeof(PetscCount)));
     91:   PetscCallHIP(hipMalloc((void **)&coo_d->Bjmap1, (coo_h->Bnnz + 1) * sizeof(PetscCount)));
     92:   PetscCallHIP(hipMalloc((void **)&coo_d->Bperm1, coo_h->Btot1 * sizeof(PetscCount)));
     93:   PetscCallHIP(hipMalloc((void **)&coo_d->Aimap2, coo_h->Annz2 * sizeof(PetscCount)));
     94:   PetscCallHIP(hipMalloc((void **)&coo_d->Ajmap2, (coo_h->Annz2 + 1) * sizeof(PetscCount)));
     95:   PetscCallHIP(hipMalloc((void **)&coo_d->Aperm2, coo_h->Atot2 * sizeof(PetscCount)));
     96:   PetscCallHIP(hipMalloc((void **)&coo_d->Bimap2, coo_h->Bnnz2 * sizeof(PetscCount)));
     97:   PetscCallHIP(hipMalloc((void **)&coo_d->Bjmap2, (coo_h->Bnnz2 + 1) * sizeof(PetscCount)));
     98:   PetscCallHIP(hipMalloc((void **)&coo_d->Bperm2, coo_h->Btot2 * sizeof(PetscCount)));
     99:   PetscCallHIP(hipMalloc((void **)&coo_d->Cperm1, coo_h->sendlen * sizeof(PetscCount)));
    100:   PetscCallHIP(hipMalloc((void **)&coo_d->sendbuf, coo_h->sendlen * sizeof(PetscScalar)));
    101:   PetscCallHIP(hipMalloc((void **)&coo_d->recvbuf, coo_h->recvlen * sizeof(PetscScalar)));

    103:   PetscCallHIP(hipMemcpy(coo_d->Ajmap1, coo_h->Ajmap1, (coo_h->Annz + 1) * sizeof(PetscCount), hipMemcpyHostToDevice));
    104:   PetscCallHIP(hipMemcpy(coo_d->Aperm1, coo_h->Aperm1, coo_h->Atot1 * sizeof(PetscCount), hipMemcpyHostToDevice));
    105:   PetscCallHIP(hipMemcpy(coo_d->Bjmap1, coo_h->Bjmap1, (coo_h->Bnnz + 1) * sizeof(PetscCount), hipMemcpyHostToDevice));
    106:   PetscCallHIP(hipMemcpy(coo_d->Bperm1, coo_h->Bperm1, coo_h->Btot1 * sizeof(PetscCount), hipMemcpyHostToDevice));
    107:   PetscCallHIP(hipMemcpy(coo_d->Aimap2, coo_h->Aimap2, coo_h->Annz2 * sizeof(PetscCount), hipMemcpyHostToDevice));
    108:   PetscCallHIP(hipMemcpy(coo_d->Ajmap2, coo_h->Ajmap2, (coo_h->Annz2 + 1) * sizeof(PetscCount), hipMemcpyHostToDevice));
    109:   PetscCallHIP(hipMemcpy(coo_d->Aperm2, coo_h->Aperm2, coo_h->Atot2 * sizeof(PetscCount), hipMemcpyHostToDevice));
    110:   PetscCallHIP(hipMemcpy(coo_d->Bimap2, coo_h->Bimap2, coo_h->Bnnz2 * sizeof(PetscCount), hipMemcpyHostToDevice));
    111:   PetscCallHIP(hipMemcpy(coo_d->Bjmap2, coo_h->Bjmap2, (coo_h->Bnnz2 + 1) * sizeof(PetscCount), hipMemcpyHostToDevice));
    112:   PetscCallHIP(hipMemcpy(coo_d->Bperm2, coo_h->Bperm2, coo_h->Btot2 * sizeof(PetscCount), hipMemcpyHostToDevice));
    113:   PetscCallHIP(hipMemcpy(coo_d->Cperm1, coo_h->Cperm1, coo_h->sendlen * sizeof(PetscCount), hipMemcpyHostToDevice));

    115:   // Put the COO struct in a container and then attach that to the matrix
    116:   PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", coo_d, MatCOOStructDestroy_MPIAIJHIPSPARSE));
    117:   PetscFunctionReturn(PETSC_SUCCESS);
    118: }

    120: __global__ static void MatPackCOOValues(const PetscScalar kv[], PetscCount nnz, const PetscCount perm[], PetscScalar buf[])
    121: {
    122:   PetscCount       i         = blockIdx.x * blockDim.x + threadIdx.x;
    123:   const PetscCount grid_size = gridDim.x * blockDim.x;
    124:   for (; i < nnz; i += grid_size) buf[i] = kv[perm[i]];
    125: }

    127: __global__ static void MatAddLocalCOOValues(const PetscScalar kv[], InsertMode imode, PetscCount Annz, const PetscCount Ajmap1[], const PetscCount Aperm1[], PetscScalar Aa[], PetscCount Bnnz, const PetscCount Bjmap1[], const PetscCount Bperm1[], PetscScalar Ba[])
    128: {
    129:   PetscCount       i         = blockIdx.x * blockDim.x + threadIdx.x;
    130:   const PetscCount grid_size = gridDim.x * blockDim.x;
    131:   for (; i < Annz + Bnnz; i += grid_size) {
    132:     PetscScalar sum = 0.0;
    133:     if (i < Annz) {
    134:       for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += kv[Aperm1[k]];
    135:       Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
    136:     } else {
    137:       i -= Annz;
    138:       for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += kv[Bperm1[k]];
    139:       Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
    140:     }
    141:   }
    142: }

    144: __global__ static void MatAddRemoteCOOValues(const PetscScalar kv[], PetscCount Annz2, const PetscCount Aimap2[], const PetscCount Ajmap2[], const PetscCount Aperm2[], PetscScalar Aa[], PetscCount Bnnz2, const PetscCount Bimap2[], const PetscCount Bjmap2[], const PetscCount Bperm2[], PetscScalar Ba[])
    145: {
    146:   PetscCount       i         = blockIdx.x * blockDim.x + threadIdx.x;
    147:   const PetscCount grid_size = gridDim.x * blockDim.x;
    148:   for (; i < Annz2 + Bnnz2; i += grid_size) {
    149:     if (i < Annz2) {
    150:       for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += kv[Aperm2[k]];
    151:     } else {
    152:       i -= Annz2;
    153:       for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += kv[Bperm2[k]];
    154:     }
    155:   }
    156: }

    158: static PetscErrorCode MatSetValuesCOO_MPIAIJHIPSPARSE(Mat mat, const PetscScalar v[], InsertMode imode)
    159: {
    160:   Mat_MPIAIJ          *mpiaij = static_cast<Mat_MPIAIJ *>(mat->data);
    161:   Mat                  A = mpiaij->A, B = mpiaij->B;
    162:   PetscScalar         *Aa, *Ba;
    163:   const PetscScalar   *v1 = v;
    164:   PetscMemType         memtype;
    165:   PetscContainer       container;
    166:   MatCOOStruct_MPIAIJ *coo;

    168:   PetscFunctionBegin;
    169:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
    170:   PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
    171:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));

    173:   const auto &Annz   = coo->Annz;
    174:   const auto &Annz2  = coo->Annz2;
    175:   const auto &Bnnz   = coo->Bnnz;
    176:   const auto &Bnnz2  = coo->Bnnz2;
    177:   const auto &vsend  = coo->sendbuf;
    178:   const auto &v2     = coo->recvbuf;
    179:   const auto &Ajmap1 = coo->Ajmap1;
    180:   const auto &Ajmap2 = coo->Ajmap2;
    181:   const auto &Aimap2 = coo->Aimap2;
    182:   const auto &Bjmap1 = coo->Bjmap1;
    183:   const auto &Bjmap2 = coo->Bjmap2;
    184:   const auto &Bimap2 = coo->Bimap2;
    185:   const auto &Aperm1 = coo->Aperm1;
    186:   const auto &Aperm2 = coo->Aperm2;
    187:   const auto &Bperm1 = coo->Bperm1;
    188:   const auto &Bperm2 = coo->Bperm2;
    189:   const auto &Cperm1 = coo->Cperm1;

    191:   PetscCall(PetscGetMemType(v, &memtype));
    192:   if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we need to copy it to device */
    193:     PetscCallHIP(hipMalloc((void **)&v1, coo->n * sizeof(PetscScalar)));
    194:     PetscCallHIP(hipMemcpy((void *)v1, v, coo->n * sizeof(PetscScalar), hipMemcpyHostToDevice));
    195:   }

    197:   if (imode == INSERT_VALUES) {
    198:     PetscCall(MatSeqAIJHIPSPARSEGetArrayWrite(A, &Aa)); /* write matrix values */
    199:     PetscCall(MatSeqAIJHIPSPARSEGetArrayWrite(B, &Ba));
    200:   } else {
    201:     PetscCall(MatSeqAIJHIPSPARSEGetArray(A, &Aa)); /* read & write matrix values */
    202:     PetscCall(MatSeqAIJHIPSPARSEGetArray(B, &Ba));
    203:   }

    205:   PetscCall(PetscLogGpuTimeBegin());
    206:   /* Pack entries to be sent to remote */
    207:   if (coo->sendlen) {
    208:     hipLaunchKernelGGL(HIP_KERNEL_NAME(MatPackCOOValues), dim3((coo->sendlen + 255) / 256), dim3(256), 0, PetscDefaultHipStream, v1, coo->sendlen, Cperm1, vsend);
    209:     PetscCallHIP(hipPeekAtLastError());
    210:   }

    212:   /* Send remote entries to their owner and overlap the communication with local computation */
    213:   PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HIP, vsend, PETSC_MEMTYPE_HIP, v2, MPI_REPLACE));
    214:   /* Add local entries to A and B */
    215:   if (Annz + Bnnz > 0) {
    216:     hipLaunchKernelGGL(HIP_KERNEL_NAME(MatAddLocalCOOValues), dim3((Annz + Bnnz + 255) / 256), dim3(256), 0, PetscDefaultHipStream, v1, imode, Annz, Ajmap1, Aperm1, Aa, Bnnz, Bjmap1, Bperm1, Ba);
    217:     PetscCallHIP(hipPeekAtLastError());
    218:   }
    219:   PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, vsend, v2, MPI_REPLACE));

    221:   /* Add received remote entries to A and B */
    222:   if (Annz2 + Bnnz2 > 0) {
    223:     hipLaunchKernelGGL(HIP_KERNEL_NAME(MatAddRemoteCOOValues), dim3((Annz2 + Bnnz2 + 255) / 256), dim3(256), 0, PetscDefaultHipStream, v2, Annz2, Aimap2, Ajmap2, Aperm2, Aa, Bnnz2, Bimap2, Bjmap2, Bperm2, Ba);
    224:     PetscCallHIP(hipPeekAtLastError());
    225:   }
    226:   PetscCall(PetscLogGpuTimeEnd());

    228:   if (imode == INSERT_VALUES) {
    229:     PetscCall(MatSeqAIJHIPSPARSERestoreArrayWrite(A, &Aa));
    230:     PetscCall(MatSeqAIJHIPSPARSERestoreArrayWrite(B, &Ba));
    231:   } else {
    232:     PetscCall(MatSeqAIJHIPSPARSERestoreArray(A, &Aa));
    233:     PetscCall(MatSeqAIJHIPSPARSERestoreArray(B, &Ba));
    234:   }
    235:   if (PetscMemTypeHost(memtype)) PetscCallHIP(hipFree((void *)v1));
    236:   mat->offloadmask = PETSC_OFFLOAD_GPU;
    237:   PetscFunctionReturn(PETSC_SUCCESS);
    238: }

    240: static PetscErrorCode MatMPIAIJGetLocalMatMerge_MPIAIJHIPSPARSE(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
    241: {
    242:   Mat             Ad, Ao;
    243:   const PetscInt *cmap;

    245:   PetscFunctionBegin;
    246:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
    247:   PetscCall(MatSeqAIJHIPSPARSEMergeMats(Ad, Ao, scall, A_loc));
    248:   if (glob) {
    249:     PetscInt cst, i, dn, on, *gidx;

    251:     PetscCall(MatGetLocalSize(Ad, NULL, &dn));
    252:     PetscCall(MatGetLocalSize(Ao, NULL, &on));
    253:     PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
    254:     PetscCall(PetscMalloc1(dn + on, &gidx));
    255:     for (i = 0; i < dn; i++) gidx[i] = cst + i;
    256:     for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
    257:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
    258:   }
    259:   PetscFunctionReturn(PETSC_SUCCESS);
    260: }

    262: static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJHIPSPARSE(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
    263: {
    264:   Mat_MPIAIJ          *b               = (Mat_MPIAIJ *)B->data;
    265:   Mat_MPIAIJHIPSPARSE *hipsparseStruct = (Mat_MPIAIJHIPSPARSE *)b->spptr;
    266:   PetscInt             i;

    268:   PetscFunctionBegin;
    269:   if (B->hash_active) {
    270:     B->ops[0]      = b->cops;
    271:     B->hash_active = PETSC_FALSE;
    272:   }
    273:   PetscCall(PetscLayoutSetUp(B->rmap));
    274:   PetscCall(PetscLayoutSetUp(B->cmap));
    275:   if (PetscDefined(USE_DEBUG) && d_nnz) {
    276:     for (i = 0; i < B->rmap->n; i++) PetscCheck(d_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "d_nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, d_nnz[i]);
    277:   }
    278:   if (PetscDefined(USE_DEBUG) && o_nnz) {
    279:     for (i = 0; i < B->rmap->n; i++) PetscCheck(o_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "o_nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, o_nnz[i]);
    280:   }
    281: #if defined(PETSC_USE_CTABLE)
    282:   PetscCall(PetscHMapIDestroy(&b->colmap));
    283: #else
    284:   PetscCall(PetscFree(b->colmap));
    285: #endif
    286:   PetscCall(PetscFree(b->garray));
    287:   PetscCall(VecDestroy(&b->lvec));
    288:   PetscCall(VecScatterDestroy(&b->Mvctx));
    289:   /* Because the B will have been resized we simply destroy it and create a new one each time */
    290:   PetscCall(MatDestroy(&b->B));
    291:   if (!b->A) {
    292:     PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
    293:     PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
    294:   }
    295:   if (!b->B) {
    296:     PetscMPIInt size;
    297:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
    298:     PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
    299:     PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
    300:   }
    301:   PetscCall(MatSetType(b->A, MATSEQAIJHIPSPARSE));
    302:   PetscCall(MatSetType(b->B, MATSEQAIJHIPSPARSE));
    303:   PetscCall(MatBindToCPU(b->A, B->boundtocpu));
    304:   PetscCall(MatBindToCPU(b->B, B->boundtocpu));
    305:   PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
    306:   PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
    307:   PetscCall(MatHIPSPARSESetFormat(b->A, MAT_HIPSPARSE_MULT, hipsparseStruct->diagGPUMatFormat));
    308:   PetscCall(MatHIPSPARSESetFormat(b->B, MAT_HIPSPARSE_MULT, hipsparseStruct->offdiagGPUMatFormat));
    309:   B->preallocated  = PETSC_TRUE;
    310:   B->was_assembled = PETSC_FALSE;
    311:   B->assembled     = PETSC_FALSE;
    312:   PetscFunctionReturn(PETSC_SUCCESS);
    313: }

    315: static PetscErrorCode MatMult_MPIAIJHIPSPARSE(Mat A, Vec xx, Vec yy)
    316: {
    317:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

    319:   PetscFunctionBegin;
    320:   PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
    321:   PetscCall((*a->A->ops->mult)(a->A, xx, yy));
    322:   PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
    323:   PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
    324:   PetscFunctionReturn(PETSC_SUCCESS);
    325: }

    327: static PetscErrorCode MatZeroEntries_MPIAIJHIPSPARSE(Mat A)
    328: {
    329:   Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;

    331:   PetscFunctionBegin;
    332:   PetscCall(MatZeroEntries(l->A));
    333:   PetscCall(MatZeroEntries(l->B));
    334:   PetscFunctionReturn(PETSC_SUCCESS);
    335: }

    337: static PetscErrorCode MatMultAdd_MPIAIJHIPSPARSE(Mat A, Vec xx, Vec yy, Vec zz)
    338: {
    339:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

    341:   PetscFunctionBegin;
    342:   PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
    343:   PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
    344:   PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
    345:   PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
    346:   PetscFunctionReturn(PETSC_SUCCESS);
    347: }

    349: static PetscErrorCode MatMultTranspose_MPIAIJHIPSPARSE(Mat A, Vec xx, Vec yy)
    350: {
    351:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

    353:   PetscFunctionBegin;
    354:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
    355:   PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
    356:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
    357:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
    358:   PetscFunctionReturn(PETSC_SUCCESS);
    359: }

    361: static PetscErrorCode MatHIPSPARSESetFormat_MPIAIJHIPSPARSE(Mat A, MatHIPSPARSEFormatOperation op, MatHIPSPARSEStorageFormat format)
    362: {
    363:   Mat_MPIAIJ          *a               = (Mat_MPIAIJ *)A->data;
    364:   Mat_MPIAIJHIPSPARSE *hipsparseStruct = (Mat_MPIAIJHIPSPARSE *)a->spptr;

    366:   PetscFunctionBegin;
    367:   switch (op) {
    368:   case MAT_HIPSPARSE_MULT_DIAG:
    369:     hipsparseStruct->diagGPUMatFormat = format;
    370:     break;
    371:   case MAT_HIPSPARSE_MULT_OFFDIAG:
    372:     hipsparseStruct->offdiagGPUMatFormat = format;
    373:     break;
    374:   case MAT_HIPSPARSE_ALL:
    375:     hipsparseStruct->diagGPUMatFormat    = format;
    376:     hipsparseStruct->offdiagGPUMatFormat = format;
    377:     break;
    378:   default:
    379:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported operation %d for MatHIPSPARSEFormatOperation. Only MAT_HIPSPARSE_MULT_DIAG, MAT_HIPSPARSE_MULT_DIAG, and MAT_HIPSPARSE_MULT_ALL are currently supported.", op);
    380:   }
    381:   PetscFunctionReturn(PETSC_SUCCESS);
    382: }

    384: static PetscErrorCode MatSetFromOptions_MPIAIJHIPSPARSE(Mat A, PetscOptionItems PetscOptionsObject)
    385: {
    386:   MatHIPSPARSEStorageFormat format;
    387:   PetscBool                 flg;
    388:   Mat_MPIAIJ               *a               = (Mat_MPIAIJ *)A->data;
    389:   Mat_MPIAIJHIPSPARSE      *hipsparseStruct = (Mat_MPIAIJHIPSPARSE *)a->spptr;

    391:   PetscFunctionBegin;
    392:   PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJHIPSPARSE options");
    393:   if (A->factortype == MAT_FACTOR_NONE) {
    394:     PetscCall(PetscOptionsEnum("-mat_hipsparse_mult_diag_storage_format", "sets storage format of the diagonal blocks of (mpi)aijhipsparse gpu matrices for SpMV", "MatHIPSPARSESetFormat", MatHIPSPARSEStorageFormats, (PetscEnum)hipsparseStruct->diagGPUMatFormat, (PetscEnum *)&format, &flg));
    395:     if (flg) PetscCall(MatHIPSPARSESetFormat(A, MAT_HIPSPARSE_MULT_DIAG, format));
    396:     PetscCall(PetscOptionsEnum("-mat_hipsparse_mult_offdiag_storage_format", "sets storage format of the off-diagonal blocks (mpi)aijhipsparse gpu matrices for SpMV", "MatHIPSPARSESetFormat", MatHIPSPARSEStorageFormats, (PetscEnum)hipsparseStruct->offdiagGPUMatFormat, (PetscEnum *)&format, &flg));
    397:     if (flg) PetscCall(MatHIPSPARSESetFormat(A, MAT_HIPSPARSE_MULT_OFFDIAG, format));
    398:     PetscCall(PetscOptionsEnum("-mat_hipsparse_storage_format", "sets storage format of the diagonal and off-diagonal blocks (mpi)aijhipsparse gpu matrices for SpMV", "MatHIPSPARSESetFormat", MatHIPSPARSEStorageFormats, (PetscEnum)hipsparseStruct->diagGPUMatFormat, (PetscEnum *)&format, &flg));
    399:     if (flg) PetscCall(MatHIPSPARSESetFormat(A, MAT_HIPSPARSE_ALL, format));
    400:   }
    401:   PetscOptionsHeadEnd();
    402:   PetscFunctionReturn(PETSC_SUCCESS);
    403: }

    405: static PetscErrorCode MatAssemblyEnd_MPIAIJHIPSPARSE(Mat A, MatAssemblyType mode)
    406: {
    407:   Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;

    409:   PetscFunctionBegin;
    410:   PetscCall(MatAssemblyEnd_MPIAIJ(A, mode));
    411:   if (mpiaij->lvec) PetscCall(VecSetType(mpiaij->lvec, VECSEQHIP));
    412:   PetscFunctionReturn(PETSC_SUCCESS);
    413: }

    415: static PetscErrorCode MatDestroy_MPIAIJHIPSPARSE(Mat A)
    416: {
    417:   Mat_MPIAIJ          *aij             = (Mat_MPIAIJ *)A->data;
    418:   Mat_MPIAIJHIPSPARSE *hipsparseStruct = (Mat_MPIAIJHIPSPARSE *)aij->spptr;

    420:   PetscFunctionBegin;
    421:   PetscCheck(hipsparseStruct, PETSC_COMM_SELF, PETSC_ERR_COR, "Missing spptr");
    422:   PetscCallCXX(delete hipsparseStruct);
    423:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJSetPreallocation_C", NULL));
    424:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", NULL));
    425:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
    426:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
    427:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatHIPSPARSESetFormat_C", NULL));
    428:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_mpiaijhipsparse_hypre_C", NULL));
    429:   PetscCall(MatDestroy_MPIAIJ(A));
    430:   PetscFunctionReturn(PETSC_SUCCESS);
    431: }

    433: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat B, MatType mtype, MatReuse reuse, Mat *newmat)
    434: {
    435:   Mat_MPIAIJ *a;
    436:   Mat         A;

    438:   PetscFunctionBegin;
    439:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_HIP));
    440:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(B, MAT_COPY_VALUES, newmat));
    441:   else if (reuse == MAT_REUSE_MATRIX) PetscCall(MatCopy(B, *newmat, SAME_NONZERO_PATTERN));
    442:   A             = *newmat;
    443:   A->boundtocpu = PETSC_FALSE;
    444:   PetscCall(PetscFree(A->defaultvectype));
    445:   PetscCall(PetscStrallocpy(VECHIP, &A->defaultvectype));

    447:   a = (Mat_MPIAIJ *)A->data;
    448:   if (a->A) PetscCall(MatSetType(a->A, MATSEQAIJHIPSPARSE));
    449:   if (a->B) PetscCall(MatSetType(a->B, MATSEQAIJHIPSPARSE));
    450:   if (a->lvec) PetscCall(VecSetType(a->lvec, VECSEQHIP));

    452:   if (reuse != MAT_REUSE_MATRIX && !a->spptr) PetscCallCXX(a->spptr = new Mat_MPIAIJHIPSPARSE);

    454:   A->ops->assemblyend           = MatAssemblyEnd_MPIAIJHIPSPARSE;
    455:   A->ops->mult                  = MatMult_MPIAIJHIPSPARSE;
    456:   A->ops->multadd               = MatMultAdd_MPIAIJHIPSPARSE;
    457:   A->ops->multtranspose         = MatMultTranspose_MPIAIJHIPSPARSE;
    458:   A->ops->setfromoptions        = MatSetFromOptions_MPIAIJHIPSPARSE;
    459:   A->ops->destroy               = MatDestroy_MPIAIJHIPSPARSE;
    460:   A->ops->zeroentries           = MatZeroEntries_MPIAIJHIPSPARSE;
    461:   A->ops->productsetfromoptions = MatProductSetFromOptions_MPIAIJBACKEND;

    463:   PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATMPIAIJHIPSPARSE));
    464:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", MatMPIAIJGetLocalMatMerge_MPIAIJHIPSPARSE));
    465:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJHIPSPARSE));
    466:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatHIPSPARSESetFormat_C", MatHIPSPARSESetFormat_MPIAIJHIPSPARSE));
    467:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJHIPSPARSE));
    468:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJHIPSPARSE));
    469: #if defined(PETSC_HAVE_HYPRE)
    470:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_mpiaijhipsparse_hypre_C", MatConvert_AIJ_HYPRE));
    471: #endif
    472:   PetscFunctionReturn(PETSC_SUCCESS);
    473: }

    475: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJHIPSPARSE(Mat A)
    476: {
    477:   PetscFunctionBegin;
    478:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_HIP));
    479:   PetscCall(MatCreate_MPIAIJ(A));
    480:   PetscCall(MatConvert_MPIAIJ_MPIAIJHIPSPARSE(A, MATMPIAIJHIPSPARSE, MAT_INPLACE_MATRIX, &A));
    481:   PetscFunctionReturn(PETSC_SUCCESS);
    482: }

    484: /*@
    485:   MatCreateAIJHIPSPARSE - Creates a sparse matrix in AIJ (compressed row) format
    486:   (the default parallel PETSc format).

    488:   Collective

    490:   Input Parameters:
    491: + comm  - MPI communicator, set to `PETSC_COMM_SELF`
    492: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
    493:            This value should be the same as the local size used in creating the
    494:            y vector for the matrix-vector product y = Ax.
    495: . n     - This value should be the same as the local size used in creating the
    496:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
    497:        calculated if `N` is given) For square matrices `n` is almost always `m`.
    498: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
    499: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
    500: . d_nz  - number of nonzeros per row (same for all rows), for the "diagonal" portion of the matrix
    501: . d_nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`, for the "diagonal" portion of the matrix
    502: . o_nz  - number of nonzeros per row (same for all rows), for the "off-diagonal" portion of the matrix
    503: - o_nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`, for the "off-diagonal" portion of the matrix

    505:   Output Parameter:
    506: . A - the matrix

    508:   Level: intermediate

    510:   Notes:
    511:   This matrix will ultimately pushed down to AMD GPUs and use the HIPSPARSE library for
    512:   calculations. For good matrix assembly performance the user should preallocate the matrix
    513:   storage by setting the parameter `nz` (or the array `nnz`).

    515:   It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
    516:   MatXXXXSetPreallocation() paradigm instead of this routine directly.
    517:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

    519:   If `d_nnz` (`o_nnz`) is given then `d_nz` (`o_nz`) is ignored

    521:   The `MATAIJ` format (compressed row storage), is fully compatible with standard Fortran
    522:   storage.  That is, the stored row and column indices can begin at
    523:   either one (as in Fortran) or zero.

    525:   Specify the preallocated storage with either `d_nz` (`o_nz`) or `d_nnz` (`o_nnz`) (not both).
    526:   Set `d_nz` (`o_nz`) = `PETSC_DEFAULT` and `d_nnz` (`o_nnz`) = `NULL` for PETSc to control dynamic memory
    527:   allocation.

    529: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATMPIAIJHIPSPARSE`, `MATAIJHIPSPARSE`
    530: @*/
    531: PetscErrorCode MatCreateAIJHIPSPARSE(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
    532: {
    533:   PetscMPIInt size;

    535:   PetscFunctionBegin;
    536:   PetscCall(MatCreate(comm, A));
    537:   PetscCall(MatSetSizes(*A, m, n, M, N));
    538:   PetscCallMPI(MPI_Comm_size(comm, &size));
    539:   if (size > 1) {
    540:     PetscCall(MatSetType(*A, MATMPIAIJHIPSPARSE));
    541:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
    542:   } else {
    543:     PetscCall(MatSetType(*A, MATSEQAIJHIPSPARSE));
    544:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
    545:   }
    546:   PetscFunctionReturn(PETSC_SUCCESS);
    547: }

    549: /*MC
    550:    MATAIJHIPSPARSE - A matrix type to be used for sparse matrices; it is as same as `MATMPIAIJHIPSPARSE`.

    552:    A matrix type whose data resides on GPUs. These matrices can be in either
    553:    CSR, ELL, or Hybrid format. All matrix calculations are performed on AMD GPUs using the HIPSPARSE library.

    555:    This matrix type is identical to `MATSEQAIJHIPSPARSE` when constructed with a single process communicator,
    556:    and `MATMPIAIJHIPSPARSE` otherwise.  As a result, for single process communicators,
    557:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
    558:    for communicators controlling multiple processes.  It is recommended that you call both of
    559:    the above preallocation routines for simplicity.

    561:    Options Database Keys:
    562: +  -mat_type mpiaijhipsparse - sets the matrix type to `MATMPIAIJHIPSPARSE`
    563: .  -mat_hipsparse_storage_format csr - sets the storage format of diagonal and off-diagonal matrices. Other options include ell (ellpack) or hyb (hybrid).
    564: .  -mat_hipsparse_mult_diag_storage_format csr - sets the storage format of diagonal matrix. Other options include ell (ellpack) or hyb (hybrid).
    565: -  -mat_hipsparse_mult_offdiag_storage_format csr - sets the storage format of off-diagonal matrix. Other options include ell (ellpack) or hyb (hybrid).

    567:   Level: beginner

    569: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJHIPSPARSE()`, `MATSEQAIJHIPSPARSE`, `MATMPIAIJHIPSPARSE`, `MatCreateSeqAIJHIPSPARSE()`, `MatHIPSPARSESetFormat()`, `MatHIPSPARSEStorageFormat`, `MatHIPSPARSEFormatOperation`
    570: M*/

    572: /*MC
    573:    MATMPIAIJHIPSPARSE - A matrix type to be used for sparse matrices; it is as same as `MATAIJHIPSPARSE`.

    575:   Level: beginner

    577: .seealso: [](ch_matrices), `Mat`, `MATAIJHIPSPARSE`, `MATSEQAIJHIPSPARSE`
    578: M*/