Actual source code: mpiaij.c

  1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
  2: #include <petsc/private/vecimpl.h>
  3: #include <petsc/private/sfimpl.h>
  4: #include <petsc/private/isimpl.h>
  5: #include <petscblaslapack.h>
  6: #include <petscsf.h>
  7: #include <petsc/private/hashmapi.h>

  9: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
 10: {
 11:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

 13:   PetscFunctionBegin;
 14:   PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
 15:   PetscCall(MatStashDestroy_Private(&mat->stash));
 16:   PetscCall(VecDestroy(&aij->diag));
 17:   PetscCall(MatDestroy(&aij->A));
 18:   PetscCall(MatDestroy(&aij->B));
 19: #if defined(PETSC_USE_CTABLE)
 20:   PetscCall(PetscHMapIDestroy(&aij->colmap));
 21: #else
 22:   PetscCall(PetscFree(aij->colmap));
 23: #endif
 24:   PetscCall(PetscFree(aij->garray));
 25:   PetscCall(VecDestroy(&aij->lvec));
 26:   PetscCall(VecScatterDestroy(&aij->Mvctx));
 27:   PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
 28:   PetscCall(PetscFree(aij->ld));

 30:   PetscCall(PetscFree(mat->data));

 32:   /* may be created by MatCreateMPIAIJSumSeqAIJSymbolic */
 33:   PetscCall(PetscObjectCompose((PetscObject)mat, "MatMergeSeqsToMPI", NULL));

 35:   PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
 36:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
 37:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
 38:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
 39:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
 40:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
 41:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
 42:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
 43:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
 44:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
 45: #if defined(PETSC_HAVE_CUDA)
 46:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
 47: #endif
 48: #if defined(PETSC_HAVE_HIP)
 49:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
 50: #endif
 51: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
 52:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
 53: #endif
 54:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
 55: #if defined(PETSC_HAVE_ELEMENTAL)
 56:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
 57: #endif
 58: #if defined(PETSC_HAVE_SCALAPACK)
 59:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
 60: #endif
 61: #if defined(PETSC_HAVE_HYPRE)
 62:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
 63:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
 64: #endif
 65:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
 66:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
 67:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
 68:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
 69:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
 70:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
 71: #if defined(PETSC_HAVE_MKL_SPARSE)
 72:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
 73: #endif
 74:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
 75:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
 76:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
 77:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
 78:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
 79:   PetscFunctionReturn(PETSC_SUCCESS);
 80: }

 82: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and  MatAssemblyEnd_MPI_Hash() */
 83: #define TYPE AIJ
 84: #define TYPE_AIJ
 85: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
 86: #undef TYPE
 87: #undef TYPE_AIJ

 89: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
 90: {
 91:   Mat B;

 93:   PetscFunctionBegin;
 94:   PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
 95:   PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
 96:   PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
 97:   PetscCall(MatDestroy(&B));
 98:   PetscFunctionReturn(PETSC_SUCCESS);
 99: }

101: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
102: {
103:   Mat B;

105:   PetscFunctionBegin;
106:   PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
107:   PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
108:   PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
109:   PetscFunctionReturn(PETSC_SUCCESS);
110: }

112: /*MC
113:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

115:    This matrix type is identical to` MATSEQAIJ` when constructed with a single process communicator,
116:    and `MATMPIAIJ` otherwise.  As a result, for single process communicators,
117:   `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
118:   for communicators controlling multiple processes.  It is recommended that you call both of
119:   the above preallocation routines for simplicity.

121:    Options Database Key:
122: . -mat_type aij - sets the matrix type to `MATAIJ` during a call to `MatSetFromOptions()`

124:   Developer Note:
125:   Level: beginner

127:     Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, `MATAIJKOKKOS`,and also automatically switches over to use inodes when
128:    enough exist.

130: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`
131: M*/

133: /*MC
134:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

136:    This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
137:    and `MATMPIAIJCRL` otherwise.  As a result, for single process communicators,
138:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
139:   for communicators controlling multiple processes.  It is recommended that you call both of
140:   the above preallocation routines for simplicity.

142:    Options Database Key:
143: . -mat_type aijcrl - sets the matrix type to `MATMPIAIJCRL` during a call to `MatSetFromOptions()`

145:   Level: beginner

147: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
148: M*/

150: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
151: {
152:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

154:   PetscFunctionBegin;
155: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
156:   A->boundtocpu = flg;
157: #endif
158:   if (a->A) PetscCall(MatBindToCPU(a->A, flg));
159:   if (a->B) PetscCall(MatBindToCPU(a->B, flg));

161:   /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
162:    * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
163:    * to differ from the parent matrix. */
164:   if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
165:   if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));

167:   PetscFunctionReturn(PETSC_SUCCESS);
168: }

170: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
171: {
172:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;

174:   PetscFunctionBegin;
175:   if (mat->A) {
176:     PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
177:     PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
178:   }
179:   PetscFunctionReturn(PETSC_SUCCESS);
180: }

182: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
183: {
184:   Mat_MPIAIJ      *mat = (Mat_MPIAIJ *)M->data;
185:   Mat_SeqAIJ      *a   = (Mat_SeqAIJ *)mat->A->data;
186:   Mat_SeqAIJ      *b   = (Mat_SeqAIJ *)mat->B->data;
187:   const PetscInt  *ia, *ib;
188:   const MatScalar *aa, *bb, *aav, *bav;
189:   PetscInt         na, nb, i, j, *rows, cnt = 0, n0rows;
190:   PetscInt         m = M->rmap->n, rstart = M->rmap->rstart;

192:   PetscFunctionBegin;
193:   *keptrows = NULL;

195:   ia = a->i;
196:   ib = b->i;
197:   PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
198:   PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
199:   for (i = 0; i < m; i++) {
200:     na = ia[i + 1] - ia[i];
201:     nb = ib[i + 1] - ib[i];
202:     if (!na && !nb) {
203:       cnt++;
204:       goto ok1;
205:     }
206:     aa = aav + ia[i];
207:     for (j = 0; j < na; j++) {
208:       if (aa[j] != 0.0) goto ok1;
209:     }
210:     bb = bav ? bav + ib[i] : NULL;
211:     for (j = 0; j < nb; j++) {
212:       if (bb[j] != 0.0) goto ok1;
213:     }
214:     cnt++;
215:   ok1:;
216:   }
217:   PetscCall(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
218:   if (!n0rows) {
219:     PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
220:     PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
221:     PetscFunctionReturn(PETSC_SUCCESS);
222:   }
223:   PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
224:   cnt = 0;
225:   for (i = 0; i < m; i++) {
226:     na = ia[i + 1] - ia[i];
227:     nb = ib[i + 1] - ib[i];
228:     if (!na && !nb) continue;
229:     aa = aav + ia[i];
230:     for (j = 0; j < na; j++) {
231:       if (aa[j] != 0.0) {
232:         rows[cnt++] = rstart + i;
233:         goto ok2;
234:       }
235:     }
236:     bb = bav ? bav + ib[i] : NULL;
237:     for (j = 0; j < nb; j++) {
238:       if (bb[j] != 0.0) {
239:         rows[cnt++] = rstart + i;
240:         goto ok2;
241:       }
242:     }
243:   ok2:;
244:   }
245:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
246:   PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
247:   PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
248:   PetscFunctionReturn(PETSC_SUCCESS);
249: }

251: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
252: {
253:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
254:   PetscBool   cong;

256:   PetscFunctionBegin;
257:   PetscCall(MatHasCongruentLayouts(Y, &cong));
258:   if (Y->assembled && cong) {
259:     PetscCall(MatDiagonalSet(aij->A, D, is));
260:   } else {
261:     PetscCall(MatDiagonalSet_Default(Y, D, is));
262:   }
263:   PetscFunctionReturn(PETSC_SUCCESS);
264: }

266: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
267: {
268:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
269:   PetscInt    i, rstart, nrows, *rows;

271:   PetscFunctionBegin;
272:   *zrows = NULL;
273:   PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
274:   PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
275:   for (i = 0; i < nrows; i++) rows[i] += rstart;
276:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
277:   PetscFunctionReturn(PETSC_SUCCESS);
278: }

280: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
281: {
282:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)A->data;
283:   PetscInt           i, m, n, *garray = aij->garray;
284:   Mat_SeqAIJ        *a_aij = (Mat_SeqAIJ *)aij->A->data;
285:   Mat_SeqAIJ        *b_aij = (Mat_SeqAIJ *)aij->B->data;
286:   PetscReal         *work;
287:   const PetscScalar *dummy;

289:   PetscFunctionBegin;
290:   PetscCall(MatGetSize(A, &m, &n));
291:   PetscCall(PetscCalloc1(n, &work));
292:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
293:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
294:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
295:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
296:   if (type == NORM_2) {
297:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
298:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
299:   } else if (type == NORM_1) {
300:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
301:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
302:   } else if (type == NORM_INFINITY) {
303:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
304:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
305:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
306:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
307:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
308:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
309:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
310:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
311:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
312:   if (type == NORM_INFINITY) {
313:     PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
314:   } else {
315:     PetscCall(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
316:   }
317:   PetscCall(PetscFree(work));
318:   if (type == NORM_2) {
319:     for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
320:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
321:     for (i = 0; i < n; i++) reductions[i] /= m;
322:   }
323:   PetscFunctionReturn(PETSC_SUCCESS);
324: }

326: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
327: {
328:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)A->data;
329:   IS              sis, gis;
330:   const PetscInt *isis, *igis;
331:   PetscInt        n, *iis, nsis, ngis, rstart, i;

333:   PetscFunctionBegin;
334:   PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
335:   PetscCall(MatFindNonzeroRows(a->B, &gis));
336:   PetscCall(ISGetSize(gis, &ngis));
337:   PetscCall(ISGetSize(sis, &nsis));
338:   PetscCall(ISGetIndices(sis, &isis));
339:   PetscCall(ISGetIndices(gis, &igis));

341:   PetscCall(PetscMalloc1(ngis + nsis, &iis));
342:   PetscCall(PetscArraycpy(iis, igis, ngis));
343:   PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
344:   n = ngis + nsis;
345:   PetscCall(PetscSortRemoveDupsInt(&n, iis));
346:   PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
347:   for (i = 0; i < n; i++) iis[i] += rstart;
348:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));

350:   PetscCall(ISRestoreIndices(sis, &isis));
351:   PetscCall(ISRestoreIndices(gis, &igis));
352:   PetscCall(ISDestroy(&sis));
353:   PetscCall(ISDestroy(&gis));
354:   PetscFunctionReturn(PETSC_SUCCESS);
355: }

357: /*
358:   Local utility routine that creates a mapping from the global column
359: number to the local number in the off-diagonal part of the local
360: storage of the matrix.  When PETSC_USE_CTABLE is used this is scalable at
361: a slightly higher hash table cost; without it it is not scalable (each processor
362: has an order N integer array but is fast to access.
363: */
364: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
365: {
366:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
367:   PetscInt    n   = aij->B->cmap->n, i;

369:   PetscFunctionBegin;
370:   PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
371: #if defined(PETSC_USE_CTABLE)
372:   PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
373:   for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
374: #else
375:   PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
376:   for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
377: #endif
378:   PetscFunctionReturn(PETSC_SUCCESS);
379: }

381: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
382:   do { \
383:     if (col <= lastcol1) low1 = 0; \
384:     else high1 = nrow1; \
385:     lastcol1 = col; \
386:     while (high1 - low1 > 5) { \
387:       t = (low1 + high1) / 2; \
388:       if (rp1[t] > col) high1 = t; \
389:       else low1 = t; \
390:     } \
391:     for (_i = low1; _i < high1; _i++) { \
392:       if (rp1[_i] > col) break; \
393:       if (rp1[_i] == col) { \
394:         if (addv == ADD_VALUES) { \
395:           ap1[_i] += value; \
396:           /* Not sure LogFlops will slow dow the code or not */ \
397:           (void)PetscLogFlops(1.0); \
398:         } else ap1[_i] = value; \
399:         goto a_noinsert; \
400:       } \
401:     } \
402:     if (value == 0.0 && ignorezeroentries && row != col) { \
403:       low1  = 0; \
404:       high1 = nrow1; \
405:       goto a_noinsert; \
406:     } \
407:     if (nonew == 1) { \
408:       low1  = 0; \
409:       high1 = nrow1; \
410:       goto a_noinsert; \
411:     } \
412:     PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
413:     MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
414:     N = nrow1++ - 1; \
415:     a->nz++; \
416:     high1++; \
417:     /* shift up all the later entries in this row */ \
418:     PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
419:     PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
420:     rp1[_i] = col; \
421:     ap1[_i] = value; \
422:     A->nonzerostate++; \
423:   a_noinsert:; \
424:     ailen[row] = nrow1; \
425:   } while (0)

427: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
428:   do { \
429:     if (col <= lastcol2) low2 = 0; \
430:     else high2 = nrow2; \
431:     lastcol2 = col; \
432:     while (high2 - low2 > 5) { \
433:       t = (low2 + high2) / 2; \
434:       if (rp2[t] > col) high2 = t; \
435:       else low2 = t; \
436:     } \
437:     for (_i = low2; _i < high2; _i++) { \
438:       if (rp2[_i] > col) break; \
439:       if (rp2[_i] == col) { \
440:         if (addv == ADD_VALUES) { \
441:           ap2[_i] += value; \
442:           (void)PetscLogFlops(1.0); \
443:         } else ap2[_i] = value; \
444:         goto b_noinsert; \
445:       } \
446:     } \
447:     if (value == 0.0 && ignorezeroentries) { \
448:       low2  = 0; \
449:       high2 = nrow2; \
450:       goto b_noinsert; \
451:     } \
452:     if (nonew == 1) { \
453:       low2  = 0; \
454:       high2 = nrow2; \
455:       goto b_noinsert; \
456:     } \
457:     PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
458:     MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
459:     N = nrow2++ - 1; \
460:     b->nz++; \
461:     high2++; \
462:     /* shift up all the later entries in this row */ \
463:     PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
464:     PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465:     rp2[_i] = col; \
466:     ap2[_i] = value; \
467:     B->nonzerostate++; \
468:   b_noinsert:; \
469:     bilen[row] = nrow2; \
470:   } while (0)

472: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
473: {
474:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)A->data;
475:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
476:   PetscInt     l, *garray                         = mat->garray, diag;
477:   PetscScalar *aa, *ba;

479:   PetscFunctionBegin;
480:   /* code only works for square matrices A */

482:   /* find size of row to the left of the diagonal part */
483:   PetscCall(MatGetOwnershipRange(A, &diag, NULL));
484:   row = row - diag;
485:   for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
486:     if (garray[b->j[b->i[row] + l]] > diag) break;
487:   }
488:   if (l) {
489:     PetscCall(MatSeqAIJGetArray(mat->B, &ba));
490:     PetscCall(PetscArraycpy(ba + b->i[row], v, l));
491:     PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
492:   }

494:   /* diagonal part */
495:   if (a->i[row + 1] - a->i[row]) {
496:     PetscCall(MatSeqAIJGetArray(mat->A, &aa));
497:     PetscCall(PetscArraycpy(aa + a->i[row], v + l, (a->i[row + 1] - a->i[row])));
498:     PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
499:   }

501:   /* right of diagonal part */
502:   if (b->i[row + 1] - b->i[row] - l) {
503:     PetscCall(MatSeqAIJGetArray(mat->B, &ba));
504:     PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
505:     PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
506:   }
507:   PetscFunctionReturn(PETSC_SUCCESS);
508: }

510: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
511: {
512:   Mat_MPIAIJ *aij   = (Mat_MPIAIJ *)mat->data;
513:   PetscScalar value = 0.0;
514:   PetscInt    i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
515:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
516:   PetscBool   roworiented = aij->roworiented;

518:   /* Some Variables required in the macro */
519:   Mat         A     = aij->A;
520:   Mat_SeqAIJ *a     = (Mat_SeqAIJ *)A->data;
521:   PetscInt   *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
522:   PetscBool   ignorezeroentries = a->ignorezeroentries;
523:   Mat         B                 = aij->B;
524:   Mat_SeqAIJ *b                 = (Mat_SeqAIJ *)B->data;
525:   PetscInt   *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
526:   MatScalar  *aa, *ba;
527:   PetscInt   *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
528:   PetscInt    nonew;
529:   MatScalar  *ap1, *ap2;

531:   PetscFunctionBegin;
532:   PetscCall(MatSeqAIJGetArray(A, &aa));
533:   PetscCall(MatSeqAIJGetArray(B, &ba));
534:   for (i = 0; i < m; i++) {
535:     if (im[i] < 0) continue;
536:     PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
537:     if (im[i] >= rstart && im[i] < rend) {
538:       row      = im[i] - rstart;
539:       lastcol1 = -1;
540:       rp1      = aj ? aj + ai[row] : NULL;
541:       ap1      = aa ? aa + ai[row] : NULL;
542:       rmax1    = aimax[row];
543:       nrow1    = ailen[row];
544:       low1     = 0;
545:       high1    = nrow1;
546:       lastcol2 = -1;
547:       rp2      = bj ? bj + bi[row] : NULL;
548:       ap2      = ba ? ba + bi[row] : NULL;
549:       rmax2    = bimax[row];
550:       nrow2    = bilen[row];
551:       low2     = 0;
552:       high2    = nrow2;

554:       for (j = 0; j < n; j++) {
555:         if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
556:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
557:         if (in[j] >= cstart && in[j] < cend) {
558:           col   = in[j] - cstart;
559:           nonew = a->nonew;
560:           MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
561:         } else if (in[j] < 0) {
562:           continue;
563:         } else {
564:           PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
565:           if (mat->was_assembled) {
566:             if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
567: #if defined(PETSC_USE_CTABLE)
568:             PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
569:             col--;
570: #else
571:             col = aij->colmap[in[j]] - 1;
572: #endif
573:             if (col < 0 && !((Mat_SeqAIJ *)(aij->B->data))->nonew) { /* col < 0 means in[j] is a new col for B */
574:               PetscCall(MatDisAssemble_MPIAIJ(mat));                 /* Change aij->B from reduced/local format to expanded/global format */
575:               col = in[j];
576:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
577:               B     = aij->B;
578:               b     = (Mat_SeqAIJ *)B->data;
579:               bimax = b->imax;
580:               bi    = b->i;
581:               bilen = b->ilen;
582:               bj    = b->j;
583:               ba    = b->a;
584:               rp2   = bj + bi[row];
585:               ap2   = ba + bi[row];
586:               rmax2 = bimax[row];
587:               nrow2 = bilen[row];
588:               low2  = 0;
589:               high2 = nrow2;
590:               bm    = aij->B->rmap->n;
591:               ba    = b->a;
592:             } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
593:               if (1 == ((Mat_SeqAIJ *)(aij->B->data))->nonew) {
594:                 PetscCall(PetscInfo(mat, "Skipping of insertion of new nonzero location in off-diagonal portion of matrix %g(%" PetscInt_FMT ",%" PetscInt_FMT ")\n", (double)PetscRealPart(value), im[i], in[j]));
595:               } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
596:             }
597:           } else col = in[j];
598:           nonew = b->nonew;
599:           MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
600:         }
601:       }
602:     } else {
603:       PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
604:       if (!aij->donotstash) {
605:         mat->assembled = PETSC_FALSE;
606:         if (roworiented) {
607:           PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v ? v + i * n : NULL, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
608:         } else {
609:           PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v ? v + i : NULL, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
610:         }
611:       }
612:     }
613:   }
614:   PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
615:   PetscCall(MatSeqAIJRestoreArray(B, &ba));
616:   PetscFunctionReturn(PETSC_SUCCESS);
617: }

619: /*
620:     This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
621:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
622:     No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
623: */
624: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
625: {
626:   Mat_MPIAIJ *aij    = (Mat_MPIAIJ *)mat->data;
627:   Mat         A      = aij->A; /* diagonal part of the matrix */
628:   Mat         B      = aij->B; /* off-diagonal part of the matrix */
629:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
630:   Mat_SeqAIJ *b      = (Mat_SeqAIJ *)B->data;
631:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
632:   PetscInt   *ailen = a->ilen, *aj = a->j;
633:   PetscInt   *bilen = b->ilen, *bj = b->j;
634:   PetscInt    am          = aij->A->rmap->n, j;
635:   PetscInt    diag_so_far = 0, dnz;
636:   PetscInt    offd_so_far = 0, onz;

638:   PetscFunctionBegin;
639:   /* Iterate over all rows of the matrix */
640:   for (j = 0; j < am; j++) {
641:     dnz = onz = 0;
642:     /*  Iterate over all non-zero columns of the current row */
643:     for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
644:       /* If column is in the diagonal */
645:       if (mat_j[col] >= cstart && mat_j[col] < cend) {
646:         aj[diag_so_far++] = mat_j[col] - cstart;
647:         dnz++;
648:       } else { /* off-diagonal entries */
649:         bj[offd_so_far++] = mat_j[col];
650:         onz++;
651:       }
652:     }
653:     ailen[j] = dnz;
654:     bilen[j] = onz;
655:   }
656:   PetscFunctionReturn(PETSC_SUCCESS);
657: }

659: /*
660:     This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
661:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
662:     No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
663:     Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
664:     would not be true and the more complex MatSetValues_MPIAIJ has to be used.
665: */
666: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
667: {
668:   Mat_MPIAIJ  *aij  = (Mat_MPIAIJ *)mat->data;
669:   Mat          A    = aij->A; /* diagonal part of the matrix */
670:   Mat          B    = aij->B; /* off-diagonal part of the matrix */
671:   Mat_SeqAIJ  *aijd = (Mat_SeqAIJ *)(aij->A)->data, *aijo = (Mat_SeqAIJ *)(aij->B)->data;
672:   Mat_SeqAIJ  *a      = (Mat_SeqAIJ *)A->data;
673:   Mat_SeqAIJ  *b      = (Mat_SeqAIJ *)B->data;
674:   PetscInt     cstart = mat->cmap->rstart, cend = mat->cmap->rend;
675:   PetscInt    *ailen = a->ilen, *aj = a->j;
676:   PetscInt    *bilen = b->ilen, *bj = b->j;
677:   PetscInt     am          = aij->A->rmap->n, j;
678:   PetscInt    *full_diag_i = aijd->i, *full_offd_i = aijo->i; /* These variables can also include non-local elements, which are set at a later point. */
679:   PetscInt     col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
680:   PetscScalar *aa = a->a, *ba = b->a;

682:   PetscFunctionBegin;
683:   /* Iterate over all rows of the matrix */
684:   for (j = 0; j < am; j++) {
685:     dnz_row = onz_row = 0;
686:     rowstart_offd     = full_offd_i[j];
687:     rowstart_diag     = full_diag_i[j];
688:     /*  Iterate over all non-zero columns of the current row */
689:     for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
690:       /* If column is in the diagonal */
691:       if (mat_j[col] >= cstart && mat_j[col] < cend) {
692:         aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
693:         aa[rowstart_diag + dnz_row] = mat_a[col];
694:         dnz_row++;
695:       } else { /* off-diagonal entries */
696:         bj[rowstart_offd + onz_row] = mat_j[col];
697:         ba[rowstart_offd + onz_row] = mat_a[col];
698:         onz_row++;
699:       }
700:     }
701:     ailen[j] = dnz_row;
702:     bilen[j] = onz_row;
703:   }
704:   PetscFunctionReturn(PETSC_SUCCESS);
705: }

707: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
708: {
709:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
710:   PetscInt    i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
711:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;

713:   PetscFunctionBegin;
714:   for (i = 0; i < m; i++) {
715:     if (idxm[i] < 0) continue; /* negative row */
716:     PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
717:     PetscCheck(idxm[i] >= rstart && idxm[i] < rend, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported, row requested %" PetscInt_FMT " range [%" PetscInt_FMT " %" PetscInt_FMT ")", idxm[i], rstart, rend);
718:     row = idxm[i] - rstart;
719:     for (j = 0; j < n; j++) {
720:       if (idxn[j] < 0) continue; /* negative column */
721:       PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
722:       if (idxn[j] >= cstart && idxn[j] < cend) {
723:         col = idxn[j] - cstart;
724:         PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
725:       } else {
726:         if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
727: #if defined(PETSC_USE_CTABLE)
728:         PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
729:         col--;
730: #else
731:         col = aij->colmap[idxn[j]] - 1;
732: #endif
733:         if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
734:         else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
735:       }
736:     }
737:   }
738:   PetscFunctionReturn(PETSC_SUCCESS);
739: }

741: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
742: {
743:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
744:   PetscInt    nstash, reallocs;

746:   PetscFunctionBegin;
747:   if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);

749:   PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
750:   PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
751:   PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
752:   PetscFunctionReturn(PETSC_SUCCESS);
753: }

755: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
756: {
757:   Mat_MPIAIJ  *aij = (Mat_MPIAIJ *)mat->data;
758:   PetscMPIInt  n;
759:   PetscInt     i, j, rstart, ncols, flg;
760:   PetscInt    *row, *col;
761:   PetscBool    other_disassembled;
762:   PetscScalar *val;

764:   /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */

766:   PetscFunctionBegin;
767:   if (!aij->donotstash && !mat->nooffprocentries) {
768:     while (1) {
769:       PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
770:       if (!flg) break;

772:       for (i = 0; i < n;) {
773:         /* Now identify the consecutive vals belonging to the same row */
774:         for (j = i, rstart = row[j]; j < n; j++) {
775:           if (row[j] != rstart) break;
776:         }
777:         if (j < n) ncols = j - i;
778:         else ncols = n - i;
779:         /* Now assemble all these values with a single function call */
780:         PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
781:         i = j;
782:       }
783:     }
784:     PetscCall(MatStashScatterEnd_Private(&mat->stash));
785:   }
786: #if defined(PETSC_HAVE_DEVICE)
787:   if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
788:   /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
789:   if (mat->boundtocpu) {
790:     PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
791:     PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
792:   }
793: #endif
794:   PetscCall(MatAssemblyBegin(aij->A, mode));
795:   PetscCall(MatAssemblyEnd(aij->A, mode));

797:   /* determine if any processor has disassembled, if so we must
798:      also disassemble ourself, in order that we may reassemble. */
799:   /*
800:      if nonzero structure of submatrix B cannot change then we know that
801:      no processor disassembled thus we can skip this stuff
802:   */
803:   if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
804:     PetscCall(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
805:     if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
806:       PetscCall(MatDisAssemble_MPIAIJ(mat));
807:     }
808:   }
809:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
810:   PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
811: #if defined(PETSC_HAVE_DEVICE)
812:   if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
813: #endif
814:   PetscCall(MatAssemblyBegin(aij->B, mode));
815:   PetscCall(MatAssemblyEnd(aij->B, mode));

817:   PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));

819:   aij->rowvalues = NULL;

821:   PetscCall(VecDestroy(&aij->diag));

823:   /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
824:   if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
825:     PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
826:     PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
827:   }
828: #if defined(PETSC_HAVE_DEVICE)
829:   mat->offloadmask = PETSC_OFFLOAD_BOTH;
830: #endif
831:   PetscFunctionReturn(PETSC_SUCCESS);
832: }

834: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
835: {
836:   Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;

838:   PetscFunctionBegin;
839:   PetscCall(MatZeroEntries(l->A));
840:   PetscCall(MatZeroEntries(l->B));
841:   PetscFunctionReturn(PETSC_SUCCESS);
842: }

844: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
845: {
846:   Mat_MPIAIJ      *mat = (Mat_MPIAIJ *)A->data;
847:   PetscObjectState sA, sB;
848:   PetscInt        *lrows;
849:   PetscInt         r, len;
850:   PetscBool        cong, lch, gch;

852:   PetscFunctionBegin;
853:   /* get locally owned rows */
854:   PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
855:   PetscCall(MatHasCongruentLayouts(A, &cong));
856:   /* fix right hand side if needed */
857:   if (x && b) {
858:     const PetscScalar *xx;
859:     PetscScalar       *bb;

861:     PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
862:     PetscCall(VecGetArrayRead(x, &xx));
863:     PetscCall(VecGetArray(b, &bb));
864:     for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
865:     PetscCall(VecRestoreArrayRead(x, &xx));
866:     PetscCall(VecRestoreArray(b, &bb));
867:   }

869:   sA = mat->A->nonzerostate;
870:   sB = mat->B->nonzerostate;

872:   if (diag != 0.0 && cong) {
873:     PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
874:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
875:   } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
876:     Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
877:     Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
878:     PetscInt    nnwA, nnwB;
879:     PetscBool   nnzA, nnzB;

881:     nnwA = aijA->nonew;
882:     nnwB = aijB->nonew;
883:     nnzA = aijA->keepnonzeropattern;
884:     nnzB = aijB->keepnonzeropattern;
885:     if (!nnzA) {
886:       PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
887:       aijA->nonew = 0;
888:     }
889:     if (!nnzB) {
890:       PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
891:       aijB->nonew = 0;
892:     }
893:     /* Must zero here before the next loop */
894:     PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
895:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
896:     for (r = 0; r < len; ++r) {
897:       const PetscInt row = lrows[r] + A->rmap->rstart;
898:       if (row >= A->cmap->N) continue;
899:       PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
900:     }
901:     aijA->nonew = nnwA;
902:     aijB->nonew = nnwB;
903:   } else {
904:     PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
905:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
906:   }
907:   PetscCall(PetscFree(lrows));
908:   PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
909:   PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));

911:   /* reduce nonzerostate */
912:   lch = (PetscBool)(sA != mat->A->nonzerostate || sB != mat->B->nonzerostate);
913:   PetscCall(MPIU_Allreduce(&lch, &gch, 1, MPIU_BOOL, MPI_LOR, PetscObjectComm((PetscObject)A)));
914:   if (gch) A->nonzerostate++;
915:   PetscFunctionReturn(PETSC_SUCCESS);
916: }

918: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
919: {
920:   Mat_MPIAIJ        *l = (Mat_MPIAIJ *)A->data;
921:   PetscMPIInt        n = A->rmap->n;
922:   PetscInt           i, j, r, m, len = 0;
923:   PetscInt          *lrows, *owners = A->rmap->range;
924:   PetscMPIInt        p = 0;
925:   PetscSFNode       *rrows;
926:   PetscSF            sf;
927:   const PetscScalar *xx;
928:   PetscScalar       *bb, *mask, *aij_a;
929:   Vec                xmask, lmask;
930:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ *)l->B->data;
931:   const PetscInt    *aj, *ii, *ridx;
932:   PetscScalar       *aa;

934:   PetscFunctionBegin;
935:   /* Create SF where leaves are input rows and roots are owned rows */
936:   PetscCall(PetscMalloc1(n, &lrows));
937:   for (r = 0; r < n; ++r) lrows[r] = -1;
938:   PetscCall(PetscMalloc1(N, &rrows));
939:   for (r = 0; r < N; ++r) {
940:     const PetscInt idx = rows[r];
941:     PetscCheck(idx >= 0 && A->rmap->N > idx, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range [0,%" PetscInt_FMT ")", idx, A->rmap->N);
942:     if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
943:       PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
944:     }
945:     rrows[r].rank  = p;
946:     rrows[r].index = rows[r] - owners[p];
947:   }
948:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
949:   PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
950:   /* Collect flags for rows to be zeroed */
951:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
952:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
953:   PetscCall(PetscSFDestroy(&sf));
954:   /* Compress and put in row numbers */
955:   for (r = 0; r < n; ++r)
956:     if (lrows[r] >= 0) lrows[len++] = r;
957:   /* zero diagonal part of matrix */
958:   PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
959:   /* handle off-diagonal part of matrix */
960:   PetscCall(MatCreateVecs(A, &xmask, NULL));
961:   PetscCall(VecDuplicate(l->lvec, &lmask));
962:   PetscCall(VecGetArray(xmask, &bb));
963:   for (i = 0; i < len; i++) bb[lrows[i]] = 1;
964:   PetscCall(VecRestoreArray(xmask, &bb));
965:   PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
966:   PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
967:   PetscCall(VecDestroy(&xmask));
968:   if (x && b) { /* this code is buggy when the row and column layout don't match */
969:     PetscBool cong;

971:     PetscCall(MatHasCongruentLayouts(A, &cong));
972:     PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
973:     PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
974:     PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
975:     PetscCall(VecGetArrayRead(l->lvec, &xx));
976:     PetscCall(VecGetArray(b, &bb));
977:   }
978:   PetscCall(VecGetArray(lmask, &mask));
979:   /* remove zeroed rows of off-diagonal matrix */
980:   PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
981:   ii = aij->i;
982:   for (i = 0; i < len; i++) PetscCall(PetscArrayzero(aij_a + ii[lrows[i]], ii[lrows[i] + 1] - ii[lrows[i]]));
983:   /* loop over all elements of off process part of matrix zeroing removed columns*/
984:   if (aij->compressedrow.use) {
985:     m    = aij->compressedrow.nrows;
986:     ii   = aij->compressedrow.i;
987:     ridx = aij->compressedrow.rindex;
988:     for (i = 0; i < m; i++) {
989:       n  = ii[i + 1] - ii[i];
990:       aj = aij->j + ii[i];
991:       aa = aij_a + ii[i];

993:       for (j = 0; j < n; j++) {
994:         if (PetscAbsScalar(mask[*aj])) {
995:           if (b) bb[*ridx] -= *aa * xx[*aj];
996:           *aa = 0.0;
997:         }
998:         aa++;
999:         aj++;
1000:       }
1001:       ridx++;
1002:     }
1003:   } else { /* do not use compressed row format */
1004:     m = l->B->rmap->n;
1005:     for (i = 0; i < m; i++) {
1006:       n  = ii[i + 1] - ii[i];
1007:       aj = aij->j + ii[i];
1008:       aa = aij_a + ii[i];
1009:       for (j = 0; j < n; j++) {
1010:         if (PetscAbsScalar(mask[*aj])) {
1011:           if (b) bb[i] -= *aa * xx[*aj];
1012:           *aa = 0.0;
1013:         }
1014:         aa++;
1015:         aj++;
1016:       }
1017:     }
1018:   }
1019:   if (x && b) {
1020:     PetscCall(VecRestoreArray(b, &bb));
1021:     PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1022:   }
1023:   PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1024:   PetscCall(VecRestoreArray(lmask, &mask));
1025:   PetscCall(VecDestroy(&lmask));
1026:   PetscCall(PetscFree(lrows));

1028:   /* only change matrix nonzero state if pattern was allowed to be changed */
1029:   if (!((Mat_SeqAIJ *)(l->A->data))->keepnonzeropattern) {
1030:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1031:     PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1032:   }
1033:   PetscFunctionReturn(PETSC_SUCCESS);
1034: }

1036: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1037: {
1038:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1039:   PetscInt    nt;
1040:   VecScatter  Mvctx = a->Mvctx;

1042:   PetscFunctionBegin;
1043:   PetscCall(VecGetLocalSize(xx, &nt));
1044:   PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt);
1045:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1046:   PetscUseTypeMethod(a->A, mult, xx, yy);
1047:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1048:   PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1049:   PetscFunctionReturn(PETSC_SUCCESS);
1050: }

1052: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1053: {
1054:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1056:   PetscFunctionBegin;
1057:   PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1058:   PetscFunctionReturn(PETSC_SUCCESS);
1059: }

1061: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1062: {
1063:   Mat_MPIAIJ *a     = (Mat_MPIAIJ *)A->data;
1064:   VecScatter  Mvctx = a->Mvctx;

1066:   PetscFunctionBegin;
1067:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1068:   PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1069:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1070:   PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1071:   PetscFunctionReturn(PETSC_SUCCESS);
1072: }

1074: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1075: {
1076:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1078:   PetscFunctionBegin;
1079:   /* do nondiagonal part */
1080:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1081:   /* do local part */
1082:   PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1083:   /* add partial results together */
1084:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1085:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1086:   PetscFunctionReturn(PETSC_SUCCESS);
1087: }

1089: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1090: {
1091:   MPI_Comm    comm;
1092:   Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1093:   Mat         Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1094:   IS          Me, Notme;
1095:   PetscInt    M, N, first, last, *notme, i;
1096:   PetscBool   lf;
1097:   PetscMPIInt size;

1099:   PetscFunctionBegin;
1100:   /* Easy test: symmetric diagonal block */
1101:   PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1102:   PetscCall(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1103:   if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1104:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1105:   PetscCallMPI(MPI_Comm_size(comm, &size));
1106:   if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);

1108:   /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1109:   PetscCall(MatGetSize(Amat, &M, &N));
1110:   PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1111:   PetscCall(PetscMalloc1(N - last + first, &notme));
1112:   for (i = 0; i < first; i++) notme[i] = i;
1113:   for (i = last; i < M; i++) notme[i - last + first] = i;
1114:   PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1115:   PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1116:   PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1117:   Aoff = Aoffs[0];
1118:   PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1119:   Boff = Boffs[0];
1120:   PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1121:   PetscCall(MatDestroyMatrices(1, &Aoffs));
1122:   PetscCall(MatDestroyMatrices(1, &Boffs));
1123:   PetscCall(ISDestroy(&Me));
1124:   PetscCall(ISDestroy(&Notme));
1125:   PetscCall(PetscFree(notme));
1126:   PetscFunctionReturn(PETSC_SUCCESS);
1127: }

1129: static PetscErrorCode MatIsSymmetric_MPIAIJ(Mat A, PetscReal tol, PetscBool *f)
1130: {
1131:   PetscFunctionBegin;
1132:   PetscCall(MatIsTranspose_MPIAIJ(A, A, tol, f));
1133:   PetscFunctionReturn(PETSC_SUCCESS);
1134: }

1136: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1137: {
1138:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1140:   PetscFunctionBegin;
1141:   /* do nondiagonal part */
1142:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1143:   /* do local part */
1144:   PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1145:   /* add partial results together */
1146:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1147:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1148:   PetscFunctionReturn(PETSC_SUCCESS);
1149: }

1151: /*
1152:   This only works correctly for square matrices where the subblock A->A is the
1153:    diagonal block
1154: */
1155: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1156: {
1157:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1159:   PetscFunctionBegin;
1160:   PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1161:   PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition");
1162:   PetscCall(MatGetDiagonal(a->A, v));
1163:   PetscFunctionReturn(PETSC_SUCCESS);
1164: }

1166: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1167: {
1168:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1170:   PetscFunctionBegin;
1171:   PetscCall(MatScale(a->A, aa));
1172:   PetscCall(MatScale(a->B, aa));
1173:   PetscFunctionReturn(PETSC_SUCCESS);
1174: }

1176: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1177: {
1178:   Mat_MPIAIJ        *aij    = (Mat_MPIAIJ *)mat->data;
1179:   Mat_SeqAIJ        *A      = (Mat_SeqAIJ *)aij->A->data;
1180:   Mat_SeqAIJ        *B      = (Mat_SeqAIJ *)aij->B->data;
1181:   const PetscInt    *garray = aij->garray;
1182:   const PetscScalar *aa, *ba;
1183:   PetscInt           header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1184:   PetscInt64         nz, hnz;
1185:   PetscInt          *rowlens;
1186:   PetscInt          *colidxs;
1187:   PetscScalar       *matvals;
1188:   PetscMPIInt        rank;

1190:   PetscFunctionBegin;
1191:   PetscCall(PetscViewerSetUp(viewer));

1193:   M  = mat->rmap->N;
1194:   N  = mat->cmap->N;
1195:   m  = mat->rmap->n;
1196:   rs = mat->rmap->rstart;
1197:   cs = mat->cmap->rstart;
1198:   nz = A->nz + B->nz;

1200:   /* write matrix header */
1201:   header[0] = MAT_FILE_CLASSID;
1202:   header[1] = M;
1203:   header[2] = N;
1204:   PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1205:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1206:   if (rank == 0) {
1207:     if (hnz > PETSC_MAX_INT) header[3] = PETSC_MAX_INT;
1208:     else header[3] = (PetscInt)hnz;
1209:   }
1210:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

1212:   /* fill in and store row lengths  */
1213:   PetscCall(PetscMalloc1(m, &rowlens));
1214:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1215:   PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1216:   PetscCall(PetscFree(rowlens));

1218:   /* fill in and store column indices */
1219:   PetscCall(PetscMalloc1(nz, &colidxs));
1220:   for (cnt = 0, i = 0; i < m; i++) {
1221:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1222:       if (garray[B->j[jb]] > cs) break;
1223:       colidxs[cnt++] = garray[B->j[jb]];
1224:     }
1225:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1226:     for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1227:   }
1228:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1229:   PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1230:   PetscCall(PetscFree(colidxs));

1232:   /* fill in and store nonzero values */
1233:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1234:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1235:   PetscCall(PetscMalloc1(nz, &matvals));
1236:   for (cnt = 0, i = 0; i < m; i++) {
1237:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1238:       if (garray[B->j[jb]] > cs) break;
1239:       matvals[cnt++] = ba[jb];
1240:     }
1241:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1242:     for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1243:   }
1244:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1245:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1246:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1247:   PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1248:   PetscCall(PetscFree(matvals));

1250:   /* write block size option to the viewer's .info file */
1251:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1252:   PetscFunctionReturn(PETSC_SUCCESS);
1253: }

1255: #include <petscdraw.h>
1256: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1257: {
1258:   Mat_MPIAIJ       *aij  = (Mat_MPIAIJ *)mat->data;
1259:   PetscMPIInt       rank = aij->rank, size = aij->size;
1260:   PetscBool         isdraw, iascii, isbinary;
1261:   PetscViewer       sviewer;
1262:   PetscViewerFormat format;

1264:   PetscFunctionBegin;
1265:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1266:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1267:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1268:   if (iascii) {
1269:     PetscCall(PetscViewerGetFormat(viewer, &format));
1270:     if (format == PETSC_VIEWER_LOAD_BALANCE) {
1271:       PetscInt i, nmax = 0, nmin = PETSC_MAX_INT, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)(aij->A->data))->nz + ((Mat_SeqAIJ *)(aij->B->data))->nz;
1272:       PetscCall(PetscMalloc1(size, &nz));
1273:       PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1274:       for (i = 0; i < (PetscInt)size; i++) {
1275:         nmax = PetscMax(nmax, nz[i]);
1276:         nmin = PetscMin(nmin, nz[i]);
1277:         navg += nz[i];
1278:       }
1279:       PetscCall(PetscFree(nz));
1280:       navg = navg / size;
1281:       PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT "  avg %" PetscInt_FMT "  max %" PetscInt_FMT "\n", nmin, navg, nmax));
1282:       PetscFunctionReturn(PETSC_SUCCESS);
1283:     }
1284:     PetscCall(PetscViewerGetFormat(viewer, &format));
1285:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1286:       MatInfo   info;
1287:       PetscInt *inodes = NULL;

1289:       PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1290:       PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1291:       PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1292:       PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1293:       if (!inodes) {
1294:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1295:                                                      (double)info.memory));
1296:       } else {
1297:         PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %g, using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used, (PetscInt)info.nz_allocated,
1298:                                                      (double)info.memory));
1299:       }
1300:       PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1301:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1302:       PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1303:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1304:       PetscCall(PetscViewerFlush(viewer));
1305:       PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1306:       PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1307:       PetscCall(VecScatterView(aij->Mvctx, viewer));
1308:       PetscFunctionReturn(PETSC_SUCCESS);
1309:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1310:       PetscInt inodecount, inodelimit, *inodes;
1311:       PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1312:       if (inodes) {
1313:         PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1314:       } else {
1315:         PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1316:       }
1317:       PetscFunctionReturn(PETSC_SUCCESS);
1318:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1319:       PetscFunctionReturn(PETSC_SUCCESS);
1320:     }
1321:   } else if (isbinary) {
1322:     if (size == 1) {
1323:       PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1324:       PetscCall(MatView(aij->A, viewer));
1325:     } else {
1326:       PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1327:     }
1328:     PetscFunctionReturn(PETSC_SUCCESS);
1329:   } else if (iascii && size == 1) {
1330:     PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1331:     PetscCall(MatView(aij->A, viewer));
1332:     PetscFunctionReturn(PETSC_SUCCESS);
1333:   } else if (isdraw) {
1334:     PetscDraw draw;
1335:     PetscBool isnull;
1336:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1337:     PetscCall(PetscDrawIsNull(draw, &isnull));
1338:     if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1339:   }

1341:   { /* assemble the entire matrix onto first processor */
1342:     Mat A = NULL, Av;
1343:     IS  isrow, iscol;

1345:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1346:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1347:     PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1348:     PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1349:     /*  The commented code uses MatCreateSubMatrices instead */
1350:     /*
1351:     Mat *AA, A = NULL, Av;
1352:     IS  isrow,iscol;

1354:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1355:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1356:     PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1357:     if (rank == 0) {
1358:        PetscCall(PetscObjectReference((PetscObject)AA[0]));
1359:        A    = AA[0];
1360:        Av   = AA[0];
1361:     }
1362:     PetscCall(MatDestroySubMatrices(1,&AA));
1363: */
1364:     PetscCall(ISDestroy(&iscol));
1365:     PetscCall(ISDestroy(&isrow));
1366:     /*
1367:        Everyone has to call to draw the matrix since the graphics waits are
1368:        synchronized across all processors that share the PetscDraw object
1369:     */
1370:     PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1371:     if (rank == 0) {
1372:       if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1373:       PetscCall(MatView_SeqAIJ(Av, sviewer));
1374:     }
1375:     PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1376:     PetscCall(PetscViewerFlush(viewer));
1377:     PetscCall(MatDestroy(&A));
1378:   }
1379:   PetscFunctionReturn(PETSC_SUCCESS);
1380: }

1382: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1383: {
1384:   PetscBool iascii, isdraw, issocket, isbinary;

1386:   PetscFunctionBegin;
1387:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1388:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1389:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1390:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1391:   if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1392:   PetscFunctionReturn(PETSC_SUCCESS);
1393: }

1395: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1396: {
1397:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1398:   Vec         bb1 = NULL;
1399:   PetscBool   hasop;

1401:   PetscFunctionBegin;
1402:   if (flag == SOR_APPLY_UPPER) {
1403:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1404:     PetscFunctionReturn(PETSC_SUCCESS);
1405:   }

1407:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));

1409:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1410:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1411:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1412:       its--;
1413:     }

1415:     while (its--) {
1416:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1417:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1419:       /* update rhs: bb1 = bb - B*x */
1420:       PetscCall(VecScale(mat->lvec, -1.0));
1421:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1423:       /* local sweep */
1424:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1425:     }
1426:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1427:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1428:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1429:       its--;
1430:     }
1431:     while (its--) {
1432:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1433:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1435:       /* update rhs: bb1 = bb - B*x */
1436:       PetscCall(VecScale(mat->lvec, -1.0));
1437:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1439:       /* local sweep */
1440:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1441:     }
1442:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1443:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1444:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1445:       its--;
1446:     }
1447:     while (its--) {
1448:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1449:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1451:       /* update rhs: bb1 = bb - B*x */
1452:       PetscCall(VecScale(mat->lvec, -1.0));
1453:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1455:       /* local sweep */
1456:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1457:     }
1458:   } else if (flag & SOR_EISENSTAT) {
1459:     Vec xx1;

1461:     PetscCall(VecDuplicate(bb, &xx1));
1462:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));

1464:     PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1465:     PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1466:     if (!mat->diag) {
1467:       PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1468:       PetscCall(MatGetDiagonal(matin, mat->diag));
1469:     }
1470:     PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1471:     if (hasop) {
1472:       PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1473:     } else {
1474:       PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1475:     }
1476:     PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));

1478:     PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));

1480:     /* local sweep */
1481:     PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1482:     PetscCall(VecAXPY(xx, 1.0, xx1));
1483:     PetscCall(VecDestroy(&xx1));
1484:   } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");

1486:   PetscCall(VecDestroy(&bb1));

1488:   matin->factorerrortype = mat->A->factorerrortype;
1489:   PetscFunctionReturn(PETSC_SUCCESS);
1490: }

1492: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1493: {
1494:   Mat             aA, aB, Aperm;
1495:   const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1496:   PetscScalar    *aa, *ba;
1497:   PetscInt        i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1498:   PetscSF         rowsf, sf;
1499:   IS              parcolp = NULL;
1500:   PetscBool       done;

1502:   PetscFunctionBegin;
1503:   PetscCall(MatGetLocalSize(A, &m, &n));
1504:   PetscCall(ISGetIndices(rowp, &rwant));
1505:   PetscCall(ISGetIndices(colp, &cwant));
1506:   PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));

1508:   /* Invert row permutation to find out where my rows should go */
1509:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1510:   PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1511:   PetscCall(PetscSFSetFromOptions(rowsf));
1512:   for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1513:   PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1514:   PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));

1516:   /* Invert column permutation to find out where my columns should go */
1517:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1518:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1519:   PetscCall(PetscSFSetFromOptions(sf));
1520:   for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1521:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1522:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1523:   PetscCall(PetscSFDestroy(&sf));

1525:   PetscCall(ISRestoreIndices(rowp, &rwant));
1526:   PetscCall(ISRestoreIndices(colp, &cwant));
1527:   PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));

1529:   /* Find out where my gcols should go */
1530:   PetscCall(MatGetSize(aB, NULL, &ng));
1531:   PetscCall(PetscMalloc1(ng, &gcdest));
1532:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1533:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1534:   PetscCall(PetscSFSetFromOptions(sf));
1535:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1536:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1537:   PetscCall(PetscSFDestroy(&sf));

1539:   PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1540:   PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1541:   PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1542:   for (i = 0; i < m; i++) {
1543:     PetscInt    row = rdest[i];
1544:     PetscMPIInt rowner;
1545:     PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1546:     for (j = ai[i]; j < ai[i + 1]; j++) {
1547:       PetscInt    col = cdest[aj[j]];
1548:       PetscMPIInt cowner;
1549:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1550:       if (rowner == cowner) dnnz[i]++;
1551:       else onnz[i]++;
1552:     }
1553:     for (j = bi[i]; j < bi[i + 1]; j++) {
1554:       PetscInt    col = gcdest[bj[j]];
1555:       PetscMPIInt cowner;
1556:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1557:       if (rowner == cowner) dnnz[i]++;
1558:       else onnz[i]++;
1559:     }
1560:   }
1561:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1562:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1563:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1564:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1565:   PetscCall(PetscSFDestroy(&rowsf));

1567:   PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1568:   PetscCall(MatSeqAIJGetArray(aA, &aa));
1569:   PetscCall(MatSeqAIJGetArray(aB, &ba));
1570:   for (i = 0; i < m; i++) {
1571:     PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1572:     PetscInt  j0, rowlen;
1573:     rowlen = ai[i + 1] - ai[i];
1574:     for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1575:       for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1576:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1577:     }
1578:     rowlen = bi[i + 1] - bi[i];
1579:     for (j0 = j = 0; j < rowlen; j0 = j) {
1580:       for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1581:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1582:     }
1583:   }
1584:   PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1585:   PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1586:   PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1587:   PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1588:   PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1589:   PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1590:   PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1591:   PetscCall(PetscFree3(work, rdest, cdest));
1592:   PetscCall(PetscFree(gcdest));
1593:   if (parcolp) PetscCall(ISDestroy(&colp));
1594:   *B = Aperm;
1595:   PetscFunctionReturn(PETSC_SUCCESS);
1596: }

1598: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1599: {
1600:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1602:   PetscFunctionBegin;
1603:   PetscCall(MatGetSize(aij->B, NULL, nghosts));
1604:   if (ghosts) *ghosts = aij->garray;
1605:   PetscFunctionReturn(PETSC_SUCCESS);
1606: }

1608: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1609: {
1610:   Mat_MPIAIJ    *mat = (Mat_MPIAIJ *)matin->data;
1611:   Mat            A = mat->A, B = mat->B;
1612:   PetscLogDouble isend[5], irecv[5];

1614:   PetscFunctionBegin;
1615:   info->block_size = 1.0;
1616:   PetscCall(MatGetInfo(A, MAT_LOCAL, info));

1618:   isend[0] = info->nz_used;
1619:   isend[1] = info->nz_allocated;
1620:   isend[2] = info->nz_unneeded;
1621:   isend[3] = info->memory;
1622:   isend[4] = info->mallocs;

1624:   PetscCall(MatGetInfo(B, MAT_LOCAL, info));

1626:   isend[0] += info->nz_used;
1627:   isend[1] += info->nz_allocated;
1628:   isend[2] += info->nz_unneeded;
1629:   isend[3] += info->memory;
1630:   isend[4] += info->mallocs;
1631:   if (flag == MAT_LOCAL) {
1632:     info->nz_used      = isend[0];
1633:     info->nz_allocated = isend[1];
1634:     info->nz_unneeded  = isend[2];
1635:     info->memory       = isend[3];
1636:     info->mallocs      = isend[4];
1637:   } else if (flag == MAT_GLOBAL_MAX) {
1638:     PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));

1640:     info->nz_used      = irecv[0];
1641:     info->nz_allocated = irecv[1];
1642:     info->nz_unneeded  = irecv[2];
1643:     info->memory       = irecv[3];
1644:     info->mallocs      = irecv[4];
1645:   } else if (flag == MAT_GLOBAL_SUM) {
1646:     PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));

1648:     info->nz_used      = irecv[0];
1649:     info->nz_allocated = irecv[1];
1650:     info->nz_unneeded  = irecv[2];
1651:     info->memory       = irecv[3];
1652:     info->mallocs      = irecv[4];
1653:   }
1654:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1655:   info->fill_ratio_needed = 0;
1656:   info->factor_mallocs    = 0;
1657:   PetscFunctionReturn(PETSC_SUCCESS);
1658: }

1660: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1661: {
1662:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1664:   PetscFunctionBegin;
1665:   switch (op) {
1666:   case MAT_NEW_NONZERO_LOCATIONS:
1667:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1668:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1669:   case MAT_KEEP_NONZERO_PATTERN:
1670:   case MAT_NEW_NONZERO_LOCATION_ERR:
1671:   case MAT_USE_INODES:
1672:   case MAT_IGNORE_ZERO_ENTRIES:
1673:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1674:     MatCheckPreallocated(A, 1);
1675:     PetscCall(MatSetOption(a->A, op, flg));
1676:     PetscCall(MatSetOption(a->B, op, flg));
1677:     break;
1678:   case MAT_ROW_ORIENTED:
1679:     MatCheckPreallocated(A, 1);
1680:     a->roworiented = flg;

1682:     PetscCall(MatSetOption(a->A, op, flg));
1683:     PetscCall(MatSetOption(a->B, op, flg));
1684:     break;
1685:   case MAT_FORCE_DIAGONAL_ENTRIES:
1686:   case MAT_SORTED_FULL:
1687:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1688:     break;
1689:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1690:     a->donotstash = flg;
1691:     break;
1692:   /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1693:   case MAT_SPD:
1694:   case MAT_SYMMETRIC:
1695:   case MAT_STRUCTURALLY_SYMMETRIC:
1696:   case MAT_HERMITIAN:
1697:   case MAT_SYMMETRY_ETERNAL:
1698:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1699:   case MAT_SPD_ETERNAL:
1700:     /* if the diagonal matrix is square it inherits some of the properties above */
1701:     break;
1702:   case MAT_SUBMAT_SINGLEIS:
1703:     A->submat_singleis = flg;
1704:     break;
1705:   case MAT_STRUCTURE_ONLY:
1706:     /* The option is handled directly by MatSetOption() */
1707:     break;
1708:   default:
1709:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1710:   }
1711:   PetscFunctionReturn(PETSC_SUCCESS);
1712: }

1714: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1715: {
1716:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)matin->data;
1717:   PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1718:   PetscInt     i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1719:   PetscInt     nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1720:   PetscInt    *cmap, *idx_p;

1722:   PetscFunctionBegin;
1723:   PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1724:   mat->getrowactive = PETSC_TRUE;

1726:   if (!mat->rowvalues && (idx || v)) {
1727:     /*
1728:         allocate enough space to hold information from the longest row.
1729:     */
1730:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1731:     PetscInt    max = 1, tmp;
1732:     for (i = 0; i < matin->rmap->n; i++) {
1733:       tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1734:       if (max < tmp) max = tmp;
1735:     }
1736:     PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1737:   }

1739:   PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1740:   lrow = row - rstart;

1742:   pvA = &vworkA;
1743:   pcA = &cworkA;
1744:   pvB = &vworkB;
1745:   pcB = &cworkB;
1746:   if (!v) {
1747:     pvA = NULL;
1748:     pvB = NULL;
1749:   }
1750:   if (!idx) {
1751:     pcA = NULL;
1752:     if (!v) pcB = NULL;
1753:   }
1754:   PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1755:   PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1756:   nztot = nzA + nzB;

1758:   cmap = mat->garray;
1759:   if (v || idx) {
1760:     if (nztot) {
1761:       /* Sort by increasing column numbers, assuming A and B already sorted */
1762:       PetscInt imark = -1;
1763:       if (v) {
1764:         *v = v_p = mat->rowvalues;
1765:         for (i = 0; i < nzB; i++) {
1766:           if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1767:           else break;
1768:         }
1769:         imark = i;
1770:         for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1771:         for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1772:       }
1773:       if (idx) {
1774:         *idx = idx_p = mat->rowindices;
1775:         if (imark > -1) {
1776:           for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1777:         } else {
1778:           for (i = 0; i < nzB; i++) {
1779:             if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1780:             else break;
1781:           }
1782:           imark = i;
1783:         }
1784:         for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1785:         for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1786:       }
1787:     } else {
1788:       if (idx) *idx = NULL;
1789:       if (v) *v = NULL;
1790:     }
1791:   }
1792:   *nz = nztot;
1793:   PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1794:   PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1795:   PetscFunctionReturn(PETSC_SUCCESS);
1796: }

1798: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1799: {
1800:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1802:   PetscFunctionBegin;
1803:   PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1804:   aij->getrowactive = PETSC_FALSE;
1805:   PetscFunctionReturn(PETSC_SUCCESS);
1806: }

1808: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1809: {
1810:   Mat_MPIAIJ      *aij  = (Mat_MPIAIJ *)mat->data;
1811:   Mat_SeqAIJ      *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1812:   PetscInt         i, j, cstart = mat->cmap->rstart;
1813:   PetscReal        sum = 0.0;
1814:   const MatScalar *v, *amata, *bmata;

1816:   PetscFunctionBegin;
1817:   if (aij->size == 1) {
1818:     PetscCall(MatNorm(aij->A, type, norm));
1819:   } else {
1820:     PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1821:     PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1822:     if (type == NORM_FROBENIUS) {
1823:       v = amata;
1824:       for (i = 0; i < amat->nz; i++) {
1825:         sum += PetscRealPart(PetscConj(*v) * (*v));
1826:         v++;
1827:       }
1828:       v = bmata;
1829:       for (i = 0; i < bmat->nz; i++) {
1830:         sum += PetscRealPart(PetscConj(*v) * (*v));
1831:         v++;
1832:       }
1833:       PetscCall(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1834:       *norm = PetscSqrtReal(*norm);
1835:       PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1836:     } else if (type == NORM_1) { /* max column norm */
1837:       PetscReal *tmp, *tmp2;
1838:       PetscInt  *jj, *garray = aij->garray;
1839:       PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1840:       PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1841:       *norm = 0.0;
1842:       v     = amata;
1843:       jj    = amat->j;
1844:       for (j = 0; j < amat->nz; j++) {
1845:         tmp[cstart + *jj++] += PetscAbsScalar(*v);
1846:         v++;
1847:       }
1848:       v  = bmata;
1849:       jj = bmat->j;
1850:       for (j = 0; j < bmat->nz; j++) {
1851:         tmp[garray[*jj++]] += PetscAbsScalar(*v);
1852:         v++;
1853:       }
1854:       PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1855:       for (j = 0; j < mat->cmap->N; j++) {
1856:         if (tmp2[j] > *norm) *norm = tmp2[j];
1857:       }
1858:       PetscCall(PetscFree(tmp));
1859:       PetscCall(PetscFree(tmp2));
1860:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1861:     } else if (type == NORM_INFINITY) { /* max row norm */
1862:       PetscReal ntemp = 0.0;
1863:       for (j = 0; j < aij->A->rmap->n; j++) {
1864:         v   = amata + amat->i[j];
1865:         sum = 0.0;
1866:         for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1867:           sum += PetscAbsScalar(*v);
1868:           v++;
1869:         }
1870:         v = bmata + bmat->i[j];
1871:         for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1872:           sum += PetscAbsScalar(*v);
1873:           v++;
1874:         }
1875:         if (sum > ntemp) ntemp = sum;
1876:       }
1877:       PetscCall(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1878:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1879:     } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1880:     PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1881:     PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1882:   }
1883:   PetscFunctionReturn(PETSC_SUCCESS);
1884: }

1886: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1887: {
1888:   Mat_MPIAIJ      *a    = (Mat_MPIAIJ *)A->data, *b;
1889:   Mat_SeqAIJ      *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1890:   PetscInt         M = A->rmap->N, N = A->cmap->N, ma, na, mb, nb, row, *cols, *cols_tmp, *B_diag_ilen, i, ncol, A_diag_ncol;
1891:   const PetscInt  *ai, *aj, *bi, *bj, *B_diag_i;
1892:   Mat              B, A_diag, *B_diag;
1893:   const MatScalar *pbv, *bv;

1895:   PetscFunctionBegin;
1896:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1897:   ma = A->rmap->n;
1898:   na = A->cmap->n;
1899:   mb = a->B->rmap->n;
1900:   nb = a->B->cmap->n;
1901:   ai = Aloc->i;
1902:   aj = Aloc->j;
1903:   bi = Bloc->i;
1904:   bj = Bloc->j;
1905:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1906:     PetscInt            *d_nnz, *g_nnz, *o_nnz;
1907:     PetscSFNode         *oloc;
1908:     PETSC_UNUSED PetscSF sf;

1910:     PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1911:     /* compute d_nnz for preallocation */
1912:     PetscCall(PetscArrayzero(d_nnz, na));
1913:     for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1914:     /* compute local off-diagonal contributions */
1915:     PetscCall(PetscArrayzero(g_nnz, nb));
1916:     for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1917:     /* map those to global */
1918:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1919:     PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1920:     PetscCall(PetscSFSetFromOptions(sf));
1921:     PetscCall(PetscArrayzero(o_nnz, na));
1922:     PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1923:     PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1924:     PetscCall(PetscSFDestroy(&sf));

1926:     PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1927:     PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1928:     PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1929:     PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1930:     PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1931:     PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1932:   } else {
1933:     B = *matout;
1934:     PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1935:   }

1937:   b           = (Mat_MPIAIJ *)B->data;
1938:   A_diag      = a->A;
1939:   B_diag      = &b->A;
1940:   sub_B_diag  = (Mat_SeqAIJ *)(*B_diag)->data;
1941:   A_diag_ncol = A_diag->cmap->N;
1942:   B_diag_ilen = sub_B_diag->ilen;
1943:   B_diag_i    = sub_B_diag->i;

1945:   /* Set ilen for diagonal of B */
1946:   for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];

1948:   /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1949:   very quickly (=without using MatSetValues), because all writes are local. */
1950:   PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1951:   PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));

1953:   /* copy over the B part */
1954:   PetscCall(PetscMalloc1(bi[mb], &cols));
1955:   PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1956:   pbv = bv;
1957:   row = A->rmap->rstart;
1958:   for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1959:   cols_tmp = cols;
1960:   for (i = 0; i < mb; i++) {
1961:     ncol = bi[i + 1] - bi[i];
1962:     PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1963:     row++;
1964:     if (pbv) pbv += ncol;
1965:     if (cols_tmp) cols_tmp += ncol;
1966:   }
1967:   PetscCall(PetscFree(cols));
1968:   PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));

1970:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1971:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1972:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1973:     *matout = B;
1974:   } else {
1975:     PetscCall(MatHeaderMerge(A, &B));
1976:   }
1977:   PetscFunctionReturn(PETSC_SUCCESS);
1978: }

1980: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1981: {
1982:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1983:   Mat         a = aij->A, b = aij->B;
1984:   PetscInt    s1, s2, s3;

1986:   PetscFunctionBegin;
1987:   PetscCall(MatGetLocalSize(mat, &s2, &s3));
1988:   if (rr) {
1989:     PetscCall(VecGetLocalSize(rr, &s1));
1990:     PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1991:     /* Overlap communication with computation. */
1992:     PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1993:   }
1994:   if (ll) {
1995:     PetscCall(VecGetLocalSize(ll, &s1));
1996:     PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1997:     PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1998:   }
1999:   /* scale  the diagonal block */
2000:   PetscUseTypeMethod(a, diagonalscale, ll, rr);

2002:   if (rr) {
2003:     /* Do a scatter end and then right scale the off-diagonal block */
2004:     PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2005:     PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2006:   }
2007:   PetscFunctionReturn(PETSC_SUCCESS);
2008: }

2010: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2011: {
2012:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2014:   PetscFunctionBegin;
2015:   PetscCall(MatSetUnfactored(a->A));
2016:   PetscFunctionReturn(PETSC_SUCCESS);
2017: }

2019: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2020: {
2021:   Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2022:   Mat         a, b, c, d;
2023:   PetscBool   flg;

2025:   PetscFunctionBegin;
2026:   a = matA->A;
2027:   b = matA->B;
2028:   c = matB->A;
2029:   d = matB->B;

2031:   PetscCall(MatEqual(a, c, &flg));
2032:   if (flg) PetscCall(MatEqual(b, d, &flg));
2033:   PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2034:   PetscFunctionReturn(PETSC_SUCCESS);
2035: }

2037: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2038: {
2039:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2040:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2042:   PetscFunctionBegin;
2043:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2044:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2045:     /* because of the column compression in the off-processor part of the matrix a->B,
2046:        the number of columns in a->B and b->B may be different, hence we cannot call
2047:        the MatCopy() directly on the two parts. If need be, we can provide a more
2048:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2049:        then copying the submatrices */
2050:     PetscCall(MatCopy_Basic(A, B, str));
2051:   } else {
2052:     PetscCall(MatCopy(a->A, b->A, str));
2053:     PetscCall(MatCopy(a->B, b->B, str));
2054:   }
2055:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
2056:   PetscFunctionReturn(PETSC_SUCCESS);
2057: }

2059: /*
2060:    Computes the number of nonzeros per row needed for preallocation when X and Y
2061:    have different nonzero structure.
2062: */
2063: PetscErrorCode MatAXPYGetPreallocation_MPIX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *xltog, const PetscInt *yi, const PetscInt *yj, const PetscInt *yltog, PetscInt *nnz)
2064: {
2065:   PetscInt i, j, k, nzx, nzy;

2067:   PetscFunctionBegin;
2068:   /* Set the number of nonzeros in the new matrix */
2069:   for (i = 0; i < m; i++) {
2070:     const PetscInt *xjj = xj + xi[i], *yjj = yj + yi[i];
2071:     nzx    = xi[i + 1] - xi[i];
2072:     nzy    = yi[i + 1] - yi[i];
2073:     nnz[i] = 0;
2074:     for (j = 0, k = 0; j < nzx; j++) {                                /* Point in X */
2075:       for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2076:       if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++;             /* Skip duplicate */
2077:       nnz[i]++;
2078:     }
2079:     for (; k < nzy; k++) nnz[i]++;
2080:   }
2081:   PetscFunctionReturn(PETSC_SUCCESS);
2082: }

2084: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2085: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2086: {
2087:   PetscInt    m = Y->rmap->N;
2088:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2089:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

2091:   PetscFunctionBegin;
2092:   PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2093:   PetscFunctionReturn(PETSC_SUCCESS);
2094: }

2096: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2097: {
2098:   Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;

2100:   PetscFunctionBegin;
2101:   if (str == SAME_NONZERO_PATTERN) {
2102:     PetscCall(MatAXPY(yy->A, a, xx->A, str));
2103:     PetscCall(MatAXPY(yy->B, a, xx->B, str));
2104:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2105:     PetscCall(MatAXPY_Basic(Y, a, X, str));
2106:   } else {
2107:     Mat       B;
2108:     PetscInt *nnz_d, *nnz_o;

2110:     PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2111:     PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2112:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2113:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2114:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2115:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2116:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2117:     PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2118:     PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2119:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2120:     PetscCall(MatHeaderMerge(Y, &B));
2121:     PetscCall(PetscFree(nnz_d));
2122:     PetscCall(PetscFree(nnz_o));
2123:   }
2124:   PetscFunctionReturn(PETSC_SUCCESS);
2125: }

2127: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);

2129: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2130: {
2131:   PetscFunctionBegin;
2132:   if (PetscDefined(USE_COMPLEX)) {
2133:     Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2135:     PetscCall(MatConjugate_SeqAIJ(aij->A));
2136:     PetscCall(MatConjugate_SeqAIJ(aij->B));
2137:   }
2138:   PetscFunctionReturn(PETSC_SUCCESS);
2139: }

2141: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2142: {
2143:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2145:   PetscFunctionBegin;
2146:   PetscCall(MatRealPart(a->A));
2147:   PetscCall(MatRealPart(a->B));
2148:   PetscFunctionReturn(PETSC_SUCCESS);
2149: }

2151: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2152: {
2153:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2155:   PetscFunctionBegin;
2156:   PetscCall(MatImaginaryPart(a->A));
2157:   PetscCall(MatImaginaryPart(a->B));
2158:   PetscFunctionReturn(PETSC_SUCCESS);
2159: }

2161: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2162: {
2163:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
2164:   PetscInt           i, *idxb = NULL, m = A->rmap->n;
2165:   PetscScalar       *va, *vv;
2166:   Vec                vB, vA;
2167:   const PetscScalar *vb;

2169:   PetscFunctionBegin;
2170:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2171:   PetscCall(MatGetRowMaxAbs(a->A, vA, idx));

2173:   PetscCall(VecGetArrayWrite(vA, &va));
2174:   if (idx) {
2175:     for (i = 0; i < m; i++) {
2176:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2177:     }
2178:   }

2180:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2181:   PetscCall(PetscMalloc1(m, &idxb));
2182:   PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));

2184:   PetscCall(VecGetArrayWrite(v, &vv));
2185:   PetscCall(VecGetArrayRead(vB, &vb));
2186:   for (i = 0; i < m; i++) {
2187:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2188:       vv[i] = vb[i];
2189:       if (idx) idx[i] = a->garray[idxb[i]];
2190:     } else {
2191:       vv[i] = va[i];
2192:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2193:     }
2194:   }
2195:   PetscCall(VecRestoreArrayWrite(vA, &vv));
2196:   PetscCall(VecRestoreArrayWrite(vA, &va));
2197:   PetscCall(VecRestoreArrayRead(vB, &vb));
2198:   PetscCall(PetscFree(idxb));
2199:   PetscCall(VecDestroy(&vA));
2200:   PetscCall(VecDestroy(&vB));
2201:   PetscFunctionReturn(PETSC_SUCCESS);
2202: }

2204: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2205: {
2206:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2207:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2208:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2209:   PetscInt          *cmap = mat->garray;
2210:   PetscInt          *diagIdx, *offdiagIdx;
2211:   Vec                diagV, offdiagV;
2212:   PetscScalar       *a, *diagA, *offdiagA;
2213:   const PetscScalar *ba, *bav;
2214:   PetscInt           r, j, col, ncols, *bi, *bj;
2215:   Mat                B = mat->B;
2216:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2218:   PetscFunctionBegin;
2219:   /* When a process holds entire A and other processes have no entry */
2220:   if (A->cmap->N == n) {
2221:     PetscCall(VecGetArrayWrite(v, &diagA));
2222:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2223:     PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2224:     PetscCall(VecDestroy(&diagV));
2225:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2226:     PetscFunctionReturn(PETSC_SUCCESS);
2227:   } else if (n == 0) {
2228:     if (m) {
2229:       PetscCall(VecGetArrayWrite(v, &a));
2230:       for (r = 0; r < m; r++) {
2231:         a[r] = 0.0;
2232:         if (idx) idx[r] = -1;
2233:       }
2234:       PetscCall(VecRestoreArrayWrite(v, &a));
2235:     }
2236:     PetscFunctionReturn(PETSC_SUCCESS);
2237:   }

2239:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2240:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2241:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2242:   PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));

2244:   /* Get offdiagIdx[] for implicit 0.0 */
2245:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2246:   ba = bav;
2247:   bi = b->i;
2248:   bj = b->j;
2249:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2250:   for (r = 0; r < m; r++) {
2251:     ncols = bi[r + 1] - bi[r];
2252:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2253:       offdiagA[r]   = *ba;
2254:       offdiagIdx[r] = cmap[0];
2255:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2256:       offdiagA[r] = 0.0;

2258:       /* Find first hole in the cmap */
2259:       for (j = 0; j < ncols; j++) {
2260:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2261:         if (col > j && j < cstart) {
2262:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2263:           break;
2264:         } else if (col > j + n && j >= cstart) {
2265:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2266:           break;
2267:         }
2268:       }
2269:       if (j == ncols && ncols < A->cmap->N - n) {
2270:         /* a hole is outside compressed Bcols */
2271:         if (ncols == 0) {
2272:           if (cstart) {
2273:             offdiagIdx[r] = 0;
2274:           } else offdiagIdx[r] = cend;
2275:         } else { /* ncols > 0 */
2276:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2277:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2278:         }
2279:       }
2280:     }

2282:     for (j = 0; j < ncols; j++) {
2283:       if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2284:         offdiagA[r]   = *ba;
2285:         offdiagIdx[r] = cmap[*bj];
2286:       }
2287:       ba++;
2288:       bj++;
2289:     }
2290:   }

2292:   PetscCall(VecGetArrayWrite(v, &a));
2293:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2294:   for (r = 0; r < m; ++r) {
2295:     if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2296:       a[r] = diagA[r];
2297:       if (idx) idx[r] = cstart + diagIdx[r];
2298:     } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2299:       a[r] = diagA[r];
2300:       if (idx) {
2301:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2302:           idx[r] = cstart + diagIdx[r];
2303:         } else idx[r] = offdiagIdx[r];
2304:       }
2305:     } else {
2306:       a[r] = offdiagA[r];
2307:       if (idx) idx[r] = offdiagIdx[r];
2308:     }
2309:   }
2310:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2311:   PetscCall(VecRestoreArrayWrite(v, &a));
2312:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2313:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2314:   PetscCall(VecDestroy(&diagV));
2315:   PetscCall(VecDestroy(&offdiagV));
2316:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2317:   PetscFunctionReturn(PETSC_SUCCESS);
2318: }

2320: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2321: {
2322:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2323:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2324:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2325:   PetscInt          *cmap = mat->garray;
2326:   PetscInt          *diagIdx, *offdiagIdx;
2327:   Vec                diagV, offdiagV;
2328:   PetscScalar       *a, *diagA, *offdiagA;
2329:   const PetscScalar *ba, *bav;
2330:   PetscInt           r, j, col, ncols, *bi, *bj;
2331:   Mat                B = mat->B;
2332:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2334:   PetscFunctionBegin;
2335:   /* When a process holds entire A and other processes have no entry */
2336:   if (A->cmap->N == n) {
2337:     PetscCall(VecGetArrayWrite(v, &diagA));
2338:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2339:     PetscCall(MatGetRowMin(mat->A, diagV, idx));
2340:     PetscCall(VecDestroy(&diagV));
2341:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2342:     PetscFunctionReturn(PETSC_SUCCESS);
2343:   } else if (n == 0) {
2344:     if (m) {
2345:       PetscCall(VecGetArrayWrite(v, &a));
2346:       for (r = 0; r < m; r++) {
2347:         a[r] = PETSC_MAX_REAL;
2348:         if (idx) idx[r] = -1;
2349:       }
2350:       PetscCall(VecRestoreArrayWrite(v, &a));
2351:     }
2352:     PetscFunctionReturn(PETSC_SUCCESS);
2353:   }

2355:   PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2356:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2357:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2358:   PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));

2360:   /* Get offdiagIdx[] for implicit 0.0 */
2361:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2362:   ba = bav;
2363:   bi = b->i;
2364:   bj = b->j;
2365:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2366:   for (r = 0; r < m; r++) {
2367:     ncols = bi[r + 1] - bi[r];
2368:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2369:       offdiagA[r]   = *ba;
2370:       offdiagIdx[r] = cmap[0];
2371:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2372:       offdiagA[r] = 0.0;

2374:       /* Find first hole in the cmap */
2375:       for (j = 0; j < ncols; j++) {
2376:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2377:         if (col > j && j < cstart) {
2378:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2379:           break;
2380:         } else if (col > j + n && j >= cstart) {
2381:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2382:           break;
2383:         }
2384:       }
2385:       if (j == ncols && ncols < A->cmap->N - n) {
2386:         /* a hole is outside compressed Bcols */
2387:         if (ncols == 0) {
2388:           if (cstart) {
2389:             offdiagIdx[r] = 0;
2390:           } else offdiagIdx[r] = cend;
2391:         } else { /* ncols > 0 */
2392:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2393:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2394:         }
2395:       }
2396:     }

2398:     for (j = 0; j < ncols; j++) {
2399:       if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2400:         offdiagA[r]   = *ba;
2401:         offdiagIdx[r] = cmap[*bj];
2402:       }
2403:       ba++;
2404:       bj++;
2405:     }
2406:   }

2408:   PetscCall(VecGetArrayWrite(v, &a));
2409:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2410:   for (r = 0; r < m; ++r) {
2411:     if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2412:       a[r] = diagA[r];
2413:       if (idx) idx[r] = cstart + diagIdx[r];
2414:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2415:       a[r] = diagA[r];
2416:       if (idx) {
2417:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2418:           idx[r] = cstart + diagIdx[r];
2419:         } else idx[r] = offdiagIdx[r];
2420:       }
2421:     } else {
2422:       a[r] = offdiagA[r];
2423:       if (idx) idx[r] = offdiagIdx[r];
2424:     }
2425:   }
2426:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2427:   PetscCall(VecRestoreArrayWrite(v, &a));
2428:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2429:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2430:   PetscCall(VecDestroy(&diagV));
2431:   PetscCall(VecDestroy(&offdiagV));
2432:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2433:   PetscFunctionReturn(PETSC_SUCCESS);
2434: }

2436: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2437: {
2438:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2439:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2440:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2441:   PetscInt          *cmap = mat->garray;
2442:   PetscInt          *diagIdx, *offdiagIdx;
2443:   Vec                diagV, offdiagV;
2444:   PetscScalar       *a, *diagA, *offdiagA;
2445:   const PetscScalar *ba, *bav;
2446:   PetscInt           r, j, col, ncols, *bi, *bj;
2447:   Mat                B = mat->B;
2448:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2450:   PetscFunctionBegin;
2451:   /* When a process holds entire A and other processes have no entry */
2452:   if (A->cmap->N == n) {
2453:     PetscCall(VecGetArrayWrite(v, &diagA));
2454:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2455:     PetscCall(MatGetRowMax(mat->A, diagV, idx));
2456:     PetscCall(VecDestroy(&diagV));
2457:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2458:     PetscFunctionReturn(PETSC_SUCCESS);
2459:   } else if (n == 0) {
2460:     if (m) {
2461:       PetscCall(VecGetArrayWrite(v, &a));
2462:       for (r = 0; r < m; r++) {
2463:         a[r] = PETSC_MIN_REAL;
2464:         if (idx) idx[r] = -1;
2465:       }
2466:       PetscCall(VecRestoreArrayWrite(v, &a));
2467:     }
2468:     PetscFunctionReturn(PETSC_SUCCESS);
2469:   }

2471:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2472:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2473:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2474:   PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));

2476:   /* Get offdiagIdx[] for implicit 0.0 */
2477:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2478:   ba = bav;
2479:   bi = b->i;
2480:   bj = b->j;
2481:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2482:   for (r = 0; r < m; r++) {
2483:     ncols = bi[r + 1] - bi[r];
2484:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2485:       offdiagA[r]   = *ba;
2486:       offdiagIdx[r] = cmap[0];
2487:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2488:       offdiagA[r] = 0.0;

2490:       /* Find first hole in the cmap */
2491:       for (j = 0; j < ncols; j++) {
2492:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2493:         if (col > j && j < cstart) {
2494:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2495:           break;
2496:         } else if (col > j + n && j >= cstart) {
2497:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2498:           break;
2499:         }
2500:       }
2501:       if (j == ncols && ncols < A->cmap->N - n) {
2502:         /* a hole is outside compressed Bcols */
2503:         if (ncols == 0) {
2504:           if (cstart) {
2505:             offdiagIdx[r] = 0;
2506:           } else offdiagIdx[r] = cend;
2507:         } else { /* ncols > 0 */
2508:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2509:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2510:         }
2511:       }
2512:     }

2514:     for (j = 0; j < ncols; j++) {
2515:       if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2516:         offdiagA[r]   = *ba;
2517:         offdiagIdx[r] = cmap[*bj];
2518:       }
2519:       ba++;
2520:       bj++;
2521:     }
2522:   }

2524:   PetscCall(VecGetArrayWrite(v, &a));
2525:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2526:   for (r = 0; r < m; ++r) {
2527:     if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2528:       a[r] = diagA[r];
2529:       if (idx) idx[r] = cstart + diagIdx[r];
2530:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2531:       a[r] = diagA[r];
2532:       if (idx) {
2533:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2534:           idx[r] = cstart + diagIdx[r];
2535:         } else idx[r] = offdiagIdx[r];
2536:       }
2537:     } else {
2538:       a[r] = offdiagA[r];
2539:       if (idx) idx[r] = offdiagIdx[r];
2540:     }
2541:   }
2542:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2543:   PetscCall(VecRestoreArrayWrite(v, &a));
2544:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2545:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2546:   PetscCall(VecDestroy(&diagV));
2547:   PetscCall(VecDestroy(&offdiagV));
2548:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2549:   PetscFunctionReturn(PETSC_SUCCESS);
2550: }

2552: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2553: {
2554:   Mat *dummy;

2556:   PetscFunctionBegin;
2557:   PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2558:   *newmat = *dummy;
2559:   PetscCall(PetscFree(dummy));
2560:   PetscFunctionReturn(PETSC_SUCCESS);
2561: }

2563: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2564: {
2565:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2567:   PetscFunctionBegin;
2568:   PetscCall(MatInvertBlockDiagonal(a->A, values));
2569:   A->factorerrortype = a->A->factorerrortype;
2570:   PetscFunctionReturn(PETSC_SUCCESS);
2571: }

2573: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2574: {
2575:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;

2577:   PetscFunctionBegin;
2578:   PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2579:   PetscCall(MatSetRandom(aij->A, rctx));
2580:   if (x->assembled) {
2581:     PetscCall(MatSetRandom(aij->B, rctx));
2582:   } else {
2583:     PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2584:   }
2585:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2586:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2587:   PetscFunctionReturn(PETSC_SUCCESS);
2588: }

2590: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2591: {
2592:   PetscFunctionBegin;
2593:   if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2594:   else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2595:   PetscFunctionReturn(PETSC_SUCCESS);
2596: }

2598: /*@
2599:   MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank

2601:   Not Collective

2603:   Input Parameter:
2604: . A - the matrix

2606:   Output Parameter:
2607: . nz - the number of nonzeros

2609:   Level: advanced

2611: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2612: @*/
2613: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2614: {
2615:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2616:   Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2617:   PetscBool   isaij;

2619:   PetscFunctionBegin;
2620:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2621:   PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2622:   *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2623:   PetscFunctionReturn(PETSC_SUCCESS);
2624: }

2626: /*@
2627:   MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap

2629:   Collective

2631:   Input Parameters:
2632: + A  - the matrix
2633: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)

2635:   Level: advanced

2637: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2638: @*/
2639: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2640: {
2641:   PetscFunctionBegin;
2642:   PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2643:   PetscFunctionReturn(PETSC_SUCCESS);
2644: }

2646: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2647: {
2648:   PetscBool sc = PETSC_FALSE, flg;

2650:   PetscFunctionBegin;
2651:   PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2652:   if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2653:   PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2654:   if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2655:   PetscOptionsHeadEnd();
2656:   PetscFunctionReturn(PETSC_SUCCESS);
2657: }

2659: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2660: {
2661:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2662:   Mat_SeqAIJ *aij  = (Mat_SeqAIJ *)maij->A->data;

2664:   PetscFunctionBegin;
2665:   if (!Y->preallocated) {
2666:     PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2667:   } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2668:     PetscInt nonew = aij->nonew;
2669:     PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2670:     aij->nonew = nonew;
2671:   }
2672:   PetscCall(MatShift_Basic(Y, a));
2673:   PetscFunctionReturn(PETSC_SUCCESS);
2674: }

2676: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2677: {
2678:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2680:   PetscFunctionBegin;
2681:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2682:   PetscCall(MatMissingDiagonal(a->A, missing, d));
2683:   if (d) {
2684:     PetscInt rstart;
2685:     PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2686:     *d += rstart;
2687:   }
2688:   PetscFunctionReturn(PETSC_SUCCESS);
2689: }

2691: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2692: {
2693:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2695:   PetscFunctionBegin;
2696:   PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2697:   PetscFunctionReturn(PETSC_SUCCESS);
2698: }

2700: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2701: {
2702:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2704:   PetscFunctionBegin;
2705:   PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep));        // possibly keep zero diagonal coefficients
2706:   PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2707:   PetscFunctionReturn(PETSC_SUCCESS);
2708: }

2710: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2711:                                        MatGetRow_MPIAIJ,
2712:                                        MatRestoreRow_MPIAIJ,
2713:                                        MatMult_MPIAIJ,
2714:                                        /* 4*/ MatMultAdd_MPIAIJ,
2715:                                        MatMultTranspose_MPIAIJ,
2716:                                        MatMultTransposeAdd_MPIAIJ,
2717:                                        NULL,
2718:                                        NULL,
2719:                                        NULL,
2720:                                        /*10*/ NULL,
2721:                                        NULL,
2722:                                        NULL,
2723:                                        MatSOR_MPIAIJ,
2724:                                        MatTranspose_MPIAIJ,
2725:                                        /*15*/ MatGetInfo_MPIAIJ,
2726:                                        MatEqual_MPIAIJ,
2727:                                        MatGetDiagonal_MPIAIJ,
2728:                                        MatDiagonalScale_MPIAIJ,
2729:                                        MatNorm_MPIAIJ,
2730:                                        /*20*/ MatAssemblyBegin_MPIAIJ,
2731:                                        MatAssemblyEnd_MPIAIJ,
2732:                                        MatSetOption_MPIAIJ,
2733:                                        MatZeroEntries_MPIAIJ,
2734:                                        /*24*/ MatZeroRows_MPIAIJ,
2735:                                        NULL,
2736:                                        NULL,
2737:                                        NULL,
2738:                                        NULL,
2739:                                        /*29*/ MatSetUp_MPI_Hash,
2740:                                        NULL,
2741:                                        NULL,
2742:                                        MatGetDiagonalBlock_MPIAIJ,
2743:                                        NULL,
2744:                                        /*34*/ MatDuplicate_MPIAIJ,
2745:                                        NULL,
2746:                                        NULL,
2747:                                        NULL,
2748:                                        NULL,
2749:                                        /*39*/ MatAXPY_MPIAIJ,
2750:                                        MatCreateSubMatrices_MPIAIJ,
2751:                                        MatIncreaseOverlap_MPIAIJ,
2752:                                        MatGetValues_MPIAIJ,
2753:                                        MatCopy_MPIAIJ,
2754:                                        /*44*/ MatGetRowMax_MPIAIJ,
2755:                                        MatScale_MPIAIJ,
2756:                                        MatShift_MPIAIJ,
2757:                                        MatDiagonalSet_MPIAIJ,
2758:                                        MatZeroRowsColumns_MPIAIJ,
2759:                                        /*49*/ MatSetRandom_MPIAIJ,
2760:                                        MatGetRowIJ_MPIAIJ,
2761:                                        MatRestoreRowIJ_MPIAIJ,
2762:                                        NULL,
2763:                                        NULL,
2764:                                        /*54*/ MatFDColoringCreate_MPIXAIJ,
2765:                                        NULL,
2766:                                        MatSetUnfactored_MPIAIJ,
2767:                                        MatPermute_MPIAIJ,
2768:                                        NULL,
2769:                                        /*59*/ MatCreateSubMatrix_MPIAIJ,
2770:                                        MatDestroy_MPIAIJ,
2771:                                        MatView_MPIAIJ,
2772:                                        NULL,
2773:                                        NULL,
2774:                                        /*64*/ NULL,
2775:                                        MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2776:                                        NULL,
2777:                                        NULL,
2778:                                        NULL,
2779:                                        /*69*/ MatGetRowMaxAbs_MPIAIJ,
2780:                                        MatGetRowMinAbs_MPIAIJ,
2781:                                        NULL,
2782:                                        NULL,
2783:                                        NULL,
2784:                                        NULL,
2785:                                        /*75*/ MatFDColoringApply_AIJ,
2786:                                        MatSetFromOptions_MPIAIJ,
2787:                                        NULL,
2788:                                        NULL,
2789:                                        MatFindZeroDiagonals_MPIAIJ,
2790:                                        /*80*/ NULL,
2791:                                        NULL,
2792:                                        NULL,
2793:                                        /*83*/ MatLoad_MPIAIJ,
2794:                                        MatIsSymmetric_MPIAIJ,
2795:                                        NULL,
2796:                                        NULL,
2797:                                        NULL,
2798:                                        NULL,
2799:                                        /*89*/ NULL,
2800:                                        NULL,
2801:                                        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2802:                                        NULL,
2803:                                        NULL,
2804:                                        /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2805:                                        NULL,
2806:                                        NULL,
2807:                                        NULL,
2808:                                        MatBindToCPU_MPIAIJ,
2809:                                        /*99*/ MatProductSetFromOptions_MPIAIJ,
2810:                                        NULL,
2811:                                        NULL,
2812:                                        MatConjugate_MPIAIJ,
2813:                                        NULL,
2814:                                        /*104*/ MatSetValuesRow_MPIAIJ,
2815:                                        MatRealPart_MPIAIJ,
2816:                                        MatImaginaryPart_MPIAIJ,
2817:                                        NULL,
2818:                                        NULL,
2819:                                        /*109*/ NULL,
2820:                                        NULL,
2821:                                        MatGetRowMin_MPIAIJ,
2822:                                        NULL,
2823:                                        MatMissingDiagonal_MPIAIJ,
2824:                                        /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2825:                                        NULL,
2826:                                        MatGetGhosts_MPIAIJ,
2827:                                        NULL,
2828:                                        NULL,
2829:                                        /*119*/ MatMultDiagonalBlock_MPIAIJ,
2830:                                        NULL,
2831:                                        NULL,
2832:                                        NULL,
2833:                                        MatGetMultiProcBlock_MPIAIJ,
2834:                                        /*124*/ MatFindNonzeroRows_MPIAIJ,
2835:                                        MatGetColumnReductions_MPIAIJ,
2836:                                        MatInvertBlockDiagonal_MPIAIJ,
2837:                                        MatInvertVariableBlockDiagonal_MPIAIJ,
2838:                                        MatCreateSubMatricesMPI_MPIAIJ,
2839:                                        /*129*/ NULL,
2840:                                        NULL,
2841:                                        NULL,
2842:                                        MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2843:                                        NULL,
2844:                                        /*134*/ NULL,
2845:                                        NULL,
2846:                                        NULL,
2847:                                        NULL,
2848:                                        NULL,
2849:                                        /*139*/ MatSetBlockSizes_MPIAIJ,
2850:                                        NULL,
2851:                                        NULL,
2852:                                        MatFDColoringSetUp_MPIXAIJ,
2853:                                        MatFindOffBlockDiagonalEntries_MPIAIJ,
2854:                                        MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2855:                                        /*145*/ NULL,
2856:                                        NULL,
2857:                                        NULL,
2858:                                        MatCreateGraph_Simple_AIJ,
2859:                                        NULL,
2860:                                        /*150*/ NULL,
2861:                                        MatEliminateZeros_MPIAIJ};

2863: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2864: {
2865:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2867:   PetscFunctionBegin;
2868:   PetscCall(MatStoreValues(aij->A));
2869:   PetscCall(MatStoreValues(aij->B));
2870:   PetscFunctionReturn(PETSC_SUCCESS);
2871: }

2873: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2874: {
2875:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2877:   PetscFunctionBegin;
2878:   PetscCall(MatRetrieveValues(aij->A));
2879:   PetscCall(MatRetrieveValues(aij->B));
2880:   PetscFunctionReturn(PETSC_SUCCESS);
2881: }

2883: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2884: {
2885:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2886:   PetscMPIInt size;

2888:   PetscFunctionBegin;
2889:   if (B->hash_active) {
2890:     B->ops[0]      = b->cops;
2891:     B->hash_active = PETSC_FALSE;
2892:   }
2893:   PetscCall(PetscLayoutSetUp(B->rmap));
2894:   PetscCall(PetscLayoutSetUp(B->cmap));

2896: #if defined(PETSC_USE_CTABLE)
2897:   PetscCall(PetscHMapIDestroy(&b->colmap));
2898: #else
2899:   PetscCall(PetscFree(b->colmap));
2900: #endif
2901:   PetscCall(PetscFree(b->garray));
2902:   PetscCall(VecDestroy(&b->lvec));
2903:   PetscCall(VecScatterDestroy(&b->Mvctx));

2905:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
2906:   PetscCall(MatDestroy(&b->B));
2907:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2908:   PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2909:   PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2910:   PetscCall(MatSetType(b->B, MATSEQAIJ));

2912:   PetscCall(MatDestroy(&b->A));
2913:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2914:   PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2915:   PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2916:   PetscCall(MatSetType(b->A, MATSEQAIJ));

2918:   PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2919:   PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2920:   B->preallocated  = PETSC_TRUE;
2921:   B->was_assembled = PETSC_FALSE;
2922:   B->assembled     = PETSC_FALSE;
2923:   PetscFunctionReturn(PETSC_SUCCESS);
2924: }

2926: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2927: {
2928:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2930:   PetscFunctionBegin;
2932:   PetscCall(PetscLayoutSetUp(B->rmap));
2933:   PetscCall(PetscLayoutSetUp(B->cmap));

2935: #if defined(PETSC_USE_CTABLE)
2936:   PetscCall(PetscHMapIDestroy(&b->colmap));
2937: #else
2938:   PetscCall(PetscFree(b->colmap));
2939: #endif
2940:   PetscCall(PetscFree(b->garray));
2941:   PetscCall(VecDestroy(&b->lvec));
2942:   PetscCall(VecScatterDestroy(&b->Mvctx));

2944:   PetscCall(MatResetPreallocation(b->A));
2945:   PetscCall(MatResetPreallocation(b->B));
2946:   B->preallocated  = PETSC_TRUE;
2947:   B->was_assembled = PETSC_FALSE;
2948:   B->assembled     = PETSC_FALSE;
2949:   PetscFunctionReturn(PETSC_SUCCESS);
2950: }

2952: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2953: {
2954:   Mat         mat;
2955:   Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;

2957:   PetscFunctionBegin;
2958:   *newmat = NULL;
2959:   PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2960:   PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2961:   PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2962:   PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2963:   a = (Mat_MPIAIJ *)mat->data;

2965:   mat->factortype = matin->factortype;
2966:   mat->assembled  = matin->assembled;
2967:   mat->insertmode = NOT_SET_VALUES;

2969:   a->size         = oldmat->size;
2970:   a->rank         = oldmat->rank;
2971:   a->donotstash   = oldmat->donotstash;
2972:   a->roworiented  = oldmat->roworiented;
2973:   a->rowindices   = NULL;
2974:   a->rowvalues    = NULL;
2975:   a->getrowactive = PETSC_FALSE;

2977:   PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2978:   PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2979:   if (matin->hash_active) {
2980:     PetscCall(MatSetUp(mat));
2981:   } else {
2982:     mat->preallocated = matin->preallocated;
2983:     if (oldmat->colmap) {
2984: #if defined(PETSC_USE_CTABLE)
2985:       PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
2986: #else
2987:       PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
2988:       PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
2989: #endif
2990:     } else a->colmap = NULL;
2991:     if (oldmat->garray) {
2992:       PetscInt len;
2993:       len = oldmat->B->cmap->n;
2994:       PetscCall(PetscMalloc1(len + 1, &a->garray));
2995:       if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
2996:     } else a->garray = NULL;

2998:     /* It may happen MatDuplicate is called with a non-assembled matrix
2999:       In fact, MatDuplicate only requires the matrix to be preallocated
3000:       This may happen inside a DMCreateMatrix_Shell */
3001:     if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3002:     if (oldmat->Mvctx) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3003:     PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3004:     PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3005:   }
3006:   PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3007:   *newmat = mat;
3008:   PetscFunctionReturn(PETSC_SUCCESS);
3009: }

3011: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3012: {
3013:   PetscBool isbinary, ishdf5;

3015:   PetscFunctionBegin;
3018:   /* force binary viewer to load .info file if it has not yet done so */
3019:   PetscCall(PetscViewerSetUp(viewer));
3020:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3021:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3022:   if (isbinary) {
3023:     PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3024:   } else if (ishdf5) {
3025: #if defined(PETSC_HAVE_HDF5)
3026:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3027: #else
3028:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3029: #endif
3030:   } else {
3031:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
3032:   }
3033:   PetscFunctionReturn(PETSC_SUCCESS);
3034: }

3036: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3037: {
3038:   PetscInt     header[4], M, N, m, nz, rows, cols, sum, i;
3039:   PetscInt    *rowidxs, *colidxs;
3040:   PetscScalar *matvals;

3042:   PetscFunctionBegin;
3043:   PetscCall(PetscViewerSetUp(viewer));

3045:   /* read in matrix header */
3046:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3047:   PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3048:   M  = header[1];
3049:   N  = header[2];
3050:   nz = header[3];
3051:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3052:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3053:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");

3055:   /* set block sizes from the viewer's .info file */
3056:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3057:   /* set global sizes if not set already */
3058:   if (mat->rmap->N < 0) mat->rmap->N = M;
3059:   if (mat->cmap->N < 0) mat->cmap->N = N;
3060:   PetscCall(PetscLayoutSetUp(mat->rmap));
3061:   PetscCall(PetscLayoutSetUp(mat->cmap));

3063:   /* check if the matrix sizes are correct */
3064:   PetscCall(MatGetSize(mat, &rows, &cols));
3065:   PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);

3067:   /* read in row lengths and build row indices */
3068:   PetscCall(MatGetLocalSize(mat, &m, NULL));
3069:   PetscCall(PetscMalloc1(m + 1, &rowidxs));
3070:   PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3071:   rowidxs[0] = 0;
3072:   for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3073:   if (nz != PETSC_MAX_INT) {
3074:     PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3075:     PetscCheck(sum == nz, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
3076:   }

3078:   /* read in column indices and matrix values */
3079:   PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3080:   PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3081:   PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3082:   /* store matrix indices and values */
3083:   PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3084:   PetscCall(PetscFree(rowidxs));
3085:   PetscCall(PetscFree2(colidxs, matvals));
3086:   PetscFunctionReturn(PETSC_SUCCESS);
3087: }

3089: /* Not scalable because of ISAllGather() unless getting all columns. */
3090: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3091: {
3092:   IS          iscol_local;
3093:   PetscBool   isstride;
3094:   PetscMPIInt lisstride = 0, gisstride;

3096:   PetscFunctionBegin;
3097:   /* check if we are grabbing all columns*/
3098:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));

3100:   if (isstride) {
3101:     PetscInt start, len, mstart, mlen;
3102:     PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3103:     PetscCall(ISGetLocalSize(iscol, &len));
3104:     PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3105:     if (mstart == start && mlen - mstart == len) lisstride = 1;
3106:   }

3108:   PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3109:   if (gisstride) {
3110:     PetscInt N;
3111:     PetscCall(MatGetSize(mat, NULL, &N));
3112:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3113:     PetscCall(ISSetIdentity(iscol_local));
3114:     PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3115:   } else {
3116:     PetscInt cbs;
3117:     PetscCall(ISGetBlockSize(iscol, &cbs));
3118:     PetscCall(ISAllGather(iscol, &iscol_local));
3119:     PetscCall(ISSetBlockSize(iscol_local, cbs));
3120:   }

3122:   *isseq = iscol_local;
3123:   PetscFunctionReturn(PETSC_SUCCESS);
3124: }

3126: /*
3127:  Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3128:  (see MatCreateSubMatrix_MPIAIJ_nonscalable)

3130:  Input Parameters:
3131: +   mat - matrix
3132: .   isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3133:            i.e., mat->rstart <= isrow[i] < mat->rend
3134: -   iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3135:            i.e., mat->cstart <= iscol[i] < mat->cend

3137:  Output Parameters:
3138: +   isrow_d - sequential row index set for retrieving mat->A
3139: .   iscol_d - sequential  column index set for retrieving mat->A
3140: .   iscol_o - sequential column index set for retrieving mat->B
3141: -   garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3142:  */
3143: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3144: {
3145:   Vec             x, cmap;
3146:   const PetscInt *is_idx;
3147:   PetscScalar    *xarray, *cmaparray;
3148:   PetscInt        ncols, isstart, *idx, m, rstart, *cmap1, count;
3149:   Mat_MPIAIJ     *a    = (Mat_MPIAIJ *)mat->data;
3150:   Mat             B    = a->B;
3151:   Vec             lvec = a->lvec, lcmap;
3152:   PetscInt        i, cstart, cend, Bn = B->cmap->N;
3153:   MPI_Comm        comm;
3154:   VecScatter      Mvctx = a->Mvctx;

3156:   PetscFunctionBegin;
3157:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3158:   PetscCall(ISGetLocalSize(iscol, &ncols));

3160:   /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3161:   PetscCall(MatCreateVecs(mat, &x, NULL));
3162:   PetscCall(VecSet(x, -1.0));
3163:   PetscCall(VecDuplicate(x, &cmap));
3164:   PetscCall(VecSet(cmap, -1.0));

3166:   /* Get start indices */
3167:   PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3168:   isstart -= ncols;
3169:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));

3171:   PetscCall(ISGetIndices(iscol, &is_idx));
3172:   PetscCall(VecGetArray(x, &xarray));
3173:   PetscCall(VecGetArray(cmap, &cmaparray));
3174:   PetscCall(PetscMalloc1(ncols, &idx));
3175:   for (i = 0; i < ncols; i++) {
3176:     xarray[is_idx[i] - cstart]    = (PetscScalar)is_idx[i];
3177:     cmaparray[is_idx[i] - cstart] = i + isstart;        /* global index of iscol[i] */
3178:     idx[i]                        = is_idx[i] - cstart; /* local index of iscol[i]  */
3179:   }
3180:   PetscCall(VecRestoreArray(x, &xarray));
3181:   PetscCall(VecRestoreArray(cmap, &cmaparray));
3182:   PetscCall(ISRestoreIndices(iscol, &is_idx));

3184:   /* Get iscol_d */
3185:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3186:   PetscCall(ISGetBlockSize(iscol, &i));
3187:   PetscCall(ISSetBlockSize(*iscol_d, i));

3189:   /* Get isrow_d */
3190:   PetscCall(ISGetLocalSize(isrow, &m));
3191:   rstart = mat->rmap->rstart;
3192:   PetscCall(PetscMalloc1(m, &idx));
3193:   PetscCall(ISGetIndices(isrow, &is_idx));
3194:   for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3195:   PetscCall(ISRestoreIndices(isrow, &is_idx));

3197:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3198:   PetscCall(ISGetBlockSize(isrow, &i));
3199:   PetscCall(ISSetBlockSize(*isrow_d, i));

3201:   /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3202:   PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3203:   PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));

3205:   PetscCall(VecDuplicate(lvec, &lcmap));

3207:   PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3208:   PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));

3210:   /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3211:   /* off-process column indices */
3212:   count = 0;
3213:   PetscCall(PetscMalloc1(Bn, &idx));
3214:   PetscCall(PetscMalloc1(Bn, &cmap1));

3216:   PetscCall(VecGetArray(lvec, &xarray));
3217:   PetscCall(VecGetArray(lcmap, &cmaparray));
3218:   for (i = 0; i < Bn; i++) {
3219:     if (PetscRealPart(xarray[i]) > -1.0) {
3220:       idx[count]   = i;                                     /* local column index in off-diagonal part B */
3221:       cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3222:       count++;
3223:     }
3224:   }
3225:   PetscCall(VecRestoreArray(lvec, &xarray));
3226:   PetscCall(VecRestoreArray(lcmap, &cmaparray));

3228:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3229:   /* cannot ensure iscol_o has same blocksize as iscol! */

3231:   PetscCall(PetscFree(idx));
3232:   *garray = cmap1;

3234:   PetscCall(VecDestroy(&x));
3235:   PetscCall(VecDestroy(&cmap));
3236:   PetscCall(VecDestroy(&lcmap));
3237:   PetscFunctionReturn(PETSC_SUCCESS);
3238: }

3240: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3241: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3242: {
3243:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3244:   Mat         M = NULL;
3245:   MPI_Comm    comm;
3246:   IS          iscol_d, isrow_d, iscol_o;
3247:   Mat         Asub = NULL, Bsub = NULL;
3248:   PetscInt    n;

3250:   PetscFunctionBegin;
3251:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));

3253:   if (call == MAT_REUSE_MATRIX) {
3254:     /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3255:     PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3256:     PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");

3258:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3259:     PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");

3261:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3262:     PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");

3264:     /* Update diagonal and off-diagonal portions of submat */
3265:     asub = (Mat_MPIAIJ *)(*submat)->data;
3266:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3267:     PetscCall(ISGetLocalSize(iscol_o, &n));
3268:     if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3269:     PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3270:     PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));

3272:   } else { /* call == MAT_INITIAL_MATRIX) */
3273:     const PetscInt *garray;
3274:     PetscInt        BsubN;

3276:     /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3277:     PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));

3279:     /* Create local submatrices Asub and Bsub */
3280:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3281:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));

3283:     /* Create submatrix M */
3284:     PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));

3286:     /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3287:     asub = (Mat_MPIAIJ *)M->data;

3289:     PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3290:     n = asub->B->cmap->N;
3291:     if (BsubN > n) {
3292:       /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3293:       const PetscInt *idx;
3294:       PetscInt        i, j, *idx_new, *subgarray = asub->garray;
3295:       PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));

3297:       PetscCall(PetscMalloc1(n, &idx_new));
3298:       j = 0;
3299:       PetscCall(ISGetIndices(iscol_o, &idx));
3300:       for (i = 0; i < n; i++) {
3301:         if (j >= BsubN) break;
3302:         while (subgarray[i] > garray[j]) j++;

3304:         if (subgarray[i] == garray[j]) {
3305:           idx_new[i] = idx[j++];
3306:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "subgarray[%" PetscInt_FMT "]=%" PetscInt_FMT " cannot < garray[%" PetscInt_FMT "]=%" PetscInt_FMT, i, subgarray[i], j, garray[j]);
3307:       }
3308:       PetscCall(ISRestoreIndices(iscol_o, &idx));

3310:       PetscCall(ISDestroy(&iscol_o));
3311:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));

3313:     } else if (BsubN < n) {
3314:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Columns of Bsub (%" PetscInt_FMT ") cannot be smaller than B's (%" PetscInt_FMT ")", BsubN, asub->B->cmap->N);
3315:     }

3317:     PetscCall(PetscFree(garray));
3318:     *submat = M;

3320:     /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3321:     PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3322:     PetscCall(ISDestroy(&isrow_d));

3324:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3325:     PetscCall(ISDestroy(&iscol_d));

3327:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3328:     PetscCall(ISDestroy(&iscol_o));
3329:   }
3330:   PetscFunctionReturn(PETSC_SUCCESS);
3331: }

3333: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3334: {
3335:   IS        iscol_local = NULL, isrow_d;
3336:   PetscInt  csize;
3337:   PetscInt  n, i, j, start, end;
3338:   PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3339:   MPI_Comm  comm;

3341:   PetscFunctionBegin;
3342:   /* If isrow has same processor distribution as mat,
3343:      call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3344:   if (call == MAT_REUSE_MATRIX) {
3345:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3346:     if (isrow_d) {
3347:       sameRowDist  = PETSC_TRUE;
3348:       tsameDist[1] = PETSC_TRUE; /* sameColDist */
3349:     } else {
3350:       PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3351:       if (iscol_local) {
3352:         sameRowDist  = PETSC_TRUE;
3353:         tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3354:       }
3355:     }
3356:   } else {
3357:     /* Check if isrow has same processor distribution as mat */
3358:     sameDist[0] = PETSC_FALSE;
3359:     PetscCall(ISGetLocalSize(isrow, &n));
3360:     if (!n) {
3361:       sameDist[0] = PETSC_TRUE;
3362:     } else {
3363:       PetscCall(ISGetMinMax(isrow, &i, &j));
3364:       PetscCall(MatGetOwnershipRange(mat, &start, &end));
3365:       if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3366:     }

3368:     /* Check if iscol has same processor distribution as mat */
3369:     sameDist[1] = PETSC_FALSE;
3370:     PetscCall(ISGetLocalSize(iscol, &n));
3371:     if (!n) {
3372:       sameDist[1] = PETSC_TRUE;
3373:     } else {
3374:       PetscCall(ISGetMinMax(iscol, &i, &j));
3375:       PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3376:       if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3377:     }

3379:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3380:     PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3381:     sameRowDist = tsameDist[0];
3382:   }

3384:   if (sameRowDist) {
3385:     if (tsameDist[1]) { /* sameRowDist & sameColDist */
3386:       /* isrow and iscol have same processor distribution as mat */
3387:       PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3388:       PetscFunctionReturn(PETSC_SUCCESS);
3389:     } else { /* sameRowDist */
3390:       /* isrow has same processor distribution as mat */
3391:       if (call == MAT_INITIAL_MATRIX) {
3392:         PetscBool sorted;
3393:         PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3394:         PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3395:         PetscCall(ISGetSize(iscol, &i));
3396:         PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);

3398:         PetscCall(ISSorted(iscol_local, &sorted));
3399:         if (sorted) {
3400:           /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3401:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3402:           PetscFunctionReturn(PETSC_SUCCESS);
3403:         }
3404:       } else { /* call == MAT_REUSE_MATRIX */
3405:         IS iscol_sub;
3406:         PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3407:         if (iscol_sub) {
3408:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3409:           PetscFunctionReturn(PETSC_SUCCESS);
3410:         }
3411:       }
3412:     }
3413:   }

3415:   /* General case: iscol -> iscol_local which has global size of iscol */
3416:   if (call == MAT_REUSE_MATRIX) {
3417:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3418:     PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3419:   } else {
3420:     if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3421:   }

3423:   PetscCall(ISGetLocalSize(iscol, &csize));
3424:   PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));

3426:   if (call == MAT_INITIAL_MATRIX) {
3427:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3428:     PetscCall(ISDestroy(&iscol_local));
3429:   }
3430:   PetscFunctionReturn(PETSC_SUCCESS);
3431: }

3433: /*@C
3434:   MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3435:   and "off-diagonal" part of the matrix in CSR format.

3437:   Collective

3439:   Input Parameters:
3440: + comm   - MPI communicator
3441: . A      - "diagonal" portion of matrix
3442: . B      - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3443: - garray - global index of `B` columns

3445:   Output Parameter:
3446: . mat - the matrix, with input `A` as its local diagonal matrix

3448:   Level: advanced

3450:   Notes:
3451:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.

3453:   `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.

3455: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3456: @*/
3457: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3458: {
3459:   Mat_MPIAIJ        *maij;
3460:   Mat_SeqAIJ        *b  = (Mat_SeqAIJ *)B->data, *bnew;
3461:   PetscInt          *oi = b->i, *oj = b->j, i, nz, col;
3462:   const PetscScalar *oa;
3463:   Mat                Bnew;
3464:   PetscInt           m, n, N;
3465:   MatType            mpi_mat_type;

3467:   PetscFunctionBegin;
3468:   PetscCall(MatCreate(comm, mat));
3469:   PetscCall(MatGetSize(A, &m, &n));
3470:   PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3471:   PetscCheck(PetscAbs(A->rmap->bs) == PetscAbs(B->rmap->bs), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "A row bs %" PetscInt_FMT " != B row bs %" PetscInt_FMT, A->rmap->bs, B->rmap->bs);
3472:   /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3473:   /* PetscCheck(A->cmap->bs == B->cmap->bs,PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"A column bs %" PetscInt_FMT " != B column bs %" PetscInt_FMT,A->cmap->bs,B->cmap->bs); */

3475:   /* Get global columns of mat */
3476:   PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));

3478:   PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3479:   /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3480:   PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3481:   PetscCall(MatSetType(*mat, mpi_mat_type));

3483:   if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3484:   maij = (Mat_MPIAIJ *)(*mat)->data;

3486:   (*mat)->preallocated = PETSC_TRUE;

3488:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
3489:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

3491:   /* Set A as diagonal portion of *mat */
3492:   maij->A = A;

3494:   nz = oi[m];
3495:   for (i = 0; i < nz; i++) {
3496:     col   = oj[i];
3497:     oj[i] = garray[col];
3498:   }

3500:   /* Set Bnew as off-diagonal portion of *mat */
3501:   PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3502:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3503:   PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3504:   bnew        = (Mat_SeqAIJ *)Bnew->data;
3505:   bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3506:   maij->B     = Bnew;

3508:   PetscCheck(B->rmap->N == Bnew->rmap->N, PETSC_COMM_SELF, PETSC_ERR_PLIB, "BN %" PetscInt_FMT " != BnewN %" PetscInt_FMT, B->rmap->N, Bnew->rmap->N);

3510:   b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3511:   b->free_a       = PETSC_FALSE;
3512:   b->free_ij      = PETSC_FALSE;
3513:   PetscCall(MatDestroy(&B));

3515:   bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3516:   bnew->free_a       = PETSC_TRUE;
3517:   bnew->free_ij      = PETSC_TRUE;

3519:   /* condense columns of maij->B */
3520:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3521:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3522:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3523:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3524:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3525:   PetscFunctionReturn(PETSC_SUCCESS);
3526: }

3528: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);

3530: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3531: {
3532:   PetscInt        i, m, n, rstart, row, rend, nz, j, bs, cbs;
3533:   PetscInt       *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3534:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)mat->data;
3535:   Mat             M, Msub, B = a->B;
3536:   MatScalar      *aa;
3537:   Mat_SeqAIJ     *aij;
3538:   PetscInt       *garray = a->garray, *colsub, Ncols;
3539:   PetscInt        count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3540:   IS              iscol_sub, iscmap;
3541:   const PetscInt *is_idx, *cmap;
3542:   PetscBool       allcolumns = PETSC_FALSE;
3543:   MPI_Comm        comm;

3545:   PetscFunctionBegin;
3546:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3547:   if (call == MAT_REUSE_MATRIX) {
3548:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3549:     PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3550:     PetscCall(ISGetLocalSize(iscol_sub, &count));

3552:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3553:     PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");

3555:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3556:     PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");

3558:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));

3560:   } else { /* call == MAT_INITIAL_MATRIX) */
3561:     PetscBool flg;

3563:     PetscCall(ISGetLocalSize(iscol, &n));
3564:     PetscCall(ISGetSize(iscol, &Ncols));

3566:     /* (1) iscol -> nonscalable iscol_local */
3567:     /* Check for special case: each processor gets entire matrix columns */
3568:     PetscCall(ISIdentity(iscol_local, &flg));
3569:     if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3570:     PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3571:     if (allcolumns) {
3572:       iscol_sub = iscol_local;
3573:       PetscCall(PetscObjectReference((PetscObject)iscol_local));
3574:       PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));

3576:     } else {
3577:       /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3578:       PetscInt *idx, *cmap1, k;
3579:       PetscCall(PetscMalloc1(Ncols, &idx));
3580:       PetscCall(PetscMalloc1(Ncols, &cmap1));
3581:       PetscCall(ISGetIndices(iscol_local, &is_idx));
3582:       count = 0;
3583:       k     = 0;
3584:       for (i = 0; i < Ncols; i++) {
3585:         j = is_idx[i];
3586:         if (j >= cstart && j < cend) {
3587:           /* diagonal part of mat */
3588:           idx[count]     = j;
3589:           cmap1[count++] = i; /* column index in submat */
3590:         } else if (Bn) {
3591:           /* off-diagonal part of mat */
3592:           if (j == garray[k]) {
3593:             idx[count]     = j;
3594:             cmap1[count++] = i; /* column index in submat */
3595:           } else if (j > garray[k]) {
3596:             while (j > garray[k] && k < Bn - 1) k++;
3597:             if (j == garray[k]) {
3598:               idx[count]     = j;
3599:               cmap1[count++] = i; /* column index in submat */
3600:             }
3601:           }
3602:         }
3603:       }
3604:       PetscCall(ISRestoreIndices(iscol_local, &is_idx));

3606:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3607:       PetscCall(ISGetBlockSize(iscol, &cbs));
3608:       PetscCall(ISSetBlockSize(iscol_sub, cbs));

3610:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3611:     }

3613:     /* (3) Create sequential Msub */
3614:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3615:   }

3617:   PetscCall(ISGetLocalSize(iscol_sub, &count));
3618:   aij = (Mat_SeqAIJ *)(Msub)->data;
3619:   ii  = aij->i;
3620:   PetscCall(ISGetIndices(iscmap, &cmap));

3622:   /*
3623:       m - number of local rows
3624:       Ncols - number of columns (same on all processors)
3625:       rstart - first row in new global matrix generated
3626:   */
3627:   PetscCall(MatGetSize(Msub, &m, NULL));

3629:   if (call == MAT_INITIAL_MATRIX) {
3630:     /* (4) Create parallel newmat */
3631:     PetscMPIInt rank, size;
3632:     PetscInt    csize;

3634:     PetscCallMPI(MPI_Comm_size(comm, &size));
3635:     PetscCallMPI(MPI_Comm_rank(comm, &rank));

3637:     /*
3638:         Determine the number of non-zeros in the diagonal and off-diagonal
3639:         portions of the matrix in order to do correct preallocation
3640:     */

3642:     /* first get start and end of "diagonal" columns */
3643:     PetscCall(ISGetLocalSize(iscol, &csize));
3644:     if (csize == PETSC_DECIDE) {
3645:       PetscCall(ISGetSize(isrow, &mglobal));
3646:       if (mglobal == Ncols) { /* square matrix */
3647:         nlocal = m;
3648:       } else {
3649:         nlocal = Ncols / size + ((Ncols % size) > rank);
3650:       }
3651:     } else {
3652:       nlocal = csize;
3653:     }
3654:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3655:     rstart = rend - nlocal;
3656:     PetscCheck(rank != size - 1 || rend == Ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Local column sizes %" PetscInt_FMT " do not add up to total number of columns %" PetscInt_FMT, rend, Ncols);

3658:     /* next, compute all the lengths */
3659:     jj = aij->j;
3660:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3661:     olens = dlens + m;
3662:     for (i = 0; i < m; i++) {
3663:       jend = ii[i + 1] - ii[i];
3664:       olen = 0;
3665:       dlen = 0;
3666:       for (j = 0; j < jend; j++) {
3667:         if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3668:         else dlen++;
3669:         jj++;
3670:       }
3671:       olens[i] = olen;
3672:       dlens[i] = dlen;
3673:     }

3675:     PetscCall(ISGetBlockSize(isrow, &bs));
3676:     PetscCall(ISGetBlockSize(iscol, &cbs));

3678:     PetscCall(MatCreate(comm, &M));
3679:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3680:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3681:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3682:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3683:     PetscCall(PetscFree(dlens));

3685:   } else { /* call == MAT_REUSE_MATRIX */
3686:     M = *newmat;
3687:     PetscCall(MatGetLocalSize(M, &i, NULL));
3688:     PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3689:     PetscCall(MatZeroEntries(M));
3690:     /*
3691:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3692:        rather than the slower MatSetValues().
3693:     */
3694:     M->was_assembled = PETSC_TRUE;
3695:     M->assembled     = PETSC_FALSE;
3696:   }

3698:   /* (5) Set values of Msub to *newmat */
3699:   PetscCall(PetscMalloc1(count, &colsub));
3700:   PetscCall(MatGetOwnershipRange(M, &rstart, NULL));

3702:   jj = aij->j;
3703:   PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3704:   for (i = 0; i < m; i++) {
3705:     row = rstart + i;
3706:     nz  = ii[i + 1] - ii[i];
3707:     for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3708:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3709:     jj += nz;
3710:     aa += nz;
3711:   }
3712:   PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3713:   PetscCall(ISRestoreIndices(iscmap, &cmap));

3715:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3716:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));

3718:   PetscCall(PetscFree(colsub));

3720:   /* save Msub, iscol_sub and iscmap used in processor for next request */
3721:   if (call == MAT_INITIAL_MATRIX) {
3722:     *newmat = M;
3723:     PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubMatrix", (PetscObject)Msub));
3724:     PetscCall(MatDestroy(&Msub));

3726:     PetscCall(PetscObjectCompose((PetscObject)(*newmat), "SubIScol", (PetscObject)iscol_sub));
3727:     PetscCall(ISDestroy(&iscol_sub));

3729:     PetscCall(PetscObjectCompose((PetscObject)(*newmat), "Subcmap", (PetscObject)iscmap));
3730:     PetscCall(ISDestroy(&iscmap));

3732:     if (iscol_local) {
3733:       PetscCall(PetscObjectCompose((PetscObject)(*newmat), "ISAllGather", (PetscObject)iscol_local));
3734:       PetscCall(ISDestroy(&iscol_local));
3735:     }
3736:   }
3737:   PetscFunctionReturn(PETSC_SUCCESS);
3738: }

3740: /*
3741:     Not great since it makes two copies of the submatrix, first an SeqAIJ
3742:   in local and then by concatenating the local matrices the end result.
3743:   Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()

3745:   This requires a sequential iscol with all indices.
3746: */
3747: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3748: {
3749:   PetscMPIInt rank, size;
3750:   PetscInt    i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3751:   PetscInt   *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3752:   Mat         M, Mreuse;
3753:   MatScalar  *aa, *vwork;
3754:   MPI_Comm    comm;
3755:   Mat_SeqAIJ *aij;
3756:   PetscBool   colflag, allcolumns = PETSC_FALSE;

3758:   PetscFunctionBegin;
3759:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3760:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
3761:   PetscCallMPI(MPI_Comm_size(comm, &size));

3763:   /* Check for special case: each processor gets entire matrix columns */
3764:   PetscCall(ISIdentity(iscol, &colflag));
3765:   PetscCall(ISGetLocalSize(iscol, &n));
3766:   if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3767:   PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));

3769:   if (call == MAT_REUSE_MATRIX) {
3770:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3771:     PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3772:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3773:   } else {
3774:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3775:   }

3777:   /*
3778:       m - number of local rows
3779:       n - number of columns (same on all processors)
3780:       rstart - first row in new global matrix generated
3781:   */
3782:   PetscCall(MatGetSize(Mreuse, &m, &n));
3783:   PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3784:   if (call == MAT_INITIAL_MATRIX) {
3785:     aij = (Mat_SeqAIJ *)(Mreuse)->data;
3786:     ii  = aij->i;
3787:     jj  = aij->j;

3789:     /*
3790:         Determine the number of non-zeros in the diagonal and off-diagonal
3791:         portions of the matrix in order to do correct preallocation
3792:     */

3794:     /* first get start and end of "diagonal" columns */
3795:     if (csize == PETSC_DECIDE) {
3796:       PetscCall(ISGetSize(isrow, &mglobal));
3797:       if (mglobal == n) { /* square matrix */
3798:         nlocal = m;
3799:       } else {
3800:         nlocal = n / size + ((n % size) > rank);
3801:       }
3802:     } else {
3803:       nlocal = csize;
3804:     }
3805:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3806:     rstart = rend - nlocal;
3807:     PetscCheck(rank != size - 1 || rend == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Local column sizes %" PetscInt_FMT " do not add up to total number of columns %" PetscInt_FMT, rend, n);

3809:     /* next, compute all the lengths */
3810:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3811:     olens = dlens + m;
3812:     for (i = 0; i < m; i++) {
3813:       jend = ii[i + 1] - ii[i];
3814:       olen = 0;
3815:       dlen = 0;
3816:       for (j = 0; j < jend; j++) {
3817:         if (*jj < rstart || *jj >= rend) olen++;
3818:         else dlen++;
3819:         jj++;
3820:       }
3821:       olens[i] = olen;
3822:       dlens[i] = dlen;
3823:     }
3824:     PetscCall(MatCreate(comm, &M));
3825:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3826:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3827:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3828:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3829:     PetscCall(PetscFree(dlens));
3830:   } else {
3831:     PetscInt ml, nl;

3833:     M = *newmat;
3834:     PetscCall(MatGetLocalSize(M, &ml, &nl));
3835:     PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3836:     PetscCall(MatZeroEntries(M));
3837:     /*
3838:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3839:        rather than the slower MatSetValues().
3840:     */
3841:     M->was_assembled = PETSC_TRUE;
3842:     M->assembled     = PETSC_FALSE;
3843:   }
3844:   PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3845:   aij = (Mat_SeqAIJ *)(Mreuse)->data;
3846:   ii  = aij->i;
3847:   jj  = aij->j;

3849:   /* trigger copy to CPU if needed */
3850:   PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3851:   for (i = 0; i < m; i++) {
3852:     row   = rstart + i;
3853:     nz    = ii[i + 1] - ii[i];
3854:     cwork = jj;
3855:     jj += nz;
3856:     vwork = aa;
3857:     aa += nz;
3858:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3859:   }
3860:   PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));

3862:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3863:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3864:   *newmat = M;

3866:   /* save submatrix used in processor for next request */
3867:   if (call == MAT_INITIAL_MATRIX) {
3868:     PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3869:     PetscCall(MatDestroy(&Mreuse));
3870:   }
3871:   PetscFunctionReturn(PETSC_SUCCESS);
3872: }

3874: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3875: {
3876:   PetscInt        m, cstart, cend, j, nnz, i, d, *ld;
3877:   PetscInt       *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3878:   const PetscInt *JJ;
3879:   PetscBool       nooffprocentries;
3880:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)B->data;

3882:   PetscFunctionBegin;
3883:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

3885:   PetscCall(PetscLayoutSetUp(B->rmap));
3886:   PetscCall(PetscLayoutSetUp(B->cmap));
3887:   m      = B->rmap->n;
3888:   cstart = B->cmap->rstart;
3889:   cend   = B->cmap->rend;
3890:   rstart = B->rmap->rstart;

3892:   PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));

3894:   if (PetscDefined(USE_DEBUG)) {
3895:     for (i = 0; i < m; i++) {
3896:       nnz = Ii[i + 1] - Ii[i];
3897:       JJ  = J ? J + Ii[i] : NULL;
3898:       PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3899:       PetscCheck(!nnz || !(JJ[0] < 0), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " starts with negative column index %" PetscInt_FMT, i, JJ[0]);
3900:       PetscCheck(!nnz || !(JJ[nnz - 1] >= B->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Row %" PetscInt_FMT " ends with too large a column index %" PetscInt_FMT " (max allowed %" PetscInt_FMT ")", i, JJ[nnz - 1], B->cmap->N);
3901:     }
3902:   }

3904:   for (i = 0; i < m; i++) {
3905:     nnz     = Ii[i + 1] - Ii[i];
3906:     JJ      = J ? J + Ii[i] : NULL;
3907:     nnz_max = PetscMax(nnz_max, nnz);
3908:     d       = 0;
3909:     for (j = 0; j < nnz; j++) {
3910:       if (cstart <= JJ[j] && JJ[j] < cend) d++;
3911:     }
3912:     d_nnz[i] = d;
3913:     o_nnz[i] = nnz - d;
3914:   }
3915:   PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3916:   PetscCall(PetscFree2(d_nnz, o_nnz));

3918:   for (i = 0; i < m; i++) {
3919:     ii = i + rstart;
3920:     PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], J ? J + Ii[i] : NULL, v ? v + Ii[i] : NULL, INSERT_VALUES));
3921:   }
3922:   nooffprocentries    = B->nooffprocentries;
3923:   B->nooffprocentries = PETSC_TRUE;
3924:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3925:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3926:   B->nooffprocentries = nooffprocentries;

3928:   /* count number of entries below block diagonal */
3929:   PetscCall(PetscFree(Aij->ld));
3930:   PetscCall(PetscCalloc1(m, &ld));
3931:   Aij->ld = ld;
3932:   for (i = 0; i < m; i++) {
3933:     nnz = Ii[i + 1] - Ii[i];
3934:     j   = 0;
3935:     while (j < nnz && J[j] < cstart) j++;
3936:     ld[i] = j;
3937:     if (J) J += nnz;
3938:   }

3940:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3941:   PetscFunctionReturn(PETSC_SUCCESS);
3942: }

3944: /*@
3945:   MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3946:   (the default parallel PETSc format).

3948:   Collective

3950:   Input Parameters:
3951: + B - the matrix
3952: . i - the indices into j for the start of each local row (starts with zero)
3953: . j - the column indices for each local row (starts with zero)
3954: - v - optional values in the matrix

3956:   Level: developer

3958:   Notes:
3959:   The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3960:   thus you CANNOT change the matrix entries by changing the values of `v` after you have
3961:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

3963:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

3965:   A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.

3967:   You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.

3969:   If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3970:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

3972:   The format which is used for the sparse matrix input, is equivalent to a
3973:   row-major ordering.. i.e for the following matrix, the input data expected is
3974:   as shown
3975: .vb
3976:         1 0 0
3977:         2 0 3     P0
3978:        -------
3979:         4 5 6     P1

3981:      Process0 [P0] rows_owned=[0,1]
3982:         i =  {0,1,3}  [size = nrow+1  = 2+1]
3983:         j =  {0,0,2}  [size = 3]
3984:         v =  {1,2,3}  [size = 3]

3986:      Process1 [P1] rows_owned=[2]
3987:         i =  {0,3}    [size = nrow+1  = 1+1]
3988:         j =  {0,1,2}  [size = 3]
3989:         v =  {4,5,6}  [size = 3]
3990: .ve

3992: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
3993:           `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
3994: @*/
3995: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3996: {
3997:   PetscFunctionBegin;
3998:   PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
3999:   PetscFunctionReturn(PETSC_SUCCESS);
4000: }

4002: /*@C
4003:   MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4004:   (the default parallel PETSc format).  For good matrix assembly performance
4005:   the user should preallocate the matrix storage by setting the parameters
4006:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4008:   Collective

4010:   Input Parameters:
4011: + B     - the matrix
4012: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4013:            (same value is used for all local rows)
4014: . d_nnz - array containing the number of nonzeros in the various rows of the
4015:            DIAGONAL portion of the local submatrix (possibly different for each row)
4016:            or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4017:            The size of this array is equal to the number of local rows, i.e 'm'.
4018:            For matrices that will be factored, you must leave room for (and set)
4019:            the diagonal entry even if it is zero.
4020: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4021:            submatrix (same value is used for all local rows).
4022: - o_nnz - array containing the number of nonzeros in the various rows of the
4023:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4024:            each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4025:            structure. The size of this array is equal to the number
4026:            of local rows, i.e 'm'.

4028:   Example Usage:
4029:   Consider the following 8x8 matrix with 34 non-zero values, that is
4030:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4031:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4032:   as follows

4034: .vb
4035:             1  2  0  |  0  3  0  |  0  4
4036:     Proc0   0  5  6  |  7  0  0  |  8  0
4037:             9  0 10  | 11  0  0  | 12  0
4038:     -------------------------------------
4039:            13  0 14  | 15 16 17  |  0  0
4040:     Proc1   0 18  0  | 19 20 21  |  0  0
4041:             0  0  0  | 22 23  0  | 24  0
4042:     -------------------------------------
4043:     Proc2  25 26 27  |  0  0 28  | 29  0
4044:            30  0  0  | 31 32 33  |  0 34
4045: .ve

4047:   This can be represented as a collection of submatrices as
4048: .vb
4049:       A B C
4050:       D E F
4051:       G H I
4052: .ve

4054:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4055:   owned by proc1, G,H,I are owned by proc2.

4057:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4058:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4059:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4061:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4062:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4063:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4064:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4065:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4066:   matrix, ans [DF] as another `MATSEQAIJ` matrix.

4068:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4069:   allocated for every row of the local diagonal submatrix, and `o_nz`
4070:   storage locations are allocated for every row of the OFF-DIAGONAL submat.
4071:   One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4072:   rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4073:   In this case, the values of `d_nz`, `o_nz` are
4074: .vb
4075:      proc0  dnz = 2, o_nz = 2
4076:      proc1  dnz = 3, o_nz = 2
4077:      proc2  dnz = 1, o_nz = 4
4078: .ve
4079:   We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4080:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4081:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4082:   34 values.

4084:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4085:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4086:   In the above case the values for `d_nnz`, `o_nnz` are
4087: .vb
4088:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4089:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4090:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4091: .ve
4092:   Here the space allocated is sum of all the above values i.e 34, and
4093:   hence pre-allocation is perfect.

4095:   Level: intermediate

4097:   Notes:
4098:   If the *_nnz parameter is given then the *_nz parameter is ignored

4100:   The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4101:   storage.  The stored row and column indices begin with zero.
4102:   See [Sparse Matrices](sec_matsparse) for details.

4104:   The parallel matrix is partitioned such that the first m0 rows belong to
4105:   process 0, the next m1 rows belong to process 1, the next m2 rows belong
4106:   to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

4108:   The DIAGONAL portion of the local submatrix of a processor can be defined
4109:   as the submatrix which is obtained by extraction the part corresponding to
4110:   the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4111:   first row that belongs to the processor, r2 is the last row belonging to
4112:   the this processor, and c1-c2 is range of indices of the local part of a
4113:   vector suitable for applying the matrix to.  This is an mxn matrix.  In the
4114:   common case of a square matrix, the row and column ranges are the same and
4115:   the DIAGONAL part is also square. The remaining portion of the local
4116:   submatrix (mxN) constitute the OFF-DIAGONAL portion.

4118:   If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.

4120:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
4121:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4122:   You can also run with the option `-info` and look for messages with the string
4123:   malloc in them to see if additional memory allocation was needed.

4125: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4126:           `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4127: @*/
4128: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4129: {
4130:   PetscFunctionBegin;
4133:   PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4134:   PetscFunctionReturn(PETSC_SUCCESS);
4135: }

4137: /*@
4138:   MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4139:   CSR format for the local rows.

4141:   Collective

4143:   Input Parameters:
4144: + comm - MPI communicator
4145: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
4146: . n    - This value should be the same as the local size used in creating the
4147:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4148:        calculated if N is given) For square matrices n is almost always m.
4149: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4150: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4151: . i    - row indices (of length m+1); that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4152: . j    - global column indices
4153: - a    - optional matrix values

4155:   Output Parameter:
4156: . mat - the matrix

4158:   Level: intermediate

4160:   Notes:
4161:   The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4162:   thus you CANNOT change the matrix entries by changing the values of a[] after you have
4163:   called this routine. Use `MatCreateMPIAIJWithSplitArray()` to avoid needing to copy the arrays.

4165:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

4167:   Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`

4169:   If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4170:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

4172:   The format which is used for the sparse matrix input, is equivalent to a
4173:   row-major ordering.. i.e for the following matrix, the input data expected is
4174:   as shown
4175: .vb
4176:         1 0 0
4177:         2 0 3     P0
4178:        -------
4179:         4 5 6     P1

4181:      Process0 [P0] rows_owned=[0,1]
4182:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4183:         j =  {0,0,2}  [size = 3]
4184:         v =  {1,2,3}  [size = 3]

4186:      Process1 [P1] rows_owned=[2]
4187:         i =  {0,3}    [size = nrow+1  = 1+1]
4188:         j =  {0,1,2}  [size = 3]
4189:         v =  {4,5,6}  [size = 3]
4190: .ve

4192: .seealso: [](ch_matrices), `Mat`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4193:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4194: @*/
4195: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4196: {
4197:   PetscFunctionBegin;
4198:   PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4199:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4200:   PetscCall(MatCreate(comm, mat));
4201:   PetscCall(MatSetSizes(*mat, m, n, M, N));
4202:   /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4203:   PetscCall(MatSetType(*mat, MATMPIAIJ));
4204:   PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4205:   PetscFunctionReturn(PETSC_SUCCESS);
4206: }

4208: /*@
4209:   MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4210:   CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4211:   from `MatCreateMPIAIJWithArrays()`

4213:   Deprecated: Use `MatUpdateMPIAIJWithArray()`

4215:   Collective

4217:   Input Parameters:
4218: + mat - the matrix
4219: . m   - number of local rows (Cannot be `PETSC_DECIDE`)
4220: . n   - This value should be the same as the local size used in creating the
4221:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4222:        calculated if N is given) For square matrices n is almost always m.
4223: . M   - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4224: . N   - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4225: . Ii  - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4226: . J   - column indices
4227: - v   - matrix values

4229:   Level: deprecated

4231: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4232:           `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4233: @*/
4234: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4235: {
4236:   PetscInt        nnz, i;
4237:   PetscBool       nooffprocentries;
4238:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4239:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4240:   PetscScalar    *ad, *ao;
4241:   PetscInt        ldi, Iii, md;
4242:   const PetscInt *Adi = Ad->i;
4243:   PetscInt       *ld  = Aij->ld;

4245:   PetscFunctionBegin;
4246:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4247:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4248:   PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4249:   PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");

4251:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4252:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));

4254:   for (i = 0; i < m; i++) {
4255:     if (PetscDefined(USE_DEBUG)) {
4256:       for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4257:         PetscCheck(J[j] >= J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", j - Ii[i], J[j], i);
4258:         PetscCheck(J[j] != J[j - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", j - Ii[i], J[j], i);
4259:       }
4260:     }
4261:     nnz = Ii[i + 1] - Ii[i];
4262:     Iii = Ii[i];
4263:     ldi = ld[i];
4264:     md  = Adi[i + 1] - Adi[i];
4265:     PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4266:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4267:     PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4268:     ad += md;
4269:     ao += nnz - md;
4270:   }
4271:   nooffprocentries      = mat->nooffprocentries;
4272:   mat->nooffprocentries = PETSC_TRUE;
4273:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4274:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4275:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4276:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4277:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4278:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4279:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4280:   mat->nooffprocentries = nooffprocentries;
4281:   PetscFunctionReturn(PETSC_SUCCESS);
4282: }

4284: /*@
4285:   MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values

4287:   Collective

4289:   Input Parameters:
4290: + mat - the matrix
4291: - v   - matrix values, stored by row

4293:   Level: intermediate

4295:   Notes:
4296:   The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`

4298:   The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly

4300: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4301:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4302: @*/
4303: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4304: {
4305:   PetscInt        nnz, i, m;
4306:   PetscBool       nooffprocentries;
4307:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4308:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4309:   Mat_SeqAIJ     *Ao  = (Mat_SeqAIJ *)Aij->B->data;
4310:   PetscScalar    *ad, *ao;
4311:   const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4312:   PetscInt        ldi, Iii, md;
4313:   PetscInt       *ld = Aij->ld;

4315:   PetscFunctionBegin;
4316:   m = mat->rmap->n;

4318:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4319:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4320:   Iii = 0;
4321:   for (i = 0; i < m; i++) {
4322:     nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4323:     ldi = ld[i];
4324:     md  = Adi[i + 1] - Adi[i];
4325:     PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4326:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4327:     PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4328:     ad += md;
4329:     ao += nnz - md;
4330:     Iii += nnz;
4331:   }
4332:   nooffprocentries      = mat->nooffprocentries;
4333:   mat->nooffprocentries = PETSC_TRUE;
4334:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4335:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4336:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4337:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4338:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4339:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4340:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4341:   mat->nooffprocentries = nooffprocentries;
4342:   PetscFunctionReturn(PETSC_SUCCESS);
4343: }

4345: /*@C
4346:   MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4347:   (the default parallel PETSc format).  For good matrix assembly performance
4348:   the user should preallocate the matrix storage by setting the parameters
4349:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4351:   Collective

4353:   Input Parameters:
4354: + comm  - MPI communicator
4355: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4356:            This value should be the same as the local size used in creating the
4357:            y vector for the matrix-vector product y = Ax.
4358: . n     - This value should be the same as the local size used in creating the
4359:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4360:        calculated if N is given) For square matrices n is almost always m.
4361: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4362: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4363: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4364:            (same value is used for all local rows)
4365: . d_nnz - array containing the number of nonzeros in the various rows of the
4366:            DIAGONAL portion of the local submatrix (possibly different for each row)
4367:            or `NULL`, if `d_nz` is used to specify the nonzero structure.
4368:            The size of this array is equal to the number of local rows, i.e 'm'.
4369: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4370:            submatrix (same value is used for all local rows).
4371: - o_nnz - array containing the number of nonzeros in the various rows of the
4372:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4373:            each row) or `NULL`, if `o_nz` is used to specify the nonzero
4374:            structure. The size of this array is equal to the number
4375:            of local rows, i.e 'm'.

4377:   Output Parameter:
4378: . A - the matrix

4380:   Options Database Keys:
4381: + -mat_no_inode                     - Do not use inodes
4382: . -mat_inode_limit <limit>          - Sets inode limit (max limit=5)
4383: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4384:         See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4385:         Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.

4387:   Level: intermediate

4389:   Notes:
4390:   It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4391:   MatXXXXSetPreallocation() paradigm instead of this routine directly.
4392:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

4394:   If the *_nnz parameter is given then the *_nz parameter is ignored

4396:   The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4397:   processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4398:   storage requirements for this matrix.

4400:   If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one
4401:   processor than it must be used on all processors that share the object for
4402:   that argument.

4404:   The user MUST specify either the local or global matrix dimensions
4405:   (possibly both).

4407:   The parallel matrix is partitioned across processors such that the
4408:   first m0 rows belong to process 0, the next m1 rows belong to
4409:   process 1, the next m2 rows belong to process 2 etc.. where
4410:   m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4411:   values corresponding to [m x N] submatrix.

4413:   The columns are logically partitioned with the n0 columns belonging
4414:   to 0th partition, the next n1 columns belonging to the next
4415:   partition etc.. where n0,n1,n2... are the input parameter 'n'.

4417:   The DIAGONAL portion of the local submatrix on any given processor
4418:   is the submatrix corresponding to the rows and columns m,n
4419:   corresponding to the given processor. i.e diagonal matrix on
4420:   process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4421:   etc. The remaining portion of the local submatrix [m x (N-n)]
4422:   constitute the OFF-DIAGONAL portion. The example below better
4423:   illustrates this concept.

4425:   For a square global matrix we define each processor's diagonal portion
4426:   to be its local rows and the corresponding columns (a square submatrix);
4427:   each processor's off-diagonal portion encompasses the remainder of the
4428:   local matrix (a rectangular submatrix).

4430:   If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.

4432:   When calling this routine with a single process communicator, a matrix of
4433:   type `MATSEQAIJ` is returned.  If a matrix of type `MATMPIAIJ` is desired for this
4434:   type of communicator, use the construction mechanism
4435: .vb
4436:   MatCreate(..., &A);
4437:   MatSetType(A, MATMPIAIJ);
4438:   MatSetSizes(A, m, n, M, N);
4439:   MatMPIAIJSetPreallocation(A, ...);
4440: .ve

4442:   By default, this format uses inodes (identical nodes) when possible.
4443:   We search for consecutive rows with the same nonzero structure, thereby
4444:   reusing matrix information to achieve increased efficiency.

4446:   Example Usage:
4447:   Consider the following 8x8 matrix with 34 non-zero values, that is
4448:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4449:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4450:   as follows

4452: .vb
4453:             1  2  0  |  0  3  0  |  0  4
4454:     Proc0   0  5  6  |  7  0  0  |  8  0
4455:             9  0 10  | 11  0  0  | 12  0
4456:     -------------------------------------
4457:            13  0 14  | 15 16 17  |  0  0
4458:     Proc1   0 18  0  | 19 20 21  |  0  0
4459:             0  0  0  | 22 23  0  | 24  0
4460:     -------------------------------------
4461:     Proc2  25 26 27  |  0  0 28  | 29  0
4462:            30  0  0  | 31 32 33  |  0 34
4463: .ve

4465:   This can be represented as a collection of submatrices as

4467: .vb
4468:       A B C
4469:       D E F
4470:       G H I
4471: .ve

4473:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4474:   owned by proc1, G,H,I are owned by proc2.

4476:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4477:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4478:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4480:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4481:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4482:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4483:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4484:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4485:   matrix, ans [DF] as another SeqAIJ matrix.

4487:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4488:   allocated for every row of the local diagonal submatrix, and `o_nz`
4489:   storage locations are allocated for every row of the OFF-DIAGONAL submat.
4490:   One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4491:   rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4492:   In this case, the values of `d_nz`,`o_nz` are
4493: .vb
4494:      proc0  dnz = 2, o_nz = 2
4495:      proc1  dnz = 3, o_nz = 2
4496:      proc2  dnz = 1, o_nz = 4
4497: .ve
4498:   We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4499:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4500:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4501:   34 values.

4503:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4504:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4505:   In the above case the values for d_nnz,o_nnz are
4506: .vb
4507:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4508:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4509:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4510: .ve
4511:   Here the space allocated is sum of all the above values i.e 34, and
4512:   hence pre-allocation is perfect.

4514: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4515:           `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4516: @*/
4517: PetscErrorCode MatCreateAIJ(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)
4518: {
4519:   PetscMPIInt size;

4521:   PetscFunctionBegin;
4522:   PetscCall(MatCreate(comm, A));
4523:   PetscCall(MatSetSizes(*A, m, n, M, N));
4524:   PetscCallMPI(MPI_Comm_size(comm, &size));
4525:   if (size > 1) {
4526:     PetscCall(MatSetType(*A, MATMPIAIJ));
4527:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4528:   } else {
4529:     PetscCall(MatSetType(*A, MATSEQAIJ));
4530:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4531:   }
4532:   PetscFunctionReturn(PETSC_SUCCESS);
4533: }

4535: /*MC
4536:     MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix

4538:     Synopsis:
4539:     MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4541:     Not Collective

4543:     Input Parameter:
4544: .   A - the `MATMPIAIJ` matrix

4546:     Output Parameters:
4547: +   Ad - the diagonal portion of the matrix
4548: .   Ao - the off-diagonal portion of the matrix
4549: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4550: -   ierr - error code

4552:      Level: advanced

4554:     Note:
4555:     Use  `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4557: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4558: M*/

4560: /*MC
4561:     MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4563:     Synopsis:
4564:     MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4566:     Not Collective

4568:     Input Parameters:
4569: +   A - the `MATMPIAIJ` matrix
4570: .   Ad - the diagonal portion of the matrix
4571: .   Ao - the off-diagonal portion of the matrix
4572: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4573: -   ierr - error code

4575:      Level: advanced

4577: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4578: M*/

4580: /*@C
4581:   MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix

4583:   Not Collective

4585:   Input Parameter:
4586: . A - The `MATMPIAIJ` matrix

4588:   Output Parameters:
4589: + Ad     - The local diagonal block as a `MATSEQAIJ` matrix
4590: . Ao     - The local off-diagonal block as a `MATSEQAIJ` matrix
4591: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix

4593:   Level: intermediate

4595:   Note:
4596:   The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4597:   in `Ad` are in [0, Nc) where Nc is the number of local columns. The columns are `Ao` are in [0, Nco), where Nco is
4598:   the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4599:   local column numbers to global column numbers in the original matrix.

4601:   Fortran Notes:
4602:   `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`

4604: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4605: @*/
4606: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4607: {
4608:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4609:   PetscBool   flg;

4611:   PetscFunctionBegin;
4612:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4613:   PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4614:   if (Ad) *Ad = a->A;
4615:   if (Ao) *Ao = a->B;
4616:   if (colmap) *colmap = a->garray;
4617:   PetscFunctionReturn(PETSC_SUCCESS);
4618: }

4620: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4621: {
4622:   PetscInt     m, N, i, rstart, nnz, Ii;
4623:   PetscInt    *indx;
4624:   PetscScalar *values;
4625:   MatType      rootType;

4627:   PetscFunctionBegin;
4628:   PetscCall(MatGetSize(inmat, &m, &N));
4629:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4630:     PetscInt *dnz, *onz, sum, bs, cbs;

4632:     if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4633:     /* Check sum(n) = N */
4634:     PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4635:     PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);

4637:     PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4638:     rstart -= m;

4640:     MatPreallocateBegin(comm, m, n, dnz, onz);
4641:     for (i = 0; i < m; i++) {
4642:       PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4643:       PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4644:       PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4645:     }

4647:     PetscCall(MatCreate(comm, outmat));
4648:     PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4649:     PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4650:     PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4651:     PetscCall(MatGetRootType_Private(inmat, &rootType));
4652:     PetscCall(MatSetType(*outmat, rootType));
4653:     PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4654:     PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4655:     MatPreallocateEnd(dnz, onz);
4656:     PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4657:   }

4659:   /* numeric phase */
4660:   PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4661:   for (i = 0; i < m; i++) {
4662:     PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4663:     Ii = i + rstart;
4664:     PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4665:     PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4666:   }
4667:   PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4668:   PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4669:   PetscFunctionReturn(PETSC_SUCCESS);
4670: }

4672: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4673: {
4674:   Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;

4676:   PetscFunctionBegin;
4677:   if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4678:   PetscCall(PetscFree(merge->id_r));
4679:   PetscCall(PetscFree(merge->len_s));
4680:   PetscCall(PetscFree(merge->len_r));
4681:   PetscCall(PetscFree(merge->bi));
4682:   PetscCall(PetscFree(merge->bj));
4683:   PetscCall(PetscFree(merge->buf_ri[0]));
4684:   PetscCall(PetscFree(merge->buf_ri));
4685:   PetscCall(PetscFree(merge->buf_rj[0]));
4686:   PetscCall(PetscFree(merge->buf_rj));
4687:   PetscCall(PetscFree(merge->coi));
4688:   PetscCall(PetscFree(merge->coj));
4689:   PetscCall(PetscFree(merge->owners_co));
4690:   PetscCall(PetscLayoutDestroy(&merge->rowmap));
4691:   PetscCall(PetscFree(merge));
4692:   PetscFunctionReturn(PETSC_SUCCESS);
4693: }

4695: #include <../src/mat/utils/freespace.h>
4696: #include <petscbt.h>

4698: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4699: {
4700:   MPI_Comm             comm;
4701:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4702:   PetscMPIInt          size, rank, taga, *len_s;
4703:   PetscInt             N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4704:   PetscInt             proc, m;
4705:   PetscInt           **buf_ri, **buf_rj;
4706:   PetscInt             k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4707:   PetscInt             nrows, **buf_ri_k, **nextrow, **nextai;
4708:   MPI_Request         *s_waits, *r_waits;
4709:   MPI_Status          *status;
4710:   const MatScalar     *aa, *a_a;
4711:   MatScalar          **abuf_r, *ba_i;
4712:   Mat_Merge_SeqsToMPI *merge;
4713:   PetscContainer       container;

4715:   PetscFunctionBegin;
4716:   PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4717:   PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));

4719:   PetscCallMPI(MPI_Comm_size(comm, &size));
4720:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4722:   PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4723:   PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4724:   PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4725:   PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4726:   aa = a_a;

4728:   bi     = merge->bi;
4729:   bj     = merge->bj;
4730:   buf_ri = merge->buf_ri;
4731:   buf_rj = merge->buf_rj;

4733:   PetscCall(PetscMalloc1(size, &status));
4734:   owners = merge->rowmap->range;
4735:   len_s  = merge->len_s;

4737:   /* send and recv matrix values */
4738:   PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4739:   PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));

4741:   PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4742:   for (proc = 0, k = 0; proc < size; proc++) {
4743:     if (!len_s[proc]) continue;
4744:     i = owners[proc];
4745:     PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4746:     k++;
4747:   }

4749:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4750:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4751:   PetscCall(PetscFree(status));

4753:   PetscCall(PetscFree(s_waits));
4754:   PetscCall(PetscFree(r_waits));

4756:   /* insert mat values of mpimat */
4757:   PetscCall(PetscMalloc1(N, &ba_i));
4758:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4760:   for (k = 0; k < merge->nrecv; k++) {
4761:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4762:     nrows       = *(buf_ri_k[k]);
4763:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4764:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4765:   }

4767:   /* set values of ba */
4768:   m = merge->rowmap->n;
4769:   for (i = 0; i < m; i++) {
4770:     arow = owners[rank] + i;
4771:     bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4772:     bnzi = bi[i + 1] - bi[i];
4773:     PetscCall(PetscArrayzero(ba_i, bnzi));

4775:     /* add local non-zero vals of this proc's seqmat into ba */
4776:     anzi   = ai[arow + 1] - ai[arow];
4777:     aj     = a->j + ai[arow];
4778:     aa     = a_a + ai[arow];
4779:     nextaj = 0;
4780:     for (j = 0; nextaj < anzi; j++) {
4781:       if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4782:         ba_i[j] += aa[nextaj++];
4783:       }
4784:     }

4786:     /* add received vals into ba */
4787:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4788:       /* i-th row */
4789:       if (i == *nextrow[k]) {
4790:         anzi   = *(nextai[k] + 1) - *nextai[k];
4791:         aj     = buf_rj[k] + *(nextai[k]);
4792:         aa     = abuf_r[k] + *(nextai[k]);
4793:         nextaj = 0;
4794:         for (j = 0; nextaj < anzi; j++) {
4795:           if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4796:             ba_i[j] += aa[nextaj++];
4797:           }
4798:         }
4799:         nextrow[k]++;
4800:         nextai[k]++;
4801:       }
4802:     }
4803:     PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4804:   }
4805:   PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4806:   PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4807:   PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));

4809:   PetscCall(PetscFree(abuf_r[0]));
4810:   PetscCall(PetscFree(abuf_r));
4811:   PetscCall(PetscFree(ba_i));
4812:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4813:   PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4814:   PetscFunctionReturn(PETSC_SUCCESS);
4815: }

4817: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4818: {
4819:   Mat                  B_mpi;
4820:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4821:   PetscMPIInt          size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4822:   PetscInt           **buf_rj, **buf_ri, **buf_ri_k;
4823:   PetscInt             M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4824:   PetscInt             len, proc, *dnz, *onz, bs, cbs;
4825:   PetscInt             k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4826:   PetscInt             nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4827:   MPI_Request         *si_waits, *sj_waits, *ri_waits, *rj_waits;
4828:   MPI_Status          *status;
4829:   PetscFreeSpaceList   free_space = NULL, current_space = NULL;
4830:   PetscBT              lnkbt;
4831:   Mat_Merge_SeqsToMPI *merge;
4832:   PetscContainer       container;

4834:   PetscFunctionBegin;
4835:   PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));

4837:   /* make sure it is a PETSc comm */
4838:   PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4839:   PetscCallMPI(MPI_Comm_size(comm, &size));
4840:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4842:   PetscCall(PetscNew(&merge));
4843:   PetscCall(PetscMalloc1(size, &status));

4845:   /* determine row ownership */
4846:   PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4847:   PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4848:   PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4849:   PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4850:   PetscCall(PetscLayoutSetUp(merge->rowmap));
4851:   PetscCall(PetscMalloc1(size, &len_si));
4852:   PetscCall(PetscMalloc1(size, &merge->len_s));

4854:   m      = merge->rowmap->n;
4855:   owners = merge->rowmap->range;

4857:   /* determine the number of messages to send, their lengths */
4858:   len_s = merge->len_s;

4860:   len          = 0; /* length of buf_si[] */
4861:   merge->nsend = 0;
4862:   for (proc = 0; proc < size; proc++) {
4863:     len_si[proc] = 0;
4864:     if (proc == rank) {
4865:       len_s[proc] = 0;
4866:     } else {
4867:       len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4868:       len_s[proc]  = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4869:     }
4870:     if (len_s[proc]) {
4871:       merge->nsend++;
4872:       nrows = 0;
4873:       for (i = owners[proc]; i < owners[proc + 1]; i++) {
4874:         if (ai[i + 1] > ai[i]) nrows++;
4875:       }
4876:       len_si[proc] = 2 * (nrows + 1);
4877:       len += len_si[proc];
4878:     }
4879:   }

4881:   /* determine the number and length of messages to receive for ij-structure */
4882:   PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4883:   PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));

4885:   /* post the Irecv of j-structure */
4886:   PetscCall(PetscCommGetNewTag(comm, &tagj));
4887:   PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));

4889:   /* post the Isend of j-structure */
4890:   PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));

4892:   for (proc = 0, k = 0; proc < size; proc++) {
4893:     if (!len_s[proc]) continue;
4894:     i = owners[proc];
4895:     PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4896:     k++;
4897:   }

4899:   /* receives and sends of j-structure are complete */
4900:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4901:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));

4903:   /* send and recv i-structure */
4904:   PetscCall(PetscCommGetNewTag(comm, &tagi));
4905:   PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));

4907:   PetscCall(PetscMalloc1(len + 1, &buf_s));
4908:   buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4909:   for (proc = 0, k = 0; proc < size; proc++) {
4910:     if (!len_s[proc]) continue;
4911:     /* form outgoing message for i-structure:
4912:          buf_si[0]:                 nrows to be sent
4913:                [1:nrows]:           row index (global)
4914:                [nrows+1:2*nrows+1]: i-structure index
4915:     */
4916:     nrows       = len_si[proc] / 2 - 1;
4917:     buf_si_i    = buf_si + nrows + 1;
4918:     buf_si[0]   = nrows;
4919:     buf_si_i[0] = 0;
4920:     nrows       = 0;
4921:     for (i = owners[proc]; i < owners[proc + 1]; i++) {
4922:       anzi = ai[i + 1] - ai[i];
4923:       if (anzi) {
4924:         buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4925:         buf_si[nrows + 1]   = i - owners[proc];       /* local row index */
4926:         nrows++;
4927:       }
4928:     }
4929:     PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4930:     k++;
4931:     buf_si += len_si[proc];
4932:   }

4934:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4935:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));

4937:   PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4938:   for (i = 0; i < merge->nrecv; i++) PetscCall(PetscInfo(seqmat, "recv len_ri=%d, len_rj=%d from [%d]\n", len_ri[i], merge->len_r[i], merge->id_r[i]));

4940:   PetscCall(PetscFree(len_si));
4941:   PetscCall(PetscFree(len_ri));
4942:   PetscCall(PetscFree(rj_waits));
4943:   PetscCall(PetscFree2(si_waits, sj_waits));
4944:   PetscCall(PetscFree(ri_waits));
4945:   PetscCall(PetscFree(buf_s));
4946:   PetscCall(PetscFree(status));

4948:   /* compute a local seq matrix in each processor */
4949:   /* allocate bi array and free space for accumulating nonzero column info */
4950:   PetscCall(PetscMalloc1(m + 1, &bi));
4951:   bi[0] = 0;

4953:   /* create and initialize a linked list */
4954:   nlnk = N + 1;
4955:   PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));

4957:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4958:   len = ai[owners[rank + 1]] - ai[owners[rank]];
4959:   PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));

4961:   current_space = free_space;

4963:   /* determine symbolic info for each local row */
4964:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4966:   for (k = 0; k < merge->nrecv; k++) {
4967:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4968:     nrows       = *buf_ri_k[k];
4969:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4970:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4971:   }

4973:   MatPreallocateBegin(comm, m, n, dnz, onz);
4974:   len = 0;
4975:   for (i = 0; i < m; i++) {
4976:     bnzi = 0;
4977:     /* add local non-zero cols of this proc's seqmat into lnk */
4978:     arow = owners[rank] + i;
4979:     anzi = ai[arow + 1] - ai[arow];
4980:     aj   = a->j + ai[arow];
4981:     PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4982:     bnzi += nlnk;
4983:     /* add received col data into lnk */
4984:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4985:       if (i == *nextrow[k]) {            /* i-th row */
4986:         anzi = *(nextai[k] + 1) - *nextai[k];
4987:         aj   = buf_rj[k] + *nextai[k];
4988:         PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
4989:         bnzi += nlnk;
4990:         nextrow[k]++;
4991:         nextai[k]++;
4992:       }
4993:     }
4994:     if (len < bnzi) len = bnzi; /* =max(bnzi) */

4996:     /* if free space is not available, make more free space */
4997:     if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), &current_space));
4998:     /* copy data into free space, then initialize lnk */
4999:     PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5000:     PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));

5002:     current_space->array += bnzi;
5003:     current_space->local_used += bnzi;
5004:     current_space->local_remaining -= bnzi;

5006:     bi[i + 1] = bi[i] + bnzi;
5007:   }

5009:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));

5011:   PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5012:   PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5013:   PetscCall(PetscLLDestroy(lnk, lnkbt));

5015:   /* create symbolic parallel matrix B_mpi */
5016:   PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5017:   PetscCall(MatCreate(comm, &B_mpi));
5018:   if (n == PETSC_DECIDE) {
5019:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5020:   } else {
5021:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5022:   }
5023:   PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5024:   PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5025:   PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5026:   MatPreallocateEnd(dnz, onz);
5027:   PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));

5029:   /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5030:   B_mpi->assembled = PETSC_FALSE;
5031:   merge->bi        = bi;
5032:   merge->bj        = bj;
5033:   merge->buf_ri    = buf_ri;
5034:   merge->buf_rj    = buf_rj;
5035:   merge->coi       = NULL;
5036:   merge->coj       = NULL;
5037:   merge->owners_co = NULL;

5039:   PetscCall(PetscCommDestroy(&comm));

5041:   /* attach the supporting struct to B_mpi for reuse */
5042:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5043:   PetscCall(PetscContainerSetPointer(container, merge));
5044:   PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5045:   PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5046:   PetscCall(PetscContainerDestroy(&container));
5047:   *mpimat = B_mpi;

5049:   PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5050:   PetscFunctionReturn(PETSC_SUCCESS);
5051: }

5053: /*@C
5054:   MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5055:   matrices from each processor

5057:   Collective

5059:   Input Parameters:
5060: + comm   - the communicators the parallel matrix will live on
5061: . seqmat - the input sequential matrices
5062: . m      - number of local rows (or `PETSC_DECIDE`)
5063: . n      - number of local columns (or `PETSC_DECIDE`)
5064: - scall  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5066:   Output Parameter:
5067: . mpimat - the parallel matrix generated

5069:   Level: advanced

5071:   Note:
5072:   The dimensions of the sequential matrix in each processor MUST be the same.
5073:   The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5074:   destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.

5076: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5077: @*/
5078: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5079: {
5080:   PetscMPIInt size;

5082:   PetscFunctionBegin;
5083:   PetscCallMPI(MPI_Comm_size(comm, &size));
5084:   if (size == 1) {
5085:     PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5086:     if (scall == MAT_INITIAL_MATRIX) {
5087:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5088:     } else {
5089:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5090:     }
5091:     PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5092:     PetscFunctionReturn(PETSC_SUCCESS);
5093:   }
5094:   PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5095:   if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5096:   PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5097:   PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5098:   PetscFunctionReturn(PETSC_SUCCESS);
5099: }

5101: /*@
5102:   MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.

5104:   Not Collective

5106:   Input Parameter:
5107: . A - the matrix

5109:   Output Parameter:
5110: . A_loc - the local sequential matrix generated

5112:   Level: developer

5114:   Notes:
5115:   The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5116:   with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5117:   `n` is the global column count obtained with `MatGetSize()`

5119:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5121:   For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.

5123:   Destroy the matrix with `MatDestroy()`

5125: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5126: @*/
5127: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5128: {
5129:   PetscBool mpi;

5131:   PetscFunctionBegin;
5132:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5133:   if (mpi) {
5134:     PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5135:   } else {
5136:     *A_loc = A;
5137:     PetscCall(PetscObjectReference((PetscObject)*A_loc));
5138:   }
5139:   PetscFunctionReturn(PETSC_SUCCESS);
5140: }

5142: /*@
5143:   MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.

5145:   Not Collective

5147:   Input Parameters:
5148: + A     - the matrix
5149: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5151:   Output Parameter:
5152: . A_loc - the local sequential matrix generated

5154:   Level: developer

5156:   Notes:
5157:   The matrix is created by taking all `A`'s local rows and putting them into a sequential
5158:   matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5159:   `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.

5161:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5163:   When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5164:   with its reference count increased by one. Hence changing values of `A_loc` changes `A`. If `MAT_REUSE_MATRIX` is requested on a sequential matrix
5165:   then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5166:   and then call this routine with `MAT_REUSE_MATRIX`. In this case, one can modify the values of `A_loc` without affecting the original sequential matrix.

5168: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5169: @*/
5170: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5171: {
5172:   Mat_MPIAIJ        *mpimat = (Mat_MPIAIJ *)A->data;
5173:   Mat_SeqAIJ        *mat, *a, *b;
5174:   PetscInt          *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5175:   const PetscScalar *aa, *ba, *aav, *bav;
5176:   PetscScalar       *ca, *cam;
5177:   PetscMPIInt        size;
5178:   PetscInt           am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5179:   PetscInt          *ci, *cj, col, ncols_d, ncols_o, jo;
5180:   PetscBool          match;

5182:   PetscFunctionBegin;
5183:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5184:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5185:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5186:   if (size == 1) {
5187:     if (scall == MAT_INITIAL_MATRIX) {
5188:       PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5189:       *A_loc = mpimat->A;
5190:     } else if (scall == MAT_REUSE_MATRIX) {
5191:       PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5192:     }
5193:     PetscFunctionReturn(PETSC_SUCCESS);
5194:   }

5196:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5197:   a  = (Mat_SeqAIJ *)(mpimat->A)->data;
5198:   b  = (Mat_SeqAIJ *)(mpimat->B)->data;
5199:   ai = a->i;
5200:   aj = a->j;
5201:   bi = b->i;
5202:   bj = b->j;
5203:   PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5204:   PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5205:   aa = aav;
5206:   ba = bav;
5207:   if (scall == MAT_INITIAL_MATRIX) {
5208:     PetscCall(PetscMalloc1(1 + am, &ci));
5209:     ci[0] = 0;
5210:     for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5211:     PetscCall(PetscMalloc1(1 + ci[am], &cj));
5212:     PetscCall(PetscMalloc1(1 + ci[am], &ca));
5213:     k = 0;
5214:     for (i = 0; i < am; i++) {
5215:       ncols_o = bi[i + 1] - bi[i];
5216:       ncols_d = ai[i + 1] - ai[i];
5217:       /* off-diagonal portion of A */
5218:       for (jo = 0; jo < ncols_o; jo++) {
5219:         col = cmap[*bj];
5220:         if (col >= cstart) break;
5221:         cj[k] = col;
5222:         bj++;
5223:         ca[k++] = *ba++;
5224:       }
5225:       /* diagonal portion of A */
5226:       for (j = 0; j < ncols_d; j++) {
5227:         cj[k]   = cstart + *aj++;
5228:         ca[k++] = *aa++;
5229:       }
5230:       /* off-diagonal portion of A */
5231:       for (j = jo; j < ncols_o; j++) {
5232:         cj[k]   = cmap[*bj++];
5233:         ca[k++] = *ba++;
5234:       }
5235:     }
5236:     /* put together the new matrix */
5237:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5238:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5239:     /* Since these are PETSc arrays, change flags to free them as necessary. */
5240:     mat          = (Mat_SeqAIJ *)(*A_loc)->data;
5241:     mat->free_a  = PETSC_TRUE;
5242:     mat->free_ij = PETSC_TRUE;
5243:     mat->nonew   = 0;
5244:   } else if (scall == MAT_REUSE_MATRIX) {
5245:     mat = (Mat_SeqAIJ *)(*A_loc)->data;
5246:     ci  = mat->i;
5247:     cj  = mat->j;
5248:     PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5249:     for (i = 0; i < am; i++) {
5250:       /* off-diagonal portion of A */
5251:       ncols_o = bi[i + 1] - bi[i];
5252:       for (jo = 0; jo < ncols_o; jo++) {
5253:         col = cmap[*bj];
5254:         if (col >= cstart) break;
5255:         *cam++ = *ba++;
5256:         bj++;
5257:       }
5258:       /* diagonal portion of A */
5259:       ncols_d = ai[i + 1] - ai[i];
5260:       for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5261:       /* off-diagonal portion of A */
5262:       for (j = jo; j < ncols_o; j++) {
5263:         *cam++ = *ba++;
5264:         bj++;
5265:       }
5266:     }
5267:     PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5268:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5269:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5270:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5271:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5272:   PetscFunctionReturn(PETSC_SUCCESS);
5273: }

5275: /*@
5276:   MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5277:   mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part

5279:   Not Collective

5281:   Input Parameters:
5282: + A     - the matrix
5283: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5285:   Output Parameters:
5286: + glob  - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5287: - A_loc - the local sequential matrix generated

5289:   Level: developer

5291:   Note:
5292:   This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5293:   part, then those associated with the off-diagonal part (in its local ordering)

5295: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5296: @*/
5297: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5298: {
5299:   Mat             Ao, Ad;
5300:   const PetscInt *cmap;
5301:   PetscMPIInt     size;
5302:   PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);

5304:   PetscFunctionBegin;
5305:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5306:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5307:   if (size == 1) {
5308:     if (scall == MAT_INITIAL_MATRIX) {
5309:       PetscCall(PetscObjectReference((PetscObject)Ad));
5310:       *A_loc = Ad;
5311:     } else if (scall == MAT_REUSE_MATRIX) {
5312:       PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5313:     }
5314:     if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5315:     PetscFunctionReturn(PETSC_SUCCESS);
5316:   }
5317:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5318:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5319:   if (f) {
5320:     PetscCall((*f)(A, scall, glob, A_loc));
5321:   } else {
5322:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)Ad->data;
5323:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)Ao->data;
5324:     Mat_SeqAIJ        *c;
5325:     PetscInt          *ai = a->i, *aj = a->j;
5326:     PetscInt          *bi = b->i, *bj = b->j;
5327:     PetscInt          *ci, *cj;
5328:     const PetscScalar *aa, *ba;
5329:     PetscScalar       *ca;
5330:     PetscInt           i, j, am, dn, on;

5332:     PetscCall(MatGetLocalSize(Ad, &am, &dn));
5333:     PetscCall(MatGetLocalSize(Ao, NULL, &on));
5334:     PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5335:     PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5336:     if (scall == MAT_INITIAL_MATRIX) {
5337:       PetscInt k;
5338:       PetscCall(PetscMalloc1(1 + am, &ci));
5339:       PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5340:       PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5341:       ci[0] = 0;
5342:       for (i = 0, k = 0; i < am; i++) {
5343:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5344:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5345:         ci[i + 1]              = ci[i] + ncols_o + ncols_d;
5346:         /* diagonal portion of A */
5347:         for (j = 0; j < ncols_d; j++, k++) {
5348:           cj[k] = *aj++;
5349:           ca[k] = *aa++;
5350:         }
5351:         /* off-diagonal portion of A */
5352:         for (j = 0; j < ncols_o; j++, k++) {
5353:           cj[k] = dn + *bj++;
5354:           ca[k] = *ba++;
5355:         }
5356:       }
5357:       /* put together the new matrix */
5358:       PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5359:       /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5360:       /* Since these are PETSc arrays, change flags to free them as necessary. */
5361:       c          = (Mat_SeqAIJ *)(*A_loc)->data;
5362:       c->free_a  = PETSC_TRUE;
5363:       c->free_ij = PETSC_TRUE;
5364:       c->nonew   = 0;
5365:       PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5366:     } else if (scall == MAT_REUSE_MATRIX) {
5367:       PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5368:       for (i = 0; i < am; i++) {
5369:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5370:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5371:         /* diagonal portion of A */
5372:         for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5373:         /* off-diagonal portion of A */
5374:         for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5375:       }
5376:       PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5377:     } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5378:     PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5379:     PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5380:     if (glob) {
5381:       PetscInt cst, *gidx;

5383:       PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5384:       PetscCall(PetscMalloc1(dn + on, &gidx));
5385:       for (i = 0; i < dn; i++) gidx[i] = cst + i;
5386:       for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5387:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5388:     }
5389:   }
5390:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5391:   PetscFunctionReturn(PETSC_SUCCESS);
5392: }

5394: /*@C
5395:   MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns

5397:   Not Collective

5399:   Input Parameters:
5400: + A     - the matrix
5401: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5402: . row   - index set of rows to extract (or `NULL`)
5403: - col   - index set of columns to extract (or `NULL`)

5405:   Output Parameter:
5406: . A_loc - the local sequential matrix generated

5408:   Level: developer

5410: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5411: @*/
5412: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5413: {
5414:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5415:   PetscInt    i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5416:   IS          isrowa, iscola;
5417:   Mat        *aloc;
5418:   PetscBool   match;

5420:   PetscFunctionBegin;
5421:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5422:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5423:   PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5424:   if (!row) {
5425:     start = A->rmap->rstart;
5426:     end   = A->rmap->rend;
5427:     PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5428:   } else {
5429:     isrowa = *row;
5430:   }
5431:   if (!col) {
5432:     start = A->cmap->rstart;
5433:     cmap  = a->garray;
5434:     nzA   = a->A->cmap->n;
5435:     nzB   = a->B->cmap->n;
5436:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5437:     ncols = 0;
5438:     for (i = 0; i < nzB; i++) {
5439:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5440:       else break;
5441:     }
5442:     imark = i;
5443:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5444:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5445:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5446:   } else {
5447:     iscola = *col;
5448:   }
5449:   if (scall != MAT_INITIAL_MATRIX) {
5450:     PetscCall(PetscMalloc1(1, &aloc));
5451:     aloc[0] = *A_loc;
5452:   }
5453:   PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5454:   if (!col) { /* attach global id of condensed columns */
5455:     PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5456:   }
5457:   *A_loc = aloc[0];
5458:   PetscCall(PetscFree(aloc));
5459:   if (!row) PetscCall(ISDestroy(&isrowa));
5460:   if (!col) PetscCall(ISDestroy(&iscola));
5461:   PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5462:   PetscFunctionReturn(PETSC_SUCCESS);
5463: }

5465: /*
5466:  * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5467:  * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5468:  * on a global size.
5469:  * */
5470: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5471: {
5472:   Mat_MPIAIJ            *p  = (Mat_MPIAIJ *)P->data;
5473:   Mat_SeqAIJ            *pd = (Mat_SeqAIJ *)(p->A)->data, *po = (Mat_SeqAIJ *)(p->B)->data, *p_oth;
5474:   PetscInt               plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5475:   PetscMPIInt            owner;
5476:   PetscSFNode           *iremote, *oiremote;
5477:   const PetscInt        *lrowindices;
5478:   PetscSF                sf, osf;
5479:   PetscInt               pcstart, *roffsets, *loffsets, *pnnz, j;
5480:   PetscInt               ontotalcols, dntotalcols, ntotalcols, nout;
5481:   MPI_Comm               comm;
5482:   ISLocalToGlobalMapping mapping;
5483:   const PetscScalar     *pd_a, *po_a;

5485:   PetscFunctionBegin;
5486:   PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5487:   /* plocalsize is the number of roots
5488:    * nrows is the number of leaves
5489:    * */
5490:   PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5491:   PetscCall(ISGetLocalSize(rows, &nrows));
5492:   PetscCall(PetscCalloc1(nrows, &iremote));
5493:   PetscCall(ISGetIndices(rows, &lrowindices));
5494:   for (i = 0; i < nrows; i++) {
5495:     /* Find a remote index and an owner for a row
5496:      * The row could be local or remote
5497:      * */
5498:     owner = 0;
5499:     lidx  = 0;
5500:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5501:     iremote[i].index = lidx;
5502:     iremote[i].rank  = owner;
5503:   }
5504:   /* Create SF to communicate how many nonzero columns for each row */
5505:   PetscCall(PetscSFCreate(comm, &sf));
5506:   /* SF will figure out the number of nonzero columns for each row, and their
5507:    * offsets
5508:    * */
5509:   PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5510:   PetscCall(PetscSFSetFromOptions(sf));
5511:   PetscCall(PetscSFSetUp(sf));

5513:   PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5514:   PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5515:   PetscCall(PetscCalloc1(nrows, &pnnz));
5516:   roffsets[0] = 0;
5517:   roffsets[1] = 0;
5518:   for (i = 0; i < plocalsize; i++) {
5519:     /* diagonal */
5520:     nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5521:     /* off-diagonal */
5522:     nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5523:     /* compute offsets so that we relative location for each row */
5524:     roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5525:     roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5526:   }
5527:   PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5528:   PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5529:   /* 'r' means root, and 'l' means leaf */
5530:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5531:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5532:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5533:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5534:   PetscCall(PetscSFDestroy(&sf));
5535:   PetscCall(PetscFree(roffsets));
5536:   PetscCall(PetscFree(nrcols));
5537:   dntotalcols = 0;
5538:   ontotalcols = 0;
5539:   ncol        = 0;
5540:   for (i = 0; i < nrows; i++) {
5541:     pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5542:     ncol    = PetscMax(pnnz[i], ncol);
5543:     /* diagonal */
5544:     dntotalcols += nlcols[i * 2 + 0];
5545:     /* off-diagonal */
5546:     ontotalcols += nlcols[i * 2 + 1];
5547:   }
5548:   /* We do not need to figure the right number of columns
5549:    * since all the calculations will be done by going through the raw data
5550:    * */
5551:   PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5552:   PetscCall(MatSetUp(*P_oth));
5553:   PetscCall(PetscFree(pnnz));
5554:   p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5555:   /* diagonal */
5556:   PetscCall(PetscCalloc1(dntotalcols, &iremote));
5557:   /* off-diagonal */
5558:   PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5559:   /* diagonal */
5560:   PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5561:   /* off-diagonal */
5562:   PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5563:   dntotalcols = 0;
5564:   ontotalcols = 0;
5565:   ntotalcols  = 0;
5566:   for (i = 0; i < nrows; i++) {
5567:     owner = 0;
5568:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5569:     /* Set iremote for diag matrix */
5570:     for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5571:       iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5572:       iremote[dntotalcols].rank  = owner;
5573:       /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5574:       ilocal[dntotalcols++] = ntotalcols++;
5575:     }
5576:     /* off-diagonal */
5577:     for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5578:       oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5579:       oiremote[ontotalcols].rank  = owner;
5580:       oilocal[ontotalcols++]      = ntotalcols++;
5581:     }
5582:   }
5583:   PetscCall(ISRestoreIndices(rows, &lrowindices));
5584:   PetscCall(PetscFree(loffsets));
5585:   PetscCall(PetscFree(nlcols));
5586:   PetscCall(PetscSFCreate(comm, &sf));
5587:   /* P serves as roots and P_oth is leaves
5588:    * Diag matrix
5589:    * */
5590:   PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5591:   PetscCall(PetscSFSetFromOptions(sf));
5592:   PetscCall(PetscSFSetUp(sf));

5594:   PetscCall(PetscSFCreate(comm, &osf));
5595:   /* off-diagonal */
5596:   PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5597:   PetscCall(PetscSFSetFromOptions(osf));
5598:   PetscCall(PetscSFSetUp(osf));
5599:   PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5600:   PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5601:   /* operate on the matrix internal data to save memory */
5602:   PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5603:   PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5604:   PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5605:   /* Convert to global indices for diag matrix */
5606:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5607:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5608:   /* We want P_oth store global indices */
5609:   PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5610:   /* Use memory scalable approach */
5611:   PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5612:   PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5613:   PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5614:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5615:   /* Convert back to local indices */
5616:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5617:   PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5618:   nout = 0;
5619:   PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5620:   PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5621:   PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5622:   /* Exchange values */
5623:   PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5624:   PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5625:   PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5626:   PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5627:   /* Stop PETSc from shrinking memory */
5628:   for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5629:   PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5630:   PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5631:   /* Attach PetscSF objects to P_oth so that we can reuse it later */
5632:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5633:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5634:   PetscCall(PetscSFDestroy(&sf));
5635:   PetscCall(PetscSFDestroy(&osf));
5636:   PetscFunctionReturn(PETSC_SUCCESS);
5637: }

5639: /*
5640:  * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5641:  * This supports MPIAIJ and MAIJ
5642:  * */
5643: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5644: {
5645:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5646:   Mat_SeqAIJ *p_oth;
5647:   IS          rows, map;
5648:   PetscHMapI  hamp;
5649:   PetscInt    i, htsize, *rowindices, off, *mapping, key, count;
5650:   MPI_Comm    comm;
5651:   PetscSF     sf, osf;
5652:   PetscBool   has;

5654:   PetscFunctionBegin;
5655:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5656:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5657:   /* If it is the first time, create an index set of off-diag nonzero columns of A,
5658:    *  and then create a submatrix (that often is an overlapping matrix)
5659:    * */
5660:   if (reuse == MAT_INITIAL_MATRIX) {
5661:     /* Use a hash table to figure out unique keys */
5662:     PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5663:     PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5664:     count = 0;
5665:     /* Assume that  a->g is sorted, otherwise the following does not make sense */
5666:     for (i = 0; i < a->B->cmap->n; i++) {
5667:       key = a->garray[i] / dof;
5668:       PetscCall(PetscHMapIHas(hamp, key, &has));
5669:       if (!has) {
5670:         mapping[i] = count;
5671:         PetscCall(PetscHMapISet(hamp, key, count++));
5672:       } else {
5673:         /* Current 'i' has the same value the previous step */
5674:         mapping[i] = count - 1;
5675:       }
5676:     }
5677:     PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5678:     PetscCall(PetscHMapIGetSize(hamp, &htsize));
5679:     PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5680:     PetscCall(PetscCalloc1(htsize, &rowindices));
5681:     off = 0;
5682:     PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5683:     PetscCall(PetscHMapIDestroy(&hamp));
5684:     PetscCall(PetscSortInt(htsize, rowindices));
5685:     PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5686:     /* In case, the matrix was already created but users want to recreate the matrix */
5687:     PetscCall(MatDestroy(P_oth));
5688:     PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5689:     PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5690:     PetscCall(ISDestroy(&map));
5691:     PetscCall(ISDestroy(&rows));
5692:   } else if (reuse == MAT_REUSE_MATRIX) {
5693:     /* If matrix was already created, we simply update values using SF objects
5694:      * that as attached to the matrix earlier.
5695:      */
5696:     const PetscScalar *pd_a, *po_a;

5698:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5699:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5700:     PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5701:     p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5702:     /* Update values in place */
5703:     PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5704:     PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5705:     PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5706:     PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5707:     PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5708:     PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5709:     PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5710:     PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5711:   } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5712:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5713:   PetscFunctionReturn(PETSC_SUCCESS);
5714: }

5716: /*@C
5717:   MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`

5719:   Collective

5721:   Input Parameters:
5722: + A     - the first matrix in `MATMPIAIJ` format
5723: . B     - the second matrix in `MATMPIAIJ` format
5724: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5726:   Output Parameters:
5727: + rowb  - On input index sets of rows of B to extract (or `NULL`), modified on output
5728: . colb  - On input index sets of columns of B to extract (or `NULL`), modified on output
5729: - B_seq - the sequential matrix generated

5731:   Level: developer

5733: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5734: @*/
5735: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5736: {
5737:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5738:   PetscInt   *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5739:   IS          isrowb, iscolb;
5740:   Mat        *bseq = NULL;

5742:   PetscFunctionBegin;
5743:   PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
5744:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5745:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));

5747:   if (scall == MAT_INITIAL_MATRIX) {
5748:     start = A->cmap->rstart;
5749:     cmap  = a->garray;
5750:     nzA   = a->A->cmap->n;
5751:     nzB   = a->B->cmap->n;
5752:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5753:     ncols = 0;
5754:     for (i = 0; i < nzB; i++) { /* row < local row index */
5755:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5756:       else break;
5757:     }
5758:     imark = i;
5759:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;   /* local rows */
5760:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5761:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5762:     PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5763:   } else {
5764:     PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5765:     isrowb = *rowb;
5766:     iscolb = *colb;
5767:     PetscCall(PetscMalloc1(1, &bseq));
5768:     bseq[0] = *B_seq;
5769:   }
5770:   PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5771:   *B_seq = bseq[0];
5772:   PetscCall(PetscFree(bseq));
5773:   if (!rowb) {
5774:     PetscCall(ISDestroy(&isrowb));
5775:   } else {
5776:     *rowb = isrowb;
5777:   }
5778:   if (!colb) {
5779:     PetscCall(ISDestroy(&iscolb));
5780:   } else {
5781:     *colb = iscolb;
5782:   }
5783:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5784:   PetscFunctionReturn(PETSC_SUCCESS);
5785: }

5787: /*
5788:     MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5789:     of the OFF-DIAGONAL portion of local A

5791:     Collective

5793:    Input Parameters:
5794: +    A,B - the matrices in `MATMPIAIJ` format
5795: -    scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5797:    Output Parameter:
5798: +    startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5799: .    startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5800: .    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5801: -    B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N

5803:     Developer Note:
5804:     This directly accesses information inside the VecScatter associated with the matrix-vector product
5805:      for this matrix. This is not desirable..

5807:     Level: developer

5809: */
5810: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5811: {
5812:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
5813:   Mat_SeqAIJ        *b_oth;
5814:   VecScatter         ctx;
5815:   MPI_Comm           comm;
5816:   const PetscMPIInt *rprocs, *sprocs;
5817:   const PetscInt    *srow, *rstarts, *sstarts;
5818:   PetscInt          *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5819:   PetscInt           i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5820:   PetscScalar       *b_otha, *bufa, *bufA, *vals = NULL;
5821:   MPI_Request       *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5822:   PetscMPIInt        size, tag, rank, nreqs;

5824:   PetscFunctionBegin;
5825:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5826:   PetscCallMPI(MPI_Comm_size(comm, &size));

5828:   PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
5829:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5830:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5831:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

5833:   if (size == 1) {
5834:     startsj_s = NULL;
5835:     bufa_ptr  = NULL;
5836:     *B_oth    = NULL;
5837:     PetscFunctionReturn(PETSC_SUCCESS);
5838:   }

5840:   ctx = a->Mvctx;
5841:   tag = ((PetscObject)ctx)->tag;

5843:   PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5844:   /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5845:   PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5846:   PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5847:   PetscCall(PetscMalloc1(nreqs, &reqs));
5848:   rwaits = reqs;
5849:   swaits = reqs + nrecvs;

5851:   if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5852:   if (scall == MAT_INITIAL_MATRIX) {
5853:     /* i-array */
5854:     /*  post receives */
5855:     if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5856:     for (i = 0; i < nrecvs; i++) {
5857:       rowlen = rvalues + rstarts[i] * rbs;
5858:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5859:       PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5860:     }

5862:     /* pack the outgoing message */
5863:     PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));

5865:     sstartsj[0] = 0;
5866:     rstartsj[0] = 0;
5867:     len         = 0; /* total length of j or a array to be sent */
5868:     if (nsends) {
5869:       k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5870:       PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5871:     }
5872:     for (i = 0; i < nsends; i++) {
5873:       rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5874:       nrows  = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5875:       for (j = 0; j < nrows; j++) {
5876:         row = srow[k] + B->rmap->range[rank]; /* global row idx */
5877:         for (l = 0; l < sbs; l++) {
5878:           PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */

5880:           rowlen[j * sbs + l] = ncols;

5882:           len += ncols;
5883:           PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5884:         }
5885:         k++;
5886:       }
5887:       PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));

5889:       sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5890:     }
5891:     /* recvs and sends of i-array are completed */
5892:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5893:     PetscCall(PetscFree(svalues));

5895:     /* allocate buffers for sending j and a arrays */
5896:     PetscCall(PetscMalloc1(len + 1, &bufj));
5897:     PetscCall(PetscMalloc1(len + 1, &bufa));

5899:     /* create i-array of B_oth */
5900:     PetscCall(PetscMalloc1(aBn + 2, &b_othi));

5902:     b_othi[0] = 0;
5903:     len       = 0; /* total length of j or a array to be received */
5904:     k         = 0;
5905:     for (i = 0; i < nrecvs; i++) {
5906:       rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5907:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5908:       for (j = 0; j < nrows; j++) {
5909:         b_othi[k + 1] = b_othi[k] + rowlen[j];
5910:         PetscCall(PetscIntSumError(rowlen[j], len, &len));
5911:         k++;
5912:       }
5913:       rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5914:     }
5915:     PetscCall(PetscFree(rvalues));

5917:     /* allocate space for j and a arrays of B_oth */
5918:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5919:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));

5921:     /* j-array */
5922:     /*  post receives of j-array */
5923:     for (i = 0; i < nrecvs; i++) {
5924:       nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5925:       PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5926:     }

5928:     /* pack the outgoing message j-array */
5929:     if (nsends) k = sstarts[0];
5930:     for (i = 0; i < nsends; i++) {
5931:       nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5932:       bufJ  = bufj + sstartsj[i];
5933:       for (j = 0; j < nrows; j++) {
5934:         row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5935:         for (ll = 0; ll < sbs; ll++) {
5936:           PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5937:           for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5938:           PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5939:         }
5940:       }
5941:       PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5942:     }

5944:     /* recvs and sends of j-array are completed */
5945:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5946:   } else if (scall == MAT_REUSE_MATRIX) {
5947:     sstartsj = *startsj_s;
5948:     rstartsj = *startsj_r;
5949:     bufa     = *bufa_ptr;
5950:     b_oth    = (Mat_SeqAIJ *)(*B_oth)->data;
5951:     PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5952:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");

5954:   /* a-array */
5955:   /*  post receives of a-array */
5956:   for (i = 0; i < nrecvs; i++) {
5957:     nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5958:     PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5959:   }

5961:   /* pack the outgoing message a-array */
5962:   if (nsends) k = sstarts[0];
5963:   for (i = 0; i < nsends; i++) {
5964:     nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5965:     bufA  = bufa + sstartsj[i];
5966:     for (j = 0; j < nrows; j++) {
5967:       row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5968:       for (ll = 0; ll < sbs; ll++) {
5969:         PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5970:         for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5971:         PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5972:       }
5973:     }
5974:     PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5975:   }
5976:   /* recvs and sends of a-array are completed */
5977:   if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5978:   PetscCall(PetscFree(reqs));

5980:   if (scall == MAT_INITIAL_MATRIX) {
5981:     /* put together the new matrix */
5982:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));

5984:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5985:     /* Since these are PETSc arrays, change flags to free them as necessary. */
5986:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
5987:     b_oth->free_a  = PETSC_TRUE;
5988:     b_oth->free_ij = PETSC_TRUE;
5989:     b_oth->nonew   = 0;

5991:     PetscCall(PetscFree(bufj));
5992:     if (!startsj_s || !bufa_ptr) {
5993:       PetscCall(PetscFree2(sstartsj, rstartsj));
5994:       PetscCall(PetscFree(bufa_ptr));
5995:     } else {
5996:       *startsj_s = sstartsj;
5997:       *startsj_r = rstartsj;
5998:       *bufa_ptr  = bufa;
5999:     }
6000:   } else if (scall == MAT_REUSE_MATRIX) {
6001:     PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6002:   }

6004:   PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6005:   PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6006:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6007:   PetscFunctionReturn(PETSC_SUCCESS);
6008: }

6010: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6011: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6012: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6013: #if defined(PETSC_HAVE_MKL_SPARSE)
6014: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6015: #endif
6016: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6017: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6018: #if defined(PETSC_HAVE_ELEMENTAL)
6019: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6020: #endif
6021: #if defined(PETSC_HAVE_SCALAPACK)
6022: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6023: #endif
6024: #if defined(PETSC_HAVE_HYPRE)
6025: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6026: #endif
6027: #if defined(PETSC_HAVE_CUDA)
6028: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6029: #endif
6030: #if defined(PETSC_HAVE_HIP)
6031: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6032: #endif
6033: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6034: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6035: #endif
6036: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6037: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6038: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

6040: /*
6041:     Computes (B'*A')' since computing B*A directly is untenable

6043:                n                       p                          p
6044:         [             ]       [             ]         [                 ]
6045:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
6046:         [             ]       [             ]         [                 ]

6048: */
6049: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6050: {
6051:   Mat At, Bt, Ct;

6053:   PetscFunctionBegin;
6054:   PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6055:   PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6056:   PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6057:   PetscCall(MatDestroy(&At));
6058:   PetscCall(MatDestroy(&Bt));
6059:   PetscCall(MatTransposeSetPrecursor(Ct, C));
6060:   PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6061:   PetscCall(MatDestroy(&Ct));
6062:   PetscFunctionReturn(PETSC_SUCCESS);
6063: }

6065: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6066: {
6067:   PetscBool cisdense;

6069:   PetscFunctionBegin;
6070:   PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n);
6071:   PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6072:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
6073:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6074:   if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6075:   PetscCall(MatSetUp(C));

6077:   C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6078:   PetscFunctionReturn(PETSC_SUCCESS);
6079: }

6081: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6082: {
6083:   Mat_Product *product = C->product;
6084:   Mat          A = product->A, B = product->B;

6086:   PetscFunctionBegin;
6087:   PetscCheck(A->cmap->rstart == B->rmap->rstart && A->cmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")",
6088:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6089:   C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6090:   C->ops->productsymbolic = MatProductSymbolic_AB;
6091:   PetscFunctionReturn(PETSC_SUCCESS);
6092: }

6094: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6095: {
6096:   Mat_Product *product = C->product;

6098:   PetscFunctionBegin;
6099:   if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6100:   PetscFunctionReturn(PETSC_SUCCESS);
6101: }

6103: /*
6104:    Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix

6106:   Input Parameters:

6108:     j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6109:     j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)

6111:     mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat

6113:     For Set1, j1[] contains column indices of the nonzeros.
6114:     For the k-th row (0<=k<m), [rowBegin1[k],rowEnd1[k]) index into j1[] and point to the begin/end nonzero in row k
6115:     respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6116:     but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.

6118:     Similar for Set2.

6120:     This routine merges the two sets of nonzeros row by row and removes repeats.

6122:   Output Parameters: (memory is allocated by the caller)

6124:     i[],j[]: the CSR of the merged matrix, which has m rows.
6125:     imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6126:     imap2[]: similar to imap1[], but for Set2.
6127:     Note we order nonzeros row-by-row and from left to right.
6128: */
6129: static PetscErrorCode MatMergeEntries_Internal(Mat mat, const PetscInt j1[], const PetscInt j2[], const PetscCount rowBegin1[], const PetscCount rowEnd1[], const PetscCount rowBegin2[], const PetscCount rowEnd2[], const PetscCount jmap1[], const PetscCount jmap2[], PetscCount imap1[], PetscCount imap2[], PetscInt i[], PetscInt j[])
6130: {
6131:   PetscInt   r, m; /* Row index of mat */
6132:   PetscCount t, t1, t2, b1, e1, b2, e2;

6134:   PetscFunctionBegin;
6135:   PetscCall(MatGetLocalSize(mat, &m, NULL));
6136:   t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6137:   i[0]        = 0;
6138:   for (r = 0; r < m; r++) { /* Do row by row merging */
6139:     b1 = rowBegin1[r];
6140:     e1 = rowEnd1[r];
6141:     b2 = rowBegin2[r];
6142:     e2 = rowEnd2[r];
6143:     while (b1 < e1 && b2 < e2) {
6144:       if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6145:         j[t]      = j1[b1];
6146:         imap1[t1] = t;
6147:         imap2[t2] = t;
6148:         b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6149:         b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6150:         t1++;
6151:         t2++;
6152:         t++;
6153:       } else if (j1[b1] < j2[b2]) {
6154:         j[t]      = j1[b1];
6155:         imap1[t1] = t;
6156:         b1 += jmap1[t1 + 1] - jmap1[t1];
6157:         t1++;
6158:         t++;
6159:       } else {
6160:         j[t]      = j2[b2];
6161:         imap2[t2] = t;
6162:         b2 += jmap2[t2 + 1] - jmap2[t2];
6163:         t2++;
6164:         t++;
6165:       }
6166:     }
6167:     /* Merge the remaining in either j1[] or j2[] */
6168:     while (b1 < e1) {
6169:       j[t]      = j1[b1];
6170:       imap1[t1] = t;
6171:       b1 += jmap1[t1 + 1] - jmap1[t1];
6172:       t1++;
6173:       t++;
6174:     }
6175:     while (b2 < e2) {
6176:       j[t]      = j2[b2];
6177:       imap2[t2] = t;
6178:       b2 += jmap2[t2 + 1] - jmap2[t2];
6179:       t2++;
6180:       t++;
6181:     }
6182:     i[r + 1] = t;
6183:   }
6184:   PetscFunctionReturn(PETSC_SUCCESS);
6185: }

6187: /*
6188:   Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block

6190:   Input Parameters:
6191:     mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6192:     n,i[],j[],perm[]: there are n input entries, belonging to m rows. Row/col indices of the entries are stored in i[] and j[]
6193:       respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.

6195:       i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6196:       i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.

6198:   Output Parameters:
6199:     j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6200:     rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6201:       They contain indices pointing to j[]. For 0<=r<m, [rowBegin[r],rowMid[r]) point to begin/end entries of row r of the diagonal block,
6202:       and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.

6204:     Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6205:       Atot: number of entries belonging to the diagonal block.
6206:       Annz: number of unique nonzeros belonging to the diagonal block.
6207:       Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6208:         repeats (i.e., same 'i,j' pair).
6209:       Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6210:         is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.

6212:       Atot: number of entries belonging to the diagonal block
6213:       Annz: number of unique nonzeros belonging to the diagonal block.

6215:     Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.

6217:     Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6218: */
6219: static PetscErrorCode MatSplitEntries_Internal(Mat mat, PetscCount n, const PetscInt i[], PetscInt j[], PetscCount perm[], PetscCount rowBegin[], PetscCount rowMid[], PetscCount rowEnd[], PetscCount *Atot_, PetscCount **Aperm_, PetscCount *Annz_, PetscCount **Ajmap_, PetscCount *Btot_, PetscCount **Bperm_, PetscCount *Bnnz_, PetscCount **Bjmap_)
6220: {
6221:   PetscInt    cstart, cend, rstart, rend, row, col;
6222:   PetscCount  Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6223:   PetscCount  Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6224:   PetscCount  k, m, p, q, r, s, mid;
6225:   PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;

6227:   PetscFunctionBegin;
6228:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6229:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6230:   m = rend - rstart;

6232:   /* Skip negative rows */
6233:   for (k = 0; k < n; k++)
6234:     if (i[k] >= 0) break;

6236:   /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6237:      fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6238:   */
6239:   while (k < n) {
6240:     row = i[k];
6241:     /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6242:     for (s = k; s < n; s++)
6243:       if (i[s] != row) break;

6245:     /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6246:     for (p = k; p < s; p++) {
6247:       if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT;
6248:       else PetscAssert((j[p] >= 0) && (j[p] <= mat->cmap->N), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index %" PetscInt_FMT " is out of range", j[p]);
6249:     }
6250:     PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6251:     PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6252:     rowBegin[row - rstart] = k;
6253:     rowMid[row - rstart]   = mid;
6254:     rowEnd[row - rstart]   = s;

6256:     /* Count nonzeros of this diag/offdiag row, which might have repeats */
6257:     Atot += mid - k;
6258:     Btot += s - mid;

6260:     /* Count unique nonzeros of this diag row */
6261:     for (p = k; p < mid;) {
6262:       col = j[p];
6263:       do {
6264:         j[p] += PETSC_MAX_INT; /* Revert the modified diagonal indices */
6265:         p++;
6266:       } while (p < mid && j[p] == col);
6267:       Annz++;
6268:     }

6270:     /* Count unique nonzeros of this offdiag row */
6271:     for (p = mid; p < s;) {
6272:       col = j[p];
6273:       do {
6274:         p++;
6275:       } while (p < s && j[p] == col);
6276:       Bnnz++;
6277:     }
6278:     k = s;
6279:   }

6281:   /* Allocation according to Atot, Btot, Annz, Bnnz */
6282:   PetscCall(PetscMalloc1(Atot, &Aperm));
6283:   PetscCall(PetscMalloc1(Btot, &Bperm));
6284:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6285:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));

6287:   /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6288:   Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6289:   for (r = 0; r < m; r++) {
6290:     k   = rowBegin[r];
6291:     mid = rowMid[r];
6292:     s   = rowEnd[r];
6293:     PetscCall(PetscArraycpy(Aperm + Atot, perm + k, mid - k));
6294:     PetscCall(PetscArraycpy(Bperm + Btot, perm + mid, s - mid));
6295:     Atot += mid - k;
6296:     Btot += s - mid;

6298:     /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6299:     for (p = k; p < mid;) {
6300:       col = j[p];
6301:       q   = p;
6302:       do {
6303:         p++;
6304:       } while (p < mid && j[p] == col);
6305:       Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6306:       Annz++;
6307:     }

6309:     for (p = mid; p < s;) {
6310:       col = j[p];
6311:       q   = p;
6312:       do {
6313:         p++;
6314:       } while (p < s && j[p] == col);
6315:       Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6316:       Bnnz++;
6317:     }
6318:   }
6319:   /* Output */
6320:   *Aperm_ = Aperm;
6321:   *Annz_  = Annz;
6322:   *Atot_  = Atot;
6323:   *Ajmap_ = Ajmap;
6324:   *Bperm_ = Bperm;
6325:   *Bnnz_  = Bnnz;
6326:   *Btot_  = Btot;
6327:   *Bjmap_ = Bjmap;
6328:   PetscFunctionReturn(PETSC_SUCCESS);
6329: }

6331: /*
6332:   Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix

6334:   Input Parameters:
6335:     nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6336:     nnz:  number of unique nonzeros in the merged matrix
6337:     imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6338:     jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set

6340:   Output Parameter: (memory is allocated by the caller)
6341:     jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set

6343:   Example:
6344:     nnz1 = 4
6345:     nnz  = 6
6346:     imap = [1,3,4,5]
6347:     jmap = [0,3,5,6,7]
6348:    then,
6349:     jmap_new = [0,0,3,3,5,6,7]
6350: */
6351: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6352: {
6353:   PetscCount k, p;

6355:   PetscFunctionBegin;
6356:   jmap_new[0] = 0;
6357:   p           = nnz;                /* p loops over jmap_new[] backwards */
6358:   for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6359:     for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6360:   }
6361:   for (; p >= 0; p--) jmap_new[p] = jmap[0];
6362:   PetscFunctionReturn(PETSC_SUCCESS);
6363: }

6365: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6366: {
6367:   MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;

6369:   PetscFunctionBegin;
6370:   PetscCall(PetscSFDestroy(&coo->sf));
6371:   PetscCall(PetscFree(coo->Aperm1));
6372:   PetscCall(PetscFree(coo->Bperm1));
6373:   PetscCall(PetscFree(coo->Ajmap1));
6374:   PetscCall(PetscFree(coo->Bjmap1));
6375:   PetscCall(PetscFree(coo->Aimap2));
6376:   PetscCall(PetscFree(coo->Bimap2));
6377:   PetscCall(PetscFree(coo->Aperm2));
6378:   PetscCall(PetscFree(coo->Bperm2));
6379:   PetscCall(PetscFree(coo->Ajmap2));
6380:   PetscCall(PetscFree(coo->Bjmap2));
6381:   PetscCall(PetscFree(coo->Cperm1));
6382:   PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6383:   PetscCall(PetscFree(coo));
6384:   PetscFunctionReturn(PETSC_SUCCESS);
6385: }

6387: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6388: {
6389:   MPI_Comm             comm;
6390:   PetscMPIInt          rank, size;
6391:   PetscInt             m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6392:   PetscCount           k, p, q, rem;                           /* Loop variables over coo arrays */
6393:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6394:   PetscContainer       container;
6395:   MatCOOStruct_MPIAIJ *coo;

6397:   PetscFunctionBegin;
6398:   PetscCall(PetscFree(mpiaij->garray));
6399:   PetscCall(VecDestroy(&mpiaij->lvec));
6400: #if defined(PETSC_USE_CTABLE)
6401:   PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6402: #else
6403:   PetscCall(PetscFree(mpiaij->colmap));
6404: #endif
6405:   PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6406:   mat->assembled     = PETSC_FALSE;
6407:   mat->was_assembled = PETSC_FALSE;

6409:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6410:   PetscCallMPI(MPI_Comm_size(comm, &size));
6411:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
6412:   PetscCall(PetscLayoutSetUp(mat->rmap));
6413:   PetscCall(PetscLayoutSetUp(mat->cmap));
6414:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6415:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6416:   PetscCall(MatGetLocalSize(mat, &m, &n));
6417:   PetscCall(MatGetSize(mat, &M, &N));

6419:   /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6420:   /* entries come first, then local rows, then remote rows.                     */
6421:   PetscCount n1 = coo_n, *perm1;
6422:   PetscInt  *i1 = coo_i, *j1 = coo_j;

6424:   PetscCall(PetscMalloc1(n1, &perm1));
6425:   for (k = 0; k < n1; k++) perm1[k] = k;

6427:   /* Manipulate indices so that entries with negative row or col indices will have smallest
6428:      row indices, local entries will have greater but negative row indices, and remote entries
6429:      will have positive row indices.
6430:   */
6431:   for (k = 0; k < n1; k++) {
6432:     if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT;                /* e.g., -2^31, minimal to move them ahead */
6433:     else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_MAX_INT; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_MAX_INT, -1] */
6434:     else {
6435:       PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6436:       if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6437:     }
6438:   }

6440:   /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6441:   PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));

6443:   /* Advance k to the first entry we need to take care of */
6444:   for (k = 0; k < n1; k++)
6445:     if (i1[k] > PETSC_MIN_INT) break;
6446:   PetscInt i1start = k;

6448:   PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6449:   for (; k < rem; k++) i1[k] += PETSC_MAX_INT;                                    /* Revert row indices of local rows*/

6451:   /*           Send remote rows to their owner                                  */
6452:   /* Find which rows should be sent to which remote ranks*/
6453:   PetscInt        nsend = 0; /* Number of MPI ranks to send data to */
6454:   PetscMPIInt    *sendto;    /* [nsend], storing remote ranks */
6455:   PetscInt       *nentries;  /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6456:   const PetscInt *ranges;
6457:   PetscInt        maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */

6459:   PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6460:   PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6461:   for (k = rem; k < n1;) {
6462:     PetscMPIInt owner;
6463:     PetscInt    firstRow, lastRow;

6465:     /* Locate a row range */
6466:     firstRow = i1[k]; /* first row of this owner */
6467:     PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6468:     lastRow = ranges[owner + 1] - 1; /* last row of this owner */

6470:     /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6471:     PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));

6473:     /* All entries in [k,p) belong to this remote owner */
6474:     if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6475:       PetscMPIInt *sendto2;
6476:       PetscInt    *nentries2;
6477:       PetscInt     maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;

6479:       PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6480:       PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6481:       PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6482:       PetscCall(PetscFree2(sendto, nentries2));
6483:       sendto   = sendto2;
6484:       nentries = nentries2;
6485:       maxNsend = maxNsend2;
6486:     }
6487:     sendto[nsend]   = owner;
6488:     nentries[nsend] = p - k;
6489:     PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6490:     nsend++;
6491:     k = p;
6492:   }

6494:   /* Build 1st SF to know offsets on remote to send data */
6495:   PetscSF      sf1;
6496:   PetscInt     nroots = 1, nroots2 = 0;
6497:   PetscInt     nleaves = nsend, nleaves2 = 0;
6498:   PetscInt    *offsets;
6499:   PetscSFNode *iremote;

6501:   PetscCall(PetscSFCreate(comm, &sf1));
6502:   PetscCall(PetscMalloc1(nsend, &iremote));
6503:   PetscCall(PetscMalloc1(nsend, &offsets));
6504:   for (k = 0; k < nsend; k++) {
6505:     iremote[k].rank  = sendto[k];
6506:     iremote[k].index = 0;
6507:     nleaves2 += nentries[k];
6508:     PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6509:   }
6510:   PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6511:   PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6512:   PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6513:   PetscCall(PetscSFDestroy(&sf1));
6514:   PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT "", nleaves2, n1 - rem);

6516:   /* Build 2nd SF to send remote COOs to their owner */
6517:   PetscSF sf2;
6518:   nroots  = nroots2;
6519:   nleaves = nleaves2;
6520:   PetscCall(PetscSFCreate(comm, &sf2));
6521:   PetscCall(PetscSFSetFromOptions(sf2));
6522:   PetscCall(PetscMalloc1(nleaves, &iremote));
6523:   p = 0;
6524:   for (k = 0; k < nsend; k++) {
6525:     PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6526:     for (q = 0; q < nentries[k]; q++, p++) {
6527:       iremote[p].rank  = sendto[k];
6528:       iremote[p].index = offsets[k] + q;
6529:     }
6530:   }
6531:   PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));

6533:   /* Send the remote COOs to their owner */
6534:   PetscInt    n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6535:   PetscCount *perm2;                 /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6536:   PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6537:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6538:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6539:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6540:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));

6542:   PetscCall(PetscFree(offsets));
6543:   PetscCall(PetscFree2(sendto, nentries));

6545:   /* Sort received COOs by row along with the permutation array     */
6546:   for (k = 0; k < n2; k++) perm2[k] = k;
6547:   PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));

6549:   /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6550:   PetscCount *Cperm1;
6551:   PetscCall(PetscMalloc1(nleaves, &Cperm1));
6552:   PetscCall(PetscArraycpy(Cperm1, perm1 + rem, nleaves));

6554:   /* Support for HYPRE matrices, kind of a hack.
6555:      Swap min column with diagonal so that diagonal values will go first */
6556:   PetscBool   hypre;
6557:   const char *name;
6558:   PetscCall(PetscObjectGetName((PetscObject)mat, &name));
6559:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
6560:   if (hypre) {
6561:     PetscInt *minj;
6562:     PetscBT   hasdiag;

6564:     PetscCall(PetscBTCreate(m, &hasdiag));
6565:     PetscCall(PetscMalloc1(m, &minj));
6566:     for (k = 0; k < m; k++) minj[k] = PETSC_MAX_INT;
6567:     for (k = i1start; k < rem; k++) {
6568:       if (j1[k] < cstart || j1[k] >= cend) continue;
6569:       const PetscInt rindex = i1[k] - rstart;
6570:       if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6571:       minj[rindex] = PetscMin(minj[rindex], j1[k]);
6572:     }
6573:     for (k = 0; k < n2; k++) {
6574:       if (j2[k] < cstart || j2[k] >= cend) continue;
6575:       const PetscInt rindex = i2[k] - rstart;
6576:       if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6577:       minj[rindex] = PetscMin(minj[rindex], j2[k]);
6578:     }
6579:     for (k = i1start; k < rem; k++) {
6580:       const PetscInt rindex = i1[k] - rstart;
6581:       if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6582:       if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6583:       else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6584:     }
6585:     for (k = 0; k < n2; k++) {
6586:       const PetscInt rindex = i2[k] - rstart;
6587:       if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6588:       if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6589:       else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6590:     }
6591:     PetscCall(PetscBTDestroy(&hasdiag));
6592:     PetscCall(PetscFree(minj));
6593:   }

6595:   /* Split local COOs and received COOs into diag/offdiag portions */
6596:   PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6597:   PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6598:   PetscCount  Annz1, Bnnz1, Atot1, Btot1;
6599:   PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6600:   PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6601:   PetscCount  Annz2, Bnnz2, Atot2, Btot2;

6603:   PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6604:   PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6605:   PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6606:   PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));

6608:   /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6609:   PetscInt *Ai, *Bi;
6610:   PetscInt *Aj, *Bj;

6612:   PetscCall(PetscMalloc1(m + 1, &Ai));
6613:   PetscCall(PetscMalloc1(m + 1, &Bi));
6614:   PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6615:   PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));

6617:   PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6618:   PetscCall(PetscMalloc1(Annz1, &Aimap1));
6619:   PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6620:   PetscCall(PetscMalloc1(Annz2, &Aimap2));
6621:   PetscCall(PetscMalloc1(Bnnz2, &Bimap2));

6623:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6624:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));

6626:   /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we     */
6627:   /* expect nonzeros in A/B most likely have local contributing entries        */
6628:   PetscInt    Annz = Ai[m];
6629:   PetscInt    Bnnz = Bi[m];
6630:   PetscCount *Ajmap1_new, *Bjmap1_new;

6632:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6633:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));

6635:   PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6636:   PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));

6638:   PetscCall(PetscFree(Aimap1));
6639:   PetscCall(PetscFree(Ajmap1));
6640:   PetscCall(PetscFree(Bimap1));
6641:   PetscCall(PetscFree(Bjmap1));
6642:   PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6643:   PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6644:   PetscCall(PetscFree(perm1));
6645:   PetscCall(PetscFree3(i2, j2, perm2));

6647:   Ajmap1 = Ajmap1_new;
6648:   Bjmap1 = Bjmap1_new;

6650:   /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6651:   if (Annz < Annz1 + Annz2) {
6652:     PetscInt *Aj_new;
6653:     PetscCall(PetscMalloc1(Annz, &Aj_new));
6654:     PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6655:     PetscCall(PetscFree(Aj));
6656:     Aj = Aj_new;
6657:   }

6659:   if (Bnnz < Bnnz1 + Bnnz2) {
6660:     PetscInt *Bj_new;
6661:     PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6662:     PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6663:     PetscCall(PetscFree(Bj));
6664:     Bj = Bj_new;
6665:   }

6667:   /* Create new submatrices for on-process and off-process coupling                  */
6668:   PetscScalar     *Aa, *Ba;
6669:   MatType          rtype;
6670:   Mat_SeqAIJ      *a, *b;
6671:   PetscObjectState state;
6672:   PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6673:   PetscCall(PetscCalloc1(Bnnz, &Ba));
6674:   /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6675:   if (cstart) {
6676:     for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6677:   }
6678:   PetscCall(MatDestroy(&mpiaij->A));
6679:   PetscCall(MatDestroy(&mpiaij->B));
6680:   PetscCall(MatGetRootType_Private(mat, &rtype));
6681:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6682:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6683:   PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6684:   mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6685:   state              = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6686:   PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));

6688:   a               = (Mat_SeqAIJ *)mpiaij->A->data;
6689:   b               = (Mat_SeqAIJ *)mpiaij->B->data;
6690:   a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6691:   a->free_a = b->free_a = PETSC_TRUE;
6692:   a->free_ij = b->free_ij = PETSC_TRUE;

6694:   /* conversion must happen AFTER multiply setup */
6695:   PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6696:   PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6697:   PetscCall(VecDestroy(&mpiaij->lvec));
6698:   PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));

6700:   // Put the COO struct in a container and then attach that to the matrix
6701:   PetscCall(PetscMalloc1(1, &coo));
6702:   coo->n       = coo_n;
6703:   coo->sf      = sf2;
6704:   coo->sendlen = nleaves;
6705:   coo->recvlen = nroots;
6706:   coo->Annz    = Annz;
6707:   coo->Bnnz    = Bnnz;
6708:   coo->Annz2   = Annz2;
6709:   coo->Bnnz2   = Bnnz2;
6710:   coo->Atot1   = Atot1;
6711:   coo->Atot2   = Atot2;
6712:   coo->Btot1   = Btot1;
6713:   coo->Btot2   = Btot2;
6714:   coo->Ajmap1  = Ajmap1;
6715:   coo->Aperm1  = Aperm1;
6716:   coo->Bjmap1  = Bjmap1;
6717:   coo->Bperm1  = Bperm1;
6718:   coo->Aimap2  = Aimap2;
6719:   coo->Ajmap2  = Ajmap2;
6720:   coo->Aperm2  = Aperm2;
6721:   coo->Bimap2  = Bimap2;
6722:   coo->Bjmap2  = Bjmap2;
6723:   coo->Bperm2  = Bperm2;
6724:   coo->Cperm1  = Cperm1;
6725:   // Allocate in preallocation. If not used, it has zero cost on host
6726:   PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6727:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6728:   PetscCall(PetscContainerSetPointer(container, coo));
6729:   PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6730:   PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6731:   PetscCall(PetscContainerDestroy(&container));
6732:   PetscFunctionReturn(PETSC_SUCCESS);
6733: }

6735: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6736: {
6737:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6738:   Mat                  A = mpiaij->A, B = mpiaij->B;
6739:   PetscScalar         *Aa, *Ba;
6740:   PetscScalar         *sendbuf, *recvbuf;
6741:   const PetscCount    *Ajmap1, *Ajmap2, *Aimap2;
6742:   const PetscCount    *Bjmap1, *Bjmap2, *Bimap2;
6743:   const PetscCount    *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6744:   const PetscCount    *Cperm1;
6745:   PetscContainer       container;
6746:   MatCOOStruct_MPIAIJ *coo;

6748:   PetscFunctionBegin;
6749:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6750:   PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6751:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6752:   sendbuf = coo->sendbuf;
6753:   recvbuf = coo->recvbuf;
6754:   Ajmap1  = coo->Ajmap1;
6755:   Ajmap2  = coo->Ajmap2;
6756:   Aimap2  = coo->Aimap2;
6757:   Bjmap1  = coo->Bjmap1;
6758:   Bjmap2  = coo->Bjmap2;
6759:   Bimap2  = coo->Bimap2;
6760:   Aperm1  = coo->Aperm1;
6761:   Aperm2  = coo->Aperm2;
6762:   Bperm1  = coo->Bperm1;
6763:   Bperm2  = coo->Bperm2;
6764:   Cperm1  = coo->Cperm1;

6766:   PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6767:   PetscCall(MatSeqAIJGetArray(B, &Ba));

6769:   /* Pack entries to be sent to remote */
6770:   for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];

6772:   /* Send remote entries to their owner and overlap the communication with local computation */
6773:   PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6774:   /* Add local entries to A and B */
6775:   for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6776:     PetscScalar sum = 0.0;                     /* Do partial summation first to improve numerical stability */
6777:     for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6778:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6779:   }
6780:   for (PetscCount i = 0; i < coo->Bnnz; i++) {
6781:     PetscScalar sum = 0.0;
6782:     for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6783:     Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6784:   }
6785:   PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));

6787:   /* Add received remote entries to A and B */
6788:   for (PetscCount i = 0; i < coo->Annz2; i++) {
6789:     for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6790:   }
6791:   for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6792:     for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6793:   }
6794:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6795:   PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6796:   PetscFunctionReturn(PETSC_SUCCESS);
6797: }

6799: /*MC
6800:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

6802:    Options Database Keys:
6803: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`

6805:    Level: beginner

6807:    Notes:
6808:    `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6809:     in this case the values associated with the rows and columns one passes in are set to zero
6810:     in the matrix

6812:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6813:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

6815: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6816: M*/
6817: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6818: {
6819:   Mat_MPIAIJ *b;
6820:   PetscMPIInt size;

6822:   PetscFunctionBegin;
6823:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

6825:   PetscCall(PetscNew(&b));
6826:   B->data       = (void *)b;
6827:   B->ops[0]     = MatOps_Values;
6828:   B->assembled  = PETSC_FALSE;
6829:   B->insertmode = NOT_SET_VALUES;
6830:   b->size       = size;

6832:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));

6834:   /* build cache for off array entries formed */
6835:   PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));

6837:   b->donotstash  = PETSC_FALSE;
6838:   b->colmap      = NULL;
6839:   b->garray      = NULL;
6840:   b->roworiented = PETSC_TRUE;

6842:   /* stuff used for matrix vector multiply */
6843:   b->lvec  = NULL;
6844:   b->Mvctx = NULL;

6846:   /* stuff for MatGetRow() */
6847:   b->rowindices   = NULL;
6848:   b->rowvalues    = NULL;
6849:   b->getrowactive = PETSC_FALSE;

6851:   /* flexible pointer used in CUSPARSE classes */
6852:   b->spptr = NULL;

6854:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6855:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6856:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6857:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6858:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6859:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6860:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6861:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6862:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6863:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6864: #if defined(PETSC_HAVE_CUDA)
6865:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6866: #endif
6867: #if defined(PETSC_HAVE_HIP)
6868:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6869: #endif
6870: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6871:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6872: #endif
6873: #if defined(PETSC_HAVE_MKL_SPARSE)
6874:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6875: #endif
6876:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6877:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6878:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6879:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6880: #if defined(PETSC_HAVE_ELEMENTAL)
6881:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6882: #endif
6883: #if defined(PETSC_HAVE_SCALAPACK)
6884:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6885: #endif
6886:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6887:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6888: #if defined(PETSC_HAVE_HYPRE)
6889:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6890:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6891: #endif
6892:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6893:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6894:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6895:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6896:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6897:   PetscFunctionReturn(PETSC_SUCCESS);
6898: }

6900: /*@C
6901:   MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6902:   and "off-diagonal" part of the matrix in CSR format.

6904:   Collective

6906:   Input Parameters:
6907: + comm - MPI communicator
6908: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
6909: . n    - This value should be the same as the local size used in creating the
6910:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
6911:        calculated if `N` is given) For square matrices `n` is almost always `m`.
6912: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6913: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6914: . i    - row indices for "diagonal" portion of matrix; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
6915: . j    - column indices, which must be local, i.e., based off the start column of the diagonal portion
6916: . a    - matrix values
6917: . oi   - row indices for "off-diagonal" portion of matrix; that is oi[0] = 0, oi[row] = oi[row-1] + number of elements in that row of the matrix
6918: . oj   - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6919: - oa   - matrix values

6921:   Output Parameter:
6922: . mat - the matrix

6924:   Level: advanced

6926:   Notes:
6927:   The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6928:   must free the arrays once the matrix has been destroyed and not before.

6930:   The `i` and `j` indices are 0 based

6932:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix

6934:   This sets local rows and cannot be used to set off-processor values.

6936:   Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6937:   legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6938:   not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6939:   the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6940:   keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6941:   communication if it is known that only local entries will be set.

6943: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6944:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6945: @*/
6946: PetscErrorCode MatCreateMPIAIJWithSplitArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt i[], PetscInt j[], PetscScalar a[], PetscInt oi[], PetscInt oj[], PetscScalar oa[], Mat *mat)
6947: {
6948:   Mat_MPIAIJ *maij;

6950:   PetscFunctionBegin;
6951:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6952:   PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6953:   PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6954:   PetscCall(MatCreate(comm, mat));
6955:   PetscCall(MatSetSizes(*mat, m, n, M, N));
6956:   PetscCall(MatSetType(*mat, MATMPIAIJ));
6957:   maij = (Mat_MPIAIJ *)(*mat)->data;

6959:   (*mat)->preallocated = PETSC_TRUE;

6961:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
6962:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

6964:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6965:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));

6967:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6968:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
6969:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
6970:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
6971:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
6972:   PetscFunctionReturn(PETSC_SUCCESS);
6973: }

6975: typedef struct {
6976:   Mat       *mp;    /* intermediate products */
6977:   PetscBool *mptmp; /* is the intermediate product temporary ? */
6978:   PetscInt   cp;    /* number of intermediate products */

6980:   /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
6981:   PetscInt    *startsj_s, *startsj_r;
6982:   PetscScalar *bufa;
6983:   Mat          P_oth;

6985:   /* may take advantage of merging product->B */
6986:   Mat Bloc; /* B-local by merging diag and off-diag */

6988:   /* cusparse does not have support to split between symbolic and numeric phases.
6989:      When api_user is true, we don't need to update the numerical values
6990:      of the temporary storage */
6991:   PetscBool reusesym;

6993:   /* support for COO values insertion */
6994:   PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
6995:   PetscInt   **own;           /* own[i] points to address of on-process COO indices for Mat mp[i] */
6996:   PetscInt   **off;           /* off[i] points to address of off-process COO indices for Mat mp[i] */
6997:   PetscBool    hasoffproc;    /* if true, have off-process values insertion (i.e. AtB or PtAP) */
6998:   PetscSF      sf;            /* used for non-local values insertion and memory malloc */
6999:   PetscMemType mtype;

7001:   /* customization */
7002:   PetscBool abmerge;
7003:   PetscBool P_oth_bind;
7004: } MatMatMPIAIJBACKEND;

7006: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7007: {
7008:   MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7009:   PetscInt             i;

7011:   PetscFunctionBegin;
7012:   PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7013:   PetscCall(PetscFree(mmdata->bufa));
7014:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7015:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7016:   PetscCall(MatDestroy(&mmdata->P_oth));
7017:   PetscCall(MatDestroy(&mmdata->Bloc));
7018:   PetscCall(PetscSFDestroy(&mmdata->sf));
7019:   for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7020:   PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7021:   PetscCall(PetscFree(mmdata->own[0]));
7022:   PetscCall(PetscFree(mmdata->own));
7023:   PetscCall(PetscFree(mmdata->off[0]));
7024:   PetscCall(PetscFree(mmdata->off));
7025:   PetscCall(PetscFree(mmdata));
7026:   PetscFunctionReturn(PETSC_SUCCESS);
7027: }

7029: /* Copy selected n entries with indices in idx[] of A to v[].
7030:    If idx is NULL, copy the whole data array of A to v[]
7031:  */
7032: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7033: {
7034:   PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);

7036:   PetscFunctionBegin;
7037:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7038:   if (f) {
7039:     PetscCall((*f)(A, n, idx, v));
7040:   } else {
7041:     const PetscScalar *vv;

7043:     PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7044:     if (n && idx) {
7045:       PetscScalar    *w  = v;
7046:       const PetscInt *oi = idx;
7047:       PetscInt        j;

7049:       for (j = 0; j < n; j++) *w++ = vv[*oi++];
7050:     } else {
7051:       PetscCall(PetscArraycpy(v, vv, n));
7052:     }
7053:     PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7054:   }
7055:   PetscFunctionReturn(PETSC_SUCCESS);
7056: }

7058: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7059: {
7060:   MatMatMPIAIJBACKEND *mmdata;
7061:   PetscInt             i, n_d, n_o;

7063:   PetscFunctionBegin;
7064:   MatCheckProduct(C, 1);
7065:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7066:   mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7067:   if (!mmdata->reusesym) { /* update temporary matrices */
7068:     if (mmdata->P_oth) PetscCall(MatGetBrowsOfAoCols_MPIAIJ(C->product->A, C->product->B, MAT_REUSE_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7069:     if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7070:   }
7071:   mmdata->reusesym = PETSC_FALSE;

7073:   for (i = 0; i < mmdata->cp; i++) {
7074:     PetscCheck(mmdata->mp[i]->ops->productnumeric, PetscObjectComm((PetscObject)mmdata->mp[i]), PETSC_ERR_PLIB, "Missing numeric op for %s", MatProductTypes[mmdata->mp[i]->product->type]);
7075:     PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7076:   }
7077:   for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7078:     PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];

7080:     if (mmdata->mptmp[i]) continue;
7081:     if (noff) {
7082:       PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];

7084:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7085:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7086:       n_o += noff;
7087:       n_d += nown;
7088:     } else {
7089:       Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;

7091:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7092:       n_d += mm->nz;
7093:     }
7094:   }
7095:   if (mmdata->hasoffproc) { /* offprocess insertion */
7096:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7097:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7098:   }
7099:   PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7100:   PetscFunctionReturn(PETSC_SUCCESS);
7101: }

7103: /* Support for Pt * A, A * P, or Pt * A * P */
7104: #define MAX_NUMBER_INTERMEDIATE 4
7105: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7106: {
7107:   Mat_Product           *product = C->product;
7108:   Mat                    A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7109:   Mat_MPIAIJ            *a, *p;
7110:   MatMatMPIAIJBACKEND   *mmdata;
7111:   ISLocalToGlobalMapping P_oth_l2g = NULL;
7112:   IS                     glob      = NULL;
7113:   const char            *prefix;
7114:   char                   pprefix[256];
7115:   const PetscInt        *globidx, *P_oth_idx;
7116:   PetscInt               i, j, cp, m, n, M, N, *coo_i, *coo_j;
7117:   PetscCount             ncoo, ncoo_d, ncoo_o, ncoo_oown;
7118:   PetscInt               cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7119:                                                                                          /* type-0: consecutive, start from 0; type-1: consecutive with */
7120:                                                                                          /* a base offset; type-2: sparse with a local to global map table */
7121:   const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE];       /* col/row local to global map array (table) for type-2 map type */

7123:   MatProductType ptype;
7124:   PetscBool      mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7125:   PetscMPIInt    size;

7127:   PetscFunctionBegin;
7128:   MatCheckProduct(C, 1);
7129:   PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7130:   ptype = product->type;
7131:   if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7132:     ptype                                          = MATPRODUCT_AB;
7133:     product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7134:   }
7135:   switch (ptype) {
7136:   case MATPRODUCT_AB:
7137:     A          = product->A;
7138:     P          = product->B;
7139:     m          = A->rmap->n;
7140:     n          = P->cmap->n;
7141:     M          = A->rmap->N;
7142:     N          = P->cmap->N;
7143:     hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7144:     break;
7145:   case MATPRODUCT_AtB:
7146:     P          = product->A;
7147:     A          = product->B;
7148:     m          = P->cmap->n;
7149:     n          = A->cmap->n;
7150:     M          = P->cmap->N;
7151:     N          = A->cmap->N;
7152:     hasoffproc = PETSC_TRUE;
7153:     break;
7154:   case MATPRODUCT_PtAP:
7155:     A          = product->A;
7156:     P          = product->B;
7157:     m          = P->cmap->n;
7158:     n          = P->cmap->n;
7159:     M          = P->cmap->N;
7160:     N          = P->cmap->N;
7161:     hasoffproc = PETSC_TRUE;
7162:     break;
7163:   default:
7164:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7165:   }
7166:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7167:   if (size == 1) hasoffproc = PETSC_FALSE;

7169:   /* defaults */
7170:   for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7171:     mp[i]    = NULL;
7172:     mptmp[i] = PETSC_FALSE;
7173:     rmapt[i] = -1;
7174:     cmapt[i] = -1;
7175:     rmapa[i] = NULL;
7176:     cmapa[i] = NULL;
7177:   }

7179:   /* customization */
7180:   PetscCall(PetscNew(&mmdata));
7181:   mmdata->reusesym = product->api_user;
7182:   if (ptype == MATPRODUCT_AB) {
7183:     if (product->api_user) {
7184:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7185:       PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7186:       PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7187:       PetscOptionsEnd();
7188:     } else {
7189:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7190:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7191:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7192:       PetscOptionsEnd();
7193:     }
7194:   } else if (ptype == MATPRODUCT_PtAP) {
7195:     if (product->api_user) {
7196:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7197:       PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7198:       PetscOptionsEnd();
7199:     } else {
7200:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7201:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7202:       PetscOptionsEnd();
7203:     }
7204:   }
7205:   a = (Mat_MPIAIJ *)A->data;
7206:   p = (Mat_MPIAIJ *)P->data;
7207:   PetscCall(MatSetSizes(C, m, n, M, N));
7208:   PetscCall(PetscLayoutSetUp(C->rmap));
7209:   PetscCall(PetscLayoutSetUp(C->cmap));
7210:   PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7211:   PetscCall(MatGetOptionsPrefix(C, &prefix));

7213:   cp = 0;
7214:   switch (ptype) {
7215:   case MATPRODUCT_AB: /* A * P */
7216:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));

7218:     /* A_diag * P_local (merged or not) */
7219:     if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7220:       /* P is product->B */
7221:       PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7222:       PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7223:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7224:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7225:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7226:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7227:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7228:       mp[cp]->product->api_user = product->api_user;
7229:       PetscCall(MatProductSetFromOptions(mp[cp]));
7230:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7231:       PetscCall(ISGetIndices(glob, &globidx));
7232:       rmapt[cp] = 1;
7233:       cmapt[cp] = 2;
7234:       cmapa[cp] = globidx;
7235:       mptmp[cp] = PETSC_FALSE;
7236:       cp++;
7237:     } else { /* A_diag * P_diag and A_diag * P_off */
7238:       PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7239:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7240:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7241:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7242:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7243:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7244:       mp[cp]->product->api_user = product->api_user;
7245:       PetscCall(MatProductSetFromOptions(mp[cp]));
7246:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7247:       rmapt[cp] = 1;
7248:       cmapt[cp] = 1;
7249:       mptmp[cp] = PETSC_FALSE;
7250:       cp++;
7251:       PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7252:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7253:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7254:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7255:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7256:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7257:       mp[cp]->product->api_user = product->api_user;
7258:       PetscCall(MatProductSetFromOptions(mp[cp]));
7259:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7260:       rmapt[cp] = 1;
7261:       cmapt[cp] = 2;
7262:       cmapa[cp] = p->garray;
7263:       mptmp[cp] = PETSC_FALSE;
7264:       cp++;
7265:     }

7267:     /* A_off * P_other */
7268:     if (mmdata->P_oth) {
7269:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7270:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7271:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7272:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7273:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7274:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7275:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7276:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7277:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7278:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7279:       mp[cp]->product->api_user = product->api_user;
7280:       PetscCall(MatProductSetFromOptions(mp[cp]));
7281:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7282:       rmapt[cp] = 1;
7283:       cmapt[cp] = 2;
7284:       cmapa[cp] = P_oth_idx;
7285:       mptmp[cp] = PETSC_FALSE;
7286:       cp++;
7287:     }
7288:     break;

7290:   case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7291:     /* A is product->B */
7292:     PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7293:     if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7294:       PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7295:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7296:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7297:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7298:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7299:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7300:       mp[cp]->product->api_user = product->api_user;
7301:       PetscCall(MatProductSetFromOptions(mp[cp]));
7302:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7303:       PetscCall(ISGetIndices(glob, &globidx));
7304:       rmapt[cp] = 2;
7305:       rmapa[cp] = globidx;
7306:       cmapt[cp] = 2;
7307:       cmapa[cp] = globidx;
7308:       mptmp[cp] = PETSC_FALSE;
7309:       cp++;
7310:     } else {
7311:       PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7312:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7313:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7314:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7315:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7316:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7317:       mp[cp]->product->api_user = product->api_user;
7318:       PetscCall(MatProductSetFromOptions(mp[cp]));
7319:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7320:       PetscCall(ISGetIndices(glob, &globidx));
7321:       rmapt[cp] = 1;
7322:       cmapt[cp] = 2;
7323:       cmapa[cp] = globidx;
7324:       mptmp[cp] = PETSC_FALSE;
7325:       cp++;
7326:       PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7327:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7328:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7329:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7330:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7331:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7332:       mp[cp]->product->api_user = product->api_user;
7333:       PetscCall(MatProductSetFromOptions(mp[cp]));
7334:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7335:       rmapt[cp] = 2;
7336:       rmapa[cp] = p->garray;
7337:       cmapt[cp] = 2;
7338:       cmapa[cp] = globidx;
7339:       mptmp[cp] = PETSC_FALSE;
7340:       cp++;
7341:     }
7342:     break;
7343:   case MATPRODUCT_PtAP:
7344:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7345:     /* P is product->B */
7346:     PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7347:     PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7348:     PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7349:     PetscCall(MatProductSetFill(mp[cp], product->fill));
7350:     PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7351:     PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7352:     PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7353:     mp[cp]->product->api_user = product->api_user;
7354:     PetscCall(MatProductSetFromOptions(mp[cp]));
7355:     PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7356:     PetscCall(ISGetIndices(glob, &globidx));
7357:     rmapt[cp] = 2;
7358:     rmapa[cp] = globidx;
7359:     cmapt[cp] = 2;
7360:     cmapa[cp] = globidx;
7361:     mptmp[cp] = PETSC_FALSE;
7362:     cp++;
7363:     if (mmdata->P_oth) {
7364:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7365:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7366:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)(a->B))->type_name));
7367:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7368:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7369:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7370:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7371:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7372:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7373:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7374:       mp[cp]->product->api_user = product->api_user;
7375:       PetscCall(MatProductSetFromOptions(mp[cp]));
7376:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7377:       mptmp[cp] = PETSC_TRUE;
7378:       cp++;
7379:       PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7380:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7381:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7382:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7383:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7384:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7385:       mp[cp]->product->api_user = product->api_user;
7386:       PetscCall(MatProductSetFromOptions(mp[cp]));
7387:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7388:       rmapt[cp] = 2;
7389:       rmapa[cp] = globidx;
7390:       cmapt[cp] = 2;
7391:       cmapa[cp] = P_oth_idx;
7392:       mptmp[cp] = PETSC_FALSE;
7393:       cp++;
7394:     }
7395:     break;
7396:   default:
7397:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7398:   }
7399:   /* sanity check */
7400:   if (size > 1)
7401:     for (i = 0; i < cp; i++) PetscCheck(rmapt[i] != 2 || hasoffproc, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unexpected offproc map type for product %" PetscInt_FMT, i);

7403:   PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7404:   for (i = 0; i < cp; i++) {
7405:     mmdata->mp[i]    = mp[i];
7406:     mmdata->mptmp[i] = mptmp[i];
7407:   }
7408:   mmdata->cp             = cp;
7409:   C->product->data       = mmdata;
7410:   C->product->destroy    = MatDestroy_MatMatMPIAIJBACKEND;
7411:   C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;

7413:   /* memory type */
7414:   mmdata->mtype = PETSC_MEMTYPE_HOST;
7415:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7416:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7417:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7418:   if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7419:   else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7420:   else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;

7422:   /* prepare coo coordinates for values insertion */

7424:   /* count total nonzeros of those intermediate seqaij Mats
7425:     ncoo_d:    # of nonzeros of matrices that do not have offproc entries
7426:     ncoo_o:    # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7427:     ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7428:   */
7429:   for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7430:     Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7431:     if (mptmp[cp]) continue;
7432:     if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7433:       const PetscInt *rmap = rmapa[cp];
7434:       const PetscInt  mr   = mp[cp]->rmap->n;
7435:       const PetscInt  rs   = C->rmap->rstart;
7436:       const PetscInt  re   = C->rmap->rend;
7437:       const PetscInt *ii   = mm->i;
7438:       for (i = 0; i < mr; i++) {
7439:         const PetscInt gr = rmap[i];
7440:         const PetscInt nz = ii[i + 1] - ii[i];
7441:         if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7442:         else ncoo_oown += nz;                  /* this row is local */
7443:       }
7444:     } else ncoo_d += mm->nz;
7445:   }

7447:   /*
7448:     ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc

7450:     ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.

7452:     off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].

7454:     off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7455:     own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7456:     so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.

7458:     coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7459:     Ex. coo_i[]: the beginning part (of size ncoo_d + ncoo_oown) stores i of local nonzeros, and the remaining part stores i of nonzeros I will receive.
7460:   */
7461:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7462:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));

7464:   /* gather (i,j) of nonzeros inserted by remote procs */
7465:   if (hasoffproc) {
7466:     PetscSF  msf;
7467:     PetscInt ncoo2, *coo_i2, *coo_j2;

7469:     PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7470:     PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7471:     PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */

7473:     for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7474:       Mat_SeqAIJ *mm     = (Mat_SeqAIJ *)mp[cp]->data;
7475:       PetscInt   *idxoff = mmdata->off[cp];
7476:       PetscInt   *idxown = mmdata->own[cp];
7477:       if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7478:         const PetscInt *rmap = rmapa[cp];
7479:         const PetscInt *cmap = cmapa[cp];
7480:         const PetscInt *ii   = mm->i;
7481:         PetscInt       *coi  = coo_i + ncoo_o;
7482:         PetscInt       *coj  = coo_j + ncoo_o;
7483:         const PetscInt  mr   = mp[cp]->rmap->n;
7484:         const PetscInt  rs   = C->rmap->rstart;
7485:         const PetscInt  re   = C->rmap->rend;
7486:         const PetscInt  cs   = C->cmap->rstart;
7487:         for (i = 0; i < mr; i++) {
7488:           const PetscInt *jj = mm->j + ii[i];
7489:           const PetscInt  gr = rmap[i];
7490:           const PetscInt  nz = ii[i + 1] - ii[i];
7491:           if (gr < rs || gr >= re) { /* this is an offproc row */
7492:             for (j = ii[i]; j < ii[i + 1]; j++) {
7493:               *coi++    = gr;
7494:               *idxoff++ = j;
7495:             }
7496:             if (!cmapt[cp]) { /* already global */
7497:               for (j = 0; j < nz; j++) *coj++ = jj[j];
7498:             } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7499:               for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7500:             } else { /* offdiag */
7501:               for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7502:             }
7503:             ncoo_o += nz;
7504:           } else { /* this is a local row */
7505:             for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7506:           }
7507:         }
7508:       }
7509:       mmdata->off[cp + 1] = idxoff;
7510:       mmdata->own[cp + 1] = idxown;
7511:     }

7513:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7514:     PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7515:     PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7516:     PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7517:     ncoo = ncoo_d + ncoo_oown + ncoo2;
7518:     PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7519:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7520:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7521:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7522:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7523:     PetscCall(PetscFree2(coo_i, coo_j));
7524:     /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7525:     PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7526:     coo_i = coo_i2;
7527:     coo_j = coo_j2;
7528:   } else { /* no offproc values insertion */
7529:     ncoo = ncoo_d;
7530:     PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));

7532:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7533:     PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7534:     PetscCall(PetscSFSetUp(mmdata->sf));
7535:   }
7536:   mmdata->hasoffproc = hasoffproc;

7538:   /* gather (i,j) of nonzeros inserted locally */
7539:   for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7540:     Mat_SeqAIJ     *mm   = (Mat_SeqAIJ *)mp[cp]->data;
7541:     PetscInt       *coi  = coo_i + ncoo_d;
7542:     PetscInt       *coj  = coo_j + ncoo_d;
7543:     const PetscInt *jj   = mm->j;
7544:     const PetscInt *ii   = mm->i;
7545:     const PetscInt *cmap = cmapa[cp];
7546:     const PetscInt *rmap = rmapa[cp];
7547:     const PetscInt  mr   = mp[cp]->rmap->n;
7548:     const PetscInt  rs   = C->rmap->rstart;
7549:     const PetscInt  re   = C->rmap->rend;
7550:     const PetscInt  cs   = C->cmap->rstart;

7552:     if (mptmp[cp]) continue;
7553:     if (rmapt[cp] == 1) { /* consecutive rows */
7554:       /* fill coo_i */
7555:       for (i = 0; i < mr; i++) {
7556:         const PetscInt gr = i + rs;
7557:         for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7558:       }
7559:       /* fill coo_j */
7560:       if (!cmapt[cp]) { /* type-0, already global */
7561:         PetscCall(PetscArraycpy(coj, jj, mm->nz));
7562:       } else if (cmapt[cp] == 1) {                        /* type-1, local to global for consecutive columns of C */
7563:         for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7564:       } else {                                            /* type-2, local to global for sparse columns */
7565:         for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7566:       }
7567:       ncoo_d += mm->nz;
7568:     } else if (rmapt[cp] == 2) { /* sparse rows */
7569:       for (i = 0; i < mr; i++) {
7570:         const PetscInt *jj = mm->j + ii[i];
7571:         const PetscInt  gr = rmap[i];
7572:         const PetscInt  nz = ii[i + 1] - ii[i];
7573:         if (gr >= rs && gr < re) { /* local rows */
7574:           for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7575:           if (!cmapt[cp]) { /* type-0, already global */
7576:             for (j = 0; j < nz; j++) *coj++ = jj[j];
7577:           } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7578:             for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7579:           } else { /* type-2, local to global for sparse columns */
7580:             for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7581:           }
7582:           ncoo_d += nz;
7583:         }
7584:       }
7585:     }
7586:   }
7587:   if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7588:   PetscCall(ISDestroy(&glob));
7589:   if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7590:   PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7591:   /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7592:   PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));

7594:   /* preallocate with COO data */
7595:   PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7596:   PetscCall(PetscFree2(coo_i, coo_j));
7597:   PetscFunctionReturn(PETSC_SUCCESS);
7598: }

7600: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7601: {
7602:   Mat_Product *product = mat->product;
7603: #if defined(PETSC_HAVE_DEVICE)
7604:   PetscBool match  = PETSC_FALSE;
7605:   PetscBool usecpu = PETSC_FALSE;
7606: #else
7607:   PetscBool match = PETSC_TRUE;
7608: #endif

7610:   PetscFunctionBegin;
7611:   MatCheckProduct(mat, 1);
7612: #if defined(PETSC_HAVE_DEVICE)
7613:   if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7614:   if (match) { /* we can always fallback to the CPU if requested */
7615:     switch (product->type) {
7616:     case MATPRODUCT_AB:
7617:       if (product->api_user) {
7618:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7619:         PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7620:         PetscOptionsEnd();
7621:       } else {
7622:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7623:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7624:         PetscOptionsEnd();
7625:       }
7626:       break;
7627:     case MATPRODUCT_AtB:
7628:       if (product->api_user) {
7629:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7630:         PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7631:         PetscOptionsEnd();
7632:       } else {
7633:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7634:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7635:         PetscOptionsEnd();
7636:       }
7637:       break;
7638:     case MATPRODUCT_PtAP:
7639:       if (product->api_user) {
7640:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7641:         PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7642:         PetscOptionsEnd();
7643:       } else {
7644:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7645:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7646:         PetscOptionsEnd();
7647:       }
7648:       break;
7649:     default:
7650:       break;
7651:     }
7652:     match = (PetscBool)!usecpu;
7653:   }
7654: #endif
7655:   if (match) {
7656:     switch (product->type) {
7657:     case MATPRODUCT_AB:
7658:     case MATPRODUCT_AtB:
7659:     case MATPRODUCT_PtAP:
7660:       mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7661:       break;
7662:     default:
7663:       break;
7664:     }
7665:   }
7666:   /* fallback to MPIAIJ ops */
7667:   if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7668:   PetscFunctionReturn(PETSC_SUCCESS);
7669: }

7671: /*
7672:    Produces a set of block column indices of the matrix row, one for each block represented in the original row

7674:    n - the number of block indices in cc[]
7675:    cc - the block indices (must be large enough to contain the indices)
7676: */
7677: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7678: {
7679:   PetscInt        cnt = -1, nidx, j;
7680:   const PetscInt *idx;

7682:   PetscFunctionBegin;
7683:   PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7684:   if (nidx) {
7685:     cnt     = 0;
7686:     cc[cnt] = idx[0] / bs;
7687:     for (j = 1; j < nidx; j++) {
7688:       if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7689:     }
7690:   }
7691:   PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7692:   *n = cnt + 1;
7693:   PetscFunctionReturn(PETSC_SUCCESS);
7694: }

7696: /*
7697:     Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows

7699:     ncollapsed - the number of block indices
7700:     collapsed - the block indices (must be large enough to contain the indices)
7701: */
7702: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7703: {
7704:   PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;

7706:   PetscFunctionBegin;
7707:   PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7708:   for (i = start + 1; i < start + bs; i++) {
7709:     PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7710:     PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7711:     cprevtmp = cprev;
7712:     cprev    = merged;
7713:     merged   = cprevtmp;
7714:   }
7715:   *ncollapsed = nprev;
7716:   if (collapsed) *collapsed = cprev;
7717:   PetscFunctionReturn(PETSC_SUCCESS);
7718: }

7720: /*
7721:  MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix

7723:  Input Parameter:
7724:  . Amat - matrix
7725:  - symmetrize - make the result symmetric
7726:  + scale - scale with diagonal

7728:  Output Parameter:
7729:  . a_Gmat - output scalar graph >= 0

7731: */
7732: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, Mat *a_Gmat)
7733: {
7734:   PetscInt  Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7735:   MPI_Comm  comm;
7736:   Mat       Gmat;
7737:   PetscBool ismpiaij, isseqaij;
7738:   Mat       a, b, c;
7739:   MatType   jtype;

7741:   PetscFunctionBegin;
7742:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7743:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7744:   PetscCall(MatGetSize(Amat, &MM, &NN));
7745:   PetscCall(MatGetBlockSize(Amat, &bs));
7746:   nloc = (Iend - Istart) / bs;

7748:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7749:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7750:   PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");

7752:   /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7753:   /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7754:      implementation */
7755:   if (bs > 1) {
7756:     PetscCall(MatGetType(Amat, &jtype));
7757:     PetscCall(MatCreate(comm, &Gmat));
7758:     PetscCall(MatSetType(Gmat, jtype));
7759:     PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7760:     PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7761:     if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7762:       PetscInt  *d_nnz, *o_nnz;
7763:       MatScalar *aa, val, *AA;
7764:       PetscInt  *aj, *ai, *AJ, nc, nmax = 0;
7765:       if (isseqaij) {
7766:         a = Amat;
7767:         b = NULL;
7768:       } else {
7769:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7770:         a             = d->A;
7771:         b             = d->B;
7772:       }
7773:       PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7774:       PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7775:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7776:         PetscInt       *nnz = (c == a) ? d_nnz : o_nnz;
7777:         const PetscInt *cols1, *cols2;
7778:         for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7779:           PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7780:           nnz[brow / bs] = nc2 / bs;
7781:           if (nc2 % bs) ok = 0;
7782:           if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7783:           for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7784:             PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7785:             if (nc1 != nc2) ok = 0;
7786:             else {
7787:               for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7788:                 if (cols1[jj] != cols2[jj]) ok = 0;
7789:                 if (cols1[jj] % bs != jj % bs) ok = 0;
7790:               }
7791:             }
7792:             PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7793:           }
7794:           PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7795:           if (!ok) {
7796:             PetscCall(PetscFree2(d_nnz, o_nnz));
7797:             PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7798:             goto old_bs;
7799:           }
7800:         }
7801:       }
7802:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7803:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7804:       PetscCall(PetscFree2(d_nnz, o_nnz));
7805:       PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7806:       // diag
7807:       for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7808:         Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7809:         ai               = aseq->i;
7810:         n                = ai[brow + 1] - ai[brow];
7811:         aj               = aseq->j + ai[brow];
7812:         for (int k = 0; k < n; k += bs) {        // block columns
7813:           AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7814:           val        = 0;
7815:           for (int ii = 0; ii < bs; ii++) { // rows in block
7816:             aa = aseq->a + ai[brow + ii] + k;
7817:             for (int jj = 0; jj < bs; jj++) {         // columns in block
7818:               val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7819:             }
7820:           }
7821:           PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7822:           AA[k / bs] = val;
7823:         }
7824:         grow = Istart / bs + brow / bs;
7825:         PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7826:       }
7827:       // off-diag
7828:       if (ismpiaij) {
7829:         Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)Amat->data;
7830:         const PetscScalar *vals;
7831:         const PetscInt    *cols, *garray = aij->garray;
7832:         PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7833:         for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7834:           PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7835:           for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7836:             PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7837:             AA[k / bs] = 0;
7838:             AJ[cidx]   = garray[cols[k]] / bs;
7839:           }
7840:           nc = ncols / bs;
7841:           PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7842:           for (int ii = 0; ii < bs; ii++) { // rows in block
7843:             PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7844:             for (int k = 0; k < ncols; k += bs) {
7845:               for (int jj = 0; jj < bs; jj++) { // cols in block
7846:                 PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7847:                 AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7848:               }
7849:             }
7850:             PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7851:           }
7852:           grow = Istart / bs + brow / bs;
7853:           PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7854:         }
7855:       }
7856:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7857:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7858:       PetscCall(PetscFree2(AA, AJ));
7859:     } else {
7860:       const PetscScalar *vals;
7861:       const PetscInt    *idx;
7862:       PetscInt          *d_nnz, *o_nnz, *w0, *w1, *w2;
7863:     old_bs:
7864:       /*
7865:        Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7866:        */
7867:       PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7868:       PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7869:       if (isseqaij) {
7870:         PetscInt max_d_nnz;
7871:         /*
7872:          Determine exact preallocation count for (sequential) scalar matrix
7873:          */
7874:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7875:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7876:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7877:         for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7878:         PetscCall(PetscFree3(w0, w1, w2));
7879:       } else if (ismpiaij) {
7880:         Mat             Daij, Oaij;
7881:         const PetscInt *garray;
7882:         PetscInt        max_d_nnz;
7883:         PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7884:         /*
7885:          Determine exact preallocation count for diagonal block portion of scalar matrix
7886:          */
7887:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7888:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7889:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7890:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7891:         PetscCall(PetscFree3(w0, w1, w2));
7892:         /*
7893:          Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7894:          */
7895:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7896:           o_nnz[jj] = 0;
7897:           for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7898:             PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7899:             o_nnz[jj] += ncols;
7900:             PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7901:           }
7902:           if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7903:         }
7904:       } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7905:       /* get scalar copy (norms) of matrix */
7906:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7907:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7908:       PetscCall(PetscFree2(d_nnz, o_nnz));
7909:       for (Ii = Istart; Ii < Iend; Ii++) {
7910:         PetscInt dest_row = Ii / bs;
7911:         PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7912:         for (jj = 0; jj < ncols; jj++) {
7913:           PetscInt    dest_col = idx[jj] / bs;
7914:           PetscScalar sv       = PetscAbs(PetscRealPart(vals[jj]));
7915:           PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7916:         }
7917:         PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7918:       }
7919:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7920:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7921:     }
7922:   } else {
7923:     if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7924:     else {
7925:       Gmat = Amat;
7926:       PetscCall(PetscObjectReference((PetscObject)Gmat));
7927:     }
7928:     if (isseqaij) {
7929:       a = Gmat;
7930:       b = NULL;
7931:     } else {
7932:       Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7933:       a             = d->A;
7934:       b             = d->B;
7935:     }
7936:     if (filter >= 0 || scale) {
7937:       /* take absolute value of each entry */
7938:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7939:         MatInfo      info;
7940:         PetscScalar *avals;
7941:         PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7942:         PetscCall(MatSeqAIJGetArray(c, &avals));
7943:         for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
7944:         PetscCall(MatSeqAIJRestoreArray(c, &avals));
7945:       }
7946:     }
7947:   }
7948:   if (symmetrize) {
7949:     PetscBool isset, issym;
7950:     PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
7951:     if (!isset || !issym) {
7952:       Mat matTrans;
7953:       PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
7954:       PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
7955:       PetscCall(MatDestroy(&matTrans));
7956:     }
7957:     PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
7958:   } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
7959:   if (scale) {
7960:     /* scale c for all diagonal values = 1 or -1 */
7961:     Vec diag;
7962:     PetscCall(MatCreateVecs(Gmat, &diag, NULL));
7963:     PetscCall(MatGetDiagonal(Gmat, diag));
7964:     PetscCall(VecReciprocal(diag));
7965:     PetscCall(VecSqrtAbs(diag));
7966:     PetscCall(MatDiagonalScale(Gmat, diag, diag));
7967:     PetscCall(VecDestroy(&diag));
7968:   }
7969:   PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));

7971:   if (filter >= 0) {
7972:     PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
7973:     PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
7974:   }
7975:   *a_Gmat = Gmat;
7976:   PetscFunctionReturn(PETSC_SUCCESS);
7977: }

7979: /*
7980:     Special version for direct calls from Fortran
7981: */
7982: #include <petsc/private/fortranimpl.h>

7984: /* Change these macros so can be used in void function */
7985: /* Identical to PetscCallVoid, except it assigns to *_ierr */
7986: #undef PetscCall
7987: #define PetscCall(...) \
7988:   do { \
7989:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
7990:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
7991:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
7992:       return; \
7993:     } \
7994:   } while (0)

7996: #undef SETERRQ
7997: #define SETERRQ(comm, ierr, ...) \
7998:   do { \
7999:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8000:     return; \
8001:   } while (0)

8003: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8004:   #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8005: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8006:   #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8007: #else
8008: #endif
8009: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8010: {
8011:   Mat         mat = *mmat;
8012:   PetscInt    m = *mm, n = *mn;
8013:   InsertMode  addv = *maddv;
8014:   Mat_MPIAIJ *aij  = (Mat_MPIAIJ *)mat->data;
8015:   PetscScalar value;

8017:   MatCheckPreallocated(mat, 1);
8018:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8019:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8020:   {
8021:     PetscInt  i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8022:     PetscInt  cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8023:     PetscBool roworiented = aij->roworiented;

8025:     /* Some Variables required in the macro */
8026:     Mat         A     = aij->A;
8027:     Mat_SeqAIJ *a     = (Mat_SeqAIJ *)A->data;
8028:     PetscInt   *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8029:     MatScalar  *aa;
8030:     PetscBool   ignorezeroentries = (((a->ignorezeroentries) && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8031:     Mat         B                 = aij->B;
8032:     Mat_SeqAIJ *b                 = (Mat_SeqAIJ *)B->data;
8033:     PetscInt   *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8034:     MatScalar  *ba;
8035:     /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8036:      * cannot use "#if defined" inside a macro. */
8037:     PETSC_UNUSED PetscBool inserted = PETSC_FALSE;

8039:     PetscInt  *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8040:     PetscInt   nonew = a->nonew;
8041:     MatScalar *ap1, *ap2;

8043:     PetscFunctionBegin;
8044:     PetscCall(MatSeqAIJGetArray(A, &aa));
8045:     PetscCall(MatSeqAIJGetArray(B, &ba));
8046:     for (i = 0; i < m; i++) {
8047:       if (im[i] < 0) continue;
8048:       PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
8049:       if (im[i] >= rstart && im[i] < rend) {
8050:         row      = im[i] - rstart;
8051:         lastcol1 = -1;
8052:         rp1      = aj + ai[row];
8053:         ap1      = aa + ai[row];
8054:         rmax1    = aimax[row];
8055:         nrow1    = ailen[row];
8056:         low1     = 0;
8057:         high1    = nrow1;
8058:         lastcol2 = -1;
8059:         rp2      = bj + bi[row];
8060:         ap2      = ba + bi[row];
8061:         rmax2    = bimax[row];
8062:         nrow2    = bilen[row];
8063:         low2     = 0;
8064:         high2    = nrow2;

8066:         for (j = 0; j < n; j++) {
8067:           if (roworiented) value = v[i * n + j];
8068:           else value = v[i + j * m];
8069:           if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8070:           if (in[j] >= cstart && in[j] < cend) {
8071:             col = in[j] - cstart;
8072:             MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8073:           } else if (in[j] < 0) continue;
8074:           else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8075:             SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8076:           } else {
8077:             if (mat->was_assembled) {
8078:               if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8079: #if defined(PETSC_USE_CTABLE)
8080:               PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8081:               col--;
8082: #else
8083:               col = aij->colmap[in[j]] - 1;
8084: #endif
8085:               if (col < 0 && !((Mat_SeqAIJ *)(aij->A->data))->nonew) {
8086:                 PetscCall(MatDisAssemble_MPIAIJ(mat));
8087:                 col = in[j];
8088:                 /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8089:                 B        = aij->B;
8090:                 b        = (Mat_SeqAIJ *)B->data;
8091:                 bimax    = b->imax;
8092:                 bi       = b->i;
8093:                 bilen    = b->ilen;
8094:                 bj       = b->j;
8095:                 rp2      = bj + bi[row];
8096:                 ap2      = ba + bi[row];
8097:                 rmax2    = bimax[row];
8098:                 nrow2    = bilen[row];
8099:                 low2     = 0;
8100:                 high2    = nrow2;
8101:                 bm       = aij->B->rmap->n;
8102:                 ba       = b->a;
8103:                 inserted = PETSC_FALSE;
8104:               }
8105:             } else col = in[j];
8106:             MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8107:           }
8108:         }
8109:       } else if (!aij->donotstash) {
8110:         if (roworiented) {
8111:           PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8112:         } else {
8113:           PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8114:         }
8115:       }
8116:     }
8117:     PetscCall(MatSeqAIJRestoreArray(A, &aa));
8118:     PetscCall(MatSeqAIJRestoreArray(B, &ba));
8119:   }
8120:   PetscFunctionReturnVoid();
8121: }

8123: /* Undefining these here since they were redefined from their original definition above! No
8124:  * other PETSc functions should be defined past this point, as it is impossible to recover the
8125:  * original definitions */
8126: #undef PetscCall
8127: #undef SETERRQ