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));
166:   PetscFunctionReturn(PETSC_SUCCESS);
167: }

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

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

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

191:   PetscFunctionBegin;
192:   *keptrows = NULL;

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

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

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

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

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

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

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

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

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

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

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

356: /*
357:   Local utility routine that creates a mapping from the global column
358: number to the local number in the off-diagonal part of the local
359: storage of the matrix.  When PETSC_USE_CTABLE is used this is scalable at
360: a slightly higher hash table cost; without it it is not scalable (each processor
361: has an order N integer array but is fast to access.
362: */
363: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
364: {
365:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
366:   PetscInt    n   = aij->B->cmap->n, i;

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

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

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

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

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

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

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

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

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

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

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

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

618: /*
619:     This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
620:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
621:     No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
622: */
623: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
624: {
625:   Mat_MPIAIJ *aij    = (Mat_MPIAIJ *)mat->data;
626:   Mat         A      = aij->A; /* diagonal part of the matrix */
627:   Mat         B      = aij->B; /* off-diagonal part of the matrix */
628:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
629:   Mat_SeqAIJ *b      = (Mat_SeqAIJ *)B->data;
630:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
631:   PetscInt   *ailen = a->ilen, *aj = a->j;
632:   PetscInt   *bilen = b->ilen, *bj = b->j;
633:   PetscInt    am          = aij->A->rmap->n, j;
634:   PetscInt    diag_so_far = 0, dnz;
635:   PetscInt    offd_so_far = 0, onz;

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

658: /*
659:     This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
660:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
661:     No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
662:     Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
663:     would not be true and the more complex MatSetValues_MPIAIJ has to be used.
664: */
665: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
666: {
667:   Mat_MPIAIJ  *aij  = (Mat_MPIAIJ *)mat->data;
668:   Mat          A    = aij->A; /* diagonal part of the matrix */
669:   Mat          B    = aij->B; /* off-diagonal part of the matrix */
670:   Mat_SeqAIJ  *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
671:   Mat_SeqAIJ  *a      = (Mat_SeqAIJ *)A->data;
672:   Mat_SeqAIJ  *b      = (Mat_SeqAIJ *)B->data;
673:   PetscInt     cstart = mat->cmap->rstart, cend = mat->cmap->rend;
674:   PetscInt    *ailen = a->ilen, *aj = a->j;
675:   PetscInt    *bilen = b->ilen, *bj = b->j;
676:   PetscInt     am          = aij->A->rmap->n, j;
677:   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. */
678:   PetscInt     col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
679:   PetscScalar *aa = a->a, *ba = b->a;

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

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

712:   PetscFunctionBegin;
713:   for (i = 0; i < m; i++) {
714:     if (idxm[i] < 0) continue; /* negative row */
715:     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);
716:     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);
717:     row = idxm[i] - rstart;
718:     for (j = 0; j < n; j++) {
719:       if (idxn[j] < 0) continue; /* negative column */
720:       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);
721:       if (idxn[j] >= cstart && idxn[j] < cend) {
722:         col = idxn[j] - cstart;
723:         PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
724:       } else {
725:         if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
726: #if defined(PETSC_USE_CTABLE)
727:         PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
728:         col--;
729: #else
730:         col = aij->colmap[idxn[j]] - 1;
731: #endif
732:         if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
733:         else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
734:       }
735:     }
736:   }
737:   PetscFunctionReturn(PETSC_SUCCESS);
738: }

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

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

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

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

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

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

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

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

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

818:   aij->rowvalues = NULL;

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

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

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

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

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

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

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

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

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

906:   /* only change matrix nonzero state if pattern was allowed to be changed */
907:   if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
908:     PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
909:     PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
910:   }
911:   PetscFunctionReturn(PETSC_SUCCESS);
912: }

914: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
915: {
916:   Mat_MPIAIJ        *l = (Mat_MPIAIJ *)A->data;
917:   PetscMPIInt        n = A->rmap->n;
918:   PetscInt           i, j, r, m, len = 0;
919:   PetscInt          *lrows, *owners = A->rmap->range;
920:   PetscMPIInt        p = 0;
921:   PetscSFNode       *rrows;
922:   PetscSF            sf;
923:   const PetscScalar *xx;
924:   PetscScalar       *bb, *mask, *aij_a;
925:   Vec                xmask, lmask;
926:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ *)l->B->data;
927:   const PetscInt    *aj, *ii, *ridx;
928:   PetscScalar       *aa;

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

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

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

1024:   /* only change matrix nonzero state if pattern was allowed to be changed */
1025:   if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1026:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1027:     PetscCall(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1028:   }
1029:   PetscFunctionReturn(PETSC_SUCCESS);
1030: }

1032: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1033: {
1034:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1035:   PetscInt    nt;
1036:   VecScatter  Mvctx = a->Mvctx;

1038:   PetscFunctionBegin;
1039:   PetscCall(VecGetLocalSize(xx, &nt));
1040:   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);
1041:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1042:   PetscUseTypeMethod(a->A, mult, xx, yy);
1043:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1044:   PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1045:   PetscFunctionReturn(PETSC_SUCCESS);
1046: }

1048: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1049: {
1050:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1052:   PetscFunctionBegin;
1053:   PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1054:   PetscFunctionReturn(PETSC_SUCCESS);
1055: }

1057: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1058: {
1059:   Mat_MPIAIJ *a     = (Mat_MPIAIJ *)A->data;
1060:   VecScatter  Mvctx = a->Mvctx;

1062:   PetscFunctionBegin;
1063:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064:   PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1065:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1066:   PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1067:   PetscFunctionReturn(PETSC_SUCCESS);
1068: }

1070: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1071: {
1072:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1074:   PetscFunctionBegin;
1075:   /* do nondiagonal part */
1076:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1077:   /* do local part */
1078:   PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1079:   /* add partial results together */
1080:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1081:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1082:   PetscFunctionReturn(PETSC_SUCCESS);
1083: }

1085: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1086: {
1087:   MPI_Comm    comm;
1088:   Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1089:   Mat         Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1090:   IS          Me, Notme;
1091:   PetscInt    M, N, first, last, *notme, i;
1092:   PetscBool   lf;
1093:   PetscMPIInt size;

1095:   PetscFunctionBegin;
1096:   /* Easy test: symmetric diagonal block */
1097:   PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1098:   PetscCall(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1099:   if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1100:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1101:   PetscCallMPI(MPI_Comm_size(comm, &size));
1102:   if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);

1104:   /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1105:   PetscCall(MatGetSize(Amat, &M, &N));
1106:   PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1107:   PetscCall(PetscMalloc1(N - last + first, &notme));
1108:   for (i = 0; i < first; i++) notme[i] = i;
1109:   for (i = last; i < M; i++) notme[i - last + first] = i;
1110:   PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1111:   PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1112:   PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1113:   Aoff = Aoffs[0];
1114:   PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1115:   Boff = Boffs[0];
1116:   PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1117:   PetscCall(MatDestroyMatrices(1, &Aoffs));
1118:   PetscCall(MatDestroyMatrices(1, &Boffs));
1119:   PetscCall(ISDestroy(&Me));
1120:   PetscCall(ISDestroy(&Notme));
1121:   PetscCall(PetscFree(notme));
1122:   PetscFunctionReturn(PETSC_SUCCESS);
1123: }

1125: static PetscErrorCode MatIsSymmetric_MPIAIJ(Mat A, PetscReal tol, PetscBool *f)
1126: {
1127:   PetscFunctionBegin;
1128:   PetscCall(MatIsTranspose_MPIAIJ(A, A, tol, f));
1129:   PetscFunctionReturn(PETSC_SUCCESS);
1130: }

1132: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1133: {
1134:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1136:   PetscFunctionBegin;
1137:   /* do nondiagonal part */
1138:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1139:   /* do local part */
1140:   PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1141:   /* add partial results together */
1142:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1143:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1144:   PetscFunctionReturn(PETSC_SUCCESS);
1145: }

1147: /*
1148:   This only works correctly for square matrices where the subblock A->A is the
1149:    diagonal block
1150: */
1151: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1152: {
1153:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1155:   PetscFunctionBegin;
1156:   PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1157:   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");
1158:   PetscCall(MatGetDiagonal(a->A, v));
1159:   PetscFunctionReturn(PETSC_SUCCESS);
1160: }

1162: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1163: {
1164:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1166:   PetscFunctionBegin;
1167:   PetscCall(MatScale(a->A, aa));
1168:   PetscCall(MatScale(a->B, aa));
1169:   PetscFunctionReturn(PETSC_SUCCESS);
1170: }

1172: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1173: {
1174:   Mat_MPIAIJ        *aij    = (Mat_MPIAIJ *)mat->data;
1175:   Mat_SeqAIJ        *A      = (Mat_SeqAIJ *)aij->A->data;
1176:   Mat_SeqAIJ        *B      = (Mat_SeqAIJ *)aij->B->data;
1177:   const PetscInt    *garray = aij->garray;
1178:   const PetscScalar *aa, *ba;
1179:   PetscInt           header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1180:   PetscInt64         nz, hnz;
1181:   PetscInt          *rowlens;
1182:   PetscInt          *colidxs;
1183:   PetscScalar       *matvals;
1184:   PetscMPIInt        rank;

1186:   PetscFunctionBegin;
1187:   PetscCall(PetscViewerSetUp(viewer));

1189:   M  = mat->rmap->N;
1190:   N  = mat->cmap->N;
1191:   m  = mat->rmap->n;
1192:   rs = mat->rmap->rstart;
1193:   cs = mat->cmap->rstart;
1194:   nz = A->nz + B->nz;

1196:   /* write matrix header */
1197:   header[0] = MAT_FILE_CLASSID;
1198:   header[1] = M;
1199:   header[2] = N;
1200:   PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1201:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1202:   if (rank == 0) {
1203:     if (hnz > PETSC_MAX_INT) header[3] = PETSC_MAX_INT;
1204:     else header[3] = (PetscInt)hnz;
1205:   }
1206:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

1208:   /* fill in and store row lengths  */
1209:   PetscCall(PetscMalloc1(m, &rowlens));
1210:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1211:   PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1212:   PetscCall(PetscFree(rowlens));

1214:   /* fill in and store column indices */
1215:   PetscCall(PetscMalloc1(nz, &colidxs));
1216:   for (cnt = 0, i = 0; i < m; i++) {
1217:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1218:       if (garray[B->j[jb]] > cs) break;
1219:       colidxs[cnt++] = garray[B->j[jb]];
1220:     }
1221:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1222:     for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1223:   }
1224:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1225:   PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1226:   PetscCall(PetscFree(colidxs));

1228:   /* fill in and store nonzero values */
1229:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1230:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1231:   PetscCall(PetscMalloc1(nz, &matvals));
1232:   for (cnt = 0, i = 0; i < m; i++) {
1233:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1234:       if (garray[B->j[jb]] > cs) break;
1235:       matvals[cnt++] = ba[jb];
1236:     }
1237:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1238:     for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1239:   }
1240:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1241:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1242:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1243:   PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1244:   PetscCall(PetscFree(matvals));

1246:   /* write block size option to the viewer's .info file */
1247:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1248:   PetscFunctionReturn(PETSC_SUCCESS);
1249: }

1251: #include <petscdraw.h>
1252: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1253: {
1254:   Mat_MPIAIJ       *aij  = (Mat_MPIAIJ *)mat->data;
1255:   PetscMPIInt       rank = aij->rank, size = aij->size;
1256:   PetscBool         isdraw, iascii, isbinary;
1257:   PetscViewer       sviewer;
1258:   PetscViewerFormat format;

1260:   PetscFunctionBegin;
1261:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1262:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1263:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1264:   if (iascii) {
1265:     PetscCall(PetscViewerGetFormat(viewer, &format));
1266:     if (format == PETSC_VIEWER_LOAD_BALANCE) {
1267:       PetscInt i, nmax = 0, nmin = PETSC_MAX_INT, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1268:       PetscCall(PetscMalloc1(size, &nz));
1269:       PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1270:       for (i = 0; i < (PetscInt)size; i++) {
1271:         nmax = PetscMax(nmax, nz[i]);
1272:         nmin = PetscMin(nmin, nz[i]);
1273:         navg += nz[i];
1274:       }
1275:       PetscCall(PetscFree(nz));
1276:       navg = navg / size;
1277:       PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT "  avg %" PetscInt_FMT "  max %" PetscInt_FMT "\n", nmin, navg, nmax));
1278:       PetscFunctionReturn(PETSC_SUCCESS);
1279:     }
1280:     PetscCall(PetscViewerGetFormat(viewer, &format));
1281:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1282:       MatInfo   info;
1283:       PetscInt *inodes = NULL;

1285:       PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1286:       PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1287:       PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1288:       PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1289:       if (!inodes) {
1290:         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,
1291:                                                      (double)info.memory));
1292:       } else {
1293:         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,
1294:                                                      (double)info.memory));
1295:       }
1296:       PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1297:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1298:       PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1299:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1300:       PetscCall(PetscViewerFlush(viewer));
1301:       PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1302:       PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1303:       PetscCall(VecScatterView(aij->Mvctx, viewer));
1304:       PetscFunctionReturn(PETSC_SUCCESS);
1305:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1306:       PetscInt inodecount, inodelimit, *inodes;
1307:       PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1308:       if (inodes) {
1309:         PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1310:       } else {
1311:         PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1312:       }
1313:       PetscFunctionReturn(PETSC_SUCCESS);
1314:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1315:       PetscFunctionReturn(PETSC_SUCCESS);
1316:     }
1317:   } else if (isbinary) {
1318:     if (size == 1) {
1319:       PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1320:       PetscCall(MatView(aij->A, viewer));
1321:     } else {
1322:       PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1323:     }
1324:     PetscFunctionReturn(PETSC_SUCCESS);
1325:   } else if (iascii && size == 1) {
1326:     PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1327:     PetscCall(MatView(aij->A, viewer));
1328:     PetscFunctionReturn(PETSC_SUCCESS);
1329:   } else if (isdraw) {
1330:     PetscDraw draw;
1331:     PetscBool isnull;
1332:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1333:     PetscCall(PetscDrawIsNull(draw, &isnull));
1334:     if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1335:   }

1337:   { /* assemble the entire matrix onto first processor */
1338:     Mat A = NULL, Av;
1339:     IS  isrow, iscol;

1341:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1342:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1343:     PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1344:     PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1345:     /*  The commented code uses MatCreateSubMatrices instead */
1346:     /*
1347:     Mat *AA, A = NULL, Av;
1348:     IS  isrow,iscol;

1350:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1351:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1352:     PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1353:     if (rank == 0) {
1354:        PetscCall(PetscObjectReference((PetscObject)AA[0]));
1355:        A    = AA[0];
1356:        Av   = AA[0];
1357:     }
1358:     PetscCall(MatDestroySubMatrices(1,&AA));
1359: */
1360:     PetscCall(ISDestroy(&iscol));
1361:     PetscCall(ISDestroy(&isrow));
1362:     /*
1363:        Everyone has to call to draw the matrix since the graphics waits are
1364:        synchronized across all processors that share the PetscDraw object
1365:     */
1366:     PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1367:     if (rank == 0) {
1368:       if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1369:       PetscCall(MatView_SeqAIJ(Av, sviewer));
1370:     }
1371:     PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1372:     PetscCall(MatDestroy(&A));
1373:   }
1374:   PetscFunctionReturn(PETSC_SUCCESS);
1375: }

1377: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1378: {
1379:   PetscBool iascii, isdraw, issocket, isbinary;

1381:   PetscFunctionBegin;
1382:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1383:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1384:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1385:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1386:   if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1387:   PetscFunctionReturn(PETSC_SUCCESS);
1388: }

1390: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1391: {
1392:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1393:   Vec         bb1 = NULL;
1394:   PetscBool   hasop;

1396:   PetscFunctionBegin;
1397:   if (flag == SOR_APPLY_UPPER) {
1398:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1399:     PetscFunctionReturn(PETSC_SUCCESS);
1400:   }

1402:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));

1404:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1405:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1406:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1407:       its--;
1408:     }

1410:     while (its--) {
1411:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1412:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1414:       /* update rhs: bb1 = bb - B*x */
1415:       PetscCall(VecScale(mat->lvec, -1.0));
1416:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1418:       /* local sweep */
1419:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1420:     }
1421:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1422:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1423:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1424:       its--;
1425:     }
1426:     while (its--) {
1427:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1428:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1430:       /* update rhs: bb1 = bb - B*x */
1431:       PetscCall(VecScale(mat->lvec, -1.0));
1432:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1434:       /* local sweep */
1435:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1436:     }
1437:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1438:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1439:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1440:       its--;
1441:     }
1442:     while (its--) {
1443:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1444:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1446:       /* update rhs: bb1 = bb - B*x */
1447:       PetscCall(VecScale(mat->lvec, -1.0));
1448:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1450:       /* local sweep */
1451:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1452:     }
1453:   } else if (flag & SOR_EISENSTAT) {
1454:     Vec xx1;

1456:     PetscCall(VecDuplicate(bb, &xx1));
1457:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));

1459:     PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1460:     PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1461:     if (!mat->diag) {
1462:       PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1463:       PetscCall(MatGetDiagonal(matin, mat->diag));
1464:     }
1465:     PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1466:     if (hasop) {
1467:       PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1468:     } else {
1469:       PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1470:     }
1471:     PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));

1473:     PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));

1475:     /* local sweep */
1476:     PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1477:     PetscCall(VecAXPY(xx, 1.0, xx1));
1478:     PetscCall(VecDestroy(&xx1));
1479:   } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");

1481:   PetscCall(VecDestroy(&bb1));

1483:   matin->factorerrortype = mat->A->factorerrortype;
1484:   PetscFunctionReturn(PETSC_SUCCESS);
1485: }

1487: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1488: {
1489:   Mat             aA, aB, Aperm;
1490:   const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1491:   PetscScalar    *aa, *ba;
1492:   PetscInt        i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1493:   PetscSF         rowsf, sf;
1494:   IS              parcolp = NULL;
1495:   PetscBool       done;

1497:   PetscFunctionBegin;
1498:   PetscCall(MatGetLocalSize(A, &m, &n));
1499:   PetscCall(ISGetIndices(rowp, &rwant));
1500:   PetscCall(ISGetIndices(colp, &cwant));
1501:   PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));

1503:   /* Invert row permutation to find out where my rows should go */
1504:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1505:   PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1506:   PetscCall(PetscSFSetFromOptions(rowsf));
1507:   for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1508:   PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1509:   PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));

1511:   /* Invert column permutation to find out where my columns should go */
1512:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1513:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1514:   PetscCall(PetscSFSetFromOptions(sf));
1515:   for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1516:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1517:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1518:   PetscCall(PetscSFDestroy(&sf));

1520:   PetscCall(ISRestoreIndices(rowp, &rwant));
1521:   PetscCall(ISRestoreIndices(colp, &cwant));
1522:   PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));

1524:   /* Find out where my gcols should go */
1525:   PetscCall(MatGetSize(aB, NULL, &ng));
1526:   PetscCall(PetscMalloc1(ng, &gcdest));
1527:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1528:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1529:   PetscCall(PetscSFSetFromOptions(sf));
1530:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1531:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1532:   PetscCall(PetscSFDestroy(&sf));

1534:   PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1535:   PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1536:   PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1537:   for (i = 0; i < m; i++) {
1538:     PetscInt    row = rdest[i];
1539:     PetscMPIInt rowner;
1540:     PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1541:     for (j = ai[i]; j < ai[i + 1]; j++) {
1542:       PetscInt    col = cdest[aj[j]];
1543:       PetscMPIInt cowner;
1544:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1545:       if (rowner == cowner) dnnz[i]++;
1546:       else onnz[i]++;
1547:     }
1548:     for (j = bi[i]; j < bi[i + 1]; j++) {
1549:       PetscInt    col = gcdest[bj[j]];
1550:       PetscMPIInt cowner;
1551:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1552:       if (rowner == cowner) dnnz[i]++;
1553:       else onnz[i]++;
1554:     }
1555:   }
1556:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1557:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1558:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1559:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1560:   PetscCall(PetscSFDestroy(&rowsf));

1562:   PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1563:   PetscCall(MatSeqAIJGetArray(aA, &aa));
1564:   PetscCall(MatSeqAIJGetArray(aB, &ba));
1565:   for (i = 0; i < m; i++) {
1566:     PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1567:     PetscInt  j0, rowlen;
1568:     rowlen = ai[i + 1] - ai[i];
1569:     for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1570:       for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1571:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1572:     }
1573:     rowlen = bi[i + 1] - bi[i];
1574:     for (j0 = j = 0; j < rowlen; j0 = j) {
1575:       for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1576:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1577:     }
1578:   }
1579:   PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1580:   PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1581:   PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1582:   PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1583:   PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1584:   PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1585:   PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1586:   PetscCall(PetscFree3(work, rdest, cdest));
1587:   PetscCall(PetscFree(gcdest));
1588:   if (parcolp) PetscCall(ISDestroy(&colp));
1589:   *B = Aperm;
1590:   PetscFunctionReturn(PETSC_SUCCESS);
1591: }

1593: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1594: {
1595:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1597:   PetscFunctionBegin;
1598:   PetscCall(MatGetSize(aij->B, NULL, nghosts));
1599:   if (ghosts) *ghosts = aij->garray;
1600:   PetscFunctionReturn(PETSC_SUCCESS);
1601: }

1603: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1604: {
1605:   Mat_MPIAIJ    *mat = (Mat_MPIAIJ *)matin->data;
1606:   Mat            A = mat->A, B = mat->B;
1607:   PetscLogDouble isend[5], irecv[5];

1609:   PetscFunctionBegin;
1610:   info->block_size = 1.0;
1611:   PetscCall(MatGetInfo(A, MAT_LOCAL, info));

1613:   isend[0] = info->nz_used;
1614:   isend[1] = info->nz_allocated;
1615:   isend[2] = info->nz_unneeded;
1616:   isend[3] = info->memory;
1617:   isend[4] = info->mallocs;

1619:   PetscCall(MatGetInfo(B, MAT_LOCAL, info));

1621:   isend[0] += info->nz_used;
1622:   isend[1] += info->nz_allocated;
1623:   isend[2] += info->nz_unneeded;
1624:   isend[3] += info->memory;
1625:   isend[4] += info->mallocs;
1626:   if (flag == MAT_LOCAL) {
1627:     info->nz_used      = isend[0];
1628:     info->nz_allocated = isend[1];
1629:     info->nz_unneeded  = isend[2];
1630:     info->memory       = isend[3];
1631:     info->mallocs      = isend[4];
1632:   } else if (flag == MAT_GLOBAL_MAX) {
1633:     PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));

1635:     info->nz_used      = irecv[0];
1636:     info->nz_allocated = irecv[1];
1637:     info->nz_unneeded  = irecv[2];
1638:     info->memory       = irecv[3];
1639:     info->mallocs      = irecv[4];
1640:   } else if (flag == MAT_GLOBAL_SUM) {
1641:     PetscCall(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));

1643:     info->nz_used      = irecv[0];
1644:     info->nz_allocated = irecv[1];
1645:     info->nz_unneeded  = irecv[2];
1646:     info->memory       = irecv[3];
1647:     info->mallocs      = irecv[4];
1648:   }
1649:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1650:   info->fill_ratio_needed = 0;
1651:   info->factor_mallocs    = 0;
1652:   PetscFunctionReturn(PETSC_SUCCESS);
1653: }

1655: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1656: {
1657:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1659:   PetscFunctionBegin;
1660:   switch (op) {
1661:   case MAT_NEW_NONZERO_LOCATIONS:
1662:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1663:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1664:   case MAT_KEEP_NONZERO_PATTERN:
1665:   case MAT_NEW_NONZERO_LOCATION_ERR:
1666:   case MAT_USE_INODES:
1667:   case MAT_IGNORE_ZERO_ENTRIES:
1668:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1669:     MatCheckPreallocated(A, 1);
1670:     PetscCall(MatSetOption(a->A, op, flg));
1671:     PetscCall(MatSetOption(a->B, op, flg));
1672:     break;
1673:   case MAT_ROW_ORIENTED:
1674:     MatCheckPreallocated(A, 1);
1675:     a->roworiented = flg;

1677:     PetscCall(MatSetOption(a->A, op, flg));
1678:     PetscCall(MatSetOption(a->B, op, flg));
1679:     break;
1680:   case MAT_FORCE_DIAGONAL_ENTRIES:
1681:   case MAT_SORTED_FULL:
1682:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1683:     break;
1684:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1685:     a->donotstash = flg;
1686:     break;
1687:   /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1688:   case MAT_SPD:
1689:   case MAT_SYMMETRIC:
1690:   case MAT_STRUCTURALLY_SYMMETRIC:
1691:   case MAT_HERMITIAN:
1692:   case MAT_SYMMETRY_ETERNAL:
1693:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1694:   case MAT_SPD_ETERNAL:
1695:     /* if the diagonal matrix is square it inherits some of the properties above */
1696:     break;
1697:   case MAT_SUBMAT_SINGLEIS:
1698:     A->submat_singleis = flg;
1699:     break;
1700:   case MAT_STRUCTURE_ONLY:
1701:     /* The option is handled directly by MatSetOption() */
1702:     break;
1703:   default:
1704:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1705:   }
1706:   PetscFunctionReturn(PETSC_SUCCESS);
1707: }

1709: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1710: {
1711:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)matin->data;
1712:   PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1713:   PetscInt     i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1714:   PetscInt     nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1715:   PetscInt    *cmap, *idx_p;

1717:   PetscFunctionBegin;
1718:   PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1719:   mat->getrowactive = PETSC_TRUE;

1721:   if (!mat->rowvalues && (idx || v)) {
1722:     /*
1723:         allocate enough space to hold information from the longest row.
1724:     */
1725:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1726:     PetscInt    max = 1, tmp;
1727:     for (i = 0; i < matin->rmap->n; i++) {
1728:       tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1729:       if (max < tmp) max = tmp;
1730:     }
1731:     PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1732:   }

1734:   PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1735:   lrow = row - rstart;

1737:   pvA = &vworkA;
1738:   pcA = &cworkA;
1739:   pvB = &vworkB;
1740:   pcB = &cworkB;
1741:   if (!v) {
1742:     pvA = NULL;
1743:     pvB = NULL;
1744:   }
1745:   if (!idx) {
1746:     pcA = NULL;
1747:     if (!v) pcB = NULL;
1748:   }
1749:   PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1750:   PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1751:   nztot = nzA + nzB;

1753:   cmap = mat->garray;
1754:   if (v || idx) {
1755:     if (nztot) {
1756:       /* Sort by increasing column numbers, assuming A and B already sorted */
1757:       PetscInt imark = -1;
1758:       if (v) {
1759:         *v = v_p = mat->rowvalues;
1760:         for (i = 0; i < nzB; i++) {
1761:           if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1762:           else break;
1763:         }
1764:         imark = i;
1765:         for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1766:         for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1767:       }
1768:       if (idx) {
1769:         *idx = idx_p = mat->rowindices;
1770:         if (imark > -1) {
1771:           for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1772:         } else {
1773:           for (i = 0; i < nzB; i++) {
1774:             if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1775:             else break;
1776:           }
1777:           imark = i;
1778:         }
1779:         for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1780:         for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1781:       }
1782:     } else {
1783:       if (idx) *idx = NULL;
1784:       if (v) *v = NULL;
1785:     }
1786:   }
1787:   *nz = nztot;
1788:   PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1789:   PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1790:   PetscFunctionReturn(PETSC_SUCCESS);
1791: }

1793: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1794: {
1795:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1797:   PetscFunctionBegin;
1798:   PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1799:   aij->getrowactive = PETSC_FALSE;
1800:   PetscFunctionReturn(PETSC_SUCCESS);
1801: }

1803: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1804: {
1805:   Mat_MPIAIJ      *aij  = (Mat_MPIAIJ *)mat->data;
1806:   Mat_SeqAIJ      *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1807:   PetscInt         i, j, cstart = mat->cmap->rstart;
1808:   PetscReal        sum = 0.0;
1809:   const MatScalar *v, *amata, *bmata;

1811:   PetscFunctionBegin;
1812:   if (aij->size == 1) {
1813:     PetscCall(MatNorm(aij->A, type, norm));
1814:   } else {
1815:     PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1816:     PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1817:     if (type == NORM_FROBENIUS) {
1818:       v = amata;
1819:       for (i = 0; i < amat->nz; i++) {
1820:         sum += PetscRealPart(PetscConj(*v) * (*v));
1821:         v++;
1822:       }
1823:       v = bmata;
1824:       for (i = 0; i < bmat->nz; i++) {
1825:         sum += PetscRealPart(PetscConj(*v) * (*v));
1826:         v++;
1827:       }
1828:       PetscCall(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1829:       *norm = PetscSqrtReal(*norm);
1830:       PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1831:     } else if (type == NORM_1) { /* max column norm */
1832:       PetscReal *tmp, *tmp2;
1833:       PetscInt  *jj, *garray = aij->garray;
1834:       PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1835:       PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1836:       *norm = 0.0;
1837:       v     = amata;
1838:       jj    = amat->j;
1839:       for (j = 0; j < amat->nz; j++) {
1840:         tmp[cstart + *jj++] += PetscAbsScalar(*v);
1841:         v++;
1842:       }
1843:       v  = bmata;
1844:       jj = bmat->j;
1845:       for (j = 0; j < bmat->nz; j++) {
1846:         tmp[garray[*jj++]] += PetscAbsScalar(*v);
1847:         v++;
1848:       }
1849:       PetscCall(MPIU_Allreduce(tmp, tmp2, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1850:       for (j = 0; j < mat->cmap->N; j++) {
1851:         if (tmp2[j] > *norm) *norm = tmp2[j];
1852:       }
1853:       PetscCall(PetscFree(tmp));
1854:       PetscCall(PetscFree(tmp2));
1855:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1856:     } else if (type == NORM_INFINITY) { /* max row norm */
1857:       PetscReal ntemp = 0.0;
1858:       for (j = 0; j < aij->A->rmap->n; j++) {
1859:         v   = PetscSafePointerPlusOffset(amata, amat->i[j]);
1860:         sum = 0.0;
1861:         for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1862:           sum += PetscAbsScalar(*v);
1863:           v++;
1864:         }
1865:         v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1866:         for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1867:           sum += PetscAbsScalar(*v);
1868:           v++;
1869:         }
1870:         if (sum > ntemp) ntemp = sum;
1871:       }
1872:       PetscCall(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1873:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1874:     } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1875:     PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1876:     PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1877:   }
1878:   PetscFunctionReturn(PETSC_SUCCESS);
1879: }

1881: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1882: {
1883:   Mat_MPIAIJ      *a    = (Mat_MPIAIJ *)A->data, *b;
1884:   Mat_SeqAIJ      *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1885:   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;
1886:   const PetscInt  *ai, *aj, *bi, *bj, *B_diag_i;
1887:   Mat              B, A_diag, *B_diag;
1888:   const MatScalar *pbv, *bv;

1890:   PetscFunctionBegin;
1891:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1892:   ma = A->rmap->n;
1893:   na = A->cmap->n;
1894:   mb = a->B->rmap->n;
1895:   nb = a->B->cmap->n;
1896:   ai = Aloc->i;
1897:   aj = Aloc->j;
1898:   bi = Bloc->i;
1899:   bj = Bloc->j;
1900:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1901:     PetscInt            *d_nnz, *g_nnz, *o_nnz;
1902:     PetscSFNode         *oloc;
1903:     PETSC_UNUSED PetscSF sf;

1905:     PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1906:     /* compute d_nnz for preallocation */
1907:     PetscCall(PetscArrayzero(d_nnz, na));
1908:     for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1909:     /* compute local off-diagonal contributions */
1910:     PetscCall(PetscArrayzero(g_nnz, nb));
1911:     for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1912:     /* map those to global */
1913:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1914:     PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1915:     PetscCall(PetscSFSetFromOptions(sf));
1916:     PetscCall(PetscArrayzero(o_nnz, na));
1917:     PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1918:     PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1919:     PetscCall(PetscSFDestroy(&sf));

1921:     PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1922:     PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1923:     PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1924:     PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1925:     PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1926:     PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1927:   } else {
1928:     B = *matout;
1929:     PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1930:   }

1932:   b           = (Mat_MPIAIJ *)B->data;
1933:   A_diag      = a->A;
1934:   B_diag      = &b->A;
1935:   sub_B_diag  = (Mat_SeqAIJ *)(*B_diag)->data;
1936:   A_diag_ncol = A_diag->cmap->N;
1937:   B_diag_ilen = sub_B_diag->ilen;
1938:   B_diag_i    = sub_B_diag->i;

1940:   /* Set ilen for diagonal of B */
1941:   for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];

1943:   /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1944:   very quickly (=without using MatSetValues), because all writes are local. */
1945:   PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1946:   PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));

1948:   /* copy over the B part */
1949:   PetscCall(PetscMalloc1(bi[mb], &cols));
1950:   PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1951:   pbv = bv;
1952:   row = A->rmap->rstart;
1953:   for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1954:   cols_tmp = cols;
1955:   for (i = 0; i < mb; i++) {
1956:     ncol = bi[i + 1] - bi[i];
1957:     PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1958:     row++;
1959:     if (pbv) pbv += ncol;
1960:     if (cols_tmp) cols_tmp += ncol;
1961:   }
1962:   PetscCall(PetscFree(cols));
1963:   PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));

1965:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1966:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1967:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1968:     *matout = B;
1969:   } else {
1970:     PetscCall(MatHeaderMerge(A, &B));
1971:   }
1972:   PetscFunctionReturn(PETSC_SUCCESS);
1973: }

1975: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1976: {
1977:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1978:   Mat         a = aij->A, b = aij->B;
1979:   PetscInt    s1, s2, s3;

1981:   PetscFunctionBegin;
1982:   PetscCall(MatGetLocalSize(mat, &s2, &s3));
1983:   if (rr) {
1984:     PetscCall(VecGetLocalSize(rr, &s1));
1985:     PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1986:     /* Overlap communication with computation. */
1987:     PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1988:   }
1989:   if (ll) {
1990:     PetscCall(VecGetLocalSize(ll, &s1));
1991:     PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1992:     PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1993:   }
1994:   /* scale  the diagonal block */
1995:   PetscUseTypeMethod(a, diagonalscale, ll, rr);

1997:   if (rr) {
1998:     /* Do a scatter end and then right scale the off-diagonal block */
1999:     PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2000:     PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2001:   }
2002:   PetscFunctionReturn(PETSC_SUCCESS);
2003: }

2005: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2006: {
2007:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2009:   PetscFunctionBegin;
2010:   PetscCall(MatSetUnfactored(a->A));
2011:   PetscFunctionReturn(PETSC_SUCCESS);
2012: }

2014: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2015: {
2016:   Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2017:   Mat         a, b, c, d;
2018:   PetscBool   flg;

2020:   PetscFunctionBegin;
2021:   a = matA->A;
2022:   b = matA->B;
2023:   c = matB->A;
2024:   d = matB->B;

2026:   PetscCall(MatEqual(a, c, &flg));
2027:   if (flg) PetscCall(MatEqual(b, d, &flg));
2028:   PetscCall(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2029:   PetscFunctionReturn(PETSC_SUCCESS);
2030: }

2032: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2033: {
2034:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2035:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2037:   PetscFunctionBegin;
2038:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2039:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2040:     /* because of the column compression in the off-processor part of the matrix a->B,
2041:        the number of columns in a->B and b->B may be different, hence we cannot call
2042:        the MatCopy() directly on the two parts. If need be, we can provide a more
2043:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2044:        then copying the submatrices */
2045:     PetscCall(MatCopy_Basic(A, B, str));
2046:   } else {
2047:     PetscCall(MatCopy(a->A, b->A, str));
2048:     PetscCall(MatCopy(a->B, b->B, str));
2049:   }
2050:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
2051:   PetscFunctionReturn(PETSC_SUCCESS);
2052: }

2054: /*
2055:    Computes the number of nonzeros per row needed for preallocation when X and Y
2056:    have different nonzero structure.
2057: */
2058: 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)
2059: {
2060:   PetscInt i, j, k, nzx, nzy;

2062:   PetscFunctionBegin;
2063:   /* Set the number of nonzeros in the new matrix */
2064:   for (i = 0; i < m; i++) {
2065:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2066:     nzx    = xi[i + 1] - xi[i];
2067:     nzy    = yi[i + 1] - yi[i];
2068:     nnz[i] = 0;
2069:     for (j = 0, k = 0; j < nzx; j++) {                                /* Point in X */
2070:       for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2071:       if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++;             /* Skip duplicate */
2072:       nnz[i]++;
2073:     }
2074:     for (; k < nzy; k++) nnz[i]++;
2075:   }
2076:   PetscFunctionReturn(PETSC_SUCCESS);
2077: }

2079: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2080: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2081: {
2082:   PetscInt    m = Y->rmap->N;
2083:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2084:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

2086:   PetscFunctionBegin;
2087:   PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2088:   PetscFunctionReturn(PETSC_SUCCESS);
2089: }

2091: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2092: {
2093:   Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;

2095:   PetscFunctionBegin;
2096:   if (str == SAME_NONZERO_PATTERN) {
2097:     PetscCall(MatAXPY(yy->A, a, xx->A, str));
2098:     PetscCall(MatAXPY(yy->B, a, xx->B, str));
2099:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2100:     PetscCall(MatAXPY_Basic(Y, a, X, str));
2101:   } else {
2102:     Mat       B;
2103:     PetscInt *nnz_d, *nnz_o;

2105:     PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2106:     PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2107:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2108:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2109:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2110:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2111:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2112:     PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2113:     PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2114:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2115:     PetscCall(MatHeaderMerge(Y, &B));
2116:     PetscCall(PetscFree(nnz_d));
2117:     PetscCall(PetscFree(nnz_o));
2118:   }
2119:   PetscFunctionReturn(PETSC_SUCCESS);
2120: }

2122: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);

2124: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2125: {
2126:   PetscFunctionBegin;
2127:   if (PetscDefined(USE_COMPLEX)) {
2128:     Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2130:     PetscCall(MatConjugate_SeqAIJ(aij->A));
2131:     PetscCall(MatConjugate_SeqAIJ(aij->B));
2132:   }
2133:   PetscFunctionReturn(PETSC_SUCCESS);
2134: }

2136: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2137: {
2138:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2140:   PetscFunctionBegin;
2141:   PetscCall(MatRealPart(a->A));
2142:   PetscCall(MatRealPart(a->B));
2143:   PetscFunctionReturn(PETSC_SUCCESS);
2144: }

2146: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2147: {
2148:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2150:   PetscFunctionBegin;
2151:   PetscCall(MatImaginaryPart(a->A));
2152:   PetscCall(MatImaginaryPart(a->B));
2153:   PetscFunctionReturn(PETSC_SUCCESS);
2154: }

2156: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2157: {
2158:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
2159:   PetscInt           i, *idxb = NULL, m = A->rmap->n;
2160:   PetscScalar       *va, *vv;
2161:   Vec                vB, vA;
2162:   const PetscScalar *vb;

2164:   PetscFunctionBegin;
2165:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2166:   PetscCall(MatGetRowMaxAbs(a->A, vA, idx));

2168:   PetscCall(VecGetArrayWrite(vA, &va));
2169:   if (idx) {
2170:     for (i = 0; i < m; i++) {
2171:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2172:     }
2173:   }

2175:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2176:   PetscCall(PetscMalloc1(m, &idxb));
2177:   PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));

2179:   PetscCall(VecGetArrayWrite(v, &vv));
2180:   PetscCall(VecGetArrayRead(vB, &vb));
2181:   for (i = 0; i < m; i++) {
2182:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2183:       vv[i] = vb[i];
2184:       if (idx) idx[i] = a->garray[idxb[i]];
2185:     } else {
2186:       vv[i] = va[i];
2187:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2188:     }
2189:   }
2190:   PetscCall(VecRestoreArrayWrite(vA, &vv));
2191:   PetscCall(VecRestoreArrayWrite(vA, &va));
2192:   PetscCall(VecRestoreArrayRead(vB, &vb));
2193:   PetscCall(PetscFree(idxb));
2194:   PetscCall(VecDestroy(&vA));
2195:   PetscCall(VecDestroy(&vB));
2196:   PetscFunctionReturn(PETSC_SUCCESS);
2197: }

2199: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2200: {
2201:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2202:   PetscInt    m = A->rmap->n;
2203:   Vec         vB, vA;

2205:   PetscFunctionBegin;
2206:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vA));
2207:   PetscCall(MatGetRowSumAbs(a->A, vA));
2208:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &vB));
2209:   PetscCall(MatGetRowSumAbs(a->B, vB));
2210:   PetscCall(VecAXPY(vA, 1.0, vB));
2211:   PetscCall(VecDestroy(&vB));
2212:   PetscCall(VecCopy(vA, v));
2213:   PetscCall(VecDestroy(&vA));
2214:   PetscFunctionReturn(PETSC_SUCCESS);
2215: }

2217: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2218: {
2219:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2220:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2221:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2222:   PetscInt          *cmap = mat->garray;
2223:   PetscInt          *diagIdx, *offdiagIdx;
2224:   Vec                diagV, offdiagV;
2225:   PetscScalar       *a, *diagA, *offdiagA;
2226:   const PetscScalar *ba, *bav;
2227:   PetscInt           r, j, col, ncols, *bi, *bj;
2228:   Mat                B = mat->B;
2229:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2231:   PetscFunctionBegin;
2232:   /* When a process holds entire A and other processes have no entry */
2233:   if (A->cmap->N == n) {
2234:     PetscCall(VecGetArrayWrite(v, &diagA));
2235:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2236:     PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2237:     PetscCall(VecDestroy(&diagV));
2238:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2239:     PetscFunctionReturn(PETSC_SUCCESS);
2240:   } else if (n == 0) {
2241:     if (m) {
2242:       PetscCall(VecGetArrayWrite(v, &a));
2243:       for (r = 0; r < m; r++) {
2244:         a[r] = 0.0;
2245:         if (idx) idx[r] = -1;
2246:       }
2247:       PetscCall(VecRestoreArrayWrite(v, &a));
2248:     }
2249:     PetscFunctionReturn(PETSC_SUCCESS);
2250:   }

2252:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2253:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2254:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2255:   PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));

2257:   /* Get offdiagIdx[] for implicit 0.0 */
2258:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2259:   ba = bav;
2260:   bi = b->i;
2261:   bj = b->j;
2262:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2263:   for (r = 0; r < m; r++) {
2264:     ncols = bi[r + 1] - bi[r];
2265:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2266:       offdiagA[r]   = *ba;
2267:       offdiagIdx[r] = cmap[0];
2268:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2269:       offdiagA[r] = 0.0;

2271:       /* Find first hole in the cmap */
2272:       for (j = 0; j < ncols; j++) {
2273:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2274:         if (col > j && j < cstart) {
2275:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2276:           break;
2277:         } else if (col > j + n && j >= cstart) {
2278:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2279:           break;
2280:         }
2281:       }
2282:       if (j == ncols && ncols < A->cmap->N - n) {
2283:         /* a hole is outside compressed Bcols */
2284:         if (ncols == 0) {
2285:           if (cstart) {
2286:             offdiagIdx[r] = 0;
2287:           } else offdiagIdx[r] = cend;
2288:         } else { /* ncols > 0 */
2289:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2290:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2291:         }
2292:       }
2293:     }

2295:     for (j = 0; j < ncols; j++) {
2296:       if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2297:         offdiagA[r]   = *ba;
2298:         offdiagIdx[r] = cmap[*bj];
2299:       }
2300:       ba++;
2301:       bj++;
2302:     }
2303:   }

2305:   PetscCall(VecGetArrayWrite(v, &a));
2306:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2307:   for (r = 0; r < m; ++r) {
2308:     if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2309:       a[r] = diagA[r];
2310:       if (idx) idx[r] = cstart + diagIdx[r];
2311:     } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2312:       a[r] = diagA[r];
2313:       if (idx) {
2314:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2315:           idx[r] = cstart + diagIdx[r];
2316:         } else idx[r] = offdiagIdx[r];
2317:       }
2318:     } else {
2319:       a[r] = offdiagA[r];
2320:       if (idx) idx[r] = offdiagIdx[r];
2321:     }
2322:   }
2323:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2324:   PetscCall(VecRestoreArrayWrite(v, &a));
2325:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2326:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2327:   PetscCall(VecDestroy(&diagV));
2328:   PetscCall(VecDestroy(&offdiagV));
2329:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2330:   PetscFunctionReturn(PETSC_SUCCESS);
2331: }

2333: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2334: {
2335:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2336:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2337:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2338:   PetscInt          *cmap = mat->garray;
2339:   PetscInt          *diagIdx, *offdiagIdx;
2340:   Vec                diagV, offdiagV;
2341:   PetscScalar       *a, *diagA, *offdiagA;
2342:   const PetscScalar *ba, *bav;
2343:   PetscInt           r, j, col, ncols, *bi, *bj;
2344:   Mat                B = mat->B;
2345:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2347:   PetscFunctionBegin;
2348:   /* When a process holds entire A and other processes have no entry */
2349:   if (A->cmap->N == n) {
2350:     PetscCall(VecGetArrayWrite(v, &diagA));
2351:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2352:     PetscCall(MatGetRowMin(mat->A, diagV, idx));
2353:     PetscCall(VecDestroy(&diagV));
2354:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2355:     PetscFunctionReturn(PETSC_SUCCESS);
2356:   } else if (n == 0) {
2357:     if (m) {
2358:       PetscCall(VecGetArrayWrite(v, &a));
2359:       for (r = 0; r < m; r++) {
2360:         a[r] = PETSC_MAX_REAL;
2361:         if (idx) idx[r] = -1;
2362:       }
2363:       PetscCall(VecRestoreArrayWrite(v, &a));
2364:     }
2365:     PetscFunctionReturn(PETSC_SUCCESS);
2366:   }

2368:   PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2369:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2370:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2371:   PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));

2373:   /* Get offdiagIdx[] for implicit 0.0 */
2374:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2375:   ba = bav;
2376:   bi = b->i;
2377:   bj = b->j;
2378:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2379:   for (r = 0; r < m; r++) {
2380:     ncols = bi[r + 1] - bi[r];
2381:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2382:       offdiagA[r]   = *ba;
2383:       offdiagIdx[r] = cmap[0];
2384:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2385:       offdiagA[r] = 0.0;

2387:       /* Find first hole in the cmap */
2388:       for (j = 0; j < ncols; j++) {
2389:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2390:         if (col > j && j < cstart) {
2391:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2392:           break;
2393:         } else if (col > j + n && j >= cstart) {
2394:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2395:           break;
2396:         }
2397:       }
2398:       if (j == ncols && ncols < A->cmap->N - n) {
2399:         /* a hole is outside compressed Bcols */
2400:         if (ncols == 0) {
2401:           if (cstart) {
2402:             offdiagIdx[r] = 0;
2403:           } else offdiagIdx[r] = cend;
2404:         } else { /* ncols > 0 */
2405:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2406:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2407:         }
2408:       }
2409:     }

2411:     for (j = 0; j < ncols; j++) {
2412:       if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2413:         offdiagA[r]   = *ba;
2414:         offdiagIdx[r] = cmap[*bj];
2415:       }
2416:       ba++;
2417:       bj++;
2418:     }
2419:   }

2421:   PetscCall(VecGetArrayWrite(v, &a));
2422:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2423:   for (r = 0; r < m; ++r) {
2424:     if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2425:       a[r] = diagA[r];
2426:       if (idx) idx[r] = cstart + diagIdx[r];
2427:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2428:       a[r] = diagA[r];
2429:       if (idx) {
2430:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2431:           idx[r] = cstart + diagIdx[r];
2432:         } else idx[r] = offdiagIdx[r];
2433:       }
2434:     } else {
2435:       a[r] = offdiagA[r];
2436:       if (idx) idx[r] = offdiagIdx[r];
2437:     }
2438:   }
2439:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2440:   PetscCall(VecRestoreArrayWrite(v, &a));
2441:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2442:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2443:   PetscCall(VecDestroy(&diagV));
2444:   PetscCall(VecDestroy(&offdiagV));
2445:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2446:   PetscFunctionReturn(PETSC_SUCCESS);
2447: }

2449: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2450: {
2451:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2452:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2453:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2454:   PetscInt          *cmap = mat->garray;
2455:   PetscInt          *diagIdx, *offdiagIdx;
2456:   Vec                diagV, offdiagV;
2457:   PetscScalar       *a, *diagA, *offdiagA;
2458:   const PetscScalar *ba, *bav;
2459:   PetscInt           r, j, col, ncols, *bi, *bj;
2460:   Mat                B = mat->B;
2461:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2463:   PetscFunctionBegin;
2464:   /* When a process holds entire A and other processes have no entry */
2465:   if (A->cmap->N == n) {
2466:     PetscCall(VecGetArrayWrite(v, &diagA));
2467:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2468:     PetscCall(MatGetRowMax(mat->A, diagV, idx));
2469:     PetscCall(VecDestroy(&diagV));
2470:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2471:     PetscFunctionReturn(PETSC_SUCCESS);
2472:   } else if (n == 0) {
2473:     if (m) {
2474:       PetscCall(VecGetArrayWrite(v, &a));
2475:       for (r = 0; r < m; r++) {
2476:         a[r] = PETSC_MIN_REAL;
2477:         if (idx) idx[r] = -1;
2478:       }
2479:       PetscCall(VecRestoreArrayWrite(v, &a));
2480:     }
2481:     PetscFunctionReturn(PETSC_SUCCESS);
2482:   }

2484:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2485:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2486:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2487:   PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));

2489:   /* Get offdiagIdx[] for implicit 0.0 */
2490:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2491:   ba = bav;
2492:   bi = b->i;
2493:   bj = b->j;
2494:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2495:   for (r = 0; r < m; r++) {
2496:     ncols = bi[r + 1] - bi[r];
2497:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2498:       offdiagA[r]   = *ba;
2499:       offdiagIdx[r] = cmap[0];
2500:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2501:       offdiagA[r] = 0.0;

2503:       /* Find first hole in the cmap */
2504:       for (j = 0; j < ncols; j++) {
2505:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2506:         if (col > j && j < cstart) {
2507:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2508:           break;
2509:         } else if (col > j + n && j >= cstart) {
2510:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2511:           break;
2512:         }
2513:       }
2514:       if (j == ncols && ncols < A->cmap->N - n) {
2515:         /* a hole is outside compressed Bcols */
2516:         if (ncols == 0) {
2517:           if (cstart) {
2518:             offdiagIdx[r] = 0;
2519:           } else offdiagIdx[r] = cend;
2520:         } else { /* ncols > 0 */
2521:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2522:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2523:         }
2524:       }
2525:     }

2527:     for (j = 0; j < ncols; j++) {
2528:       if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2529:         offdiagA[r]   = *ba;
2530:         offdiagIdx[r] = cmap[*bj];
2531:       }
2532:       ba++;
2533:       bj++;
2534:     }
2535:   }

2537:   PetscCall(VecGetArrayWrite(v, &a));
2538:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2539:   for (r = 0; r < m; ++r) {
2540:     if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2541:       a[r] = diagA[r];
2542:       if (idx) idx[r] = cstart + diagIdx[r];
2543:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2544:       a[r] = diagA[r];
2545:       if (idx) {
2546:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2547:           idx[r] = cstart + diagIdx[r];
2548:         } else idx[r] = offdiagIdx[r];
2549:       }
2550:     } else {
2551:       a[r] = offdiagA[r];
2552:       if (idx) idx[r] = offdiagIdx[r];
2553:     }
2554:   }
2555:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2556:   PetscCall(VecRestoreArrayWrite(v, &a));
2557:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2558:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2559:   PetscCall(VecDestroy(&diagV));
2560:   PetscCall(VecDestroy(&offdiagV));
2561:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2562:   PetscFunctionReturn(PETSC_SUCCESS);
2563: }

2565: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2566: {
2567:   Mat *dummy;

2569:   PetscFunctionBegin;
2570:   PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2571:   *newmat = *dummy;
2572:   PetscCall(PetscFree(dummy));
2573:   PetscFunctionReturn(PETSC_SUCCESS);
2574: }

2576: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2577: {
2578:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2580:   PetscFunctionBegin;
2581:   PetscCall(MatInvertBlockDiagonal(a->A, values));
2582:   A->factorerrortype = a->A->factorerrortype;
2583:   PetscFunctionReturn(PETSC_SUCCESS);
2584: }

2586: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2587: {
2588:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;

2590:   PetscFunctionBegin;
2591:   PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2592:   PetscCall(MatSetRandom(aij->A, rctx));
2593:   if (x->assembled) {
2594:     PetscCall(MatSetRandom(aij->B, rctx));
2595:   } else {
2596:     PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2597:   }
2598:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2599:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2600:   PetscFunctionReturn(PETSC_SUCCESS);
2601: }

2603: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2604: {
2605:   PetscFunctionBegin;
2606:   if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2607:   else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2608:   PetscFunctionReturn(PETSC_SUCCESS);
2609: }

2611: /*@
2612:   MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank

2614:   Not Collective

2616:   Input Parameter:
2617: . A - the matrix

2619:   Output Parameter:
2620: . nz - the number of nonzeros

2622:   Level: advanced

2624: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2625: @*/
2626: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2627: {
2628:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2629:   Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2630:   PetscBool   isaij;

2632:   PetscFunctionBegin;
2633:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2634:   PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2635:   *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2636:   PetscFunctionReturn(PETSC_SUCCESS);
2637: }

2639: /*@
2640:   MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap

2642:   Collective

2644:   Input Parameters:
2645: + A  - the matrix
2646: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)

2648:   Level: advanced

2650: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2651: @*/
2652: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2653: {
2654:   PetscFunctionBegin;
2655:   PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2656:   PetscFunctionReturn(PETSC_SUCCESS);
2657: }

2659: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2660: {
2661:   PetscBool sc = PETSC_FALSE, flg;

2663:   PetscFunctionBegin;
2664:   PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2665:   if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2666:   PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2667:   if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2668:   PetscOptionsHeadEnd();
2669:   PetscFunctionReturn(PETSC_SUCCESS);
2670: }

2672: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2673: {
2674:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2675:   Mat_SeqAIJ *aij  = (Mat_SeqAIJ *)maij->A->data;

2677:   PetscFunctionBegin;
2678:   if (!Y->preallocated) {
2679:     PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2680:   } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2681:     PetscInt nonew = aij->nonew;
2682:     PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2683:     aij->nonew = nonew;
2684:   }
2685:   PetscCall(MatShift_Basic(Y, a));
2686:   PetscFunctionReturn(PETSC_SUCCESS);
2687: }

2689: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2690: {
2691:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2693:   PetscFunctionBegin;
2694:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2695:   PetscCall(MatMissingDiagonal(a->A, missing, d));
2696:   if (d) {
2697:     PetscInt rstart;
2698:     PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2699:     *d += rstart;
2700:   }
2701:   PetscFunctionReturn(PETSC_SUCCESS);
2702: }

2704: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2705: {
2706:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2708:   PetscFunctionBegin;
2709:   PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2710:   PetscFunctionReturn(PETSC_SUCCESS);
2711: }

2713: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2714: {
2715:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2717:   PetscFunctionBegin;
2718:   PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep));        // possibly keep zero diagonal coefficients
2719:   PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2720:   PetscFunctionReturn(PETSC_SUCCESS);
2721: }

2723: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2724:                                        MatGetRow_MPIAIJ,
2725:                                        MatRestoreRow_MPIAIJ,
2726:                                        MatMult_MPIAIJ,
2727:                                        /* 4*/ MatMultAdd_MPIAIJ,
2728:                                        MatMultTranspose_MPIAIJ,
2729:                                        MatMultTransposeAdd_MPIAIJ,
2730:                                        NULL,
2731:                                        NULL,
2732:                                        NULL,
2733:                                        /*10*/ NULL,
2734:                                        NULL,
2735:                                        NULL,
2736:                                        MatSOR_MPIAIJ,
2737:                                        MatTranspose_MPIAIJ,
2738:                                        /*15*/ MatGetInfo_MPIAIJ,
2739:                                        MatEqual_MPIAIJ,
2740:                                        MatGetDiagonal_MPIAIJ,
2741:                                        MatDiagonalScale_MPIAIJ,
2742:                                        MatNorm_MPIAIJ,
2743:                                        /*20*/ MatAssemblyBegin_MPIAIJ,
2744:                                        MatAssemblyEnd_MPIAIJ,
2745:                                        MatSetOption_MPIAIJ,
2746:                                        MatZeroEntries_MPIAIJ,
2747:                                        /*24*/ MatZeroRows_MPIAIJ,
2748:                                        NULL,
2749:                                        NULL,
2750:                                        NULL,
2751:                                        NULL,
2752:                                        /*29*/ MatSetUp_MPI_Hash,
2753:                                        NULL,
2754:                                        NULL,
2755:                                        MatGetDiagonalBlock_MPIAIJ,
2756:                                        NULL,
2757:                                        /*34*/ MatDuplicate_MPIAIJ,
2758:                                        NULL,
2759:                                        NULL,
2760:                                        NULL,
2761:                                        NULL,
2762:                                        /*39*/ MatAXPY_MPIAIJ,
2763:                                        MatCreateSubMatrices_MPIAIJ,
2764:                                        MatIncreaseOverlap_MPIAIJ,
2765:                                        MatGetValues_MPIAIJ,
2766:                                        MatCopy_MPIAIJ,
2767:                                        /*44*/ MatGetRowMax_MPIAIJ,
2768:                                        MatScale_MPIAIJ,
2769:                                        MatShift_MPIAIJ,
2770:                                        MatDiagonalSet_MPIAIJ,
2771:                                        MatZeroRowsColumns_MPIAIJ,
2772:                                        /*49*/ MatSetRandom_MPIAIJ,
2773:                                        MatGetRowIJ_MPIAIJ,
2774:                                        MatRestoreRowIJ_MPIAIJ,
2775:                                        NULL,
2776:                                        NULL,
2777:                                        /*54*/ MatFDColoringCreate_MPIXAIJ,
2778:                                        NULL,
2779:                                        MatSetUnfactored_MPIAIJ,
2780:                                        MatPermute_MPIAIJ,
2781:                                        NULL,
2782:                                        /*59*/ MatCreateSubMatrix_MPIAIJ,
2783:                                        MatDestroy_MPIAIJ,
2784:                                        MatView_MPIAIJ,
2785:                                        NULL,
2786:                                        NULL,
2787:                                        /*64*/ NULL,
2788:                                        MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2789:                                        NULL,
2790:                                        NULL,
2791:                                        NULL,
2792:                                        /*69*/ MatGetRowMaxAbs_MPIAIJ,
2793:                                        MatGetRowMinAbs_MPIAIJ,
2794:                                        NULL,
2795:                                        NULL,
2796:                                        NULL,
2797:                                        NULL,
2798:                                        /*75*/ MatFDColoringApply_AIJ,
2799:                                        MatSetFromOptions_MPIAIJ,
2800:                                        NULL,
2801:                                        NULL,
2802:                                        MatFindZeroDiagonals_MPIAIJ,
2803:                                        /*80*/ NULL,
2804:                                        NULL,
2805:                                        NULL,
2806:                                        /*83*/ MatLoad_MPIAIJ,
2807:                                        MatIsSymmetric_MPIAIJ,
2808:                                        NULL,
2809:                                        NULL,
2810:                                        NULL,
2811:                                        NULL,
2812:                                        /*89*/ NULL,
2813:                                        NULL,
2814:                                        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2815:                                        NULL,
2816:                                        NULL,
2817:                                        /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2818:                                        NULL,
2819:                                        NULL,
2820:                                        NULL,
2821:                                        MatBindToCPU_MPIAIJ,
2822:                                        /*99*/ MatProductSetFromOptions_MPIAIJ,
2823:                                        NULL,
2824:                                        NULL,
2825:                                        MatConjugate_MPIAIJ,
2826:                                        NULL,
2827:                                        /*104*/ MatSetValuesRow_MPIAIJ,
2828:                                        MatRealPart_MPIAIJ,
2829:                                        MatImaginaryPart_MPIAIJ,
2830:                                        NULL,
2831:                                        NULL,
2832:                                        /*109*/ NULL,
2833:                                        NULL,
2834:                                        MatGetRowMin_MPIAIJ,
2835:                                        NULL,
2836:                                        MatMissingDiagonal_MPIAIJ,
2837:                                        /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2838:                                        NULL,
2839:                                        MatGetGhosts_MPIAIJ,
2840:                                        NULL,
2841:                                        NULL,
2842:                                        /*119*/ MatMultDiagonalBlock_MPIAIJ,
2843:                                        NULL,
2844:                                        NULL,
2845:                                        NULL,
2846:                                        MatGetMultiProcBlock_MPIAIJ,
2847:                                        /*124*/ MatFindNonzeroRows_MPIAIJ,
2848:                                        MatGetColumnReductions_MPIAIJ,
2849:                                        MatInvertBlockDiagonal_MPIAIJ,
2850:                                        MatInvertVariableBlockDiagonal_MPIAIJ,
2851:                                        MatCreateSubMatricesMPI_MPIAIJ,
2852:                                        /*129*/ NULL,
2853:                                        NULL,
2854:                                        NULL,
2855:                                        MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2856:                                        NULL,
2857:                                        /*134*/ NULL,
2858:                                        NULL,
2859:                                        NULL,
2860:                                        NULL,
2861:                                        NULL,
2862:                                        /*139*/ MatSetBlockSizes_MPIAIJ,
2863:                                        NULL,
2864:                                        NULL,
2865:                                        MatFDColoringSetUp_MPIXAIJ,
2866:                                        MatFindOffBlockDiagonalEntries_MPIAIJ,
2867:                                        MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2868:                                        /*145*/ NULL,
2869:                                        NULL,
2870:                                        NULL,
2871:                                        MatCreateGraph_Simple_AIJ,
2872:                                        NULL,
2873:                                        /*150*/ NULL,
2874:                                        MatEliminateZeros_MPIAIJ,
2875:                                        MatGetRowSumAbs_MPIAIJ};

2877: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2878: {
2879:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2881:   PetscFunctionBegin;
2882:   PetscCall(MatStoreValues(aij->A));
2883:   PetscCall(MatStoreValues(aij->B));
2884:   PetscFunctionReturn(PETSC_SUCCESS);
2885: }

2887: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2888: {
2889:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2891:   PetscFunctionBegin;
2892:   PetscCall(MatRetrieveValues(aij->A));
2893:   PetscCall(MatRetrieveValues(aij->B));
2894:   PetscFunctionReturn(PETSC_SUCCESS);
2895: }

2897: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2898: {
2899:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2900:   PetscMPIInt size;

2902:   PetscFunctionBegin;
2903:   if (B->hash_active) {
2904:     B->ops[0]      = b->cops;
2905:     B->hash_active = PETSC_FALSE;
2906:   }
2907:   PetscCall(PetscLayoutSetUp(B->rmap));
2908:   PetscCall(PetscLayoutSetUp(B->cmap));

2910: #if defined(PETSC_USE_CTABLE)
2911:   PetscCall(PetscHMapIDestroy(&b->colmap));
2912: #else
2913:   PetscCall(PetscFree(b->colmap));
2914: #endif
2915:   PetscCall(PetscFree(b->garray));
2916:   PetscCall(VecDestroy(&b->lvec));
2917:   PetscCall(VecScatterDestroy(&b->Mvctx));

2919:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

2921:   MatSeqXAIJGetOptions_Private(b->B);
2922:   PetscCall(MatDestroy(&b->B));
2923:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2924:   PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2925:   PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2926:   PetscCall(MatSetType(b->B, MATSEQAIJ));
2927:   MatSeqXAIJRestoreOptions_Private(b->B);

2929:   MatSeqXAIJGetOptions_Private(b->A);
2930:   PetscCall(MatDestroy(&b->A));
2931:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2932:   PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2933:   PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2934:   PetscCall(MatSetType(b->A, MATSEQAIJ));
2935:   MatSeqXAIJRestoreOptions_Private(b->A);

2937:   PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2938:   PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2939:   B->preallocated  = PETSC_TRUE;
2940:   B->was_assembled = PETSC_FALSE;
2941:   B->assembled     = PETSC_FALSE;
2942:   PetscFunctionReturn(PETSC_SUCCESS);
2943: }

2945: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2946: {
2947:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2949:   PetscFunctionBegin;
2951:   PetscCall(PetscLayoutSetUp(B->rmap));
2952:   PetscCall(PetscLayoutSetUp(B->cmap));

2954: #if defined(PETSC_USE_CTABLE)
2955:   PetscCall(PetscHMapIDestroy(&b->colmap));
2956: #else
2957:   PetscCall(PetscFree(b->colmap));
2958: #endif
2959:   PetscCall(PetscFree(b->garray));
2960:   PetscCall(VecDestroy(&b->lvec));
2961:   PetscCall(VecScatterDestroy(&b->Mvctx));

2963:   PetscCall(MatResetPreallocation(b->A));
2964:   PetscCall(MatResetPreallocation(b->B));
2965:   B->preallocated  = PETSC_TRUE;
2966:   B->was_assembled = PETSC_FALSE;
2967:   B->assembled     = PETSC_FALSE;
2968:   PetscFunctionReturn(PETSC_SUCCESS);
2969: }

2971: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2972: {
2973:   Mat         mat;
2974:   Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;

2976:   PetscFunctionBegin;
2977:   *newmat = NULL;
2978:   PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2979:   PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2980:   PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2981:   PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2982:   a = (Mat_MPIAIJ *)mat->data;

2984:   mat->factortype = matin->factortype;
2985:   mat->assembled  = matin->assembled;
2986:   mat->insertmode = NOT_SET_VALUES;

2988:   a->size         = oldmat->size;
2989:   a->rank         = oldmat->rank;
2990:   a->donotstash   = oldmat->donotstash;
2991:   a->roworiented  = oldmat->roworiented;
2992:   a->rowindices   = NULL;
2993:   a->rowvalues    = NULL;
2994:   a->getrowactive = PETSC_FALSE;

2996:   PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2997:   PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2998:   if (matin->hash_active) {
2999:     PetscCall(MatSetUp(mat));
3000:   } else {
3001:     mat->preallocated = matin->preallocated;
3002:     if (oldmat->colmap) {
3003: #if defined(PETSC_USE_CTABLE)
3004:       PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3005: #else
3006:       PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3007:       PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3008: #endif
3009:     } else a->colmap = NULL;
3010:     if (oldmat->garray) {
3011:       PetscInt len;
3012:       len = oldmat->B->cmap->n;
3013:       PetscCall(PetscMalloc1(len + 1, &a->garray));
3014:       if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3015:     } else a->garray = NULL;

3017:     /* It may happen MatDuplicate is called with a non-assembled matrix
3018:       In fact, MatDuplicate only requires the matrix to be preallocated
3019:       This may happen inside a DMCreateMatrix_Shell */
3020:     if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3021:     if (oldmat->Mvctx) PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
3022:     PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3023:     PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3024:   }
3025:   PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3026:   *newmat = mat;
3027:   PetscFunctionReturn(PETSC_SUCCESS);
3028: }

3030: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3031: {
3032:   PetscBool isbinary, ishdf5;

3034:   PetscFunctionBegin;
3037:   /* force binary viewer to load .info file if it has not yet done so */
3038:   PetscCall(PetscViewerSetUp(viewer));
3039:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3040:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3041:   if (isbinary) {
3042:     PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3043:   } else if (ishdf5) {
3044: #if defined(PETSC_HAVE_HDF5)
3045:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3046: #else
3047:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3048: #endif
3049:   } else {
3050:     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);
3051:   }
3052:   PetscFunctionReturn(PETSC_SUCCESS);
3053: }

3055: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3056: {
3057:   PetscInt     header[4], M, N, m, nz, rows, cols, sum, i;
3058:   PetscInt    *rowidxs, *colidxs;
3059:   PetscScalar *matvals;

3061:   PetscFunctionBegin;
3062:   PetscCall(PetscViewerSetUp(viewer));

3064:   /* read in matrix header */
3065:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3066:   PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3067:   M  = header[1];
3068:   N  = header[2];
3069:   nz = header[3];
3070:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3071:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3072:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");

3074:   /* set block sizes from the viewer's .info file */
3075:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3076:   /* set global sizes if not set already */
3077:   if (mat->rmap->N < 0) mat->rmap->N = M;
3078:   if (mat->cmap->N < 0) mat->cmap->N = N;
3079:   PetscCall(PetscLayoutSetUp(mat->rmap));
3080:   PetscCall(PetscLayoutSetUp(mat->cmap));

3082:   /* check if the matrix sizes are correct */
3083:   PetscCall(MatGetSize(mat, &rows, &cols));
3084:   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);

3086:   /* read in row lengths and build row indices */
3087:   PetscCall(MatGetLocalSize(mat, &m, NULL));
3088:   PetscCall(PetscMalloc1(m + 1, &rowidxs));
3089:   PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3090:   rowidxs[0] = 0;
3091:   for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3092:   if (nz != PETSC_MAX_INT) {
3093:     PetscCall(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3094:     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);
3095:   }

3097:   /* read in column indices and matrix values */
3098:   PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3099:   PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3100:   PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3101:   /* store matrix indices and values */
3102:   PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3103:   PetscCall(PetscFree(rowidxs));
3104:   PetscCall(PetscFree2(colidxs, matvals));
3105:   PetscFunctionReturn(PETSC_SUCCESS);
3106: }

3108: /* Not scalable because of ISAllGather() unless getting all columns. */
3109: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3110: {
3111:   IS          iscol_local;
3112:   PetscBool   isstride;
3113:   PetscMPIInt lisstride = 0, gisstride;

3115:   PetscFunctionBegin;
3116:   /* check if we are grabbing all columns*/
3117:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));

3119:   if (isstride) {
3120:     PetscInt start, len, mstart, mlen;
3121:     PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3122:     PetscCall(ISGetLocalSize(iscol, &len));
3123:     PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3124:     if (mstart == start && mlen - mstart == len) lisstride = 1;
3125:   }

3127:   PetscCall(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3128:   if (gisstride) {
3129:     PetscInt N;
3130:     PetscCall(MatGetSize(mat, NULL, &N));
3131:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3132:     PetscCall(ISSetIdentity(iscol_local));
3133:     PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3134:   } else {
3135:     PetscInt cbs;
3136:     PetscCall(ISGetBlockSize(iscol, &cbs));
3137:     PetscCall(ISAllGather(iscol, &iscol_local));
3138:     PetscCall(ISSetBlockSize(iscol_local, cbs));
3139:   }

3141:   *isseq = iscol_local;
3142:   PetscFunctionReturn(PETSC_SUCCESS);
3143: }

3145: /*
3146:  Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3147:  (see MatCreateSubMatrix_MPIAIJ_nonscalable)

3149:  Input Parameters:
3150: +   mat - matrix
3151: .   isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3152:            i.e., mat->rstart <= isrow[i] < mat->rend
3153: -   iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3154:            i.e., mat->cstart <= iscol[i] < mat->cend

3156:  Output Parameters:
3157: +   isrow_d - sequential row index set for retrieving mat->A
3158: .   iscol_d - sequential  column index set for retrieving mat->A
3159: .   iscol_o - sequential column index set for retrieving mat->B
3160: -   garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3161:  */
3162: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3163: {
3164:   Vec             x, cmap;
3165:   const PetscInt *is_idx;
3166:   PetscScalar    *xarray, *cmaparray;
3167:   PetscInt        ncols, isstart, *idx, m, rstart, *cmap1, count;
3168:   Mat_MPIAIJ     *a    = (Mat_MPIAIJ *)mat->data;
3169:   Mat             B    = a->B;
3170:   Vec             lvec = a->lvec, lcmap;
3171:   PetscInt        i, cstart, cend, Bn = B->cmap->N;
3172:   MPI_Comm        comm;
3173:   VecScatter      Mvctx = a->Mvctx;

3175:   PetscFunctionBegin;
3176:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3177:   PetscCall(ISGetLocalSize(iscol, &ncols));

3179:   /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3180:   PetscCall(MatCreateVecs(mat, &x, NULL));
3181:   PetscCall(VecSet(x, -1.0));
3182:   PetscCall(VecDuplicate(x, &cmap));
3183:   PetscCall(VecSet(cmap, -1.0));

3185:   /* Get start indices */
3186:   PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3187:   isstart -= ncols;
3188:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));

3190:   PetscCall(ISGetIndices(iscol, &is_idx));
3191:   PetscCall(VecGetArray(x, &xarray));
3192:   PetscCall(VecGetArray(cmap, &cmaparray));
3193:   PetscCall(PetscMalloc1(ncols, &idx));
3194:   for (i = 0; i < ncols; i++) {
3195:     xarray[is_idx[i] - cstart]    = (PetscScalar)is_idx[i];
3196:     cmaparray[is_idx[i] - cstart] = i + isstart;        /* global index of iscol[i] */
3197:     idx[i]                        = is_idx[i] - cstart; /* local index of iscol[i]  */
3198:   }
3199:   PetscCall(VecRestoreArray(x, &xarray));
3200:   PetscCall(VecRestoreArray(cmap, &cmaparray));
3201:   PetscCall(ISRestoreIndices(iscol, &is_idx));

3203:   /* Get iscol_d */
3204:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3205:   PetscCall(ISGetBlockSize(iscol, &i));
3206:   PetscCall(ISSetBlockSize(*iscol_d, i));

3208:   /* Get isrow_d */
3209:   PetscCall(ISGetLocalSize(isrow, &m));
3210:   rstart = mat->rmap->rstart;
3211:   PetscCall(PetscMalloc1(m, &idx));
3212:   PetscCall(ISGetIndices(isrow, &is_idx));
3213:   for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3214:   PetscCall(ISRestoreIndices(isrow, &is_idx));

3216:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3217:   PetscCall(ISGetBlockSize(isrow, &i));
3218:   PetscCall(ISSetBlockSize(*isrow_d, i));

3220:   /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3221:   PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3222:   PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));

3224:   PetscCall(VecDuplicate(lvec, &lcmap));

3226:   PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3227:   PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));

3229:   /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3230:   /* off-process column indices */
3231:   count = 0;
3232:   PetscCall(PetscMalloc1(Bn, &idx));
3233:   PetscCall(PetscMalloc1(Bn, &cmap1));

3235:   PetscCall(VecGetArray(lvec, &xarray));
3236:   PetscCall(VecGetArray(lcmap, &cmaparray));
3237:   for (i = 0; i < Bn; i++) {
3238:     if (PetscRealPart(xarray[i]) > -1.0) {
3239:       idx[count]   = i;                                     /* local column index in off-diagonal part B */
3240:       cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3241:       count++;
3242:     }
3243:   }
3244:   PetscCall(VecRestoreArray(lvec, &xarray));
3245:   PetscCall(VecRestoreArray(lcmap, &cmaparray));

3247:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3248:   /* cannot ensure iscol_o has same blocksize as iscol! */

3250:   PetscCall(PetscFree(idx));
3251:   *garray = cmap1;

3253:   PetscCall(VecDestroy(&x));
3254:   PetscCall(VecDestroy(&cmap));
3255:   PetscCall(VecDestroy(&lcmap));
3256:   PetscFunctionReturn(PETSC_SUCCESS);
3257: }

3259: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3260: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3261: {
3262:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3263:   Mat         M = NULL;
3264:   MPI_Comm    comm;
3265:   IS          iscol_d, isrow_d, iscol_o;
3266:   Mat         Asub = NULL, Bsub = NULL;
3267:   PetscInt    n;

3269:   PetscFunctionBegin;
3270:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));

3272:   if (call == MAT_REUSE_MATRIX) {
3273:     /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3274:     PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3275:     PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");

3277:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3278:     PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");

3280:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3281:     PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");

3283:     /* Update diagonal and off-diagonal portions of submat */
3284:     asub = (Mat_MPIAIJ *)(*submat)->data;
3285:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3286:     PetscCall(ISGetLocalSize(iscol_o, &n));
3287:     if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3288:     PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3289:     PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));

3291:   } else { /* call == MAT_INITIAL_MATRIX) */
3292:     const PetscInt *garray;
3293:     PetscInt        BsubN;

3295:     /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3296:     PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));

3298:     /* Create local submatrices Asub and Bsub */
3299:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3300:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));

3302:     /* Create submatrix M */
3303:     PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));

3305:     /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3306:     asub = (Mat_MPIAIJ *)M->data;

3308:     PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3309:     n = asub->B->cmap->N;
3310:     if (BsubN > n) {
3311:       /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3312:       const PetscInt *idx;
3313:       PetscInt        i, j, *idx_new, *subgarray = asub->garray;
3314:       PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));

3316:       PetscCall(PetscMalloc1(n, &idx_new));
3317:       j = 0;
3318:       PetscCall(ISGetIndices(iscol_o, &idx));
3319:       for (i = 0; i < n; i++) {
3320:         if (j >= BsubN) break;
3321:         while (subgarray[i] > garray[j]) j++;

3323:         if (subgarray[i] == garray[j]) {
3324:           idx_new[i] = idx[j++];
3325:         } 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]);
3326:       }
3327:       PetscCall(ISRestoreIndices(iscol_o, &idx));

3329:       PetscCall(ISDestroy(&iscol_o));
3330:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));

3332:     } else if (BsubN < n) {
3333:       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);
3334:     }

3336:     PetscCall(PetscFree(garray));
3337:     *submat = M;

3339:     /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3340:     PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3341:     PetscCall(ISDestroy(&isrow_d));

3343:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3344:     PetscCall(ISDestroy(&iscol_d));

3346:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3347:     PetscCall(ISDestroy(&iscol_o));
3348:   }
3349:   PetscFunctionReturn(PETSC_SUCCESS);
3350: }

3352: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3353: {
3354:   IS        iscol_local = NULL, isrow_d;
3355:   PetscInt  csize;
3356:   PetscInt  n, i, j, start, end;
3357:   PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3358:   MPI_Comm  comm;

3360:   PetscFunctionBegin;
3361:   /* If isrow has same processor distribution as mat,
3362:      call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3363:   if (call == MAT_REUSE_MATRIX) {
3364:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3365:     if (isrow_d) {
3366:       sameRowDist  = PETSC_TRUE;
3367:       tsameDist[1] = PETSC_TRUE; /* sameColDist */
3368:     } else {
3369:       PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3370:       if (iscol_local) {
3371:         sameRowDist  = PETSC_TRUE;
3372:         tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3373:       }
3374:     }
3375:   } else {
3376:     /* Check if isrow has same processor distribution as mat */
3377:     sameDist[0] = PETSC_FALSE;
3378:     PetscCall(ISGetLocalSize(isrow, &n));
3379:     if (!n) {
3380:       sameDist[0] = PETSC_TRUE;
3381:     } else {
3382:       PetscCall(ISGetMinMax(isrow, &i, &j));
3383:       PetscCall(MatGetOwnershipRange(mat, &start, &end));
3384:       if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3385:     }

3387:     /* Check if iscol has same processor distribution as mat */
3388:     sameDist[1] = PETSC_FALSE;
3389:     PetscCall(ISGetLocalSize(iscol, &n));
3390:     if (!n) {
3391:       sameDist[1] = PETSC_TRUE;
3392:     } else {
3393:       PetscCall(ISGetMinMax(iscol, &i, &j));
3394:       PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3395:       if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3396:     }

3398:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3399:     PetscCall(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3400:     sameRowDist = tsameDist[0];
3401:   }

3403:   if (sameRowDist) {
3404:     if (tsameDist[1]) { /* sameRowDist & sameColDist */
3405:       /* isrow and iscol have same processor distribution as mat */
3406:       PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3407:       PetscFunctionReturn(PETSC_SUCCESS);
3408:     } else { /* sameRowDist */
3409:       /* isrow has same processor distribution as mat */
3410:       if (call == MAT_INITIAL_MATRIX) {
3411:         PetscBool sorted;
3412:         PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3413:         PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3414:         PetscCall(ISGetSize(iscol, &i));
3415:         PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);

3417:         PetscCall(ISSorted(iscol_local, &sorted));
3418:         if (sorted) {
3419:           /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3420:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3421:           PetscFunctionReturn(PETSC_SUCCESS);
3422:         }
3423:       } else { /* call == MAT_REUSE_MATRIX */
3424:         IS iscol_sub;
3425:         PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3426:         if (iscol_sub) {
3427:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3428:           PetscFunctionReturn(PETSC_SUCCESS);
3429:         }
3430:       }
3431:     }
3432:   }

3434:   /* General case: iscol -> iscol_local which has global size of iscol */
3435:   if (call == MAT_REUSE_MATRIX) {
3436:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3437:     PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3438:   } else {
3439:     if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3440:   }

3442:   PetscCall(ISGetLocalSize(iscol, &csize));
3443:   PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));

3445:   if (call == MAT_INITIAL_MATRIX) {
3446:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3447:     PetscCall(ISDestroy(&iscol_local));
3448:   }
3449:   PetscFunctionReturn(PETSC_SUCCESS);
3450: }

3452: /*@C
3453:   MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3454:   and "off-diagonal" part of the matrix in CSR format.

3456:   Collective

3458:   Input Parameters:
3459: + comm   - MPI communicator
3460: . A      - "diagonal" portion of matrix
3461: . B      - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3462: - garray - global index of `B` columns

3464:   Output Parameter:
3465: . mat - the matrix, with input `A` as its local diagonal matrix

3467:   Level: advanced

3469:   Notes:
3470:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.

3472:   `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.

3474: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3475: @*/
3476: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3477: {
3478:   Mat_MPIAIJ        *maij;
3479:   Mat_SeqAIJ        *b  = (Mat_SeqAIJ *)B->data, *bnew;
3480:   PetscInt          *oi = b->i, *oj = b->j, i, nz, col;
3481:   const PetscScalar *oa;
3482:   Mat                Bnew;
3483:   PetscInt           m, n, N;
3484:   MatType            mpi_mat_type;

3486:   PetscFunctionBegin;
3487:   PetscCall(MatCreate(comm, mat));
3488:   PetscCall(MatGetSize(A, &m, &n));
3489:   PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3490:   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);
3491:   /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3492:   /* 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); */

3494:   /* Get global columns of mat */
3495:   PetscCall(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));

3497:   PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3498:   /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3499:   PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3500:   PetscCall(MatSetType(*mat, mpi_mat_type));

3502:   if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3503:   maij = (Mat_MPIAIJ *)(*mat)->data;

3505:   (*mat)->preallocated = PETSC_TRUE;

3507:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
3508:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

3510:   /* Set A as diagonal portion of *mat */
3511:   maij->A = A;

3513:   nz = oi[m];
3514:   for (i = 0; i < nz; i++) {
3515:     col   = oj[i];
3516:     oj[i] = garray[col];
3517:   }

3519:   /* Set Bnew as off-diagonal portion of *mat */
3520:   PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3521:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3522:   PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3523:   bnew        = (Mat_SeqAIJ *)Bnew->data;
3524:   bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3525:   maij->B     = Bnew;

3527:   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);

3529:   b->singlemalloc = PETSC_FALSE; /* B arrays are shared by Bnew */
3530:   b->free_a       = PETSC_FALSE;
3531:   b->free_ij      = PETSC_FALSE;
3532:   PetscCall(MatDestroy(&B));

3534:   bnew->singlemalloc = PETSC_TRUE; /* arrays will be freed by MatDestroy(&Bnew) */
3535:   bnew->free_a       = PETSC_TRUE;
3536:   bnew->free_ij      = PETSC_TRUE;

3538:   /* condense columns of maij->B */
3539:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3540:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3541:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3542:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3543:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3544:   PetscFunctionReturn(PETSC_SUCCESS);
3545: }

3547: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);

3549: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3550: {
3551:   PetscInt        i, m, n, rstart, row, rend, nz, j, bs, cbs;
3552:   PetscInt       *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3553:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)mat->data;
3554:   Mat             M, Msub, B = a->B;
3555:   MatScalar      *aa;
3556:   Mat_SeqAIJ     *aij;
3557:   PetscInt       *garray = a->garray, *colsub, Ncols;
3558:   PetscInt        count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3559:   IS              iscol_sub, iscmap;
3560:   const PetscInt *is_idx, *cmap;
3561:   PetscBool       allcolumns = PETSC_FALSE;
3562:   MPI_Comm        comm;

3564:   PetscFunctionBegin;
3565:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3566:   if (call == MAT_REUSE_MATRIX) {
3567:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3568:     PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3569:     PetscCall(ISGetLocalSize(iscol_sub, &count));

3571:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3572:     PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");

3574:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3575:     PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");

3577:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));

3579:   } else { /* call == MAT_INITIAL_MATRIX) */
3580:     PetscBool flg;

3582:     PetscCall(ISGetLocalSize(iscol, &n));
3583:     PetscCall(ISGetSize(iscol, &Ncols));

3585:     /* (1) iscol -> nonscalable iscol_local */
3586:     /* Check for special case: each processor gets entire matrix columns */
3587:     PetscCall(ISIdentity(iscol_local, &flg));
3588:     if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3589:     PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3590:     if (allcolumns) {
3591:       iscol_sub = iscol_local;
3592:       PetscCall(PetscObjectReference((PetscObject)iscol_local));
3593:       PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));

3595:     } else {
3596:       /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3597:       PetscInt *idx, *cmap1, k;
3598:       PetscCall(PetscMalloc1(Ncols, &idx));
3599:       PetscCall(PetscMalloc1(Ncols, &cmap1));
3600:       PetscCall(ISGetIndices(iscol_local, &is_idx));
3601:       count = 0;
3602:       k     = 0;
3603:       for (i = 0; i < Ncols; i++) {
3604:         j = is_idx[i];
3605:         if (j >= cstart && j < cend) {
3606:           /* diagonal part of mat */
3607:           idx[count]     = j;
3608:           cmap1[count++] = i; /* column index in submat */
3609:         } else if (Bn) {
3610:           /* off-diagonal part of mat */
3611:           if (j == garray[k]) {
3612:             idx[count]     = j;
3613:             cmap1[count++] = i; /* column index in submat */
3614:           } else if (j > garray[k]) {
3615:             while (j > garray[k] && k < Bn - 1) k++;
3616:             if (j == garray[k]) {
3617:               idx[count]     = j;
3618:               cmap1[count++] = i; /* column index in submat */
3619:             }
3620:           }
3621:         }
3622:       }
3623:       PetscCall(ISRestoreIndices(iscol_local, &is_idx));

3625:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3626:       PetscCall(ISGetBlockSize(iscol, &cbs));
3627:       PetscCall(ISSetBlockSize(iscol_sub, cbs));

3629:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3630:     }

3632:     /* (3) Create sequential Msub */
3633:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3634:   }

3636:   PetscCall(ISGetLocalSize(iscol_sub, &count));
3637:   aij = (Mat_SeqAIJ *)(Msub)->data;
3638:   ii  = aij->i;
3639:   PetscCall(ISGetIndices(iscmap, &cmap));

3641:   /*
3642:       m - number of local rows
3643:       Ncols - number of columns (same on all processors)
3644:       rstart - first row in new global matrix generated
3645:   */
3646:   PetscCall(MatGetSize(Msub, &m, NULL));

3648:   if (call == MAT_INITIAL_MATRIX) {
3649:     /* (4) Create parallel newmat */
3650:     PetscMPIInt rank, size;
3651:     PetscInt    csize;

3653:     PetscCallMPI(MPI_Comm_size(comm, &size));
3654:     PetscCallMPI(MPI_Comm_rank(comm, &rank));

3656:     /*
3657:         Determine the number of non-zeros in the diagonal and off-diagonal
3658:         portions of the matrix in order to do correct preallocation
3659:     */

3661:     /* first get start and end of "diagonal" columns */
3662:     PetscCall(ISGetLocalSize(iscol, &csize));
3663:     if (csize == PETSC_DECIDE) {
3664:       PetscCall(ISGetSize(isrow, &mglobal));
3665:       if (mglobal == Ncols) { /* square matrix */
3666:         nlocal = m;
3667:       } else {
3668:         nlocal = Ncols / size + ((Ncols % size) > rank);
3669:       }
3670:     } else {
3671:       nlocal = csize;
3672:     }
3673:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3674:     rstart = rend - nlocal;
3675:     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);

3677:     /* next, compute all the lengths */
3678:     jj = aij->j;
3679:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3680:     olens = dlens + m;
3681:     for (i = 0; i < m; i++) {
3682:       jend = ii[i + 1] - ii[i];
3683:       olen = 0;
3684:       dlen = 0;
3685:       for (j = 0; j < jend; j++) {
3686:         if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3687:         else dlen++;
3688:         jj++;
3689:       }
3690:       olens[i] = olen;
3691:       dlens[i] = dlen;
3692:     }

3694:     PetscCall(ISGetBlockSize(isrow, &bs));
3695:     PetscCall(ISGetBlockSize(iscol, &cbs));

3697:     PetscCall(MatCreate(comm, &M));
3698:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3699:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3700:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3701:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3702:     PetscCall(PetscFree(dlens));

3704:   } else { /* call == MAT_REUSE_MATRIX */
3705:     M = *newmat;
3706:     PetscCall(MatGetLocalSize(M, &i, NULL));
3707:     PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3708:     PetscCall(MatZeroEntries(M));
3709:     /*
3710:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3711:        rather than the slower MatSetValues().
3712:     */
3713:     M->was_assembled = PETSC_TRUE;
3714:     M->assembled     = PETSC_FALSE;
3715:   }

3717:   /* (5) Set values of Msub to *newmat */
3718:   PetscCall(PetscMalloc1(count, &colsub));
3719:   PetscCall(MatGetOwnershipRange(M, &rstart, NULL));

3721:   jj = aij->j;
3722:   PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3723:   for (i = 0; i < m; i++) {
3724:     row = rstart + i;
3725:     nz  = ii[i + 1] - ii[i];
3726:     for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3727:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3728:     jj += nz;
3729:     aa += nz;
3730:   }
3731:   PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3732:   PetscCall(ISRestoreIndices(iscmap, &cmap));

3734:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3735:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));

3737:   PetscCall(PetscFree(colsub));

3739:   /* save Msub, iscol_sub and iscmap used in processor for next request */
3740:   if (call == MAT_INITIAL_MATRIX) {
3741:     *newmat = M;
3742:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3743:     PetscCall(MatDestroy(&Msub));

3745:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3746:     PetscCall(ISDestroy(&iscol_sub));

3748:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3749:     PetscCall(ISDestroy(&iscmap));

3751:     if (iscol_local) {
3752:       PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3753:       PetscCall(ISDestroy(&iscol_local));
3754:     }
3755:   }
3756:   PetscFunctionReturn(PETSC_SUCCESS);
3757: }

3759: /*
3760:     Not great since it makes two copies of the submatrix, first an SeqAIJ
3761:   in local and then by concatenating the local matrices the end result.
3762:   Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()

3764:   This requires a sequential iscol with all indices.
3765: */
3766: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3767: {
3768:   PetscMPIInt rank, size;
3769:   PetscInt    i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3770:   PetscInt   *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3771:   Mat         M, Mreuse;
3772:   MatScalar  *aa, *vwork;
3773:   MPI_Comm    comm;
3774:   Mat_SeqAIJ *aij;
3775:   PetscBool   colflag, allcolumns = PETSC_FALSE;

3777:   PetscFunctionBegin;
3778:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3779:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
3780:   PetscCallMPI(MPI_Comm_size(comm, &size));

3782:   /* Check for special case: each processor gets entire matrix columns */
3783:   PetscCall(ISIdentity(iscol, &colflag));
3784:   PetscCall(ISGetLocalSize(iscol, &n));
3785:   if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3786:   PetscCall(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));

3788:   if (call == MAT_REUSE_MATRIX) {
3789:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3790:     PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3791:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3792:   } else {
3793:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3794:   }

3796:   /*
3797:       m - number of local rows
3798:       n - number of columns (same on all processors)
3799:       rstart - first row in new global matrix generated
3800:   */
3801:   PetscCall(MatGetSize(Mreuse, &m, &n));
3802:   PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3803:   if (call == MAT_INITIAL_MATRIX) {
3804:     aij = (Mat_SeqAIJ *)(Mreuse)->data;
3805:     ii  = aij->i;
3806:     jj  = aij->j;

3808:     /*
3809:         Determine the number of non-zeros in the diagonal and off-diagonal
3810:         portions of the matrix in order to do correct preallocation
3811:     */

3813:     /* first get start and end of "diagonal" columns */
3814:     if (csize == PETSC_DECIDE) {
3815:       PetscCall(ISGetSize(isrow, &mglobal));
3816:       if (mglobal == n) { /* square matrix */
3817:         nlocal = m;
3818:       } else {
3819:         nlocal = n / size + ((n % size) > rank);
3820:       }
3821:     } else {
3822:       nlocal = csize;
3823:     }
3824:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3825:     rstart = rend - nlocal;
3826:     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);

3828:     /* next, compute all the lengths */
3829:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3830:     olens = dlens + m;
3831:     for (i = 0; i < m; i++) {
3832:       jend = ii[i + 1] - ii[i];
3833:       olen = 0;
3834:       dlen = 0;
3835:       for (j = 0; j < jend; j++) {
3836:         if (*jj < rstart || *jj >= rend) olen++;
3837:         else dlen++;
3838:         jj++;
3839:       }
3840:       olens[i] = olen;
3841:       dlens[i] = dlen;
3842:     }
3843:     PetscCall(MatCreate(comm, &M));
3844:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3845:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3846:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3847:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3848:     PetscCall(PetscFree(dlens));
3849:   } else {
3850:     PetscInt ml, nl;

3852:     M = *newmat;
3853:     PetscCall(MatGetLocalSize(M, &ml, &nl));
3854:     PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3855:     PetscCall(MatZeroEntries(M));
3856:     /*
3857:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3858:        rather than the slower MatSetValues().
3859:     */
3860:     M->was_assembled = PETSC_TRUE;
3861:     M->assembled     = PETSC_FALSE;
3862:   }
3863:   PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3864:   aij = (Mat_SeqAIJ *)(Mreuse)->data;
3865:   ii  = aij->i;
3866:   jj  = aij->j;

3868:   /* trigger copy to CPU if needed */
3869:   PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3870:   for (i = 0; i < m; i++) {
3871:     row   = rstart + i;
3872:     nz    = ii[i + 1] - ii[i];
3873:     cwork = jj;
3874:     jj    = PetscSafePointerPlusOffset(jj, nz);
3875:     vwork = aa;
3876:     aa    = PetscSafePointerPlusOffset(aa, nz);
3877:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3878:   }
3879:   PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));

3881:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3882:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3883:   *newmat = M;

3885:   /* save submatrix used in processor for next request */
3886:   if (call == MAT_INITIAL_MATRIX) {
3887:     PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3888:     PetscCall(MatDestroy(&Mreuse));
3889:   }
3890:   PetscFunctionReturn(PETSC_SUCCESS);
3891: }

3893: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3894: {
3895:   PetscInt        m, cstart, cend, j, nnz, i, d, *ld;
3896:   PetscInt       *d_nnz, *o_nnz, nnz_max = 0, rstart, ii;
3897:   const PetscInt *JJ;
3898:   PetscBool       nooffprocentries;
3899:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)B->data;

3901:   PetscFunctionBegin;
3902:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

3904:   PetscCall(PetscLayoutSetUp(B->rmap));
3905:   PetscCall(PetscLayoutSetUp(B->cmap));
3906:   m      = B->rmap->n;
3907:   cstart = B->cmap->rstart;
3908:   cend   = B->cmap->rend;
3909:   rstart = B->rmap->rstart;

3911:   PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));

3913:   if (PetscDefined(USE_DEBUG)) {
3914:     for (i = 0; i < m; i++) {
3915:       nnz = Ii[i + 1] - Ii[i];
3916:       JJ  = PetscSafePointerPlusOffset(J, Ii[i]);
3917:       PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3918:       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]);
3919:       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);
3920:     }
3921:   }

3923:   for (i = 0; i < m; i++) {
3924:     nnz     = Ii[i + 1] - Ii[i];
3925:     JJ      = PetscSafePointerPlusOffset(J, Ii[i]);
3926:     nnz_max = PetscMax(nnz_max, nnz);
3927:     d       = 0;
3928:     for (j = 0; j < nnz; j++) {
3929:       if (cstart <= JJ[j] && JJ[j] < cend) d++;
3930:     }
3931:     d_nnz[i] = d;
3932:     o_nnz[i] = nnz - d;
3933:   }
3934:   PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3935:   PetscCall(PetscFree2(d_nnz, o_nnz));

3937:   for (i = 0; i < m; i++) {
3938:     ii = i + rstart;
3939:     PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i]), PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES));
3940:   }
3941:   nooffprocentries    = B->nooffprocentries;
3942:   B->nooffprocentries = PETSC_TRUE;
3943:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3944:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3945:   B->nooffprocentries = nooffprocentries;

3947:   /* count number of entries below block diagonal */
3948:   PetscCall(PetscFree(Aij->ld));
3949:   PetscCall(PetscCalloc1(m, &ld));
3950:   Aij->ld = ld;
3951:   for (i = 0; i < m; i++) {
3952:     nnz = Ii[i + 1] - Ii[i];
3953:     j   = 0;
3954:     while (j < nnz && J[j] < cstart) j++;
3955:     ld[i] = j;
3956:     if (J) J += nnz;
3957:   }

3959:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3960:   PetscFunctionReturn(PETSC_SUCCESS);
3961: }

3963: /*@
3964:   MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3965:   (the default parallel PETSc format).

3967:   Collective

3969:   Input Parameters:
3970: + B - the matrix
3971: . i - the indices into `j` for the start of each local row (indices start with zero)
3972: . j - the column indices for each local row (indices start with zero)
3973: - v - optional values in the matrix

3975:   Level: developer

3977:   Notes:
3978:   The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3979:   thus you CANNOT change the matrix entries by changing the values of `v` after you have
3980:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

3982:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

3984:   A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.

3986:   You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.

3988:   If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3989:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

3991:   The format which is used for the sparse matrix input, is equivalent to a
3992:   row-major ordering.. i.e for the following matrix, the input data expected is
3993:   as shown
3994: .vb
3995:         1 0 0
3996:         2 0 3     P0
3997:        -------
3998:         4 5 6     P1

4000:      Process0 [P0] rows_owned=[0,1]
4001:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4002:         j =  {0,0,2}  [size = 3]
4003:         v =  {1,2,3}  [size = 3]

4005:      Process1 [P1] rows_owned=[2]
4006:         i =  {0,3}    [size = nrow+1  = 1+1]
4007:         j =  {0,1,2}  [size = 3]
4008:         v =  {4,5,6}  [size = 3]
4009: .ve

4011: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4012:           `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4013: @*/
4014: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4015: {
4016:   PetscFunctionBegin;
4017:   PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4018:   PetscFunctionReturn(PETSC_SUCCESS);
4019: }

4021: /*@C
4022:   MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4023:   (the default parallel PETSc format).  For good matrix assembly performance
4024:   the user should preallocate the matrix storage by setting the parameters
4025:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4027:   Collective

4029:   Input Parameters:
4030: + B     - the matrix
4031: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4032:            (same value is used for all local rows)
4033: . d_nnz - array containing the number of nonzeros in the various rows of the
4034:            DIAGONAL portion of the local submatrix (possibly different for each row)
4035:            or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4036:            The size of this array is equal to the number of local rows, i.e 'm'.
4037:            For matrices that will be factored, you must leave room for (and set)
4038:            the diagonal entry even if it is zero.
4039: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4040:            submatrix (same value is used for all local rows).
4041: - o_nnz - array containing the number of nonzeros in the various rows of the
4042:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4043:            each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4044:            structure. The size of this array is equal to the number
4045:            of local rows, i.e 'm'.

4047:   Example Usage:
4048:   Consider the following 8x8 matrix with 34 non-zero values, that is
4049:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4050:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4051:   as follows

4053: .vb
4054:             1  2  0  |  0  3  0  |  0  4
4055:     Proc0   0  5  6  |  7  0  0  |  8  0
4056:             9  0 10  | 11  0  0  | 12  0
4057:     -------------------------------------
4058:            13  0 14  | 15 16 17  |  0  0
4059:     Proc1   0 18  0  | 19 20 21  |  0  0
4060:             0  0  0  | 22 23  0  | 24  0
4061:     -------------------------------------
4062:     Proc2  25 26 27  |  0  0 28  | 29  0
4063:            30  0  0  | 31 32 33  |  0 34
4064: .ve

4066:   This can be represented as a collection of submatrices as
4067: .vb
4068:       A B C
4069:       D E F
4070:       G H I
4071: .ve

4073:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4074:   owned by proc1, G,H,I are owned by proc2.

4076:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4077:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4078:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4080:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4081:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4082:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4083:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4084:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4085:   matrix, ans [DF] as another `MATSEQAIJ` matrix.

4087:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4088:   allocated for every row of the local diagonal submatrix, and `o_nz`
4089:   storage locations are allocated for every row of the OFF-DIAGONAL submat.
4090:   One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4091:   rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4092:   In this case, the values of `d_nz`, `o_nz` are
4093: .vb
4094:      proc0  dnz = 2, o_nz = 2
4095:      proc1  dnz = 3, o_nz = 2
4096:      proc2  dnz = 1, o_nz = 4
4097: .ve
4098:   We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4099:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4100:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4101:   34 values.

4103:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4104:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4105:   In the above case the values for `d_nnz`, `o_nnz` are
4106: .vb
4107:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4108:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4109:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4110: .ve
4111:   Here the space allocated is sum of all the above values i.e 34, and
4112:   hence pre-allocation is perfect.

4114:   Level: intermediate

4116:   Notes:
4117:   If the *_nnz parameter is given then the *_nz parameter is ignored

4119:   The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4120:   storage.  The stored row and column indices begin with zero.
4121:   See [Sparse Matrices](sec_matsparse) for details.

4123:   The parallel matrix is partitioned such that the first m0 rows belong to
4124:   process 0, the next m1 rows belong to process 1, the next m2 rows belong
4125:   to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

4127:   The DIAGONAL portion of the local submatrix of a processor can be defined
4128:   as the submatrix which is obtained by extraction the part corresponding to
4129:   the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4130:   first row that belongs to the processor, r2 is the last row belonging to
4131:   the this processor, and c1-c2 is range of indices of the local part of a
4132:   vector suitable for applying the matrix to.  This is an mxn matrix.  In the
4133:   common case of a square matrix, the row and column ranges are the same and
4134:   the DIAGONAL part is also square. The remaining portion of the local
4135:   submatrix (mxN) constitute the OFF-DIAGONAL portion.

4137:   If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.

4139:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
4140:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4141:   You can also run with the option `-info` and look for messages with the string
4142:   malloc in them to see if additional memory allocation was needed.

4144: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4145:           `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4146: @*/
4147: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4148: {
4149:   PetscFunctionBegin;
4152:   PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4153:   PetscFunctionReturn(PETSC_SUCCESS);
4154: }

4156: /*@
4157:   MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4158:   CSR format for the local rows.

4160:   Collective

4162:   Input Parameters:
4163: + comm - MPI communicator
4164: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
4165: . n    - This value should be the same as the local size used in creating the
4166:          x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4167:          calculated if `N` is given) For square matrices n is almost always `m`.
4168: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4169: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4170: . 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
4171: . j    - global column indices
4172: - a    - optional matrix values

4174:   Output Parameter:
4175: . mat - the matrix

4177:   Level: intermediate

4179:   Notes:
4180:   The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4181:   thus you CANNOT change the matrix entries by changing the values of a[] after you have
4182:   called this routine. Use `MatCreateMPIAIJWithSplitArray()` to avoid needing to copy the arrays.

4184:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

4186:   Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`

4188:   If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4189:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

4191:   The format which is used for the sparse matrix input, is equivalent to a
4192:   row-major ordering.. i.e for the following matrix, the input data expected is
4193:   as shown
4194: .vb
4195:         1 0 0
4196:         2 0 3     P0
4197:        -------
4198:         4 5 6     P1

4200:      Process0 [P0] rows_owned=[0,1]
4201:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4202:         j =  {0,0,2}  [size = 3]
4203:         v =  {1,2,3}  [size = 3]

4205:      Process1 [P1] rows_owned=[2]
4206:         i =  {0,3}    [size = nrow+1  = 1+1]
4207:         j =  {0,1,2}  [size = 3]
4208:         v =  {4,5,6}  [size = 3]
4209: .ve

4211: .seealso: [](ch_matrices), `Mat`, `MATMPIAIK`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4212:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4213: @*/
4214: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4215: {
4216:   PetscFunctionBegin;
4217:   PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4218:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4219:   PetscCall(MatCreate(comm, mat));
4220:   PetscCall(MatSetSizes(*mat, m, n, M, N));
4221:   /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4222:   PetscCall(MatSetType(*mat, MATMPIAIJ));
4223:   PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4224:   PetscFunctionReturn(PETSC_SUCCESS);
4225: }

4227: /*@
4228:   MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4229:   CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4230:   from `MatCreateMPIAIJWithArrays()`

4232:   Deprecated: Use `MatUpdateMPIAIJWithArray()`

4234:   Collective

4236:   Input Parameters:
4237: + mat - the matrix
4238: . m   - number of local rows (Cannot be `PETSC_DECIDE`)
4239: . n   - This value should be the same as the local size used in creating the
4240:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4241:        calculated if N is given) For square matrices n is almost always m.
4242: . M   - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4243: . N   - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4244: . Ii  - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4245: . J   - column indices
4246: - v   - matrix values

4248:   Level: deprecated

4250: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4251:           `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4252: @*/
4253: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4254: {
4255:   PetscInt        nnz, i;
4256:   PetscBool       nooffprocentries;
4257:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4258:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4259:   PetscScalar    *ad, *ao;
4260:   PetscInt        ldi, Iii, md;
4261:   const PetscInt *Adi = Ad->i;
4262:   PetscInt       *ld  = Aij->ld;

4264:   PetscFunctionBegin;
4265:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4266:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4267:   PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4268:   PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");

4270:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4271:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));

4273:   for (i = 0; i < m; i++) {
4274:     if (PetscDefined(USE_DEBUG)) {
4275:       for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4276:         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);
4277:         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);
4278:       }
4279:     }
4280:     nnz = Ii[i + 1] - Ii[i];
4281:     Iii = Ii[i];
4282:     ldi = ld[i];
4283:     md  = Adi[i + 1] - Adi[i];
4284:     PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4285:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4286:     PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4287:     ad += md;
4288:     ao += nnz - md;
4289:   }
4290:   nooffprocentries      = mat->nooffprocentries;
4291:   mat->nooffprocentries = PETSC_TRUE;
4292:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4293:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4294:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4295:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4296:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4297:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4298:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4299:   mat->nooffprocentries = nooffprocentries;
4300:   PetscFunctionReturn(PETSC_SUCCESS);
4301: }

4303: /*@
4304:   MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values

4306:   Collective

4308:   Input Parameters:
4309: + mat - the matrix
4310: - v   - matrix values, stored by row

4312:   Level: intermediate

4314:   Notes:
4315:   The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`

4317:   The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly

4319: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4320:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4321: @*/
4322: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4323: {
4324:   PetscInt        nnz, i, m;
4325:   PetscBool       nooffprocentries;
4326:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4327:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4328:   Mat_SeqAIJ     *Ao  = (Mat_SeqAIJ *)Aij->B->data;
4329:   PetscScalar    *ad, *ao;
4330:   const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4331:   PetscInt        ldi, Iii, md;
4332:   PetscInt       *ld = Aij->ld;

4334:   PetscFunctionBegin;
4335:   m = mat->rmap->n;

4337:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4338:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4339:   Iii = 0;
4340:   for (i = 0; i < m; i++) {
4341:     nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4342:     ldi = ld[i];
4343:     md  = Adi[i + 1] - Adi[i];
4344:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4345:     ad += md;
4346:     if (ao) {
4347:       PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4348:       PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4349:       ao += nnz - md;
4350:     }
4351:     Iii += nnz;
4352:   }
4353:   nooffprocentries      = mat->nooffprocentries;
4354:   mat->nooffprocentries = PETSC_TRUE;
4355:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4356:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4357:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4358:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4359:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4360:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4361:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4362:   mat->nooffprocentries = nooffprocentries;
4363:   PetscFunctionReturn(PETSC_SUCCESS);
4364: }

4366: /*@C
4367:   MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4368:   (the default parallel PETSc format).  For good matrix assembly performance
4369:   the user should preallocate the matrix storage by setting the parameters
4370:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4372:   Collective

4374:   Input Parameters:
4375: + comm  - MPI communicator
4376: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4377:            This value should be the same as the local size used in creating the
4378:            y vector for the matrix-vector product y = Ax.
4379: . n     - This value should be the same as the local size used in creating the
4380:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4381:        calculated if N is given) For square matrices n is almost always m.
4382: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4383: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4384: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4385:            (same value is used for all local rows)
4386: . d_nnz - array containing the number of nonzeros in the various rows of the
4387:            DIAGONAL portion of the local submatrix (possibly different for each row)
4388:            or `NULL`, if `d_nz` is used to specify the nonzero structure.
4389:            The size of this array is equal to the number of local rows, i.e 'm'.
4390: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4391:            submatrix (same value is used for all local rows).
4392: - o_nnz - array containing the number of nonzeros in the various rows of the
4393:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4394:            each row) or `NULL`, if `o_nz` is used to specify the nonzero
4395:            structure. The size of this array is equal to the number
4396:            of local rows, i.e 'm'.

4398:   Output Parameter:
4399: . A - the matrix

4401:   Options Database Keys:
4402: + -mat_no_inode                     - Do not use inodes
4403: . -mat_inode_limit <limit>          - Sets inode limit (max limit=5)
4404: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4405:         See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the vecscatter be viewed as a matrix.
4406:         Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.

4408:   Level: intermediate

4410:   Notes:
4411:   It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4412:   MatXXXXSetPreallocation() paradigm instead of this routine directly.
4413:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

4415:   If the *_nnz parameter is given then the *_nz parameter is ignored

4417:   The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4418:   processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4419:   storage requirements for this matrix.

4421:   If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one
4422:   processor than it must be used on all processors that share the object for
4423:   that argument.

4425:   The user MUST specify either the local or global matrix dimensions
4426:   (possibly both).

4428:   The parallel matrix is partitioned across processors such that the
4429:   first m0 rows belong to process 0, the next m1 rows belong to
4430:   process 1, the next m2 rows belong to process 2 etc.. where
4431:   m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4432:   values corresponding to [m x N] submatrix.

4434:   The columns are logically partitioned with the n0 columns belonging
4435:   to 0th partition, the next n1 columns belonging to the next
4436:   partition etc.. where n0,n1,n2... are the input parameter 'n'.

4438:   The DIAGONAL portion of the local submatrix on any given processor
4439:   is the submatrix corresponding to the rows and columns m,n
4440:   corresponding to the given processor. i.e diagonal matrix on
4441:   process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4442:   etc. The remaining portion of the local submatrix [m x (N-n)]
4443:   constitute the OFF-DIAGONAL portion. The example below better
4444:   illustrates this concept.

4446:   For a square global matrix we define each processor's diagonal portion
4447:   to be its local rows and the corresponding columns (a square submatrix);
4448:   each processor's off-diagonal portion encompasses the remainder of the
4449:   local matrix (a rectangular submatrix).

4451:   If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.

4453:   When calling this routine with a single process communicator, a matrix of
4454:   type `MATSEQAIJ` is returned.  If a matrix of type `MATMPIAIJ` is desired for this
4455:   type of communicator, use the construction mechanism
4456: .vb
4457:   MatCreate(..., &A);
4458:   MatSetType(A, MATMPIAIJ);
4459:   MatSetSizes(A, m, n, M, N);
4460:   MatMPIAIJSetPreallocation(A, ...);
4461: .ve

4463:   By default, this format uses inodes (identical nodes) when possible.
4464:   We search for consecutive rows with the same nonzero structure, thereby
4465:   reusing matrix information to achieve increased efficiency.

4467:   Example Usage:
4468:   Consider the following 8x8 matrix with 34 non-zero values, that is
4469:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4470:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4471:   as follows

4473: .vb
4474:             1  2  0  |  0  3  0  |  0  4
4475:     Proc0   0  5  6  |  7  0  0  |  8  0
4476:             9  0 10  | 11  0  0  | 12  0
4477:     -------------------------------------
4478:            13  0 14  | 15 16 17  |  0  0
4479:     Proc1   0 18  0  | 19 20 21  |  0  0
4480:             0  0  0  | 22 23  0  | 24  0
4481:     -------------------------------------
4482:     Proc2  25 26 27  |  0  0 28  | 29  0
4483:            30  0  0  | 31 32 33  |  0 34
4484: .ve

4486:   This can be represented as a collection of submatrices as

4488: .vb
4489:       A B C
4490:       D E F
4491:       G H I
4492: .ve

4494:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4495:   owned by proc1, G,H,I are owned by proc2.

4497:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4498:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4499:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4501:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4502:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4503:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4504:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4505:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4506:   matrix, ans [DF] as another SeqAIJ matrix.

4508:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4509:   allocated for every row of the local diagonal submatrix, and `o_nz`
4510:   storage locations are allocated for every row of the OFF-DIAGONAL submat.
4511:   One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
4512:   rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4513:   In this case, the values of `d_nz`,`o_nz` are
4514: .vb
4515:      proc0  dnz = 2, o_nz = 2
4516:      proc1  dnz = 3, o_nz = 2
4517:      proc2  dnz = 1, o_nz = 4
4518: .ve
4519:   We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4520:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4521:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4522:   34 values.

4524:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4525:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4526:   In the above case the values for d_nnz,o_nnz are
4527: .vb
4528:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4529:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4530:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4531: .ve
4532:   Here the space allocated is sum of all the above values i.e 34, and
4533:   hence pre-allocation is perfect.

4535: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4536:           `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`
4537: @*/
4538: 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)
4539: {
4540:   PetscMPIInt size;

4542:   PetscFunctionBegin;
4543:   PetscCall(MatCreate(comm, A));
4544:   PetscCall(MatSetSizes(*A, m, n, M, N));
4545:   PetscCallMPI(MPI_Comm_size(comm, &size));
4546:   if (size > 1) {
4547:     PetscCall(MatSetType(*A, MATMPIAIJ));
4548:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4549:   } else {
4550:     PetscCall(MatSetType(*A, MATSEQAIJ));
4551:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4552:   }
4553:   PetscFunctionReturn(PETSC_SUCCESS);
4554: }

4556: /*MC
4557:     MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix

4559:     Synopsis:
4560:     MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4562:     Not Collective

4564:     Input Parameter:
4565: .   A - the `MATMPIAIJ` matrix

4567:     Output Parameters:
4568: +   Ad - the diagonal portion of the matrix
4569: .   Ao - the off-diagonal portion of the matrix
4570: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4571: -   ierr - error code

4573:      Level: advanced

4575:     Note:
4576:     Use  `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4578: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4579: M*/

4581: /*MC
4582:     MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4584:     Synopsis:
4585:     MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4587:     Not Collective

4589:     Input Parameters:
4590: +   A - the `MATMPIAIJ` matrix
4591: .   Ad - the diagonal portion of the matrix
4592: .   Ao - the off-diagonal portion of the matrix
4593: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4594: -   ierr - error code

4596:      Level: advanced

4598: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4599: M*/

4601: /*@C
4602:   MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix

4604:   Not Collective

4606:   Input Parameter:
4607: . A - The `MATMPIAIJ` matrix

4609:   Output Parameters:
4610: + Ad     - The local diagonal block as a `MATSEQAIJ` matrix
4611: . Ao     - The local off-diagonal block as a `MATSEQAIJ` matrix
4612: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix

4614:   Level: intermediate

4616:   Note:
4617:   The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4618:   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
4619:   the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4620:   local column numbers to global column numbers in the original matrix.

4622:   Fortran Notes:
4623:   `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`

4625: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4626: @*/
4627: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4628: {
4629:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4630:   PetscBool   flg;

4632:   PetscFunctionBegin;
4633:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4634:   PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4635:   if (Ad) *Ad = a->A;
4636:   if (Ao) *Ao = a->B;
4637:   if (colmap) *colmap = a->garray;
4638:   PetscFunctionReturn(PETSC_SUCCESS);
4639: }

4641: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4642: {
4643:   PetscInt     m, N, i, rstart, nnz, Ii;
4644:   PetscInt    *indx;
4645:   PetscScalar *values;
4646:   MatType      rootType;

4648:   PetscFunctionBegin;
4649:   PetscCall(MatGetSize(inmat, &m, &N));
4650:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4651:     PetscInt *dnz, *onz, sum, bs, cbs;

4653:     if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4654:     /* Check sum(n) = N */
4655:     PetscCall(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4656:     PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);

4658:     PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4659:     rstart -= m;

4661:     MatPreallocateBegin(comm, m, n, dnz, onz);
4662:     for (i = 0; i < m; i++) {
4663:       PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4664:       PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4665:       PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4666:     }

4668:     PetscCall(MatCreate(comm, outmat));
4669:     PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4670:     PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4671:     PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4672:     PetscCall(MatGetRootType_Private(inmat, &rootType));
4673:     PetscCall(MatSetType(*outmat, rootType));
4674:     PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4675:     PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4676:     MatPreallocateEnd(dnz, onz);
4677:     PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4678:   }

4680:   /* numeric phase */
4681:   PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4682:   for (i = 0; i < m; i++) {
4683:     PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4684:     Ii = i + rstart;
4685:     PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4686:     PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4687:   }
4688:   PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4689:   PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4690:   PetscFunctionReturn(PETSC_SUCCESS);
4691: }

4693: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4694: {
4695:   Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;

4697:   PetscFunctionBegin;
4698:   if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4699:   PetscCall(PetscFree(merge->id_r));
4700:   PetscCall(PetscFree(merge->len_s));
4701:   PetscCall(PetscFree(merge->len_r));
4702:   PetscCall(PetscFree(merge->bi));
4703:   PetscCall(PetscFree(merge->bj));
4704:   PetscCall(PetscFree(merge->buf_ri[0]));
4705:   PetscCall(PetscFree(merge->buf_ri));
4706:   PetscCall(PetscFree(merge->buf_rj[0]));
4707:   PetscCall(PetscFree(merge->buf_rj));
4708:   PetscCall(PetscFree(merge->coi));
4709:   PetscCall(PetscFree(merge->coj));
4710:   PetscCall(PetscFree(merge->owners_co));
4711:   PetscCall(PetscLayoutDestroy(&merge->rowmap));
4712:   PetscCall(PetscFree(merge));
4713:   PetscFunctionReturn(PETSC_SUCCESS);
4714: }

4716: #include <../src/mat/utils/freespace.h>
4717: #include <petscbt.h>

4719: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4720: {
4721:   MPI_Comm             comm;
4722:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4723:   PetscMPIInt          size, rank, taga, *len_s;
4724:   PetscInt             N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj;
4725:   PetscInt             proc, m;
4726:   PetscInt           **buf_ri, **buf_rj;
4727:   PetscInt             k, anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4728:   PetscInt             nrows, **buf_ri_k, **nextrow, **nextai;
4729:   MPI_Request         *s_waits, *r_waits;
4730:   MPI_Status          *status;
4731:   const MatScalar     *aa, *a_a;
4732:   MatScalar          **abuf_r, *ba_i;
4733:   Mat_Merge_SeqsToMPI *merge;
4734:   PetscContainer       container;

4736:   PetscFunctionBegin;
4737:   PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4738:   PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));

4740:   PetscCallMPI(MPI_Comm_size(comm, &size));
4741:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4743:   PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4744:   PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4745:   PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4746:   PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4747:   aa = a_a;

4749:   bi     = merge->bi;
4750:   bj     = merge->bj;
4751:   buf_ri = merge->buf_ri;
4752:   buf_rj = merge->buf_rj;

4754:   PetscCall(PetscMalloc1(size, &status));
4755:   owners = merge->rowmap->range;
4756:   len_s  = merge->len_s;

4758:   /* send and recv matrix values */
4759:   PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4760:   PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));

4762:   PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4763:   for (proc = 0, k = 0; proc < size; proc++) {
4764:     if (!len_s[proc]) continue;
4765:     i = owners[proc];
4766:     PetscCallMPI(MPI_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4767:     k++;
4768:   }

4770:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4771:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4772:   PetscCall(PetscFree(status));

4774:   PetscCall(PetscFree(s_waits));
4775:   PetscCall(PetscFree(r_waits));

4777:   /* insert mat values of mpimat */
4778:   PetscCall(PetscMalloc1(N, &ba_i));
4779:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4781:   for (k = 0; k < merge->nrecv; k++) {
4782:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4783:     nrows       = *buf_ri_k[k];
4784:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4785:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4786:   }

4788:   /* set values of ba */
4789:   m = merge->rowmap->n;
4790:   for (i = 0; i < m; i++) {
4791:     arow = owners[rank] + i;
4792:     bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4793:     bnzi = bi[i + 1] - bi[i];
4794:     PetscCall(PetscArrayzero(ba_i, bnzi));

4796:     /* add local non-zero vals of this proc's seqmat into ba */
4797:     anzi   = ai[arow + 1] - ai[arow];
4798:     aj     = a->j + ai[arow];
4799:     aa     = a_a + ai[arow];
4800:     nextaj = 0;
4801:     for (j = 0; nextaj < anzi; j++) {
4802:       if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4803:         ba_i[j] += aa[nextaj++];
4804:       }
4805:     }

4807:     /* add received vals into ba */
4808:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4809:       /* i-th row */
4810:       if (i == *nextrow[k]) {
4811:         anzi   = *(nextai[k] + 1) - *nextai[k];
4812:         aj     = buf_rj[k] + *nextai[k];
4813:         aa     = abuf_r[k] + *nextai[k];
4814:         nextaj = 0;
4815:         for (j = 0; nextaj < anzi; j++) {
4816:           if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4817:             ba_i[j] += aa[nextaj++];
4818:           }
4819:         }
4820:         nextrow[k]++;
4821:         nextai[k]++;
4822:       }
4823:     }
4824:     PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4825:   }
4826:   PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4827:   PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4828:   PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));

4830:   PetscCall(PetscFree(abuf_r[0]));
4831:   PetscCall(PetscFree(abuf_r));
4832:   PetscCall(PetscFree(ba_i));
4833:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4834:   PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4835:   PetscFunctionReturn(PETSC_SUCCESS);
4836: }

4838: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4839: {
4840:   Mat                  B_mpi;
4841:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4842:   PetscMPIInt          size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4843:   PetscInt           **buf_rj, **buf_ri, **buf_ri_k;
4844:   PetscInt             M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4845:   PetscInt             len, proc, *dnz, *onz, bs, cbs;
4846:   PetscInt             k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4847:   PetscInt             nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4848:   MPI_Request         *si_waits, *sj_waits, *ri_waits, *rj_waits;
4849:   MPI_Status          *status;
4850:   PetscFreeSpaceList   free_space = NULL, current_space = NULL;
4851:   PetscBT              lnkbt;
4852:   Mat_Merge_SeqsToMPI *merge;
4853:   PetscContainer       container;

4855:   PetscFunctionBegin;
4856:   PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));

4858:   /* make sure it is a PETSc comm */
4859:   PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4860:   PetscCallMPI(MPI_Comm_size(comm, &size));
4861:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4863:   PetscCall(PetscNew(&merge));
4864:   PetscCall(PetscMalloc1(size, &status));

4866:   /* determine row ownership */
4867:   PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4868:   PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4869:   PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4870:   PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4871:   PetscCall(PetscLayoutSetUp(merge->rowmap));
4872:   PetscCall(PetscMalloc1(size, &len_si));
4873:   PetscCall(PetscMalloc1(size, &merge->len_s));

4875:   m      = merge->rowmap->n;
4876:   owners = merge->rowmap->range;

4878:   /* determine the number of messages to send, their lengths */
4879:   len_s = merge->len_s;

4881:   len          = 0; /* length of buf_si[] */
4882:   merge->nsend = 0;
4883:   for (proc = 0; proc < size; proc++) {
4884:     len_si[proc] = 0;
4885:     if (proc == rank) {
4886:       len_s[proc] = 0;
4887:     } else {
4888:       len_si[proc] = owners[proc + 1] - owners[proc] + 1;
4889:       len_s[proc]  = ai[owners[proc + 1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4890:     }
4891:     if (len_s[proc]) {
4892:       merge->nsend++;
4893:       nrows = 0;
4894:       for (i = owners[proc]; i < owners[proc + 1]; i++) {
4895:         if (ai[i + 1] > ai[i]) nrows++;
4896:       }
4897:       len_si[proc] = 2 * (nrows + 1);
4898:       len += len_si[proc];
4899:     }
4900:   }

4902:   /* determine the number and length of messages to receive for ij-structure */
4903:   PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4904:   PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));

4906:   /* post the Irecv of j-structure */
4907:   PetscCall(PetscCommGetNewTag(comm, &tagj));
4908:   PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));

4910:   /* post the Isend of j-structure */
4911:   PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));

4913:   for (proc = 0, k = 0; proc < size; proc++) {
4914:     if (!len_s[proc]) continue;
4915:     i = owners[proc];
4916:     PetscCallMPI(MPI_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4917:     k++;
4918:   }

4920:   /* receives and sends of j-structure are complete */
4921:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4922:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));

4924:   /* send and recv i-structure */
4925:   PetscCall(PetscCommGetNewTag(comm, &tagi));
4926:   PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));

4928:   PetscCall(PetscMalloc1(len + 1, &buf_s));
4929:   buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4930:   for (proc = 0, k = 0; proc < size; proc++) {
4931:     if (!len_s[proc]) continue;
4932:     /* form outgoing message for i-structure:
4933:          buf_si[0]:                 nrows to be sent
4934:                [1:nrows]:           row index (global)
4935:                [nrows+1:2*nrows+1]: i-structure index
4936:     */
4937:     nrows       = len_si[proc] / 2 - 1;
4938:     buf_si_i    = buf_si + nrows + 1;
4939:     buf_si[0]   = nrows;
4940:     buf_si_i[0] = 0;
4941:     nrows       = 0;
4942:     for (i = owners[proc]; i < owners[proc + 1]; i++) {
4943:       anzi = ai[i + 1] - ai[i];
4944:       if (anzi) {
4945:         buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4946:         buf_si[nrows + 1]   = i - owners[proc];       /* local row index */
4947:         nrows++;
4948:       }
4949:     }
4950:     PetscCallMPI(MPI_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4951:     k++;
4952:     buf_si += len_si[proc];
4953:   }

4955:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4956:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));

4958:   PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4959:   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]));

4961:   PetscCall(PetscFree(len_si));
4962:   PetscCall(PetscFree(len_ri));
4963:   PetscCall(PetscFree(rj_waits));
4964:   PetscCall(PetscFree2(si_waits, sj_waits));
4965:   PetscCall(PetscFree(ri_waits));
4966:   PetscCall(PetscFree(buf_s));
4967:   PetscCall(PetscFree(status));

4969:   /* compute a local seq matrix in each processor */
4970:   /* allocate bi array and free space for accumulating nonzero column info */
4971:   PetscCall(PetscMalloc1(m + 1, &bi));
4972:   bi[0] = 0;

4974:   /* create and initialize a linked list */
4975:   nlnk = N + 1;
4976:   PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));

4978:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4979:   len = ai[owners[rank + 1]] - ai[owners[rank]];
4980:   PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));

4982:   current_space = free_space;

4984:   /* determine symbolic info for each local row */
4985:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4987:   for (k = 0; k < merge->nrecv; k++) {
4988:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4989:     nrows       = *buf_ri_k[k];
4990:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4991:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4992:   }

4994:   MatPreallocateBegin(comm, m, n, dnz, onz);
4995:   len = 0;
4996:   for (i = 0; i < m; i++) {
4997:     bnzi = 0;
4998:     /* add local non-zero cols of this proc's seqmat into lnk */
4999:     arow = owners[rank] + i;
5000:     anzi = ai[arow + 1] - ai[arow];
5001:     aj   = a->j + ai[arow];
5002:     PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5003:     bnzi += nlnk;
5004:     /* add received col data into lnk */
5005:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5006:       if (i == *nextrow[k]) {            /* i-th row */
5007:         anzi = *(nextai[k] + 1) - *nextai[k];
5008:         aj   = buf_rj[k] + *nextai[k];
5009:         PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5010:         bnzi += nlnk;
5011:         nextrow[k]++;
5012:         nextai[k]++;
5013:       }
5014:     }
5015:     if (len < bnzi) len = bnzi; /* =max(bnzi) */

5017:     /* if free space is not available, make more free space */
5018:     if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), &current_space));
5019:     /* copy data into free space, then initialize lnk */
5020:     PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5021:     PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));

5023:     current_space->array += bnzi;
5024:     current_space->local_used += bnzi;
5025:     current_space->local_remaining -= bnzi;

5027:     bi[i + 1] = bi[i] + bnzi;
5028:   }

5030:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));

5032:   PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5033:   PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5034:   PetscCall(PetscLLDestroy(lnk, lnkbt));

5036:   /* create symbolic parallel matrix B_mpi */
5037:   PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5038:   PetscCall(MatCreate(comm, &B_mpi));
5039:   if (n == PETSC_DECIDE) {
5040:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5041:   } else {
5042:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5043:   }
5044:   PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5045:   PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5046:   PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5047:   MatPreallocateEnd(dnz, onz);
5048:   PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));

5050:   /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5051:   B_mpi->assembled = PETSC_FALSE;
5052:   merge->bi        = bi;
5053:   merge->bj        = bj;
5054:   merge->buf_ri    = buf_ri;
5055:   merge->buf_rj    = buf_rj;
5056:   merge->coi       = NULL;
5057:   merge->coj       = NULL;
5058:   merge->owners_co = NULL;

5060:   PetscCall(PetscCommDestroy(&comm));

5062:   /* attach the supporting struct to B_mpi for reuse */
5063:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5064:   PetscCall(PetscContainerSetPointer(container, merge));
5065:   PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5066:   PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5067:   PetscCall(PetscContainerDestroy(&container));
5068:   *mpimat = B_mpi;

5070:   PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5071:   PetscFunctionReturn(PETSC_SUCCESS);
5072: }

5074: /*@C
5075:   MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5076:   matrices from each processor

5078:   Collective

5080:   Input Parameters:
5081: + comm   - the communicators the parallel matrix will live on
5082: . seqmat - the input sequential matrices
5083: . m      - number of local rows (or `PETSC_DECIDE`)
5084: . n      - number of local columns (or `PETSC_DECIDE`)
5085: - scall  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5087:   Output Parameter:
5088: . mpimat - the parallel matrix generated

5090:   Level: advanced

5092:   Note:
5093:   The dimensions of the sequential matrix in each processor MUST be the same.
5094:   The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5095:   destroyed when mpimat is destroyed. Call `PetscObjectQuery()` to access seqmat.

5097: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5098: @*/
5099: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5100: {
5101:   PetscMPIInt size;

5103:   PetscFunctionBegin;
5104:   PetscCallMPI(MPI_Comm_size(comm, &size));
5105:   if (size == 1) {
5106:     PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5107:     if (scall == MAT_INITIAL_MATRIX) {
5108:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5109:     } else {
5110:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5111:     }
5112:     PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5113:     PetscFunctionReturn(PETSC_SUCCESS);
5114:   }
5115:   PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5116:   if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5117:   PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5118:   PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5119:   PetscFunctionReturn(PETSC_SUCCESS);
5120: }

5122: /*@
5123:   MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.

5125:   Not Collective

5127:   Input Parameter:
5128: . A - the matrix

5130:   Output Parameter:
5131: . A_loc - the local sequential matrix generated

5133:   Level: developer

5135:   Notes:
5136:   The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5137:   with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5138:   `n` is the global column count obtained with `MatGetSize()`

5140:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5142:   For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.

5144:   Destroy the matrix with `MatDestroy()`

5146: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5147: @*/
5148: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5149: {
5150:   PetscBool mpi;

5152:   PetscFunctionBegin;
5153:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5154:   if (mpi) {
5155:     PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5156:   } else {
5157:     *A_loc = A;
5158:     PetscCall(PetscObjectReference((PetscObject)*A_loc));
5159:   }
5160:   PetscFunctionReturn(PETSC_SUCCESS);
5161: }

5163: /*@
5164:   MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.

5166:   Not Collective

5168:   Input Parameters:
5169: + A     - the matrix
5170: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5172:   Output Parameter:
5173: . A_loc - the local sequential matrix generated

5175:   Level: developer

5177:   Notes:
5178:   The matrix is created by taking all `A`'s local rows and putting them into a sequential
5179:   matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5180:   `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.

5182:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5184:   When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5185:   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
5186:   then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5187:   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.

5189: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5190: @*/
5191: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5192: {
5193:   Mat_MPIAIJ        *mpimat = (Mat_MPIAIJ *)A->data;
5194:   Mat_SeqAIJ        *mat, *a, *b;
5195:   PetscInt          *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5196:   const PetscScalar *aa, *ba, *aav, *bav;
5197:   PetscScalar       *ca, *cam;
5198:   PetscMPIInt        size;
5199:   PetscInt           am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5200:   PetscInt          *ci, *cj, col, ncols_d, ncols_o, jo;
5201:   PetscBool          match;

5203:   PetscFunctionBegin;
5204:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5205:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5206:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5207:   if (size == 1) {
5208:     if (scall == MAT_INITIAL_MATRIX) {
5209:       PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5210:       *A_loc = mpimat->A;
5211:     } else if (scall == MAT_REUSE_MATRIX) {
5212:       PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5213:     }
5214:     PetscFunctionReturn(PETSC_SUCCESS);
5215:   }

5217:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5218:   a  = (Mat_SeqAIJ *)mpimat->A->data;
5219:   b  = (Mat_SeqAIJ *)mpimat->B->data;
5220:   ai = a->i;
5221:   aj = a->j;
5222:   bi = b->i;
5223:   bj = b->j;
5224:   PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5225:   PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5226:   aa = aav;
5227:   ba = bav;
5228:   if (scall == MAT_INITIAL_MATRIX) {
5229:     PetscCall(PetscMalloc1(1 + am, &ci));
5230:     ci[0] = 0;
5231:     for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5232:     PetscCall(PetscMalloc1(1 + ci[am], &cj));
5233:     PetscCall(PetscMalloc1(1 + ci[am], &ca));
5234:     k = 0;
5235:     for (i = 0; i < am; i++) {
5236:       ncols_o = bi[i + 1] - bi[i];
5237:       ncols_d = ai[i + 1] - ai[i];
5238:       /* off-diagonal portion of A */
5239:       for (jo = 0; jo < ncols_o; jo++) {
5240:         col = cmap[*bj];
5241:         if (col >= cstart) break;
5242:         cj[k] = col;
5243:         bj++;
5244:         ca[k++] = *ba++;
5245:       }
5246:       /* diagonal portion of A */
5247:       for (j = 0; j < ncols_d; j++) {
5248:         cj[k]   = cstart + *aj++;
5249:         ca[k++] = *aa++;
5250:       }
5251:       /* off-diagonal portion of A */
5252:       for (j = jo; j < ncols_o; j++) {
5253:         cj[k]   = cmap[*bj++];
5254:         ca[k++] = *ba++;
5255:       }
5256:     }
5257:     /* put together the new matrix */
5258:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5259:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5260:     /* Since these are PETSc arrays, change flags to free them as necessary. */
5261:     mat          = (Mat_SeqAIJ *)(*A_loc)->data;
5262:     mat->free_a  = PETSC_TRUE;
5263:     mat->free_ij = PETSC_TRUE;
5264:     mat->nonew   = 0;
5265:   } else if (scall == MAT_REUSE_MATRIX) {
5266:     mat = (Mat_SeqAIJ *)(*A_loc)->data;
5267:     ci  = mat->i;
5268:     cj  = mat->j;
5269:     PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5270:     for (i = 0; i < am; i++) {
5271:       /* off-diagonal portion of A */
5272:       ncols_o = bi[i + 1] - bi[i];
5273:       for (jo = 0; jo < ncols_o; jo++) {
5274:         col = cmap[*bj];
5275:         if (col >= cstart) break;
5276:         *cam++ = *ba++;
5277:         bj++;
5278:       }
5279:       /* diagonal portion of A */
5280:       ncols_d = ai[i + 1] - ai[i];
5281:       for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5282:       /* off-diagonal portion of A */
5283:       for (j = jo; j < ncols_o; j++) {
5284:         *cam++ = *ba++;
5285:         bj++;
5286:       }
5287:     }
5288:     PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5289:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5290:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5291:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5292:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5293:   PetscFunctionReturn(PETSC_SUCCESS);
5294: }

5296: /*@
5297:   MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5298:   mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part

5300:   Not Collective

5302:   Input Parameters:
5303: + A     - the matrix
5304: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5306:   Output Parameters:
5307: + glob  - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5308: - A_loc - the local sequential matrix generated

5310:   Level: developer

5312:   Note:
5313:   This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5314:   part, then those associated with the off-diagonal part (in its local ordering)

5316: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5317: @*/
5318: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5319: {
5320:   Mat             Ao, Ad;
5321:   const PetscInt *cmap;
5322:   PetscMPIInt     size;
5323:   PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);

5325:   PetscFunctionBegin;
5326:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5327:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5328:   if (size == 1) {
5329:     if (scall == MAT_INITIAL_MATRIX) {
5330:       PetscCall(PetscObjectReference((PetscObject)Ad));
5331:       *A_loc = Ad;
5332:     } else if (scall == MAT_REUSE_MATRIX) {
5333:       PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5334:     }
5335:     if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5336:     PetscFunctionReturn(PETSC_SUCCESS);
5337:   }
5338:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5339:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5340:   if (f) {
5341:     PetscCall((*f)(A, scall, glob, A_loc));
5342:   } else {
5343:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)Ad->data;
5344:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)Ao->data;
5345:     Mat_SeqAIJ        *c;
5346:     PetscInt          *ai = a->i, *aj = a->j;
5347:     PetscInt          *bi = b->i, *bj = b->j;
5348:     PetscInt          *ci, *cj;
5349:     const PetscScalar *aa, *ba;
5350:     PetscScalar       *ca;
5351:     PetscInt           i, j, am, dn, on;

5353:     PetscCall(MatGetLocalSize(Ad, &am, &dn));
5354:     PetscCall(MatGetLocalSize(Ao, NULL, &on));
5355:     PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5356:     PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5357:     if (scall == MAT_INITIAL_MATRIX) {
5358:       PetscInt k;
5359:       PetscCall(PetscMalloc1(1 + am, &ci));
5360:       PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5361:       PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5362:       ci[0] = 0;
5363:       for (i = 0, k = 0; i < am; i++) {
5364:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5365:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5366:         ci[i + 1]              = ci[i] + ncols_o + ncols_d;
5367:         /* diagonal portion of A */
5368:         for (j = 0; j < ncols_d; j++, k++) {
5369:           cj[k] = *aj++;
5370:           ca[k] = *aa++;
5371:         }
5372:         /* off-diagonal portion of A */
5373:         for (j = 0; j < ncols_o; j++, k++) {
5374:           cj[k] = dn + *bj++;
5375:           ca[k] = *ba++;
5376:         }
5377:       }
5378:       /* put together the new matrix */
5379:       PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5380:       /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5381:       /* Since these are PETSc arrays, change flags to free them as necessary. */
5382:       c          = (Mat_SeqAIJ *)(*A_loc)->data;
5383:       c->free_a  = PETSC_TRUE;
5384:       c->free_ij = PETSC_TRUE;
5385:       c->nonew   = 0;
5386:       PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5387:     } else if (scall == MAT_REUSE_MATRIX) {
5388:       PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5389:       for (i = 0; i < am; i++) {
5390:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5391:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5392:         /* diagonal portion of A */
5393:         for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5394:         /* off-diagonal portion of A */
5395:         for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5396:       }
5397:       PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5398:     } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5399:     PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5400:     PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5401:     if (glob) {
5402:       PetscInt cst, *gidx;

5404:       PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5405:       PetscCall(PetscMalloc1(dn + on, &gidx));
5406:       for (i = 0; i < dn; i++) gidx[i] = cst + i;
5407:       for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5408:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5409:     }
5410:   }
5411:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5412:   PetscFunctionReturn(PETSC_SUCCESS);
5413: }

5415: /*@C
5416:   MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns

5418:   Not Collective

5420:   Input Parameters:
5421: + A     - the matrix
5422: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5423: . row   - index set of rows to extract (or `NULL`)
5424: - col   - index set of columns to extract (or `NULL`)

5426:   Output Parameter:
5427: . A_loc - the local sequential matrix generated

5429:   Level: developer

5431: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5432: @*/
5433: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5434: {
5435:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5436:   PetscInt    i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5437:   IS          isrowa, iscola;
5438:   Mat        *aloc;
5439:   PetscBool   match;

5441:   PetscFunctionBegin;
5442:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5443:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5444:   PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5445:   if (!row) {
5446:     start = A->rmap->rstart;
5447:     end   = A->rmap->rend;
5448:     PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5449:   } else {
5450:     isrowa = *row;
5451:   }
5452:   if (!col) {
5453:     start = A->cmap->rstart;
5454:     cmap  = a->garray;
5455:     nzA   = a->A->cmap->n;
5456:     nzB   = a->B->cmap->n;
5457:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5458:     ncols = 0;
5459:     for (i = 0; i < nzB; i++) {
5460:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5461:       else break;
5462:     }
5463:     imark = i;
5464:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5465:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5466:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5467:   } else {
5468:     iscola = *col;
5469:   }
5470:   if (scall != MAT_INITIAL_MATRIX) {
5471:     PetscCall(PetscMalloc1(1, &aloc));
5472:     aloc[0] = *A_loc;
5473:   }
5474:   PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5475:   if (!col) { /* attach global id of condensed columns */
5476:     PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5477:   }
5478:   *A_loc = aloc[0];
5479:   PetscCall(PetscFree(aloc));
5480:   if (!row) PetscCall(ISDestroy(&isrowa));
5481:   if (!col) PetscCall(ISDestroy(&iscola));
5482:   PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5483:   PetscFunctionReturn(PETSC_SUCCESS);
5484: }

5486: /*
5487:  * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5488:  * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5489:  * on a global size.
5490:  * */
5491: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5492: {
5493:   Mat_MPIAIJ            *p  = (Mat_MPIAIJ *)P->data;
5494:   Mat_SeqAIJ            *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5495:   PetscInt               plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5496:   PetscMPIInt            owner;
5497:   PetscSFNode           *iremote, *oiremote;
5498:   const PetscInt        *lrowindices;
5499:   PetscSF                sf, osf;
5500:   PetscInt               pcstart, *roffsets, *loffsets, *pnnz, j;
5501:   PetscInt               ontotalcols, dntotalcols, ntotalcols, nout;
5502:   MPI_Comm               comm;
5503:   ISLocalToGlobalMapping mapping;
5504:   const PetscScalar     *pd_a, *po_a;

5506:   PetscFunctionBegin;
5507:   PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5508:   /* plocalsize is the number of roots
5509:    * nrows is the number of leaves
5510:    * */
5511:   PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5512:   PetscCall(ISGetLocalSize(rows, &nrows));
5513:   PetscCall(PetscCalloc1(nrows, &iremote));
5514:   PetscCall(ISGetIndices(rows, &lrowindices));
5515:   for (i = 0; i < nrows; i++) {
5516:     /* Find a remote index and an owner for a row
5517:      * The row could be local or remote
5518:      * */
5519:     owner = 0;
5520:     lidx  = 0;
5521:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5522:     iremote[i].index = lidx;
5523:     iremote[i].rank  = owner;
5524:   }
5525:   /* Create SF to communicate how many nonzero columns for each row */
5526:   PetscCall(PetscSFCreate(comm, &sf));
5527:   /* SF will figure out the number of nonzero columns for each row, and their
5528:    * offsets
5529:    * */
5530:   PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5531:   PetscCall(PetscSFSetFromOptions(sf));
5532:   PetscCall(PetscSFSetUp(sf));

5534:   PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5535:   PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5536:   PetscCall(PetscCalloc1(nrows, &pnnz));
5537:   roffsets[0] = 0;
5538:   roffsets[1] = 0;
5539:   for (i = 0; i < plocalsize; i++) {
5540:     /* diagonal */
5541:     nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5542:     /* off-diagonal */
5543:     nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5544:     /* compute offsets so that we relative location for each row */
5545:     roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5546:     roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5547:   }
5548:   PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5549:   PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5550:   /* 'r' means root, and 'l' means leaf */
5551:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5552:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5553:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5554:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5555:   PetscCall(PetscSFDestroy(&sf));
5556:   PetscCall(PetscFree(roffsets));
5557:   PetscCall(PetscFree(nrcols));
5558:   dntotalcols = 0;
5559:   ontotalcols = 0;
5560:   ncol        = 0;
5561:   for (i = 0; i < nrows; i++) {
5562:     pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5563:     ncol    = PetscMax(pnnz[i], ncol);
5564:     /* diagonal */
5565:     dntotalcols += nlcols[i * 2 + 0];
5566:     /* off-diagonal */
5567:     ontotalcols += nlcols[i * 2 + 1];
5568:   }
5569:   /* We do not need to figure the right number of columns
5570:    * since all the calculations will be done by going through the raw data
5571:    * */
5572:   PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5573:   PetscCall(MatSetUp(*P_oth));
5574:   PetscCall(PetscFree(pnnz));
5575:   p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5576:   /* diagonal */
5577:   PetscCall(PetscCalloc1(dntotalcols, &iremote));
5578:   /* off-diagonal */
5579:   PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5580:   /* diagonal */
5581:   PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5582:   /* off-diagonal */
5583:   PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5584:   dntotalcols = 0;
5585:   ontotalcols = 0;
5586:   ntotalcols  = 0;
5587:   for (i = 0; i < nrows; i++) {
5588:     owner = 0;
5589:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5590:     /* Set iremote for diag matrix */
5591:     for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5592:       iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5593:       iremote[dntotalcols].rank  = owner;
5594:       /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5595:       ilocal[dntotalcols++] = ntotalcols++;
5596:     }
5597:     /* off-diagonal */
5598:     for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5599:       oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5600:       oiremote[ontotalcols].rank  = owner;
5601:       oilocal[ontotalcols++]      = ntotalcols++;
5602:     }
5603:   }
5604:   PetscCall(ISRestoreIndices(rows, &lrowindices));
5605:   PetscCall(PetscFree(loffsets));
5606:   PetscCall(PetscFree(nlcols));
5607:   PetscCall(PetscSFCreate(comm, &sf));
5608:   /* P serves as roots and P_oth is leaves
5609:    * Diag matrix
5610:    * */
5611:   PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5612:   PetscCall(PetscSFSetFromOptions(sf));
5613:   PetscCall(PetscSFSetUp(sf));

5615:   PetscCall(PetscSFCreate(comm, &osf));
5616:   /* off-diagonal */
5617:   PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5618:   PetscCall(PetscSFSetFromOptions(osf));
5619:   PetscCall(PetscSFSetUp(osf));
5620:   PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5621:   PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5622:   /* operate on the matrix internal data to save memory */
5623:   PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5624:   PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5625:   PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5626:   /* Convert to global indices for diag matrix */
5627:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5628:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5629:   /* We want P_oth store global indices */
5630:   PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5631:   /* Use memory scalable approach */
5632:   PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5633:   PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5634:   PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5635:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5636:   /* Convert back to local indices */
5637:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5638:   PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5639:   nout = 0;
5640:   PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5641:   PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5642:   PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5643:   /* Exchange values */
5644:   PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5645:   PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5646:   PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5647:   PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5648:   /* Stop PETSc from shrinking memory */
5649:   for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5650:   PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5651:   PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5652:   /* Attach PetscSF objects to P_oth so that we can reuse it later */
5653:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5654:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5655:   PetscCall(PetscSFDestroy(&sf));
5656:   PetscCall(PetscSFDestroy(&osf));
5657:   PetscFunctionReturn(PETSC_SUCCESS);
5658: }

5660: /*
5661:  * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5662:  * This supports MPIAIJ and MAIJ
5663:  * */
5664: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5665: {
5666:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5667:   Mat_SeqAIJ *p_oth;
5668:   IS          rows, map;
5669:   PetscHMapI  hamp;
5670:   PetscInt    i, htsize, *rowindices, off, *mapping, key, count;
5671:   MPI_Comm    comm;
5672:   PetscSF     sf, osf;
5673:   PetscBool   has;

5675:   PetscFunctionBegin;
5676:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5677:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5678:   /* If it is the first time, create an index set of off-diag nonzero columns of A,
5679:    *  and then create a submatrix (that often is an overlapping matrix)
5680:    * */
5681:   if (reuse == MAT_INITIAL_MATRIX) {
5682:     /* Use a hash table to figure out unique keys */
5683:     PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5684:     PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5685:     count = 0;
5686:     /* Assume that  a->g is sorted, otherwise the following does not make sense */
5687:     for (i = 0; i < a->B->cmap->n; i++) {
5688:       key = a->garray[i] / dof;
5689:       PetscCall(PetscHMapIHas(hamp, key, &has));
5690:       if (!has) {
5691:         mapping[i] = count;
5692:         PetscCall(PetscHMapISet(hamp, key, count++));
5693:       } else {
5694:         /* Current 'i' has the same value the previous step */
5695:         mapping[i] = count - 1;
5696:       }
5697:     }
5698:     PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5699:     PetscCall(PetscHMapIGetSize(hamp, &htsize));
5700:     PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5701:     PetscCall(PetscCalloc1(htsize, &rowindices));
5702:     off = 0;
5703:     PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5704:     PetscCall(PetscHMapIDestroy(&hamp));
5705:     PetscCall(PetscSortInt(htsize, rowindices));
5706:     PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5707:     /* In case, the matrix was already created but users want to recreate the matrix */
5708:     PetscCall(MatDestroy(P_oth));
5709:     PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5710:     PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5711:     PetscCall(ISDestroy(&map));
5712:     PetscCall(ISDestroy(&rows));
5713:   } else if (reuse == MAT_REUSE_MATRIX) {
5714:     /* If matrix was already created, we simply update values using SF objects
5715:      * that as attached to the matrix earlier.
5716:      */
5717:     const PetscScalar *pd_a, *po_a;

5719:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5720:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5721:     PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5722:     p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5723:     /* Update values in place */
5724:     PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5725:     PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5726:     PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5727:     PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5728:     PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5729:     PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5730:     PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5731:     PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5732:   } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5733:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5734:   PetscFunctionReturn(PETSC_SUCCESS);
5735: }

5737: /*@C
5738:   MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`

5740:   Collective

5742:   Input Parameters:
5743: + A     - the first matrix in `MATMPIAIJ` format
5744: . B     - the second matrix in `MATMPIAIJ` format
5745: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5747:   Output Parameters:
5748: + rowb  - On input index sets of rows of B to extract (or `NULL`), modified on output
5749: . colb  - On input index sets of columns of B to extract (or `NULL`), modified on output
5750: - B_seq - the sequential matrix generated

5752:   Level: developer

5754: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5755: @*/
5756: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5757: {
5758:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5759:   PetscInt   *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5760:   IS          isrowb, iscolb;
5761:   Mat        *bseq = NULL;

5763:   PetscFunctionBegin;
5764:   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 ")",
5765:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5766:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));

5768:   if (scall == MAT_INITIAL_MATRIX) {
5769:     start = A->cmap->rstart;
5770:     cmap  = a->garray;
5771:     nzA   = a->A->cmap->n;
5772:     nzB   = a->B->cmap->n;
5773:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5774:     ncols = 0;
5775:     for (i = 0; i < nzB; i++) { /* row < local row index */
5776:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5777:       else break;
5778:     }
5779:     imark = i;
5780:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;   /* local rows */
5781:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5782:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5783:     PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5784:   } else {
5785:     PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5786:     isrowb = *rowb;
5787:     iscolb = *colb;
5788:     PetscCall(PetscMalloc1(1, &bseq));
5789:     bseq[0] = *B_seq;
5790:   }
5791:   PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5792:   *B_seq = bseq[0];
5793:   PetscCall(PetscFree(bseq));
5794:   if (!rowb) {
5795:     PetscCall(ISDestroy(&isrowb));
5796:   } else {
5797:     *rowb = isrowb;
5798:   }
5799:   if (!colb) {
5800:     PetscCall(ISDestroy(&iscolb));
5801:   } else {
5802:     *colb = iscolb;
5803:   }
5804:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5805:   PetscFunctionReturn(PETSC_SUCCESS);
5806: }

5808: /*
5809:     MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5810:     of the OFF-DIAGONAL portion of local A

5812:     Collective

5814:    Input Parameters:
5815: +    A,B - the matrices in `MATMPIAIJ` format
5816: -    scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5818:    Output Parameter:
5819: +    startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5820: .    startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5821: .    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5822: -    B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N

5824:     Developer Note:
5825:     This directly accesses information inside the VecScatter associated with the matrix-vector product
5826:      for this matrix. This is not desirable..

5828:     Level: developer

5830: */
5831: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5832: {
5833:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
5834:   Mat_SeqAIJ        *b_oth;
5835:   VecScatter         ctx;
5836:   MPI_Comm           comm;
5837:   const PetscMPIInt *rprocs, *sprocs;
5838:   const PetscInt    *srow, *rstarts, *sstarts;
5839:   PetscInt          *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5840:   PetscInt           i, j, k = 0, l, ll, nrecvs, nsends, nrows, *rstartsj = NULL, *sstartsj, len;
5841:   PetscScalar       *b_otha, *bufa, *bufA, *vals = NULL;
5842:   MPI_Request       *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5843:   PetscMPIInt        size, tag, rank, nreqs;

5845:   PetscFunctionBegin;
5846:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5847:   PetscCallMPI(MPI_Comm_size(comm, &size));

5849:   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 ")",
5850:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5851:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5852:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

5854:   if (size == 1) {
5855:     startsj_s = NULL;
5856:     bufa_ptr  = NULL;
5857:     *B_oth    = NULL;
5858:     PetscFunctionReturn(PETSC_SUCCESS);
5859:   }

5861:   ctx = a->Mvctx;
5862:   tag = ((PetscObject)ctx)->tag;

5864:   PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5865:   /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5866:   PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5867:   PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5868:   PetscCall(PetscMalloc1(nreqs, &reqs));
5869:   rwaits = reqs;
5870:   swaits = PetscSafePointerPlusOffset(reqs, nrecvs);

5872:   if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5873:   if (scall == MAT_INITIAL_MATRIX) {
5874:     /* i-array */
5875:     /*  post receives */
5876:     if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5877:     for (i = 0; i < nrecvs; i++) {
5878:       rowlen = rvalues + rstarts[i] * rbs;
5879:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5880:       PetscCallMPI(MPI_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5881:     }

5883:     /* pack the outgoing message */
5884:     PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));

5886:     sstartsj[0] = 0;
5887:     rstartsj[0] = 0;
5888:     len         = 0; /* total length of j or a array to be sent */
5889:     if (nsends) {
5890:       k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5891:       PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5892:     }
5893:     for (i = 0; i < nsends; i++) {
5894:       rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5895:       nrows  = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5896:       for (j = 0; j < nrows; j++) {
5897:         row = srow[k] + B->rmap->range[rank]; /* global row idx */
5898:         for (l = 0; l < sbs; l++) {
5899:           PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */

5901:           rowlen[j * sbs + l] = ncols;

5903:           len += ncols;
5904:           PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5905:         }
5906:         k++;
5907:       }
5908:       PetscCallMPI(MPI_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));

5910:       sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5911:     }
5912:     /* recvs and sends of i-array are completed */
5913:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5914:     PetscCall(PetscFree(svalues));

5916:     /* allocate buffers for sending j and a arrays */
5917:     PetscCall(PetscMalloc1(len + 1, &bufj));
5918:     PetscCall(PetscMalloc1(len + 1, &bufa));

5920:     /* create i-array of B_oth */
5921:     PetscCall(PetscMalloc1(aBn + 2, &b_othi));

5923:     b_othi[0] = 0;
5924:     len       = 0; /* total length of j or a array to be received */
5925:     k         = 0;
5926:     for (i = 0; i < nrecvs; i++) {
5927:       rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5928:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5929:       for (j = 0; j < nrows; j++) {
5930:         b_othi[k + 1] = b_othi[k] + rowlen[j];
5931:         PetscCall(PetscIntSumError(rowlen[j], len, &len));
5932:         k++;
5933:       }
5934:       rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5935:     }
5936:     PetscCall(PetscFree(rvalues));

5938:     /* allocate space for j and a arrays of B_oth */
5939:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5940:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));

5942:     /* j-array */
5943:     /*  post receives of j-array */
5944:     for (i = 0; i < nrecvs; i++) {
5945:       nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5946:       PetscCallMPI(MPI_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5947:     }

5949:     /* pack the outgoing message j-array */
5950:     if (nsends) k = sstarts[0];
5951:     for (i = 0; i < nsends; i++) {
5952:       nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5953:       bufJ  = bufj + sstartsj[i];
5954:       for (j = 0; j < nrows; j++) {
5955:         row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5956:         for (ll = 0; ll < sbs; ll++) {
5957:           PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5958:           for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5959:           PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5960:         }
5961:       }
5962:       PetscCallMPI(MPI_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5963:     }

5965:     /* recvs and sends of j-array are completed */
5966:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5967:   } else if (scall == MAT_REUSE_MATRIX) {
5968:     sstartsj = *startsj_s;
5969:     rstartsj = *startsj_r;
5970:     bufa     = *bufa_ptr;
5971:     b_oth    = (Mat_SeqAIJ *)(*B_oth)->data;
5972:     PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5973:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");

5975:   /* a-array */
5976:   /*  post receives of a-array */
5977:   for (i = 0; i < nrecvs; i++) {
5978:     nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5979:     PetscCallMPI(MPI_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5980:   }

5982:   /* pack the outgoing message a-array */
5983:   if (nsends) k = sstarts[0];
5984:   for (i = 0; i < nsends; i++) {
5985:     nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5986:     bufA  = bufa + sstartsj[i];
5987:     for (j = 0; j < nrows; j++) {
5988:       row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5989:       for (ll = 0; ll < sbs; ll++) {
5990:         PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5991:         for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5992:         PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5993:       }
5994:     }
5995:     PetscCallMPI(MPI_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5996:   }
5997:   /* recvs and sends of a-array are completed */
5998:   if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5999:   PetscCall(PetscFree(reqs));

6001:   if (scall == MAT_INITIAL_MATRIX) {
6002:     /* put together the new matrix */
6003:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));

6005:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6006:     /* Since these are PETSc arrays, change flags to free them as necessary. */
6007:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
6008:     b_oth->free_a  = PETSC_TRUE;
6009:     b_oth->free_ij = PETSC_TRUE;
6010:     b_oth->nonew   = 0;

6012:     PetscCall(PetscFree(bufj));
6013:     if (!startsj_s || !bufa_ptr) {
6014:       PetscCall(PetscFree2(sstartsj, rstartsj));
6015:       PetscCall(PetscFree(bufa_ptr));
6016:     } else {
6017:       *startsj_s = sstartsj;
6018:       *startsj_r = rstartsj;
6019:       *bufa_ptr  = bufa;
6020:     }
6021:   } else if (scall == MAT_REUSE_MATRIX) {
6022:     PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6023:   }

6025:   PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6026:   PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6027:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6028:   PetscFunctionReturn(PETSC_SUCCESS);
6029: }

6031: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6032: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6033: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6034: #if defined(PETSC_HAVE_MKL_SPARSE)
6035: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6036: #endif
6037: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6038: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6039: #if defined(PETSC_HAVE_ELEMENTAL)
6040: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6041: #endif
6042: #if defined(PETSC_HAVE_SCALAPACK)
6043: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6044: #endif
6045: #if defined(PETSC_HAVE_HYPRE)
6046: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6047: #endif
6048: #if defined(PETSC_HAVE_CUDA)
6049: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6050: #endif
6051: #if defined(PETSC_HAVE_HIP)
6052: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6053: #endif
6054: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6055: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6056: #endif
6057: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6058: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6059: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

6061: /*
6062:     Computes (B'*A')' since computing B*A directly is untenable

6064:                n                       p                          p
6065:         [             ]       [             ]         [                 ]
6066:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
6067:         [             ]       [             ]         [                 ]

6069: */
6070: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6071: {
6072:   Mat At, Bt, Ct;

6074:   PetscFunctionBegin;
6075:   PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6076:   PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6077:   PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &Ct));
6078:   PetscCall(MatDestroy(&At));
6079:   PetscCall(MatDestroy(&Bt));
6080:   PetscCall(MatTransposeSetPrecursor(Ct, C));
6081:   PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6082:   PetscCall(MatDestroy(&Ct));
6083:   PetscFunctionReturn(PETSC_SUCCESS);
6084: }

6086: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6087: {
6088:   PetscBool cisdense;

6090:   PetscFunctionBegin;
6091:   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);
6092:   PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6093:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
6094:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6095:   if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6096:   PetscCall(MatSetUp(C));

6098:   C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6099:   PetscFunctionReturn(PETSC_SUCCESS);
6100: }

6102: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6103: {
6104:   Mat_Product *product = C->product;
6105:   Mat          A = product->A, B = product->B;

6107:   PetscFunctionBegin;
6108:   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 ")",
6109:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6110:   C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6111:   C->ops->productsymbolic = MatProductSymbolic_AB;
6112:   PetscFunctionReturn(PETSC_SUCCESS);
6113: }

6115: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6116: {
6117:   Mat_Product *product = C->product;

6119:   PetscFunctionBegin;
6120:   if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6121:   PetscFunctionReturn(PETSC_SUCCESS);
6122: }

6124: /*
6125:    Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix

6127:   Input Parameters:

6129:     j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6130:     j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)

6132:     mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat

6134:     For Set1, j1[] contains column indices of the nonzeros.
6135:     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
6136:     respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6137:     but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.

6139:     Similar for Set2.

6141:     This routine merges the two sets of nonzeros row by row and removes repeats.

6143:   Output Parameters: (memory is allocated by the caller)

6145:     i[],j[]: the CSR of the merged matrix, which has m rows.
6146:     imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6147:     imap2[]: similar to imap1[], but for Set2.
6148:     Note we order nonzeros row-by-row and from left to right.
6149: */
6150: 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[])
6151: {
6152:   PetscInt   r, m; /* Row index of mat */
6153:   PetscCount t, t1, t2, b1, e1, b2, e2;

6155:   PetscFunctionBegin;
6156:   PetscCall(MatGetLocalSize(mat, &m, NULL));
6157:   t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6158:   i[0]        = 0;
6159:   for (r = 0; r < m; r++) { /* Do row by row merging */
6160:     b1 = rowBegin1[r];
6161:     e1 = rowEnd1[r];
6162:     b2 = rowBegin2[r];
6163:     e2 = rowEnd2[r];
6164:     while (b1 < e1 && b2 < e2) {
6165:       if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6166:         j[t]      = j1[b1];
6167:         imap1[t1] = t;
6168:         imap2[t2] = t;
6169:         b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6170:         b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6171:         t1++;
6172:         t2++;
6173:         t++;
6174:       } else if (j1[b1] < j2[b2]) {
6175:         j[t]      = j1[b1];
6176:         imap1[t1] = t;
6177:         b1 += jmap1[t1 + 1] - jmap1[t1];
6178:         t1++;
6179:         t++;
6180:       } else {
6181:         j[t]      = j2[b2];
6182:         imap2[t2] = t;
6183:         b2 += jmap2[t2 + 1] - jmap2[t2];
6184:         t2++;
6185:         t++;
6186:       }
6187:     }
6188:     /* Merge the remaining in either j1[] or j2[] */
6189:     while (b1 < e1) {
6190:       j[t]      = j1[b1];
6191:       imap1[t1] = t;
6192:       b1 += jmap1[t1 + 1] - jmap1[t1];
6193:       t1++;
6194:       t++;
6195:     }
6196:     while (b2 < e2) {
6197:       j[t]      = j2[b2];
6198:       imap2[t2] = t;
6199:       b2 += jmap2[t2 + 1] - jmap2[t2];
6200:       t2++;
6201:       t++;
6202:     }
6203:     i[r + 1] = t;
6204:   }
6205:   PetscFunctionReturn(PETSC_SUCCESS);
6206: }

6208: /*
6209:   Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block

6211:   Input Parameters:
6212:     mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6213:     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[]
6214:       respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.

6216:       i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6217:       i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.

6219:   Output Parameters:
6220:     j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6221:     rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6222:       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,
6223:       and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.

6225:     Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6226:       Atot: number of entries belonging to the diagonal block.
6227:       Annz: number of unique nonzeros belonging to the diagonal block.
6228:       Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6229:         repeats (i.e., same 'i,j' pair).
6230:       Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6231:         is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.

6233:       Atot: number of entries belonging to the diagonal block
6234:       Annz: number of unique nonzeros belonging to the diagonal block.

6236:     Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.

6238:     Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6239: */
6240: 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_)
6241: {
6242:   PetscInt    cstart, cend, rstart, rend, row, col;
6243:   PetscCount  Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6244:   PetscCount  Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6245:   PetscCount  k, m, p, q, r, s, mid;
6246:   PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;

6248:   PetscFunctionBegin;
6249:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6250:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6251:   m = rend - rstart;

6253:   /* Skip negative rows */
6254:   for (k = 0; k < n; k++)
6255:     if (i[k] >= 0) break;

6257:   /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6258:      fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6259:   */
6260:   while (k < n) {
6261:     row = i[k];
6262:     /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6263:     for (s = k; s < n; s++)
6264:       if (i[s] != row) break;

6266:     /* Shift diag columns to range of [-PETSC_MAX_INT, -1] */
6267:     for (p = k; p < s; p++) {
6268:       if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_MAX_INT;
6269:       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]);
6270:     }
6271:     PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6272:     PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6273:     rowBegin[row - rstart] = k;
6274:     rowMid[row - rstart]   = mid;
6275:     rowEnd[row - rstart]   = s;

6277:     /* Count nonzeros of this diag/offdiag row, which might have repeats */
6278:     Atot += mid - k;
6279:     Btot += s - mid;

6281:     /* Count unique nonzeros of this diag row */
6282:     for (p = k; p < mid;) {
6283:       col = j[p];
6284:       do {
6285:         j[p] += PETSC_MAX_INT; /* Revert the modified diagonal indices */
6286:         p++;
6287:       } while (p < mid && j[p] == col);
6288:       Annz++;
6289:     }

6291:     /* Count unique nonzeros of this offdiag row */
6292:     for (p = mid; p < s;) {
6293:       col = j[p];
6294:       do {
6295:         p++;
6296:       } while (p < s && j[p] == col);
6297:       Bnnz++;
6298:     }
6299:     k = s;
6300:   }

6302:   /* Allocation according to Atot, Btot, Annz, Bnnz */
6303:   PetscCall(PetscMalloc1(Atot, &Aperm));
6304:   PetscCall(PetscMalloc1(Btot, &Bperm));
6305:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6306:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));

6308:   /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6309:   Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6310:   for (r = 0; r < m; r++) {
6311:     k   = rowBegin[r];
6312:     mid = rowMid[r];
6313:     s   = rowEnd[r];
6314:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6315:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6316:     Atot += mid - k;
6317:     Btot += s - mid;

6319:     /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6320:     for (p = k; p < mid;) {
6321:       col = j[p];
6322:       q   = p;
6323:       do {
6324:         p++;
6325:       } while (p < mid && j[p] == col);
6326:       Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6327:       Annz++;
6328:     }

6330:     for (p = mid; p < s;) {
6331:       col = j[p];
6332:       q   = p;
6333:       do {
6334:         p++;
6335:       } while (p < s && j[p] == col);
6336:       Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6337:       Bnnz++;
6338:     }
6339:   }
6340:   /* Output */
6341:   *Aperm_ = Aperm;
6342:   *Annz_  = Annz;
6343:   *Atot_  = Atot;
6344:   *Ajmap_ = Ajmap;
6345:   *Bperm_ = Bperm;
6346:   *Bnnz_  = Bnnz;
6347:   *Btot_  = Btot;
6348:   *Bjmap_ = Bjmap;
6349:   PetscFunctionReturn(PETSC_SUCCESS);
6350: }

6352: /*
6353:   Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix

6355:   Input Parameters:
6356:     nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6357:     nnz:  number of unique nonzeros in the merged matrix
6358:     imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6359:     jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set

6361:   Output Parameter: (memory is allocated by the caller)
6362:     jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set

6364:   Example:
6365:     nnz1 = 4
6366:     nnz  = 6
6367:     imap = [1,3,4,5]
6368:     jmap = [0,3,5,6,7]
6369:    then,
6370:     jmap_new = [0,0,3,3,5,6,7]
6371: */
6372: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6373: {
6374:   PetscCount k, p;

6376:   PetscFunctionBegin;
6377:   jmap_new[0] = 0;
6378:   p           = nnz;                /* p loops over jmap_new[] backwards */
6379:   for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6380:     for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6381:   }
6382:   for (; p >= 0; p--) jmap_new[p] = jmap[0];
6383:   PetscFunctionReturn(PETSC_SUCCESS);
6384: }

6386: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6387: {
6388:   MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;

6390:   PetscFunctionBegin;
6391:   PetscCall(PetscSFDestroy(&coo->sf));
6392:   PetscCall(PetscFree(coo->Aperm1));
6393:   PetscCall(PetscFree(coo->Bperm1));
6394:   PetscCall(PetscFree(coo->Ajmap1));
6395:   PetscCall(PetscFree(coo->Bjmap1));
6396:   PetscCall(PetscFree(coo->Aimap2));
6397:   PetscCall(PetscFree(coo->Bimap2));
6398:   PetscCall(PetscFree(coo->Aperm2));
6399:   PetscCall(PetscFree(coo->Bperm2));
6400:   PetscCall(PetscFree(coo->Ajmap2));
6401:   PetscCall(PetscFree(coo->Bjmap2));
6402:   PetscCall(PetscFree(coo->Cperm1));
6403:   PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6404:   PetscCall(PetscFree(coo));
6405:   PetscFunctionReturn(PETSC_SUCCESS);
6406: }

6408: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6409: {
6410:   MPI_Comm             comm;
6411:   PetscMPIInt          rank, size;
6412:   PetscInt             m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6413:   PetscCount           k, p, q, rem;                           /* Loop variables over coo arrays */
6414:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6415:   PetscContainer       container;
6416:   MatCOOStruct_MPIAIJ *coo;

6418:   PetscFunctionBegin;
6419:   PetscCall(PetscFree(mpiaij->garray));
6420:   PetscCall(VecDestroy(&mpiaij->lvec));
6421: #if defined(PETSC_USE_CTABLE)
6422:   PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6423: #else
6424:   PetscCall(PetscFree(mpiaij->colmap));
6425: #endif
6426:   PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6427:   mat->assembled     = PETSC_FALSE;
6428:   mat->was_assembled = PETSC_FALSE;

6430:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6431:   PetscCallMPI(MPI_Comm_size(comm, &size));
6432:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
6433:   PetscCall(PetscLayoutSetUp(mat->rmap));
6434:   PetscCall(PetscLayoutSetUp(mat->cmap));
6435:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6436:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6437:   PetscCall(MatGetLocalSize(mat, &m, &n));
6438:   PetscCall(MatGetSize(mat, &M, &N));

6440:   /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6441:   /* entries come first, then local rows, then remote rows.                     */
6442:   PetscCount n1 = coo_n, *perm1;
6443:   PetscInt  *i1 = coo_i, *j1 = coo_j;

6445:   PetscCall(PetscMalloc1(n1, &perm1));
6446:   for (k = 0; k < n1; k++) perm1[k] = k;

6448:   /* Manipulate indices so that entries with negative row or col indices will have smallest
6449:      row indices, local entries will have greater but negative row indices, and remote entries
6450:      will have positive row indices.
6451:   */
6452:   for (k = 0; k < n1; k++) {
6453:     if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_MIN_INT;                /* e.g., -2^31, minimal to move them ahead */
6454:     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] */
6455:     else {
6456:       PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6457:       if (mpiaij->donotstash) i1[k] = PETSC_MIN_INT; /* Ignore offproc entries as if they had negative indices */
6458:     }
6459:   }

6461:   /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6462:   PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));

6464:   /* Advance k to the first entry we need to take care of */
6465:   for (k = 0; k < n1; k++)
6466:     if (i1[k] > PETSC_MIN_INT) break;
6467:   PetscInt i1start = k;

6469:   PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_MAX_INT, &rem)); /* rem is upper bound of the last local row */
6470:   for (; k < rem; k++) i1[k] += PETSC_MAX_INT;                                    /* Revert row indices of local rows*/

6472:   /*           Send remote rows to their owner                                  */
6473:   /* Find which rows should be sent to which remote ranks*/
6474:   PetscInt        nsend = 0; /* Number of MPI ranks to send data to */
6475:   PetscMPIInt    *sendto;    /* [nsend], storing remote ranks */
6476:   PetscInt       *nentries;  /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6477:   const PetscInt *ranges;
6478:   PetscInt        maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */

6480:   PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6481:   PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6482:   for (k = rem; k < n1;) {
6483:     PetscMPIInt owner;
6484:     PetscInt    firstRow, lastRow;

6486:     /* Locate a row range */
6487:     firstRow = i1[k]; /* first row of this owner */
6488:     PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6489:     lastRow = ranges[owner + 1] - 1; /* last row of this owner */

6491:     /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6492:     PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));

6494:     /* All entries in [k,p) belong to this remote owner */
6495:     if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6496:       PetscMPIInt *sendto2;
6497:       PetscInt    *nentries2;
6498:       PetscInt     maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;

6500:       PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6501:       PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6502:       PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6503:       PetscCall(PetscFree2(sendto, nentries2));
6504:       sendto   = sendto2;
6505:       nentries = nentries2;
6506:       maxNsend = maxNsend2;
6507:     }
6508:     sendto[nsend]   = owner;
6509:     nentries[nsend] = p - k;
6510:     PetscCall(PetscCountCast(p - k, &nentries[nsend]));
6511:     nsend++;
6512:     k = p;
6513:   }

6515:   /* Build 1st SF to know offsets on remote to send data */
6516:   PetscSF      sf1;
6517:   PetscInt     nroots = 1, nroots2 = 0;
6518:   PetscInt     nleaves = nsend, nleaves2 = 0;
6519:   PetscInt    *offsets;
6520:   PetscSFNode *iremote;

6522:   PetscCall(PetscSFCreate(comm, &sf1));
6523:   PetscCall(PetscMalloc1(nsend, &iremote));
6524:   PetscCall(PetscMalloc1(nsend, &offsets));
6525:   for (k = 0; k < nsend; k++) {
6526:     iremote[k].rank  = sendto[k];
6527:     iremote[k].index = 0;
6528:     nleaves2 += nentries[k];
6529:     PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6530:   }
6531:   PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6532:   PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6533:   PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6534:   PetscCall(PetscSFDestroy(&sf1));
6535:   PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);

6537:   /* Build 2nd SF to send remote COOs to their owner */
6538:   PetscSF sf2;
6539:   nroots  = nroots2;
6540:   nleaves = nleaves2;
6541:   PetscCall(PetscSFCreate(comm, &sf2));
6542:   PetscCall(PetscSFSetFromOptions(sf2));
6543:   PetscCall(PetscMalloc1(nleaves, &iremote));
6544:   p = 0;
6545:   for (k = 0; k < nsend; k++) {
6546:     PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6547:     for (q = 0; q < nentries[k]; q++, p++) {
6548:       iremote[p].rank  = sendto[k];
6549:       iremote[p].index = offsets[k] + q;
6550:     }
6551:   }
6552:   PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));

6554:   /* Send the remote COOs to their owner */
6555:   PetscInt    n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6556:   PetscCount *perm2;                 /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6557:   PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6558:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1 + rem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6559:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1 + rem, i2, MPI_REPLACE));
6560:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1 + rem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6561:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1 + rem, j2, MPI_REPLACE));

6563:   PetscCall(PetscFree(offsets));
6564:   PetscCall(PetscFree2(sendto, nentries));

6566:   /* Sort received COOs by row along with the permutation array     */
6567:   for (k = 0; k < n2; k++) perm2[k] = k;
6568:   PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));

6570:   /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6571:   PetscCount *Cperm1;
6572:   PetscCall(PetscMalloc1(nleaves, &Cperm1));
6573:   PetscCall(PetscArraycpy(Cperm1, perm1 + rem, nleaves));

6575:   /* Support for HYPRE matrices, kind of a hack.
6576:      Swap min column with diagonal so that diagonal values will go first */
6577:   PetscBool   hypre;
6578:   const char *name;
6579:   PetscCall(PetscObjectGetName((PetscObject)mat, &name));
6580:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
6581:   if (hypre) {
6582:     PetscInt *minj;
6583:     PetscBT   hasdiag;

6585:     PetscCall(PetscBTCreate(m, &hasdiag));
6586:     PetscCall(PetscMalloc1(m, &minj));
6587:     for (k = 0; k < m; k++) minj[k] = PETSC_MAX_INT;
6588:     for (k = i1start; k < rem; k++) {
6589:       if (j1[k] < cstart || j1[k] >= cend) continue;
6590:       const PetscInt rindex = i1[k] - rstart;
6591:       if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6592:       minj[rindex] = PetscMin(minj[rindex], j1[k]);
6593:     }
6594:     for (k = 0; k < n2; k++) {
6595:       if (j2[k] < cstart || j2[k] >= cend) continue;
6596:       const PetscInt rindex = i2[k] - rstart;
6597:       if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6598:       minj[rindex] = PetscMin(minj[rindex], j2[k]);
6599:     }
6600:     for (k = i1start; k < rem; k++) {
6601:       const PetscInt rindex = i1[k] - rstart;
6602:       if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6603:       if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6604:       else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6605:     }
6606:     for (k = 0; k < n2; k++) {
6607:       const PetscInt rindex = i2[k] - rstart;
6608:       if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6609:       if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6610:       else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6611:     }
6612:     PetscCall(PetscBTDestroy(&hasdiag));
6613:     PetscCall(PetscFree(minj));
6614:   }

6616:   /* Split local COOs and received COOs into diag/offdiag portions */
6617:   PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6618:   PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6619:   PetscCount  Annz1, Bnnz1, Atot1, Btot1;
6620:   PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6621:   PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6622:   PetscCount  Annz2, Bnnz2, Atot2, Btot2;

6624:   PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6625:   PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6626:   PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6627:   PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));

6629:   /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6630:   PetscInt *Ai, *Bi;
6631:   PetscInt *Aj, *Bj;

6633:   PetscCall(PetscMalloc1(m + 1, &Ai));
6634:   PetscCall(PetscMalloc1(m + 1, &Bi));
6635:   PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6636:   PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));

6638:   PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6639:   PetscCall(PetscMalloc1(Annz1, &Aimap1));
6640:   PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6641:   PetscCall(PetscMalloc1(Annz2, &Aimap2));
6642:   PetscCall(PetscMalloc1(Bnnz2, &Bimap2));

6644:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6645:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));

6647:   /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we     */
6648:   /* expect nonzeros in A/B most likely have local contributing entries        */
6649:   PetscInt    Annz = Ai[m];
6650:   PetscInt    Bnnz = Bi[m];
6651:   PetscCount *Ajmap1_new, *Bjmap1_new;

6653:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6654:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));

6656:   PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6657:   PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));

6659:   PetscCall(PetscFree(Aimap1));
6660:   PetscCall(PetscFree(Ajmap1));
6661:   PetscCall(PetscFree(Bimap1));
6662:   PetscCall(PetscFree(Bjmap1));
6663:   PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6664:   PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6665:   PetscCall(PetscFree(perm1));
6666:   PetscCall(PetscFree3(i2, j2, perm2));

6668:   Ajmap1 = Ajmap1_new;
6669:   Bjmap1 = Bjmap1_new;

6671:   /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6672:   if (Annz < Annz1 + Annz2) {
6673:     PetscInt *Aj_new;
6674:     PetscCall(PetscMalloc1(Annz, &Aj_new));
6675:     PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6676:     PetscCall(PetscFree(Aj));
6677:     Aj = Aj_new;
6678:   }

6680:   if (Bnnz < Bnnz1 + Bnnz2) {
6681:     PetscInt *Bj_new;
6682:     PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6683:     PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6684:     PetscCall(PetscFree(Bj));
6685:     Bj = Bj_new;
6686:   }

6688:   /* Create new submatrices for on-process and off-process coupling                  */
6689:   PetscScalar     *Aa, *Ba;
6690:   MatType          rtype;
6691:   Mat_SeqAIJ      *a, *b;
6692:   PetscObjectState state;
6693:   PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6694:   PetscCall(PetscCalloc1(Bnnz, &Ba));
6695:   /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6696:   if (cstart) {
6697:     for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6698:   }

6700:   PetscCall(MatGetRootType_Private(mat, &rtype));

6702:   MatSeqXAIJGetOptions_Private(mpiaij->A);
6703:   PetscCall(MatDestroy(&mpiaij->A));
6704:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6705:   PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6706:   MatSeqXAIJRestoreOptions_Private(mpiaij->A);

6708:   MatSeqXAIJGetOptions_Private(mpiaij->B);
6709:   PetscCall(MatDestroy(&mpiaij->B));
6710:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6711:   PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6712:   MatSeqXAIJRestoreOptions_Private(mpiaij->B);

6714:   PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6715:   mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6716:   state              = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6717:   PetscCall(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));

6719:   a               = (Mat_SeqAIJ *)mpiaij->A->data;
6720:   b               = (Mat_SeqAIJ *)mpiaij->B->data;
6721:   a->singlemalloc = b->singlemalloc = PETSC_FALSE; /* Let newmat own Ai,Aj,Aa,Bi,Bj,Ba */
6722:   a->free_a = b->free_a = PETSC_TRUE;
6723:   a->free_ij = b->free_ij = PETSC_TRUE;

6725:   /* conversion must happen AFTER multiply setup */
6726:   PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6727:   PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6728:   PetscCall(VecDestroy(&mpiaij->lvec));
6729:   PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));

6731:   // Put the COO struct in a container and then attach that to the matrix
6732:   PetscCall(PetscMalloc1(1, &coo));
6733:   coo->n       = coo_n;
6734:   coo->sf      = sf2;
6735:   coo->sendlen = nleaves;
6736:   coo->recvlen = nroots;
6737:   coo->Annz    = Annz;
6738:   coo->Bnnz    = Bnnz;
6739:   coo->Annz2   = Annz2;
6740:   coo->Bnnz2   = Bnnz2;
6741:   coo->Atot1   = Atot1;
6742:   coo->Atot2   = Atot2;
6743:   coo->Btot1   = Btot1;
6744:   coo->Btot2   = Btot2;
6745:   coo->Ajmap1  = Ajmap1;
6746:   coo->Aperm1  = Aperm1;
6747:   coo->Bjmap1  = Bjmap1;
6748:   coo->Bperm1  = Bperm1;
6749:   coo->Aimap2  = Aimap2;
6750:   coo->Ajmap2  = Ajmap2;
6751:   coo->Aperm2  = Aperm2;
6752:   coo->Bimap2  = Bimap2;
6753:   coo->Bjmap2  = Bjmap2;
6754:   coo->Bperm2  = Bperm2;
6755:   coo->Cperm1  = Cperm1;
6756:   // Allocate in preallocation. If not used, it has zero cost on host
6757:   PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6758:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6759:   PetscCall(PetscContainerSetPointer(container, coo));
6760:   PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6761:   PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6762:   PetscCall(PetscContainerDestroy(&container));
6763:   PetscFunctionReturn(PETSC_SUCCESS);
6764: }

6766: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6767: {
6768:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6769:   Mat                  A = mpiaij->A, B = mpiaij->B;
6770:   PetscScalar         *Aa, *Ba;
6771:   PetscScalar         *sendbuf, *recvbuf;
6772:   const PetscCount    *Ajmap1, *Ajmap2, *Aimap2;
6773:   const PetscCount    *Bjmap1, *Bjmap2, *Bimap2;
6774:   const PetscCount    *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6775:   const PetscCount    *Cperm1;
6776:   PetscContainer       container;
6777:   MatCOOStruct_MPIAIJ *coo;

6779:   PetscFunctionBegin;
6780:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6781:   PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6782:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6783:   sendbuf = coo->sendbuf;
6784:   recvbuf = coo->recvbuf;
6785:   Ajmap1  = coo->Ajmap1;
6786:   Ajmap2  = coo->Ajmap2;
6787:   Aimap2  = coo->Aimap2;
6788:   Bjmap1  = coo->Bjmap1;
6789:   Bjmap2  = coo->Bjmap2;
6790:   Bimap2  = coo->Bimap2;
6791:   Aperm1  = coo->Aperm1;
6792:   Aperm2  = coo->Aperm2;
6793:   Bperm1  = coo->Bperm1;
6794:   Bperm2  = coo->Bperm2;
6795:   Cperm1  = coo->Cperm1;

6797:   PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6798:   PetscCall(MatSeqAIJGetArray(B, &Ba));

6800:   /* Pack entries to be sent to remote */
6801:   for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];

6803:   /* Send remote entries to their owner and overlap the communication with local computation */
6804:   PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6805:   /* Add local entries to A and B */
6806:   for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6807:     PetscScalar sum = 0.0;                     /* Do partial summation first to improve numerical stability */
6808:     for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6809:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6810:   }
6811:   for (PetscCount i = 0; i < coo->Bnnz; i++) {
6812:     PetscScalar sum = 0.0;
6813:     for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6814:     Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6815:   }
6816:   PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));

6818:   /* Add received remote entries to A and B */
6819:   for (PetscCount i = 0; i < coo->Annz2; i++) {
6820:     for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6821:   }
6822:   for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6823:     for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6824:   }
6825:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6826:   PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6827:   PetscFunctionReturn(PETSC_SUCCESS);
6828: }

6830: /*MC
6831:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

6833:    Options Database Keys:
6834: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`

6836:    Level: beginner

6838:    Notes:
6839:    `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6840:     in this case the values associated with the rows and columns one passes in are set to zero
6841:     in the matrix

6843:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6844:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

6846: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6847: M*/
6848: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6849: {
6850:   Mat_MPIAIJ *b;
6851:   PetscMPIInt size;

6853:   PetscFunctionBegin;
6854:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

6856:   PetscCall(PetscNew(&b));
6857:   B->data       = (void *)b;
6858:   B->ops[0]     = MatOps_Values;
6859:   B->assembled  = PETSC_FALSE;
6860:   B->insertmode = NOT_SET_VALUES;
6861:   b->size       = size;

6863:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));

6865:   /* build cache for off array entries formed */
6866:   PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));

6868:   b->donotstash  = PETSC_FALSE;
6869:   b->colmap      = NULL;
6870:   b->garray      = NULL;
6871:   b->roworiented = PETSC_TRUE;

6873:   /* stuff used for matrix vector multiply */
6874:   b->lvec  = NULL;
6875:   b->Mvctx = NULL;

6877:   /* stuff for MatGetRow() */
6878:   b->rowindices   = NULL;
6879:   b->rowvalues    = NULL;
6880:   b->getrowactive = PETSC_FALSE;

6882:   /* flexible pointer used in CUSPARSE classes */
6883:   b->spptr = NULL;

6885:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6886:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6887:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6888:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6889:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6890:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6891:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6892:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6893:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6894:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6895: #if defined(PETSC_HAVE_CUDA)
6896:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6897: #endif
6898: #if defined(PETSC_HAVE_HIP)
6899:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6900: #endif
6901: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6902:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6903: #endif
6904: #if defined(PETSC_HAVE_MKL_SPARSE)
6905:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6906: #endif
6907:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6908:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6909:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6910:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6911: #if defined(PETSC_HAVE_ELEMENTAL)
6912:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6913: #endif
6914: #if defined(PETSC_HAVE_SCALAPACK)
6915:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6916: #endif
6917:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6919: #if defined(PETSC_HAVE_HYPRE)
6920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6921:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6922: #endif
6923:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6924:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6925:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6926:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6927:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6928:   PetscFunctionReturn(PETSC_SUCCESS);
6929: }

6931: /*@C
6932:   MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6933:   and "off-diagonal" part of the matrix in CSR format.

6935:   Collective

6937:   Input Parameters:
6938: + comm - MPI communicator
6939: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
6940: . n    - This value should be the same as the local size used in creating the
6941:          x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6942:          calculated if `N` is given) For square matrices `n` is almost always `m`.
6943: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6944: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6945: . 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
6946: . j    - column indices, which must be local, i.e., based off the start column of the diagonal portion
6947: . a    - matrix values
6948: . 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
6949: . oj   - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6950: - oa   - matrix values

6952:   Output Parameter:
6953: . mat - the matrix

6955:   Level: advanced

6957:   Notes:
6958:   The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
6959:   must free the arrays once the matrix has been destroyed and not before.

6961:   The `i` and `j` indices are 0 based

6963:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix

6965:   This sets local rows and cannot be used to set off-processor values.

6967:   Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6968:   legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6969:   not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6970:   the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6971:   keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6972:   communication if it is known that only local entries will be set.

6974: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6975:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6976: @*/
6977: 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)
6978: {
6979:   Mat_MPIAIJ *maij;

6981:   PetscFunctionBegin;
6982:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6983:   PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6984:   PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6985:   PetscCall(MatCreate(comm, mat));
6986:   PetscCall(MatSetSizes(*mat, m, n, M, N));
6987:   PetscCall(MatSetType(*mat, MATMPIAIJ));
6988:   maij = (Mat_MPIAIJ *)(*mat)->data;

6990:   (*mat)->preallocated = PETSC_TRUE;

6992:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
6993:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

6995:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
6996:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));

6998:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
6999:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7000:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7001:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7002:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7003:   PetscFunctionReturn(PETSC_SUCCESS);
7004: }

7006: typedef struct {
7007:   Mat       *mp;    /* intermediate products */
7008:   PetscBool *mptmp; /* is the intermediate product temporary ? */
7009:   PetscInt   cp;    /* number of intermediate products */

7011:   /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7012:   PetscInt    *startsj_s, *startsj_r;
7013:   PetscScalar *bufa;
7014:   Mat          P_oth;

7016:   /* may take advantage of merging product->B */
7017:   Mat Bloc; /* B-local by merging diag and off-diag */

7019:   /* cusparse does not have support to split between symbolic and numeric phases.
7020:      When api_user is true, we don't need to update the numerical values
7021:      of the temporary storage */
7022:   PetscBool reusesym;

7024:   /* support for COO values insertion */
7025:   PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7026:   PetscInt   **own;           /* own[i] points to address of on-process COO indices for Mat mp[i] */
7027:   PetscInt   **off;           /* off[i] points to address of off-process COO indices for Mat mp[i] */
7028:   PetscBool    hasoffproc;    /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7029:   PetscSF      sf;            /* used for non-local values insertion and memory malloc */
7030:   PetscMemType mtype;

7032:   /* customization */
7033:   PetscBool abmerge;
7034:   PetscBool P_oth_bind;
7035: } MatMatMPIAIJBACKEND;

7037: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7038: {
7039:   MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7040:   PetscInt             i;

7042:   PetscFunctionBegin;
7043:   PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7044:   PetscCall(PetscFree(mmdata->bufa));
7045:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7046:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7047:   PetscCall(MatDestroy(&mmdata->P_oth));
7048:   PetscCall(MatDestroy(&mmdata->Bloc));
7049:   PetscCall(PetscSFDestroy(&mmdata->sf));
7050:   for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7051:   PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7052:   PetscCall(PetscFree(mmdata->own[0]));
7053:   PetscCall(PetscFree(mmdata->own));
7054:   PetscCall(PetscFree(mmdata->off[0]));
7055:   PetscCall(PetscFree(mmdata->off));
7056:   PetscCall(PetscFree(mmdata));
7057:   PetscFunctionReturn(PETSC_SUCCESS);
7058: }

7060: /* Copy selected n entries with indices in idx[] of A to v[].
7061:    If idx is NULL, copy the whole data array of A to v[]
7062:  */
7063: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7064: {
7065:   PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);

7067:   PetscFunctionBegin;
7068:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7069:   if (f) {
7070:     PetscCall((*f)(A, n, idx, v));
7071:   } else {
7072:     const PetscScalar *vv;

7074:     PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7075:     if (n && idx) {
7076:       PetscScalar    *w  = v;
7077:       const PetscInt *oi = idx;
7078:       PetscInt        j;

7080:       for (j = 0; j < n; j++) *w++ = vv[*oi++];
7081:     } else {
7082:       PetscCall(PetscArraycpy(v, vv, n));
7083:     }
7084:     PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7085:   }
7086:   PetscFunctionReturn(PETSC_SUCCESS);
7087: }

7089: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7090: {
7091:   MatMatMPIAIJBACKEND *mmdata;
7092:   PetscInt             i, n_d, n_o;

7094:   PetscFunctionBegin;
7095:   MatCheckProduct(C, 1);
7096:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7097:   mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7098:   if (!mmdata->reusesym) { /* update temporary matrices */
7099:     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));
7100:     if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7101:   }
7102:   mmdata->reusesym = PETSC_FALSE;

7104:   for (i = 0; i < mmdata->cp; i++) {
7105:     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]);
7106:     PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7107:   }
7108:   for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7109:     PetscInt noff = mmdata->off[i + 1] - mmdata->off[i];

7111:     if (mmdata->mptmp[i]) continue;
7112:     if (noff) {
7113:       PetscInt nown = mmdata->own[i + 1] - mmdata->own[i];

7115:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7116:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7117:       n_o += noff;
7118:       n_d += nown;
7119:     } else {
7120:       Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;

7122:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7123:       n_d += mm->nz;
7124:     }
7125:   }
7126:   if (mmdata->hasoffproc) { /* offprocess insertion */
7127:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7128:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7129:   }
7130:   PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7131:   PetscFunctionReturn(PETSC_SUCCESS);
7132: }

7134: /* Support for Pt * A, A * P, or Pt * A * P */
7135: #define MAX_NUMBER_INTERMEDIATE 4
7136: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7137: {
7138:   Mat_Product           *product = C->product;
7139:   Mat                    A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7140:   Mat_MPIAIJ            *a, *p;
7141:   MatMatMPIAIJBACKEND   *mmdata;
7142:   ISLocalToGlobalMapping P_oth_l2g = NULL;
7143:   IS                     glob      = NULL;
7144:   const char            *prefix;
7145:   char                   pprefix[256];
7146:   const PetscInt        *globidx, *P_oth_idx;
7147:   PetscInt               i, j, cp, m, n, M, N, *coo_i, *coo_j;
7148:   PetscCount             ncoo, ncoo_d, ncoo_o, ncoo_oown;
7149:   PetscInt               cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7150:                                                                                          /* type-0: consecutive, start from 0; type-1: consecutive with */
7151:                                                                                          /* a base offset; type-2: sparse with a local to global map table */
7152:   const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE];       /* col/row local to global map array (table) for type-2 map type */

7154:   MatProductType ptype;
7155:   PetscBool      mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7156:   PetscMPIInt    size;

7158:   PetscFunctionBegin;
7159:   MatCheckProduct(C, 1);
7160:   PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7161:   ptype = product->type;
7162:   if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7163:     ptype                                          = MATPRODUCT_AB;
7164:     product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7165:   }
7166:   switch (ptype) {
7167:   case MATPRODUCT_AB:
7168:     A          = product->A;
7169:     P          = product->B;
7170:     m          = A->rmap->n;
7171:     n          = P->cmap->n;
7172:     M          = A->rmap->N;
7173:     N          = P->cmap->N;
7174:     hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7175:     break;
7176:   case MATPRODUCT_AtB:
7177:     P          = product->A;
7178:     A          = product->B;
7179:     m          = P->cmap->n;
7180:     n          = A->cmap->n;
7181:     M          = P->cmap->N;
7182:     N          = A->cmap->N;
7183:     hasoffproc = PETSC_TRUE;
7184:     break;
7185:   case MATPRODUCT_PtAP:
7186:     A          = product->A;
7187:     P          = product->B;
7188:     m          = P->cmap->n;
7189:     n          = P->cmap->n;
7190:     M          = P->cmap->N;
7191:     N          = P->cmap->N;
7192:     hasoffproc = PETSC_TRUE;
7193:     break;
7194:   default:
7195:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7196:   }
7197:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7198:   if (size == 1) hasoffproc = PETSC_FALSE;

7200:   /* defaults */
7201:   for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7202:     mp[i]    = NULL;
7203:     mptmp[i] = PETSC_FALSE;
7204:     rmapt[i] = -1;
7205:     cmapt[i] = -1;
7206:     rmapa[i] = NULL;
7207:     cmapa[i] = NULL;
7208:   }

7210:   /* customization */
7211:   PetscCall(PetscNew(&mmdata));
7212:   mmdata->reusesym = product->api_user;
7213:   if (ptype == MATPRODUCT_AB) {
7214:     if (product->api_user) {
7215:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7216:       PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7217:       PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7218:       PetscOptionsEnd();
7219:     } else {
7220:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7221:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7222:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7223:       PetscOptionsEnd();
7224:     }
7225:   } else if (ptype == MATPRODUCT_PtAP) {
7226:     if (product->api_user) {
7227:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7228:       PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7229:       PetscOptionsEnd();
7230:     } else {
7231:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7232:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7233:       PetscOptionsEnd();
7234:     }
7235:   }
7236:   a = (Mat_MPIAIJ *)A->data;
7237:   p = (Mat_MPIAIJ *)P->data;
7238:   PetscCall(MatSetSizes(C, m, n, M, N));
7239:   PetscCall(PetscLayoutSetUp(C->rmap));
7240:   PetscCall(PetscLayoutSetUp(C->cmap));
7241:   PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7242:   PetscCall(MatGetOptionsPrefix(C, &prefix));

7244:   cp = 0;
7245:   switch (ptype) {
7246:   case MATPRODUCT_AB: /* A * P */
7247:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));

7249:     /* A_diag * P_local (merged or not) */
7250:     if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7251:       /* P is product->B */
7252:       PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7253:       PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7254:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7255:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7256:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7257:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7258:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7259:       mp[cp]->product->api_user = product->api_user;
7260:       PetscCall(MatProductSetFromOptions(mp[cp]));
7261:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7262:       PetscCall(ISGetIndices(glob, &globidx));
7263:       rmapt[cp] = 1;
7264:       cmapt[cp] = 2;
7265:       cmapa[cp] = globidx;
7266:       mptmp[cp] = PETSC_FALSE;
7267:       cp++;
7268:     } else { /* A_diag * P_diag and A_diag * P_off */
7269:       PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7270:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7271:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7272:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7273:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7274:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7275:       mp[cp]->product->api_user = product->api_user;
7276:       PetscCall(MatProductSetFromOptions(mp[cp]));
7277:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7278:       rmapt[cp] = 1;
7279:       cmapt[cp] = 1;
7280:       mptmp[cp] = PETSC_FALSE;
7281:       cp++;
7282:       PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7283:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7284:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7285:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7286:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7287:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7288:       mp[cp]->product->api_user = product->api_user;
7289:       PetscCall(MatProductSetFromOptions(mp[cp]));
7290:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7291:       rmapt[cp] = 1;
7292:       cmapt[cp] = 2;
7293:       cmapa[cp] = p->garray;
7294:       mptmp[cp] = PETSC_FALSE;
7295:       cp++;
7296:     }

7298:     /* A_off * P_other */
7299:     if (mmdata->P_oth) {
7300:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7301:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7302:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7303:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7304:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7305:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7306:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7307:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7308:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7309:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7310:       mp[cp]->product->api_user = product->api_user;
7311:       PetscCall(MatProductSetFromOptions(mp[cp]));
7312:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7313:       rmapt[cp] = 1;
7314:       cmapt[cp] = 2;
7315:       cmapa[cp] = P_oth_idx;
7316:       mptmp[cp] = PETSC_FALSE;
7317:       cp++;
7318:     }
7319:     break;

7321:   case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7322:     /* A is product->B */
7323:     PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7324:     if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7325:       PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7326:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7327:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7328:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7329:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7330:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7331:       mp[cp]->product->api_user = product->api_user;
7332:       PetscCall(MatProductSetFromOptions(mp[cp]));
7333:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7334:       PetscCall(ISGetIndices(glob, &globidx));
7335:       rmapt[cp] = 2;
7336:       rmapa[cp] = globidx;
7337:       cmapt[cp] = 2;
7338:       cmapa[cp] = globidx;
7339:       mptmp[cp] = PETSC_FALSE;
7340:       cp++;
7341:     } else {
7342:       PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7343:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7344:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7345:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7346:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7347:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7348:       mp[cp]->product->api_user = product->api_user;
7349:       PetscCall(MatProductSetFromOptions(mp[cp]));
7350:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7351:       PetscCall(ISGetIndices(glob, &globidx));
7352:       rmapt[cp] = 1;
7353:       cmapt[cp] = 2;
7354:       cmapa[cp] = globidx;
7355:       mptmp[cp] = PETSC_FALSE;
7356:       cp++;
7357:       PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7358:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7359:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7360:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7361:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7362:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7363:       mp[cp]->product->api_user = product->api_user;
7364:       PetscCall(MatProductSetFromOptions(mp[cp]));
7365:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7366:       rmapt[cp] = 2;
7367:       rmapa[cp] = p->garray;
7368:       cmapt[cp] = 2;
7369:       cmapa[cp] = globidx;
7370:       mptmp[cp] = PETSC_FALSE;
7371:       cp++;
7372:     }
7373:     break;
7374:   case MATPRODUCT_PtAP:
7375:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7376:     /* P is product->B */
7377:     PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7378:     PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7379:     PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7380:     PetscCall(MatProductSetFill(mp[cp], product->fill));
7381:     PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7382:     PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7383:     PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7384:     mp[cp]->product->api_user = product->api_user;
7385:     PetscCall(MatProductSetFromOptions(mp[cp]));
7386:     PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7387:     PetscCall(ISGetIndices(glob, &globidx));
7388:     rmapt[cp] = 2;
7389:     rmapa[cp] = globidx;
7390:     cmapt[cp] = 2;
7391:     cmapa[cp] = globidx;
7392:     mptmp[cp] = PETSC_FALSE;
7393:     cp++;
7394:     if (mmdata->P_oth) {
7395:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7396:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7397:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7398:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7399:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7400:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7401:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7402:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7403:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7404:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7405:       mp[cp]->product->api_user = product->api_user;
7406:       PetscCall(MatProductSetFromOptions(mp[cp]));
7407:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7408:       mptmp[cp] = PETSC_TRUE;
7409:       cp++;
7410:       PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7411:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7412:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7413:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7414:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7415:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7416:       mp[cp]->product->api_user = product->api_user;
7417:       PetscCall(MatProductSetFromOptions(mp[cp]));
7418:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7419:       rmapt[cp] = 2;
7420:       rmapa[cp] = globidx;
7421:       cmapt[cp] = 2;
7422:       cmapa[cp] = P_oth_idx;
7423:       mptmp[cp] = PETSC_FALSE;
7424:       cp++;
7425:     }
7426:     break;
7427:   default:
7428:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7429:   }
7430:   /* sanity check */
7431:   if (size > 1)
7432:     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);

7434:   PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7435:   for (i = 0; i < cp; i++) {
7436:     mmdata->mp[i]    = mp[i];
7437:     mmdata->mptmp[i] = mptmp[i];
7438:   }
7439:   mmdata->cp             = cp;
7440:   C->product->data       = mmdata;
7441:   C->product->destroy    = MatDestroy_MatMatMPIAIJBACKEND;
7442:   C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;

7444:   /* memory type */
7445:   mmdata->mtype = PETSC_MEMTYPE_HOST;
7446:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7447:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7448:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7449:   if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7450:   else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7451:   else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;

7453:   /* prepare coo coordinates for values insertion */

7455:   /* count total nonzeros of those intermediate seqaij Mats
7456:     ncoo_d:    # of nonzeros of matrices that do not have offproc entries
7457:     ncoo_o:    # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7458:     ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7459:   */
7460:   for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7461:     Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7462:     if (mptmp[cp]) continue;
7463:     if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7464:       const PetscInt *rmap = rmapa[cp];
7465:       const PetscInt  mr   = mp[cp]->rmap->n;
7466:       const PetscInt  rs   = C->rmap->rstart;
7467:       const PetscInt  re   = C->rmap->rend;
7468:       const PetscInt *ii   = mm->i;
7469:       for (i = 0; i < mr; i++) {
7470:         const PetscInt gr = rmap[i];
7471:         const PetscInt nz = ii[i + 1] - ii[i];
7472:         if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7473:         else ncoo_oown += nz;                  /* this row is local */
7474:       }
7475:     } else ncoo_d += mm->nz;
7476:   }

7478:   /*
7479:     ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc

7481:     ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.

7483:     off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].

7485:     off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7486:     own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7487:     so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.

7489:     coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7490:     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.
7491:   */
7492:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7493:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));

7495:   /* gather (i,j) of nonzeros inserted by remote procs */
7496:   if (hasoffproc) {
7497:     PetscSF  msf;
7498:     PetscInt ncoo2, *coo_i2, *coo_j2;

7500:     PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7501:     PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7502:     PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */

7504:     for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7505:       Mat_SeqAIJ *mm     = (Mat_SeqAIJ *)mp[cp]->data;
7506:       PetscInt   *idxoff = mmdata->off[cp];
7507:       PetscInt   *idxown = mmdata->own[cp];
7508:       if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7509:         const PetscInt *rmap = rmapa[cp];
7510:         const PetscInt *cmap = cmapa[cp];
7511:         const PetscInt *ii   = mm->i;
7512:         PetscInt       *coi  = coo_i + ncoo_o;
7513:         PetscInt       *coj  = coo_j + ncoo_o;
7514:         const PetscInt  mr   = mp[cp]->rmap->n;
7515:         const PetscInt  rs   = C->rmap->rstart;
7516:         const PetscInt  re   = C->rmap->rend;
7517:         const PetscInt  cs   = C->cmap->rstart;
7518:         for (i = 0; i < mr; i++) {
7519:           const PetscInt *jj = mm->j + ii[i];
7520:           const PetscInt  gr = rmap[i];
7521:           const PetscInt  nz = ii[i + 1] - ii[i];
7522:           if (gr < rs || gr >= re) { /* this is an offproc row */
7523:             for (j = ii[i]; j < ii[i + 1]; j++) {
7524:               *coi++    = gr;
7525:               *idxoff++ = j;
7526:             }
7527:             if (!cmapt[cp]) { /* already global */
7528:               for (j = 0; j < nz; j++) *coj++ = jj[j];
7529:             } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7530:               for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7531:             } else { /* offdiag */
7532:               for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7533:             }
7534:             ncoo_o += nz;
7535:           } else { /* this is a local row */
7536:             for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7537:           }
7538:         }
7539:       }
7540:       mmdata->off[cp + 1] = idxoff;
7541:       mmdata->own[cp + 1] = idxown;
7542:     }

7544:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7545:     PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, ncoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7546:     PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7547:     PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7548:     ncoo = ncoo_d + ncoo_oown + ncoo2;
7549:     PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7550:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7551:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7552:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7553:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7554:     PetscCall(PetscFree2(coo_i, coo_j));
7555:     /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7556:     PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7557:     coo_i = coo_i2;
7558:     coo_j = coo_j2;
7559:   } else { /* no offproc values insertion */
7560:     ncoo = ncoo_d;
7561:     PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));

7563:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7564:     PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7565:     PetscCall(PetscSFSetUp(mmdata->sf));
7566:   }
7567:   mmdata->hasoffproc = hasoffproc;

7569:   /* gather (i,j) of nonzeros inserted locally */
7570:   for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7571:     Mat_SeqAIJ     *mm   = (Mat_SeqAIJ *)mp[cp]->data;
7572:     PetscInt       *coi  = coo_i + ncoo_d;
7573:     PetscInt       *coj  = coo_j + ncoo_d;
7574:     const PetscInt *jj   = mm->j;
7575:     const PetscInt *ii   = mm->i;
7576:     const PetscInt *cmap = cmapa[cp];
7577:     const PetscInt *rmap = rmapa[cp];
7578:     const PetscInt  mr   = mp[cp]->rmap->n;
7579:     const PetscInt  rs   = C->rmap->rstart;
7580:     const PetscInt  re   = C->rmap->rend;
7581:     const PetscInt  cs   = C->cmap->rstart;

7583:     if (mptmp[cp]) continue;
7584:     if (rmapt[cp] == 1) { /* consecutive rows */
7585:       /* fill coo_i */
7586:       for (i = 0; i < mr; i++) {
7587:         const PetscInt gr = i + rs;
7588:         for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7589:       }
7590:       /* fill coo_j */
7591:       if (!cmapt[cp]) { /* type-0, already global */
7592:         PetscCall(PetscArraycpy(coj, jj, mm->nz));
7593:       } else if (cmapt[cp] == 1) {                        /* type-1, local to global for consecutive columns of C */
7594:         for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7595:       } else {                                            /* type-2, local to global for sparse columns */
7596:         for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7597:       }
7598:       ncoo_d += mm->nz;
7599:     } else if (rmapt[cp] == 2) { /* sparse rows */
7600:       for (i = 0; i < mr; i++) {
7601:         const PetscInt *jj = mm->j + ii[i];
7602:         const PetscInt  gr = rmap[i];
7603:         const PetscInt  nz = ii[i + 1] - ii[i];
7604:         if (gr >= rs && gr < re) { /* local rows */
7605:           for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7606:           if (!cmapt[cp]) { /* type-0, already global */
7607:             for (j = 0; j < nz; j++) *coj++ = jj[j];
7608:           } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7609:             for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7610:           } else { /* type-2, local to global for sparse columns */
7611:             for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7612:           }
7613:           ncoo_d += nz;
7614:         }
7615:       }
7616:     }
7617:   }
7618:   if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7619:   PetscCall(ISDestroy(&glob));
7620:   if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7621:   PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7622:   /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7623:   PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));

7625:   /* preallocate with COO data */
7626:   PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7627:   PetscCall(PetscFree2(coo_i, coo_j));
7628:   PetscFunctionReturn(PETSC_SUCCESS);
7629: }

7631: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7632: {
7633:   Mat_Product *product = mat->product;
7634: #if defined(PETSC_HAVE_DEVICE)
7635:   PetscBool match  = PETSC_FALSE;
7636:   PetscBool usecpu = PETSC_FALSE;
7637: #else
7638:   PetscBool match = PETSC_TRUE;
7639: #endif

7641:   PetscFunctionBegin;
7642:   MatCheckProduct(mat, 1);
7643: #if defined(PETSC_HAVE_DEVICE)
7644:   if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7645:   if (match) { /* we can always fallback to the CPU if requested */
7646:     switch (product->type) {
7647:     case MATPRODUCT_AB:
7648:       if (product->api_user) {
7649:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7650:         PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7651:         PetscOptionsEnd();
7652:       } else {
7653:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7654:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7655:         PetscOptionsEnd();
7656:       }
7657:       break;
7658:     case MATPRODUCT_AtB:
7659:       if (product->api_user) {
7660:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7661:         PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7662:         PetscOptionsEnd();
7663:       } else {
7664:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7665:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7666:         PetscOptionsEnd();
7667:       }
7668:       break;
7669:     case MATPRODUCT_PtAP:
7670:       if (product->api_user) {
7671:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7672:         PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7673:         PetscOptionsEnd();
7674:       } else {
7675:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7676:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7677:         PetscOptionsEnd();
7678:       }
7679:       break;
7680:     default:
7681:       break;
7682:     }
7683:     match = (PetscBool)!usecpu;
7684:   }
7685: #endif
7686:   if (match) {
7687:     switch (product->type) {
7688:     case MATPRODUCT_AB:
7689:     case MATPRODUCT_AtB:
7690:     case MATPRODUCT_PtAP:
7691:       mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7692:       break;
7693:     default:
7694:       break;
7695:     }
7696:   }
7697:   /* fallback to MPIAIJ ops */
7698:   if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7699:   PetscFunctionReturn(PETSC_SUCCESS);
7700: }

7702: /*
7703:    Produces a set of block column indices of the matrix row, one for each block represented in the original row

7705:    n - the number of block indices in cc[]
7706:    cc - the block indices (must be large enough to contain the indices)
7707: */
7708: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7709: {
7710:   PetscInt        cnt = -1, nidx, j;
7711:   const PetscInt *idx;

7713:   PetscFunctionBegin;
7714:   PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7715:   if (nidx) {
7716:     cnt     = 0;
7717:     cc[cnt] = idx[0] / bs;
7718:     for (j = 1; j < nidx; j++) {
7719:       if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7720:     }
7721:   }
7722:   PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7723:   *n = cnt + 1;
7724:   PetscFunctionReturn(PETSC_SUCCESS);
7725: }

7727: /*
7728:     Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows

7730:     ncollapsed - the number of block indices
7731:     collapsed - the block indices (must be large enough to contain the indices)
7732: */
7733: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7734: {
7735:   PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;

7737:   PetscFunctionBegin;
7738:   PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7739:   for (i = start + 1; i < start + bs; i++) {
7740:     PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7741:     PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7742:     cprevtmp = cprev;
7743:     cprev    = merged;
7744:     merged   = cprevtmp;
7745:   }
7746:   *ncollapsed = nprev;
7747:   if (collapsed) *collapsed = cprev;
7748:   PetscFunctionReturn(PETSC_SUCCESS);
7749: }

7751: /*
7752:  MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix

7754:  Input Parameter:
7755:  . Amat - matrix
7756:  - symmetrize - make the result symmetric
7757:  + scale - scale with diagonal

7759:  Output Parameter:
7760:  . a_Gmat - output scalar graph >= 0

7762: */
7763: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7764: {
7765:   PetscInt  Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7766:   MPI_Comm  comm;
7767:   Mat       Gmat;
7768:   PetscBool ismpiaij, isseqaij;
7769:   Mat       a, b, c;
7770:   MatType   jtype;

7772:   PetscFunctionBegin;
7773:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7774:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7775:   PetscCall(MatGetSize(Amat, &MM, &NN));
7776:   PetscCall(MatGetBlockSize(Amat, &bs));
7777:   nloc = (Iend - Istart) / bs;

7779:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7780:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7781:   PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");

7783:   /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7784:   /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7785:      implementation */
7786:   if (bs > 1) {
7787:     PetscCall(MatGetType(Amat, &jtype));
7788:     PetscCall(MatCreate(comm, &Gmat));
7789:     PetscCall(MatSetType(Gmat, jtype));
7790:     PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7791:     PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7792:     if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7793:       PetscInt  *d_nnz, *o_nnz;
7794:       MatScalar *aa, val, *AA;
7795:       PetscInt  *aj, *ai, *AJ, nc, nmax = 0;
7796:       if (isseqaij) {
7797:         a = Amat;
7798:         b = NULL;
7799:       } else {
7800:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7801:         a             = d->A;
7802:         b             = d->B;
7803:       }
7804:       PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7805:       PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7806:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7807:         PetscInt       *nnz = (c == a) ? d_nnz : o_nnz;
7808:         const PetscInt *cols1, *cols2;
7809:         for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7810:           PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7811:           nnz[brow / bs] = nc2 / bs;
7812:           if (nc2 % bs) ok = 0;
7813:           if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7814:           for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7815:             PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7816:             if (nc1 != nc2) ok = 0;
7817:             else {
7818:               for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7819:                 if (cols1[jj] != cols2[jj]) ok = 0;
7820:                 if (cols1[jj] % bs != jj % bs) ok = 0;
7821:               }
7822:             }
7823:             PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7824:           }
7825:           PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7826:           if (!ok) {
7827:             PetscCall(PetscFree2(d_nnz, o_nnz));
7828:             PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7829:             goto old_bs;
7830:           }
7831:         }
7832:       }
7833:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7834:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7835:       PetscCall(PetscFree2(d_nnz, o_nnz));
7836:       PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7837:       // diag
7838:       for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7839:         Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;
7840:         ai               = aseq->i;
7841:         n                = ai[brow + 1] - ai[brow];
7842:         aj               = aseq->j + ai[brow];
7843:         for (int k = 0; k < n; k += bs) {        // block columns
7844:           AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7845:           val        = 0;
7846:           if (index_size == 0) {
7847:             for (int ii = 0; ii < bs; ii++) { // rows in block
7848:               aa = aseq->a + ai[brow + ii] + k;
7849:               for (int jj = 0; jj < bs; jj++) {         // columns in block
7850:                 val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7851:               }
7852:             }
7853:           } else {                                       // use (index,index) value if provided
7854:             for (int iii = 0; iii < index_size; iii++) { // rows in block
7855:               int ii = index[iii];
7856:               aa     = aseq->a + ai[brow + ii] + k;
7857:               for (int jjj = 0; jjj < index_size; jjj++) { // columns in block
7858:                 int jj = index[jjj];
7859:                 val += PetscAbs(PetscRealPart(aa[jj]));
7860:               }
7861:             }
7862:           }
7863:           PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7864:           AA[k / bs] = val;
7865:         }
7866:         grow = Istart / bs + brow / bs;
7867:         PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, INSERT_VALUES));
7868:       }
7869:       // off-diag
7870:       if (ismpiaij) {
7871:         Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)Amat->data;
7872:         const PetscScalar *vals;
7873:         const PetscInt    *cols, *garray = aij->garray;
7874:         PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7875:         for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7876:           PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7877:           for (int k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7878:             PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7879:             AA[k / bs] = 0;
7880:             AJ[cidx]   = garray[cols[k]] / bs;
7881:           }
7882:           nc = ncols / bs;
7883:           PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7884:           if (index_size == 0) {
7885:             for (int ii = 0; ii < bs; ii++) { // rows in block
7886:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7887:               for (int k = 0; k < ncols; k += bs) {
7888:                 for (int jj = 0; jj < bs; jj++) { // cols in block
7889:                   PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7890:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7891:                 }
7892:               }
7893:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7894:             }
7895:           } else {                                       // use (index,index) value if provided
7896:             for (int iii = 0; iii < index_size; iii++) { // rows in block
7897:               int ii = index[iii];
7898:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7899:               for (int k = 0; k < ncols; k += bs) {
7900:                 for (int jjj = 0; jjj < index_size; jjj++) { // cols in block
7901:                   int jj = index[jjj];
7902:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7903:                 }
7904:               }
7905:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7906:             }
7907:           }
7908:           grow = Istart / bs + brow / bs;
7909:           PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, INSERT_VALUES));
7910:         }
7911:       }
7912:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7913:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7914:       PetscCall(PetscFree2(AA, AJ));
7915:     } else {
7916:       const PetscScalar *vals;
7917:       const PetscInt    *idx;
7918:       PetscInt          *d_nnz, *o_nnz, *w0, *w1, *w2;
7919:     old_bs:
7920:       /*
7921:        Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7922:        */
7923:       PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7924:       PetscCall(PetscMalloc2(nloc, &d_nnz, isseqaij ? 0 : nloc, &o_nnz));
7925:       if (isseqaij) {
7926:         PetscInt max_d_nnz;
7927:         /*
7928:          Determine exact preallocation count for (sequential) scalar matrix
7929:          */
7930:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7931:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7932:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7933:         for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7934:         PetscCall(PetscFree3(w0, w1, w2));
7935:       } else if (ismpiaij) {
7936:         Mat             Daij, Oaij;
7937:         const PetscInt *garray;
7938:         PetscInt        max_d_nnz;
7939:         PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7940:         /*
7941:          Determine exact preallocation count for diagonal block portion of scalar matrix
7942:          */
7943:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7944:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7945:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7946:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7947:         PetscCall(PetscFree3(w0, w1, w2));
7948:         /*
7949:          Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7950:          */
7951:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7952:           o_nnz[jj] = 0;
7953:           for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7954:             PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7955:             o_nnz[jj] += ncols;
7956:             PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7957:           }
7958:           if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7959:         }
7960:       } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7961:       /* get scalar copy (norms) of matrix */
7962:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7963:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7964:       PetscCall(PetscFree2(d_nnz, o_nnz));
7965:       for (Ii = Istart; Ii < Iend; Ii++) {
7966:         PetscInt dest_row = Ii / bs;
7967:         PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
7968:         for (jj = 0; jj < ncols; jj++) {
7969:           PetscInt    dest_col = idx[jj] / bs;
7970:           PetscScalar sv       = PetscAbs(PetscRealPart(vals[jj]));
7971:           PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
7972:         }
7973:         PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
7974:       }
7975:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7976:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7977:     }
7978:   } else {
7979:     if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
7980:     else {
7981:       Gmat = Amat;
7982:       PetscCall(PetscObjectReference((PetscObject)Gmat));
7983:     }
7984:     if (isseqaij) {
7985:       a = Gmat;
7986:       b = NULL;
7987:     } else {
7988:       Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
7989:       a             = d->A;
7990:       b             = d->B;
7991:     }
7992:     if (filter >= 0 || scale) {
7993:       /* take absolute value of each entry */
7994:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7995:         MatInfo      info;
7996:         PetscScalar *avals;
7997:         PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
7998:         PetscCall(MatSeqAIJGetArray(c, &avals));
7999:         for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8000:         PetscCall(MatSeqAIJRestoreArray(c, &avals));
8001:       }
8002:     }
8003:   }
8004:   if (symmetrize) {
8005:     PetscBool isset, issym;
8006:     PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8007:     if (!isset || !issym) {
8008:       Mat matTrans;
8009:       PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8010:       PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8011:       PetscCall(MatDestroy(&matTrans));
8012:     }
8013:     PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8014:   } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8015:   if (scale) {
8016:     /* scale c for all diagonal values = 1 or -1 */
8017:     Vec diag;
8018:     PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8019:     PetscCall(MatGetDiagonal(Gmat, diag));
8020:     PetscCall(VecReciprocal(diag));
8021:     PetscCall(VecSqrtAbs(diag));
8022:     PetscCall(MatDiagonalScale(Gmat, diag, diag));
8023:     PetscCall(VecDestroy(&diag));
8024:   }
8025:   PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));

8027:   if (filter >= 0) {
8028:     PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8029:     PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8030:   }
8031:   *a_Gmat = Gmat;
8032:   PetscFunctionReturn(PETSC_SUCCESS);
8033: }

8035: /*
8036:     Special version for direct calls from Fortran
8037: */
8038: #include <petsc/private/fortranimpl.h>

8040: /* Change these macros so can be used in void function */
8041: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8042: #undef PetscCall
8043: #define PetscCall(...) \
8044:   do { \
8045:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8046:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
8047:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8048:       return; \
8049:     } \
8050:   } while (0)

8052: #undef SETERRQ
8053: #define SETERRQ(comm, ierr, ...) \
8054:   do { \
8055:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8056:     return; \
8057:   } while (0)

8059: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8060:   #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8061: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8062:   #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8063: #else
8064: #endif
8065: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8066: {
8067:   Mat         mat = *mmat;
8068:   PetscInt    m = *mm, n = *mn;
8069:   InsertMode  addv = *maddv;
8070:   Mat_MPIAIJ *aij  = (Mat_MPIAIJ *)mat->data;
8071:   PetscScalar value;

8073:   MatCheckPreallocated(mat, 1);
8074:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8075:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8076:   {
8077:     PetscInt  i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8078:     PetscInt  cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8079:     PetscBool roworiented = aij->roworiented;

8081:     /* Some Variables required in the macro */
8082:     Mat         A     = aij->A;
8083:     Mat_SeqAIJ *a     = (Mat_SeqAIJ *)A->data;
8084:     PetscInt   *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8085:     MatScalar  *aa;
8086:     PetscBool   ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8087:     Mat         B                 = aij->B;
8088:     Mat_SeqAIJ *b                 = (Mat_SeqAIJ *)B->data;
8089:     PetscInt   *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8090:     MatScalar  *ba;
8091:     /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8092:      * cannot use "#if defined" inside a macro. */
8093:     PETSC_UNUSED PetscBool inserted = PETSC_FALSE;

8095:     PetscInt  *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8096:     PetscInt   nonew = a->nonew;
8097:     MatScalar *ap1, *ap2;

8099:     PetscFunctionBegin;
8100:     PetscCall(MatSeqAIJGetArray(A, &aa));
8101:     PetscCall(MatSeqAIJGetArray(B, &ba));
8102:     for (i = 0; i < m; i++) {
8103:       if (im[i] < 0) continue;
8104:       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);
8105:       if (im[i] >= rstart && im[i] < rend) {
8106:         row      = im[i] - rstart;
8107:         lastcol1 = -1;
8108:         rp1      = aj + ai[row];
8109:         ap1      = aa + ai[row];
8110:         rmax1    = aimax[row];
8111:         nrow1    = ailen[row];
8112:         low1     = 0;
8113:         high1    = nrow1;
8114:         lastcol2 = -1;
8115:         rp2      = bj + bi[row];
8116:         ap2      = ba + bi[row];
8117:         rmax2    = bimax[row];
8118:         nrow2    = bilen[row];
8119:         low2     = 0;
8120:         high2    = nrow2;

8122:         for (j = 0; j < n; j++) {
8123:           if (roworiented) value = v[i * n + j];
8124:           else value = v[i + j * m];
8125:           if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8126:           if (in[j] >= cstart && in[j] < cend) {
8127:             col = in[j] - cstart;
8128:             MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8129:           } else if (in[j] < 0) continue;
8130:           else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8131:             SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8132:           } else {
8133:             if (mat->was_assembled) {
8134:               if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8135: #if defined(PETSC_USE_CTABLE)
8136:               PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8137:               col--;
8138: #else
8139:               col = aij->colmap[in[j]] - 1;
8140: #endif
8141:               if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8142:                 PetscCall(MatDisAssemble_MPIAIJ(mat));
8143:                 col = in[j];
8144:                 /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8145:                 B        = aij->B;
8146:                 b        = (Mat_SeqAIJ *)B->data;
8147:                 bimax    = b->imax;
8148:                 bi       = b->i;
8149:                 bilen    = b->ilen;
8150:                 bj       = b->j;
8151:                 rp2      = bj + bi[row];
8152:                 ap2      = ba + bi[row];
8153:                 rmax2    = bimax[row];
8154:                 nrow2    = bilen[row];
8155:                 low2     = 0;
8156:                 high2    = nrow2;
8157:                 bm       = aij->B->rmap->n;
8158:                 ba       = b->a;
8159:                 inserted = PETSC_FALSE;
8160:               }
8161:             } else col = in[j];
8162:             MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8163:           }
8164:         }
8165:       } else if (!aij->donotstash) {
8166:         if (roworiented) {
8167:           PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8168:         } else {
8169:           PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8170:         }
8171:       }
8172:     }
8173:     PetscCall(MatSeqAIJRestoreArray(A, &aa));
8174:     PetscCall(MatSeqAIJRestoreArray(B, &ba));
8175:   }
8176:   PetscFunctionReturnVoid();
8177: }

8179: /* Undefining these here since they were redefined from their original definition above! No
8180:  * other PETSc functions should be defined past this point, as it is impossible to recover the
8181:  * original definitions */
8182: #undef PetscCall
8183: #undef SETERRQ