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:   PetscCallMPI(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;
287:   PetscMPIInt        in;

289:   PetscFunctionBegin;
290:   PetscCall(MatGetSize(A, &m, &n));
291:   PetscCall(PetscCalloc1(n, &work));
292:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
293:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
294:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
295:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
296:   if (type == NORM_2) {
297:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i] * a_aij->a[i]);
298:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i] * b_aij->a[i]);
299:   } else if (type == NORM_1) {
300:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
301:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
302:   } else if (type == NORM_INFINITY) {
303:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
304:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]), work[garray[b_aij->j[i]]]);
305:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
306:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscRealPart(a_aij->a[i]);
307:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
308:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
309:     for (i = 0; i < a_aij->i[aij->A->rmap->n]; i++) work[A->cmap->rstart + a_aij->j[i]] += PetscImaginaryPart(a_aij->a[i]);
310:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
311:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
312:   PetscCall(PetscMPIIntCast(n, &in));
313:   if (type == NORM_INFINITY) {
314:     PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
315:   } else {
316:     PetscCallMPI(MPIU_Allreduce(work, reductions, in, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
317:   }
318:   PetscCall(PetscFree(work));
319:   if (type == NORM_2) {
320:     for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
321:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
322:     for (i = 0; i < n; i++) reductions[i] /= m;
323:   }
324:   PetscFunctionReturn(PETSC_SUCCESS);
325: }

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

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

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

351:   PetscCall(ISRestoreIndices(sis, &isis));
352:   PetscCall(ISRestoreIndices(gis, &igis));
353:   PetscCall(ISDestroy(&sis));
354:   PetscCall(ISDestroy(&gis));
355:   PetscFunctionReturn(PETSC_SUCCESS);
356: }

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

370:   PetscFunctionBegin;
371:   PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
372: #if defined(PETSC_USE_CTABLE)
373:   PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
374:   for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
375: #else
376:   PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
377:   for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
378: #endif
379:   PetscFunctionReturn(PETSC_SUCCESS);
380: }

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

427: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
428:   do { \
429:     if (col <= lastcol2) low2 = 0; \
430:     else high2 = nrow2; \
431:     lastcol2 = col; \
432:     while (high2 - low2 > 5) { \
433:       t = (low2 + high2) / 2; \
434:       if (rp2[t] > col) high2 = t; \
435:       else low2 = t; \
436:     } \
437:     for (_i = low2; _i < high2; _i++) { \
438:       if (rp2[_i] > col) break; \
439:       if (rp2[_i] == col) { \
440:         if (addv == ADD_VALUES) { \
441:           ap2[_i] += value; \
442:           (void)PetscLogFlops(1.0); \
443:         } else ap2[_i] = value; \
444:         goto b_noinsert; \
445:       } \
446:     } \
447:     if (value == 0.0 && ignorezeroentries) { \
448:       low2  = 0; \
449:       high2 = nrow2; \
450:       goto b_noinsert; \
451:     } \
452:     if (nonew == 1) { \
453:       low2  = 0; \
454:       high2 = nrow2; \
455:       goto b_noinsert; \
456:     } \
457:     PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
458:     MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
459:     N = nrow2++ - 1; \
460:     b->nz++; \
461:     high2++; \
462:     /* shift up all the later entries in this row */ \
463:     PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
464:     PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
465:     rp2[_i] = col; \
466:     ap2[_i] = value; \
467:   b_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, PETSC_FALSE));  /* 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   = PetscSafePointerPlusOffset(bj, bi[row]);
584:               ap2   = PetscSafePointerPlusOffset(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:     PetscCallMPI(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, PETSC_FALSE));
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:     PetscCallMPI(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:     PetscCallMPI(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:   PetscInt           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:     PetscCallMPI(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:   PetscCallMPI(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 MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1126: {
1127:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1129:   PetscFunctionBegin;
1130:   /* do nondiagonal part */
1131:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1132:   /* do local part */
1133:   PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1134:   /* add partial results together */
1135:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1136:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1137:   PetscFunctionReturn(PETSC_SUCCESS);
1138: }

1140: /*
1141:   This only works correctly for square matrices where the subblock A->A is the
1142:    diagonal block
1143: */
1144: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1145: {
1146:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1148:   PetscFunctionBegin;
1149:   PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1150:   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");
1151:   PetscCall(MatGetDiagonal(a->A, v));
1152:   PetscFunctionReturn(PETSC_SUCCESS);
1153: }

1155: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1156: {
1157:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1159:   PetscFunctionBegin;
1160:   PetscCall(MatScale(a->A, aa));
1161:   PetscCall(MatScale(a->B, aa));
1162:   PetscFunctionReturn(PETSC_SUCCESS);
1163: }

1165: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1166: {
1167:   Mat_MPIAIJ        *aij    = (Mat_MPIAIJ *)mat->data;
1168:   Mat_SeqAIJ        *A      = (Mat_SeqAIJ *)aij->A->data;
1169:   Mat_SeqAIJ        *B      = (Mat_SeqAIJ *)aij->B->data;
1170:   const PetscInt    *garray = aij->garray;
1171:   const PetscScalar *aa, *ba;
1172:   PetscInt           header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1173:   PetscInt64         nz, hnz;
1174:   PetscInt          *rowlens;
1175:   PetscInt          *colidxs;
1176:   PetscScalar       *matvals;
1177:   PetscMPIInt        rank;

1179:   PetscFunctionBegin;
1180:   PetscCall(PetscViewerSetUp(viewer));

1182:   M  = mat->rmap->N;
1183:   N  = mat->cmap->N;
1184:   m  = mat->rmap->n;
1185:   rs = mat->rmap->rstart;
1186:   cs = mat->cmap->rstart;
1187:   nz = A->nz + B->nz;

1189:   /* write matrix header */
1190:   header[0] = MAT_FILE_CLASSID;
1191:   header[1] = M;
1192:   header[2] = N;
1193:   PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1194:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1195:   if (rank == 0) {
1196:     if (hnz > PETSC_INT_MAX) header[3] = PETSC_INT_MAX;
1197:     else header[3] = (PetscInt)hnz;
1198:   }
1199:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

1201:   /* fill in and store row lengths  */
1202:   PetscCall(PetscMalloc1(m, &rowlens));
1203:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1204:   PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1205:   PetscCall(PetscFree(rowlens));

1207:   /* fill in and store column indices */
1208:   PetscCall(PetscMalloc1(nz, &colidxs));
1209:   for (cnt = 0, i = 0; i < m; i++) {
1210:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1211:       if (garray[B->j[jb]] > cs) break;
1212:       colidxs[cnt++] = garray[B->j[jb]];
1213:     }
1214:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1215:     for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1216:   }
1217:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1218:   PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1219:   PetscCall(PetscFree(colidxs));

1221:   /* fill in and store nonzero values */
1222:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1223:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1224:   PetscCall(PetscMalloc1(nz, &matvals));
1225:   for (cnt = 0, i = 0; i < m; i++) {
1226:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1227:       if (garray[B->j[jb]] > cs) break;
1228:       matvals[cnt++] = ba[jb];
1229:     }
1230:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1231:     for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1232:   }
1233:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1234:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1235:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1236:   PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1237:   PetscCall(PetscFree(matvals));

1239:   /* write block size option to the viewer's .info file */
1240:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1241:   PetscFunctionReturn(PETSC_SUCCESS);
1242: }

1244: #include <petscdraw.h>
1245: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1246: {
1247:   Mat_MPIAIJ       *aij  = (Mat_MPIAIJ *)mat->data;
1248:   PetscMPIInt       rank = aij->rank, size = aij->size;
1249:   PetscBool         isdraw, iascii, isbinary;
1250:   PetscViewer       sviewer;
1251:   PetscViewerFormat format;

1253:   PetscFunctionBegin;
1254:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1255:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1256:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1257:   if (iascii) {
1258:     PetscCall(PetscViewerGetFormat(viewer, &format));
1259:     if (format == PETSC_VIEWER_LOAD_BALANCE) {
1260:       PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1261:       PetscCall(PetscMalloc1(size, &nz));
1262:       PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1263:       for (i = 0; i < (PetscInt)size; i++) {
1264:         nmax = PetscMax(nmax, nz[i]);
1265:         nmin = PetscMin(nmin, nz[i]);
1266:         navg += nz[i];
1267:       }
1268:       PetscCall(PetscFree(nz));
1269:       navg = navg / size;
1270:       PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT "  avg %" PetscInt_FMT "  max %" PetscInt_FMT "\n", nmin, navg, nmax));
1271:       PetscFunctionReturn(PETSC_SUCCESS);
1272:     }
1273:     PetscCall(PetscViewerGetFormat(viewer, &format));
1274:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1275:       MatInfo   info;
1276:       PetscInt *inodes = NULL;

1278:       PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1279:       PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1280:       PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1281:       PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1282:       if (!inodes) {
1283:         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,
1284:                                                      (double)info.memory));
1285:       } else {
1286:         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,
1287:                                                      (double)info.memory));
1288:       }
1289:       PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1290:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1291:       PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1292:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1293:       PetscCall(PetscViewerFlush(viewer));
1294:       PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1295:       PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1296:       PetscCall(VecScatterView(aij->Mvctx, viewer));
1297:       PetscFunctionReturn(PETSC_SUCCESS);
1298:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1299:       PetscInt inodecount, inodelimit, *inodes;
1300:       PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1301:       if (inodes) {
1302:         PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1303:       } else {
1304:         PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1305:       }
1306:       PetscFunctionReturn(PETSC_SUCCESS);
1307:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1308:       PetscFunctionReturn(PETSC_SUCCESS);
1309:     }
1310:   } else if (isbinary) {
1311:     if (size == 1) {
1312:       PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1313:       PetscCall(MatView(aij->A, viewer));
1314:     } else {
1315:       PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1316:     }
1317:     PetscFunctionReturn(PETSC_SUCCESS);
1318:   } else if (iascii && size == 1) {
1319:     PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1320:     PetscCall(MatView(aij->A, viewer));
1321:     PetscFunctionReturn(PETSC_SUCCESS);
1322:   } else if (isdraw) {
1323:     PetscDraw draw;
1324:     PetscBool isnull;
1325:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1326:     PetscCall(PetscDrawIsNull(draw, &isnull));
1327:     if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1328:   }

1330:   { /* assemble the entire matrix onto first processor */
1331:     Mat A = NULL, Av;
1332:     IS  isrow, iscol;

1334:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1335:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1336:     PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1337:     PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1338:     /*  The commented code uses MatCreateSubMatrices instead */
1339:     /*
1340:     Mat *AA, A = NULL, Av;
1341:     IS  isrow,iscol;

1343:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1344:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1345:     PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1346:     if (rank == 0) {
1347:        PetscCall(PetscObjectReference((PetscObject)AA[0]));
1348:        A    = AA[0];
1349:        Av   = AA[0];
1350:     }
1351:     PetscCall(MatDestroySubMatrices(1,&AA));
1352: */
1353:     PetscCall(ISDestroy(&iscol));
1354:     PetscCall(ISDestroy(&isrow));
1355:     /*
1356:        Everyone has to call to draw the matrix since the graphics waits are
1357:        synchronized across all processors that share the PetscDraw object
1358:     */
1359:     PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1360:     if (rank == 0) {
1361:       if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1362:       PetscCall(MatView_SeqAIJ(Av, sviewer));
1363:     }
1364:     PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1365:     PetscCall(MatDestroy(&A));
1366:   }
1367:   PetscFunctionReturn(PETSC_SUCCESS);
1368: }

1370: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1371: {
1372:   PetscBool iascii, isdraw, issocket, isbinary;

1374:   PetscFunctionBegin;
1375:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1376:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1377:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1378:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1379:   if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1380:   PetscFunctionReturn(PETSC_SUCCESS);
1381: }

1383: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1384: {
1385:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1386:   Vec         bb1 = NULL;
1387:   PetscBool   hasop;

1389:   PetscFunctionBegin;
1390:   if (flag == SOR_APPLY_UPPER) {
1391:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1392:     PetscFunctionReturn(PETSC_SUCCESS);
1393:   }

1395:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));

1397:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1398:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1399:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1400:       its--;
1401:     }

1403:     while (its--) {
1404:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1405:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1407:       /* update rhs: bb1 = bb - B*x */
1408:       PetscCall(VecScale(mat->lvec, -1.0));
1409:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1411:       /* local sweep */
1412:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1413:     }
1414:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1415:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1416:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1417:       its--;
1418:     }
1419:     while (its--) {
1420:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1421:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1423:       /* update rhs: bb1 = bb - B*x */
1424:       PetscCall(VecScale(mat->lvec, -1.0));
1425:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1427:       /* local sweep */
1428:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1429:     }
1430:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1431:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1432:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1433:       its--;
1434:     }
1435:     while (its--) {
1436:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1437:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1439:       /* update rhs: bb1 = bb - B*x */
1440:       PetscCall(VecScale(mat->lvec, -1.0));
1441:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1443:       /* local sweep */
1444:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1445:     }
1446:   } else if (flag & SOR_EISENSTAT) {
1447:     Vec xx1;

1449:     PetscCall(VecDuplicate(bb, &xx1));
1450:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));

1452:     PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1453:     PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454:     if (!mat->diag) {
1455:       PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1456:       PetscCall(MatGetDiagonal(matin, mat->diag));
1457:     }
1458:     PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1459:     if (hasop) {
1460:       PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1461:     } else {
1462:       PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1463:     }
1464:     PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));

1466:     PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));

1468:     /* local sweep */
1469:     PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1470:     PetscCall(VecAXPY(xx, 1.0, xx1));
1471:     PetscCall(VecDestroy(&xx1));
1472:   } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");

1474:   PetscCall(VecDestroy(&bb1));

1476:   matin->factorerrortype = mat->A->factorerrortype;
1477:   PetscFunctionReturn(PETSC_SUCCESS);
1478: }

1480: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1481: {
1482:   Mat             aA, aB, Aperm;
1483:   const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1484:   PetscScalar    *aa, *ba;
1485:   PetscInt        i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1486:   PetscSF         rowsf, sf;
1487:   IS              parcolp = NULL;
1488:   PetscBool       done;

1490:   PetscFunctionBegin;
1491:   PetscCall(MatGetLocalSize(A, &m, &n));
1492:   PetscCall(ISGetIndices(rowp, &rwant));
1493:   PetscCall(ISGetIndices(colp, &cwant));
1494:   PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));

1496:   /* Invert row permutation to find out where my rows should go */
1497:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1498:   PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1499:   PetscCall(PetscSFSetFromOptions(rowsf));
1500:   for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1501:   PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1502:   PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));

1504:   /* Invert column permutation to find out where my columns should go */
1505:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1506:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1507:   PetscCall(PetscSFSetFromOptions(sf));
1508:   for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1509:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1510:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1511:   PetscCall(PetscSFDestroy(&sf));

1513:   PetscCall(ISRestoreIndices(rowp, &rwant));
1514:   PetscCall(ISRestoreIndices(colp, &cwant));
1515:   PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));

1517:   /* Find out where my gcols should go */
1518:   PetscCall(MatGetSize(aB, NULL, &ng));
1519:   PetscCall(PetscMalloc1(ng, &gcdest));
1520:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1521:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1522:   PetscCall(PetscSFSetFromOptions(sf));
1523:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1524:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1525:   PetscCall(PetscSFDestroy(&sf));

1527:   PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1528:   PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1529:   PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1530:   for (i = 0; i < m; i++) {
1531:     PetscInt    row = rdest[i];
1532:     PetscMPIInt rowner;
1533:     PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1534:     for (j = ai[i]; j < ai[i + 1]; j++) {
1535:       PetscInt    col = cdest[aj[j]];
1536:       PetscMPIInt cowner;
1537:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1538:       if (rowner == cowner) dnnz[i]++;
1539:       else onnz[i]++;
1540:     }
1541:     for (j = bi[i]; j < bi[i + 1]; j++) {
1542:       PetscInt    col = gcdest[bj[j]];
1543:       PetscMPIInt cowner;
1544:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1545:       if (rowner == cowner) dnnz[i]++;
1546:       else onnz[i]++;
1547:     }
1548:   }
1549:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1550:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1551:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1552:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1553:   PetscCall(PetscSFDestroy(&rowsf));

1555:   PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1556:   PetscCall(MatSeqAIJGetArray(aA, &aa));
1557:   PetscCall(MatSeqAIJGetArray(aB, &ba));
1558:   for (i = 0; i < m; i++) {
1559:     PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1560:     PetscInt  j0, rowlen;
1561:     rowlen = ai[i + 1] - ai[i];
1562:     for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1563:       for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1564:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1565:     }
1566:     rowlen = bi[i + 1] - bi[i];
1567:     for (j0 = j = 0; j < rowlen; j0 = j) {
1568:       for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1569:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1570:     }
1571:   }
1572:   PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1573:   PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1574:   PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1575:   PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1576:   PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1577:   PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1578:   PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1579:   PetscCall(PetscFree3(work, rdest, cdest));
1580:   PetscCall(PetscFree(gcdest));
1581:   if (parcolp) PetscCall(ISDestroy(&colp));
1582:   *B = Aperm;
1583:   PetscFunctionReturn(PETSC_SUCCESS);
1584: }

1586: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1587: {
1588:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1590:   PetscFunctionBegin;
1591:   PetscCall(MatGetSize(aij->B, NULL, nghosts));
1592:   if (ghosts) *ghosts = aij->garray;
1593:   PetscFunctionReturn(PETSC_SUCCESS);
1594: }

1596: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1597: {
1598:   Mat_MPIAIJ    *mat = (Mat_MPIAIJ *)matin->data;
1599:   Mat            A = mat->A, B = mat->B;
1600:   PetscLogDouble isend[5], irecv[5];

1602:   PetscFunctionBegin;
1603:   info->block_size = 1.0;
1604:   PetscCall(MatGetInfo(A, MAT_LOCAL, info));

1606:   isend[0] = info->nz_used;
1607:   isend[1] = info->nz_allocated;
1608:   isend[2] = info->nz_unneeded;
1609:   isend[3] = info->memory;
1610:   isend[4] = info->mallocs;

1612:   PetscCall(MatGetInfo(B, MAT_LOCAL, info));

1614:   isend[0] += info->nz_used;
1615:   isend[1] += info->nz_allocated;
1616:   isend[2] += info->nz_unneeded;
1617:   isend[3] += info->memory;
1618:   isend[4] += info->mallocs;
1619:   if (flag == MAT_LOCAL) {
1620:     info->nz_used      = isend[0];
1621:     info->nz_allocated = isend[1];
1622:     info->nz_unneeded  = isend[2];
1623:     info->memory       = isend[3];
1624:     info->mallocs      = isend[4];
1625:   } else if (flag == MAT_GLOBAL_MAX) {
1626:     PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));

1628:     info->nz_used      = irecv[0];
1629:     info->nz_allocated = irecv[1];
1630:     info->nz_unneeded  = irecv[2];
1631:     info->memory       = irecv[3];
1632:     info->mallocs      = irecv[4];
1633:   } else if (flag == MAT_GLOBAL_SUM) {
1634:     PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));

1636:     info->nz_used      = irecv[0];
1637:     info->nz_allocated = irecv[1];
1638:     info->nz_unneeded  = irecv[2];
1639:     info->memory       = irecv[3];
1640:     info->mallocs      = irecv[4];
1641:   }
1642:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1643:   info->fill_ratio_needed = 0;
1644:   info->factor_mallocs    = 0;
1645:   PetscFunctionReturn(PETSC_SUCCESS);
1646: }

1648: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1649: {
1650:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1652:   PetscFunctionBegin;
1653:   switch (op) {
1654:   case MAT_NEW_NONZERO_LOCATIONS:
1655:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1656:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1657:   case MAT_KEEP_NONZERO_PATTERN:
1658:   case MAT_NEW_NONZERO_LOCATION_ERR:
1659:   case MAT_USE_INODES:
1660:   case MAT_IGNORE_ZERO_ENTRIES:
1661:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1662:     MatCheckPreallocated(A, 1);
1663:     PetscCall(MatSetOption(a->A, op, flg));
1664:     PetscCall(MatSetOption(a->B, op, flg));
1665:     break;
1666:   case MAT_ROW_ORIENTED:
1667:     MatCheckPreallocated(A, 1);
1668:     a->roworiented = flg;

1670:     PetscCall(MatSetOption(a->A, op, flg));
1671:     PetscCall(MatSetOption(a->B, op, flg));
1672:     break;
1673:   case MAT_FORCE_DIAGONAL_ENTRIES:
1674:   case MAT_SORTED_FULL:
1675:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1676:     break;
1677:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1678:     a->donotstash = flg;
1679:     break;
1680:   /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1681:   case MAT_SPD:
1682:   case MAT_SYMMETRIC:
1683:   case MAT_STRUCTURALLY_SYMMETRIC:
1684:   case MAT_HERMITIAN:
1685:   case MAT_SYMMETRY_ETERNAL:
1686:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1687:   case MAT_SPD_ETERNAL:
1688:     /* if the diagonal matrix is square it inherits some of the properties above */
1689:     break;
1690:   case MAT_SUBMAT_SINGLEIS:
1691:     A->submat_singleis = flg;
1692:     break;
1693:   case MAT_STRUCTURE_ONLY:
1694:     /* The option is handled directly by MatSetOption() */
1695:     break;
1696:   default:
1697:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1698:   }
1699:   PetscFunctionReturn(PETSC_SUCCESS);
1700: }

1702: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1703: {
1704:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)matin->data;
1705:   PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1706:   PetscInt     i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1707:   PetscInt     nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1708:   PetscInt    *cmap, *idx_p;

1710:   PetscFunctionBegin;
1711:   PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1712:   mat->getrowactive = PETSC_TRUE;

1714:   if (!mat->rowvalues && (idx || v)) {
1715:     /*
1716:         allocate enough space to hold information from the longest row.
1717:     */
1718:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1719:     PetscInt    max = 1, tmp;
1720:     for (i = 0; i < matin->rmap->n; i++) {
1721:       tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1722:       if (max < tmp) max = tmp;
1723:     }
1724:     PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1725:   }

1727:   PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1728:   lrow = row - rstart;

1730:   pvA = &vworkA;
1731:   pcA = &cworkA;
1732:   pvB = &vworkB;
1733:   pcB = &cworkB;
1734:   if (!v) {
1735:     pvA = NULL;
1736:     pvB = NULL;
1737:   }
1738:   if (!idx) {
1739:     pcA = NULL;
1740:     if (!v) pcB = NULL;
1741:   }
1742:   PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1743:   PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1744:   nztot = nzA + nzB;

1746:   cmap = mat->garray;
1747:   if (v || idx) {
1748:     if (nztot) {
1749:       /* Sort by increasing column numbers, assuming A and B already sorted */
1750:       PetscInt imark = -1;
1751:       if (v) {
1752:         *v = v_p = mat->rowvalues;
1753:         for (i = 0; i < nzB; i++) {
1754:           if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1755:           else break;
1756:         }
1757:         imark = i;
1758:         for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1759:         for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1760:       }
1761:       if (idx) {
1762:         *idx = idx_p = mat->rowindices;
1763:         if (imark > -1) {
1764:           for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1765:         } else {
1766:           for (i = 0; i < nzB; i++) {
1767:             if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1768:             else break;
1769:           }
1770:           imark = i;
1771:         }
1772:         for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1773:         for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1774:       }
1775:     } else {
1776:       if (idx) *idx = NULL;
1777:       if (v) *v = NULL;
1778:     }
1779:   }
1780:   *nz = nztot;
1781:   PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1782:   PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1783:   PetscFunctionReturn(PETSC_SUCCESS);
1784: }

1786: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1787: {
1788:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1790:   PetscFunctionBegin;
1791:   PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1792:   aij->getrowactive = PETSC_FALSE;
1793:   PetscFunctionReturn(PETSC_SUCCESS);
1794: }

1796: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1797: {
1798:   Mat_MPIAIJ      *aij  = (Mat_MPIAIJ *)mat->data;
1799:   Mat_SeqAIJ      *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1800:   PetscInt         i, j, cstart = mat->cmap->rstart;
1801:   PetscReal        sum = 0.0;
1802:   const MatScalar *v, *amata, *bmata;
1803:   PetscMPIInt      iN;

1805:   PetscFunctionBegin;
1806:   if (aij->size == 1) {
1807:     PetscCall(MatNorm(aij->A, type, norm));
1808:   } else {
1809:     PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1810:     PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1811:     if (type == NORM_FROBENIUS) {
1812:       v = amata;
1813:       for (i = 0; i < amat->nz; i++) {
1814:         sum += PetscRealPart(PetscConj(*v) * (*v));
1815:         v++;
1816:       }
1817:       v = bmata;
1818:       for (i = 0; i < bmat->nz; i++) {
1819:         sum += PetscRealPart(PetscConj(*v) * (*v));
1820:         v++;
1821:       }
1822:       PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1823:       *norm = PetscSqrtReal(*norm);
1824:       PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1825:     } else if (type == NORM_1) { /* max column norm */
1826:       PetscReal *tmp, *tmp2;
1827:       PetscInt  *jj, *garray = aij->garray;
1828:       PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1829:       PetscCall(PetscMalloc1(mat->cmap->N + 1, &tmp2));
1830:       *norm = 0.0;
1831:       v     = amata;
1832:       jj    = amat->j;
1833:       for (j = 0; j < amat->nz; j++) {
1834:         tmp[cstart + *jj++] += PetscAbsScalar(*v);
1835:         v++;
1836:       }
1837:       v  = bmata;
1838:       jj = bmat->j;
1839:       for (j = 0; j < bmat->nz; j++) {
1840:         tmp[garray[*jj++]] += PetscAbsScalar(*v);
1841:         v++;
1842:       }
1843:       PetscCall(PetscMPIIntCast(mat->cmap->N, &iN));
1844:       PetscCallMPI(MPIU_Allreduce(tmp, tmp2, iN, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1845:       for (j = 0; j < mat->cmap->N; j++) {
1846:         if (tmp2[j] > *norm) *norm = tmp2[j];
1847:       }
1848:       PetscCall(PetscFree(tmp));
1849:       PetscCall(PetscFree(tmp2));
1850:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1851:     } else if (type == NORM_INFINITY) { /* max row norm */
1852:       PetscReal ntemp = 0.0;
1853:       for (j = 0; j < aij->A->rmap->n; j++) {
1854:         v   = PetscSafePointerPlusOffset(amata, amat->i[j]);
1855:         sum = 0.0;
1856:         for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1857:           sum += PetscAbsScalar(*v);
1858:           v++;
1859:         }
1860:         v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1861:         for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1862:           sum += PetscAbsScalar(*v);
1863:           v++;
1864:         }
1865:         if (sum > ntemp) ntemp = sum;
1866:       }
1867:       PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1868:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1869:     } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1870:     PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1871:     PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1872:   }
1873:   PetscFunctionReturn(PETSC_SUCCESS);
1874: }

1876: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1877: {
1878:   Mat_MPIAIJ      *a    = (Mat_MPIAIJ *)A->data, *b;
1879:   Mat_SeqAIJ      *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1880:   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;
1881:   const PetscInt  *ai, *aj, *bi, *bj, *B_diag_i;
1882:   Mat              B, A_diag, *B_diag;
1883:   const MatScalar *pbv, *bv;

1885:   PetscFunctionBegin;
1886:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1887:   ma = A->rmap->n;
1888:   na = A->cmap->n;
1889:   mb = a->B->rmap->n;
1890:   nb = a->B->cmap->n;
1891:   ai = Aloc->i;
1892:   aj = Aloc->j;
1893:   bi = Bloc->i;
1894:   bj = Bloc->j;
1895:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1896:     PetscInt            *d_nnz, *g_nnz, *o_nnz;
1897:     PetscSFNode         *oloc;
1898:     PETSC_UNUSED PetscSF sf;

1900:     PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1901:     /* compute d_nnz for preallocation */
1902:     PetscCall(PetscArrayzero(d_nnz, na));
1903:     for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1904:     /* compute local off-diagonal contributions */
1905:     PetscCall(PetscArrayzero(g_nnz, nb));
1906:     for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1907:     /* map those to global */
1908:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1909:     PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1910:     PetscCall(PetscSFSetFromOptions(sf));
1911:     PetscCall(PetscArrayzero(o_nnz, na));
1912:     PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1913:     PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1914:     PetscCall(PetscSFDestroy(&sf));

1916:     PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1917:     PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1918:     PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1919:     PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1920:     PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1921:     PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1922:   } else {
1923:     B = *matout;
1924:     PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1925:   }

1927:   b           = (Mat_MPIAIJ *)B->data;
1928:   A_diag      = a->A;
1929:   B_diag      = &b->A;
1930:   sub_B_diag  = (Mat_SeqAIJ *)(*B_diag)->data;
1931:   A_diag_ncol = A_diag->cmap->N;
1932:   B_diag_ilen = sub_B_diag->ilen;
1933:   B_diag_i    = sub_B_diag->i;

1935:   /* Set ilen for diagonal of B */
1936:   for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];

1938:   /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1939:   very quickly (=without using MatSetValues), because all writes are local. */
1940:   PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1941:   PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));

1943:   /* copy over the B part */
1944:   PetscCall(PetscMalloc1(bi[mb], &cols));
1945:   PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1946:   pbv = bv;
1947:   row = A->rmap->rstart;
1948:   for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1949:   cols_tmp = cols;
1950:   for (i = 0; i < mb; i++) {
1951:     ncol = bi[i + 1] - bi[i];
1952:     PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1953:     row++;
1954:     if (pbv) pbv += ncol;
1955:     if (cols_tmp) cols_tmp += ncol;
1956:   }
1957:   PetscCall(PetscFree(cols));
1958:   PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));

1960:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1961:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1962:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1963:     *matout = B;
1964:   } else {
1965:     PetscCall(MatHeaderMerge(A, &B));
1966:   }
1967:   PetscFunctionReturn(PETSC_SUCCESS);
1968: }

1970: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1971: {
1972:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1973:   Mat         a = aij->A, b = aij->B;
1974:   PetscInt    s1, s2, s3;

1976:   PetscFunctionBegin;
1977:   PetscCall(MatGetLocalSize(mat, &s2, &s3));
1978:   if (rr) {
1979:     PetscCall(VecGetLocalSize(rr, &s1));
1980:     PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1981:     /* Overlap communication with computation. */
1982:     PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1983:   }
1984:   if (ll) {
1985:     PetscCall(VecGetLocalSize(ll, &s1));
1986:     PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
1987:     PetscUseTypeMethod(b, diagonalscale, ll, NULL);
1988:   }
1989:   /* scale  the diagonal block */
1990:   PetscUseTypeMethod(a, diagonalscale, ll, rr);

1992:   if (rr) {
1993:     /* Do a scatter end and then right scale the off-diagonal block */
1994:     PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1995:     PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
1996:   }
1997:   PetscFunctionReturn(PETSC_SUCCESS);
1998: }

2000: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2001: {
2002:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2004:   PetscFunctionBegin;
2005:   PetscCall(MatSetUnfactored(a->A));
2006:   PetscFunctionReturn(PETSC_SUCCESS);
2007: }

2009: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2010: {
2011:   Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2012:   Mat         a, b, c, d;
2013:   PetscBool   flg;

2015:   PetscFunctionBegin;
2016:   a = matA->A;
2017:   b = matA->B;
2018:   c = matB->A;
2019:   d = matB->B;

2021:   PetscCall(MatEqual(a, c, &flg));
2022:   if (flg) PetscCall(MatEqual(b, d, &flg));
2023:   PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2024:   PetscFunctionReturn(PETSC_SUCCESS);
2025: }

2027: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2028: {
2029:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2030:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2032:   PetscFunctionBegin;
2033:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2034:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2035:     /* because of the column compression in the off-processor part of the matrix a->B,
2036:        the number of columns in a->B and b->B may be different, hence we cannot call
2037:        the MatCopy() directly on the two parts. If need be, we can provide a more
2038:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2039:        then copying the submatrices */
2040:     PetscCall(MatCopy_Basic(A, B, str));
2041:   } else {
2042:     PetscCall(MatCopy(a->A, b->A, str));
2043:     PetscCall(MatCopy(a->B, b->B, str));
2044:   }
2045:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
2046:   PetscFunctionReturn(PETSC_SUCCESS);
2047: }

2049: /*
2050:    Computes the number of nonzeros per row needed for preallocation when X and Y
2051:    have different nonzero structure.
2052: */
2053: 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)
2054: {
2055:   PetscInt i, j, k, nzx, nzy;

2057:   PetscFunctionBegin;
2058:   /* Set the number of nonzeros in the new matrix */
2059:   for (i = 0; i < m; i++) {
2060:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2061:     nzx    = xi[i + 1] - xi[i];
2062:     nzy    = yi[i + 1] - yi[i];
2063:     nnz[i] = 0;
2064:     for (j = 0, k = 0; j < nzx; j++) {                                /* Point in X */
2065:       for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2066:       if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++;             /* Skip duplicate */
2067:       nnz[i]++;
2068:     }
2069:     for (; k < nzy; k++) nnz[i]++;
2070:   }
2071:   PetscFunctionReturn(PETSC_SUCCESS);
2072: }

2074: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2075: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2076: {
2077:   PetscInt    m = Y->rmap->N;
2078:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2079:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

2081:   PetscFunctionBegin;
2082:   PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2083:   PetscFunctionReturn(PETSC_SUCCESS);
2084: }

2086: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2087: {
2088:   Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;

2090:   PetscFunctionBegin;
2091:   if (str == SAME_NONZERO_PATTERN) {
2092:     PetscCall(MatAXPY(yy->A, a, xx->A, str));
2093:     PetscCall(MatAXPY(yy->B, a, xx->B, str));
2094:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2095:     PetscCall(MatAXPY_Basic(Y, a, X, str));
2096:   } else {
2097:     Mat       B;
2098:     PetscInt *nnz_d, *nnz_o;

2100:     PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2101:     PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2102:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2103:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2104:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2105:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2106:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2107:     PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2108:     PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2109:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2110:     PetscCall(MatHeaderMerge(Y, &B));
2111:     PetscCall(PetscFree(nnz_d));
2112:     PetscCall(PetscFree(nnz_o));
2113:   }
2114:   PetscFunctionReturn(PETSC_SUCCESS);
2115: }

2117: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);

2119: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2120: {
2121:   PetscFunctionBegin;
2122:   if (PetscDefined(USE_COMPLEX)) {
2123:     Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2125:     PetscCall(MatConjugate_SeqAIJ(aij->A));
2126:     PetscCall(MatConjugate_SeqAIJ(aij->B));
2127:   }
2128:   PetscFunctionReturn(PETSC_SUCCESS);
2129: }

2131: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2132: {
2133:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2135:   PetscFunctionBegin;
2136:   PetscCall(MatRealPart(a->A));
2137:   PetscCall(MatRealPart(a->B));
2138:   PetscFunctionReturn(PETSC_SUCCESS);
2139: }

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

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

2151: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2152: {
2153:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
2154:   PetscInt           i, *idxb = NULL, m = A->rmap->n;
2155:   PetscScalar       *va, *vv;
2156:   Vec                vB, vA;
2157:   const PetscScalar *vb;

2159:   PetscFunctionBegin;
2160:   PetscCall(MatCreateVecs(a->A, NULL, &vA));
2161:   PetscCall(MatGetRowMaxAbs(a->A, vA, idx));

2163:   PetscCall(VecGetArrayWrite(vA, &va));
2164:   if (idx) {
2165:     for (i = 0; i < m; i++) {
2166:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2167:     }
2168:   }

2170:   PetscCall(MatCreateVecs(a->B, NULL, &vB));
2171:   PetscCall(PetscMalloc1(m, &idxb));
2172:   PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));

2174:   PetscCall(VecGetArrayWrite(v, &vv));
2175:   PetscCall(VecGetArrayRead(vB, &vb));
2176:   for (i = 0; i < m; i++) {
2177:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2178:       vv[i] = vb[i];
2179:       if (idx) idx[i] = a->garray[idxb[i]];
2180:     } else {
2181:       vv[i] = va[i];
2182:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2183:     }
2184:   }
2185:   PetscCall(VecRestoreArrayWrite(vA, &vv));
2186:   PetscCall(VecRestoreArrayWrite(vA, &va));
2187:   PetscCall(VecRestoreArrayRead(vB, &vb));
2188:   PetscCall(PetscFree(idxb));
2189:   PetscCall(VecDestroy(&vA));
2190:   PetscCall(VecDestroy(&vB));
2191:   PetscFunctionReturn(PETSC_SUCCESS);
2192: }

2194: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2195: {
2196:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2197:   Vec         vB, vA;

2199:   PetscFunctionBegin;
2200:   PetscCall(MatCreateVecs(a->A, NULL, &vA));
2201:   PetscCall(MatGetRowSumAbs(a->A, vA));
2202:   PetscCall(MatCreateVecs(a->B, NULL, &vB));
2203:   PetscCall(MatGetRowSumAbs(a->B, vB));
2204:   PetscCall(VecAXPY(vA, 1.0, vB));
2205:   PetscCall(VecDestroy(&vB));
2206:   PetscCall(VecCopy(vA, v));
2207:   PetscCall(VecDestroy(&vA));
2208:   PetscFunctionReturn(PETSC_SUCCESS);
2209: }

2211: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2212: {
2213:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2214:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2215:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2216:   PetscInt          *cmap = mat->garray;
2217:   PetscInt          *diagIdx, *offdiagIdx;
2218:   Vec                diagV, offdiagV;
2219:   PetscScalar       *a, *diagA, *offdiagA;
2220:   const PetscScalar *ba, *bav;
2221:   PetscInt           r, j, col, ncols, *bi, *bj;
2222:   Mat                B = mat->B;
2223:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2225:   PetscFunctionBegin;
2226:   /* When a process holds entire A and other processes have no entry */
2227:   if (A->cmap->N == n) {
2228:     PetscCall(VecGetArrayWrite(v, &diagA));
2229:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2230:     PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2231:     PetscCall(VecDestroy(&diagV));
2232:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2233:     PetscFunctionReturn(PETSC_SUCCESS);
2234:   } else if (n == 0) {
2235:     if (m) {
2236:       PetscCall(VecGetArrayWrite(v, &a));
2237:       for (r = 0; r < m; r++) {
2238:         a[r] = 0.0;
2239:         if (idx) idx[r] = -1;
2240:       }
2241:       PetscCall(VecRestoreArrayWrite(v, &a));
2242:     }
2243:     PetscFunctionReturn(PETSC_SUCCESS);
2244:   }

2246:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2247:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2248:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2249:   PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));

2251:   /* Get offdiagIdx[] for implicit 0.0 */
2252:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2253:   ba = bav;
2254:   bi = b->i;
2255:   bj = b->j;
2256:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2257:   for (r = 0; r < m; r++) {
2258:     ncols = bi[r + 1] - bi[r];
2259:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2260:       offdiagA[r]   = *ba;
2261:       offdiagIdx[r] = cmap[0];
2262:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2263:       offdiagA[r] = 0.0;

2265:       /* Find first hole in the cmap */
2266:       for (j = 0; j < ncols; j++) {
2267:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2268:         if (col > j && j < cstart) {
2269:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2270:           break;
2271:         } else if (col > j + n && j >= cstart) {
2272:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2273:           break;
2274:         }
2275:       }
2276:       if (j == ncols && ncols < A->cmap->N - n) {
2277:         /* a hole is outside compressed Bcols */
2278:         if (ncols == 0) {
2279:           if (cstart) {
2280:             offdiagIdx[r] = 0;
2281:           } else offdiagIdx[r] = cend;
2282:         } else { /* ncols > 0 */
2283:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2284:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2285:         }
2286:       }
2287:     }

2289:     for (j = 0; j < ncols; j++) {
2290:       if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2291:         offdiagA[r]   = *ba;
2292:         offdiagIdx[r] = cmap[*bj];
2293:       }
2294:       ba++;
2295:       bj++;
2296:     }
2297:   }

2299:   PetscCall(VecGetArrayWrite(v, &a));
2300:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2301:   for (r = 0; r < m; ++r) {
2302:     if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2303:       a[r] = diagA[r];
2304:       if (idx) idx[r] = cstart + diagIdx[r];
2305:     } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2306:       a[r] = diagA[r];
2307:       if (idx) {
2308:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2309:           idx[r] = cstart + diagIdx[r];
2310:         } else idx[r] = offdiagIdx[r];
2311:       }
2312:     } else {
2313:       a[r] = offdiagA[r];
2314:       if (idx) idx[r] = offdiagIdx[r];
2315:     }
2316:   }
2317:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2318:   PetscCall(VecRestoreArrayWrite(v, &a));
2319:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2320:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2321:   PetscCall(VecDestroy(&diagV));
2322:   PetscCall(VecDestroy(&offdiagV));
2323:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2324:   PetscFunctionReturn(PETSC_SUCCESS);
2325: }

2327: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2328: {
2329:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2330:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2331:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2332:   PetscInt          *cmap = mat->garray;
2333:   PetscInt          *diagIdx, *offdiagIdx;
2334:   Vec                diagV, offdiagV;
2335:   PetscScalar       *a, *diagA, *offdiagA;
2336:   const PetscScalar *ba, *bav;
2337:   PetscInt           r, j, col, ncols, *bi, *bj;
2338:   Mat                B = mat->B;
2339:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2341:   PetscFunctionBegin;
2342:   /* When a process holds entire A and other processes have no entry */
2343:   if (A->cmap->N == n) {
2344:     PetscCall(VecGetArrayWrite(v, &diagA));
2345:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2346:     PetscCall(MatGetRowMin(mat->A, diagV, idx));
2347:     PetscCall(VecDestroy(&diagV));
2348:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2349:     PetscFunctionReturn(PETSC_SUCCESS);
2350:   } else if (n == 0) {
2351:     if (m) {
2352:       PetscCall(VecGetArrayWrite(v, &a));
2353:       for (r = 0; r < m; r++) {
2354:         a[r] = PETSC_MAX_REAL;
2355:         if (idx) idx[r] = -1;
2356:       }
2357:       PetscCall(VecRestoreArrayWrite(v, &a));
2358:     }
2359:     PetscFunctionReturn(PETSC_SUCCESS);
2360:   }

2362:   PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2363:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2364:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2365:   PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));

2367:   /* Get offdiagIdx[] for implicit 0.0 */
2368:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2369:   ba = bav;
2370:   bi = b->i;
2371:   bj = b->j;
2372:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2373:   for (r = 0; r < m; r++) {
2374:     ncols = bi[r + 1] - bi[r];
2375:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2376:       offdiagA[r]   = *ba;
2377:       offdiagIdx[r] = cmap[0];
2378:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2379:       offdiagA[r] = 0.0;

2381:       /* Find first hole in the cmap */
2382:       for (j = 0; j < ncols; j++) {
2383:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2384:         if (col > j && j < cstart) {
2385:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2386:           break;
2387:         } else if (col > j + n && j >= cstart) {
2388:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2389:           break;
2390:         }
2391:       }
2392:       if (j == ncols && ncols < A->cmap->N - n) {
2393:         /* a hole is outside compressed Bcols */
2394:         if (ncols == 0) {
2395:           if (cstart) {
2396:             offdiagIdx[r] = 0;
2397:           } else offdiagIdx[r] = cend;
2398:         } else { /* ncols > 0 */
2399:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2400:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2401:         }
2402:       }
2403:     }

2405:     for (j = 0; j < ncols; j++) {
2406:       if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2407:         offdiagA[r]   = *ba;
2408:         offdiagIdx[r] = cmap[*bj];
2409:       }
2410:       ba++;
2411:       bj++;
2412:     }
2413:   }

2415:   PetscCall(VecGetArrayWrite(v, &a));
2416:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2417:   for (r = 0; r < m; ++r) {
2418:     if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2419:       a[r] = diagA[r];
2420:       if (idx) idx[r] = cstart + diagIdx[r];
2421:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2422:       a[r] = diagA[r];
2423:       if (idx) {
2424:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2425:           idx[r] = cstart + diagIdx[r];
2426:         } else idx[r] = offdiagIdx[r];
2427:       }
2428:     } else {
2429:       a[r] = offdiagA[r];
2430:       if (idx) idx[r] = offdiagIdx[r];
2431:     }
2432:   }
2433:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2434:   PetscCall(VecRestoreArrayWrite(v, &a));
2435:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2436:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2437:   PetscCall(VecDestroy(&diagV));
2438:   PetscCall(VecDestroy(&offdiagV));
2439:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2440:   PetscFunctionReturn(PETSC_SUCCESS);
2441: }

2443: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2444: {
2445:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2446:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2447:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2448:   PetscInt          *cmap = mat->garray;
2449:   PetscInt          *diagIdx, *offdiagIdx;
2450:   Vec                diagV, offdiagV;
2451:   PetscScalar       *a, *diagA, *offdiagA;
2452:   const PetscScalar *ba, *bav;
2453:   PetscInt           r, j, col, ncols, *bi, *bj;
2454:   Mat                B = mat->B;
2455:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2457:   PetscFunctionBegin;
2458:   /* When a process holds entire A and other processes have no entry */
2459:   if (A->cmap->N == n) {
2460:     PetscCall(VecGetArrayWrite(v, &diagA));
2461:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2462:     PetscCall(MatGetRowMax(mat->A, diagV, idx));
2463:     PetscCall(VecDestroy(&diagV));
2464:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2465:     PetscFunctionReturn(PETSC_SUCCESS);
2466:   } else if (n == 0) {
2467:     if (m) {
2468:       PetscCall(VecGetArrayWrite(v, &a));
2469:       for (r = 0; r < m; r++) {
2470:         a[r] = PETSC_MIN_REAL;
2471:         if (idx) idx[r] = -1;
2472:       }
2473:       PetscCall(VecRestoreArrayWrite(v, &a));
2474:     }
2475:     PetscFunctionReturn(PETSC_SUCCESS);
2476:   }

2478:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2479:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2480:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2481:   PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));

2483:   /* Get offdiagIdx[] for implicit 0.0 */
2484:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2485:   ba = bav;
2486:   bi = b->i;
2487:   bj = b->j;
2488:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2489:   for (r = 0; r < m; r++) {
2490:     ncols = bi[r + 1] - bi[r];
2491:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2492:       offdiagA[r]   = *ba;
2493:       offdiagIdx[r] = cmap[0];
2494:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2495:       offdiagA[r] = 0.0;

2497:       /* Find first hole in the cmap */
2498:       for (j = 0; j < ncols; j++) {
2499:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2500:         if (col > j && j < cstart) {
2501:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2502:           break;
2503:         } else if (col > j + n && j >= cstart) {
2504:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2505:           break;
2506:         }
2507:       }
2508:       if (j == ncols && ncols < A->cmap->N - n) {
2509:         /* a hole is outside compressed Bcols */
2510:         if (ncols == 0) {
2511:           if (cstart) {
2512:             offdiagIdx[r] = 0;
2513:           } else offdiagIdx[r] = cend;
2514:         } else { /* ncols > 0 */
2515:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2516:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2517:         }
2518:       }
2519:     }

2521:     for (j = 0; j < ncols; j++) {
2522:       if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2523:         offdiagA[r]   = *ba;
2524:         offdiagIdx[r] = cmap[*bj];
2525:       }
2526:       ba++;
2527:       bj++;
2528:     }
2529:   }

2531:   PetscCall(VecGetArrayWrite(v, &a));
2532:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2533:   for (r = 0; r < m; ++r) {
2534:     if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2535:       a[r] = diagA[r];
2536:       if (idx) idx[r] = cstart + diagIdx[r];
2537:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2538:       a[r] = diagA[r];
2539:       if (idx) {
2540:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2541:           idx[r] = cstart + diagIdx[r];
2542:         } else idx[r] = offdiagIdx[r];
2543:       }
2544:     } else {
2545:       a[r] = offdiagA[r];
2546:       if (idx) idx[r] = offdiagIdx[r];
2547:     }
2548:   }
2549:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2550:   PetscCall(VecRestoreArrayWrite(v, &a));
2551:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2552:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2553:   PetscCall(VecDestroy(&diagV));
2554:   PetscCall(VecDestroy(&offdiagV));
2555:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2556:   PetscFunctionReturn(PETSC_SUCCESS);
2557: }

2559: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2560: {
2561:   Mat *dummy;

2563:   PetscFunctionBegin;
2564:   PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2565:   *newmat = *dummy;
2566:   PetscCall(PetscFree(dummy));
2567:   PetscFunctionReturn(PETSC_SUCCESS);
2568: }

2570: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2571: {
2572:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2574:   PetscFunctionBegin;
2575:   PetscCall(MatInvertBlockDiagonal(a->A, values));
2576:   A->factorerrortype = a->A->factorerrortype;
2577:   PetscFunctionReturn(PETSC_SUCCESS);
2578: }

2580: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2581: {
2582:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;

2584:   PetscFunctionBegin;
2585:   PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2586:   PetscCall(MatSetRandom(aij->A, rctx));
2587:   if (x->assembled) {
2588:     PetscCall(MatSetRandom(aij->B, rctx));
2589:   } else {
2590:     PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2591:   }
2592:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2593:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2594:   PetscFunctionReturn(PETSC_SUCCESS);
2595: }

2597: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2598: {
2599:   PetscFunctionBegin;
2600:   if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2601:   else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2602:   PetscFunctionReturn(PETSC_SUCCESS);
2603: }

2605: /*@
2606:   MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank

2608:   Not Collective

2610:   Input Parameter:
2611: . A - the matrix

2613:   Output Parameter:
2614: . nz - the number of nonzeros

2616:   Level: advanced

2618: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2619: @*/
2620: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2621: {
2622:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2623:   Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2624:   PetscBool   isaij;

2626:   PetscFunctionBegin;
2627:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2628:   PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2629:   *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2630:   PetscFunctionReturn(PETSC_SUCCESS);
2631: }

2633: /*@
2634:   MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap

2636:   Collective

2638:   Input Parameters:
2639: + A  - the matrix
2640: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)

2642:   Level: advanced

2644: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2645: @*/
2646: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2647: {
2648:   PetscFunctionBegin;
2649:   PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2650:   PetscFunctionReturn(PETSC_SUCCESS);
2651: }

2653: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2654: {
2655:   PetscBool sc = PETSC_FALSE, flg;

2657:   PetscFunctionBegin;
2658:   PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2659:   if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2660:   PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2661:   if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2662:   PetscOptionsHeadEnd();
2663:   PetscFunctionReturn(PETSC_SUCCESS);
2664: }

2666: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2667: {
2668:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2669:   Mat_SeqAIJ *aij  = (Mat_SeqAIJ *)maij->A->data;

2671:   PetscFunctionBegin;
2672:   if (!Y->preallocated) {
2673:     PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2674:   } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2675:     PetscInt nonew = aij->nonew;
2676:     PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2677:     aij->nonew = nonew;
2678:   }
2679:   PetscCall(MatShift_Basic(Y, a));
2680:   PetscFunctionReturn(PETSC_SUCCESS);
2681: }

2683: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2684: {
2685:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2687:   PetscFunctionBegin;
2688:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2689:   PetscCall(MatMissingDiagonal(a->A, missing, d));
2690:   if (d) {
2691:     PetscInt rstart;
2692:     PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2693:     *d += rstart;
2694:   }
2695:   PetscFunctionReturn(PETSC_SUCCESS);
2696: }

2698: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2699: {
2700:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2702:   PetscFunctionBegin;
2703:   PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2704:   PetscFunctionReturn(PETSC_SUCCESS);
2705: }

2707: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2708: {
2709:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2711:   PetscFunctionBegin;
2712:   PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep));        // possibly keep zero diagonal coefficients
2713:   PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2714:   PetscFunctionReturn(PETSC_SUCCESS);
2715: }

2717: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2718:                                        MatGetRow_MPIAIJ,
2719:                                        MatRestoreRow_MPIAIJ,
2720:                                        MatMult_MPIAIJ,
2721:                                        /* 4*/ MatMultAdd_MPIAIJ,
2722:                                        MatMultTranspose_MPIAIJ,
2723:                                        MatMultTransposeAdd_MPIAIJ,
2724:                                        NULL,
2725:                                        NULL,
2726:                                        NULL,
2727:                                        /*10*/ NULL,
2728:                                        NULL,
2729:                                        NULL,
2730:                                        MatSOR_MPIAIJ,
2731:                                        MatTranspose_MPIAIJ,
2732:                                        /*15*/ MatGetInfo_MPIAIJ,
2733:                                        MatEqual_MPIAIJ,
2734:                                        MatGetDiagonal_MPIAIJ,
2735:                                        MatDiagonalScale_MPIAIJ,
2736:                                        MatNorm_MPIAIJ,
2737:                                        /*20*/ MatAssemblyBegin_MPIAIJ,
2738:                                        MatAssemblyEnd_MPIAIJ,
2739:                                        MatSetOption_MPIAIJ,
2740:                                        MatZeroEntries_MPIAIJ,
2741:                                        /*24*/ MatZeroRows_MPIAIJ,
2742:                                        NULL,
2743:                                        NULL,
2744:                                        NULL,
2745:                                        NULL,
2746:                                        /*29*/ MatSetUp_MPI_Hash,
2747:                                        NULL,
2748:                                        NULL,
2749:                                        MatGetDiagonalBlock_MPIAIJ,
2750:                                        NULL,
2751:                                        /*34*/ MatDuplicate_MPIAIJ,
2752:                                        NULL,
2753:                                        NULL,
2754:                                        NULL,
2755:                                        NULL,
2756:                                        /*39*/ MatAXPY_MPIAIJ,
2757:                                        MatCreateSubMatrices_MPIAIJ,
2758:                                        MatIncreaseOverlap_MPIAIJ,
2759:                                        MatGetValues_MPIAIJ,
2760:                                        MatCopy_MPIAIJ,
2761:                                        /*44*/ MatGetRowMax_MPIAIJ,
2762:                                        MatScale_MPIAIJ,
2763:                                        MatShift_MPIAIJ,
2764:                                        MatDiagonalSet_MPIAIJ,
2765:                                        MatZeroRowsColumns_MPIAIJ,
2766:                                        /*49*/ MatSetRandom_MPIAIJ,
2767:                                        MatGetRowIJ_MPIAIJ,
2768:                                        MatRestoreRowIJ_MPIAIJ,
2769:                                        NULL,
2770:                                        NULL,
2771:                                        /*54*/ MatFDColoringCreate_MPIXAIJ,
2772:                                        NULL,
2773:                                        MatSetUnfactored_MPIAIJ,
2774:                                        MatPermute_MPIAIJ,
2775:                                        NULL,
2776:                                        /*59*/ MatCreateSubMatrix_MPIAIJ,
2777:                                        MatDestroy_MPIAIJ,
2778:                                        MatView_MPIAIJ,
2779:                                        NULL,
2780:                                        NULL,
2781:                                        /*64*/ NULL,
2782:                                        MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2783:                                        NULL,
2784:                                        NULL,
2785:                                        NULL,
2786:                                        /*69*/ MatGetRowMaxAbs_MPIAIJ,
2787:                                        MatGetRowMinAbs_MPIAIJ,
2788:                                        NULL,
2789:                                        NULL,
2790:                                        NULL,
2791:                                        NULL,
2792:                                        /*75*/ MatFDColoringApply_AIJ,
2793:                                        MatSetFromOptions_MPIAIJ,
2794:                                        NULL,
2795:                                        NULL,
2796:                                        MatFindZeroDiagonals_MPIAIJ,
2797:                                        /*80*/ NULL,
2798:                                        NULL,
2799:                                        NULL,
2800:                                        /*83*/ MatLoad_MPIAIJ,
2801:                                        NULL,
2802:                                        NULL,
2803:                                        NULL,
2804:                                        NULL,
2805:                                        NULL,
2806:                                        /*89*/ NULL,
2807:                                        NULL,
2808:                                        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2809:                                        NULL,
2810:                                        NULL,
2811:                                        /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2812:                                        NULL,
2813:                                        NULL,
2814:                                        NULL,
2815:                                        MatBindToCPU_MPIAIJ,
2816:                                        /*99*/ MatProductSetFromOptions_MPIAIJ,
2817:                                        NULL,
2818:                                        NULL,
2819:                                        MatConjugate_MPIAIJ,
2820:                                        NULL,
2821:                                        /*104*/ MatSetValuesRow_MPIAIJ,
2822:                                        MatRealPart_MPIAIJ,
2823:                                        MatImaginaryPart_MPIAIJ,
2824:                                        NULL,
2825:                                        NULL,
2826:                                        /*109*/ NULL,
2827:                                        NULL,
2828:                                        MatGetRowMin_MPIAIJ,
2829:                                        NULL,
2830:                                        MatMissingDiagonal_MPIAIJ,
2831:                                        /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2832:                                        NULL,
2833:                                        MatGetGhosts_MPIAIJ,
2834:                                        NULL,
2835:                                        NULL,
2836:                                        /*119*/ MatMultDiagonalBlock_MPIAIJ,
2837:                                        NULL,
2838:                                        NULL,
2839:                                        NULL,
2840:                                        MatGetMultiProcBlock_MPIAIJ,
2841:                                        /*124*/ MatFindNonzeroRows_MPIAIJ,
2842:                                        MatGetColumnReductions_MPIAIJ,
2843:                                        MatInvertBlockDiagonal_MPIAIJ,
2844:                                        MatInvertVariableBlockDiagonal_MPIAIJ,
2845:                                        MatCreateSubMatricesMPI_MPIAIJ,
2846:                                        /*129*/ NULL,
2847:                                        NULL,
2848:                                        NULL,
2849:                                        MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2850:                                        NULL,
2851:                                        /*134*/ NULL,
2852:                                        NULL,
2853:                                        NULL,
2854:                                        NULL,
2855:                                        NULL,
2856:                                        /*139*/ MatSetBlockSizes_MPIAIJ,
2857:                                        NULL,
2858:                                        NULL,
2859:                                        MatFDColoringSetUp_MPIXAIJ,
2860:                                        MatFindOffBlockDiagonalEntries_MPIAIJ,
2861:                                        MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2862:                                        /*145*/ NULL,
2863:                                        NULL,
2864:                                        NULL,
2865:                                        MatCreateGraph_Simple_AIJ,
2866:                                        NULL,
2867:                                        /*150*/ NULL,
2868:                                        MatEliminateZeros_MPIAIJ,
2869:                                        MatGetRowSumAbs_MPIAIJ,
2870:                                        NULL,
2871:                                        NULL,
2872:                                        NULL};

2874: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2875: {
2876:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2878:   PetscFunctionBegin;
2879:   PetscCall(MatStoreValues(aij->A));
2880:   PetscCall(MatStoreValues(aij->B));
2881:   PetscFunctionReturn(PETSC_SUCCESS);
2882: }

2884: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2885: {
2886:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2888:   PetscFunctionBegin;
2889:   PetscCall(MatRetrieveValues(aij->A));
2890:   PetscCall(MatRetrieveValues(aij->B));
2891:   PetscFunctionReturn(PETSC_SUCCESS);
2892: }

2894: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2895: {
2896:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2897:   PetscMPIInt size;

2899:   PetscFunctionBegin;
2900:   if (B->hash_active) {
2901:     B->ops[0]      = b->cops;
2902:     B->hash_active = PETSC_FALSE;
2903:   }
2904:   PetscCall(PetscLayoutSetUp(B->rmap));
2905:   PetscCall(PetscLayoutSetUp(B->cmap));

2907: #if defined(PETSC_USE_CTABLE)
2908:   PetscCall(PetscHMapIDestroy(&b->colmap));
2909: #else
2910:   PetscCall(PetscFree(b->colmap));
2911: #endif
2912:   PetscCall(PetscFree(b->garray));
2913:   PetscCall(VecDestroy(&b->lvec));
2914:   PetscCall(VecScatterDestroy(&b->Mvctx));

2916:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

2918:   MatSeqXAIJGetOptions_Private(b->B);
2919:   PetscCall(MatDestroy(&b->B));
2920:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2921:   PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2922:   PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2923:   PetscCall(MatSetType(b->B, MATSEQAIJ));
2924:   MatSeqXAIJRestoreOptions_Private(b->B);

2926:   MatSeqXAIJGetOptions_Private(b->A);
2927:   PetscCall(MatDestroy(&b->A));
2928:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2929:   PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2930:   PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2931:   PetscCall(MatSetType(b->A, MATSEQAIJ));
2932:   MatSeqXAIJRestoreOptions_Private(b->A);

2934:   PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2935:   PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2936:   B->preallocated  = PETSC_TRUE;
2937:   B->was_assembled = PETSC_FALSE;
2938:   B->assembled     = PETSC_FALSE;
2939:   PetscFunctionReturn(PETSC_SUCCESS);
2940: }

2942: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2943: {
2944:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2946:   PetscFunctionBegin;
2948:   PetscCall(PetscLayoutSetUp(B->rmap));
2949:   PetscCall(PetscLayoutSetUp(B->cmap));
2950:   if (B->assembled || B->was_assembled) PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2951:   else {
2952: #if defined(PETSC_USE_CTABLE)
2953:     PetscCall(PetscHMapIDestroy(&b->colmap));
2954: #else
2955:     PetscCall(PetscFree(b->colmap));
2956: #endif
2957:     PetscCall(PetscFree(b->garray));
2958:     PetscCall(VecDestroy(&b->lvec));
2959:   }
2960:   PetscCall(VecScatterDestroy(&b->Mvctx));

2962:   PetscCall(MatResetPreallocation(b->A));
2963:   PetscCall(MatResetPreallocation(b->B));
2964:   B->preallocated  = PETSC_TRUE;
2965:   B->was_assembled = PETSC_FALSE;
2966:   B->assembled     = PETSC_FALSE;
2967:   PetscFunctionReturn(PETSC_SUCCESS);
2968: }

2970: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2971: {
2972:   Mat         mat;
2973:   Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;

2975:   PetscFunctionBegin;
2976:   *newmat = NULL;
2977:   PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2978:   PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2979:   PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2980:   PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2981:   a = (Mat_MPIAIJ *)mat->data;

2983:   mat->factortype = matin->factortype;
2984:   mat->assembled  = matin->assembled;
2985:   mat->insertmode = NOT_SET_VALUES;

2987:   a->size         = oldmat->size;
2988:   a->rank         = oldmat->rank;
2989:   a->donotstash   = oldmat->donotstash;
2990:   a->roworiented  = oldmat->roworiented;
2991:   a->rowindices   = NULL;
2992:   a->rowvalues    = NULL;
2993:   a->getrowactive = PETSC_FALSE;

2995:   PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
2996:   PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
2997:   if (matin->hash_active) {
2998:     PetscCall(MatSetUp(mat));
2999:   } else {
3000:     mat->preallocated = matin->preallocated;
3001:     if (oldmat->colmap) {
3002: #if defined(PETSC_USE_CTABLE)
3003:       PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3004: #else
3005:       PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3006:       PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3007: #endif
3008:     } else a->colmap = NULL;
3009:     if (oldmat->garray) {
3010:       PetscInt len;
3011:       len = oldmat->B->cmap->n;
3012:       PetscCall(PetscMalloc1(len + 1, &a->garray));
3013:       if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3014:     } else a->garray = NULL;

3016:     /* It may happen MatDuplicate is called with a non-assembled matrix
3017:       In fact, MatDuplicate only requires the matrix to be preallocated
3018:       This may happen inside a DMCreateMatrix_Shell */
3019:     if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3020:     if (oldmat->Mvctx) {
3021:       a->Mvctx = oldmat->Mvctx;
3022:       PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3023:     }
3024:     PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3025:     PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3026:   }
3027:   PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3028:   *newmat = mat;
3029:   PetscFunctionReturn(PETSC_SUCCESS);
3030: }

3032: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3033: {
3034:   PetscBool isbinary, ishdf5;

3036:   PetscFunctionBegin;
3039:   /* force binary viewer to load .info file if it has not yet done so */
3040:   PetscCall(PetscViewerSetUp(viewer));
3041:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3042:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3043:   if (isbinary) {
3044:     PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3045:   } else if (ishdf5) {
3046: #if defined(PETSC_HAVE_HDF5)
3047:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3048: #else
3049:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3050: #endif
3051:   } else {
3052:     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);
3053:   }
3054:   PetscFunctionReturn(PETSC_SUCCESS);
3055: }

3057: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3058: {
3059:   PetscInt     header[4], M, N, m, nz, rows, cols, sum, i;
3060:   PetscInt    *rowidxs, *colidxs;
3061:   PetscScalar *matvals;

3063:   PetscFunctionBegin;
3064:   PetscCall(PetscViewerSetUp(viewer));

3066:   /* read in matrix header */
3067:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3068:   PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3069:   M  = header[1];
3070:   N  = header[2];
3071:   nz = header[3];
3072:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3073:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3074:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");

3076:   /* set block sizes from the viewer's .info file */
3077:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3078:   /* set global sizes if not set already */
3079:   if (mat->rmap->N < 0) mat->rmap->N = M;
3080:   if (mat->cmap->N < 0) mat->cmap->N = N;
3081:   PetscCall(PetscLayoutSetUp(mat->rmap));
3082:   PetscCall(PetscLayoutSetUp(mat->cmap));

3084:   /* check if the matrix sizes are correct */
3085:   PetscCall(MatGetSize(mat, &rows, &cols));
3086:   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);

3088:   /* read in row lengths and build row indices */
3089:   PetscCall(MatGetLocalSize(mat, &m, NULL));
3090:   PetscCall(PetscMalloc1(m + 1, &rowidxs));
3091:   PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3092:   rowidxs[0] = 0;
3093:   for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3094:   if (nz != PETSC_INT_MAX) {
3095:     PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3096:     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);
3097:   }

3099:   /* read in column indices and matrix values */
3100:   PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3101:   PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3102:   PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3103:   /* store matrix indices and values */
3104:   PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3105:   PetscCall(PetscFree(rowidxs));
3106:   PetscCall(PetscFree2(colidxs, matvals));
3107:   PetscFunctionReturn(PETSC_SUCCESS);
3108: }

3110: /* Not scalable because of ISAllGather() unless getting all columns. */
3111: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3112: {
3113:   IS          iscol_local;
3114:   PetscBool   isstride;
3115:   PetscMPIInt lisstride = 0, gisstride;

3117:   PetscFunctionBegin;
3118:   /* check if we are grabbing all columns*/
3119:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));

3121:   if (isstride) {
3122:     PetscInt start, len, mstart, mlen;
3123:     PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3124:     PetscCall(ISGetLocalSize(iscol, &len));
3125:     PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3126:     if (mstart == start && mlen - mstart == len) lisstride = 1;
3127:   }

3129:   PetscCallMPI(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3130:   if (gisstride) {
3131:     PetscInt N;
3132:     PetscCall(MatGetSize(mat, NULL, &N));
3133:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3134:     PetscCall(ISSetIdentity(iscol_local));
3135:     PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3136:   } else {
3137:     PetscInt cbs;
3138:     PetscCall(ISGetBlockSize(iscol, &cbs));
3139:     PetscCall(ISAllGather(iscol, &iscol_local));
3140:     PetscCall(ISSetBlockSize(iscol_local, cbs));
3141:   }

3143:   *isseq = iscol_local;
3144:   PetscFunctionReturn(PETSC_SUCCESS);
3145: }

3147: /*
3148:  Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3149:  (see MatCreateSubMatrix_MPIAIJ_nonscalable)

3151:  Input Parameters:
3152: +   mat - matrix
3153: .   isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3154:            i.e., mat->rstart <= isrow[i] < mat->rend
3155: -   iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3156:            i.e., mat->cstart <= iscol[i] < mat->cend

3158:  Output Parameters:
3159: +   isrow_d - sequential row index set for retrieving mat->A
3160: .   iscol_d - sequential  column index set for retrieving mat->A
3161: .   iscol_o - sequential column index set for retrieving mat->B
3162: -   garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3163:  */
3164: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, const PetscInt *garray[])
3165: {
3166:   Vec             x, cmap;
3167:   const PetscInt *is_idx;
3168:   PetscScalar    *xarray, *cmaparray;
3169:   PetscInt        ncols, isstart, *idx, m, rstart, *cmap1, count;
3170:   Mat_MPIAIJ     *a    = (Mat_MPIAIJ *)mat->data;
3171:   Mat             B    = a->B;
3172:   Vec             lvec = a->lvec, lcmap;
3173:   PetscInt        i, cstart, cend, Bn = B->cmap->N;
3174:   MPI_Comm        comm;
3175:   VecScatter      Mvctx = a->Mvctx;

3177:   PetscFunctionBegin;
3178:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3179:   PetscCall(ISGetLocalSize(iscol, &ncols));

3181:   /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3182:   PetscCall(MatCreateVecs(mat, &x, NULL));
3183:   PetscCall(VecSet(x, -1.0));
3184:   PetscCall(VecDuplicate(x, &cmap));
3185:   PetscCall(VecSet(cmap, -1.0));

3187:   /* Get start indices */
3188:   PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3189:   isstart -= ncols;
3190:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));

3192:   PetscCall(ISGetIndices(iscol, &is_idx));
3193:   PetscCall(VecGetArray(x, &xarray));
3194:   PetscCall(VecGetArray(cmap, &cmaparray));
3195:   PetscCall(PetscMalloc1(ncols, &idx));
3196:   for (i = 0; i < ncols; i++) {
3197:     xarray[is_idx[i] - cstart]    = (PetscScalar)is_idx[i];
3198:     cmaparray[is_idx[i] - cstart] = i + isstart;        /* global index of iscol[i] */
3199:     idx[i]                        = is_idx[i] - cstart; /* local index of iscol[i]  */
3200:   }
3201:   PetscCall(VecRestoreArray(x, &xarray));
3202:   PetscCall(VecRestoreArray(cmap, &cmaparray));
3203:   PetscCall(ISRestoreIndices(iscol, &is_idx));

3205:   /* Get iscol_d */
3206:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3207:   PetscCall(ISGetBlockSize(iscol, &i));
3208:   PetscCall(ISSetBlockSize(*iscol_d, i));

3210:   /* Get isrow_d */
3211:   PetscCall(ISGetLocalSize(isrow, &m));
3212:   rstart = mat->rmap->rstart;
3213:   PetscCall(PetscMalloc1(m, &idx));
3214:   PetscCall(ISGetIndices(isrow, &is_idx));
3215:   for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3216:   PetscCall(ISRestoreIndices(isrow, &is_idx));

3218:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3219:   PetscCall(ISGetBlockSize(isrow, &i));
3220:   PetscCall(ISSetBlockSize(*isrow_d, i));

3222:   /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3223:   PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3224:   PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));

3226:   PetscCall(VecDuplicate(lvec, &lcmap));

3228:   PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3229:   PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));

3231:   /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3232:   /* off-process column indices */
3233:   count = 0;
3234:   PetscCall(PetscMalloc1(Bn, &idx));
3235:   PetscCall(PetscMalloc1(Bn, &cmap1));

3237:   PetscCall(VecGetArray(lvec, &xarray));
3238:   PetscCall(VecGetArray(lcmap, &cmaparray));
3239:   for (i = 0; i < Bn; i++) {
3240:     if (PetscRealPart(xarray[i]) > -1.0) {
3241:       idx[count]   = i;                                     /* local column index in off-diagonal part B */
3242:       cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3243:       count++;
3244:     }
3245:   }
3246:   PetscCall(VecRestoreArray(lvec, &xarray));
3247:   PetscCall(VecRestoreArray(lcmap, &cmaparray));

3249:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3250:   /* cannot ensure iscol_o has same blocksize as iscol! */

3252:   PetscCall(PetscFree(idx));
3253:   *garray = cmap1;

3255:   PetscCall(VecDestroy(&x));
3256:   PetscCall(VecDestroy(&cmap));
3257:   PetscCall(VecDestroy(&lcmap));
3258:   PetscFunctionReturn(PETSC_SUCCESS);
3259: }

3261: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3262: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3263: {
3264:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3265:   Mat         M = NULL;
3266:   MPI_Comm    comm;
3267:   IS          iscol_d, isrow_d, iscol_o;
3268:   Mat         Asub = NULL, Bsub = NULL;
3269:   PetscInt    n;

3271:   PetscFunctionBegin;
3272:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));

3274:   if (call == MAT_REUSE_MATRIX) {
3275:     /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3276:     PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3277:     PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");

3279:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3280:     PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");

3282:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3283:     PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");

3285:     /* Update diagonal and off-diagonal portions of submat */
3286:     asub = (Mat_MPIAIJ *)(*submat)->data;
3287:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3288:     PetscCall(ISGetLocalSize(iscol_o, &n));
3289:     if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3290:     PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3291:     PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));

3293:   } else { /* call == MAT_INITIAL_MATRIX) */
3294:     const PetscInt *garray;
3295:     PetscInt        BsubN;

3297:     /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3298:     PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));

3300:     /* Create local submatrices Asub and Bsub */
3301:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3302:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));

3304:     /* Create submatrix M */
3305:     PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));

3307:     /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3308:     asub = (Mat_MPIAIJ *)M->data;

3310:     PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3311:     n = asub->B->cmap->N;
3312:     if (BsubN > n) {
3313:       /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3314:       const PetscInt *idx;
3315:       PetscInt        i, j, *idx_new, *subgarray = asub->garray;
3316:       PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));

3318:       PetscCall(PetscMalloc1(n, &idx_new));
3319:       j = 0;
3320:       PetscCall(ISGetIndices(iscol_o, &idx));
3321:       for (i = 0; i < n; i++) {
3322:         if (j >= BsubN) break;
3323:         while (subgarray[i] > garray[j]) j++;

3325:         if (subgarray[i] == garray[j]) {
3326:           idx_new[i] = idx[j++];
3327:         } 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]);
3328:       }
3329:       PetscCall(ISRestoreIndices(iscol_o, &idx));

3331:       PetscCall(ISDestroy(&iscol_o));
3332:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));

3334:     } else if (BsubN < n) {
3335:       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);
3336:     }

3338:     PetscCall(PetscFree(garray));
3339:     *submat = M;

3341:     /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3342:     PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3343:     PetscCall(ISDestroy(&isrow_d));

3345:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3346:     PetscCall(ISDestroy(&iscol_d));

3348:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3349:     PetscCall(ISDestroy(&iscol_o));
3350:   }
3351:   PetscFunctionReturn(PETSC_SUCCESS);
3352: }

3354: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3355: {
3356:   IS        iscol_local = NULL, isrow_d;
3357:   PetscInt  csize;
3358:   PetscInt  n, i, j, start, end;
3359:   PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3360:   MPI_Comm  comm;

3362:   PetscFunctionBegin;
3363:   /* If isrow has same processor distribution as mat,
3364:      call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3365:   if (call == MAT_REUSE_MATRIX) {
3366:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3367:     if (isrow_d) {
3368:       sameRowDist  = PETSC_TRUE;
3369:       tsameDist[1] = PETSC_TRUE; /* sameColDist */
3370:     } else {
3371:       PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3372:       if (iscol_local) {
3373:         sameRowDist  = PETSC_TRUE;
3374:         tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3375:       }
3376:     }
3377:   } else {
3378:     /* Check if isrow has same processor distribution as mat */
3379:     sameDist[0] = PETSC_FALSE;
3380:     PetscCall(ISGetLocalSize(isrow, &n));
3381:     if (!n) {
3382:       sameDist[0] = PETSC_TRUE;
3383:     } else {
3384:       PetscCall(ISGetMinMax(isrow, &i, &j));
3385:       PetscCall(MatGetOwnershipRange(mat, &start, &end));
3386:       if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3387:     }

3389:     /* Check if iscol has same processor distribution as mat */
3390:     sameDist[1] = PETSC_FALSE;
3391:     PetscCall(ISGetLocalSize(iscol, &n));
3392:     if (!n) {
3393:       sameDist[1] = PETSC_TRUE;
3394:     } else {
3395:       PetscCall(ISGetMinMax(iscol, &i, &j));
3396:       PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3397:       if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3398:     }

3400:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3401:     PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3402:     sameRowDist = tsameDist[0];
3403:   }

3405:   if (sameRowDist) {
3406:     if (tsameDist[1]) { /* sameRowDist & sameColDist */
3407:       /* isrow and iscol have same processor distribution as mat */
3408:       PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3409:       PetscFunctionReturn(PETSC_SUCCESS);
3410:     } else { /* sameRowDist */
3411:       /* isrow has same processor distribution as mat */
3412:       if (call == MAT_INITIAL_MATRIX) {
3413:         PetscBool sorted;
3414:         PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3415:         PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3416:         PetscCall(ISGetSize(iscol, &i));
3417:         PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);

3419:         PetscCall(ISSorted(iscol_local, &sorted));
3420:         if (sorted) {
3421:           /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3422:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3423:           PetscFunctionReturn(PETSC_SUCCESS);
3424:         }
3425:       } else { /* call == MAT_REUSE_MATRIX */
3426:         IS iscol_sub;
3427:         PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3428:         if (iscol_sub) {
3429:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3430:           PetscFunctionReturn(PETSC_SUCCESS);
3431:         }
3432:       }
3433:     }
3434:   }

3436:   /* General case: iscol -> iscol_local which has global size of iscol */
3437:   if (call == MAT_REUSE_MATRIX) {
3438:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3439:     PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3440:   } else {
3441:     if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3442:   }

3444:   PetscCall(ISGetLocalSize(iscol, &csize));
3445:   PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));

3447:   if (call == MAT_INITIAL_MATRIX) {
3448:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3449:     PetscCall(ISDestroy(&iscol_local));
3450:   }
3451:   PetscFunctionReturn(PETSC_SUCCESS);
3452: }

3454: /*@C
3455:   MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3456:   and "off-diagonal" part of the matrix in CSR format.

3458:   Collective

3460:   Input Parameters:
3461: + comm   - MPI communicator
3462: . A      - "diagonal" portion of matrix
3463: . B      - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3464: - garray - global index of `B` columns

3466:   Output Parameter:
3467: . mat - the matrix, with input `A` as its local diagonal matrix

3469:   Level: advanced

3471:   Notes:
3472:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.

3474:   `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.

3476: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3477: @*/
3478: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3479: {
3480:   Mat_MPIAIJ        *maij;
3481:   Mat_SeqAIJ        *b  = (Mat_SeqAIJ *)B->data, *bnew;
3482:   PetscInt          *oi = b->i, *oj = b->j, i, nz, col;
3483:   const PetscScalar *oa;
3484:   Mat                Bnew;
3485:   PetscInt           m, n, N;
3486:   MatType            mpi_mat_type;

3488:   PetscFunctionBegin;
3489:   PetscCall(MatCreate(comm, mat));
3490:   PetscCall(MatGetSize(A, &m, &n));
3491:   PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3492:   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);
3493:   /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3494:   /* 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); */

3496:   /* Get global columns of mat */
3497:   PetscCallMPI(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));

3499:   PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3500:   /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3501:   PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3502:   PetscCall(MatSetType(*mat, mpi_mat_type));

3504:   if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3505:   maij = (Mat_MPIAIJ *)(*mat)->data;

3507:   (*mat)->preallocated = PETSC_TRUE;

3509:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
3510:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

3512:   /* Set A as diagonal portion of *mat */
3513:   maij->A = A;

3515:   nz = oi[m];
3516:   for (i = 0; i < nz; i++) {
3517:     col   = oj[i];
3518:     oj[i] = garray[col];
3519:   }

3521:   /* Set Bnew as off-diagonal portion of *mat */
3522:   PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3523:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3524:   PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3525:   bnew        = (Mat_SeqAIJ *)Bnew->data;
3526:   bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3527:   maij->B     = Bnew;

3529:   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);

3531:   b->free_a  = PETSC_FALSE;
3532:   b->free_ij = PETSC_FALSE;
3533:   PetscCall(MatDestroy(&B));

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:     PetscCallMPI(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:   PetscCallMPI(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, irstart;
3897:   const PetscInt *JJ;
3898:   PetscBool       nooffprocentries;
3899:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)B->data;

3901:   PetscFunctionBegin;
3902:   PetscCall(PetscLayoutSetUp(B->rmap));
3903:   PetscCall(PetscLayoutSetUp(B->cmap));
3904:   m       = B->rmap->n;
3905:   cstart  = B->cmap->rstart;
3906:   cend    = B->cmap->rend;
3907:   rstart  = B->rmap->rstart;
3908:   irstart = Ii[0];

3910:   PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));

3912:   if (PetscDefined(USE_DEBUG)) {
3913:     for (i = 0; i < m; i++) {
3914:       nnz = Ii[i + 1] - Ii[i];
3915:       JJ  = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3916:       PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3917:       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]);
3918:       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);
3919:     }
3920:   }

3922:   for (i = 0; i < m; i++) {
3923:     nnz     = Ii[i + 1] - Ii[i];
3924:     JJ      = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3925:     nnz_max = PetscMax(nnz_max, nnz);
3926:     d       = 0;
3927:     for (j = 0; j < nnz; j++) {
3928:       if (cstart <= JJ[j] && JJ[j] < cend) d++;
3929:     }
3930:     d_nnz[i] = d;
3931:     o_nnz[i] = nnz - d;
3932:   }
3933:   PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3934:   PetscCall(PetscFree2(d_nnz, o_nnz));

3936:   for (i = 0; i < m; i++) {
3937:     ii = i + rstart;
3938:     PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3939:   }
3940:   nooffprocentries    = B->nooffprocentries;
3941:   B->nooffprocentries = PETSC_TRUE;
3942:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3943:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3944:   B->nooffprocentries = nooffprocentries;

3946:   /* count number of entries below block diagonal */
3947:   PetscCall(PetscFree(Aij->ld));
3948:   PetscCall(PetscCalloc1(m, &ld));
3949:   Aij->ld = ld;
3950:   for (i = 0; i < m; i++) {
3951:     nnz = Ii[i + 1] - Ii[i];
3952:     j   = 0;
3953:     while (j < nnz && J[j] < cstart) j++;
3954:     ld[i] = j;
3955:     if (J) J += nnz;
3956:   }

3958:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3959:   PetscFunctionReturn(PETSC_SUCCESS);
3960: }

3962: /*@
3963:   MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3964:   (the default parallel PETSc format).

3966:   Collective

3968:   Input Parameters:
3969: + B - the matrix
3970: . i - the indices into `j` for the start of each local row (indices start with zero)
3971: . j - the column indices for each local row (indices start with zero)
3972: - v - optional values in the matrix

3974:   Level: developer

3976:   Notes:
3977:   The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3978:   thus you CANNOT change the matrix entries by changing the values of `v` after you have
3979:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

3981:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

3983:   A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.

3985:   You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.

3987:   If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
3988:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

3990:   The format which is used for the sparse matrix input, is equivalent to a
3991:   row-major ordering.. i.e for the following matrix, the input data expected is
3992:   as shown
3993: .vb
3994:         1 0 0
3995:         2 0 3     P0
3996:        -------
3997:         4 5 6     P1

3999:      Process0 [P0] rows_owned=[0,1]
4000:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4001:         j =  {0,0,2}  [size = 3]
4002:         v =  {1,2,3}  [size = 3]

4004:      Process1 [P1] rows_owned=[2]
4005:         i =  {0,3}    [size = nrow+1  = 1+1]
4006:         j =  {0,1,2}  [size = 3]
4007:         v =  {4,5,6}  [size = 3]
4008: .ve

4010: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4011:           `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4012: @*/
4013: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4014: {
4015:   PetscFunctionBegin;
4016:   PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4017:   PetscFunctionReturn(PETSC_SUCCESS);
4018: }

4020: /*@
4021:   MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4022:   (the default parallel PETSc format).  For good matrix assembly performance
4023:   the user should preallocate the matrix storage by setting the parameters
4024:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4026:   Collective

4028:   Input Parameters:
4029: + B     - the matrix
4030: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4031:            (same value is used for all local rows)
4032: . d_nnz - array containing the number of nonzeros in the various rows of the
4033:            DIAGONAL portion of the local submatrix (possibly different for each row)
4034:            or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4035:            The size of this array is equal to the number of local rows, i.e 'm'.
4036:            For matrices that will be factored, you must leave room for (and set)
4037:            the diagonal entry even if it is zero.
4038: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4039:            submatrix (same value is used for all local rows).
4040: - o_nnz - array containing the number of nonzeros in the various rows of the
4041:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4042:            each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4043:            structure. The size of this array is equal to the number
4044:            of local rows, i.e 'm'.

4046:   Example Usage:
4047:   Consider the following 8x8 matrix with 34 non-zero values, that is
4048:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4049:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4050:   as follows

4052: .vb
4053:             1  2  0  |  0  3  0  |  0  4
4054:     Proc0   0  5  6  |  7  0  0  |  8  0
4055:             9  0 10  | 11  0  0  | 12  0
4056:     -------------------------------------
4057:            13  0 14  | 15 16 17  |  0  0
4058:     Proc1   0 18  0  | 19 20 21  |  0  0
4059:             0  0  0  | 22 23  0  | 24  0
4060:     -------------------------------------
4061:     Proc2  25 26 27  |  0  0 28  | 29  0
4062:            30  0  0  | 31 32 33  |  0 34
4063: .ve

4065:   This can be represented as a collection of submatrices as
4066: .vb
4067:       A B C
4068:       D E F
4069:       G H I
4070: .ve

4072:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4073:   owned by proc1, G,H,I are owned by proc2.

4075:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4076:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4077:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4079:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4080:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4081:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4082:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4083:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4084:   matrix, and [DF] as another `MATSEQAIJ` matrix.

4086:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4087:   allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4088:   storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4089:   One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4090:   the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4091:   In this case, the values of `d_nz`, `o_nz` are
4092: .vb
4093:      proc0  dnz = 2, o_nz = 2
4094:      proc1  dnz = 3, o_nz = 2
4095:      proc2  dnz = 1, o_nz = 4
4096: .ve
4097:   We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4098:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4099:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4100:   34 values.

4102:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4103:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4104:   In the above case the values for `d_nnz`, `o_nnz` are
4105: .vb
4106:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4107:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4108:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4109: .ve
4110:   Here the space allocated is sum of all the above values i.e 34, and
4111:   hence pre-allocation is perfect.

4113:   Level: intermediate

4115:   Notes:
4116:   If the *_nnz parameter is given then the *_nz parameter is ignored

4118:   The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4119:   storage.  The stored row and column indices begin with zero.
4120:   See [Sparse Matrices](sec_matsparse) for details.

4122:   The parallel matrix is partitioned such that the first m0 rows belong to
4123:   process 0, the next m1 rows belong to process 1, the next m2 rows belong
4124:   to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

4126:   The DIAGONAL portion of the local submatrix of a processor can be defined
4127:   as the submatrix which is obtained by extraction the part corresponding to
4128:   the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4129:   first row that belongs to the processor, r2 is the last row belonging to
4130:   the this processor, and c1-c2 is range of indices of the local part of a
4131:   vector suitable for applying the matrix to.  This is an mxn matrix.  In the
4132:   common case of a square matrix, the row and column ranges are the same and
4133:   the DIAGONAL part is also square. The remaining portion of the local
4134:   submatrix (mxN) constitute the OFF-DIAGONAL portion.

4136:   If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.

4138:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
4139:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4140:   You can also run with the option `-info` and look for messages with the string
4141:   malloc in them to see if additional memory allocation was needed.

4143: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4144:           `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4145: @*/
4146: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4147: {
4148:   PetscFunctionBegin;
4151:   PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4152:   PetscFunctionReturn(PETSC_SUCCESS);
4153: }

4155: /*@
4156:   MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4157:   CSR format for the local rows.

4159:   Collective

4161:   Input Parameters:
4162: + comm - MPI communicator
4163: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
4164: . n    - This value should be the same as the local size used in creating the
4165:          x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4166:          calculated if `N` is given) For square matrices n is almost always `m`.
4167: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4168: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4169: . 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
4170: . j    - global column indices
4171: - a    - optional matrix values

4173:   Output Parameter:
4174: . mat - the matrix

4176:   Level: intermediate

4178:   Notes:
4179:   The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4180:   thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4181:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

4183:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

4185:   Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`

4187:   If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4188:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

4190:   The format which is used for the sparse matrix input, is equivalent to a
4191:   row-major ordering, i.e., for the following matrix, the input data expected is
4192:   as shown
4193: .vb
4194:         1 0 0
4195:         2 0 3     P0
4196:        -------
4197:         4 5 6     P1

4199:      Process0 [P0] rows_owned=[0,1]
4200:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4201:         j =  {0,0,2}  [size = 3]
4202:         v =  {1,2,3}  [size = 3]

4204:      Process1 [P1] rows_owned=[2]
4205:         i =  {0,3}    [size = nrow+1  = 1+1]
4206:         j =  {0,1,2}  [size = 3]
4207:         v =  {4,5,6}  [size = 3]
4208: .ve

4210: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4211:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4212: @*/
4213: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4214: {
4215:   PetscFunctionBegin;
4216:   PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4217:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4218:   PetscCall(MatCreate(comm, mat));
4219:   PetscCall(MatSetSizes(*mat, m, n, M, N));
4220:   /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4221:   PetscCall(MatSetType(*mat, MATMPIAIJ));
4222:   PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4223:   PetscFunctionReturn(PETSC_SUCCESS);
4224: }

4226: /*@
4227:   MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4228:   CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4229:   from `MatCreateMPIAIJWithArrays()`

4231:   Deprecated: Use `MatUpdateMPIAIJWithArray()`

4233:   Collective

4235:   Input Parameters:
4236: + mat - the matrix
4237: . m   - number of local rows (Cannot be `PETSC_DECIDE`)
4238: . n   - This value should be the same as the local size used in creating the
4239:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4240:        calculated if N is given) For square matrices n is almost always m.
4241: . M   - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4242: . N   - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4243: . Ii  - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4244: . J   - column indices
4245: - v   - matrix values

4247:   Level: deprecated

4249: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4250:           `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4251: @*/
4252: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4253: {
4254:   PetscInt        nnz, i;
4255:   PetscBool       nooffprocentries;
4256:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4257:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4258:   PetscScalar    *ad, *ao;
4259:   PetscInt        ldi, Iii, md;
4260:   const PetscInt *Adi = Ad->i;
4261:   PetscInt       *ld  = Aij->ld;

4263:   PetscFunctionBegin;
4264:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4265:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4266:   PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4267:   PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");

4269:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4270:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));

4272:   for (i = 0; i < m; i++) {
4273:     if (PetscDefined(USE_DEBUG)) {
4274:       for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4275:         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);
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 identical to previous entry", j - Ii[i], J[j], i);
4277:       }
4278:     }
4279:     nnz = Ii[i + 1] - Ii[i];
4280:     Iii = Ii[i];
4281:     ldi = ld[i];
4282:     md  = Adi[i + 1] - Adi[i];
4283:     PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4284:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4285:     PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4286:     ad += md;
4287:     ao += nnz - md;
4288:   }
4289:   nooffprocentries      = mat->nooffprocentries;
4290:   mat->nooffprocentries = PETSC_TRUE;
4291:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4292:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4293:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4294:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4295:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4296:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4297:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4298:   mat->nooffprocentries = nooffprocentries;
4299:   PetscFunctionReturn(PETSC_SUCCESS);
4300: }

4302: /*@
4303:   MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values

4305:   Collective

4307:   Input Parameters:
4308: + mat - the matrix
4309: - v   - matrix values, stored by row

4311:   Level: intermediate

4313:   Notes:
4314:   The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`

4316:   The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly

4318: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4319:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4320: @*/
4321: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4322: {
4323:   PetscInt        nnz, i, m;
4324:   PetscBool       nooffprocentries;
4325:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4326:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4327:   Mat_SeqAIJ     *Ao  = (Mat_SeqAIJ *)Aij->B->data;
4328:   PetscScalar    *ad, *ao;
4329:   const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4330:   PetscInt        ldi, Iii, md;
4331:   PetscInt       *ld = Aij->ld;

4333:   PetscFunctionBegin;
4334:   m = mat->rmap->n;

4336:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4337:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4338:   Iii = 0;
4339:   for (i = 0; i < m; i++) {
4340:     nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4341:     ldi = ld[i];
4342:     md  = Adi[i + 1] - Adi[i];
4343:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4344:     ad += md;
4345:     if (ao) {
4346:       PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4347:       PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4348:       ao += nnz - md;
4349:     }
4350:     Iii += nnz;
4351:   }
4352:   nooffprocentries      = mat->nooffprocentries;
4353:   mat->nooffprocentries = PETSC_TRUE;
4354:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4355:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4356:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4357:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4358:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4359:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4360:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4361:   mat->nooffprocentries = nooffprocentries;
4362:   PetscFunctionReturn(PETSC_SUCCESS);
4363: }

4365: /*@
4366:   MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4367:   (the default parallel PETSc format).  For good matrix assembly performance
4368:   the user should preallocate the matrix storage by setting the parameters
4369:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4371:   Collective

4373:   Input Parameters:
4374: + comm  - MPI communicator
4375: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4376:           This value should be the same as the local size used in creating the
4377:           y vector for the matrix-vector product y = Ax.
4378: . n     - This value should be the same as the local size used in creating the
4379:           x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4380:           calculated if N is given) For square matrices n is almost always m.
4381: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4382: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4383: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4384:           (same value is used for all local rows)
4385: . d_nnz - array containing the number of nonzeros in the various rows of the
4386:           DIAGONAL portion of the local submatrix (possibly different for each row)
4387:           or `NULL`, if `d_nz` is used to specify the nonzero structure.
4388:           The size of this array is equal to the number of local rows, i.e 'm'.
4389: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4390:           submatrix (same value is used for all local rows).
4391: - o_nnz - array containing the number of nonzeros in the various rows of the
4392:           OFF-DIAGONAL portion of the local submatrix (possibly different for
4393:           each row) or `NULL`, if `o_nz` is used to specify the nonzero
4394:           structure. The size of this array is equal to the number
4395:           of local rows, i.e 'm'.

4397:   Output Parameter:
4398: . A - the matrix

4400:   Options Database Keys:
4401: + -mat_no_inode                     - Do not use inodes
4402: . -mat_inode_limit <limit>          - Sets inode limit (max limit=5)
4403: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4404:                                       See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4405:                                       to be viewed as a matrix. Entry (i,j) is the size of message (in bytes) rank i sends to rank j in one `MatMult()` call.

4407:   Level: intermediate

4409:   Notes:
4410:   It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4411:   MatXXXXSetPreallocation() paradigm instead of this routine directly.
4412:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

4414:   If the *_nnz parameter is given then the *_nz parameter is ignored

4416:   The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4417:   processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4418:   storage requirements for this matrix.

4420:   If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one
4421:   processor than it must be used on all processors that share the object for
4422:   that argument.

4424:   If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4425:   `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.

4427:   The user MUST specify either the local or global matrix dimensions
4428:   (possibly both).

4430:   The parallel matrix is partitioned across processors such that the
4431:   first `m0` rows belong to process 0, the next `m1` rows belong to
4432:   process 1, the next `m2` rows belong to process 2, etc., where
4433:   `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4434:   values corresponding to [m x N] submatrix.

4436:   The columns are logically partitioned with the n0 columns belonging
4437:   to 0th partition, the next n1 columns belonging to the next
4438:   partition etc.. where n0,n1,n2... are the input parameter 'n'.

4440:   The DIAGONAL portion of the local submatrix on any given processor
4441:   is the submatrix corresponding to the rows and columns m,n
4442:   corresponding to the given processor. i.e diagonal matrix on
4443:   process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4444:   etc. The remaining portion of the local submatrix [m x (N-n)]
4445:   constitute the OFF-DIAGONAL portion. The example below better
4446:   illustrates this concept.

4448:   For a square global matrix we define each processor's diagonal portion
4449:   to be its local rows and the corresponding columns (a square submatrix);
4450:   each processor's off-diagonal portion encompasses the remainder of the
4451:   local matrix (a rectangular submatrix).

4453:   If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.

4455:   When calling this routine with a single process communicator, a matrix of
4456:   type `MATSEQAIJ` is returned.  If a matrix of type `MATMPIAIJ` is desired for this
4457:   type of communicator, use the construction mechanism
4458: .vb
4459:   MatCreate(..., &A);
4460:   MatSetType(A, MATMPIAIJ);
4461:   MatSetSizes(A, m, n, M, N);
4462:   MatMPIAIJSetPreallocation(A, ...);
4463: .ve

4465:   By default, this format uses inodes (identical nodes) when possible.
4466:   We search for consecutive rows with the same nonzero structure, thereby
4467:   reusing matrix information to achieve increased efficiency.

4469:   Example Usage:
4470:   Consider the following 8x8 matrix with 34 non-zero values, that is
4471:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4472:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4473:   as follows

4475: .vb
4476:             1  2  0  |  0  3  0  |  0  4
4477:     Proc0   0  5  6  |  7  0  0  |  8  0
4478:             9  0 10  | 11  0  0  | 12  0
4479:     -------------------------------------
4480:            13  0 14  | 15 16 17  |  0  0
4481:     Proc1   0 18  0  | 19 20 21  |  0  0
4482:             0  0  0  | 22 23  0  | 24  0
4483:     -------------------------------------
4484:     Proc2  25 26 27  |  0  0 28  | 29  0
4485:            30  0  0  | 31 32 33  |  0 34
4486: .ve

4488:   This can be represented as a collection of submatrices as

4490: .vb
4491:       A B C
4492:       D E F
4493:       G H I
4494: .ve

4496:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4497:   owned by proc1, G,H,I are owned by proc2.

4499:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4500:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4501:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4503:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4504:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4505:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4506:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4507:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4508:   matrix, and [DF] as another SeqAIJ matrix.

4510:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4511:   allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4512:   storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4513:   One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4514:   the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4515:   In this case, the values of `d_nz`,`o_nz` are
4516: .vb
4517:      proc0  dnz = 2, o_nz = 2
4518:      proc1  dnz = 3, o_nz = 2
4519:      proc2  dnz = 1, o_nz = 4
4520: .ve
4521:   We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4522:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4523:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4524:   34 values.

4526:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4527:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4528:   In the above case the values for d_nnz,o_nnz are
4529: .vb
4530:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4531:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4532:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4533: .ve
4534:   Here the space allocated is sum of all the above values i.e 34, and
4535:   hence pre-allocation is perfect.

4537: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4538:           `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4539:           `MatGetOwnershipRangesColumn()`, `PetscLayout`
4540: @*/
4541: 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)
4542: {
4543:   PetscMPIInt size;

4545:   PetscFunctionBegin;
4546:   PetscCall(MatCreate(comm, A));
4547:   PetscCall(MatSetSizes(*A, m, n, M, N));
4548:   PetscCallMPI(MPI_Comm_size(comm, &size));
4549:   if (size > 1) {
4550:     PetscCall(MatSetType(*A, MATMPIAIJ));
4551:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4552:   } else {
4553:     PetscCall(MatSetType(*A, MATSEQAIJ));
4554:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4555:   }
4556:   PetscFunctionReturn(PETSC_SUCCESS);
4557: }

4559: /*MC
4560:     MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix

4562:     Synopsis:
4563:     MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4565:     Not Collective

4567:     Input Parameter:
4568: .   A - the `MATMPIAIJ` matrix

4570:     Output Parameters:
4571: +   Ad - the diagonal portion of the matrix
4572: .   Ao - the off-diagonal portion of the matrix
4573: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4574: -   ierr - error code

4576:      Level: advanced

4578:     Note:
4579:     Use  `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4581: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4582: M*/

4584: /*MC
4585:     MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4587:     Synopsis:
4588:     MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4590:     Not Collective

4592:     Input Parameters:
4593: +   A - the `MATMPIAIJ` matrix
4594: .   Ad - the diagonal portion of the matrix
4595: .   Ao - the off-diagonal portion of the matrix
4596: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4597: -   ierr - error code

4599:      Level: advanced

4601: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4602: M*/

4604: /*@C
4605:   MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix

4607:   Not Collective

4609:   Input Parameter:
4610: . A - The `MATMPIAIJ` matrix

4612:   Output Parameters:
4613: + Ad     - The local diagonal block as a `MATSEQAIJ` matrix
4614: . Ao     - The local off-diagonal block as a `MATSEQAIJ` matrix
4615: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix

4617:   Level: intermediate

4619:   Note:
4620:   The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4621:   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
4622:   the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4623:   local column numbers to global column numbers in the original matrix.

4625:   Fortran Notes:
4626:   `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`

4628: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4629: @*/
4630: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4631: {
4632:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4633:   PetscBool   flg;

4635:   PetscFunctionBegin;
4636:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4637:   PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4638:   if (Ad) *Ad = a->A;
4639:   if (Ao) *Ao = a->B;
4640:   if (colmap) *colmap = a->garray;
4641:   PetscFunctionReturn(PETSC_SUCCESS);
4642: }

4644: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4645: {
4646:   PetscInt     m, N, i, rstart, nnz, Ii;
4647:   PetscInt    *indx;
4648:   PetscScalar *values;
4649:   MatType      rootType;

4651:   PetscFunctionBegin;
4652:   PetscCall(MatGetSize(inmat, &m, &N));
4653:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4654:     PetscInt *dnz, *onz, sum, bs, cbs;

4656:     if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4657:     /* Check sum(n) = N */
4658:     PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4659:     PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);

4661:     PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4662:     rstart -= m;

4664:     MatPreallocateBegin(comm, m, n, dnz, onz);
4665:     for (i = 0; i < m; i++) {
4666:       PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4667:       PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4668:       PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4669:     }

4671:     PetscCall(MatCreate(comm, outmat));
4672:     PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4673:     PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4674:     PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4675:     PetscCall(MatGetRootType_Private(inmat, &rootType));
4676:     PetscCall(MatSetType(*outmat, rootType));
4677:     PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4678:     PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4679:     MatPreallocateEnd(dnz, onz);
4680:     PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4681:   }

4683:   /* numeric phase */
4684:   PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4685:   for (i = 0; i < m; i++) {
4686:     PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4687:     Ii = i + rstart;
4688:     PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4689:     PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4690:   }
4691:   PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4692:   PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4693:   PetscFunctionReturn(PETSC_SUCCESS);
4694: }

4696: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void *data)
4697: {
4698:   Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)data;

4700:   PetscFunctionBegin;
4701:   if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4702:   PetscCall(PetscFree(merge->id_r));
4703:   PetscCall(PetscFree(merge->len_s));
4704:   PetscCall(PetscFree(merge->len_r));
4705:   PetscCall(PetscFree(merge->bi));
4706:   PetscCall(PetscFree(merge->bj));
4707:   PetscCall(PetscFree(merge->buf_ri[0]));
4708:   PetscCall(PetscFree(merge->buf_ri));
4709:   PetscCall(PetscFree(merge->buf_rj[0]));
4710:   PetscCall(PetscFree(merge->buf_rj));
4711:   PetscCall(PetscFree(merge->coi));
4712:   PetscCall(PetscFree(merge->coj));
4713:   PetscCall(PetscFree(merge->owners_co));
4714:   PetscCall(PetscLayoutDestroy(&merge->rowmap));
4715:   PetscCall(PetscFree(merge));
4716:   PetscFunctionReturn(PETSC_SUCCESS);
4717: }

4719: #include <../src/mat/utils/freespace.h>
4720: #include <petscbt.h>

4722: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4723: {
4724:   MPI_Comm             comm;
4725:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4726:   PetscMPIInt          size, rank, taga, *len_s;
4727:   PetscInt             N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4728:   PetscMPIInt          proc, k;
4729:   PetscInt           **buf_ri, **buf_rj;
4730:   PetscInt             anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4731:   PetscInt             nrows, **buf_ri_k, **nextrow, **nextai;
4732:   MPI_Request         *s_waits, *r_waits;
4733:   MPI_Status          *status;
4734:   const MatScalar     *aa, *a_a;
4735:   MatScalar          **abuf_r, *ba_i;
4736:   Mat_Merge_SeqsToMPI *merge;
4737:   PetscContainer       container;

4739:   PetscFunctionBegin;
4740:   PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4741:   PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));

4743:   PetscCallMPI(MPI_Comm_size(comm, &size));
4744:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4746:   PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4747:   PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4748:   PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4749:   PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4750:   aa = a_a;

4752:   bi     = merge->bi;
4753:   bj     = merge->bj;
4754:   buf_ri = merge->buf_ri;
4755:   buf_rj = merge->buf_rj;

4757:   PetscCall(PetscMalloc1(size, &status));
4758:   owners = merge->rowmap->range;
4759:   len_s  = merge->len_s;

4761:   /* send and recv matrix values */
4762:   PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4763:   PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));

4765:   PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4766:   for (proc = 0, k = 0; proc < size; proc++) {
4767:     if (!len_s[proc]) continue;
4768:     i = owners[proc];
4769:     PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4770:     k++;
4771:   }

4773:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4774:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4775:   PetscCall(PetscFree(status));

4777:   PetscCall(PetscFree(s_waits));
4778:   PetscCall(PetscFree(r_waits));

4780:   /* insert mat values of mpimat */
4781:   PetscCall(PetscMalloc1(N, &ba_i));
4782:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4784:   for (k = 0; k < merge->nrecv; k++) {
4785:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4786:     nrows       = *buf_ri_k[k];
4787:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4788:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4789:   }

4791:   /* set values of ba */
4792:   m = merge->rowmap->n;
4793:   for (i = 0; i < m; i++) {
4794:     arow = owners[rank] + i;
4795:     bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4796:     bnzi = bi[i + 1] - bi[i];
4797:     PetscCall(PetscArrayzero(ba_i, bnzi));

4799:     /* add local non-zero vals of this proc's seqmat into ba */
4800:     anzi   = ai[arow + 1] - ai[arow];
4801:     aj     = a->j + ai[arow];
4802:     aa     = a_a + ai[arow];
4803:     nextaj = 0;
4804:     for (j = 0; nextaj < anzi; j++) {
4805:       if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4806:         ba_i[j] += aa[nextaj++];
4807:       }
4808:     }

4810:     /* add received vals into ba */
4811:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4812:       /* i-th row */
4813:       if (i == *nextrow[k]) {
4814:         anzi   = *(nextai[k] + 1) - *nextai[k];
4815:         aj     = buf_rj[k] + *nextai[k];
4816:         aa     = abuf_r[k] + *nextai[k];
4817:         nextaj = 0;
4818:         for (j = 0; nextaj < anzi; j++) {
4819:           if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4820:             ba_i[j] += aa[nextaj++];
4821:           }
4822:         }
4823:         nextrow[k]++;
4824:         nextai[k]++;
4825:       }
4826:     }
4827:     PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4828:   }
4829:   PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4830:   PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4831:   PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));

4833:   PetscCall(PetscFree(abuf_r[0]));
4834:   PetscCall(PetscFree(abuf_r));
4835:   PetscCall(PetscFree(ba_i));
4836:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4837:   PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4838:   PetscFunctionReturn(PETSC_SUCCESS);
4839: }

4841: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4842: {
4843:   Mat                  B_mpi;
4844:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4845:   PetscMPIInt          size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4846:   PetscInt           **buf_rj, **buf_ri, **buf_ri_k;
4847:   PetscInt             M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4848:   PetscInt             len, *dnz, *onz, bs, cbs;
4849:   PetscInt             k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4850:   PetscInt             nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4851:   MPI_Request         *si_waits, *sj_waits, *ri_waits, *rj_waits;
4852:   MPI_Status          *status;
4853:   PetscFreeSpaceList   free_space = NULL, current_space = NULL;
4854:   PetscBT              lnkbt;
4855:   Mat_Merge_SeqsToMPI *merge;
4856:   PetscContainer       container;

4858:   PetscFunctionBegin;
4859:   PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));

4861:   /* make sure it is a PETSc comm */
4862:   PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4863:   PetscCallMPI(MPI_Comm_size(comm, &size));
4864:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4866:   PetscCall(PetscNew(&merge));
4867:   PetscCall(PetscMalloc1(size, &status));

4869:   /* determine row ownership */
4870:   PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4871:   PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4872:   PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4873:   PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4874:   PetscCall(PetscLayoutSetUp(merge->rowmap));
4875:   PetscCall(PetscMalloc1(size, &len_si));
4876:   PetscCall(PetscMalloc1(size, &merge->len_s));

4878:   m      = merge->rowmap->n;
4879:   owners = merge->rowmap->range;

4881:   /* determine the number of messages to send, their lengths */
4882:   len_s = merge->len_s;

4884:   len          = 0; /* length of buf_si[] */
4885:   merge->nsend = 0;
4886:   for (PetscMPIInt proc = 0; proc < size; proc++) {
4887:     len_si[proc] = 0;
4888:     if (proc == rank) {
4889:       len_s[proc] = 0;
4890:     } else {
4891:       PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4892:       PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4893:     }
4894:     if (len_s[proc]) {
4895:       merge->nsend++;
4896:       nrows = 0;
4897:       for (i = owners[proc]; i < owners[proc + 1]; i++) {
4898:         if (ai[i + 1] > ai[i]) nrows++;
4899:       }
4900:       PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4901:       len += len_si[proc];
4902:     }
4903:   }

4905:   /* determine the number and length of messages to receive for ij-structure */
4906:   PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4907:   PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));

4909:   /* post the Irecv of j-structure */
4910:   PetscCall(PetscCommGetNewTag(comm, &tagj));
4911:   PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));

4913:   /* post the Isend of j-structure */
4914:   PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));

4916:   for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4917:     if (!len_s[proc]) continue;
4918:     i = owners[proc];
4919:     PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4920:     k++;
4921:   }

4923:   /* receives and sends of j-structure are complete */
4924:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4925:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));

4927:   /* send and recv i-structure */
4928:   PetscCall(PetscCommGetNewTag(comm, &tagi));
4929:   PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));

4931:   PetscCall(PetscMalloc1(len + 1, &buf_s));
4932:   buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4933:   for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4934:     if (!len_s[proc]) continue;
4935:     /* form outgoing message for i-structure:
4936:          buf_si[0]:                 nrows to be sent
4937:                [1:nrows]:           row index (global)
4938:                [nrows+1:2*nrows+1]: i-structure index
4939:     */
4940:     nrows       = len_si[proc] / 2 - 1;
4941:     buf_si_i    = buf_si + nrows + 1;
4942:     buf_si[0]   = nrows;
4943:     buf_si_i[0] = 0;
4944:     nrows       = 0;
4945:     for (i = owners[proc]; i < owners[proc + 1]; i++) {
4946:       anzi = ai[i + 1] - ai[i];
4947:       if (anzi) {
4948:         buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4949:         buf_si[nrows + 1]   = i - owners[proc];       /* local row index */
4950:         nrows++;
4951:       }
4952:     }
4953:     PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4954:     k++;
4955:     buf_si += len_si[proc];
4956:   }

4958:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4959:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));

4961:   PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4962:   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]));

4964:   PetscCall(PetscFree(len_si));
4965:   PetscCall(PetscFree(len_ri));
4966:   PetscCall(PetscFree(rj_waits));
4967:   PetscCall(PetscFree2(si_waits, sj_waits));
4968:   PetscCall(PetscFree(ri_waits));
4969:   PetscCall(PetscFree(buf_s));
4970:   PetscCall(PetscFree(status));

4972:   /* compute a local seq matrix in each processor */
4973:   /* allocate bi array and free space for accumulating nonzero column info */
4974:   PetscCall(PetscMalloc1(m + 1, &bi));
4975:   bi[0] = 0;

4977:   /* create and initialize a linked list */
4978:   nlnk = N + 1;
4979:   PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));

4981:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4982:   len = ai[owners[rank + 1]] - ai[owners[rank]];
4983:   PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));

4985:   current_space = free_space;

4987:   /* determine symbolic info for each local row */
4988:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4990:   for (k = 0; k < merge->nrecv; k++) {
4991:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4992:     nrows       = *buf_ri_k[k];
4993:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4994:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4995:   }

4997:   MatPreallocateBegin(comm, m, n, dnz, onz);
4998:   len = 0;
4999:   for (i = 0; i < m; i++) {
5000:     bnzi = 0;
5001:     /* add local non-zero cols of this proc's seqmat into lnk */
5002:     arow = owners[rank] + i;
5003:     anzi = ai[arow + 1] - ai[arow];
5004:     aj   = a->j + ai[arow];
5005:     PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5006:     bnzi += nlnk;
5007:     /* add received col data into lnk */
5008:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5009:       if (i == *nextrow[k]) {            /* i-th row */
5010:         anzi = *(nextai[k] + 1) - *nextai[k];
5011:         aj   = buf_rj[k] + *nextai[k];
5012:         PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5013:         bnzi += nlnk;
5014:         nextrow[k]++;
5015:         nextai[k]++;
5016:       }
5017:     }
5018:     if (len < bnzi) len = bnzi; /* =max(bnzi) */

5020:     /* if free space is not available, make more free space */
5021:     if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), &current_space));
5022:     /* copy data into free space, then initialize lnk */
5023:     PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5024:     PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));

5026:     current_space->array += bnzi;
5027:     current_space->local_used += bnzi;
5028:     current_space->local_remaining -= bnzi;

5030:     bi[i + 1] = bi[i] + bnzi;
5031:   }

5033:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));

5035:   PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5036:   PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5037:   PetscCall(PetscLLDestroy(lnk, lnkbt));

5039:   /* create symbolic parallel matrix B_mpi */
5040:   PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5041:   PetscCall(MatCreate(comm, &B_mpi));
5042:   if (n == PETSC_DECIDE) {
5043:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5044:   } else {
5045:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5046:   }
5047:   PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5048:   PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5049:   PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5050:   MatPreallocateEnd(dnz, onz);
5051:   PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));

5053:   /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5054:   B_mpi->assembled = PETSC_FALSE;
5055:   merge->bi        = bi;
5056:   merge->bj        = bj;
5057:   merge->buf_ri    = buf_ri;
5058:   merge->buf_rj    = buf_rj;
5059:   merge->coi       = NULL;
5060:   merge->coj       = NULL;
5061:   merge->owners_co = NULL;

5063:   PetscCall(PetscCommDestroy(&comm));

5065:   /* attach the supporting struct to B_mpi for reuse */
5066:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5067:   PetscCall(PetscContainerSetPointer(container, merge));
5068:   PetscCall(PetscContainerSetUserDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5069:   PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5070:   PetscCall(PetscContainerDestroy(&container));
5071:   *mpimat = B_mpi;

5073:   PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5074:   PetscFunctionReturn(PETSC_SUCCESS);
5075: }

5077: /*@
5078:   MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5079:   matrices from each processor

5081:   Collective

5083:   Input Parameters:
5084: + comm   - the communicators the parallel matrix will live on
5085: . seqmat - the input sequential matrices
5086: . m      - number of local rows (or `PETSC_DECIDE`)
5087: . n      - number of local columns (or `PETSC_DECIDE`)
5088: - scall  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5090:   Output Parameter:
5091: . mpimat - the parallel matrix generated

5093:   Level: advanced

5095:   Note:
5096:   The dimensions of the sequential matrix in each processor MUST be the same.
5097:   The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5098:   destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.

5100: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5101: @*/
5102: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5103: {
5104:   PetscMPIInt size;

5106:   PetscFunctionBegin;
5107:   PetscCallMPI(MPI_Comm_size(comm, &size));
5108:   if (size == 1) {
5109:     PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5110:     if (scall == MAT_INITIAL_MATRIX) {
5111:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5112:     } else {
5113:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5114:     }
5115:     PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5116:     PetscFunctionReturn(PETSC_SUCCESS);
5117:   }
5118:   PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5119:   if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5120:   PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5121:   PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5122:   PetscFunctionReturn(PETSC_SUCCESS);
5123: }

5125: /*@
5126:   MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.

5128:   Not Collective

5130:   Input Parameter:
5131: . A - the matrix

5133:   Output Parameter:
5134: . A_loc - the local sequential matrix generated

5136:   Level: developer

5138:   Notes:
5139:   The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5140:   with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5141:   `n` is the global column count obtained with `MatGetSize()`

5143:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5145:   For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.

5147:   Destroy the matrix with `MatDestroy()`

5149: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5150: @*/
5151: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5152: {
5153:   PetscBool mpi;

5155:   PetscFunctionBegin;
5156:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5157:   if (mpi) {
5158:     PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5159:   } else {
5160:     *A_loc = A;
5161:     PetscCall(PetscObjectReference((PetscObject)*A_loc));
5162:   }
5163:   PetscFunctionReturn(PETSC_SUCCESS);
5164: }

5166: /*@
5167:   MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.

5169:   Not Collective

5171:   Input Parameters:
5172: + A     - the matrix
5173: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5175:   Output Parameter:
5176: . A_loc - the local sequential matrix generated

5178:   Level: developer

5180:   Notes:
5181:   The matrix is created by taking all `A`'s local rows and putting them into a sequential
5182:   matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5183:   `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.

5185:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5187:   When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5188:   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
5189:   then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5190:   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.

5192: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5193: @*/
5194: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5195: {
5196:   Mat_MPIAIJ        *mpimat = (Mat_MPIAIJ *)A->data;
5197:   Mat_SeqAIJ        *mat, *a, *b;
5198:   PetscInt          *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5199:   const PetscScalar *aa, *ba, *aav, *bav;
5200:   PetscScalar       *ca, *cam;
5201:   PetscMPIInt        size;
5202:   PetscInt           am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5203:   PetscInt          *ci, *cj, col, ncols_d, ncols_o, jo;
5204:   PetscBool          match;

5206:   PetscFunctionBegin;
5207:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5208:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5209:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5210:   if (size == 1) {
5211:     if (scall == MAT_INITIAL_MATRIX) {
5212:       PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5213:       *A_loc = mpimat->A;
5214:     } else if (scall == MAT_REUSE_MATRIX) {
5215:       PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5216:     }
5217:     PetscFunctionReturn(PETSC_SUCCESS);
5218:   }

5220:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5221:   a  = (Mat_SeqAIJ *)mpimat->A->data;
5222:   b  = (Mat_SeqAIJ *)mpimat->B->data;
5223:   ai = a->i;
5224:   aj = a->j;
5225:   bi = b->i;
5226:   bj = b->j;
5227:   PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5228:   PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5229:   aa = aav;
5230:   ba = bav;
5231:   if (scall == MAT_INITIAL_MATRIX) {
5232:     PetscCall(PetscMalloc1(1 + am, &ci));
5233:     ci[0] = 0;
5234:     for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5235:     PetscCall(PetscMalloc1(1 + ci[am], &cj));
5236:     PetscCall(PetscMalloc1(1 + ci[am], &ca));
5237:     k = 0;
5238:     for (i = 0; i < am; i++) {
5239:       ncols_o = bi[i + 1] - bi[i];
5240:       ncols_d = ai[i + 1] - ai[i];
5241:       /* off-diagonal portion of A */
5242:       for (jo = 0; jo < ncols_o; jo++) {
5243:         col = cmap[*bj];
5244:         if (col >= cstart) break;
5245:         cj[k] = col;
5246:         bj++;
5247:         ca[k++] = *ba++;
5248:       }
5249:       /* diagonal portion of A */
5250:       for (j = 0; j < ncols_d; j++) {
5251:         cj[k]   = cstart + *aj++;
5252:         ca[k++] = *aa++;
5253:       }
5254:       /* off-diagonal portion of A */
5255:       for (j = jo; j < ncols_o; j++) {
5256:         cj[k]   = cmap[*bj++];
5257:         ca[k++] = *ba++;
5258:       }
5259:     }
5260:     /* put together the new matrix */
5261:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5262:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5263:     /* Since these are PETSc arrays, change flags to free them as necessary. */
5264:     mat          = (Mat_SeqAIJ *)(*A_loc)->data;
5265:     mat->free_a  = PETSC_TRUE;
5266:     mat->free_ij = PETSC_TRUE;
5267:     mat->nonew   = 0;
5268:   } else if (scall == MAT_REUSE_MATRIX) {
5269:     mat = (Mat_SeqAIJ *)(*A_loc)->data;
5270:     ci  = mat->i;
5271:     cj  = mat->j;
5272:     PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5273:     for (i = 0; i < am; i++) {
5274:       /* off-diagonal portion of A */
5275:       ncols_o = bi[i + 1] - bi[i];
5276:       for (jo = 0; jo < ncols_o; jo++) {
5277:         col = cmap[*bj];
5278:         if (col >= cstart) break;
5279:         *cam++ = *ba++;
5280:         bj++;
5281:       }
5282:       /* diagonal portion of A */
5283:       ncols_d = ai[i + 1] - ai[i];
5284:       for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5285:       /* off-diagonal portion of A */
5286:       for (j = jo; j < ncols_o; j++) {
5287:         *cam++ = *ba++;
5288:         bj++;
5289:       }
5290:     }
5291:     PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5292:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5293:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5294:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5295:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5296:   PetscFunctionReturn(PETSC_SUCCESS);
5297: }

5299: /*@
5300:   MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5301:   mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part

5303:   Not Collective

5305:   Input Parameters:
5306: + A     - the matrix
5307: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5309:   Output Parameters:
5310: + glob  - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5311: - A_loc - the local sequential matrix generated

5313:   Level: developer

5315:   Note:
5316:   This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5317:   part, then those associated with the off-diagonal part (in its local ordering)

5319: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5320: @*/
5321: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5322: {
5323:   Mat             Ao, Ad;
5324:   const PetscInt *cmap;
5325:   PetscMPIInt     size;
5326:   PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);

5328:   PetscFunctionBegin;
5329:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5330:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5331:   if (size == 1) {
5332:     if (scall == MAT_INITIAL_MATRIX) {
5333:       PetscCall(PetscObjectReference((PetscObject)Ad));
5334:       *A_loc = Ad;
5335:     } else if (scall == MAT_REUSE_MATRIX) {
5336:       PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5337:     }
5338:     if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5339:     PetscFunctionReturn(PETSC_SUCCESS);
5340:   }
5341:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5342:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5343:   if (f) {
5344:     PetscCall((*f)(A, scall, glob, A_loc));
5345:   } else {
5346:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)Ad->data;
5347:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)Ao->data;
5348:     Mat_SeqAIJ        *c;
5349:     PetscInt          *ai = a->i, *aj = a->j;
5350:     PetscInt          *bi = b->i, *bj = b->j;
5351:     PetscInt          *ci, *cj;
5352:     const PetscScalar *aa, *ba;
5353:     PetscScalar       *ca;
5354:     PetscInt           i, j, am, dn, on;

5356:     PetscCall(MatGetLocalSize(Ad, &am, &dn));
5357:     PetscCall(MatGetLocalSize(Ao, NULL, &on));
5358:     PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5359:     PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5360:     if (scall == MAT_INITIAL_MATRIX) {
5361:       PetscInt k;
5362:       PetscCall(PetscMalloc1(1 + am, &ci));
5363:       PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5364:       PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5365:       ci[0] = 0;
5366:       for (i = 0, k = 0; i < am; i++) {
5367:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5368:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5369:         ci[i + 1]              = ci[i] + ncols_o + ncols_d;
5370:         /* diagonal portion of A */
5371:         for (j = 0; j < ncols_d; j++, k++) {
5372:           cj[k] = *aj++;
5373:           ca[k] = *aa++;
5374:         }
5375:         /* off-diagonal portion of A */
5376:         for (j = 0; j < ncols_o; j++, k++) {
5377:           cj[k] = dn + *bj++;
5378:           ca[k] = *ba++;
5379:         }
5380:       }
5381:       /* put together the new matrix */
5382:       PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5383:       /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5384:       /* Since these are PETSc arrays, change flags to free them as necessary. */
5385:       c          = (Mat_SeqAIJ *)(*A_loc)->data;
5386:       c->free_a  = PETSC_TRUE;
5387:       c->free_ij = PETSC_TRUE;
5388:       c->nonew   = 0;
5389:       PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5390:     } else if (scall == MAT_REUSE_MATRIX) {
5391:       PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5392:       for (i = 0; i < am; i++) {
5393:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5394:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5395:         /* diagonal portion of A */
5396:         for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5397:         /* off-diagonal portion of A */
5398:         for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5399:       }
5400:       PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5401:     } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5402:     PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5403:     PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5404:     if (glob) {
5405:       PetscInt cst, *gidx;

5407:       PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5408:       PetscCall(PetscMalloc1(dn + on, &gidx));
5409:       for (i = 0; i < dn; i++) gidx[i] = cst + i;
5410:       for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5411:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5412:     }
5413:   }
5414:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5415:   PetscFunctionReturn(PETSC_SUCCESS);
5416: }

5418: /*@C
5419:   MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns

5421:   Not Collective

5423:   Input Parameters:
5424: + A     - the matrix
5425: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5426: . row   - index set of rows to extract (or `NULL`)
5427: - col   - index set of columns to extract (or `NULL`)

5429:   Output Parameter:
5430: . A_loc - the local sequential matrix generated

5432:   Level: developer

5434: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5435: @*/
5436: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5437: {
5438:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5439:   PetscInt    i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5440:   IS          isrowa, iscola;
5441:   Mat        *aloc;
5442:   PetscBool   match;

5444:   PetscFunctionBegin;
5445:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5446:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5447:   PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5448:   if (!row) {
5449:     start = A->rmap->rstart;
5450:     end   = A->rmap->rend;
5451:     PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5452:   } else {
5453:     isrowa = *row;
5454:   }
5455:   if (!col) {
5456:     start = A->cmap->rstart;
5457:     cmap  = a->garray;
5458:     nzA   = a->A->cmap->n;
5459:     nzB   = a->B->cmap->n;
5460:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5461:     ncols = 0;
5462:     for (i = 0; i < nzB; i++) {
5463:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5464:       else break;
5465:     }
5466:     imark = i;
5467:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5468:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5469:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5470:   } else {
5471:     iscola = *col;
5472:   }
5473:   if (scall != MAT_INITIAL_MATRIX) {
5474:     PetscCall(PetscMalloc1(1, &aloc));
5475:     aloc[0] = *A_loc;
5476:   }
5477:   PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5478:   if (!col) { /* attach global id of condensed columns */
5479:     PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5480:   }
5481:   *A_loc = aloc[0];
5482:   PetscCall(PetscFree(aloc));
5483:   if (!row) PetscCall(ISDestroy(&isrowa));
5484:   if (!col) PetscCall(ISDestroy(&iscola));
5485:   PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5486:   PetscFunctionReturn(PETSC_SUCCESS);
5487: }

5489: /*
5490:  * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5491:  * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5492:  * on a global size.
5493:  * */
5494: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5495: {
5496:   Mat_MPIAIJ            *p  = (Mat_MPIAIJ *)P->data;
5497:   Mat_SeqAIJ            *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5498:   PetscInt               plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5499:   PetscMPIInt            owner;
5500:   PetscSFNode           *iremote, *oiremote;
5501:   const PetscInt        *lrowindices;
5502:   PetscSF                sf, osf;
5503:   PetscInt               pcstart, *roffsets, *loffsets, *pnnz, j;
5504:   PetscInt               ontotalcols, dntotalcols, ntotalcols, nout;
5505:   MPI_Comm               comm;
5506:   ISLocalToGlobalMapping mapping;
5507:   const PetscScalar     *pd_a, *po_a;

5509:   PetscFunctionBegin;
5510:   PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5511:   /* plocalsize is the number of roots
5512:    * nrows is the number of leaves
5513:    * */
5514:   PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5515:   PetscCall(ISGetLocalSize(rows, &nrows));
5516:   PetscCall(PetscCalloc1(nrows, &iremote));
5517:   PetscCall(ISGetIndices(rows, &lrowindices));
5518:   for (i = 0; i < nrows; i++) {
5519:     /* Find a remote index and an owner for a row
5520:      * The row could be local or remote
5521:      * */
5522:     owner = 0;
5523:     lidx  = 0;
5524:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5525:     iremote[i].index = lidx;
5526:     iremote[i].rank  = owner;
5527:   }
5528:   /* Create SF to communicate how many nonzero columns for each row */
5529:   PetscCall(PetscSFCreate(comm, &sf));
5530:   /* SF will figure out the number of nonzero columns for each row, and their
5531:    * offsets
5532:    * */
5533:   PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5534:   PetscCall(PetscSFSetFromOptions(sf));
5535:   PetscCall(PetscSFSetUp(sf));

5537:   PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5538:   PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5539:   PetscCall(PetscCalloc1(nrows, &pnnz));
5540:   roffsets[0] = 0;
5541:   roffsets[1] = 0;
5542:   for (i = 0; i < plocalsize; i++) {
5543:     /* diagonal */
5544:     nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5545:     /* off-diagonal */
5546:     nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5547:     /* compute offsets so that we relative location for each row */
5548:     roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5549:     roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5550:   }
5551:   PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5552:   PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5553:   /* 'r' means root, and 'l' means leaf */
5554:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5555:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5556:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5557:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5558:   PetscCall(PetscSFDestroy(&sf));
5559:   PetscCall(PetscFree(roffsets));
5560:   PetscCall(PetscFree(nrcols));
5561:   dntotalcols = 0;
5562:   ontotalcols = 0;
5563:   ncol        = 0;
5564:   for (i = 0; i < nrows; i++) {
5565:     pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5566:     ncol    = PetscMax(pnnz[i], ncol);
5567:     /* diagonal */
5568:     dntotalcols += nlcols[i * 2 + 0];
5569:     /* off-diagonal */
5570:     ontotalcols += nlcols[i * 2 + 1];
5571:   }
5572:   /* We do not need to figure the right number of columns
5573:    * since all the calculations will be done by going through the raw data
5574:    * */
5575:   PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5576:   PetscCall(MatSetUp(*P_oth));
5577:   PetscCall(PetscFree(pnnz));
5578:   p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5579:   /* diagonal */
5580:   PetscCall(PetscCalloc1(dntotalcols, &iremote));
5581:   /* off-diagonal */
5582:   PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5583:   /* diagonal */
5584:   PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5585:   /* off-diagonal */
5586:   PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5587:   dntotalcols = 0;
5588:   ontotalcols = 0;
5589:   ntotalcols  = 0;
5590:   for (i = 0; i < nrows; i++) {
5591:     owner = 0;
5592:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5593:     /* Set iremote for diag matrix */
5594:     for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5595:       iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5596:       iremote[dntotalcols].rank  = owner;
5597:       /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5598:       ilocal[dntotalcols++] = ntotalcols++;
5599:     }
5600:     /* off-diagonal */
5601:     for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5602:       oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5603:       oiremote[ontotalcols].rank  = owner;
5604:       oilocal[ontotalcols++]      = ntotalcols++;
5605:     }
5606:   }
5607:   PetscCall(ISRestoreIndices(rows, &lrowindices));
5608:   PetscCall(PetscFree(loffsets));
5609:   PetscCall(PetscFree(nlcols));
5610:   PetscCall(PetscSFCreate(comm, &sf));
5611:   /* P serves as roots and P_oth is leaves
5612:    * Diag matrix
5613:    * */
5614:   PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5615:   PetscCall(PetscSFSetFromOptions(sf));
5616:   PetscCall(PetscSFSetUp(sf));

5618:   PetscCall(PetscSFCreate(comm, &osf));
5619:   /* off-diagonal */
5620:   PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5621:   PetscCall(PetscSFSetFromOptions(osf));
5622:   PetscCall(PetscSFSetUp(osf));
5623:   PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5624:   PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5625:   /* operate on the matrix internal data to save memory */
5626:   PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5627:   PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5628:   PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5629:   /* Convert to global indices for diag matrix */
5630:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5631:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5632:   /* We want P_oth store global indices */
5633:   PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5634:   /* Use memory scalable approach */
5635:   PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5636:   PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5637:   PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5638:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5639:   /* Convert back to local indices */
5640:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5641:   PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5642:   nout = 0;
5643:   PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5644:   PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5645:   PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5646:   /* Exchange values */
5647:   PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5648:   PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5649:   PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5650:   PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5651:   /* Stop PETSc from shrinking memory */
5652:   for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5653:   PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5654:   PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5655:   /* Attach PetscSF objects to P_oth so that we can reuse it later */
5656:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5657:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5658:   PetscCall(PetscSFDestroy(&sf));
5659:   PetscCall(PetscSFDestroy(&osf));
5660:   PetscFunctionReturn(PETSC_SUCCESS);
5661: }

5663: /*
5664:  * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5665:  * This supports MPIAIJ and MAIJ
5666:  * */
5667: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5668: {
5669:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5670:   Mat_SeqAIJ *p_oth;
5671:   IS          rows, map;
5672:   PetscHMapI  hamp;
5673:   PetscInt    i, htsize, *rowindices, off, *mapping, key, count;
5674:   MPI_Comm    comm;
5675:   PetscSF     sf, osf;
5676:   PetscBool   has;

5678:   PetscFunctionBegin;
5679:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5680:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5681:   /* If it is the first time, create an index set of off-diag nonzero columns of A,
5682:    *  and then create a submatrix (that often is an overlapping matrix)
5683:    * */
5684:   if (reuse == MAT_INITIAL_MATRIX) {
5685:     /* Use a hash table to figure out unique keys */
5686:     PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5687:     PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5688:     count = 0;
5689:     /* Assume that  a->g is sorted, otherwise the following does not make sense */
5690:     for (i = 0; i < a->B->cmap->n; i++) {
5691:       key = a->garray[i] / dof;
5692:       PetscCall(PetscHMapIHas(hamp, key, &has));
5693:       if (!has) {
5694:         mapping[i] = count;
5695:         PetscCall(PetscHMapISet(hamp, key, count++));
5696:       } else {
5697:         /* Current 'i' has the same value the previous step */
5698:         mapping[i] = count - 1;
5699:       }
5700:     }
5701:     PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5702:     PetscCall(PetscHMapIGetSize(hamp, &htsize));
5703:     PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5704:     PetscCall(PetscCalloc1(htsize, &rowindices));
5705:     off = 0;
5706:     PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5707:     PetscCall(PetscHMapIDestroy(&hamp));
5708:     PetscCall(PetscSortInt(htsize, rowindices));
5709:     PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5710:     /* In case, the matrix was already created but users want to recreate the matrix */
5711:     PetscCall(MatDestroy(P_oth));
5712:     PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5713:     PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5714:     PetscCall(ISDestroy(&map));
5715:     PetscCall(ISDestroy(&rows));
5716:   } else if (reuse == MAT_REUSE_MATRIX) {
5717:     /* If matrix was already created, we simply update values using SF objects
5718:      * that as attached to the matrix earlier.
5719:      */
5720:     const PetscScalar *pd_a, *po_a;

5722:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5723:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5724:     PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5725:     p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5726:     /* Update values in place */
5727:     PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5728:     PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5729:     PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5730:     PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5731:     PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5732:     PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5733:     PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5734:     PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5735:   } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5736:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5737:   PetscFunctionReturn(PETSC_SUCCESS);
5738: }

5740: /*@C
5741:   MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`

5743:   Collective

5745:   Input Parameters:
5746: + A     - the first matrix in `MATMPIAIJ` format
5747: . B     - the second matrix in `MATMPIAIJ` format
5748: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5750:   Output Parameters:
5751: + rowb  - On input index sets of rows of B to extract (or `NULL`), modified on output
5752: . colb  - On input index sets of columns of B to extract (or `NULL`), modified on output
5753: - B_seq - the sequential matrix generated

5755:   Level: developer

5757: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5758: @*/
5759: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5760: {
5761:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5762:   PetscInt   *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5763:   IS          isrowb, iscolb;
5764:   Mat        *bseq = NULL;

5766:   PetscFunctionBegin;
5767:   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 ")",
5768:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5769:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));

5771:   if (scall == MAT_INITIAL_MATRIX) {
5772:     start = A->cmap->rstart;
5773:     cmap  = a->garray;
5774:     nzA   = a->A->cmap->n;
5775:     nzB   = a->B->cmap->n;
5776:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5777:     ncols = 0;
5778:     for (i = 0; i < nzB; i++) { /* row < local row index */
5779:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5780:       else break;
5781:     }
5782:     imark = i;
5783:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;   /* local rows */
5784:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5785:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5786:     PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5787:   } else {
5788:     PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5789:     isrowb = *rowb;
5790:     iscolb = *colb;
5791:     PetscCall(PetscMalloc1(1, &bseq));
5792:     bseq[0] = *B_seq;
5793:   }
5794:   PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5795:   *B_seq = bseq[0];
5796:   PetscCall(PetscFree(bseq));
5797:   if (!rowb) {
5798:     PetscCall(ISDestroy(&isrowb));
5799:   } else {
5800:     *rowb = isrowb;
5801:   }
5802:   if (!colb) {
5803:     PetscCall(ISDestroy(&iscolb));
5804:   } else {
5805:     *colb = iscolb;
5806:   }
5807:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5808:   PetscFunctionReturn(PETSC_SUCCESS);
5809: }

5811: /*
5812:     MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5813:     of the OFF-DIAGONAL portion of local A

5815:     Collective

5817:    Input Parameters:
5818: +    A,B - the matrices in `MATMPIAIJ` format
5819: -    scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5821:    Output Parameter:
5822: +    startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5823: .    startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5824: .    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5825: -    B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N

5827:     Developer Note:
5828:     This directly accesses information inside the VecScatter associated with the matrix-vector product
5829:      for this matrix. This is not desirable..

5831:     Level: developer

5833: */

5835: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5836: {
5837:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
5838:   VecScatter         ctx;
5839:   MPI_Comm           comm;
5840:   const PetscMPIInt *rprocs, *sprocs;
5841:   PetscMPIInt        nrecvs, nsends;
5842:   const PetscInt    *srow, *rstarts, *sstarts;
5843:   PetscInt          *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5844:   PetscInt           i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5845:   PetscScalar       *b_otha, *bufa, *bufA, *vals = NULL;
5846:   MPI_Request       *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5847:   PetscMPIInt        size, tag, rank, nreqs;

5849:   PetscFunctionBegin;
5850:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5851:   PetscCallMPI(MPI_Comm_size(comm, &size));

5853:   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 ")",
5854:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5855:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5856:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

5858:   if (size == 1) {
5859:     startsj_s = NULL;
5860:     bufa_ptr  = NULL;
5861:     *B_oth    = NULL;
5862:     PetscFunctionReturn(PETSC_SUCCESS);
5863:   }

5865:   ctx = a->Mvctx;
5866:   tag = ((PetscObject)ctx)->tag;

5868:   PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5869:   /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5870:   PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5871:   PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5872:   PetscCall(PetscMalloc1(nreqs, &reqs));
5873:   rwaits = reqs;
5874:   swaits = PetscSafePointerPlusOffset(reqs, nrecvs);

5876:   if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5877:   if (scall == MAT_INITIAL_MATRIX) {
5878:     /* i-array */
5879:     /*  post receives */
5880:     if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5881:     for (i = 0; i < nrecvs; i++) {
5882:       rowlen = rvalues + rstarts[i] * rbs;
5883:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5884:       PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5885:     }

5887:     /* pack the outgoing message */
5888:     PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));

5890:     sstartsj[0] = 0;
5891:     rstartsj[0] = 0;
5892:     len         = 0; /* total length of j or a array to be sent */
5893:     if (nsends) {
5894:       k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5895:       PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5896:     }
5897:     for (i = 0; i < nsends; i++) {
5898:       rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5899:       nrows  = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5900:       for (j = 0; j < nrows; j++) {
5901:         row = srow[k] + B->rmap->range[rank]; /* global row idx */
5902:         for (l = 0; l < sbs; l++) {
5903:           PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */

5905:           rowlen[j * sbs + l] = ncols;

5907:           len += ncols;
5908:           PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5909:         }
5910:         k++;
5911:       }
5912:       PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));

5914:       sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5915:     }
5916:     /* recvs and sends of i-array are completed */
5917:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5918:     PetscCall(PetscFree(svalues));

5920:     /* allocate buffers for sending j and a arrays */
5921:     PetscCall(PetscMalloc1(len + 1, &bufj));
5922:     PetscCall(PetscMalloc1(len + 1, &bufa));

5924:     /* create i-array of B_oth */
5925:     PetscCall(PetscMalloc1(aBn + 2, &b_othi));

5927:     b_othi[0] = 0;
5928:     len       = 0; /* total length of j or a array to be received */
5929:     k         = 0;
5930:     for (i = 0; i < nrecvs; i++) {
5931:       rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5932:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5933:       for (j = 0; j < nrows; j++) {
5934:         b_othi[k + 1] = b_othi[k] + rowlen[j];
5935:         PetscCall(PetscIntSumError(rowlen[j], len, &len));
5936:         k++;
5937:       }
5938:       rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5939:     }
5940:     PetscCall(PetscFree(rvalues));

5942:     /* allocate space for j and a arrays of B_oth */
5943:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5944:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));

5946:     /* j-array */
5947:     /*  post receives of j-array */
5948:     for (i = 0; i < nrecvs; i++) {
5949:       nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5950:       PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5951:     }

5953:     /* pack the outgoing message j-array */
5954:     if (nsends) k = sstarts[0];
5955:     for (i = 0; i < nsends; i++) {
5956:       nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5957:       bufJ  = bufj + sstartsj[i];
5958:       for (j = 0; j < nrows; j++) {
5959:         row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5960:         for (ll = 0; ll < sbs; ll++) {
5961:           PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5962:           for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5963:           PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5964:         }
5965:       }
5966:       PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5967:     }

5969:     /* recvs and sends of j-array are completed */
5970:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5971:   } else if (scall == MAT_REUSE_MATRIX) {
5972:     sstartsj = *startsj_s;
5973:     rstartsj = *startsj_r;
5974:     bufa     = *bufa_ptr;
5975:     PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5976:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");

5978:   /* a-array */
5979:   /*  post receives of a-array */
5980:   for (i = 0; i < nrecvs; i++) {
5981:     nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5982:     PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5983:   }

5985:   /* pack the outgoing message a-array */
5986:   if (nsends) k = sstarts[0];
5987:   for (i = 0; i < nsends; i++) {
5988:     nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5989:     bufA  = bufa + sstartsj[i];
5990:     for (j = 0; j < nrows; j++) {
5991:       row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5992:       for (ll = 0; ll < sbs; ll++) {
5993:         PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5994:         for (l = 0; l < ncols; l++) *bufA++ = vals[l];
5995:         PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
5996:       }
5997:     }
5998:     PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
5999:   }
6000:   /* recvs and sends of a-array are completed */
6001:   if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6002:   PetscCall(PetscFree(reqs));

6004:   if (scall == MAT_INITIAL_MATRIX) {
6005:     Mat_SeqAIJ *b_oth;

6007:     /* put together the new matrix */
6008:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));

6010:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6011:     /* Since these are PETSc arrays, change flags to free them as necessary. */
6012:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
6013:     b_oth->free_a  = PETSC_TRUE;
6014:     b_oth->free_ij = PETSC_TRUE;
6015:     b_oth->nonew   = 0;

6017:     PetscCall(PetscFree(bufj));
6018:     if (!startsj_s || !bufa_ptr) {
6019:       PetscCall(PetscFree2(sstartsj, rstartsj));
6020:       PetscCall(PetscFree(bufa_ptr));
6021:     } else {
6022:       *startsj_s = sstartsj;
6023:       *startsj_r = rstartsj;
6024:       *bufa_ptr  = bufa;
6025:     }
6026:   } else if (scall == MAT_REUSE_MATRIX) {
6027:     PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6028:   }

6030:   PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6031:   PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6032:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6033:   PetscFunctionReturn(PETSC_SUCCESS);
6034: }

6036: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6037: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6038: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6039: #if defined(PETSC_HAVE_MKL_SPARSE)
6040: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6041: #endif
6042: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6043: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6044: #if defined(PETSC_HAVE_ELEMENTAL)
6045: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6046: #endif
6047: #if defined(PETSC_HAVE_SCALAPACK)
6048: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6049: #endif
6050: #if defined(PETSC_HAVE_HYPRE)
6051: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6052: #endif
6053: #if defined(PETSC_HAVE_CUDA)
6054: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6055: #endif
6056: #if defined(PETSC_HAVE_HIP)
6057: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6058: #endif
6059: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6060: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6061: #endif
6062: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6063: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6064: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

6066: /*
6067:     Computes (B'*A')' since computing B*A directly is untenable

6069:                n                       p                          p
6070:         [             ]       [             ]         [                 ]
6071:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
6072:         [             ]       [             ]         [                 ]

6074: */
6075: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6076: {
6077:   Mat At, Bt, Ct;

6079:   PetscFunctionBegin;
6080:   PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6081:   PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6082:   PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6083:   PetscCall(MatDestroy(&At));
6084:   PetscCall(MatDestroy(&Bt));
6085:   PetscCall(MatTransposeSetPrecursor(Ct, C));
6086:   PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6087:   PetscCall(MatDestroy(&Ct));
6088:   PetscFunctionReturn(PETSC_SUCCESS);
6089: }

6091: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6092: {
6093:   PetscBool cisdense;

6095:   PetscFunctionBegin;
6096:   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);
6097:   PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6098:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
6099:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6100:   if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6101:   PetscCall(MatSetUp(C));

6103:   C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6104:   PetscFunctionReturn(PETSC_SUCCESS);
6105: }

6107: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6108: {
6109:   Mat_Product *product = C->product;
6110:   Mat          A = product->A, B = product->B;

6112:   PetscFunctionBegin;
6113:   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 ")",
6114:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6115:   C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6116:   C->ops->productsymbolic = MatProductSymbolic_AB;
6117:   PetscFunctionReturn(PETSC_SUCCESS);
6118: }

6120: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6121: {
6122:   Mat_Product *product = C->product;

6124:   PetscFunctionBegin;
6125:   if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6126:   PetscFunctionReturn(PETSC_SUCCESS);
6127: }

6129: /*
6130:    Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix

6132:   Input Parameters:

6134:     j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6135:     j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)

6137:     mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat

6139:     For Set1, j1[] contains column indices of the nonzeros.
6140:     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
6141:     respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6142:     but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.

6144:     Similar for Set2.

6146:     This routine merges the two sets of nonzeros row by row and removes repeats.

6148:   Output Parameters: (memory is allocated by the caller)

6150:     i[],j[]: the CSR of the merged matrix, which has m rows.
6151:     imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6152:     imap2[]: similar to imap1[], but for Set2.
6153:     Note we order nonzeros row-by-row and from left to right.
6154: */
6155: 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[])
6156: {
6157:   PetscInt   r, m; /* Row index of mat */
6158:   PetscCount t, t1, t2, b1, e1, b2, e2;

6160:   PetscFunctionBegin;
6161:   PetscCall(MatGetLocalSize(mat, &m, NULL));
6162:   t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6163:   i[0]        = 0;
6164:   for (r = 0; r < m; r++) { /* Do row by row merging */
6165:     b1 = rowBegin1[r];
6166:     e1 = rowEnd1[r];
6167:     b2 = rowBegin2[r];
6168:     e2 = rowEnd2[r];
6169:     while (b1 < e1 && b2 < e2) {
6170:       if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6171:         j[t]      = j1[b1];
6172:         imap1[t1] = t;
6173:         imap2[t2] = t;
6174:         b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6175:         b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6176:         t1++;
6177:         t2++;
6178:         t++;
6179:       } else if (j1[b1] < j2[b2]) {
6180:         j[t]      = j1[b1];
6181:         imap1[t1] = t;
6182:         b1 += jmap1[t1 + 1] - jmap1[t1];
6183:         t1++;
6184:         t++;
6185:       } else {
6186:         j[t]      = j2[b2];
6187:         imap2[t2] = t;
6188:         b2 += jmap2[t2 + 1] - jmap2[t2];
6189:         t2++;
6190:         t++;
6191:       }
6192:     }
6193:     /* Merge the remaining in either j1[] or j2[] */
6194:     while (b1 < e1) {
6195:       j[t]      = j1[b1];
6196:       imap1[t1] = t;
6197:       b1 += jmap1[t1 + 1] - jmap1[t1];
6198:       t1++;
6199:       t++;
6200:     }
6201:     while (b2 < e2) {
6202:       j[t]      = j2[b2];
6203:       imap2[t2] = t;
6204:       b2 += jmap2[t2 + 1] - jmap2[t2];
6205:       t2++;
6206:       t++;
6207:     }
6208:     PetscCall(PetscIntCast(t, i + r + 1));
6209:   }
6210:   PetscFunctionReturn(PETSC_SUCCESS);
6211: }

6213: /*
6214:   Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block

6216:   Input Parameters:
6217:     mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6218:     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[]
6219:       respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.

6221:       i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6222:       i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.

6224:   Output Parameters:
6225:     j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6226:     rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6227:       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,
6228:       and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.

6230:     Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6231:       Atot: number of entries belonging to the diagonal block.
6232:       Annz: number of unique nonzeros belonging to the diagonal block.
6233:       Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6234:         repeats (i.e., same 'i,j' pair).
6235:       Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6236:         is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.

6238:       Atot: number of entries belonging to the diagonal block
6239:       Annz: number of unique nonzeros belonging to the diagonal block.

6241:     Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.

6243:     Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6244: */
6245: 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_)
6246: {
6247:   PetscInt    cstart, cend, rstart, rend, row, col;
6248:   PetscCount  Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6249:   PetscCount  Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6250:   PetscCount  k, m, p, q, r, s, mid;
6251:   PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;

6253:   PetscFunctionBegin;
6254:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6255:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6256:   m = rend - rstart;

6258:   /* Skip negative rows */
6259:   for (k = 0; k < n; k++)
6260:     if (i[k] >= 0) break;

6262:   /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6263:      fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6264:   */
6265:   while (k < n) {
6266:     row = i[k];
6267:     /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6268:     for (s = k; s < n; s++)
6269:       if (i[s] != row) break;

6271:     /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6272:     for (p = k; p < s; p++) {
6273:       if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6274:       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]);
6275:     }
6276:     PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6277:     PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6278:     rowBegin[row - rstart] = k;
6279:     rowMid[row - rstart]   = mid;
6280:     rowEnd[row - rstart]   = s;

6282:     /* Count nonzeros of this diag/offdiag row, which might have repeats */
6283:     Atot += mid - k;
6284:     Btot += s - mid;

6286:     /* Count unique nonzeros of this diag row */
6287:     for (p = k; p < mid;) {
6288:       col = j[p];
6289:       do {
6290:         j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6291:         p++;
6292:       } while (p < mid && j[p] == col);
6293:       Annz++;
6294:     }

6296:     /* Count unique nonzeros of this offdiag row */
6297:     for (p = mid; p < s;) {
6298:       col = j[p];
6299:       do {
6300:         p++;
6301:       } while (p < s && j[p] == col);
6302:       Bnnz++;
6303:     }
6304:     k = s;
6305:   }

6307:   /* Allocation according to Atot, Btot, Annz, Bnnz */
6308:   PetscCall(PetscMalloc1(Atot, &Aperm));
6309:   PetscCall(PetscMalloc1(Btot, &Bperm));
6310:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6311:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));

6313:   /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6314:   Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6315:   for (r = 0; r < m; r++) {
6316:     k   = rowBegin[r];
6317:     mid = rowMid[r];
6318:     s   = rowEnd[r];
6319:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6320:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6321:     Atot += mid - k;
6322:     Btot += s - mid;

6324:     /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6325:     for (p = k; p < mid;) {
6326:       col = j[p];
6327:       q   = p;
6328:       do {
6329:         p++;
6330:       } while (p < mid && j[p] == col);
6331:       Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6332:       Annz++;
6333:     }

6335:     for (p = mid; p < s;) {
6336:       col = j[p];
6337:       q   = p;
6338:       do {
6339:         p++;
6340:       } while (p < s && j[p] == col);
6341:       Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6342:       Bnnz++;
6343:     }
6344:   }
6345:   /* Output */
6346:   *Aperm_ = Aperm;
6347:   *Annz_  = Annz;
6348:   *Atot_  = Atot;
6349:   *Ajmap_ = Ajmap;
6350:   *Bperm_ = Bperm;
6351:   *Bnnz_  = Bnnz;
6352:   *Btot_  = Btot;
6353:   *Bjmap_ = Bjmap;
6354:   PetscFunctionReturn(PETSC_SUCCESS);
6355: }

6357: /*
6358:   Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix

6360:   Input Parameters:
6361:     nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6362:     nnz:  number of unique nonzeros in the merged matrix
6363:     imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6364:     jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set

6366:   Output Parameter: (memory is allocated by the caller)
6367:     jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set

6369:   Example:
6370:     nnz1 = 4
6371:     nnz  = 6
6372:     imap = [1,3,4,5]
6373:     jmap = [0,3,5,6,7]
6374:    then,
6375:     jmap_new = [0,0,3,3,5,6,7]
6376: */
6377: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6378: {
6379:   PetscCount k, p;

6381:   PetscFunctionBegin;
6382:   jmap_new[0] = 0;
6383:   p           = nnz;                /* p loops over jmap_new[] backwards */
6384:   for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6385:     for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6386:   }
6387:   for (; p >= 0; p--) jmap_new[p] = jmap[0];
6388:   PetscFunctionReturn(PETSC_SUCCESS);
6389: }

6391: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void *data)
6392: {
6393:   MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)data;

6395:   PetscFunctionBegin;
6396:   PetscCall(PetscSFDestroy(&coo->sf));
6397:   PetscCall(PetscFree(coo->Aperm1));
6398:   PetscCall(PetscFree(coo->Bperm1));
6399:   PetscCall(PetscFree(coo->Ajmap1));
6400:   PetscCall(PetscFree(coo->Bjmap1));
6401:   PetscCall(PetscFree(coo->Aimap2));
6402:   PetscCall(PetscFree(coo->Bimap2));
6403:   PetscCall(PetscFree(coo->Aperm2));
6404:   PetscCall(PetscFree(coo->Bperm2));
6405:   PetscCall(PetscFree(coo->Ajmap2));
6406:   PetscCall(PetscFree(coo->Bjmap2));
6407:   PetscCall(PetscFree(coo->Cperm1));
6408:   PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6409:   PetscCall(PetscFree(coo));
6410:   PetscFunctionReturn(PETSC_SUCCESS);
6411: }

6413: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6414: {
6415:   MPI_Comm             comm;
6416:   PetscMPIInt          rank, size;
6417:   PetscInt             m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6418:   PetscCount           k, p, q, rem;                           /* Loop variables over coo arrays */
6419:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6420:   PetscContainer       container;
6421:   MatCOOStruct_MPIAIJ *coo;

6423:   PetscFunctionBegin;
6424:   PetscCall(PetscFree(mpiaij->garray));
6425:   PetscCall(VecDestroy(&mpiaij->lvec));
6426: #if defined(PETSC_USE_CTABLE)
6427:   PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6428: #else
6429:   PetscCall(PetscFree(mpiaij->colmap));
6430: #endif
6431:   PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6432:   mat->assembled     = PETSC_FALSE;
6433:   mat->was_assembled = PETSC_FALSE;

6435:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6436:   PetscCallMPI(MPI_Comm_size(comm, &size));
6437:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
6438:   PetscCall(PetscLayoutSetUp(mat->rmap));
6439:   PetscCall(PetscLayoutSetUp(mat->cmap));
6440:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6441:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6442:   PetscCall(MatGetLocalSize(mat, &m, &n));
6443:   PetscCall(MatGetSize(mat, &M, &N));

6445:   /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6446:   /* entries come first, then local rows, then remote rows.                     */
6447:   PetscCount n1 = coo_n, *perm1;
6448:   PetscInt  *i1 = coo_i, *j1 = coo_j;

6450:   PetscCall(PetscMalloc1(n1, &perm1));
6451:   for (k = 0; k < n1; k++) perm1[k] = k;

6453:   /* Manipulate indices so that entries with negative row or col indices will have smallest
6454:      row indices, local entries will have greater but negative row indices, and remote entries
6455:      will have positive row indices.
6456:   */
6457:   for (k = 0; k < n1; k++) {
6458:     if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN;                /* e.g., -2^31, minimal to move them ahead */
6459:     else if (i1[k] >= rstart && i1[k] < rend) i1[k] -= PETSC_INT_MAX; /* e.g., minus 2^31-1 to shift local rows to range of [-PETSC_INT_MAX, -1] */
6460:     else {
6461:       PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6462:       if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6463:     }
6464:   }

6466:   /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6467:   PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));

6469:   /* Advance k to the first entry we need to take care of */
6470:   for (k = 0; k < n1; k++)
6471:     if (i1[k] > PETSC_INT_MIN) break;
6472:   PetscCount i1start = k;

6474:   PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6475:   for (; k < rem; k++) i1[k] += PETSC_INT_MAX;                                    /* Revert row indices of local rows*/

6477:   /*           Send remote rows to their owner                                  */
6478:   /* Find which rows should be sent to which remote ranks*/
6479:   PetscInt        nsend = 0; /* Number of MPI ranks to send data to */
6480:   PetscMPIInt    *sendto;    /* [nsend], storing remote ranks */
6481:   PetscInt       *nentries;  /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6482:   const PetscInt *ranges;
6483:   PetscInt        maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */

6485:   PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6486:   PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6487:   for (k = rem; k < n1;) {
6488:     PetscMPIInt owner;
6489:     PetscInt    firstRow, lastRow;

6491:     /* Locate a row range */
6492:     firstRow = i1[k]; /* first row of this owner */
6493:     PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6494:     lastRow = ranges[owner + 1] - 1; /* last row of this owner */

6496:     /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6497:     PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));

6499:     /* All entries in [k,p) belong to this remote owner */
6500:     if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6501:       PetscMPIInt *sendto2;
6502:       PetscInt    *nentries2;
6503:       PetscInt     maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;

6505:       PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6506:       PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6507:       PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6508:       PetscCall(PetscFree2(sendto, nentries2));
6509:       sendto   = sendto2;
6510:       nentries = nentries2;
6511:       maxNsend = maxNsend2;
6512:     }
6513:     sendto[nsend] = owner;
6514:     PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6515:     nsend++;
6516:     k = p;
6517:   }

6519:   /* Build 1st SF to know offsets on remote to send data */
6520:   PetscSF      sf1;
6521:   PetscInt     nroots = 1, nroots2 = 0;
6522:   PetscInt     nleaves = nsend, nleaves2 = 0;
6523:   PetscInt    *offsets;
6524:   PetscSFNode *iremote;

6526:   PetscCall(PetscSFCreate(comm, &sf1));
6527:   PetscCall(PetscMalloc1(nsend, &iremote));
6528:   PetscCall(PetscMalloc1(nsend, &offsets));
6529:   for (k = 0; k < nsend; k++) {
6530:     iremote[k].rank  = sendto[k];
6531:     iremote[k].index = 0;
6532:     nleaves2 += nentries[k];
6533:     PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6534:   }
6535:   PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6536:   PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6537:   PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6538:   PetscCall(PetscSFDestroy(&sf1));
6539:   PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);

6541:   /* Build 2nd SF to send remote COOs to their owner */
6542:   PetscSF sf2;
6543:   nroots  = nroots2;
6544:   nleaves = nleaves2;
6545:   PetscCall(PetscSFCreate(comm, &sf2));
6546:   PetscCall(PetscSFSetFromOptions(sf2));
6547:   PetscCall(PetscMalloc1(nleaves, &iremote));
6548:   p = 0;
6549:   for (k = 0; k < nsend; k++) {
6550:     PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6551:     for (q = 0; q < nentries[k]; q++, p++) {
6552:       iremote[p].rank = sendto[k];
6553:       PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6554:     }
6555:   }
6556:   PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));

6558:   /* Send the remote COOs to their owner */
6559:   PetscInt    n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6560:   PetscCount *perm2;                 /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6561:   PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6562:   PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6563:   PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6564:   PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6565:   PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6566:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6567:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6568:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6569:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));

6571:   PetscCall(PetscFree(offsets));
6572:   PetscCall(PetscFree2(sendto, nentries));

6574:   /* Sort received COOs by row along with the permutation array     */
6575:   for (k = 0; k < n2; k++) perm2[k] = k;
6576:   PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));

6578:   /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6579:   PetscCount *Cperm1;
6580:   PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6581:   PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6582:   PetscCall(PetscMalloc1(nleaves, &Cperm1));
6583:   PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));

6585:   /* Support for HYPRE matrices, kind of a hack.
6586:      Swap min column with diagonal so that diagonal values will go first */
6587:   PetscBool hypre;
6588:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6589:   if (hypre) {
6590:     PetscInt *minj;
6591:     PetscBT   hasdiag;

6593:     PetscCall(PetscBTCreate(m, &hasdiag));
6594:     PetscCall(PetscMalloc1(m, &minj));
6595:     for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6596:     for (k = i1start; k < rem; k++) {
6597:       if (j1[k] < cstart || j1[k] >= cend) continue;
6598:       const PetscInt rindex = i1[k] - rstart;
6599:       if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6600:       minj[rindex] = PetscMin(minj[rindex], j1[k]);
6601:     }
6602:     for (k = 0; k < n2; k++) {
6603:       if (j2[k] < cstart || j2[k] >= cend) continue;
6604:       const PetscInt rindex = i2[k] - rstart;
6605:       if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6606:       minj[rindex] = PetscMin(minj[rindex], j2[k]);
6607:     }
6608:     for (k = i1start; k < rem; k++) {
6609:       const PetscInt rindex = i1[k] - rstart;
6610:       if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6611:       if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6612:       else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6613:     }
6614:     for (k = 0; k < n2; k++) {
6615:       const PetscInt rindex = i2[k] - rstart;
6616:       if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6617:       if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6618:       else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6619:     }
6620:     PetscCall(PetscBTDestroy(&hasdiag));
6621:     PetscCall(PetscFree(minj));
6622:   }

6624:   /* Split local COOs and received COOs into diag/offdiag portions */
6625:   PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6626:   PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6627:   PetscCount  Annz1, Bnnz1, Atot1, Btot1;
6628:   PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6629:   PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6630:   PetscCount  Annz2, Bnnz2, Atot2, Btot2;

6632:   PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6633:   PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6634:   PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6635:   PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));

6637:   /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6638:   PetscInt *Ai, *Bi;
6639:   PetscInt *Aj, *Bj;

6641:   PetscCall(PetscMalloc1(m + 1, &Ai));
6642:   PetscCall(PetscMalloc1(m + 1, &Bi));
6643:   PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6644:   PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));

6646:   PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6647:   PetscCall(PetscMalloc1(Annz1, &Aimap1));
6648:   PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6649:   PetscCall(PetscMalloc1(Annz2, &Aimap2));
6650:   PetscCall(PetscMalloc1(Bnnz2, &Bimap2));

6652:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6653:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));

6655:   /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we     */
6656:   /* expect nonzeros in A/B most likely have local contributing entries        */
6657:   PetscInt    Annz = Ai[m];
6658:   PetscInt    Bnnz = Bi[m];
6659:   PetscCount *Ajmap1_new, *Bjmap1_new;

6661:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6662:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));

6664:   PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6665:   PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));

6667:   PetscCall(PetscFree(Aimap1));
6668:   PetscCall(PetscFree(Ajmap1));
6669:   PetscCall(PetscFree(Bimap1));
6670:   PetscCall(PetscFree(Bjmap1));
6671:   PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6672:   PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6673:   PetscCall(PetscFree(perm1));
6674:   PetscCall(PetscFree3(i2, j2, perm2));

6676:   Ajmap1 = Ajmap1_new;
6677:   Bjmap1 = Bjmap1_new;

6679:   /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6680:   if (Annz < Annz1 + Annz2) {
6681:     PetscInt *Aj_new;
6682:     PetscCall(PetscMalloc1(Annz, &Aj_new));
6683:     PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6684:     PetscCall(PetscFree(Aj));
6685:     Aj = Aj_new;
6686:   }

6688:   if (Bnnz < Bnnz1 + Bnnz2) {
6689:     PetscInt *Bj_new;
6690:     PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6691:     PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6692:     PetscCall(PetscFree(Bj));
6693:     Bj = Bj_new;
6694:   }

6696:   /* Create new submatrices for on-process and off-process coupling                  */
6697:   PetscScalar     *Aa, *Ba;
6698:   MatType          rtype;
6699:   Mat_SeqAIJ      *a, *b;
6700:   PetscObjectState state;
6701:   PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6702:   PetscCall(PetscCalloc1(Bnnz, &Ba));
6703:   /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6704:   if (cstart) {
6705:     for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6706:   }

6708:   PetscCall(MatGetRootType_Private(mat, &rtype));

6710:   MatSeqXAIJGetOptions_Private(mpiaij->A);
6711:   PetscCall(MatDestroy(&mpiaij->A));
6712:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6713:   PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6714:   MatSeqXAIJRestoreOptions_Private(mpiaij->A);

6716:   MatSeqXAIJGetOptions_Private(mpiaij->B);
6717:   PetscCall(MatDestroy(&mpiaij->B));
6718:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6719:   PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6720:   MatSeqXAIJRestoreOptions_Private(mpiaij->B);

6722:   PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6723:   mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6724:   state              = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6725:   PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));

6727:   a          = (Mat_SeqAIJ *)mpiaij->A->data;
6728:   b          = (Mat_SeqAIJ *)mpiaij->B->data;
6729:   a->free_a  = PETSC_TRUE;
6730:   a->free_ij = PETSC_TRUE;
6731:   b->free_a  = PETSC_TRUE;
6732:   b->free_ij = PETSC_TRUE;
6733:   a->maxnz   = a->nz;
6734:   b->maxnz   = b->nz;

6736:   /* conversion must happen AFTER multiply setup */
6737:   PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6738:   PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6739:   PetscCall(VecDestroy(&mpiaij->lvec));
6740:   PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));

6742:   // Put the COO struct in a container and then attach that to the matrix
6743:   PetscCall(PetscMalloc1(1, &coo));
6744:   coo->n       = coo_n;
6745:   coo->sf      = sf2;
6746:   coo->sendlen = nleaves;
6747:   coo->recvlen = nroots;
6748:   coo->Annz    = Annz;
6749:   coo->Bnnz    = Bnnz;
6750:   coo->Annz2   = Annz2;
6751:   coo->Bnnz2   = Bnnz2;
6752:   coo->Atot1   = Atot1;
6753:   coo->Atot2   = Atot2;
6754:   coo->Btot1   = Btot1;
6755:   coo->Btot2   = Btot2;
6756:   coo->Ajmap1  = Ajmap1;
6757:   coo->Aperm1  = Aperm1;
6758:   coo->Bjmap1  = Bjmap1;
6759:   coo->Bperm1  = Bperm1;
6760:   coo->Aimap2  = Aimap2;
6761:   coo->Ajmap2  = Ajmap2;
6762:   coo->Aperm2  = Aperm2;
6763:   coo->Bimap2  = Bimap2;
6764:   coo->Bjmap2  = Bjmap2;
6765:   coo->Bperm2  = Bperm2;
6766:   coo->Cperm1  = Cperm1;
6767:   // Allocate in preallocation. If not used, it has zero cost on host
6768:   PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6769:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6770:   PetscCall(PetscContainerSetPointer(container, coo));
6771:   PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_MPIAIJ));
6772:   PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6773:   PetscCall(PetscContainerDestroy(&container));
6774:   PetscFunctionReturn(PETSC_SUCCESS);
6775: }

6777: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6778: {
6779:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6780:   Mat                  A = mpiaij->A, B = mpiaij->B;
6781:   PetscScalar         *Aa, *Ba;
6782:   PetscScalar         *sendbuf, *recvbuf;
6783:   const PetscCount    *Ajmap1, *Ajmap2, *Aimap2;
6784:   const PetscCount    *Bjmap1, *Bjmap2, *Bimap2;
6785:   const PetscCount    *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6786:   const PetscCount    *Cperm1;
6787:   PetscContainer       container;
6788:   MatCOOStruct_MPIAIJ *coo;

6790:   PetscFunctionBegin;
6791:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6792:   PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6793:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6794:   sendbuf = coo->sendbuf;
6795:   recvbuf = coo->recvbuf;
6796:   Ajmap1  = coo->Ajmap1;
6797:   Ajmap2  = coo->Ajmap2;
6798:   Aimap2  = coo->Aimap2;
6799:   Bjmap1  = coo->Bjmap1;
6800:   Bjmap2  = coo->Bjmap2;
6801:   Bimap2  = coo->Bimap2;
6802:   Aperm1  = coo->Aperm1;
6803:   Aperm2  = coo->Aperm2;
6804:   Bperm1  = coo->Bperm1;
6805:   Bperm2  = coo->Bperm2;
6806:   Cperm1  = coo->Cperm1;

6808:   PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6809:   PetscCall(MatSeqAIJGetArray(B, &Ba));

6811:   /* Pack entries to be sent to remote */
6812:   for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];

6814:   /* Send remote entries to their owner and overlap the communication with local computation */
6815:   PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6816:   /* Add local entries to A and B */
6817:   for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6818:     PetscScalar sum = 0.0;                     /* Do partial summation first to improve numerical stability */
6819:     for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6820:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6821:   }
6822:   for (PetscCount i = 0; i < coo->Bnnz; i++) {
6823:     PetscScalar sum = 0.0;
6824:     for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6825:     Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6826:   }
6827:   PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));

6829:   /* Add received remote entries to A and B */
6830:   for (PetscCount i = 0; i < coo->Annz2; i++) {
6831:     for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6832:   }
6833:   for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6834:     for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6835:   }
6836:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6837:   PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6838:   PetscFunctionReturn(PETSC_SUCCESS);
6839: }

6841: /*MC
6842:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

6844:    Options Database Keys:
6845: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`

6847:    Level: beginner

6849:    Notes:
6850:    `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6851:     in this case the values associated with the rows and columns one passes in are set to zero
6852:     in the matrix

6854:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6855:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

6857: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6858: M*/
6859: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6860: {
6861:   Mat_MPIAIJ *b;
6862:   PetscMPIInt size;

6864:   PetscFunctionBegin;
6865:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

6867:   PetscCall(PetscNew(&b));
6868:   B->data       = (void *)b;
6869:   B->ops[0]     = MatOps_Values;
6870:   B->assembled  = PETSC_FALSE;
6871:   B->insertmode = NOT_SET_VALUES;
6872:   b->size       = size;

6874:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));

6876:   /* build cache for off array entries formed */
6877:   PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));

6879:   b->donotstash  = PETSC_FALSE;
6880:   b->colmap      = NULL;
6881:   b->garray      = NULL;
6882:   b->roworiented = PETSC_TRUE;

6884:   /* stuff used for matrix vector multiply */
6885:   b->lvec  = NULL;
6886:   b->Mvctx = NULL;

6888:   /* stuff for MatGetRow() */
6889:   b->rowindices   = NULL;
6890:   b->rowvalues    = NULL;
6891:   b->getrowactive = PETSC_FALSE;

6893:   /* flexible pointer used in CUSPARSE classes */
6894:   b->spptr = NULL;

6896:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6897:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6898:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6899:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6900:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6901:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6902:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6903:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6904:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6905:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6906: #if defined(PETSC_HAVE_CUDA)
6907:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6908: #endif
6909: #if defined(PETSC_HAVE_HIP)
6910:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6911: #endif
6912: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6913:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6914: #endif
6915: #if defined(PETSC_HAVE_MKL_SPARSE)
6916:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6917: #endif
6918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6919:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6921:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6922: #if defined(PETSC_HAVE_ELEMENTAL)
6923:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6924: #endif
6925: #if defined(PETSC_HAVE_SCALAPACK)
6926:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6927: #endif
6928:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6929:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6930: #if defined(PETSC_HAVE_HYPRE)
6931:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6932:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6933: #endif
6934:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6935:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6936:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6937:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6938:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6939:   PetscFunctionReturn(PETSC_SUCCESS);
6940: }

6942: /*@
6943:   MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6944:   and "off-diagonal" part of the matrix in CSR format.

6946:   Collective

6948:   Input Parameters:
6949: + comm - MPI communicator
6950: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
6951: . n    - This value should be the same as the local size used in creating the
6952:          x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6953:          calculated if `N` is given) For square matrices `n` is almost always `m`.
6954: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6955: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6956: . 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
6957: . j    - column indices, which must be local, i.e., based off the start column of the diagonal portion
6958: . a    - matrix values
6959: . 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
6960: . oj   - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6961: - oa   - matrix values

6963:   Output Parameter:
6964: . mat - the matrix

6966:   Level: advanced

6968:   Notes:
6969:   The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6970:   must free the arrays once the matrix has been destroyed and not before.

6972:   The `i` and `j` indices are 0 based

6974:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix

6976:   This sets local rows and cannot be used to set off-processor values.

6978:   Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6979:   legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6980:   not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6981:   the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6982:   keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6983:   communication if it is known that only local entries will be set.

6985: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
6986:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
6987: @*/
6988: 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)
6989: {
6990:   Mat_MPIAIJ *maij;

6992:   PetscFunctionBegin;
6993:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
6994:   PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
6995:   PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
6996:   PetscCall(MatCreate(comm, mat));
6997:   PetscCall(MatSetSizes(*mat, m, n, M, N));
6998:   PetscCall(MatSetType(*mat, MATMPIAIJ));
6999:   maij = (Mat_MPIAIJ *)(*mat)->data;

7001:   (*mat)->preallocated = PETSC_TRUE;

7003:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
7004:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

7006:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
7007:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));

7009:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7010:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7011:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7012:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7013:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7014:   PetscFunctionReturn(PETSC_SUCCESS);
7015: }

7017: typedef struct {
7018:   Mat       *mp;    /* intermediate products */
7019:   PetscBool *mptmp; /* is the intermediate product temporary ? */
7020:   PetscInt   cp;    /* number of intermediate products */

7022:   /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7023:   PetscInt    *startsj_s, *startsj_r;
7024:   PetscScalar *bufa;
7025:   Mat          P_oth;

7027:   /* may take advantage of merging product->B */
7028:   Mat Bloc; /* B-local by merging diag and off-diag */

7030:   /* cusparse does not have support to split between symbolic and numeric phases.
7031:      When api_user is true, we don't need to update the numerical values
7032:      of the temporary storage */
7033:   PetscBool reusesym;

7035:   /* support for COO values insertion */
7036:   PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7037:   PetscInt   **own;           /* own[i] points to address of on-process COO indices for Mat mp[i] */
7038:   PetscInt   **off;           /* off[i] points to address of off-process COO indices for Mat mp[i] */
7039:   PetscBool    hasoffproc;    /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7040:   PetscSF      sf;            /* used for non-local values insertion and memory malloc */
7041:   PetscMemType mtype;

7043:   /* customization */
7044:   PetscBool abmerge;
7045:   PetscBool P_oth_bind;
7046: } MatMatMPIAIJBACKEND;

7048: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7049: {
7050:   MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7051:   PetscInt             i;

7053:   PetscFunctionBegin;
7054:   PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7055:   PetscCall(PetscFree(mmdata->bufa));
7056:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7057:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7058:   PetscCall(MatDestroy(&mmdata->P_oth));
7059:   PetscCall(MatDestroy(&mmdata->Bloc));
7060:   PetscCall(PetscSFDestroy(&mmdata->sf));
7061:   for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7062:   PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7063:   PetscCall(PetscFree(mmdata->own[0]));
7064:   PetscCall(PetscFree(mmdata->own));
7065:   PetscCall(PetscFree(mmdata->off[0]));
7066:   PetscCall(PetscFree(mmdata->off));
7067:   PetscCall(PetscFree(mmdata));
7068:   PetscFunctionReturn(PETSC_SUCCESS);
7069: }

7071: /* Copy selected n entries with indices in idx[] of A to v[].
7072:    If idx is NULL, copy the whole data array of A to v[]
7073:  */
7074: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7075: {
7076:   PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);

7078:   PetscFunctionBegin;
7079:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7080:   if (f) {
7081:     PetscCall((*f)(A, n, idx, v));
7082:   } else {
7083:     const PetscScalar *vv;

7085:     PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7086:     if (n && idx) {
7087:       PetscScalar    *w  = v;
7088:       const PetscInt *oi = idx;
7089:       PetscInt        j;

7091:       for (j = 0; j < n; j++) *w++ = vv[*oi++];
7092:     } else {
7093:       PetscCall(PetscArraycpy(v, vv, n));
7094:     }
7095:     PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7096:   }
7097:   PetscFunctionReturn(PETSC_SUCCESS);
7098: }

7100: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7101: {
7102:   MatMatMPIAIJBACKEND *mmdata;
7103:   PetscInt             i, n_d, n_o;

7105:   PetscFunctionBegin;
7106:   MatCheckProduct(C, 1);
7107:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7108:   mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7109:   if (!mmdata->reusesym) { /* update temporary matrices */
7110:     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));
7111:     if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7112:   }
7113:   mmdata->reusesym = PETSC_FALSE;

7115:   for (i = 0; i < mmdata->cp; i++) {
7116:     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]);
7117:     PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7118:   }
7119:   for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7120:     PetscInt noff;

7122:     PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7123:     if (mmdata->mptmp[i]) continue;
7124:     if (noff) {
7125:       PetscInt nown;

7127:       PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7128:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7129:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7130:       n_o += noff;
7131:       n_d += nown;
7132:     } else {
7133:       Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;

7135:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7136:       n_d += mm->nz;
7137:     }
7138:   }
7139:   if (mmdata->hasoffproc) { /* offprocess insertion */
7140:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7141:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7142:   }
7143:   PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7144:   PetscFunctionReturn(PETSC_SUCCESS);
7145: }

7147: /* Support for Pt * A, A * P, or Pt * A * P */
7148: #define MAX_NUMBER_INTERMEDIATE 4
7149: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7150: {
7151:   Mat_Product           *product = C->product;
7152:   Mat                    A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7153:   Mat_MPIAIJ            *a, *p;
7154:   MatMatMPIAIJBACKEND   *mmdata;
7155:   ISLocalToGlobalMapping P_oth_l2g = NULL;
7156:   IS                     glob      = NULL;
7157:   const char            *prefix;
7158:   char                   pprefix[256];
7159:   const PetscInt        *globidx, *P_oth_idx;
7160:   PetscInt               i, j, cp, m, n, M, N, *coo_i, *coo_j;
7161:   PetscCount             ncoo, ncoo_d, ncoo_o, ncoo_oown;
7162:   PetscInt               cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7163:                                                                                          /* type-0: consecutive, start from 0; type-1: consecutive with */
7164:                                                                                          /* a base offset; type-2: sparse with a local to global map table */
7165:   const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE];       /* col/row local to global map array (table) for type-2 map type */

7167:   MatProductType ptype;
7168:   PetscBool      mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7169:   PetscMPIInt    size;

7171:   PetscFunctionBegin;
7172:   MatCheckProduct(C, 1);
7173:   PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7174:   ptype = product->type;
7175:   if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7176:     ptype                                          = MATPRODUCT_AB;
7177:     product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7178:   }
7179:   switch (ptype) {
7180:   case MATPRODUCT_AB:
7181:     A          = product->A;
7182:     P          = product->B;
7183:     m          = A->rmap->n;
7184:     n          = P->cmap->n;
7185:     M          = A->rmap->N;
7186:     N          = P->cmap->N;
7187:     hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7188:     break;
7189:   case MATPRODUCT_AtB:
7190:     P          = product->A;
7191:     A          = product->B;
7192:     m          = P->cmap->n;
7193:     n          = A->cmap->n;
7194:     M          = P->cmap->N;
7195:     N          = A->cmap->N;
7196:     hasoffproc = PETSC_TRUE;
7197:     break;
7198:   case MATPRODUCT_PtAP:
7199:     A          = product->A;
7200:     P          = product->B;
7201:     m          = P->cmap->n;
7202:     n          = P->cmap->n;
7203:     M          = P->cmap->N;
7204:     N          = P->cmap->N;
7205:     hasoffproc = PETSC_TRUE;
7206:     break;
7207:   default:
7208:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7209:   }
7210:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7211:   if (size == 1) hasoffproc = PETSC_FALSE;

7213:   /* defaults */
7214:   for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7215:     mp[i]    = NULL;
7216:     mptmp[i] = PETSC_FALSE;
7217:     rmapt[i] = -1;
7218:     cmapt[i] = -1;
7219:     rmapa[i] = NULL;
7220:     cmapa[i] = NULL;
7221:   }

7223:   /* customization */
7224:   PetscCall(PetscNew(&mmdata));
7225:   mmdata->reusesym = product->api_user;
7226:   if (ptype == MATPRODUCT_AB) {
7227:     if (product->api_user) {
7228:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7229:       PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7230:       PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7231:       PetscOptionsEnd();
7232:     } else {
7233:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7234:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7235:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7236:       PetscOptionsEnd();
7237:     }
7238:   } else if (ptype == MATPRODUCT_PtAP) {
7239:     if (product->api_user) {
7240:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7241:       PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7242:       PetscOptionsEnd();
7243:     } else {
7244:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7245:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7246:       PetscOptionsEnd();
7247:     }
7248:   }
7249:   a = (Mat_MPIAIJ *)A->data;
7250:   p = (Mat_MPIAIJ *)P->data;
7251:   PetscCall(MatSetSizes(C, m, n, M, N));
7252:   PetscCall(PetscLayoutSetUp(C->rmap));
7253:   PetscCall(PetscLayoutSetUp(C->cmap));
7254:   PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7255:   PetscCall(MatGetOptionsPrefix(C, &prefix));

7257:   cp = 0;
7258:   switch (ptype) {
7259:   case MATPRODUCT_AB: /* A * P */
7260:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));

7262:     /* A_diag * P_local (merged or not) */
7263:     if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7264:       /* P is product->B */
7265:       PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7266:       PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7267:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7268:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7269:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7270:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7271:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7272:       mp[cp]->product->api_user = product->api_user;
7273:       PetscCall(MatProductSetFromOptions(mp[cp]));
7274:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7275:       PetscCall(ISGetIndices(glob, &globidx));
7276:       rmapt[cp] = 1;
7277:       cmapt[cp] = 2;
7278:       cmapa[cp] = globidx;
7279:       mptmp[cp] = PETSC_FALSE;
7280:       cp++;
7281:     } else { /* A_diag * P_diag and A_diag * P_off */
7282:       PetscCall(MatProductCreate(a->A, p->A, 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] = 1;
7293:       mptmp[cp] = PETSC_FALSE;
7294:       cp++;
7295:       PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7296:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7297:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7298:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7299:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7300:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7301:       mp[cp]->product->api_user = product->api_user;
7302:       PetscCall(MatProductSetFromOptions(mp[cp]));
7303:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7304:       rmapt[cp] = 1;
7305:       cmapt[cp] = 2;
7306:       cmapa[cp] = p->garray;
7307:       mptmp[cp] = PETSC_FALSE;
7308:       cp++;
7309:     }

7311:     /* A_off * P_other */
7312:     if (mmdata->P_oth) {
7313:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7314:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7315:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7316:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7317:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7318:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7319:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7320:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7321:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7322:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7323:       mp[cp]->product->api_user = product->api_user;
7324:       PetscCall(MatProductSetFromOptions(mp[cp]));
7325:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7326:       rmapt[cp] = 1;
7327:       cmapt[cp] = 2;
7328:       cmapa[cp] = P_oth_idx;
7329:       mptmp[cp] = PETSC_FALSE;
7330:       cp++;
7331:     }
7332:     break;

7334:   case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7335:     /* A is product->B */
7336:     PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7337:     if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7338:       PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7339:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7340:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7341:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7342:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7343:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7344:       mp[cp]->product->api_user = product->api_user;
7345:       PetscCall(MatProductSetFromOptions(mp[cp]));
7346:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7347:       PetscCall(ISGetIndices(glob, &globidx));
7348:       rmapt[cp] = 2;
7349:       rmapa[cp] = globidx;
7350:       cmapt[cp] = 2;
7351:       cmapa[cp] = globidx;
7352:       mptmp[cp] = PETSC_FALSE;
7353:       cp++;
7354:     } else {
7355:       PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7356:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7357:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7358:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7359:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7360:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7361:       mp[cp]->product->api_user = product->api_user;
7362:       PetscCall(MatProductSetFromOptions(mp[cp]));
7363:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7364:       PetscCall(ISGetIndices(glob, &globidx));
7365:       rmapt[cp] = 1;
7366:       cmapt[cp] = 2;
7367:       cmapa[cp] = globidx;
7368:       mptmp[cp] = PETSC_FALSE;
7369:       cp++;
7370:       PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7371:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7372:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7373:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7374:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7375:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7376:       mp[cp]->product->api_user = product->api_user;
7377:       PetscCall(MatProductSetFromOptions(mp[cp]));
7378:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7379:       rmapt[cp] = 2;
7380:       rmapa[cp] = p->garray;
7381:       cmapt[cp] = 2;
7382:       cmapa[cp] = globidx;
7383:       mptmp[cp] = PETSC_FALSE;
7384:       cp++;
7385:     }
7386:     break;
7387:   case MATPRODUCT_PtAP:
7388:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7389:     /* P is product->B */
7390:     PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7391:     PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7392:     PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7393:     PetscCall(MatProductSetFill(mp[cp], product->fill));
7394:     PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7395:     PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7396:     PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7397:     mp[cp]->product->api_user = product->api_user;
7398:     PetscCall(MatProductSetFromOptions(mp[cp]));
7399:     PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7400:     PetscCall(ISGetIndices(glob, &globidx));
7401:     rmapt[cp] = 2;
7402:     rmapa[cp] = globidx;
7403:     cmapt[cp] = 2;
7404:     cmapa[cp] = globidx;
7405:     mptmp[cp] = PETSC_FALSE;
7406:     cp++;
7407:     if (mmdata->P_oth) {
7408:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7409:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7410:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7411:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7412:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7413:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7414:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7415:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7416:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7417:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7418:       mp[cp]->product->api_user = product->api_user;
7419:       PetscCall(MatProductSetFromOptions(mp[cp]));
7420:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7421:       mptmp[cp] = PETSC_TRUE;
7422:       cp++;
7423:       PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7424:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7425:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7426:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7427:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7428:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7429:       mp[cp]->product->api_user = product->api_user;
7430:       PetscCall(MatProductSetFromOptions(mp[cp]));
7431:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7432:       rmapt[cp] = 2;
7433:       rmapa[cp] = globidx;
7434:       cmapt[cp] = 2;
7435:       cmapa[cp] = P_oth_idx;
7436:       mptmp[cp] = PETSC_FALSE;
7437:       cp++;
7438:     }
7439:     break;
7440:   default:
7441:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7442:   }
7443:   /* sanity check */
7444:   if (size > 1)
7445:     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);

7447:   PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7448:   for (i = 0; i < cp; i++) {
7449:     mmdata->mp[i]    = mp[i];
7450:     mmdata->mptmp[i] = mptmp[i];
7451:   }
7452:   mmdata->cp             = cp;
7453:   C->product->data       = mmdata;
7454:   C->product->destroy    = MatDestroy_MatMatMPIAIJBACKEND;
7455:   C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;

7457:   /* memory type */
7458:   mmdata->mtype = PETSC_MEMTYPE_HOST;
7459:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7460:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7461:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7462:   if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7463:   else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7464:   else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;

7466:   /* prepare coo coordinates for values insertion */

7468:   /* count total nonzeros of those intermediate seqaij Mats
7469:     ncoo_d:    # of nonzeros of matrices that do not have offproc entries
7470:     ncoo_o:    # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7471:     ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7472:   */
7473:   for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7474:     Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7475:     if (mptmp[cp]) continue;
7476:     if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7477:       const PetscInt *rmap = rmapa[cp];
7478:       const PetscInt  mr   = mp[cp]->rmap->n;
7479:       const PetscInt  rs   = C->rmap->rstart;
7480:       const PetscInt  re   = C->rmap->rend;
7481:       const PetscInt *ii   = mm->i;
7482:       for (i = 0; i < mr; i++) {
7483:         const PetscInt gr = rmap[i];
7484:         const PetscInt nz = ii[i + 1] - ii[i];
7485:         if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7486:         else ncoo_oown += nz;                  /* this row is local */
7487:       }
7488:     } else ncoo_d += mm->nz;
7489:   }

7491:   /*
7492:     ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc

7494:     ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.

7496:     off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].

7498:     off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7499:     own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7500:     so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.

7502:     coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7503:     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.
7504:   */
7505:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7506:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));

7508:   /* gather (i,j) of nonzeros inserted by remote procs */
7509:   if (hasoffproc) {
7510:     PetscSF  msf;
7511:     PetscInt ncoo2, *coo_i2, *coo_j2;

7513:     PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7514:     PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7515:     PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */

7517:     for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7518:       Mat_SeqAIJ *mm     = (Mat_SeqAIJ *)mp[cp]->data;
7519:       PetscInt   *idxoff = mmdata->off[cp];
7520:       PetscInt   *idxown = mmdata->own[cp];
7521:       if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7522:         const PetscInt *rmap = rmapa[cp];
7523:         const PetscInt *cmap = cmapa[cp];
7524:         const PetscInt *ii   = mm->i;
7525:         PetscInt       *coi  = coo_i + ncoo_o;
7526:         PetscInt       *coj  = coo_j + ncoo_o;
7527:         const PetscInt  mr   = mp[cp]->rmap->n;
7528:         const PetscInt  rs   = C->rmap->rstart;
7529:         const PetscInt  re   = C->rmap->rend;
7530:         const PetscInt  cs   = C->cmap->rstart;
7531:         for (i = 0; i < mr; i++) {
7532:           const PetscInt *jj = mm->j + ii[i];
7533:           const PetscInt  gr = rmap[i];
7534:           const PetscInt  nz = ii[i + 1] - ii[i];
7535:           if (gr < rs || gr >= re) { /* this is an offproc row */
7536:             for (j = ii[i]; j < ii[i + 1]; j++) {
7537:               *coi++    = gr;
7538:               *idxoff++ = j;
7539:             }
7540:             if (!cmapt[cp]) { /* already global */
7541:               for (j = 0; j < nz; j++) *coj++ = jj[j];
7542:             } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7543:               for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7544:             } else { /* offdiag */
7545:               for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7546:             }
7547:             ncoo_o += nz;
7548:           } else { /* this is a local row */
7549:             for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7550:           }
7551:         }
7552:       }
7553:       mmdata->off[cp + 1] = idxoff;
7554:       mmdata->own[cp + 1] = idxown;
7555:     }

7557:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7558:     PetscInt incoo_o;
7559:     PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7560:     PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7561:     PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7562:     PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7563:     ncoo = ncoo_d + ncoo_oown + ncoo2;
7564:     PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7565:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7566:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7567:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7568:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7569:     PetscCall(PetscFree2(coo_i, coo_j));
7570:     /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7571:     PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7572:     coo_i = coo_i2;
7573:     coo_j = coo_j2;
7574:   } else { /* no offproc values insertion */
7575:     ncoo = ncoo_d;
7576:     PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));

7578:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7579:     PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7580:     PetscCall(PetscSFSetUp(mmdata->sf));
7581:   }
7582:   mmdata->hasoffproc = hasoffproc;

7584:   /* gather (i,j) of nonzeros inserted locally */
7585:   for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7586:     Mat_SeqAIJ     *mm   = (Mat_SeqAIJ *)mp[cp]->data;
7587:     PetscInt       *coi  = coo_i + ncoo_d;
7588:     PetscInt       *coj  = coo_j + ncoo_d;
7589:     const PetscInt *jj   = mm->j;
7590:     const PetscInt *ii   = mm->i;
7591:     const PetscInt *cmap = cmapa[cp];
7592:     const PetscInt *rmap = rmapa[cp];
7593:     const PetscInt  mr   = mp[cp]->rmap->n;
7594:     const PetscInt  rs   = C->rmap->rstart;
7595:     const PetscInt  re   = C->rmap->rend;
7596:     const PetscInt  cs   = C->cmap->rstart;

7598:     if (mptmp[cp]) continue;
7599:     if (rmapt[cp] == 1) { /* consecutive rows */
7600:       /* fill coo_i */
7601:       for (i = 0; i < mr; i++) {
7602:         const PetscInt gr = i + rs;
7603:         for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7604:       }
7605:       /* fill coo_j */
7606:       if (!cmapt[cp]) { /* type-0, already global */
7607:         PetscCall(PetscArraycpy(coj, jj, mm->nz));
7608:       } else if (cmapt[cp] == 1) {                        /* type-1, local to global for consecutive columns of C */
7609:         for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7610:       } else {                                            /* type-2, local to global for sparse columns */
7611:         for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7612:       }
7613:       ncoo_d += mm->nz;
7614:     } else if (rmapt[cp] == 2) { /* sparse rows */
7615:       for (i = 0; i < mr; i++) {
7616:         const PetscInt *jj = mm->j + ii[i];
7617:         const PetscInt  gr = rmap[i];
7618:         const PetscInt  nz = ii[i + 1] - ii[i];
7619:         if (gr >= rs && gr < re) { /* local rows */
7620:           for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7621:           if (!cmapt[cp]) { /* type-0, already global */
7622:             for (j = 0; j < nz; j++) *coj++ = jj[j];
7623:           } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7624:             for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7625:           } else { /* type-2, local to global for sparse columns */
7626:             for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7627:           }
7628:           ncoo_d += nz;
7629:         }
7630:       }
7631:     }
7632:   }
7633:   if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7634:   PetscCall(ISDestroy(&glob));
7635:   if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7636:   PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7637:   /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7638:   PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));

7640:   /* preallocate with COO data */
7641:   PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7642:   PetscCall(PetscFree2(coo_i, coo_j));
7643:   PetscFunctionReturn(PETSC_SUCCESS);
7644: }

7646: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7647: {
7648:   Mat_Product *product = mat->product;
7649: #if defined(PETSC_HAVE_DEVICE)
7650:   PetscBool match  = PETSC_FALSE;
7651:   PetscBool usecpu = PETSC_FALSE;
7652: #else
7653:   PetscBool match = PETSC_TRUE;
7654: #endif

7656:   PetscFunctionBegin;
7657:   MatCheckProduct(mat, 1);
7658: #if defined(PETSC_HAVE_DEVICE)
7659:   if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7660:   if (match) { /* we can always fallback to the CPU if requested */
7661:     switch (product->type) {
7662:     case MATPRODUCT_AB:
7663:       if (product->api_user) {
7664:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7665:         PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7666:         PetscOptionsEnd();
7667:       } else {
7668:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7669:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7670:         PetscOptionsEnd();
7671:       }
7672:       break;
7673:     case MATPRODUCT_AtB:
7674:       if (product->api_user) {
7675:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7676:         PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7677:         PetscOptionsEnd();
7678:       } else {
7679:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7680:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7681:         PetscOptionsEnd();
7682:       }
7683:       break;
7684:     case MATPRODUCT_PtAP:
7685:       if (product->api_user) {
7686:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7687:         PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7688:         PetscOptionsEnd();
7689:       } else {
7690:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7691:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7692:         PetscOptionsEnd();
7693:       }
7694:       break;
7695:     default:
7696:       break;
7697:     }
7698:     match = (PetscBool)!usecpu;
7699:   }
7700: #endif
7701:   if (match) {
7702:     switch (product->type) {
7703:     case MATPRODUCT_AB:
7704:     case MATPRODUCT_AtB:
7705:     case MATPRODUCT_PtAP:
7706:       mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7707:       break;
7708:     default:
7709:       break;
7710:     }
7711:   }
7712:   /* fallback to MPIAIJ ops */
7713:   if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7714:   PetscFunctionReturn(PETSC_SUCCESS);
7715: }

7717: /*
7718:    Produces a set of block column indices of the matrix row, one for each block represented in the original row

7720:    n - the number of block indices in cc[]
7721:    cc - the block indices (must be large enough to contain the indices)
7722: */
7723: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7724: {
7725:   PetscInt        cnt = -1, nidx, j;
7726:   const PetscInt *idx;

7728:   PetscFunctionBegin;
7729:   PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7730:   if (nidx) {
7731:     cnt     = 0;
7732:     cc[cnt] = idx[0] / bs;
7733:     for (j = 1; j < nidx; j++) {
7734:       if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7735:     }
7736:   }
7737:   PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7738:   *n = cnt + 1;
7739:   PetscFunctionReturn(PETSC_SUCCESS);
7740: }

7742: /*
7743:     Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows

7745:     ncollapsed - the number of block indices
7746:     collapsed - the block indices (must be large enough to contain the indices)
7747: */
7748: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7749: {
7750:   PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;

7752:   PetscFunctionBegin;
7753:   PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7754:   for (i = start + 1; i < start + bs; i++) {
7755:     PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7756:     PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7757:     cprevtmp = cprev;
7758:     cprev    = merged;
7759:     merged   = cprevtmp;
7760:   }
7761:   *ncollapsed = nprev;
7762:   if (collapsed) *collapsed = cprev;
7763:   PetscFunctionReturn(PETSC_SUCCESS);
7764: }

7766: /*
7767:  MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix

7769:  Input Parameter:
7770:  . Amat - matrix
7771:  - symmetrize - make the result symmetric
7772:  + scale - scale with diagonal

7774:  Output Parameter:
7775:  . a_Gmat - output scalar graph >= 0

7777: */
7778: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7779: {
7780:   PetscInt  Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7781:   MPI_Comm  comm;
7782:   Mat       Gmat;
7783:   PetscBool ismpiaij, isseqaij;
7784:   Mat       a, b, c;
7785:   MatType   jtype;

7787:   PetscFunctionBegin;
7788:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7789:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7790:   PetscCall(MatGetSize(Amat, &MM, &NN));
7791:   PetscCall(MatGetBlockSize(Amat, &bs));
7792:   nloc = (Iend - Istart) / bs;

7794:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7795:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7796:   PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");

7798:   /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7799:   /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7800:      implementation */
7801:   if (bs > 1) {
7802:     PetscCall(MatGetType(Amat, &jtype));
7803:     PetscCall(MatCreate(comm, &Gmat));
7804:     PetscCall(MatSetType(Gmat, jtype));
7805:     PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7806:     PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7807:     if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7808:       PetscInt  *d_nnz, *o_nnz;
7809:       MatScalar *aa, val, *AA;
7810:       PetscInt  *aj, *ai, *AJ, nc, nmax = 0;

7812:       if (isseqaij) {
7813:         a = Amat;
7814:         b = NULL;
7815:       } else {
7816:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7817:         a             = d->A;
7818:         b             = d->B;
7819:       }
7820:       PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7821:       PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7822:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7823:         PetscInt       *nnz = (c == a) ? d_nnz : o_nnz;
7824:         const PetscInt *cols1, *cols2;

7826:         for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7827:           PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7828:           nnz[brow / bs] = nc2 / bs;
7829:           if (nc2 % bs) ok = 0;
7830:           if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7831:           for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7832:             PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7833:             if (nc1 != nc2) ok = 0;
7834:             else {
7835:               for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7836:                 if (cols1[jj] != cols2[jj]) ok = 0;
7837:                 if (cols1[jj] % bs != jj % bs) ok = 0;
7838:               }
7839:             }
7840:             PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7841:           }
7842:           PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7843:           if (!ok) {
7844:             PetscCall(PetscFree2(d_nnz, o_nnz));
7845:             PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7846:             goto old_bs;
7847:           }
7848:         }
7849:       }
7850:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7851:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7852:       PetscCall(PetscFree2(d_nnz, o_nnz));
7853:       PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7854:       // diag
7855:       for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7856:         Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;

7858:         ai = aseq->i;
7859:         n  = ai[brow + 1] - ai[brow];
7860:         aj = aseq->j + ai[brow];
7861:         for (PetscInt k = 0; k < n; k += bs) {   // block columns
7862:           AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7863:           val        = 0;
7864:           if (index_size == 0) {
7865:             for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7866:               aa = aseq->a + ai[brow + ii] + k;
7867:               for (PetscInt jj = 0; jj < bs; jj++) {    // columns in block
7868:                 val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7869:               }
7870:             }
7871:           } else {                                            // use (index,index) value if provided
7872:             for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7873:               PetscInt ii = index[iii];
7874:               aa          = aseq->a + ai[brow + ii] + k;
7875:               for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7876:                 PetscInt jj = index[jjj];
7877:                 val += PetscAbs(PetscRealPart(aa[jj]));
7878:               }
7879:             }
7880:           }
7881:           PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7882:           AA[k / bs] = val;
7883:         }
7884:         grow = Istart / bs + brow / bs;
7885:         PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7886:       }
7887:       // off-diag
7888:       if (ismpiaij) {
7889:         Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)Amat->data;
7890:         const PetscScalar *vals;
7891:         const PetscInt    *cols, *garray = aij->garray;

7893:         PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7894:         for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7895:           PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7896:           for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7897:             PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7898:             AA[k / bs] = 0;
7899:             AJ[cidx]   = garray[cols[k]] / bs;
7900:           }
7901:           nc = ncols / bs;
7902:           PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7903:           if (index_size == 0) {
7904:             for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7905:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7906:               for (PetscInt k = 0; k < ncols; k += bs) {
7907:                 for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7908:                   PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%d) >= nmax (%d)", (int)(k / bs), (int)nmax);
7909:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7910:                 }
7911:               }
7912:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7913:             }
7914:           } else {                                            // use (index,index) value if provided
7915:             for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7916:               PetscInt ii = index[iii];
7917:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7918:               for (PetscInt k = 0; k < ncols; k += bs) {
7919:                 for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7920:                   PetscInt jj = index[jjj];
7921:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7922:                 }
7923:               }
7924:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7925:             }
7926:           }
7927:           grow = Istart / bs + brow / bs;
7928:           PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7929:         }
7930:       }
7931:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7932:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7933:       PetscCall(PetscFree2(AA, AJ));
7934:     } else {
7935:       const PetscScalar *vals;
7936:       const PetscInt    *idx;
7937:       PetscInt          *d_nnz, *o_nnz, *w0, *w1, *w2;
7938:     old_bs:
7939:       /*
7940:        Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7941:        */
7942:       PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7943:       PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7944:       if (isseqaij) {
7945:         PetscInt max_d_nnz;

7947:         /*
7948:          Determine exact preallocation count for (sequential) scalar matrix
7949:          */
7950:         PetscCall(