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: /* defines MatSetValues_MPI_Hash(), MatAssemblyBegin_MPI_Hash(), and MatAssemblyEnd_MPI_Hash() */
 10: #define TYPE AIJ
 11: #define TYPE_AIJ
 12: #include "../src/mat/impls/aij/mpi/mpihashmat.h"
 13: #undef TYPE
 14: #undef TYPE_AIJ

 16: static PetscErrorCode MatReset_MPIAIJ(Mat mat)
 17: {
 18:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

 20:   PetscFunctionBegin;
 21:   PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
 22:   PetscCall(MatStashDestroy_Private(&mat->stash));
 23:   PetscCall(VecDestroy(&aij->diag));
 24:   PetscCall(MatDestroy(&aij->A));
 25:   PetscCall(MatDestroy(&aij->B));
 26: #if defined(PETSC_USE_CTABLE)
 27:   PetscCall(PetscHMapIDestroy(&aij->colmap));
 28: #else
 29:   PetscCall(PetscFree(aij->colmap));
 30: #endif
 31:   PetscCall(PetscFree(aij->garray));
 32:   PetscCall(VecDestroy(&aij->lvec));
 33:   PetscCall(VecScatterDestroy(&aij->Mvctx));
 34:   PetscCall(PetscFree2(aij->rowvalues, aij->rowindices));
 35:   PetscCall(PetscFree(aij->ld));
 36:   PetscFunctionReturn(PETSC_SUCCESS);
 37: }

 39: static PetscErrorCode MatResetHash_MPIAIJ(Mat mat)
 40: {
 41:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
 42:   /* Save the nonzero states of the component matrices because those are what are used to determine
 43:     the nonzero state of mat */
 44:   PetscObjectState Astate = aij->A->nonzerostate, Bstate = aij->B->nonzerostate;

 46:   PetscFunctionBegin;
 47:   PetscCall(MatReset_MPIAIJ(mat));
 48:   PetscCall(MatSetUp_MPI_Hash(mat));
 49:   aij->A->nonzerostate = ++Astate, aij->B->nonzerostate = ++Bstate;
 50:   PetscFunctionReturn(PETSC_SUCCESS);
 51: }

 53: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
 54: {
 55:   PetscFunctionBegin;
 56:   PetscCall(MatReset_MPIAIJ(mat));

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

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

 63:   PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
 64:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
 65:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
 66:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
 67:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocation_C", NULL));
 68:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetPreallocation_C", NULL));
 69:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatResetHash_C", NULL));
 70:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetPreallocationCSR_C", NULL));
 71:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
 72:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpibaij_C", NULL));
 73:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisbaij_C", NULL));
 74: #if defined(PETSC_HAVE_CUDA)
 75:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcusparse_C", NULL));
 76: #endif
 77: #if defined(PETSC_HAVE_HIP)
 78:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijhipsparse_C", NULL));
 79: #endif
 80: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
 81:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijkokkos_C", NULL));
 82: #endif
 83:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpidense_C", NULL));
 84: #if defined(PETSC_HAVE_ELEMENTAL)
 85:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_elemental_C", NULL));
 86: #endif
 87: #if defined(PETSC_HAVE_SCALAPACK)
 88:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_scalapack_C", NULL));
 89: #endif
 90: #if defined(PETSC_HAVE_HYPRE)
 91:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_hypre_C", NULL));
 92:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", NULL));
 93: #endif
 94:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
 95:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_is_mpiaij_C", NULL));
 96:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatProductSetFromOptions_mpiaij_mpiaij_C", NULL));
 97:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPIAIJSetUseScalableIncreaseOverlap_C", NULL));
 98:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijperm_C", NULL));
 99:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijsell_C", NULL));
100: #if defined(PETSC_HAVE_MKL_SPARSE)
101:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijmkl_C", NULL));
102: #endif
103:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpiaijcrl_C", NULL));
104:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_is_C", NULL));
105:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpiaij_mpisell_C", NULL));
106:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetPreallocationCOO_C", NULL));
107:   PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatSetValuesCOO_C", NULL));
108:   PetscFunctionReturn(PETSC_SUCCESS);
109: }

111: static PetscErrorCode MatGetRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
112: {
113:   Mat B;

115:   PetscFunctionBegin;
116:   PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &B));
117:   PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject)B));
118:   PetscCall(MatGetRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
119:   PetscCall(MatDestroy(&B));
120:   PetscFunctionReturn(PETSC_SUCCESS);
121: }

123: static PetscErrorCode MatRestoreRowIJ_MPIAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
124: {
125:   Mat B;

127:   PetscFunctionBegin;
128:   PetscCall(PetscObjectQuery((PetscObject)A, "MatGetRowIJ_MPIAIJ", (PetscObject *)&B));
129:   PetscCall(MatRestoreRowIJ(B, oshift, symmetric, inodecompressed, m, ia, ja, done));
130:   PetscCall(PetscObjectCompose((PetscObject)A, "MatGetRowIJ_MPIAIJ", NULL));
131:   PetscFunctionReturn(PETSC_SUCCESS);
132: }

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

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

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

146:   Developer Note:
147:   Level: beginner

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

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

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

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

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

167:   Level: beginner

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

172: static PetscErrorCode MatBindToCPU_MPIAIJ(Mat A, PetscBool flg)
173: {
174:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

176:   PetscFunctionBegin;
177: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP) || defined(PETSC_HAVE_VIENNACL)
178:   A->boundtocpu = flg;
179: #endif
180:   if (a->A) PetscCall(MatBindToCPU(a->A, flg));
181:   if (a->B) PetscCall(MatBindToCPU(a->B, flg));

183:   /* In addition to binding the diagonal and off-diagonal matrices, bind the local vectors used for matrix-vector products.
184:    * This maybe seems a little odd for a MatBindToCPU() call to do, but it makes no sense for the binding of these vectors
185:    * to differ from the parent matrix. */
186:   if (a->lvec) PetscCall(VecBindToCPU(a->lvec, flg));
187:   if (a->diag) PetscCall(VecBindToCPU(a->diag, flg));
188:   PetscFunctionReturn(PETSC_SUCCESS);
189: }

191: static PetscErrorCode MatSetBlockSizes_MPIAIJ(Mat M, PetscInt rbs, PetscInt cbs)
192: {
193:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)M->data;

195:   PetscFunctionBegin;
196:   if (mat->A) {
197:     PetscCall(MatSetBlockSizes(mat->A, rbs, cbs));
198:     PetscCall(MatSetBlockSizes(mat->B, rbs, 1));
199:   }
200:   PetscFunctionReturn(PETSC_SUCCESS);
201: }

203: static PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M, IS *keptrows)
204: {
205:   Mat_MPIAIJ      *mat = (Mat_MPIAIJ *)M->data;
206:   Mat_SeqAIJ      *a   = (Mat_SeqAIJ *)mat->A->data;
207:   Mat_SeqAIJ      *b   = (Mat_SeqAIJ *)mat->B->data;
208:   const PetscInt  *ia, *ib;
209:   const MatScalar *aa, *bb, *aav, *bav;
210:   PetscInt         na, nb, i, j, *rows, cnt = 0, n0rows;
211:   PetscInt         m = M->rmap->n, rstart = M->rmap->rstart;

213:   PetscFunctionBegin;
214:   *keptrows = NULL;

216:   ia = a->i;
217:   ib = b->i;
218:   PetscCall(MatSeqAIJGetArrayRead(mat->A, &aav));
219:   PetscCall(MatSeqAIJGetArrayRead(mat->B, &bav));
220:   for (i = 0; i < m; i++) {
221:     na = ia[i + 1] - ia[i];
222:     nb = ib[i + 1] - ib[i];
223:     if (!na && !nb) {
224:       cnt++;
225:       goto ok1;
226:     }
227:     aa = aav + ia[i];
228:     for (j = 0; j < na; j++) {
229:       if (aa[j] != 0.0) goto ok1;
230:     }
231:     bb = PetscSafePointerPlusOffset(bav, ib[i]);
232:     for (j = 0; j < nb; j++) {
233:       if (bb[j] != 0.0) goto ok1;
234:     }
235:     cnt++;
236:   ok1:;
237:   }
238:   PetscCallMPI(MPIU_Allreduce(&cnt, &n0rows, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)M)));
239:   if (!n0rows) {
240:     PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
241:     PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
242:     PetscFunctionReturn(PETSC_SUCCESS);
243:   }
244:   PetscCall(PetscMalloc1(M->rmap->n - cnt, &rows));
245:   cnt = 0;
246:   for (i = 0; i < m; i++) {
247:     na = ia[i + 1] - ia[i];
248:     nb = ib[i + 1] - ib[i];
249:     if (!na && !nb) continue;
250:     aa = aav + ia[i];
251:     for (j = 0; j < na; j++) {
252:       if (aa[j] != 0.0) {
253:         rows[cnt++] = rstart + i;
254:         goto ok2;
255:       }
256:     }
257:     bb = PetscSafePointerPlusOffset(bav, ib[i]);
258:     for (j = 0; j < nb; j++) {
259:       if (bb[j] != 0.0) {
260:         rows[cnt++] = rstart + i;
261:         goto ok2;
262:       }
263:     }
264:   ok2:;
265:   }
266:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), cnt, rows, PETSC_OWN_POINTER, keptrows));
267:   PetscCall(MatSeqAIJRestoreArrayRead(mat->A, &aav));
268:   PetscCall(MatSeqAIJRestoreArrayRead(mat->B, &bav));
269:   PetscFunctionReturn(PETSC_SUCCESS);
270: }

272: static PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y, Vec D, InsertMode is)
273: {
274:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)Y->data;
275:   PetscBool   cong;

277:   PetscFunctionBegin;
278:   PetscCall(MatHasCongruentLayouts(Y, &cong));
279:   if (Y->assembled && cong) {
280:     PetscCall(MatDiagonalSet(aij->A, D, is));
281:   } else {
282:     PetscCall(MatDiagonalSet_Default(Y, D, is));
283:   }
284:   PetscFunctionReturn(PETSC_SUCCESS);
285: }

287: static PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M, IS *zrows)
288: {
289:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)M->data;
290:   PetscInt    i, rstart, nrows, *rows;

292:   PetscFunctionBegin;
293:   *zrows = NULL;
294:   PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(aij->A, &nrows, &rows));
295:   PetscCall(MatGetOwnershipRange(M, &rstart, NULL));
296:   for (i = 0; i < nrows; i++) rows[i] += rstart;
297:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)M), nrows, rows, PETSC_OWN_POINTER, zrows));
298:   PetscFunctionReturn(PETSC_SUCCESS);
299: }

301: static PetscErrorCode MatGetColumnReductions_MPIAIJ(Mat A, PetscInt type, PetscReal *reductions)
302: {
303:   Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)A->data;
304:   PetscInt           i, m, n, *garray = aij->garray;
305:   Mat_SeqAIJ        *a_aij = (Mat_SeqAIJ *)aij->A->data;
306:   Mat_SeqAIJ        *b_aij = (Mat_SeqAIJ *)aij->B->data;
307:   PetscReal         *work;
308:   const PetscScalar *dummy;

310:   PetscFunctionBegin;
311:   PetscCall(MatGetSize(A, &m, &n));
312:   PetscCall(PetscCalloc1(n, &work));
313:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &dummy));
314:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &dummy));
315:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &dummy));
316:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &dummy));
317:   if (type == NORM_2) {
318:     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]);
319:     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]);
320:   } else if (type == NORM_1) {
321:     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]);
322:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
323:   } else if (type == NORM_INFINITY) {
324:     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]]);
325:     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]]]);
326:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
327:     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]);
328:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscRealPart(b_aij->a[i]);
329:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
330:     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]);
331:     for (i = 0; i < b_aij->i[aij->B->rmap->n]; i++) work[garray[b_aij->j[i]]] += PetscImaginaryPart(b_aij->a[i]);
332:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
333:   if (type == NORM_INFINITY) {
334:     PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)A)));
335:   } else {
336:     PetscCallMPI(MPIU_Allreduce(work, reductions, n, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)A)));
337:   }
338:   PetscCall(PetscFree(work));
339:   if (type == NORM_2) {
340:     for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
341:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
342:     for (i = 0; i < n; i++) reductions[i] /= m;
343:   }
344:   PetscFunctionReturn(PETSC_SUCCESS);
345: }

347: static PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A, IS *is)
348: {
349:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)A->data;
350:   IS              sis, gis;
351:   const PetscInt *isis, *igis;
352:   PetscInt        n, *iis, nsis, ngis, rstart, i;

354:   PetscFunctionBegin;
355:   PetscCall(MatFindOffBlockDiagonalEntries(a->A, &sis));
356:   PetscCall(MatFindNonzeroRows(a->B, &gis));
357:   PetscCall(ISGetSize(gis, &ngis));
358:   PetscCall(ISGetSize(sis, &nsis));
359:   PetscCall(ISGetIndices(sis, &isis));
360:   PetscCall(ISGetIndices(gis, &igis));

362:   PetscCall(PetscMalloc1(ngis + nsis, &iis));
363:   PetscCall(PetscArraycpy(iis, igis, ngis));
364:   PetscCall(PetscArraycpy(iis + ngis, isis, nsis));
365:   n = ngis + nsis;
366:   PetscCall(PetscSortRemoveDupsInt(&n, iis));
367:   PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
368:   for (i = 0; i < n; i++) iis[i] += rstart;
369:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), n, iis, PETSC_OWN_POINTER, is));

371:   PetscCall(ISRestoreIndices(sis, &isis));
372:   PetscCall(ISRestoreIndices(gis, &igis));
373:   PetscCall(ISDestroy(&sis));
374:   PetscCall(ISDestroy(&gis));
375:   PetscFunctionReturn(PETSC_SUCCESS);
376: }

378: /*
379:   Local utility routine that creates a mapping from the global column
380: number to the local number in the off-diagonal part of the local
381: storage of the matrix.  When PETSC_USE_CTABLE is used this is scalable at
382: a slightly higher hash table cost; without it it is not scalable (each processor
383: has an order N integer array but is fast to access.
384: */
385: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
386: {
387:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
388:   PetscInt    n   = aij->B->cmap->n, i;

390:   PetscFunctionBegin;
391:   PetscCheck(!n || aij->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPIAIJ Matrix was assembled but is missing garray");
392: #if defined(PETSC_USE_CTABLE)
393:   PetscCall(PetscHMapICreateWithSize(n, &aij->colmap));
394:   for (i = 0; i < n; i++) PetscCall(PetscHMapISet(aij->colmap, aij->garray[i] + 1, i + 1));
395: #else
396:   PetscCall(PetscCalloc1(mat->cmap->N + 1, &aij->colmap));
397:   for (i = 0; i < n; i++) aij->colmap[aij->garray[i]] = i + 1;
398: #endif
399:   PetscFunctionReturn(PETSC_SUCCESS);
400: }

402: #define MatSetValues_SeqAIJ_A_Private(row, col, value, addv, orow, ocol) \
403:   do { \
404:     if (col <= lastcol1) low1 = 0; \
405:     else high1 = nrow1; \
406:     lastcol1 = col; \
407:     while (high1 - low1 > 5) { \
408:       t = (low1 + high1) / 2; \
409:       if (rp1[t] > col) high1 = t; \
410:       else low1 = t; \
411:     } \
412:     for (_i = low1; _i < high1; _i++) { \
413:       if (rp1[_i] > col) break; \
414:       if (rp1[_i] == col) { \
415:         if (addv == ADD_VALUES) { \
416:           ap1[_i] += value; \
417:           /* Not sure LogFlops will slow dow the code or not */ \
418:           (void)PetscLogFlops(1.0); \
419:         } else ap1[_i] = value; \
420:         goto a_noinsert; \
421:       } \
422:     } \
423:     if (value == 0.0 && ignorezeroentries && row != col) { \
424:       low1  = 0; \
425:       high1 = nrow1; \
426:       goto a_noinsert; \
427:     } \
428:     if (nonew == 1) { \
429:       low1  = 0; \
430:       high1 = nrow1; \
431:       goto a_noinsert; \
432:     } \
433:     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); \
434:     MatSeqXAIJReallocateAIJ(A, am, 1, nrow1, row, col, rmax1, aa, ai, aj, rp1, ap1, aimax, nonew, MatScalar); \
435:     N = nrow1++ - 1; \
436:     a->nz++; \
437:     high1++; \
438:     /* shift up all the later entries in this row */ \
439:     PetscCall(PetscArraymove(rp1 + _i + 1, rp1 + _i, N - _i + 1)); \
440:     PetscCall(PetscArraymove(ap1 + _i + 1, ap1 + _i, N - _i + 1)); \
441:     rp1[_i] = col; \
442:     ap1[_i] = value; \
443:   a_noinsert:; \
444:     ailen[row] = nrow1; \
445:   } while (0)

447: #define MatSetValues_SeqAIJ_B_Private(row, col, value, addv, orow, ocol) \
448:   do { \
449:     if (col <= lastcol2) low2 = 0; \
450:     else high2 = nrow2; \
451:     lastcol2 = col; \
452:     while (high2 - low2 > 5) { \
453:       t = (low2 + high2) / 2; \
454:       if (rp2[t] > col) high2 = t; \
455:       else low2 = t; \
456:     } \
457:     for (_i = low2; _i < high2; _i++) { \
458:       if (rp2[_i] > col) break; \
459:       if (rp2[_i] == col) { \
460:         if (addv == ADD_VALUES) { \
461:           ap2[_i] += value; \
462:           (void)PetscLogFlops(1.0); \
463:         } else ap2[_i] = value; \
464:         goto b_noinsert; \
465:       } \
466:     } \
467:     if (value == 0.0 && ignorezeroentries) { \
468:       low2  = 0; \
469:       high2 = nrow2; \
470:       goto b_noinsert; \
471:     } \
472:     if (nonew == 1) { \
473:       low2  = 0; \
474:       high2 = nrow2; \
475:       goto b_noinsert; \
476:     } \
477:     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); \
478:     MatSeqXAIJReallocateAIJ(B, bm, 1, nrow2, row, col, rmax2, ba, bi, bj, rp2, ap2, bimax, nonew, MatScalar); \
479:     N = nrow2++ - 1; \
480:     b->nz++; \
481:     high2++; \
482:     /* shift up all the later entries in this row */ \
483:     PetscCall(PetscArraymove(rp2 + _i + 1, rp2 + _i, N - _i + 1)); \
484:     PetscCall(PetscArraymove(ap2 + _i + 1, ap2 + _i, N - _i + 1)); \
485:     rp2[_i] = col; \
486:     ap2[_i] = value; \
487:   b_noinsert:; \
488:     bilen[row] = nrow2; \
489:   } while (0)

491: static PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A, PetscInt row, const PetscScalar v[])
492: {
493:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)A->data;
494:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)mat->A->data, *b = (Mat_SeqAIJ *)mat->B->data;
495:   PetscInt     l, *garray                         = mat->garray, diag;
496:   PetscScalar *aa, *ba;

498:   PetscFunctionBegin;
499:   /* code only works for square matrices A */

501:   /* find size of row to the left of the diagonal part */
502:   PetscCall(MatGetOwnershipRange(A, &diag, NULL));
503:   row = row - diag;
504:   for (l = 0; l < b->i[row + 1] - b->i[row]; l++) {
505:     if (garray[b->j[b->i[row] + l]] > diag) break;
506:   }
507:   if (l) {
508:     PetscCall(MatSeqAIJGetArray(mat->B, &ba));
509:     PetscCall(PetscArraycpy(ba + b->i[row], v, l));
510:     PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
511:   }

513:   /* diagonal part */
514:   if (a->i[row + 1] - a->i[row]) {
515:     PetscCall(MatSeqAIJGetArray(mat->A, &aa));
516:     PetscCall(PetscArraycpy(aa + a->i[row], v + l, a->i[row + 1] - a->i[row]));
517:     PetscCall(MatSeqAIJRestoreArray(mat->A, &aa));
518:   }

520:   /* right of diagonal part */
521:   if (b->i[row + 1] - b->i[row] - l) {
522:     PetscCall(MatSeqAIJGetArray(mat->B, &ba));
523:     PetscCall(PetscArraycpy(ba + b->i[row] + l, v + l + a->i[row + 1] - a->i[row], b->i[row + 1] - b->i[row] - l));
524:     PetscCall(MatSeqAIJRestoreArray(mat->B, &ba));
525:   }
526:   PetscFunctionReturn(PETSC_SUCCESS);
527: }

529: PetscErrorCode MatSetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
530: {
531:   Mat_MPIAIJ *aij   = (Mat_MPIAIJ *)mat->data;
532:   PetscScalar value = 0.0;
533:   PetscInt    i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
534:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
535:   PetscBool   roworiented = aij->roworiented;

537:   /* Some Variables required in the macro */
538:   Mat         A     = aij->A;
539:   Mat_SeqAIJ *a     = (Mat_SeqAIJ *)A->data;
540:   PetscInt   *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
541:   PetscBool   ignorezeroentries = a->ignorezeroentries;
542:   Mat         B                 = aij->B;
543:   Mat_SeqAIJ *b                 = (Mat_SeqAIJ *)B->data;
544:   PetscInt   *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
545:   MatScalar  *aa, *ba;
546:   PetscInt   *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
547:   PetscInt    nonew;
548:   MatScalar  *ap1, *ap2;

550:   PetscFunctionBegin;
551:   PetscCall(MatSeqAIJGetArray(A, &aa));
552:   PetscCall(MatSeqAIJGetArray(B, &ba));
553:   for (i = 0; i < m; i++) {
554:     if (im[i] < 0) continue;
555:     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);
556:     if (im[i] >= rstart && im[i] < rend) {
557:       row      = im[i] - rstart;
558:       lastcol1 = -1;
559:       rp1      = PetscSafePointerPlusOffset(aj, ai[row]);
560:       ap1      = PetscSafePointerPlusOffset(aa, ai[row]);
561:       rmax1    = aimax[row];
562:       nrow1    = ailen[row];
563:       low1     = 0;
564:       high1    = nrow1;
565:       lastcol2 = -1;
566:       rp2      = PetscSafePointerPlusOffset(bj, bi[row]);
567:       ap2      = PetscSafePointerPlusOffset(ba, bi[row]);
568:       rmax2    = bimax[row];
569:       nrow2    = bilen[row];
570:       low2     = 0;
571:       high2    = nrow2;

573:       for (j = 0; j < n; j++) {
574:         if (v) value = roworiented ? v[i * n + j] : v[i + j * m];
575:         if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
576:         if (in[j] >= cstart && in[j] < cend) {
577:           col   = in[j] - cstart;
578:           nonew = a->nonew;
579:           MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
580:         } else if (in[j] < 0) {
581:           continue;
582:         } else {
583:           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);
584:           if (mat->was_assembled) {
585:             if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
586: #if defined(PETSC_USE_CTABLE)
587:             PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col)); /* map global col ids to local ones */
588:             col--;
589: #else
590:             col = aij->colmap[in[j]] - 1;
591: #endif
592:             if (col < 0 && !((Mat_SeqAIJ *)aij->B->data)->nonew) { /* col < 0 means in[j] is a new col for B */
593:               PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));  /* Change aij->B from reduced/local format to expanded/global format */
594:               col = in[j];
595:               /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
596:               B     = aij->B;
597:               b     = (Mat_SeqAIJ *)B->data;
598:               bimax = b->imax;
599:               bi    = b->i;
600:               bilen = b->ilen;
601:               bj    = b->j;
602:               ba    = b->a;
603:               rp2   = PetscSafePointerPlusOffset(bj, bi[row]);
604:               ap2   = PetscSafePointerPlusOffset(ba, bi[row]);
605:               rmax2 = bimax[row];
606:               nrow2 = bilen[row];
607:               low2  = 0;
608:               high2 = nrow2;
609:               bm    = aij->B->rmap->n;
610:               ba    = b->a;
611:             } else if (col < 0 && !(ignorezeroentries && value == 0.0)) {
612:               if (1 == ((Mat_SeqAIJ *)aij->B->data)->nonew) {
613:                 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]));
614:               } 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]);
615:             }
616:           } else col = in[j];
617:           nonew = b->nonew;
618:           MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
619:         }
620:       }
621:     } else {
622:       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]);
623:       if (!aij->donotstash) {
624:         mat->assembled = PETSC_FALSE;
625:         if (roworiented) {
626:           PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i * n), (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
627:         } else {
628:           PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, PetscSafePointerPlusOffset(v, i), m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
629:         }
630:       }
631:     }
632:   }
633:   PetscCall(MatSeqAIJRestoreArray(A, &aa)); /* aa, bb might have been free'd due to reallocation above. But we don't access them here */
634:   PetscCall(MatSeqAIJRestoreArray(B, &ba));
635:   PetscFunctionReturn(PETSC_SUCCESS);
636: }

638: /*
639:     This function sets the j and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
640:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
641:     No off-processor parts off the matrix are allowed here and mat->was_assembled has to be PETSC_FALSE.
642: */
643: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[])
644: {
645:   Mat_MPIAIJ *aij    = (Mat_MPIAIJ *)mat->data;
646:   Mat         A      = aij->A; /* diagonal part of the matrix */
647:   Mat         B      = aij->B; /* off-diagonal part of the matrix */
648:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
649:   Mat_SeqAIJ *b      = (Mat_SeqAIJ *)B->data;
650:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, col;
651:   PetscInt   *ailen = a->ilen, *aj = a->j;
652:   PetscInt   *bilen = b->ilen, *bj = b->j;
653:   PetscInt    am          = aij->A->rmap->n, j;
654:   PetscInt    diag_so_far = 0, dnz;
655:   PetscInt    offd_so_far = 0, onz;

657:   PetscFunctionBegin;
658:   /* Iterate over all rows of the matrix */
659:   for (j = 0; j < am; j++) {
660:     dnz = onz = 0;
661:     /*  Iterate over all non-zero columns of the current row */
662:     for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
663:       /* If column is in the diagonal */
664:       if (mat_j[col] >= cstart && mat_j[col] < cend) {
665:         aj[diag_so_far++] = mat_j[col] - cstart;
666:         dnz++;
667:       } else { /* off-diagonal entries */
668:         bj[offd_so_far++] = mat_j[col];
669:         onz++;
670:       }
671:     }
672:     ailen[j] = dnz;
673:     bilen[j] = onz;
674:   }
675:   PetscFunctionReturn(PETSC_SUCCESS);
676: }

678: /*
679:     This function sets the local j, a and ilen arrays (of the diagonal and off-diagonal part) of an MPIAIJ-matrix.
680:     The values in mat_i have to be sorted and the values in mat_j have to be sorted for each row (CSR-like).
681:     No off-processor parts off the matrix are allowed here, they are set at a later point by MatSetValues_MPIAIJ.
682:     Also, mat->was_assembled has to be false, otherwise the statement aj[rowstart_diag+dnz_row] = mat_j[col] - cstart;
683:     would not be true and the more complex MatSetValues_MPIAIJ has to be used.
684: */
685: PetscErrorCode MatSetValues_MPIAIJ_CopyFromCSRFormat(Mat mat, const PetscInt mat_j[], const PetscInt mat_i[], const PetscScalar mat_a[])
686: {
687:   Mat_MPIAIJ  *aij  = (Mat_MPIAIJ *)mat->data;
688:   Mat          A    = aij->A; /* diagonal part of the matrix */
689:   Mat          B    = aij->B; /* off-diagonal part of the matrix */
690:   Mat_SeqAIJ  *aijd = (Mat_SeqAIJ *)aij->A->data, *aijo = (Mat_SeqAIJ *)aij->B->data;
691:   Mat_SeqAIJ  *a      = (Mat_SeqAIJ *)A->data;
692:   Mat_SeqAIJ  *b      = (Mat_SeqAIJ *)B->data;
693:   PetscInt     cstart = mat->cmap->rstart, cend = mat->cmap->rend;
694:   PetscInt    *ailen = a->ilen, *aj = a->j;
695:   PetscInt    *bilen = b->ilen, *bj = b->j;
696:   PetscInt     am          = aij->A->rmap->n, j;
697:   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. */
698:   PetscInt     col, dnz_row, onz_row, rowstart_diag, rowstart_offd;
699:   PetscScalar *aa = a->a, *ba = b->a;

701:   PetscFunctionBegin;
702:   /* Iterate over all rows of the matrix */
703:   for (j = 0; j < am; j++) {
704:     dnz_row = onz_row = 0;
705:     rowstart_offd     = full_offd_i[j];
706:     rowstart_diag     = full_diag_i[j];
707:     /*  Iterate over all non-zero columns of the current row */
708:     for (col = mat_i[j]; col < mat_i[j + 1]; col++) {
709:       /* If column is in the diagonal */
710:       if (mat_j[col] >= cstart && mat_j[col] < cend) {
711:         aj[rowstart_diag + dnz_row] = mat_j[col] - cstart;
712:         aa[rowstart_diag + dnz_row] = mat_a[col];
713:         dnz_row++;
714:       } else { /* off-diagonal entries */
715:         bj[rowstart_offd + onz_row] = mat_j[col];
716:         ba[rowstart_offd + onz_row] = mat_a[col];
717:         onz_row++;
718:       }
719:     }
720:     ailen[j] = dnz_row;
721:     bilen[j] = onz_row;
722:   }
723:   PetscFunctionReturn(PETSC_SUCCESS);
724: }

726: static PetscErrorCode MatGetValues_MPIAIJ(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
727: {
728:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
729:   PetscInt    i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
730:   PetscInt    cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;

732:   PetscFunctionBegin;
733:   for (i = 0; i < m; i++) {
734:     if (idxm[i] < 0) continue; /* negative row */
735:     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);
736:     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);
737:     row = idxm[i] - rstart;
738:     for (j = 0; j < n; j++) {
739:       if (idxn[j] < 0) continue; /* negative column */
740:       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);
741:       if (idxn[j] >= cstart && idxn[j] < cend) {
742:         col = idxn[j] - cstart;
743:         PetscCall(MatGetValues(aij->A, 1, &row, 1, &col, v + i * n + j));
744:       } else {
745:         if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
746: #if defined(PETSC_USE_CTABLE)
747:         PetscCall(PetscHMapIGetWithDefault(aij->colmap, idxn[j] + 1, 0, &col));
748:         col--;
749: #else
750:         col = aij->colmap[idxn[j]] - 1;
751: #endif
752:         if ((col < 0) || (aij->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
753:         else PetscCall(MatGetValues(aij->B, 1, &row, 1, &col, v + i * n + j));
754:       }
755:     }
756:   }
757:   PetscFunctionReturn(PETSC_SUCCESS);
758: }

760: static PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat, MatAssemblyType mode)
761: {
762:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
763:   PetscInt    nstash, reallocs;

765:   PetscFunctionBegin;
766:   if (aij->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);

768:   PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
769:   PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
770:   PetscCall(PetscInfo(aij->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
771:   PetscFunctionReturn(PETSC_SUCCESS);
772: }

774: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat, MatAssemblyType mode)
775: {
776:   Mat_MPIAIJ  *aij = (Mat_MPIAIJ *)mat->data;
777:   PetscMPIInt  n;
778:   PetscInt     i, j, rstart, ncols, flg;
779:   PetscInt    *row, *col;
780:   PetscBool    other_disassembled;
781:   PetscScalar *val;

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

785:   PetscFunctionBegin;
786:   if (!aij->donotstash && !mat->nooffprocentries) {
787:     while (1) {
788:       PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
789:       if (!flg) break;

791:       for (i = 0; i < n;) {
792:         /* Now identify the consecutive vals belonging to the same row */
793:         for (j = i, rstart = row[j]; j < n; j++) {
794:           if (row[j] != rstart) break;
795:         }
796:         if (j < n) ncols = j - i;
797:         else ncols = n - i;
798:         /* Now assemble all these values with a single function call */
799:         PetscCall(MatSetValues_MPIAIJ(mat, 1, row + i, ncols, col + i, val + i, mat->insertmode));
800:         i = j;
801:       }
802:     }
803:     PetscCall(MatStashScatterEnd_Private(&mat->stash));
804:   }
805: #if defined(PETSC_HAVE_DEVICE)
806:   if (mat->offloadmask == PETSC_OFFLOAD_CPU) aij->A->offloadmask = PETSC_OFFLOAD_CPU;
807:   /* We call MatBindToCPU() on aij->A and aij->B here, because if MatBindToCPU_MPIAIJ() is called before assembly, it cannot bind these. */
808:   if (mat->boundtocpu) {
809:     PetscCall(MatBindToCPU(aij->A, PETSC_TRUE));
810:     PetscCall(MatBindToCPU(aij->B, PETSC_TRUE));
811:   }
812: #endif
813:   PetscCall(MatAssemblyBegin(aij->A, mode));
814:   PetscCall(MatAssemblyEnd(aij->A, mode));

816:   /* determine if any processor has disassembled, if so we must
817:      also disassemble ourself, in order that we may reassemble. */
818:   /*
819:      if nonzero structure of submatrix B cannot change then we know that
820:      no processor disassembled thus we can skip this stuff
821:   */
822:   if (!((Mat_SeqAIJ *)aij->B->data)->nonew) {
823:     PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
824:     if (mat->was_assembled && !other_disassembled) { /* mat on this rank has reduced off-diag B with local col ids, but globally it does not */
825:       PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
826:     }
827:   }
828:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPIAIJ(mat));
829:   PetscCall(MatSetOption(aij->B, MAT_USE_INODES, PETSC_FALSE));
830: #if defined(PETSC_HAVE_DEVICE)
831:   if (mat->offloadmask == PETSC_OFFLOAD_CPU && aij->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) aij->B->offloadmask = PETSC_OFFLOAD_CPU;
832: #endif
833:   PetscCall(MatAssemblyBegin(aij->B, mode));
834:   PetscCall(MatAssemblyEnd(aij->B, mode));

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

838:   aij->rowvalues = NULL;

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

842:   /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
843:   if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ *)aij->A->data)->nonew) {
844:     PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
845:     PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
846:   }
847: #if defined(PETSC_HAVE_DEVICE)
848:   mat->offloadmask = PETSC_OFFLOAD_BOTH;
849: #endif
850:   PetscFunctionReturn(PETSC_SUCCESS);
851: }

853: static PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
854: {
855:   Mat_MPIAIJ *l = (Mat_MPIAIJ *)A->data;

857:   PetscFunctionBegin;
858:   PetscCall(MatZeroEntries(l->A));
859:   PetscCall(MatZeroEntries(l->B));
860:   PetscFunctionReturn(PETSC_SUCCESS);
861: }

863: static PetscErrorCode MatZeroRows_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
864: {
865:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)A->data;
866:   PetscInt   *lrows;
867:   PetscInt    r, len;
868:   PetscBool   cong;

870:   PetscFunctionBegin;
871:   /* get locally owned rows */
872:   PetscCall(MatZeroRowsMapLocal_Private(A, N, rows, &len, &lrows));
873:   PetscCall(MatHasCongruentLayouts(A, &cong));
874:   /* fix right-hand side if needed */
875:   if (x && b) {
876:     const PetscScalar *xx;
877:     PetscScalar       *bb;

879:     PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
880:     PetscCall(VecGetArrayRead(x, &xx));
881:     PetscCall(VecGetArray(b, &bb));
882:     for (r = 0; r < len; ++r) bb[lrows[r]] = diag * xx[lrows[r]];
883:     PetscCall(VecRestoreArrayRead(x, &xx));
884:     PetscCall(VecRestoreArray(b, &bb));
885:   }

887:   if (diag != 0.0 && cong) {
888:     PetscCall(MatZeroRows(mat->A, len, lrows, diag, NULL, NULL));
889:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
890:   } else if (diag != 0.0) { /* non-square or non congruent layouts -> if keepnonzeropattern is false, we allow for new insertion */
891:     Mat_SeqAIJ *aijA = (Mat_SeqAIJ *)mat->A->data;
892:     Mat_SeqAIJ *aijB = (Mat_SeqAIJ *)mat->B->data;
893:     PetscInt    nnwA, nnwB;
894:     PetscBool   nnzA, nnzB;

896:     nnwA = aijA->nonew;
897:     nnwB = aijB->nonew;
898:     nnzA = aijA->keepnonzeropattern;
899:     nnzB = aijB->keepnonzeropattern;
900:     if (!nnzA) {
901:       PetscCall(PetscInfo(mat->A, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on diagonal block.\n"));
902:       aijA->nonew = 0;
903:     }
904:     if (!nnzB) {
905:       PetscCall(PetscInfo(mat->B, "Requested to not keep the pattern and add a nonzero diagonal; may encounter reallocations on off-diagonal block.\n"));
906:       aijB->nonew = 0;
907:     }
908:     /* Must zero here before the next loop */
909:     PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
910:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
911:     for (r = 0; r < len; ++r) {
912:       const PetscInt row = lrows[r] + A->rmap->rstart;
913:       if (row >= A->cmap->N) continue;
914:       PetscCall(MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES));
915:     }
916:     aijA->nonew = nnwA;
917:     aijB->nonew = nnwB;
918:   } else {
919:     PetscCall(MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL));
920:     PetscCall(MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL));
921:   }
922:   PetscCall(PetscFree(lrows));
923:   PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
924:   PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));

926:   /* only change matrix nonzero state if pattern was allowed to be changed */
927:   if (!((Mat_SeqAIJ *)mat->A->data)->keepnonzeropattern || !((Mat_SeqAIJ *)mat->A->data)->nonew) {
928:     PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
929:     PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
930:   }
931:   PetscFunctionReturn(PETSC_SUCCESS);
932: }

934: static PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
935: {
936:   Mat_MPIAIJ        *l = (Mat_MPIAIJ *)A->data;
937:   PetscInt           n = A->rmap->n;
938:   PetscInt           i, j, r, m, len = 0;
939:   PetscInt          *lrows, *owners = A->rmap->range;
940:   PetscMPIInt        p = 0;
941:   PetscSFNode       *rrows;
942:   PetscSF            sf;
943:   const PetscScalar *xx;
944:   PetscScalar       *bb, *mask, *aij_a;
945:   Vec                xmask, lmask;
946:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ *)l->B->data;
947:   const PetscInt    *aj, *ii, *ridx;
948:   PetscScalar       *aa;

950:   PetscFunctionBegin;
951:   /* Create SF where leaves are input rows and roots are owned rows */
952:   PetscCall(PetscMalloc1(n, &lrows));
953:   for (r = 0; r < n; ++r) lrows[r] = -1;
954:   PetscCall(PetscMalloc1(N, &rrows));
955:   for (r = 0; r < N; ++r) {
956:     const PetscInt idx = rows[r];
957:     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);
958:     if (idx < owners[p] || owners[p + 1] <= idx) { /* short-circuit the search if the last p owns this row too */
959:       PetscCall(PetscLayoutFindOwner(A->rmap, idx, &p));
960:     }
961:     rrows[r].rank  = p;
962:     rrows[r].index = rows[r] - owners[p];
963:   }
964:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
965:   PetscCall(PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER));
966:   /* Collect flags for rows to be zeroed */
967:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
968:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *)rows, lrows, MPI_LOR));
969:   PetscCall(PetscSFDestroy(&sf));
970:   /* Compress and put in row numbers */
971:   for (r = 0; r < n; ++r)
972:     if (lrows[r] >= 0) lrows[len++] = r;
973:   /* zero diagonal part of matrix */
974:   PetscCall(MatZeroRowsColumns(l->A, len, lrows, diag, x, b));
975:   /* handle off-diagonal part of matrix */
976:   PetscCall(MatCreateVecs(A, &xmask, NULL));
977:   PetscCall(VecDuplicate(l->lvec, &lmask));
978:   PetscCall(VecGetArray(xmask, &bb));
979:   for (i = 0; i < len; i++) bb[lrows[i]] = 1;
980:   PetscCall(VecRestoreArray(xmask, &bb));
981:   PetscCall(VecScatterBegin(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
982:   PetscCall(VecScatterEnd(l->Mvctx, xmask, lmask, ADD_VALUES, SCATTER_FORWARD));
983:   PetscCall(VecDestroy(&xmask));
984:   if (x && b) { /* this code is buggy when the row and column layout don't match */
985:     PetscBool cong;

987:     PetscCall(MatHasCongruentLayouts(A, &cong));
988:     PetscCheck(cong, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Need matching row/col layout");
989:     PetscCall(VecScatterBegin(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
990:     PetscCall(VecScatterEnd(l->Mvctx, x, l->lvec, INSERT_VALUES, SCATTER_FORWARD));
991:     PetscCall(VecGetArrayRead(l->lvec, &xx));
992:     PetscCall(VecGetArray(b, &bb));
993:   }
994:   PetscCall(VecGetArray(lmask, &mask));
995:   /* remove zeroed rows of off-diagonal matrix */
996:   PetscCall(MatSeqAIJGetArray(l->B, &aij_a));
997:   ii = aij->i;
998:   for (i = 0; i < len; i++) PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aij_a, ii[lrows[i]]), ii[lrows[i] + 1] - ii[lrows[i]]));
999:   /* loop over all elements of off process part of matrix zeroing removed columns*/
1000:   if (aij->compressedrow.use) {
1001:     m    = aij->compressedrow.nrows;
1002:     ii   = aij->compressedrow.i;
1003:     ridx = aij->compressedrow.rindex;
1004:     for (i = 0; i < m; i++) {
1005:       n  = ii[i + 1] - ii[i];
1006:       aj = aij->j + ii[i];
1007:       aa = aij_a + ii[i];

1009:       for (j = 0; j < n; j++) {
1010:         if (PetscAbsScalar(mask[*aj])) {
1011:           if (b) bb[*ridx] -= *aa * xx[*aj];
1012:           *aa = 0.0;
1013:         }
1014:         aa++;
1015:         aj++;
1016:       }
1017:       ridx++;
1018:     }
1019:   } else { /* do not use compressed row format */
1020:     m = l->B->rmap->n;
1021:     for (i = 0; i < m; i++) {
1022:       n  = ii[i + 1] - ii[i];
1023:       aj = aij->j + ii[i];
1024:       aa = aij_a + ii[i];
1025:       for (j = 0; j < n; j++) {
1026:         if (PetscAbsScalar(mask[*aj])) {
1027:           if (b) bb[i] -= *aa * xx[*aj];
1028:           *aa = 0.0;
1029:         }
1030:         aa++;
1031:         aj++;
1032:       }
1033:     }
1034:   }
1035:   if (x && b) {
1036:     PetscCall(VecRestoreArray(b, &bb));
1037:     PetscCall(VecRestoreArrayRead(l->lvec, &xx));
1038:   }
1039:   PetscCall(MatSeqAIJRestoreArray(l->B, &aij_a));
1040:   PetscCall(VecRestoreArray(lmask, &mask));
1041:   PetscCall(VecDestroy(&lmask));
1042:   PetscCall(PetscFree(lrows));

1044:   /* only change matrix nonzero state if pattern was allowed to be changed */
1045:   if (!((Mat_SeqAIJ *)l->A->data)->nonew) {
1046:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1047:     PetscCallMPI(MPIU_Allreduce(&state, &A->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)A)));
1048:   }
1049:   PetscFunctionReturn(PETSC_SUCCESS);
1050: }

1052: static PetscErrorCode MatMult_MPIAIJ(Mat A, Vec xx, Vec yy)
1053: {
1054:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1055:   PetscInt    nt;
1056:   VecScatter  Mvctx = a->Mvctx;

1058:   PetscFunctionBegin;
1059:   PetscCall(VecGetLocalSize(xx, &nt));
1060:   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);
1061:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1062:   PetscUseTypeMethod(a->A, mult, xx, yy);
1063:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1064:   PetscUseTypeMethod(a->B, multadd, a->lvec, yy, yy);
1065:   PetscFunctionReturn(PETSC_SUCCESS);
1066: }

1068: static PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A, Vec bb, Vec xx)
1069: {
1070:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1072:   PetscFunctionBegin;
1073:   PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
1074:   PetscFunctionReturn(PETSC_SUCCESS);
1075: }

1077: static PetscErrorCode MatMultAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1078: {
1079:   Mat_MPIAIJ *a     = (Mat_MPIAIJ *)A->data;
1080:   VecScatter  Mvctx = a->Mvctx;

1082:   PetscFunctionBegin;
1083:   PetscCall(VecScatterBegin(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1084:   PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
1085:   PetscCall(VecScatterEnd(Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
1086:   PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
1087:   PetscFunctionReturn(PETSC_SUCCESS);
1088: }

1090: static PetscErrorCode MatMultTranspose_MPIAIJ(Mat A, Vec xx, Vec yy)
1091: {
1092:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1094:   PetscFunctionBegin;
1095:   /* do nondiagonal part */
1096:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1097:   /* do local part */
1098:   PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
1099:   /* add partial results together */
1100:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1101:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
1102:   PetscFunctionReturn(PETSC_SUCCESS);
1103: }

1105: static PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
1106: {
1107:   MPI_Comm    comm;
1108:   Mat_MPIAIJ *Aij = (Mat_MPIAIJ *)Amat->data, *Bij = (Mat_MPIAIJ *)Bmat->data;
1109:   Mat         Adia = Aij->A, Bdia = Bij->A, Aoff, Boff, *Aoffs, *Boffs;
1110:   IS          Me, Notme;
1111:   PetscInt    M, N, first, last, *notme, i;
1112:   PetscBool   lf;
1113:   PetscMPIInt size;

1115:   PetscFunctionBegin;
1116:   /* Easy test: symmetric diagonal block */
1117:   PetscCall(MatIsTranspose(Adia, Bdia, tol, &lf));
1118:   PetscCallMPI(MPIU_Allreduce(&lf, f, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)Amat)));
1119:   if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
1120:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
1121:   PetscCallMPI(MPI_Comm_size(comm, &size));
1122:   if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);

1124:   /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
1125:   PetscCall(MatGetSize(Amat, &M, &N));
1126:   PetscCall(MatGetOwnershipRange(Amat, &first, &last));
1127:   PetscCall(PetscMalloc1(N - last + first, &notme));
1128:   for (i = 0; i < first; i++) notme[i] = i;
1129:   for (i = last; i < M; i++) notme[i - last + first] = i;
1130:   PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
1131:   PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
1132:   PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
1133:   Aoff = Aoffs[0];
1134:   PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
1135:   Boff = Boffs[0];
1136:   PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
1137:   PetscCall(MatDestroyMatrices(1, &Aoffs));
1138:   PetscCall(MatDestroyMatrices(1, &Boffs));
1139:   PetscCall(ISDestroy(&Me));
1140:   PetscCall(ISDestroy(&Notme));
1141:   PetscCall(PetscFree(notme));
1142:   PetscFunctionReturn(PETSC_SUCCESS);
1143: }

1145: static PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1146: {
1147:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1149:   PetscFunctionBegin;
1150:   /* do nondiagonal part */
1151:   PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
1152:   /* do local part */
1153:   PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
1154:   /* add partial results together */
1155:   PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1156:   PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
1157:   PetscFunctionReturn(PETSC_SUCCESS);
1158: }

1160: /*
1161:   This only works correctly for square matrices where the subblock A->A is the
1162:    diagonal block
1163: */
1164: static PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A, Vec v)
1165: {
1166:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1168:   PetscFunctionBegin;
1169:   PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
1170:   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");
1171:   PetscCall(MatGetDiagonal(a->A, v));
1172:   PetscFunctionReturn(PETSC_SUCCESS);
1173: }

1175: static PetscErrorCode MatScale_MPIAIJ(Mat A, PetscScalar aa)
1176: {
1177:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1179:   PetscFunctionBegin;
1180:   PetscCall(MatScale(a->A, aa));
1181:   PetscCall(MatScale(a->B, aa));
1182:   PetscFunctionReturn(PETSC_SUCCESS);
1183: }

1185: static PetscErrorCode MatView_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
1186: {
1187:   Mat_MPIAIJ        *aij    = (Mat_MPIAIJ *)mat->data;
1188:   Mat_SeqAIJ        *A      = (Mat_SeqAIJ *)aij->A->data;
1189:   Mat_SeqAIJ        *B      = (Mat_SeqAIJ *)aij->B->data;
1190:   const PetscInt    *garray = aij->garray;
1191:   const PetscScalar *aa, *ba;
1192:   PetscInt           header[4], M, N, m, rs, cs, cnt, i, ja, jb;
1193:   PetscInt64         nz, hnz;
1194:   PetscInt          *rowlens;
1195:   PetscInt          *colidxs;
1196:   PetscScalar       *matvals;
1197:   PetscMPIInt        rank;

1199:   PetscFunctionBegin;
1200:   PetscCall(PetscViewerSetUp(viewer));

1202:   M  = mat->rmap->N;
1203:   N  = mat->cmap->N;
1204:   m  = mat->rmap->n;
1205:   rs = mat->rmap->rstart;
1206:   cs = mat->cmap->rstart;
1207:   nz = A->nz + B->nz;

1209:   /* write matrix header */
1210:   header[0] = MAT_FILE_CLASSID;
1211:   header[1] = M;
1212:   header[2] = N;
1213:   PetscCallMPI(MPI_Reduce(&nz, &hnz, 1, MPIU_INT64, MPI_SUM, 0, PetscObjectComm((PetscObject)mat)));
1214:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1215:   if (rank == 0) PetscCall(PetscIntCast(hnz, &header[3]));
1216:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

1218:   /* fill in and store row lengths  */
1219:   PetscCall(PetscMalloc1(m, &rowlens));
1220:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i] + B->i[i + 1] - B->i[i];
1221:   PetscCall(PetscViewerBinaryWriteAll(viewer, rowlens, m, rs, M, PETSC_INT));
1222:   PetscCall(PetscFree(rowlens));

1224:   /* fill in and store column indices */
1225:   PetscCall(PetscMalloc1(nz, &colidxs));
1226:   for (cnt = 0, i = 0; i < m; i++) {
1227:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1228:       if (garray[B->j[jb]] > cs) break;
1229:       colidxs[cnt++] = garray[B->j[jb]];
1230:     }
1231:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) colidxs[cnt++] = A->j[ja] + cs;
1232:     for (; jb < B->i[i + 1]; jb++) colidxs[cnt++] = garray[B->j[jb]];
1233:   }
1234:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1235:   PetscCall(PetscViewerBinaryWriteAll(viewer, colidxs, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
1236:   PetscCall(PetscFree(colidxs));

1238:   /* fill in and store nonzero values */
1239:   PetscCall(MatSeqAIJGetArrayRead(aij->A, &aa));
1240:   PetscCall(MatSeqAIJGetArrayRead(aij->B, &ba));
1241:   PetscCall(PetscMalloc1(nz, &matvals));
1242:   for (cnt = 0, i = 0; i < m; i++) {
1243:     for (jb = B->i[i]; jb < B->i[i + 1]; jb++) {
1244:       if (garray[B->j[jb]] > cs) break;
1245:       matvals[cnt++] = ba[jb];
1246:     }
1247:     for (ja = A->i[i]; ja < A->i[i + 1]; ja++) matvals[cnt++] = aa[ja];
1248:     for (; jb < B->i[i + 1]; jb++) matvals[cnt++] = ba[jb];
1249:   }
1250:   PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &aa));
1251:   PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &ba));
1252:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt64_FMT, cnt, nz);
1253:   PetscCall(PetscViewerBinaryWriteAll(viewer, matvals, nz, PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
1254:   PetscCall(PetscFree(matvals));

1256:   /* write block size option to the viewer's .info file */
1257:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1258:   PetscFunctionReturn(PETSC_SUCCESS);
1259: }

1261: #include <petscdraw.h>
1262: static PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
1263: {
1264:   Mat_MPIAIJ       *aij  = (Mat_MPIAIJ *)mat->data;
1265:   PetscMPIInt       rank = aij->rank, size = aij->size;
1266:   PetscBool         isdraw, iascii, isbinary;
1267:   PetscViewer       sviewer;
1268:   PetscViewerFormat format;

1270:   PetscFunctionBegin;
1271:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1272:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1273:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1274:   if (iascii) {
1275:     PetscCall(PetscViewerGetFormat(viewer, &format));
1276:     if (format == PETSC_VIEWER_LOAD_BALANCE) {
1277:       PetscInt i, nmax = 0, nmin = PETSC_INT_MAX, navg = 0, *nz, nzlocal = ((Mat_SeqAIJ *)aij->A->data)->nz + ((Mat_SeqAIJ *)aij->B->data)->nz;
1278:       PetscCall(PetscMalloc1(size, &nz));
1279:       PetscCallMPI(MPI_Allgather(&nzlocal, 1, MPIU_INT, nz, 1, MPIU_INT, PetscObjectComm((PetscObject)mat)));
1280:       for (i = 0; i < size; i++) {
1281:         nmax = PetscMax(nmax, nz[i]);
1282:         nmin = PetscMin(nmin, nz[i]);
1283:         navg += nz[i];
1284:       }
1285:       PetscCall(PetscFree(nz));
1286:       navg = navg / size;
1287:       PetscCall(PetscViewerASCIIPrintf(viewer, "Load Balance - Nonzeros: Min %" PetscInt_FMT "  avg %" PetscInt_FMT "  max %" PetscInt_FMT "\n", nmin, navg, nmax));
1288:       PetscFunctionReturn(PETSC_SUCCESS);
1289:     }
1290:     PetscCall(PetscViewerGetFormat(viewer, &format));
1291:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1292:       MatInfo   info;
1293:       PetscInt *inodes = NULL;

1295:       PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
1296:       PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
1297:       PetscCall(MatInodeGetInodeSizes(aij->A, NULL, &inodes, NULL));
1298:       PetscCall(PetscViewerASCIIPushSynchronized(viewer));
1299:       if (!inodes) {
1300:         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,
1301:                                                      info.memory));
1302:       } else {
1303:         PetscCall(
1304:           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, info.memory));
1305:       }
1306:       PetscCall(MatGetInfo(aij->A, MAT_LOCAL, &info));
1307:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1308:       PetscCall(MatGetInfo(aij->B, MAT_LOCAL, &info));
1309:       PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
1310:       PetscCall(PetscViewerFlush(viewer));
1311:       PetscCall(PetscViewerASCIIPopSynchronized(viewer));
1312:       PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
1313:       PetscCall(VecScatterView(aij->Mvctx, viewer));
1314:       PetscFunctionReturn(PETSC_SUCCESS);
1315:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1316:       PetscInt inodecount, inodelimit, *inodes;
1317:       PetscCall(MatInodeGetInodeSizes(aij->A, &inodecount, &inodes, &inodelimit));
1318:       if (inodes) {
1319:         PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
1320:       } else {
1321:         PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
1322:       }
1323:       PetscFunctionReturn(PETSC_SUCCESS);
1324:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1325:       PetscFunctionReturn(PETSC_SUCCESS);
1326:     }
1327:   } else if (isbinary) {
1328:     if (size == 1) {
1329:       PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1330:       PetscCall(MatView(aij->A, viewer));
1331:     } else {
1332:       PetscCall(MatView_MPIAIJ_Binary(mat, viewer));
1333:     }
1334:     PetscFunctionReturn(PETSC_SUCCESS);
1335:   } else if (iascii && size == 1) {
1336:     PetscCall(PetscObjectSetName((PetscObject)aij->A, ((PetscObject)mat)->name));
1337:     PetscCall(MatView(aij->A, viewer));
1338:     PetscFunctionReturn(PETSC_SUCCESS);
1339:   } else if (isdraw) {
1340:     PetscDraw draw;
1341:     PetscBool isnull;
1342:     PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1343:     PetscCall(PetscDrawIsNull(draw, &isnull));
1344:     if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1345:   }

1347:   { /* assemble the entire matrix onto first processor */
1348:     Mat A = NULL, Av;
1349:     IS  isrow, iscol;

1351:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->rmap->N : 0, 0, 1, &isrow));
1352:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), rank == 0 ? mat->cmap->N : 0, 0, 1, &iscol));
1353:     PetscCall(MatCreateSubMatrix(mat, isrow, iscol, MAT_INITIAL_MATRIX, &A));
1354:     PetscCall(MatMPIAIJGetSeqAIJ(A, &Av, NULL, NULL));
1355:     /*  The commented code uses MatCreateSubMatrices instead */
1356:     /*
1357:     Mat *AA, A = NULL, Av;
1358:     IS  isrow,iscol;

1360:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->rmap->N : 0,0,1,&isrow));
1361:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat),rank == 0 ? mat->cmap->N : 0,0,1,&iscol));
1362:     PetscCall(MatCreateSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&AA));
1363:     if (rank == 0) {
1364:        PetscCall(PetscObjectReference((PetscObject)AA[0]));
1365:        A    = AA[0];
1366:        Av   = AA[0];
1367:     }
1368:     PetscCall(MatDestroySubMatrices(1,&AA));
1369: */
1370:     PetscCall(ISDestroy(&iscol));
1371:     PetscCall(ISDestroy(&isrow));
1372:     /*
1373:        Everyone has to call to draw the matrix since the graphics waits are
1374:        synchronized across all processors that share the PetscDraw object
1375:     */
1376:     PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1377:     if (rank == 0) {
1378:       if (((PetscObject)mat)->name) PetscCall(PetscObjectSetName((PetscObject)Av, ((PetscObject)mat)->name));
1379:       PetscCall(MatView_SeqAIJ(Av, sviewer));
1380:     }
1381:     PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
1382:     PetscCall(MatDestroy(&A));
1383:   }
1384:   PetscFunctionReturn(PETSC_SUCCESS);
1385: }

1387: PetscErrorCode MatView_MPIAIJ(Mat mat, PetscViewer viewer)
1388: {
1389:   PetscBool iascii, isdraw, issocket, isbinary;

1391:   PetscFunctionBegin;
1392:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1393:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1394:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1395:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
1396:   if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPIAIJ_ASCIIorDraworSocket(mat, viewer));
1397:   PetscFunctionReturn(PETSC_SUCCESS);
1398: }

1400: static PetscErrorCode MatSOR_MPIAIJ(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1401: {
1402:   Mat_MPIAIJ *mat = (Mat_MPIAIJ *)matin->data;
1403:   Vec         bb1 = NULL;
1404:   PetscBool   hasop;

1406:   PetscFunctionBegin;
1407:   if (flag == SOR_APPLY_UPPER) {
1408:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1409:     PetscFunctionReturn(PETSC_SUCCESS);
1410:   }

1412:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));

1414:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_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:     }

1420:     while (its--) {
1421:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1422:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1424:       /* update rhs: bb1 = bb - B*x */
1425:       PetscCall(VecScale(mat->lvec, -1.0));
1426:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1428:       /* local sweep */
1429:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1430:     }
1431:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1432:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1433:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1434:       its--;
1435:     }
1436:     while (its--) {
1437:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1438:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1440:       /* update rhs: bb1 = bb - B*x */
1441:       PetscCall(VecScale(mat->lvec, -1.0));
1442:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1444:       /* local sweep */
1445:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1446:     }
1447:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1448:     if (flag & SOR_ZERO_INITIAL_GUESS) {
1449:       PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1450:       its--;
1451:     }
1452:     while (its--) {
1453:       PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1454:       PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));

1456:       /* update rhs: bb1 = bb - B*x */
1457:       PetscCall(VecScale(mat->lvec, -1.0));
1458:       PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));

1460:       /* local sweep */
1461:       PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1462:     }
1463:   } else if (flag & SOR_EISENSTAT) {
1464:     Vec xx1;

1466:     PetscCall(VecDuplicate(bb, &xx1));
1467:     PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP), fshift, lits, 1, xx));

1469:     PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1470:     PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1471:     if (!mat->diag) {
1472:       PetscCall(MatCreateVecs(matin, &mat->diag, NULL));
1473:       PetscCall(MatGetDiagonal(matin, mat->diag));
1474:     }
1475:     PetscCall(MatHasOperation(matin, MATOP_MULT_DIAGONAL_BLOCK, &hasop));
1476:     if (hasop) {
1477:       PetscCall(MatMultDiagonalBlock(matin, xx, bb1));
1478:     } else {
1479:       PetscCall(VecPointwiseMult(bb1, mat->diag, xx));
1480:     }
1481:     PetscCall(VecAYPX(bb1, (omega - 2.0) / omega, bb));

1483:     PetscCall(MatMultAdd(mat->B, mat->lvec, bb1, bb1));

1485:     /* local sweep */
1486:     PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, (MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP), fshift, lits, 1, xx1));
1487:     PetscCall(VecAXPY(xx, 1.0, xx1));
1488:     PetscCall(VecDestroy(&xx1));
1489:   } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");

1491:   PetscCall(VecDestroy(&bb1));

1493:   matin->factorerrortype = mat->A->factorerrortype;
1494:   PetscFunctionReturn(PETSC_SUCCESS);
1495: }

1497: static PetscErrorCode MatPermute_MPIAIJ(Mat A, IS rowp, IS colp, Mat *B)
1498: {
1499:   Mat             aA, aB, Aperm;
1500:   const PetscInt *rwant, *cwant, *gcols, *ai, *bi, *aj, *bj;
1501:   PetscScalar    *aa, *ba;
1502:   PetscInt        i, j, m, n, ng, anz, bnz, *dnnz, *onnz, *tdnnz, *tonnz, *rdest, *cdest, *work, *gcdest;
1503:   PetscSF         rowsf, sf;
1504:   IS              parcolp = NULL;
1505:   PetscBool       done;

1507:   PetscFunctionBegin;
1508:   PetscCall(MatGetLocalSize(A, &m, &n));
1509:   PetscCall(ISGetIndices(rowp, &rwant));
1510:   PetscCall(ISGetIndices(colp, &cwant));
1511:   PetscCall(PetscMalloc3(PetscMax(m, n), &work, m, &rdest, n, &cdest));

1513:   /* Invert row permutation to find out where my rows should go */
1514:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &rowsf));
1515:   PetscCall(PetscSFSetGraphLayout(rowsf, A->rmap, A->rmap->n, NULL, PETSC_OWN_POINTER, rwant));
1516:   PetscCall(PetscSFSetFromOptions(rowsf));
1517:   for (i = 0; i < m; i++) work[i] = A->rmap->rstart + i;
1518:   PetscCall(PetscSFReduceBegin(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));
1519:   PetscCall(PetscSFReduceEnd(rowsf, MPIU_INT, work, rdest, MPI_REPLACE));

1521:   /* Invert column permutation to find out where my columns should go */
1522:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1523:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, A->cmap->n, NULL, PETSC_OWN_POINTER, cwant));
1524:   PetscCall(PetscSFSetFromOptions(sf));
1525:   for (i = 0; i < n; i++) work[i] = A->cmap->rstart + i;
1526:   PetscCall(PetscSFReduceBegin(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1527:   PetscCall(PetscSFReduceEnd(sf, MPIU_INT, work, cdest, MPI_REPLACE));
1528:   PetscCall(PetscSFDestroy(&sf));

1530:   PetscCall(ISRestoreIndices(rowp, &rwant));
1531:   PetscCall(ISRestoreIndices(colp, &cwant));
1532:   PetscCall(MatMPIAIJGetSeqAIJ(A, &aA, &aB, &gcols));

1534:   /* Find out where my gcols should go */
1535:   PetscCall(MatGetSize(aB, NULL, &ng));
1536:   PetscCall(PetscMalloc1(ng, &gcdest));
1537:   PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1538:   PetscCall(PetscSFSetGraphLayout(sf, A->cmap, ng, NULL, PETSC_OWN_POINTER, gcols));
1539:   PetscCall(PetscSFSetFromOptions(sf));
1540:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1541:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, cdest, gcdest, MPI_REPLACE));
1542:   PetscCall(PetscSFDestroy(&sf));

1544:   PetscCall(PetscCalloc4(m, &dnnz, m, &onnz, m, &tdnnz, m, &tonnz));
1545:   PetscCall(MatGetRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1546:   PetscCall(MatGetRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1547:   for (i = 0; i < m; i++) {
1548:     PetscInt    row = rdest[i];
1549:     PetscMPIInt rowner;
1550:     PetscCall(PetscLayoutFindOwner(A->rmap, row, &rowner));
1551:     for (j = ai[i]; j < ai[i + 1]; j++) {
1552:       PetscInt    col = cdest[aj[j]];
1553:       PetscMPIInt cowner;
1554:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner)); /* Could build an index for the columns to eliminate this search */
1555:       if (rowner == cowner) dnnz[i]++;
1556:       else onnz[i]++;
1557:     }
1558:     for (j = bi[i]; j < bi[i + 1]; j++) {
1559:       PetscInt    col = gcdest[bj[j]];
1560:       PetscMPIInt cowner;
1561:       PetscCall(PetscLayoutFindOwner(A->cmap, col, &cowner));
1562:       if (rowner == cowner) dnnz[i]++;
1563:       else onnz[i]++;
1564:     }
1565:   }
1566:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1567:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, dnnz, tdnnz, MPI_REPLACE));
1568:   PetscCall(PetscSFBcastBegin(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1569:   PetscCall(PetscSFBcastEnd(rowsf, MPIU_INT, onnz, tonnz, MPI_REPLACE));
1570:   PetscCall(PetscSFDestroy(&rowsf));

1572:   PetscCall(MatCreateAIJ(PetscObjectComm((PetscObject)A), A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N, 0, tdnnz, 0, tonnz, &Aperm));
1573:   PetscCall(MatSeqAIJGetArray(aA, &aa));
1574:   PetscCall(MatSeqAIJGetArray(aB, &ba));
1575:   for (i = 0; i < m; i++) {
1576:     PetscInt *acols = dnnz, *bcols = onnz; /* Repurpose now-unneeded arrays */
1577:     PetscInt  j0, rowlen;
1578:     rowlen = ai[i + 1] - ai[i];
1579:     for (j0 = j = 0; j < rowlen; j0 = j) { /* rowlen could be larger than number of rows m, so sum in batches */
1580:       for (; j < PetscMin(rowlen, j0 + m); j++) acols[j - j0] = cdest[aj[ai[i] + j]];
1581:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, acols, aa + ai[i] + j0, INSERT_VALUES));
1582:     }
1583:     rowlen = bi[i + 1] - bi[i];
1584:     for (j0 = j = 0; j < rowlen; j0 = j) {
1585:       for (; j < PetscMin(rowlen, j0 + m); j++) bcols[j - j0] = gcdest[bj[bi[i] + j]];
1586:       PetscCall(MatSetValues(Aperm, 1, &rdest[i], j - j0, bcols, ba + bi[i] + j0, INSERT_VALUES));
1587:     }
1588:   }
1589:   PetscCall(MatAssemblyBegin(Aperm, MAT_FINAL_ASSEMBLY));
1590:   PetscCall(MatAssemblyEnd(Aperm, MAT_FINAL_ASSEMBLY));
1591:   PetscCall(MatRestoreRowIJ(aA, 0, PETSC_FALSE, PETSC_FALSE, &anz, &ai, &aj, &done));
1592:   PetscCall(MatRestoreRowIJ(aB, 0, PETSC_FALSE, PETSC_FALSE, &bnz, &bi, &bj, &done));
1593:   PetscCall(MatSeqAIJRestoreArray(aA, &aa));
1594:   PetscCall(MatSeqAIJRestoreArray(aB, &ba));
1595:   PetscCall(PetscFree4(dnnz, onnz, tdnnz, tonnz));
1596:   PetscCall(PetscFree3(work, rdest, cdest));
1597:   PetscCall(PetscFree(gcdest));
1598:   if (parcolp) PetscCall(ISDestroy(&colp));
1599:   *B = Aperm;
1600:   PetscFunctionReturn(PETSC_SUCCESS);
1601: }

1603: static PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
1604: {
1605:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1607:   PetscFunctionBegin;
1608:   PetscCall(MatGetSize(aij->B, NULL, nghosts));
1609:   if (ghosts) *ghosts = aij->garray;
1610:   PetscFunctionReturn(PETSC_SUCCESS);
1611: }

1613: static PetscErrorCode MatGetInfo_MPIAIJ(Mat matin, MatInfoType flag, MatInfo *info)
1614: {
1615:   Mat_MPIAIJ    *mat = (Mat_MPIAIJ *)matin->data;
1616:   Mat            A = mat->A, B = mat->B;
1617:   PetscLogDouble isend[5], irecv[5];

1619:   PetscFunctionBegin;
1620:   info->block_size = 1.0;
1621:   PetscCall(MatGetInfo(A, MAT_LOCAL, info));

1623:   isend[0] = info->nz_used;
1624:   isend[1] = info->nz_allocated;
1625:   isend[2] = info->nz_unneeded;
1626:   isend[3] = info->memory;
1627:   isend[4] = info->mallocs;

1629:   PetscCall(MatGetInfo(B, MAT_LOCAL, info));

1631:   isend[0] += info->nz_used;
1632:   isend[1] += info->nz_allocated;
1633:   isend[2] += info->nz_unneeded;
1634:   isend[3] += info->memory;
1635:   isend[4] += info->mallocs;
1636:   if (flag == MAT_LOCAL) {
1637:     info->nz_used      = isend[0];
1638:     info->nz_allocated = isend[1];
1639:     info->nz_unneeded  = isend[2];
1640:     info->memory       = isend[3];
1641:     info->mallocs      = isend[4];
1642:   } else if (flag == MAT_GLOBAL_MAX) {
1643:     PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));

1645:     info->nz_used      = irecv[0];
1646:     info->nz_allocated = irecv[1];
1647:     info->nz_unneeded  = irecv[2];
1648:     info->memory       = irecv[3];
1649:     info->mallocs      = irecv[4];
1650:   } else if (flag == MAT_GLOBAL_SUM) {
1651:     PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));

1653:     info->nz_used      = irecv[0];
1654:     info->nz_allocated = irecv[1];
1655:     info->nz_unneeded  = irecv[2];
1656:     info->memory       = irecv[3];
1657:     info->mallocs      = irecv[4];
1658:   }
1659:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1660:   info->fill_ratio_needed = 0;
1661:   info->factor_mallocs    = 0;
1662:   PetscFunctionReturn(PETSC_SUCCESS);
1663: }

1665: PetscErrorCode MatSetOption_MPIAIJ(Mat A, MatOption op, PetscBool flg)
1666: {
1667:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

1669:   PetscFunctionBegin;
1670:   switch (op) {
1671:   case MAT_NEW_NONZERO_LOCATIONS:
1672:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1673:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1674:   case MAT_KEEP_NONZERO_PATTERN:
1675:   case MAT_NEW_NONZERO_LOCATION_ERR:
1676:   case MAT_USE_INODES:
1677:   case MAT_IGNORE_ZERO_ENTRIES:
1678:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1679:     MatCheckPreallocated(A, 1);
1680:     PetscCall(MatSetOption(a->A, op, flg));
1681:     PetscCall(MatSetOption(a->B, op, flg));
1682:     break;
1683:   case MAT_ROW_ORIENTED:
1684:     MatCheckPreallocated(A, 1);
1685:     a->roworiented = flg;

1687:     PetscCall(MatSetOption(a->A, op, flg));
1688:     PetscCall(MatSetOption(a->B, op, flg));
1689:     break;
1690:   case MAT_FORCE_DIAGONAL_ENTRIES:
1691:   case MAT_SORTED_FULL:
1692:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1693:     break;
1694:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1695:     a->donotstash = flg;
1696:     break;
1697:   /* Symmetry flags are handled directly by MatSetOption() and they don't affect preallocation */
1698:   case MAT_SPD:
1699:   case MAT_SYMMETRIC:
1700:   case MAT_STRUCTURALLY_SYMMETRIC:
1701:   case MAT_HERMITIAN:
1702:   case MAT_SYMMETRY_ETERNAL:
1703:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1704:   case MAT_SPD_ETERNAL:
1705:     /* if the diagonal matrix is square it inherits some of the properties above */
1706:     break;
1707:   case MAT_SUBMAT_SINGLEIS:
1708:     A->submat_singleis = flg;
1709:     break;
1710:   case MAT_STRUCTURE_ONLY:
1711:     /* The option is handled directly by MatSetOption() */
1712:     break;
1713:   default:
1714:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1715:   }
1716:   PetscFunctionReturn(PETSC_SUCCESS);
1717: }

1719: PetscErrorCode MatGetRow_MPIAIJ(Mat matin, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1720: {
1721:   Mat_MPIAIJ  *mat = (Mat_MPIAIJ *)matin->data;
1722:   PetscScalar *vworkA, *vworkB, **pvA, **pvB, *v_p;
1723:   PetscInt     i, *cworkA, *cworkB, **pcA, **pcB, cstart = matin->cmap->rstart;
1724:   PetscInt     nztot, nzA, nzB, lrow, rstart = matin->rmap->rstart, rend = matin->rmap->rend;
1725:   PetscInt    *cmap, *idx_p;

1727:   PetscFunctionBegin;
1728:   PetscCheck(!mat->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Already active");
1729:   mat->getrowactive = PETSC_TRUE;

1731:   if (!mat->rowvalues && (idx || v)) {
1732:     /*
1733:         allocate enough space to hold information from the longest row.
1734:     */
1735:     Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)mat->A->data, *Ba = (Mat_SeqAIJ *)mat->B->data;
1736:     PetscInt    max = 1, tmp;
1737:     for (i = 0; i < matin->rmap->n; i++) {
1738:       tmp = Aa->i[i + 1] - Aa->i[i] + Ba->i[i + 1] - Ba->i[i];
1739:       if (max < tmp) max = tmp;
1740:     }
1741:     PetscCall(PetscMalloc2(max, &mat->rowvalues, max, &mat->rowindices));
1742:   }

1744:   PetscCheck(row >= rstart && row < rend, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Only local rows");
1745:   lrow = row - rstart;

1747:   pvA = &vworkA;
1748:   pcA = &cworkA;
1749:   pvB = &vworkB;
1750:   pcB = &cworkB;
1751:   if (!v) {
1752:     pvA = NULL;
1753:     pvB = NULL;
1754:   }
1755:   if (!idx) {
1756:     pcA = NULL;
1757:     if (!v) pcB = NULL;
1758:   }
1759:   PetscCall((*mat->A->ops->getrow)(mat->A, lrow, &nzA, pcA, pvA));
1760:   PetscCall((*mat->B->ops->getrow)(mat->B, lrow, &nzB, pcB, pvB));
1761:   nztot = nzA + nzB;

1763:   cmap = mat->garray;
1764:   if (v || idx) {
1765:     if (nztot) {
1766:       /* Sort by increasing column numbers, assuming A and B already sorted */
1767:       PetscInt imark = -1;
1768:       if (v) {
1769:         *v = v_p = mat->rowvalues;
1770:         for (i = 0; i < nzB; i++) {
1771:           if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1772:           else break;
1773:         }
1774:         imark = i;
1775:         for (i = 0; i < nzA; i++) v_p[imark + i] = vworkA[i];
1776:         for (i = imark; i < nzB; i++) v_p[nzA + i] = vworkB[i];
1777:       }
1778:       if (idx) {
1779:         *idx = idx_p = mat->rowindices;
1780:         if (imark > -1) {
1781:           for (i = 0; i < imark; i++) idx_p[i] = cmap[cworkB[i]];
1782:         } else {
1783:           for (i = 0; i < nzB; i++) {
1784:             if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1785:             else break;
1786:           }
1787:           imark = i;
1788:         }
1789:         for (i = 0; i < nzA; i++) idx_p[imark + i] = cstart + cworkA[i];
1790:         for (i = imark; i < nzB; i++) idx_p[nzA + i] = cmap[cworkB[i]];
1791:       }
1792:     } else {
1793:       if (idx) *idx = NULL;
1794:       if (v) *v = NULL;
1795:     }
1796:   }
1797:   *nz = nztot;
1798:   PetscCall((*mat->A->ops->restorerow)(mat->A, lrow, &nzA, pcA, pvA));
1799:   PetscCall((*mat->B->ops->restorerow)(mat->B, lrow, &nzB, pcB, pvB));
1800:   PetscFunctionReturn(PETSC_SUCCESS);
1801: }

1803: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1804: {
1805:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

1807:   PetscFunctionBegin;
1808:   PetscCheck(aij->getrowactive, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "MatGetRow() must be called first");
1809:   aij->getrowactive = PETSC_FALSE;
1810:   PetscFunctionReturn(PETSC_SUCCESS);
1811: }

1813: static PetscErrorCode MatNorm_MPIAIJ(Mat mat, NormType type, PetscReal *norm)
1814: {
1815:   Mat_MPIAIJ      *aij  = (Mat_MPIAIJ *)mat->data;
1816:   Mat_SeqAIJ      *amat = (Mat_SeqAIJ *)aij->A->data, *bmat = (Mat_SeqAIJ *)aij->B->data;
1817:   PetscInt         i, j, cstart = mat->cmap->rstart;
1818:   PetscReal        sum = 0.0;
1819:   const MatScalar *v, *amata, *bmata;

1821:   PetscFunctionBegin;
1822:   if (aij->size == 1) {
1823:     PetscCall(MatNorm(aij->A, type, norm));
1824:   } else {
1825:     PetscCall(MatSeqAIJGetArrayRead(aij->A, &amata));
1826:     PetscCall(MatSeqAIJGetArrayRead(aij->B, &bmata));
1827:     if (type == NORM_FROBENIUS) {
1828:       v = amata;
1829:       for (i = 0; i < amat->nz; i++) {
1830:         sum += PetscRealPart(PetscConj(*v) * (*v));
1831:         v++;
1832:       }
1833:       v = bmata;
1834:       for (i = 0; i < bmat->nz; i++) {
1835:         sum += PetscRealPart(PetscConj(*v) * (*v));
1836:         v++;
1837:       }
1838:       PetscCallMPI(MPIU_Allreduce(&sum, norm, 1, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1839:       *norm = PetscSqrtReal(*norm);
1840:       PetscCall(PetscLogFlops(2.0 * amat->nz + 2.0 * bmat->nz));
1841:     } else if (type == NORM_1) { /* max column norm */
1842:       PetscReal *tmp;
1843:       PetscInt  *jj, *garray = aij->garray;
1844:       PetscCall(PetscCalloc1(mat->cmap->N + 1, &tmp));
1845:       *norm = 0.0;
1846:       v     = amata;
1847:       jj    = amat->j;
1848:       for (j = 0; j < amat->nz; j++) {
1849:         tmp[cstart + *jj++] += PetscAbsScalar(*v);
1850:         v++;
1851:       }
1852:       v  = bmata;
1853:       jj = bmat->j;
1854:       for (j = 0; j < bmat->nz; j++) {
1855:         tmp[garray[*jj++]] += PetscAbsScalar(*v);
1856:         v++;
1857:       }
1858:       PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, tmp, mat->cmap->N, MPIU_REAL, MPIU_SUM, PetscObjectComm((PetscObject)mat)));
1859:       for (j = 0; j < mat->cmap->N; j++) {
1860:         if (tmp[j] > *norm) *norm = tmp[j];
1861:       }
1862:       PetscCall(PetscFree(tmp));
1863:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1864:     } else if (type == NORM_INFINITY) { /* max row norm */
1865:       PetscReal ntemp = 0.0;
1866:       for (j = 0; j < aij->A->rmap->n; j++) {
1867:         v   = PetscSafePointerPlusOffset(amata, amat->i[j]);
1868:         sum = 0.0;
1869:         for (i = 0; i < amat->i[j + 1] - amat->i[j]; i++) {
1870:           sum += PetscAbsScalar(*v);
1871:           v++;
1872:         }
1873:         v = PetscSafePointerPlusOffset(bmata, bmat->i[j]);
1874:         for (i = 0; i < bmat->i[j + 1] - bmat->i[j]; i++) {
1875:           sum += PetscAbsScalar(*v);
1876:           v++;
1877:         }
1878:         if (sum > ntemp) ntemp = sum;
1879:       }
1880:       PetscCallMPI(MPIU_Allreduce(&ntemp, norm, 1, MPIU_REAL, MPIU_MAX, PetscObjectComm((PetscObject)mat)));
1881:       PetscCall(PetscLogFlops(PetscMax(amat->nz + bmat->nz - 1, 0)));
1882:     } else SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "No support for two norm");
1883:     PetscCall(MatSeqAIJRestoreArrayRead(aij->A, &amata));
1884:     PetscCall(MatSeqAIJRestoreArrayRead(aij->B, &bmata));
1885:   }
1886:   PetscFunctionReturn(PETSC_SUCCESS);
1887: }

1889: static PetscErrorCode MatTranspose_MPIAIJ(Mat A, MatReuse reuse, Mat *matout)
1890: {
1891:   Mat_MPIAIJ      *a    = (Mat_MPIAIJ *)A->data, *b;
1892:   Mat_SeqAIJ      *Aloc = (Mat_SeqAIJ *)a->A->data, *Bloc = (Mat_SeqAIJ *)a->B->data, *sub_B_diag;
1893:   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;
1894:   const PetscInt  *ai, *aj, *bi, *bj, *B_diag_i;
1895:   Mat              B, A_diag, *B_diag;
1896:   const MatScalar *pbv, *bv;

1898:   PetscFunctionBegin;
1899:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *matout));
1900:   ma = A->rmap->n;
1901:   na = A->cmap->n;
1902:   mb = a->B->rmap->n;
1903:   nb = a->B->cmap->n;
1904:   ai = Aloc->i;
1905:   aj = Aloc->j;
1906:   bi = Bloc->i;
1907:   bj = Bloc->j;
1908:   if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1909:     PetscInt            *d_nnz, *g_nnz, *o_nnz;
1910:     PetscSFNode         *oloc;
1911:     PETSC_UNUSED PetscSF sf;

1913:     PetscCall(PetscMalloc4(na, &d_nnz, na, &o_nnz, nb, &g_nnz, nb, &oloc));
1914:     /* compute d_nnz for preallocation */
1915:     PetscCall(PetscArrayzero(d_nnz, na));
1916:     for (i = 0; i < ai[ma]; i++) d_nnz[aj[i]]++;
1917:     /* compute local off-diagonal contributions */
1918:     PetscCall(PetscArrayzero(g_nnz, nb));
1919:     for (i = 0; i < bi[ma]; i++) g_nnz[bj[i]]++;
1920:     /* map those to global */
1921:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)A), &sf));
1922:     PetscCall(PetscSFSetGraphLayout(sf, A->cmap, nb, NULL, PETSC_USE_POINTER, a->garray));
1923:     PetscCall(PetscSFSetFromOptions(sf));
1924:     PetscCall(PetscArrayzero(o_nnz, na));
1925:     PetscCall(PetscSFReduceBegin(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1926:     PetscCall(PetscSFReduceEnd(sf, MPIU_INT, g_nnz, o_nnz, MPI_SUM));
1927:     PetscCall(PetscSFDestroy(&sf));

1929:     PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1930:     PetscCall(MatSetSizes(B, A->cmap->n, A->rmap->n, N, M));
1931:     PetscCall(MatSetBlockSizes(B, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1932:     PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
1933:     PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
1934:     PetscCall(PetscFree4(d_nnz, o_nnz, g_nnz, oloc));
1935:   } else {
1936:     B = *matout;
1937:     PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
1938:   }

1940:   b           = (Mat_MPIAIJ *)B->data;
1941:   A_diag      = a->A;
1942:   B_diag      = &b->A;
1943:   sub_B_diag  = (Mat_SeqAIJ *)(*B_diag)->data;
1944:   A_diag_ncol = A_diag->cmap->N;
1945:   B_diag_ilen = sub_B_diag->ilen;
1946:   B_diag_i    = sub_B_diag->i;

1948:   /* Set ilen for diagonal of B */
1949:   for (i = 0; i < A_diag_ncol; i++) B_diag_ilen[i] = B_diag_i[i + 1] - B_diag_i[i];

1951:   /* Transpose the diagonal part of the matrix. In contrast to the off-diagonal part, this can be done
1952:   very quickly (=without using MatSetValues), because all writes are local. */
1953:   PetscCall(MatTransposeSetPrecursor(A_diag, *B_diag));
1954:   PetscCall(MatTranspose(A_diag, MAT_REUSE_MATRIX, B_diag));

1956:   /* copy over the B part */
1957:   PetscCall(PetscMalloc1(bi[mb], &cols));
1958:   PetscCall(MatSeqAIJGetArrayRead(a->B, &bv));
1959:   pbv = bv;
1960:   row = A->rmap->rstart;
1961:   for (i = 0; i < bi[mb]; i++) cols[i] = a->garray[bj[i]];
1962:   cols_tmp = cols;
1963:   for (i = 0; i < mb; i++) {
1964:     ncol = bi[i + 1] - bi[i];
1965:     PetscCall(MatSetValues(B, ncol, cols_tmp, 1, &row, pbv, INSERT_VALUES));
1966:     row++;
1967:     if (pbv) pbv += ncol;
1968:     if (cols_tmp) cols_tmp += ncol;
1969:   }
1970:   PetscCall(PetscFree(cols));
1971:   PetscCall(MatSeqAIJRestoreArrayRead(a->B, &bv));

1973:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1974:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1975:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1976:     *matout = B;
1977:   } else {
1978:     PetscCall(MatHeaderMerge(A, &B));
1979:   }
1980:   PetscFunctionReturn(PETSC_SUCCESS);
1981: }

1983: static PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat, Vec ll, Vec rr)
1984: {
1985:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1986:   Mat         a = aij->A, b = aij->B;
1987:   PetscInt    s1, s2, s3;

1989:   PetscFunctionBegin;
1990:   PetscCall(MatGetLocalSize(mat, &s2, &s3));
1991:   if (rr) {
1992:     PetscCall(VecGetLocalSize(rr, &s1));
1993:     PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
1994:     /* Overlap communication with computation. */
1995:     PetscCall(VecScatterBegin(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
1996:   }
1997:   if (ll) {
1998:     PetscCall(VecGetLocalSize(ll, &s1));
1999:     PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
2000:     PetscUseTypeMethod(b, diagonalscale, ll, NULL);
2001:   }
2002:   /* scale  the diagonal block */
2003:   PetscUseTypeMethod(a, diagonalscale, ll, rr);

2005:   if (rr) {
2006:     /* Do a scatter end and then right scale the off-diagonal block */
2007:     PetscCall(VecScatterEnd(aij->Mvctx, rr, aij->lvec, INSERT_VALUES, SCATTER_FORWARD));
2008:     PetscUseTypeMethod(b, diagonalscale, NULL, aij->lvec);
2009:   }
2010:   PetscFunctionReturn(PETSC_SUCCESS);
2011: }

2013: static PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2014: {
2015:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2017:   PetscFunctionBegin;
2018:   PetscCall(MatSetUnfactored(a->A));
2019:   PetscFunctionReturn(PETSC_SUCCESS);
2020: }

2022: static PetscErrorCode MatEqual_MPIAIJ(Mat A, Mat B, PetscBool *flag)
2023: {
2024:   Mat_MPIAIJ *matB = (Mat_MPIAIJ *)B->data, *matA = (Mat_MPIAIJ *)A->data;
2025:   Mat         a, b, c, d;
2026:   PetscBool   flg;

2028:   PetscFunctionBegin;
2029:   a = matA->A;
2030:   b = matA->B;
2031:   c = matB->A;
2032:   d = matB->B;

2034:   PetscCall(MatEqual(a, c, &flg));
2035:   if (flg) PetscCall(MatEqual(b, d, &flg));
2036:   PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
2037:   PetscFunctionReturn(PETSC_SUCCESS);
2038: }

2040: static PetscErrorCode MatCopy_MPIAIJ(Mat A, Mat B, MatStructure str)
2041: {
2042:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2043:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2045:   PetscFunctionBegin;
2046:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2047:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2048:     /* because of the column compression in the off-processor part of the matrix a->B,
2049:        the number of columns in a->B and b->B may be different, hence we cannot call
2050:        the MatCopy() directly on the two parts. If need be, we can provide a more
2051:        efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2052:        then copying the submatrices */
2053:     PetscCall(MatCopy_Basic(A, B, str));
2054:   } else {
2055:     PetscCall(MatCopy(a->A, b->A, str));
2056:     PetscCall(MatCopy(a->B, b->B, str));
2057:   }
2058:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
2059:   PetscFunctionReturn(PETSC_SUCCESS);
2060: }

2062: /*
2063:    Computes the number of nonzeros per row needed for preallocation when X and Y
2064:    have different nonzero structure.
2065: */
2066: 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)
2067: {
2068:   PetscInt i, j, k, nzx, nzy;

2070:   PetscFunctionBegin;
2071:   /* Set the number of nonzeros in the new matrix */
2072:   for (i = 0; i < m; i++) {
2073:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2074:     nzx    = xi[i + 1] - xi[i];
2075:     nzy    = yi[i + 1] - yi[i];
2076:     nnz[i] = 0;
2077:     for (j = 0, k = 0; j < nzx; j++) {                                /* Point in X */
2078:       for (; k < nzy && yltog[yjj[k]] < xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2079:       if (k < nzy && yltog[yjj[k]] == xltog[xjj[j]]) k++;             /* Skip duplicate */
2080:       nnz[i]++;
2081:     }
2082:     for (; k < nzy; k++) nnz[i]++;
2083:   }
2084:   PetscFunctionReturn(PETSC_SUCCESS);
2085: }

2087: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2088: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y, const PetscInt *yltog, Mat X, const PetscInt *xltog, PetscInt *nnz)
2089: {
2090:   PetscInt    m = Y->rmap->N;
2091:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
2092:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

2094:   PetscFunctionBegin;
2095:   PetscCall(MatAXPYGetPreallocation_MPIX_private(m, x->i, x->j, xltog, y->i, y->j, yltog, nnz));
2096:   PetscFunctionReturn(PETSC_SUCCESS);
2097: }

2099: static PetscErrorCode MatAXPY_MPIAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2100: {
2101:   Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data, *yy = (Mat_MPIAIJ *)Y->data;

2103:   PetscFunctionBegin;
2104:   if (str == SAME_NONZERO_PATTERN) {
2105:     PetscCall(MatAXPY(yy->A, a, xx->A, str));
2106:     PetscCall(MatAXPY(yy->B, a, xx->B, str));
2107:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2108:     PetscCall(MatAXPY_Basic(Y, a, X, str));
2109:   } else {
2110:     Mat       B;
2111:     PetscInt *nnz_d, *nnz_o;

2113:     PetscCall(PetscMalloc1(yy->A->rmap->N, &nnz_d));
2114:     PetscCall(PetscMalloc1(yy->B->rmap->N, &nnz_o));
2115:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2116:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2117:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
2118:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
2119:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(yy->A, xx->A, nnz_d));
2120:     PetscCall(MatAXPYGetPreallocation_MPIAIJ(yy->B, yy->garray, xx->B, xx->garray, nnz_o));
2121:     PetscCall(MatMPIAIJSetPreallocation(B, 0, nnz_d, 0, nnz_o));
2122:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2123:     PetscCall(MatHeaderMerge(Y, &B));
2124:     PetscCall(PetscFree(nnz_d));
2125:     PetscCall(PetscFree(nnz_o));
2126:   }
2127:   PetscFunctionReturn(PETSC_SUCCESS);
2128: }

2130: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);

2132: static PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2133: {
2134:   PetscFunctionBegin;
2135:   if (PetscDefined(USE_COMPLEX)) {
2136:     Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2138:     PetscCall(MatConjugate_SeqAIJ(aij->A));
2139:     PetscCall(MatConjugate_SeqAIJ(aij->B));
2140:   }
2141:   PetscFunctionReturn(PETSC_SUCCESS);
2142: }

2144: static PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2145: {
2146:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2148:   PetscFunctionBegin;
2149:   PetscCall(MatRealPart(a->A));
2150:   PetscCall(MatRealPart(a->B));
2151:   PetscFunctionReturn(PETSC_SUCCESS);
2152: }

2154: static PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2155: {
2156:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2158:   PetscFunctionBegin;
2159:   PetscCall(MatImaginaryPart(a->A));
2160:   PetscCall(MatImaginaryPart(a->B));
2161:   PetscFunctionReturn(PETSC_SUCCESS);
2162: }

2164: static PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2165: {
2166:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
2167:   PetscInt           i, *idxb = NULL, m = A->rmap->n;
2168:   PetscScalar       *va, *vv;
2169:   Vec                vB, vA;
2170:   const PetscScalar *vb;

2172:   PetscFunctionBegin;
2173:   PetscCall(MatCreateVecs(a->A, NULL, &vA));
2174:   PetscCall(MatGetRowMaxAbs(a->A, vA, idx));

2176:   PetscCall(VecGetArrayWrite(vA, &va));
2177:   if (idx) {
2178:     for (i = 0; i < m; i++) {
2179:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2180:     }
2181:   }

2183:   PetscCall(MatCreateVecs(a->B, NULL, &vB));
2184:   PetscCall(PetscMalloc1(m, &idxb));
2185:   PetscCall(MatGetRowMaxAbs(a->B, vB, idxb));

2187:   PetscCall(VecGetArrayWrite(v, &vv));
2188:   PetscCall(VecGetArrayRead(vB, &vb));
2189:   for (i = 0; i < m; i++) {
2190:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2191:       vv[i] = vb[i];
2192:       if (idx) idx[i] = a->garray[idxb[i]];
2193:     } else {
2194:       vv[i] = va[i];
2195:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > a->garray[idxb[i]]) idx[i] = a->garray[idxb[i]];
2196:     }
2197:   }
2198:   PetscCall(VecRestoreArrayWrite(vA, &vv));
2199:   PetscCall(VecRestoreArrayWrite(vA, &va));
2200:   PetscCall(VecRestoreArrayRead(vB, &vb));
2201:   PetscCall(PetscFree(idxb));
2202:   PetscCall(VecDestroy(&vA));
2203:   PetscCall(VecDestroy(&vB));
2204:   PetscFunctionReturn(PETSC_SUCCESS);
2205: }

2207: static PetscErrorCode MatGetRowSumAbs_MPIAIJ(Mat A, Vec v)
2208: {
2209:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2210:   Vec         vB, vA;

2212:   PetscFunctionBegin;
2213:   PetscCall(MatCreateVecs(a->A, NULL, &vA));
2214:   PetscCall(MatGetRowSumAbs(a->A, vA));
2215:   PetscCall(MatCreateVecs(a->B, NULL, &vB));
2216:   PetscCall(MatGetRowSumAbs(a->B, vB));
2217:   PetscCall(VecAXPY(vA, 1.0, vB));
2218:   PetscCall(VecDestroy(&vB));
2219:   PetscCall(VecCopy(vA, v));
2220:   PetscCall(VecDestroy(&vA));
2221:   PetscFunctionReturn(PETSC_SUCCESS);
2222: }

2224: static PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2225: {
2226:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2227:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2228:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2229:   PetscInt          *cmap = mat->garray;
2230:   PetscInt          *diagIdx, *offdiagIdx;
2231:   Vec                diagV, offdiagV;
2232:   PetscScalar       *a, *diagA, *offdiagA;
2233:   const PetscScalar *ba, *bav;
2234:   PetscInt           r, j, col, ncols, *bi, *bj;
2235:   Mat                B = mat->B;
2236:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2238:   PetscFunctionBegin;
2239:   /* When a process holds entire A and other processes have no entry */
2240:   if (A->cmap->N == n) {
2241:     PetscCall(VecGetArrayWrite(v, &diagA));
2242:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2243:     PetscCall(MatGetRowMinAbs(mat->A, diagV, idx));
2244:     PetscCall(VecDestroy(&diagV));
2245:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2246:     PetscFunctionReturn(PETSC_SUCCESS);
2247:   } else if (n == 0) {
2248:     if (m) {
2249:       PetscCall(VecGetArrayWrite(v, &a));
2250:       for (r = 0; r < m; r++) {
2251:         a[r] = 0.0;
2252:         if (idx) idx[r] = -1;
2253:       }
2254:       PetscCall(VecRestoreArrayWrite(v, &a));
2255:     }
2256:     PetscFunctionReturn(PETSC_SUCCESS);
2257:   }

2259:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2260:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2261:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2262:   PetscCall(MatGetRowMinAbs(mat->A, diagV, diagIdx));

2264:   /* Get offdiagIdx[] for implicit 0.0 */
2265:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2266:   ba = bav;
2267:   bi = b->i;
2268:   bj = b->j;
2269:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2270:   for (r = 0; r < m; r++) {
2271:     ncols = bi[r + 1] - bi[r];
2272:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2273:       offdiagA[r]   = *ba;
2274:       offdiagIdx[r] = cmap[0];
2275:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2276:       offdiagA[r] = 0.0;

2278:       /* Find first hole in the cmap */
2279:       for (j = 0; j < ncols; j++) {
2280:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2281:         if (col > j && j < cstart) {
2282:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2283:           break;
2284:         } else if (col > j + n && j >= cstart) {
2285:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2286:           break;
2287:         }
2288:       }
2289:       if (j == ncols && ncols < A->cmap->N - n) {
2290:         /* a hole is outside compressed Bcols */
2291:         if (ncols == 0) {
2292:           if (cstart) {
2293:             offdiagIdx[r] = 0;
2294:           } else offdiagIdx[r] = cend;
2295:         } else { /* ncols > 0 */
2296:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2297:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2298:         }
2299:       }
2300:     }

2302:     for (j = 0; j < ncols; j++) {
2303:       if (PetscAbsScalar(offdiagA[r]) > PetscAbsScalar(*ba)) {
2304:         offdiagA[r]   = *ba;
2305:         offdiagIdx[r] = cmap[*bj];
2306:       }
2307:       ba++;
2308:       bj++;
2309:     }
2310:   }

2312:   PetscCall(VecGetArrayWrite(v, &a));
2313:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2314:   for (r = 0; r < m; ++r) {
2315:     if (PetscAbsScalar(diagA[r]) < PetscAbsScalar(offdiagA[r])) {
2316:       a[r] = diagA[r];
2317:       if (idx) idx[r] = cstart + diagIdx[r];
2318:     } else if (PetscAbsScalar(diagA[r]) == PetscAbsScalar(offdiagA[r])) {
2319:       a[r] = diagA[r];
2320:       if (idx) {
2321:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2322:           idx[r] = cstart + diagIdx[r];
2323:         } else idx[r] = offdiagIdx[r];
2324:       }
2325:     } else {
2326:       a[r] = offdiagA[r];
2327:       if (idx) idx[r] = offdiagIdx[r];
2328:     }
2329:   }
2330:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2331:   PetscCall(VecRestoreArrayWrite(v, &a));
2332:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2333:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2334:   PetscCall(VecDestroy(&diagV));
2335:   PetscCall(VecDestroy(&offdiagV));
2336:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2337:   PetscFunctionReturn(PETSC_SUCCESS);
2338: }

2340: static PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2341: {
2342:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2343:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2344:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2345:   PetscInt          *cmap = mat->garray;
2346:   PetscInt          *diagIdx, *offdiagIdx;
2347:   Vec                diagV, offdiagV;
2348:   PetscScalar       *a, *diagA, *offdiagA;
2349:   const PetscScalar *ba, *bav;
2350:   PetscInt           r, j, col, ncols, *bi, *bj;
2351:   Mat                B = mat->B;
2352:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2354:   PetscFunctionBegin;
2355:   /* When a process holds entire A and other processes have no entry */
2356:   if (A->cmap->N == n) {
2357:     PetscCall(VecGetArrayWrite(v, &diagA));
2358:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2359:     PetscCall(MatGetRowMin(mat->A, diagV, idx));
2360:     PetscCall(VecDestroy(&diagV));
2361:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2362:     PetscFunctionReturn(PETSC_SUCCESS);
2363:   } else if (n == 0) {
2364:     if (m) {
2365:       PetscCall(VecGetArrayWrite(v, &a));
2366:       for (r = 0; r < m; r++) {
2367:         a[r] = PETSC_MAX_REAL;
2368:         if (idx) idx[r] = -1;
2369:       }
2370:       PetscCall(VecRestoreArrayWrite(v, &a));
2371:     }
2372:     PetscFunctionReturn(PETSC_SUCCESS);
2373:   }

2375:   PetscCall(PetscCalloc2(m, &diagIdx, m, &offdiagIdx));
2376:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2377:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2378:   PetscCall(MatGetRowMin(mat->A, diagV, diagIdx));

2380:   /* Get offdiagIdx[] for implicit 0.0 */
2381:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2382:   ba = bav;
2383:   bi = b->i;
2384:   bj = b->j;
2385:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2386:   for (r = 0; r < m; r++) {
2387:     ncols = bi[r + 1] - bi[r];
2388:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2389:       offdiagA[r]   = *ba;
2390:       offdiagIdx[r] = cmap[0];
2391:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2392:       offdiagA[r] = 0.0;

2394:       /* Find first hole in the cmap */
2395:       for (j = 0; j < ncols; j++) {
2396:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2397:         if (col > j && j < cstart) {
2398:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2399:           break;
2400:         } else if (col > j + n && j >= cstart) {
2401:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2402:           break;
2403:         }
2404:       }
2405:       if (j == ncols && ncols < A->cmap->N - n) {
2406:         /* a hole is outside compressed Bcols */
2407:         if (ncols == 0) {
2408:           if (cstart) {
2409:             offdiagIdx[r] = 0;
2410:           } else offdiagIdx[r] = cend;
2411:         } else { /* ncols > 0 */
2412:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2413:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2414:         }
2415:       }
2416:     }

2418:     for (j = 0; j < ncols; j++) {
2419:       if (PetscRealPart(offdiagA[r]) > PetscRealPart(*ba)) {
2420:         offdiagA[r]   = *ba;
2421:         offdiagIdx[r] = cmap[*bj];
2422:       }
2423:       ba++;
2424:       bj++;
2425:     }
2426:   }

2428:   PetscCall(VecGetArrayWrite(v, &a));
2429:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2430:   for (r = 0; r < m; ++r) {
2431:     if (PetscRealPart(diagA[r]) < PetscRealPart(offdiagA[r])) {
2432:       a[r] = diagA[r];
2433:       if (idx) idx[r] = cstart + diagIdx[r];
2434:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2435:       a[r] = diagA[r];
2436:       if (idx) {
2437:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2438:           idx[r] = cstart + diagIdx[r];
2439:         } else idx[r] = offdiagIdx[r];
2440:       }
2441:     } else {
2442:       a[r] = offdiagA[r];
2443:       if (idx) idx[r] = offdiagIdx[r];
2444:     }
2445:   }
2446:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2447:   PetscCall(VecRestoreArrayWrite(v, &a));
2448:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2449:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2450:   PetscCall(VecDestroy(&diagV));
2451:   PetscCall(VecDestroy(&offdiagV));
2452:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2453:   PetscFunctionReturn(PETSC_SUCCESS);
2454: }

2456: static PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2457: {
2458:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ *)A->data;
2459:   PetscInt           m = A->rmap->n, n = A->cmap->n;
2460:   PetscInt           cstart = A->cmap->rstart, cend = A->cmap->rend;
2461:   PetscInt          *cmap = mat->garray;
2462:   PetscInt          *diagIdx, *offdiagIdx;
2463:   Vec                diagV, offdiagV;
2464:   PetscScalar       *a, *diagA, *offdiagA;
2465:   const PetscScalar *ba, *bav;
2466:   PetscInt           r, j, col, ncols, *bi, *bj;
2467:   Mat                B = mat->B;
2468:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;

2470:   PetscFunctionBegin;
2471:   /* When a process holds entire A and other processes have no entry */
2472:   if (A->cmap->N == n) {
2473:     PetscCall(VecGetArrayWrite(v, &diagA));
2474:     PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, 1, m, diagA, &diagV));
2475:     PetscCall(MatGetRowMax(mat->A, diagV, idx));
2476:     PetscCall(VecDestroy(&diagV));
2477:     PetscCall(VecRestoreArrayWrite(v, &diagA));
2478:     PetscFunctionReturn(PETSC_SUCCESS);
2479:   } else if (n == 0) {
2480:     if (m) {
2481:       PetscCall(VecGetArrayWrite(v, &a));
2482:       for (r = 0; r < m; r++) {
2483:         a[r] = PETSC_MIN_REAL;
2484:         if (idx) idx[r] = -1;
2485:       }
2486:       PetscCall(VecRestoreArrayWrite(v, &a));
2487:     }
2488:     PetscFunctionReturn(PETSC_SUCCESS);
2489:   }

2491:   PetscCall(PetscMalloc2(m, &diagIdx, m, &offdiagIdx));
2492:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &diagV));
2493:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, m, &offdiagV));
2494:   PetscCall(MatGetRowMax(mat->A, diagV, diagIdx));

2496:   /* Get offdiagIdx[] for implicit 0.0 */
2497:   PetscCall(MatSeqAIJGetArrayRead(B, &bav));
2498:   ba = bav;
2499:   bi = b->i;
2500:   bj = b->j;
2501:   PetscCall(VecGetArrayWrite(offdiagV, &offdiagA));
2502:   for (r = 0; r < m; r++) {
2503:     ncols = bi[r + 1] - bi[r];
2504:     if (ncols == A->cmap->N - n) { /* Brow is dense */
2505:       offdiagA[r]   = *ba;
2506:       offdiagIdx[r] = cmap[0];
2507:     } else { /* Brow is sparse so already KNOW maximum is 0.0 or higher */
2508:       offdiagA[r] = 0.0;

2510:       /* Find first hole in the cmap */
2511:       for (j = 0; j < ncols; j++) {
2512:         col = cmap[bj[j]]; /* global column number = cmap[B column number] */
2513:         if (col > j && j < cstart) {
2514:           offdiagIdx[r] = j; /* global column number of first implicit 0.0 */
2515:           break;
2516:         } else if (col > j + n && j >= cstart) {
2517:           offdiagIdx[r] = j + n; /* global column number of first implicit 0.0 */
2518:           break;
2519:         }
2520:       }
2521:       if (j == ncols && ncols < A->cmap->N - n) {
2522:         /* a hole is outside compressed Bcols */
2523:         if (ncols == 0) {
2524:           if (cstart) {
2525:             offdiagIdx[r] = 0;
2526:           } else offdiagIdx[r] = cend;
2527:         } else { /* ncols > 0 */
2528:           offdiagIdx[r] = cmap[ncols - 1] + 1;
2529:           if (offdiagIdx[r] == cstart) offdiagIdx[r] += n;
2530:         }
2531:       }
2532:     }

2534:     for (j = 0; j < ncols; j++) {
2535:       if (PetscRealPart(offdiagA[r]) < PetscRealPart(*ba)) {
2536:         offdiagA[r]   = *ba;
2537:         offdiagIdx[r] = cmap[*bj];
2538:       }
2539:       ba++;
2540:       bj++;
2541:     }
2542:   }

2544:   PetscCall(VecGetArrayWrite(v, &a));
2545:   PetscCall(VecGetArrayRead(diagV, (const PetscScalar **)&diagA));
2546:   for (r = 0; r < m; ++r) {
2547:     if (PetscRealPart(diagA[r]) > PetscRealPart(offdiagA[r])) {
2548:       a[r] = diagA[r];
2549:       if (idx) idx[r] = cstart + diagIdx[r];
2550:     } else if (PetscRealPart(diagA[r]) == PetscRealPart(offdiagA[r])) {
2551:       a[r] = diagA[r];
2552:       if (idx) {
2553:         if (cstart + diagIdx[r] <= offdiagIdx[r]) {
2554:           idx[r] = cstart + diagIdx[r];
2555:         } else idx[r] = offdiagIdx[r];
2556:       }
2557:     } else {
2558:       a[r] = offdiagA[r];
2559:       if (idx) idx[r] = offdiagIdx[r];
2560:     }
2561:   }
2562:   PetscCall(MatSeqAIJRestoreArrayRead(B, &bav));
2563:   PetscCall(VecRestoreArrayWrite(v, &a));
2564:   PetscCall(VecRestoreArrayRead(diagV, (const PetscScalar **)&diagA));
2565:   PetscCall(VecRestoreArrayWrite(offdiagV, &offdiagA));
2566:   PetscCall(VecDestroy(&diagV));
2567:   PetscCall(VecDestroy(&offdiagV));
2568:   PetscCall(PetscFree2(diagIdx, offdiagIdx));
2569:   PetscFunctionReturn(PETSC_SUCCESS);
2570: }

2572: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat, Mat *newmat)
2573: {
2574:   Mat *dummy;

2576:   PetscFunctionBegin;
2577:   PetscCall(MatCreateSubMatrix_MPIAIJ_All(mat, MAT_DO_NOT_GET_VALUES, MAT_INITIAL_MATRIX, &dummy));
2578:   *newmat = *dummy;
2579:   PetscCall(PetscFree(dummy));
2580:   PetscFunctionReturn(PETSC_SUCCESS);
2581: }

2583: static PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A, const PetscScalar **values)
2584: {
2585:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2587:   PetscFunctionBegin;
2588:   PetscCall(MatInvertBlockDiagonal(a->A, values));
2589:   A->factorerrortype = a->A->factorerrortype;
2590:   PetscFunctionReturn(PETSC_SUCCESS);
2591: }

2593: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x, PetscRandom rctx)
2594: {
2595:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)x->data;

2597:   PetscFunctionBegin;
2598:   PetscCheck(x->assembled || x->preallocated, PetscObjectComm((PetscObject)x), PETSC_ERR_ARG_WRONGSTATE, "MatSetRandom on an unassembled and unpreallocated MATMPIAIJ is not allowed");
2599:   PetscCall(MatSetRandom(aij->A, rctx));
2600:   if (x->assembled) {
2601:     PetscCall(MatSetRandom(aij->B, rctx));
2602:   } else {
2603:     PetscCall(MatSetRandomSkipColumnRange_SeqAIJ_Private(aij->B, x->cmap->rstart, x->cmap->rend, rctx));
2604:   }
2605:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
2606:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
2607:   PetscFunctionReturn(PETSC_SUCCESS);
2608: }

2610: static PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ(Mat A, PetscBool sc)
2611: {
2612:   PetscFunctionBegin;
2613:   if (sc) A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ_Scalable;
2614:   else A->ops->increaseoverlap = MatIncreaseOverlap_MPIAIJ;
2615:   PetscFunctionReturn(PETSC_SUCCESS);
2616: }

2618: /*@
2619:   MatMPIAIJGetNumberNonzeros - gets the number of nonzeros in the matrix on this MPI rank

2621:   Not Collective

2623:   Input Parameter:
2624: . A - the matrix

2626:   Output Parameter:
2627: . nz - the number of nonzeros

2629:   Level: advanced

2631: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2632: @*/
2633: PetscErrorCode MatMPIAIJGetNumberNonzeros(Mat A, PetscCount *nz)
2634: {
2635:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)A->data;
2636:   Mat_SeqAIJ *aaij = (Mat_SeqAIJ *)maij->A->data, *baij = (Mat_SeqAIJ *)maij->B->data;
2637:   PetscBool   isaij;

2639:   PetscFunctionBegin;
2640:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATMPIAIJ, &isaij));
2641:   PetscCheck(isaij, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for type %s", ((PetscObject)A)->type_name);
2642:   *nz = aaij->i[A->rmap->n] + baij->i[A->rmap->n];
2643:   PetscFunctionReturn(PETSC_SUCCESS);
2644: }

2646: /*@
2647:   MatMPIAIJSetUseScalableIncreaseOverlap - Determine if the matrix uses a scalable algorithm to compute the overlap

2649:   Collective

2651:   Input Parameters:
2652: + A  - the matrix
2653: - sc - `PETSC_TRUE` indicates use the scalable algorithm (default is not to use the scalable algorithm)

2655:   Level: advanced

2657: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`
2658: @*/
2659: PetscErrorCode MatMPIAIJSetUseScalableIncreaseOverlap(Mat A, PetscBool sc)
2660: {
2661:   PetscFunctionBegin;
2662:   PetscTryMethod(A, "MatMPIAIJSetUseScalableIncreaseOverlap_C", (Mat, PetscBool), (A, sc));
2663:   PetscFunctionReturn(PETSC_SUCCESS);
2664: }

2666: PetscErrorCode MatSetFromOptions_MPIAIJ(Mat A, PetscOptionItems *PetscOptionsObject)
2667: {
2668:   PetscBool sc = PETSC_FALSE, flg;

2670:   PetscFunctionBegin;
2671:   PetscOptionsHeadBegin(PetscOptionsObject, "MPIAIJ options");
2672:   if (A->ops->increaseoverlap == MatIncreaseOverlap_MPIAIJ_Scalable) sc = PETSC_TRUE;
2673:   PetscCall(PetscOptionsBool("-mat_increase_overlap_scalable", "Use a scalable algorithm to compute the overlap", "MatIncreaseOverlap", sc, &sc, &flg));
2674:   if (flg) PetscCall(MatMPIAIJSetUseScalableIncreaseOverlap(A, sc));
2675:   PetscOptionsHeadEnd();
2676:   PetscFunctionReturn(PETSC_SUCCESS);
2677: }

2679: static PetscErrorCode MatShift_MPIAIJ(Mat Y, PetscScalar a)
2680: {
2681:   Mat_MPIAIJ *maij = (Mat_MPIAIJ *)Y->data;
2682:   Mat_SeqAIJ *aij  = (Mat_SeqAIJ *)maij->A->data;

2684:   PetscFunctionBegin;
2685:   if (!Y->preallocated) {
2686:     PetscCall(MatMPIAIJSetPreallocation(Y, 1, NULL, 0, NULL));
2687:   } else if (!aij->nz) { /* It does not matter if diagonals of Y only partially lie in maij->A. We just need an estimated preallocation. */
2688:     PetscInt nonew = aij->nonew;
2689:     PetscCall(MatSeqAIJSetPreallocation(maij->A, 1, NULL));
2690:     aij->nonew = nonew;
2691:   }
2692:   PetscCall(MatShift_Basic(Y, a));
2693:   PetscFunctionReturn(PETSC_SUCCESS);
2694: }

2696: static PetscErrorCode MatMissingDiagonal_MPIAIJ(Mat A, PetscBool *missing, PetscInt *d)
2697: {
2698:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2700:   PetscFunctionBegin;
2701:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
2702:   PetscCall(MatMissingDiagonal(a->A, missing, d));
2703:   if (d) {
2704:     PetscInt rstart;
2705:     PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
2706:     *d += rstart;
2707:   }
2708:   PetscFunctionReturn(PETSC_SUCCESS);
2709: }

2711: static PetscErrorCode MatInvertVariableBlockDiagonal_MPIAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
2712: {
2713:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2715:   PetscFunctionBegin;
2716:   PetscCall(MatInvertVariableBlockDiagonal(a->A, nblocks, bsizes, diag));
2717:   PetscFunctionReturn(PETSC_SUCCESS);
2718: }

2720: static PetscErrorCode MatEliminateZeros_MPIAIJ(Mat A, PetscBool keep)
2721: {
2722:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;

2724:   PetscFunctionBegin;
2725:   PetscCall(MatEliminateZeros_SeqAIJ(a->A, keep));        // possibly keep zero diagonal coefficients
2726:   PetscCall(MatEliminateZeros_SeqAIJ(a->B, PETSC_FALSE)); // never keep zero diagonal coefficients
2727:   PetscFunctionReturn(PETSC_SUCCESS);
2728: }

2730: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2731:                                        MatGetRow_MPIAIJ,
2732:                                        MatRestoreRow_MPIAIJ,
2733:                                        MatMult_MPIAIJ,
2734:                                        /* 4*/ MatMultAdd_MPIAIJ,
2735:                                        MatMultTranspose_MPIAIJ,
2736:                                        MatMultTransposeAdd_MPIAIJ,
2737:                                        NULL,
2738:                                        NULL,
2739:                                        NULL,
2740:                                        /*10*/ NULL,
2741:                                        NULL,
2742:                                        NULL,
2743:                                        MatSOR_MPIAIJ,
2744:                                        MatTranspose_MPIAIJ,
2745:                                        /*15*/ MatGetInfo_MPIAIJ,
2746:                                        MatEqual_MPIAIJ,
2747:                                        MatGetDiagonal_MPIAIJ,
2748:                                        MatDiagonalScale_MPIAIJ,
2749:                                        MatNorm_MPIAIJ,
2750:                                        /*20*/ MatAssemblyBegin_MPIAIJ,
2751:                                        MatAssemblyEnd_MPIAIJ,
2752:                                        MatSetOption_MPIAIJ,
2753:                                        MatZeroEntries_MPIAIJ,
2754:                                        /*24*/ MatZeroRows_MPIAIJ,
2755:                                        NULL,
2756:                                        NULL,
2757:                                        NULL,
2758:                                        NULL,
2759:                                        /*29*/ MatSetUp_MPI_Hash,
2760:                                        NULL,
2761:                                        NULL,
2762:                                        MatGetDiagonalBlock_MPIAIJ,
2763:                                        NULL,
2764:                                        /*34*/ MatDuplicate_MPIAIJ,
2765:                                        NULL,
2766:                                        NULL,
2767:                                        NULL,
2768:                                        NULL,
2769:                                        /*39*/ MatAXPY_MPIAIJ,
2770:                                        MatCreateSubMatrices_MPIAIJ,
2771:                                        MatIncreaseOverlap_MPIAIJ,
2772:                                        MatGetValues_MPIAIJ,
2773:                                        MatCopy_MPIAIJ,
2774:                                        /*44*/ MatGetRowMax_MPIAIJ,
2775:                                        MatScale_MPIAIJ,
2776:                                        MatShift_MPIAIJ,
2777:                                        MatDiagonalSet_MPIAIJ,
2778:                                        MatZeroRowsColumns_MPIAIJ,
2779:                                        /*49*/ MatSetRandom_MPIAIJ,
2780:                                        MatGetRowIJ_MPIAIJ,
2781:                                        MatRestoreRowIJ_MPIAIJ,
2782:                                        NULL,
2783:                                        NULL,
2784:                                        /*54*/ MatFDColoringCreate_MPIXAIJ,
2785:                                        NULL,
2786:                                        MatSetUnfactored_MPIAIJ,
2787:                                        MatPermute_MPIAIJ,
2788:                                        NULL,
2789:                                        /*59*/ MatCreateSubMatrix_MPIAIJ,
2790:                                        MatDestroy_MPIAIJ,
2791:                                        MatView_MPIAIJ,
2792:                                        NULL,
2793:                                        NULL,
2794:                                        /*64*/ NULL,
2795:                                        MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2796:                                        NULL,
2797:                                        NULL,
2798:                                        NULL,
2799:                                        /*69*/ MatGetRowMaxAbs_MPIAIJ,
2800:                                        MatGetRowMinAbs_MPIAIJ,
2801:                                        NULL,
2802:                                        NULL,
2803:                                        NULL,
2804:                                        NULL,
2805:                                        /*75*/ MatFDColoringApply_AIJ,
2806:                                        MatSetFromOptions_MPIAIJ,
2807:                                        NULL,
2808:                                        NULL,
2809:                                        MatFindZeroDiagonals_MPIAIJ,
2810:                                        /*80*/ NULL,
2811:                                        NULL,
2812:                                        NULL,
2813:                                        /*83*/ MatLoad_MPIAIJ,
2814:                                        NULL,
2815:                                        NULL,
2816:                                        NULL,
2817:                                        NULL,
2818:                                        NULL,
2819:                                        /*89*/ NULL,
2820:                                        NULL,
2821:                                        MatMatMultNumeric_MPIAIJ_MPIAIJ,
2822:                                        NULL,
2823:                                        NULL,
2824:                                        /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2825:                                        NULL,
2826:                                        NULL,
2827:                                        NULL,
2828:                                        MatBindToCPU_MPIAIJ,
2829:                                        /*99*/ MatProductSetFromOptions_MPIAIJ,
2830:                                        NULL,
2831:                                        NULL,
2832:                                        MatConjugate_MPIAIJ,
2833:                                        NULL,
2834:                                        /*104*/ MatSetValuesRow_MPIAIJ,
2835:                                        MatRealPart_MPIAIJ,
2836:                                        MatImaginaryPart_MPIAIJ,
2837:                                        NULL,
2838:                                        NULL,
2839:                                        /*109*/ NULL,
2840:                                        NULL,
2841:                                        MatGetRowMin_MPIAIJ,
2842:                                        NULL,
2843:                                        MatMissingDiagonal_MPIAIJ,
2844:                                        /*114*/ MatGetSeqNonzeroStructure_MPIAIJ,
2845:                                        NULL,
2846:                                        MatGetGhosts_MPIAIJ,
2847:                                        NULL,
2848:                                        NULL,
2849:                                        /*119*/ MatMultDiagonalBlock_MPIAIJ,
2850:                                        NULL,
2851:                                        NULL,
2852:                                        NULL,
2853:                                        MatGetMultiProcBlock_MPIAIJ,
2854:                                        /*124*/ MatFindNonzeroRows_MPIAIJ,
2855:                                        MatGetColumnReductions_MPIAIJ,
2856:                                        MatInvertBlockDiagonal_MPIAIJ,
2857:                                        MatInvertVariableBlockDiagonal_MPIAIJ,
2858:                                        MatCreateSubMatricesMPI_MPIAIJ,
2859:                                        /*129*/ NULL,
2860:                                        NULL,
2861:                                        NULL,
2862:                                        MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2863:                                        NULL,
2864:                                        /*134*/ NULL,
2865:                                        NULL,
2866:                                        NULL,
2867:                                        NULL,
2868:                                        NULL,
2869:                                        /*139*/ MatSetBlockSizes_MPIAIJ,
2870:                                        NULL,
2871:                                        NULL,
2872:                                        MatFDColoringSetUp_MPIXAIJ,
2873:                                        MatFindOffBlockDiagonalEntries_MPIAIJ,
2874:                                        MatCreateMPIMatConcatenateSeqMat_MPIAIJ,
2875:                                        /*145*/ NULL,
2876:                                        NULL,
2877:                                        NULL,
2878:                                        MatCreateGraph_Simple_AIJ,
2879:                                        NULL,
2880:                                        /*150*/ NULL,
2881:                                        MatEliminateZeros_MPIAIJ,
2882:                                        MatGetRowSumAbs_MPIAIJ,
2883:                                        NULL,
2884:                                        NULL,
2885:                                        /*155*/ NULL,
2886:                                        MatCopyHashToXAIJ_MPI_Hash};

2888: static PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2889: {
2890:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2892:   PetscFunctionBegin;
2893:   PetscCall(MatStoreValues(aij->A));
2894:   PetscCall(MatStoreValues(aij->B));
2895:   PetscFunctionReturn(PETSC_SUCCESS);
2896: }

2898: static PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2899: {
2900:   Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;

2902:   PetscFunctionBegin;
2903:   PetscCall(MatRetrieveValues(aij->A));
2904:   PetscCall(MatRetrieveValues(aij->B));
2905:   PetscFunctionReturn(PETSC_SUCCESS);
2906: }

2908: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
2909: {
2910:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
2911:   PetscMPIInt size;

2913:   PetscFunctionBegin;
2914:   if (B->hash_active) {
2915:     B->ops[0]      = b->cops;
2916:     B->hash_active = PETSC_FALSE;
2917:   }
2918:   PetscCall(PetscLayoutSetUp(B->rmap));
2919:   PetscCall(PetscLayoutSetUp(B->cmap));

2921: #if defined(PETSC_USE_CTABLE)
2922:   PetscCall(PetscHMapIDestroy(&b->colmap));
2923: #else
2924:   PetscCall(PetscFree(b->colmap));
2925: #endif
2926:   PetscCall(PetscFree(b->garray));
2927:   PetscCall(VecDestroy(&b->lvec));
2928:   PetscCall(VecScatterDestroy(&b->Mvctx));

2930:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

2932:   MatSeqXAIJGetOptions_Private(b->B);
2933:   PetscCall(MatDestroy(&b->B));
2934:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
2935:   PetscCall(MatSetSizes(b->B, B->rmap->n, size > 1 ? B->cmap->N : 0, B->rmap->n, size > 1 ? B->cmap->N : 0));
2936:   PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
2937:   PetscCall(MatSetType(b->B, MATSEQAIJ));
2938:   MatSeqXAIJRestoreOptions_Private(b->B);

2940:   MatSeqXAIJGetOptions_Private(b->A);
2941:   PetscCall(MatDestroy(&b->A));
2942:   PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
2943:   PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
2944:   PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
2945:   PetscCall(MatSetType(b->A, MATSEQAIJ));
2946:   MatSeqXAIJRestoreOptions_Private(b->A);

2948:   PetscCall(MatSeqAIJSetPreallocation(b->A, d_nz, d_nnz));
2949:   PetscCall(MatSeqAIJSetPreallocation(b->B, o_nz, o_nnz));
2950:   B->preallocated  = PETSC_TRUE;
2951:   B->was_assembled = PETSC_FALSE;
2952:   B->assembled     = PETSC_FALSE;
2953:   PetscFunctionReturn(PETSC_SUCCESS);
2954: }

2956: static PetscErrorCode MatResetPreallocation_MPIAIJ(Mat B)
2957: {
2958:   Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;

2960:   PetscFunctionBegin;
2962:   PetscCall(PetscLayoutSetUp(B->rmap));
2963:   PetscCall(PetscLayoutSetUp(B->cmap));
2964:   if (B->assembled || B->was_assembled) PetscCall(MatDisAssemble_MPIAIJ(B, PETSC_TRUE));
2965:   else {
2966: #if defined(PETSC_USE_CTABLE)
2967:     PetscCall(PetscHMapIDestroy(&b->colmap));
2968: #else
2969:     PetscCall(PetscFree(b->colmap));
2970: #endif
2971:     PetscCall(PetscFree(b->garray));
2972:     PetscCall(VecDestroy(&b->lvec));
2973:   }
2974:   PetscCall(VecScatterDestroy(&b->Mvctx));

2976:   PetscCall(MatResetPreallocation(b->A));
2977:   PetscCall(MatResetPreallocation(b->B));
2978:   B->preallocated  = PETSC_TRUE;
2979:   B->was_assembled = PETSC_FALSE;
2980:   B->assembled     = PETSC_FALSE;
2981:   PetscFunctionReturn(PETSC_SUCCESS);
2982: }

2984: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
2985: {
2986:   Mat         mat;
2987:   Mat_MPIAIJ *a, *oldmat = (Mat_MPIAIJ *)matin->data;

2989:   PetscFunctionBegin;
2990:   *newmat = NULL;
2991:   PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
2992:   PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
2993:   PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
2994:   PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
2995:   a = (Mat_MPIAIJ *)mat->data;

2997:   mat->factortype = matin->factortype;
2998:   mat->assembled  = matin->assembled;
2999:   mat->insertmode = NOT_SET_VALUES;

3001:   a->size         = oldmat->size;
3002:   a->rank         = oldmat->rank;
3003:   a->donotstash   = oldmat->donotstash;
3004:   a->roworiented  = oldmat->roworiented;
3005:   a->rowindices   = NULL;
3006:   a->rowvalues    = NULL;
3007:   a->getrowactive = PETSC_FALSE;

3009:   PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
3010:   PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
3011:   if (matin->hash_active) {
3012:     PetscCall(MatSetUp(mat));
3013:   } else {
3014:     mat->preallocated = matin->preallocated;
3015:     if (oldmat->colmap) {
3016: #if defined(PETSC_USE_CTABLE)
3017:       PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
3018: #else
3019:       PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
3020:       PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
3021: #endif
3022:     } else a->colmap = NULL;
3023:     if (oldmat->garray) {
3024:       PetscInt len;
3025:       len = oldmat->B->cmap->n;
3026:       PetscCall(PetscMalloc1(len + 1, &a->garray));
3027:       if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
3028:     } else a->garray = NULL;

3030:     /* It may happen MatDuplicate is called with a non-assembled matrix
3031:       In fact, MatDuplicate only requires the matrix to be preallocated
3032:       This may happen inside a DMCreateMatrix_Shell */
3033:     if (oldmat->lvec) PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
3034:     if (oldmat->Mvctx) {
3035:       a->Mvctx = oldmat->Mvctx;
3036:       PetscCall(PetscObjectReference((PetscObject)oldmat->Mvctx));
3037:     }
3038:     PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
3039:     PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
3040:   }
3041:   PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
3042:   *newmat = mat;
3043:   PetscFunctionReturn(PETSC_SUCCESS);
3044: }

3046: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3047: {
3048:   PetscBool isbinary, ishdf5;

3050:   PetscFunctionBegin;
3053:   /* force binary viewer to load .info file if it has not yet done so */
3054:   PetscCall(PetscViewerSetUp(viewer));
3055:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3056:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
3057:   if (isbinary) {
3058:     PetscCall(MatLoad_MPIAIJ_Binary(newMat, viewer));
3059:   } else if (ishdf5) {
3060: #if defined(PETSC_HAVE_HDF5)
3061:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
3062: #else
3063:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
3064: #endif
3065:   } else {
3066:     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);
3067:   }
3068:   PetscFunctionReturn(PETSC_SUCCESS);
3069: }

3071: PetscErrorCode MatLoad_MPIAIJ_Binary(Mat mat, PetscViewer viewer)
3072: {
3073:   PetscInt     header[4], M, N, m, nz, rows, cols, sum, i;
3074:   PetscInt    *rowidxs, *colidxs;
3075:   PetscScalar *matvals;

3077:   PetscFunctionBegin;
3078:   PetscCall(PetscViewerSetUp(viewer));

3080:   /* read in matrix header */
3081:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3082:   PetscCheck(header[0] == MAT_FILE_CLASSID, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3083:   M  = header[1];
3084:   N  = header[2];
3085:   nz = header[3];
3086:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3087:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3088:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as MPIAIJ");

3090:   /* set block sizes from the viewer's .info file */
3091:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3092:   /* set global sizes if not set already */
3093:   if (mat->rmap->N < 0) mat->rmap->N = M;
3094:   if (mat->cmap->N < 0) mat->cmap->N = N;
3095:   PetscCall(PetscLayoutSetUp(mat->rmap));
3096:   PetscCall(PetscLayoutSetUp(mat->cmap));

3098:   /* check if the matrix sizes are correct */
3099:   PetscCall(MatGetSize(mat, &rows, &cols));
3100:   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);

3102:   /* read in row lengths and build row indices */
3103:   PetscCall(MatGetLocalSize(mat, &m, NULL));
3104:   PetscCall(PetscMalloc1(m + 1, &rowidxs));
3105:   PetscCall(PetscViewerBinaryReadAll(viewer, rowidxs + 1, m, PETSC_DECIDE, M, PETSC_INT));
3106:   rowidxs[0] = 0;
3107:   for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3108:   if (nz != PETSC_INT_MAX) {
3109:     PetscCallMPI(MPIU_Allreduce(&rowidxs[m], &sum, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)viewer)));
3110:     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);
3111:   }

3113:   /* read in column indices and matrix values */
3114:   PetscCall(PetscMalloc2(rowidxs[m], &colidxs, rowidxs[m], &matvals));
3115:   PetscCall(PetscViewerBinaryReadAll(viewer, colidxs, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_INT));
3116:   PetscCall(PetscViewerBinaryReadAll(viewer, matvals, rowidxs[m], PETSC_DETERMINE, PETSC_DETERMINE, PETSC_SCALAR));
3117:   /* store matrix indices and values */
3118:   PetscCall(MatMPIAIJSetPreallocationCSR(mat, rowidxs, colidxs, matvals));
3119:   PetscCall(PetscFree(rowidxs));
3120:   PetscCall(PetscFree2(colidxs, matvals));
3121:   PetscFunctionReturn(PETSC_SUCCESS);
3122: }

3124: /* Not scalable because of ISAllGather() unless getting all columns. */
3125: static PetscErrorCode ISGetSeqIS_Private(Mat mat, IS iscol, IS *isseq)
3126: {
3127:   IS          iscol_local;
3128:   PetscBool   isstride;
3129:   PetscMPIInt lisstride = 0, gisstride;

3131:   PetscFunctionBegin;
3132:   /* check if we are grabbing all columns*/
3133:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &isstride));

3135:   if (isstride) {
3136:     PetscInt start, len, mstart, mlen;
3137:     PetscCall(ISStrideGetInfo(iscol, &start, NULL));
3138:     PetscCall(ISGetLocalSize(iscol, &len));
3139:     PetscCall(MatGetOwnershipRangeColumn(mat, &mstart, &mlen));
3140:     if (mstart == start && mlen - mstart == len) lisstride = 1;
3141:   }

3143:   PetscCallMPI(MPIU_Allreduce(&lisstride, &gisstride, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
3144:   if (gisstride) {
3145:     PetscInt N;
3146:     PetscCall(MatGetSize(mat, NULL, &N));
3147:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol_local));
3148:     PetscCall(ISSetIdentity(iscol_local));
3149:     PetscCall(PetscInfo(mat, "Optimizing for obtaining all columns of the matrix; skipping ISAllGather()\n"));
3150:   } else {
3151:     PetscInt cbs;
3152:     PetscCall(ISGetBlockSize(iscol, &cbs));
3153:     PetscCall(ISAllGather(iscol, &iscol_local));
3154:     PetscCall(ISSetBlockSize(iscol_local, cbs));
3155:   }

3157:   *isseq = iscol_local;
3158:   PetscFunctionReturn(PETSC_SUCCESS);
3159: }

3161: /*
3162:  Used by MatCreateSubMatrix_MPIAIJ_SameRowColDist() to avoid ISAllGather() and global size of iscol_local
3163:  (see MatCreateSubMatrix_MPIAIJ_nonscalable)

3165:  Input Parameters:
3166: +   mat - matrix
3167: .   isrow - parallel row index set; its local indices are a subset of local columns of `mat`,
3168:            i.e., mat->rstart <= isrow[i] < mat->rend
3169: -   iscol - parallel column index set; its local indices are a subset of local columns of `mat`,
3170:            i.e., mat->cstart <= iscol[i] < mat->cend

3172:  Output Parameters:
3173: +   isrow_d - sequential row index set for retrieving mat->A
3174: .   iscol_d - sequential  column index set for retrieving mat->A
3175: .   iscol_o - sequential column index set for retrieving mat->B
3176: -   garray - column map; garray[i] indicates global location of iscol_o[i] in `iscol`
3177:  */
3178: static PetscErrorCode ISGetSeqIS_SameColDist_Private(Mat mat, IS isrow, IS iscol, IS *isrow_d, IS *iscol_d, IS *iscol_o, PetscInt *garray[])
3179: {
3180:   Vec             x, cmap;
3181:   const PetscInt *is_idx;
3182:   PetscScalar    *xarray, *cmaparray;
3183:   PetscInt        ncols, isstart, *idx, m, rstart, *cmap1, count;
3184:   Mat_MPIAIJ     *a    = (Mat_MPIAIJ *)mat->data;
3185:   Mat             B    = a->B;
3186:   Vec             lvec = a->lvec, lcmap;
3187:   PetscInt        i, cstart, cend, Bn = B->cmap->N;
3188:   MPI_Comm        comm;
3189:   VecScatter      Mvctx = a->Mvctx;

3191:   PetscFunctionBegin;
3192:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3193:   PetscCall(ISGetLocalSize(iscol, &ncols));

3195:   /* (1) iscol is a sub-column vector of mat, pad it with '-1.' to form a full vector x */
3196:   PetscCall(MatCreateVecs(mat, &x, NULL));
3197:   PetscCall(VecSet(x, -1.0));
3198:   PetscCall(VecDuplicate(x, &cmap));
3199:   PetscCall(VecSet(cmap, -1.0));

3201:   /* Get start indices */
3202:   PetscCallMPI(MPI_Scan(&ncols, &isstart, 1, MPIU_INT, MPI_SUM, comm));
3203:   isstart -= ncols;
3204:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));

3206:   PetscCall(ISGetIndices(iscol, &is_idx));
3207:   PetscCall(VecGetArray(x, &xarray));
3208:   PetscCall(VecGetArray(cmap, &cmaparray));
3209:   PetscCall(PetscMalloc1(ncols, &idx));
3210:   for (i = 0; i < ncols; i++) {
3211:     xarray[is_idx[i] - cstart]    = (PetscScalar)is_idx[i];
3212:     cmaparray[is_idx[i] - cstart] = i + isstart;        /* global index of iscol[i] */
3213:     idx[i]                        = is_idx[i] - cstart; /* local index of iscol[i]  */
3214:   }
3215:   PetscCall(VecRestoreArray(x, &xarray));
3216:   PetscCall(VecRestoreArray(cmap, &cmaparray));
3217:   PetscCall(ISRestoreIndices(iscol, &is_idx));

3219:   /* Get iscol_d */
3220:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, iscol_d));
3221:   PetscCall(ISGetBlockSize(iscol, &i));
3222:   PetscCall(ISSetBlockSize(*iscol_d, i));

3224:   /* Get isrow_d */
3225:   PetscCall(ISGetLocalSize(isrow, &m));
3226:   rstart = mat->rmap->rstart;
3227:   PetscCall(PetscMalloc1(m, &idx));
3228:   PetscCall(ISGetIndices(isrow, &is_idx));
3229:   for (i = 0; i < m; i++) idx[i] = is_idx[i] - rstart;
3230:   PetscCall(ISRestoreIndices(isrow, &is_idx));

3232:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, m, idx, PETSC_OWN_POINTER, isrow_d));
3233:   PetscCall(ISGetBlockSize(isrow, &i));
3234:   PetscCall(ISSetBlockSize(*isrow_d, i));

3236:   /* (2) Scatter x and cmap using aij->Mvctx to get their off-process portions (see MatMult_MPIAIJ) */
3237:   PetscCall(VecScatterBegin(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));
3238:   PetscCall(VecScatterEnd(Mvctx, x, lvec, INSERT_VALUES, SCATTER_FORWARD));

3240:   PetscCall(VecDuplicate(lvec, &lcmap));

3242:   PetscCall(VecScatterBegin(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));
3243:   PetscCall(VecScatterEnd(Mvctx, cmap, lcmap, INSERT_VALUES, SCATTER_FORWARD));

3245:   /* (3) create sequential iscol_o (a subset of iscol) and isgarray */
3246:   /* off-process column indices */
3247:   count = 0;
3248:   PetscCall(PetscMalloc1(Bn, &idx));
3249:   PetscCall(PetscMalloc1(Bn, &cmap1));

3251:   PetscCall(VecGetArray(lvec, &xarray));
3252:   PetscCall(VecGetArray(lcmap, &cmaparray));
3253:   for (i = 0; i < Bn; i++) {
3254:     if (PetscRealPart(xarray[i]) > -1.0) {
3255:       idx[count]   = i;                                     /* local column index in off-diagonal part B */
3256:       cmap1[count] = (PetscInt)PetscRealPart(cmaparray[i]); /* column index in submat */
3257:       count++;
3258:     }
3259:   }
3260:   PetscCall(VecRestoreArray(lvec, &xarray));
3261:   PetscCall(VecRestoreArray(lcmap, &cmaparray));

3263:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_COPY_VALUES, iscol_o));
3264:   /* cannot ensure iscol_o has same blocksize as iscol! */

3266:   PetscCall(PetscFree(idx));
3267:   *garray = cmap1;

3269:   PetscCall(VecDestroy(&x));
3270:   PetscCall(VecDestroy(&cmap));
3271:   PetscCall(VecDestroy(&lcmap));
3272:   PetscFunctionReturn(PETSC_SUCCESS);
3273: }

3275: /* isrow and iscol have same processor distribution as mat, output *submat is a submatrix of local mat */
3276: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowColDist(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *submat)
3277: {
3278:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)mat->data, *asub;
3279:   Mat         M = NULL;
3280:   MPI_Comm    comm;
3281:   IS          iscol_d, isrow_d, iscol_o;
3282:   Mat         Asub = NULL, Bsub = NULL;
3283:   PetscInt    n;

3285:   PetscFunctionBegin;
3286:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));

3288:   if (call == MAT_REUSE_MATRIX) {
3289:     /* Retrieve isrow_d, iscol_d and iscol_o from submat */
3290:     PetscCall(PetscObjectQuery((PetscObject)*submat, "isrow_d", (PetscObject *)&isrow_d));
3291:     PetscCheck(isrow_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "isrow_d passed in was not used before, cannot reuse");

3293:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_d", (PetscObject *)&iscol_d));
3294:     PetscCheck(iscol_d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_d passed in was not used before, cannot reuse");

3296:     PetscCall(PetscObjectQuery((PetscObject)*submat, "iscol_o", (PetscObject *)&iscol_o));
3297:     PetscCheck(iscol_o, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "iscol_o passed in was not used before, cannot reuse");

3299:     /* Update diagonal and off-diagonal portions of submat */
3300:     asub = (Mat_MPIAIJ *)(*submat)->data;
3301:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->A));
3302:     PetscCall(ISGetLocalSize(iscol_o, &n));
3303:     if (n) PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_REUSE_MATRIX, &asub->B));
3304:     PetscCall(MatAssemblyBegin(*submat, MAT_FINAL_ASSEMBLY));
3305:     PetscCall(MatAssemblyEnd(*submat, MAT_FINAL_ASSEMBLY));

3307:   } else { /* call == MAT_INITIAL_MATRIX) */
3308:     PetscInt *garray;
3309:     PetscInt  BsubN;

3311:     /* Create isrow_d, iscol_d, iscol_o and isgarray (replace isgarray with array?) */
3312:     PetscCall(ISGetSeqIS_SameColDist_Private(mat, isrow, iscol, &isrow_d, &iscol_d, &iscol_o, &garray));

3314:     /* Create local submatrices Asub and Bsub */
3315:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->A, isrow_d, iscol_d, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Asub));
3316:     PetscCall(MatCreateSubMatrix_SeqAIJ(a->B, isrow_d, iscol_o, PETSC_DECIDE, MAT_INITIAL_MATRIX, &Bsub));

3318:     /* Create submatrix M */
3319:     PetscCall(MatCreateMPIAIJWithSeqAIJ(comm, Asub, Bsub, garray, &M));

3321:     /* If Bsub has empty columns, compress iscol_o such that it will retrieve condensed Bsub from a->B during reuse */
3322:     asub = (Mat_MPIAIJ *)M->data;

3324:     PetscCall(ISGetLocalSize(iscol_o, &BsubN));
3325:     n = asub->B->cmap->N;
3326:     if (BsubN > n) {
3327:       /* This case can be tested using ~petsc/src/tao/bound/tutorials/runplate2_3 */
3328:       const PetscInt *idx;
3329:       PetscInt        i, j, *idx_new, *subgarray = asub->garray;
3330:       PetscCall(PetscInfo(M, "submatrix Bn %" PetscInt_FMT " != BsubN %" PetscInt_FMT ", update iscol_o\n", n, BsubN));

3332:       PetscCall(PetscMalloc1(n, &idx_new));
3333:       j = 0;
3334:       PetscCall(ISGetIndices(iscol_o, &idx));
3335:       for (i = 0; i < n; i++) {
3336:         if (j >= BsubN) break;
3337:         while (subgarray[i] > garray[j]) j++;

3339:         if (subgarray[i] == garray[j]) {
3340:           idx_new[i] = idx[j++];
3341:         } 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]);
3342:       }
3343:       PetscCall(ISRestoreIndices(iscol_o, &idx));

3345:       PetscCall(ISDestroy(&iscol_o));
3346:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, n, idx_new, PETSC_OWN_POINTER, &iscol_o));

3348:     } else if (BsubN < n) {
3349:       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);
3350:     }

3352:     PetscCall(PetscFree(garray));
3353:     *submat = M;

3355:     /* Save isrow_d, iscol_d and iscol_o used in processor for next request */
3356:     PetscCall(PetscObjectCompose((PetscObject)M, "isrow_d", (PetscObject)isrow_d));
3357:     PetscCall(ISDestroy(&isrow_d));

3359:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_d", (PetscObject)iscol_d));
3360:     PetscCall(ISDestroy(&iscol_d));

3362:     PetscCall(PetscObjectCompose((PetscObject)M, "iscol_o", (PetscObject)iscol_o));
3363:     PetscCall(ISDestroy(&iscol_o));
3364:   }
3365:   PetscFunctionReturn(PETSC_SUCCESS);
3366: }

3368: PetscErrorCode MatCreateSubMatrix_MPIAIJ(Mat mat, IS isrow, IS iscol, MatReuse call, Mat *newmat)
3369: {
3370:   IS        iscol_local = NULL, isrow_d;
3371:   PetscInt  csize;
3372:   PetscInt  n, i, j, start, end;
3373:   PetscBool sameRowDist = PETSC_FALSE, sameDist[2], tsameDist[2];
3374:   MPI_Comm  comm;

3376:   PetscFunctionBegin;
3377:   /* If isrow has same processor distribution as mat,
3378:      call MatCreateSubMatrix_MPIAIJ_SameRowDist() to avoid using a hash table with global size of iscol */
3379:   if (call == MAT_REUSE_MATRIX) {
3380:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "isrow_d", (PetscObject *)&isrow_d));
3381:     if (isrow_d) {
3382:       sameRowDist  = PETSC_TRUE;
3383:       tsameDist[1] = PETSC_TRUE; /* sameColDist */
3384:     } else {
3385:       PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_local));
3386:       if (iscol_local) {
3387:         sameRowDist  = PETSC_TRUE;
3388:         tsameDist[1] = PETSC_FALSE; /* !sameColDist */
3389:       }
3390:     }
3391:   } else {
3392:     /* Check if isrow has same processor distribution as mat */
3393:     sameDist[0] = PETSC_FALSE;
3394:     PetscCall(ISGetLocalSize(isrow, &n));
3395:     if (!n) {
3396:       sameDist[0] = PETSC_TRUE;
3397:     } else {
3398:       PetscCall(ISGetMinMax(isrow, &i, &j));
3399:       PetscCall(MatGetOwnershipRange(mat, &start, &end));
3400:       if (i >= start && j < end) sameDist[0] = PETSC_TRUE;
3401:     }

3403:     /* Check if iscol has same processor distribution as mat */
3404:     sameDist[1] = PETSC_FALSE;
3405:     PetscCall(ISGetLocalSize(iscol, &n));
3406:     if (!n) {
3407:       sameDist[1] = PETSC_TRUE;
3408:     } else {
3409:       PetscCall(ISGetMinMax(iscol, &i, &j));
3410:       PetscCall(MatGetOwnershipRangeColumn(mat, &start, &end));
3411:       if (i >= start && j < end) sameDist[1] = PETSC_TRUE;
3412:     }

3414:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3415:     PetscCallMPI(MPIU_Allreduce(&sameDist, &tsameDist, 2, MPIU_BOOL, MPI_LAND, comm));
3416:     sameRowDist = tsameDist[0];
3417:   }

3419:   if (sameRowDist) {
3420:     if (tsameDist[1]) { /* sameRowDist & sameColDist */
3421:       /* isrow and iscol have same processor distribution as mat */
3422:       PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowColDist(mat, isrow, iscol, call, newmat));
3423:       PetscFunctionReturn(PETSC_SUCCESS);
3424:     } else { /* sameRowDist */
3425:       /* isrow has same processor distribution as mat */
3426:       if (call == MAT_INITIAL_MATRIX) {
3427:         PetscBool sorted;
3428:         PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3429:         PetscCall(ISGetLocalSize(iscol_local, &n)); /* local size of iscol_local = global columns of newmat */
3430:         PetscCall(ISGetSize(iscol, &i));
3431:         PetscCheck(n == i, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "n %" PetscInt_FMT " != size of iscol %" PetscInt_FMT, n, i);

3433:         PetscCall(ISSorted(iscol_local, &sorted));
3434:         if (sorted) {
3435:           /* MatCreateSubMatrix_MPIAIJ_SameRowDist() requires iscol_local be sorted; it can have duplicate indices */
3436:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, iscol_local, MAT_INITIAL_MATRIX, newmat));
3437:           PetscFunctionReturn(PETSC_SUCCESS);
3438:         }
3439:       } else { /* call == MAT_REUSE_MATRIX */
3440:         IS iscol_sub;
3441:         PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3442:         if (iscol_sub) {
3443:           PetscCall(MatCreateSubMatrix_MPIAIJ_SameRowDist(mat, isrow, iscol, NULL, call, newmat));
3444:           PetscFunctionReturn(PETSC_SUCCESS);
3445:         }
3446:       }
3447:     }
3448:   }

3450:   /* General case: iscol -> iscol_local which has global size of iscol */
3451:   if (call == MAT_REUSE_MATRIX) {
3452:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "ISAllGather", (PetscObject *)&iscol_local));
3453:     PetscCheck(iscol_local, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3454:   } else {
3455:     if (!iscol_local) PetscCall(ISGetSeqIS_Private(mat, iscol, &iscol_local));
3456:   }

3458:   PetscCall(ISGetLocalSize(iscol, &csize));
3459:   PetscCall(MatCreateSubMatrix_MPIAIJ_nonscalable(mat, isrow, iscol_local, csize, call, newmat));

3461:   if (call == MAT_INITIAL_MATRIX) {
3462:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3463:     PetscCall(ISDestroy(&iscol_local));
3464:   }
3465:   PetscFunctionReturn(PETSC_SUCCESS);
3466: }

3468: /*@C
3469:   MatCreateMPIAIJWithSeqAIJ - creates a `MATMPIAIJ` matrix using `MATSEQAIJ` matrices that contain the "diagonal"
3470:   and "off-diagonal" part of the matrix in CSR format.

3472:   Collective

3474:   Input Parameters:
3475: + comm   - MPI communicator
3476: . A      - "diagonal" portion of matrix
3477: . B      - "off-diagonal" portion of matrix, may have empty columns, will be destroyed by this routine
3478: - garray - global index of `B` columns

3480:   Output Parameter:
3481: . mat - the matrix, with input `A` as its local diagonal matrix

3483:   Level: advanced

3485:   Notes:
3486:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix.

3488:   `A` becomes part of output mat, `B` is destroyed by this routine. The user cannot use `A` and `B` anymore.

3490: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MATSEQAIJ`, `MatCreateMPIAIJWithSplitArrays()`
3491: @*/
3492: PetscErrorCode MatCreateMPIAIJWithSeqAIJ(MPI_Comm comm, Mat A, Mat B, const PetscInt garray[], Mat *mat)
3493: {
3494:   Mat_MPIAIJ        *maij;
3495:   Mat_SeqAIJ        *b  = (Mat_SeqAIJ *)B->data, *bnew;
3496:   PetscInt          *oi = b->i, *oj = b->j, i, nz, col;
3497:   const PetscScalar *oa;
3498:   Mat                Bnew;
3499:   PetscInt           m, n, N;
3500:   MatType            mpi_mat_type;

3502:   PetscFunctionBegin;
3503:   PetscCall(MatCreate(comm, mat));
3504:   PetscCall(MatGetSize(A, &m, &n));
3505:   PetscCheck(m == B->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Am %" PetscInt_FMT " != Bm %" PetscInt_FMT, m, B->rmap->N);
3506:   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);
3507:   /* remove check below; When B is created using iscol_o from ISGetSeqIS_SameColDist_Private(), its bs may not be same as A */
3508:   /* 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); */

3510:   /* Get global columns of mat */
3511:   PetscCallMPI(MPIU_Allreduce(&n, &N, 1, MPIU_INT, MPI_SUM, comm));

3513:   PetscCall(MatSetSizes(*mat, m, n, PETSC_DECIDE, N));
3514:   /* Determine the type of MPI matrix that should be created from the type of matrix A, which holds the "diagonal" portion. */
3515:   PetscCall(MatGetMPIMatType_Private(A, &mpi_mat_type));
3516:   PetscCall(MatSetType(*mat, mpi_mat_type));

3518:   if (A->rmap->bs > 1 || A->cmap->bs > 1) PetscCall(MatSetBlockSizes(*mat, A->rmap->bs, A->cmap->bs));
3519:   maij = (Mat_MPIAIJ *)(*mat)->data;

3521:   (*mat)->preallocated = PETSC_TRUE;

3523:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
3524:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

3526:   /* Set A as diagonal portion of *mat */
3527:   maij->A = A;

3529:   nz = oi[m];
3530:   for (i = 0; i < nz; i++) {
3531:     col   = oj[i];
3532:     oj[i] = garray[col];
3533:   }

3535:   /* Set Bnew as off-diagonal portion of *mat */
3536:   PetscCall(MatSeqAIJGetArrayRead(B, &oa));
3537:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, N, oi, oj, (PetscScalar *)oa, &Bnew));
3538:   PetscCall(MatSeqAIJRestoreArrayRead(B, &oa));
3539:   bnew        = (Mat_SeqAIJ *)Bnew->data;
3540:   bnew->maxnz = b->maxnz; /* allocated nonzeros of B */
3541:   maij->B     = Bnew;

3543:   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);

3545:   b->free_a  = PETSC_FALSE;
3546:   b->free_ij = PETSC_FALSE;
3547:   PetscCall(MatDestroy(&B));

3549:   bnew->free_a  = PETSC_TRUE;
3550:   bnew->free_ij = PETSC_TRUE;

3552:   /* condense columns of maij->B */
3553:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
3554:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3555:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3556:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
3557:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3558:   PetscFunctionReturn(PETSC_SUCCESS);
3559: }

3561: extern PetscErrorCode MatCreateSubMatrices_MPIAIJ_SingleIS_Local(Mat, PetscInt, const IS[], const IS[], MatReuse, PetscBool, Mat *);

3563: PetscErrorCode MatCreateSubMatrix_MPIAIJ_SameRowDist(Mat mat, IS isrow, IS iscol, IS iscol_local, MatReuse call, Mat *newmat)
3564: {
3565:   PetscInt        i, m, n, rstart, row, rend, nz, j, bs, cbs;
3566:   PetscInt       *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3567:   Mat_MPIAIJ     *a = (Mat_MPIAIJ *)mat->data;
3568:   Mat             M, Msub, B = a->B;
3569:   MatScalar      *aa;
3570:   Mat_SeqAIJ     *aij;
3571:   PetscInt       *garray = a->garray, *colsub, Ncols;
3572:   PetscInt        count, Bn = B->cmap->N, cstart = mat->cmap->rstart, cend = mat->cmap->rend;
3573:   IS              iscol_sub, iscmap;
3574:   const PetscInt *is_idx, *cmap;
3575:   PetscBool       allcolumns = PETSC_FALSE;
3576:   MPI_Comm        comm;

3578:   PetscFunctionBegin;
3579:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3580:   if (call == MAT_REUSE_MATRIX) {
3581:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubIScol", (PetscObject *)&iscol_sub));
3582:     PetscCheck(iscol_sub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "SubIScol passed in was not used before, cannot reuse");
3583:     PetscCall(ISGetLocalSize(iscol_sub, &count));

3585:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "Subcmap", (PetscObject *)&iscmap));
3586:     PetscCheck(iscmap, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Subcmap passed in was not used before, cannot reuse");

3588:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Msub));
3589:     PetscCheck(Msub, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");

3591:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_REUSE_MATRIX, PETSC_FALSE, &Msub));

3593:   } else { /* call == MAT_INITIAL_MATRIX) */
3594:     PetscBool flg;

3596:     PetscCall(ISGetLocalSize(iscol, &n));
3597:     PetscCall(ISGetSize(iscol, &Ncols));

3599:     /* (1) iscol -> nonscalable iscol_local */
3600:     /* Check for special case: each processor gets entire matrix columns */
3601:     PetscCall(ISIdentity(iscol_local, &flg));
3602:     if (flg && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3603:     PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
3604:     if (allcolumns) {
3605:       iscol_sub = iscol_local;
3606:       PetscCall(PetscObjectReference((PetscObject)iscol_local));
3607:       PetscCall(ISCreateStride(PETSC_COMM_SELF, n, 0, 1, &iscmap));

3609:     } else {
3610:       /* (2) iscol_local -> iscol_sub and iscmap. Implementation below requires iscol_local be sorted, it can have duplicate indices */
3611:       PetscInt *idx, *cmap1, k;
3612:       PetscCall(PetscMalloc1(Ncols, &idx));
3613:       PetscCall(PetscMalloc1(Ncols, &cmap1));
3614:       PetscCall(ISGetIndices(iscol_local, &is_idx));
3615:       count = 0;
3616:       k     = 0;
3617:       for (i = 0; i < Ncols; i++) {
3618:         j = is_idx[i];
3619:         if (j >= cstart && j < cend) {
3620:           /* diagonal part of mat */
3621:           idx[count]     = j;
3622:           cmap1[count++] = i; /* column index in submat */
3623:         } else if (Bn) {
3624:           /* off-diagonal part of mat */
3625:           if (j == garray[k]) {
3626:             idx[count]     = j;
3627:             cmap1[count++] = i; /* column index in submat */
3628:           } else if (j > garray[k]) {
3629:             while (j > garray[k] && k < Bn - 1) k++;
3630:             if (j == garray[k]) {
3631:               idx[count]     = j;
3632:               cmap1[count++] = i; /* column index in submat */
3633:             }
3634:           }
3635:         }
3636:       }
3637:       PetscCall(ISRestoreIndices(iscol_local, &is_idx));

3639:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, count, idx, PETSC_OWN_POINTER, &iscol_sub));
3640:       PetscCall(ISGetBlockSize(iscol, &cbs));
3641:       PetscCall(ISSetBlockSize(iscol_sub, cbs));

3643:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)iscol_local), count, cmap1, PETSC_OWN_POINTER, &iscmap));
3644:     }

3646:     /* (3) Create sequential Msub */
3647:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol_sub, MAT_INITIAL_MATRIX, allcolumns, &Msub));
3648:   }

3650:   PetscCall(ISGetLocalSize(iscol_sub, &count));
3651:   aij = (Mat_SeqAIJ *)Msub->data;
3652:   ii  = aij->i;
3653:   PetscCall(ISGetIndices(iscmap, &cmap));

3655:   /*
3656:       m - number of local rows
3657:       Ncols - number of columns (same on all processors)
3658:       rstart - first row in new global matrix generated
3659:   */
3660:   PetscCall(MatGetSize(Msub, &m, NULL));

3662:   if (call == MAT_INITIAL_MATRIX) {
3663:     /* (4) Create parallel newmat */
3664:     PetscMPIInt rank, size;
3665:     PetscInt    csize;

3667:     PetscCallMPI(MPI_Comm_size(comm, &size));
3668:     PetscCallMPI(MPI_Comm_rank(comm, &rank));

3670:     /*
3671:         Determine the number of non-zeros in the diagonal and off-diagonal
3672:         portions of the matrix in order to do correct preallocation
3673:     */

3675:     /* first get start and end of "diagonal" columns */
3676:     PetscCall(ISGetLocalSize(iscol, &csize));
3677:     if (csize == PETSC_DECIDE) {
3678:       PetscCall(ISGetSize(isrow, &mglobal));
3679:       if (mglobal == Ncols) { /* square matrix */
3680:         nlocal = m;
3681:       } else {
3682:         nlocal = Ncols / size + ((Ncols % size) > rank);
3683:       }
3684:     } else {
3685:       nlocal = csize;
3686:     }
3687:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3688:     rstart = rend - nlocal;
3689:     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);

3691:     /* next, compute all the lengths */
3692:     jj = aij->j;
3693:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3694:     olens = dlens + m;
3695:     for (i = 0; i < m; i++) {
3696:       jend = ii[i + 1] - ii[i];
3697:       olen = 0;
3698:       dlen = 0;
3699:       for (j = 0; j < jend; j++) {
3700:         if (cmap[*jj] < rstart || cmap[*jj] >= rend) olen++;
3701:         else dlen++;
3702:         jj++;
3703:       }
3704:       olens[i] = olen;
3705:       dlens[i] = dlen;
3706:     }

3708:     PetscCall(ISGetBlockSize(isrow, &bs));
3709:     PetscCall(ISGetBlockSize(iscol, &cbs));

3711:     PetscCall(MatCreate(comm, &M));
3712:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, Ncols));
3713:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3714:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3715:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3716:     PetscCall(PetscFree(dlens));

3718:   } else { /* call == MAT_REUSE_MATRIX */
3719:     M = *newmat;
3720:     PetscCall(MatGetLocalSize(M, &i, NULL));
3721:     PetscCheck(i == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3722:     PetscCall(MatZeroEntries(M));
3723:     /*
3724:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3725:        rather than the slower MatSetValues().
3726:     */
3727:     M->was_assembled = PETSC_TRUE;
3728:     M->assembled     = PETSC_FALSE;
3729:   }

3731:   /* (5) Set values of Msub to *newmat */
3732:   PetscCall(PetscMalloc1(count, &colsub));
3733:   PetscCall(MatGetOwnershipRange(M, &rstart, NULL));

3735:   jj = aij->j;
3736:   PetscCall(MatSeqAIJGetArrayRead(Msub, (const PetscScalar **)&aa));
3737:   for (i = 0; i < m; i++) {
3738:     row = rstart + i;
3739:     nz  = ii[i + 1] - ii[i];
3740:     for (j = 0; j < nz; j++) colsub[j] = cmap[jj[j]];
3741:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, colsub, aa, INSERT_VALUES));
3742:     jj += nz;
3743:     aa += nz;
3744:   }
3745:   PetscCall(MatSeqAIJRestoreArrayRead(Msub, (const PetscScalar **)&aa));
3746:   PetscCall(ISRestoreIndices(iscmap, &cmap));

3748:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3749:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));

3751:   PetscCall(PetscFree(colsub));

3753:   /* save Msub, iscol_sub and iscmap used in processor for next request */
3754:   if (call == MAT_INITIAL_MATRIX) {
3755:     *newmat = M;
3756:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubMatrix", (PetscObject)Msub));
3757:     PetscCall(MatDestroy(&Msub));

3759:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "SubIScol", (PetscObject)iscol_sub));
3760:     PetscCall(ISDestroy(&iscol_sub));

3762:     PetscCall(PetscObjectCompose((PetscObject)*newmat, "Subcmap", (PetscObject)iscmap));
3763:     PetscCall(ISDestroy(&iscmap));

3765:     if (iscol_local) {
3766:       PetscCall(PetscObjectCompose((PetscObject)*newmat, "ISAllGather", (PetscObject)iscol_local));
3767:       PetscCall(ISDestroy(&iscol_local));
3768:     }
3769:   }
3770:   PetscFunctionReturn(PETSC_SUCCESS);
3771: }

3773: /*
3774:     Not great since it makes two copies of the submatrix, first an SeqAIJ
3775:   in local and then by concatenating the local matrices the end result.
3776:   Writing it directly would be much like MatCreateSubMatrices_MPIAIJ()

3778:   This requires a sequential iscol with all indices.
3779: */
3780: PetscErrorCode MatCreateSubMatrix_MPIAIJ_nonscalable(Mat mat, IS isrow, IS iscol, PetscInt csize, MatReuse call, Mat *newmat)
3781: {
3782:   PetscMPIInt rank, size;
3783:   PetscInt    i, m, n, rstart, row, rend, nz, *cwork, j, bs, cbs;
3784:   PetscInt   *ii, *jj, nlocal, *dlens, *olens, dlen, olen, jend, mglobal;
3785:   Mat         M, Mreuse;
3786:   MatScalar  *aa, *vwork;
3787:   MPI_Comm    comm;
3788:   Mat_SeqAIJ *aij;
3789:   PetscBool   colflag, allcolumns = PETSC_FALSE;

3791:   PetscFunctionBegin;
3792:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
3793:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
3794:   PetscCallMPI(MPI_Comm_size(comm, &size));

3796:   /* Check for special case: each processor gets entire matrix columns */
3797:   PetscCall(ISIdentity(iscol, &colflag));
3798:   PetscCall(ISGetLocalSize(iscol, &n));
3799:   if (colflag && n == mat->cmap->N) allcolumns = PETSC_TRUE;
3800:   PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &allcolumns, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));

3802:   if (call == MAT_REUSE_MATRIX) {
3803:     PetscCall(PetscObjectQuery((PetscObject)*newmat, "SubMatrix", (PetscObject *)&Mreuse));
3804:     PetscCheck(Mreuse, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Submatrix passed in was not used before, cannot reuse");
3805:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_REUSE_MATRIX, allcolumns, &Mreuse));
3806:   } else {
3807:     PetscCall(MatCreateSubMatrices_MPIAIJ_SingleIS_Local(mat, 1, &isrow, &iscol, MAT_INITIAL_MATRIX, allcolumns, &Mreuse));
3808:   }

3810:   /*
3811:       m - number of local rows
3812:       n - number of columns (same on all processors)
3813:       rstart - first row in new global matrix generated
3814:   */
3815:   PetscCall(MatGetSize(Mreuse, &m, &n));
3816:   PetscCall(MatGetBlockSizes(Mreuse, &bs, &cbs));
3817:   if (call == MAT_INITIAL_MATRIX) {
3818:     aij = (Mat_SeqAIJ *)Mreuse->data;
3819:     ii  = aij->i;
3820:     jj  = aij->j;

3822:     /*
3823:         Determine the number of non-zeros in the diagonal and off-diagonal
3824:         portions of the matrix in order to do correct preallocation
3825:     */

3827:     /* first get start and end of "diagonal" columns */
3828:     if (csize == PETSC_DECIDE) {
3829:       PetscCall(ISGetSize(isrow, &mglobal));
3830:       if (mglobal == n) { /* square matrix */
3831:         nlocal = m;
3832:       } else {
3833:         nlocal = n / size + ((n % size) > rank);
3834:       }
3835:     } else {
3836:       nlocal = csize;
3837:     }
3838:     PetscCallMPI(MPI_Scan(&nlocal, &rend, 1, MPIU_INT, MPI_SUM, comm));
3839:     rstart = rend - nlocal;
3840:     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);

3842:     /* next, compute all the lengths */
3843:     PetscCall(PetscMalloc1(2 * m + 1, &dlens));
3844:     olens = dlens + m;
3845:     for (i = 0; i < m; i++) {
3846:       jend = ii[i + 1] - ii[i];
3847:       olen = 0;
3848:       dlen = 0;
3849:       for (j = 0; j < jend; j++) {
3850:         if (*jj < rstart || *jj >= rend) olen++;
3851:         else dlen++;
3852:         jj++;
3853:       }
3854:       olens[i] = olen;
3855:       dlens[i] = dlen;
3856:     }
3857:     PetscCall(MatCreate(comm, &M));
3858:     PetscCall(MatSetSizes(M, m, nlocal, PETSC_DECIDE, n));
3859:     PetscCall(MatSetBlockSizes(M, bs, cbs));
3860:     PetscCall(MatSetType(M, ((PetscObject)mat)->type_name));
3861:     PetscCall(MatMPIAIJSetPreallocation(M, 0, dlens, 0, olens));
3862:     PetscCall(PetscFree(dlens));
3863:   } else {
3864:     PetscInt ml, nl;

3866:     M = *newmat;
3867:     PetscCall(MatGetLocalSize(M, &ml, &nl));
3868:     PetscCheck(ml == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Previous matrix must be same size/layout as request");
3869:     PetscCall(MatZeroEntries(M));
3870:     /*
3871:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3872:        rather than the slower MatSetValues().
3873:     */
3874:     M->was_assembled = PETSC_TRUE;
3875:     M->assembled     = PETSC_FALSE;
3876:   }
3877:   PetscCall(MatGetOwnershipRange(M, &rstart, &rend));
3878:   aij = (Mat_SeqAIJ *)Mreuse->data;
3879:   ii  = aij->i;
3880:   jj  = aij->j;

3882:   /* trigger copy to CPU if needed */
3883:   PetscCall(MatSeqAIJGetArrayRead(Mreuse, (const PetscScalar **)&aa));
3884:   for (i = 0; i < m; i++) {
3885:     row   = rstart + i;
3886:     nz    = ii[i + 1] - ii[i];
3887:     cwork = jj;
3888:     jj    = PetscSafePointerPlusOffset(jj, nz);
3889:     vwork = aa;
3890:     aa    = PetscSafePointerPlusOffset(aa, nz);
3891:     PetscCall(MatSetValues_MPIAIJ(M, 1, &row, nz, cwork, vwork, INSERT_VALUES));
3892:   }
3893:   PetscCall(MatSeqAIJRestoreArrayRead(Mreuse, (const PetscScalar **)&aa));

3895:   PetscCall(MatAssemblyBegin(M, MAT_FINAL_ASSEMBLY));
3896:   PetscCall(MatAssemblyEnd(M, MAT_FINAL_ASSEMBLY));
3897:   *newmat = M;

3899:   /* save submatrix used in processor for next request */
3900:   if (call == MAT_INITIAL_MATRIX) {
3901:     PetscCall(PetscObjectCompose((PetscObject)M, "SubMatrix", (PetscObject)Mreuse));
3902:     PetscCall(MatDestroy(&Mreuse));
3903:   }
3904:   PetscFunctionReturn(PETSC_SUCCESS);
3905: }

3907: static PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
3908: {
3909:   PetscInt        m, cstart, cend, j, nnz, i, d, *ld;
3910:   PetscInt       *d_nnz, *o_nnz, nnz_max = 0, rstart, ii, irstart;
3911:   const PetscInt *JJ;
3912:   PetscBool       nooffprocentries;
3913:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)B->data;

3915:   PetscFunctionBegin;
3916:   PetscCall(PetscLayoutSetUp(B->rmap));
3917:   PetscCall(PetscLayoutSetUp(B->cmap));
3918:   m       = B->rmap->n;
3919:   cstart  = B->cmap->rstart;
3920:   cend    = B->cmap->rend;
3921:   rstart  = B->rmap->rstart;
3922:   irstart = Ii[0];

3924:   PetscCall(PetscCalloc2(m, &d_nnz, m, &o_nnz));

3926:   if (PetscDefined(USE_DEBUG)) {
3927:     for (i = 0; i < m; i++) {
3928:       nnz = Ii[i + 1] - Ii[i];
3929:       JJ  = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3930:       PetscCheck(nnz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative %" PetscInt_FMT " number of columns", i, nnz);
3931:       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]);
3932:       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);
3933:     }
3934:   }

3936:   for (i = 0; i < m; i++) {
3937:     nnz     = Ii[i + 1] - Ii[i];
3938:     JJ      = PetscSafePointerPlusOffset(J, Ii[i] - irstart);
3939:     nnz_max = PetscMax(nnz_max, nnz);
3940:     d       = 0;
3941:     for (j = 0; j < nnz; j++) {
3942:       if (cstart <= JJ[j] && JJ[j] < cend) d++;
3943:     }
3944:     d_nnz[i] = d;
3945:     o_nnz[i] = nnz - d;
3946:   }
3947:   PetscCall(MatMPIAIJSetPreallocation(B, 0, d_nnz, 0, o_nnz));
3948:   PetscCall(PetscFree2(d_nnz, o_nnz));

3950:   for (i = 0; i < m; i++) {
3951:     ii = i + rstart;
3952:     PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, Ii[i + 1] - Ii[i], PetscSafePointerPlusOffset(J, Ii[i] - irstart), PetscSafePointerPlusOffset(v, Ii[i] - irstart), INSERT_VALUES));
3953:   }
3954:   nooffprocentries    = B->nooffprocentries;
3955:   B->nooffprocentries = PETSC_TRUE;
3956:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3957:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3958:   B->nooffprocentries = nooffprocentries;

3960:   /* count number of entries below block diagonal */
3961:   PetscCall(PetscFree(Aij->ld));
3962:   PetscCall(PetscCalloc1(m, &ld));
3963:   Aij->ld = ld;
3964:   for (i = 0; i < m; i++) {
3965:     nnz = Ii[i + 1] - Ii[i];
3966:     j   = 0;
3967:     while (j < nnz && J[j] < cstart) j++;
3968:     ld[i] = j;
3969:     if (J) J += nnz;
3970:   }

3972:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3973:   PetscFunctionReturn(PETSC_SUCCESS);
3974: }

3976: /*@
3977:   MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in `MATAIJ` format
3978:   (the default parallel PETSc format).

3980:   Collective

3982:   Input Parameters:
3983: + B - the matrix
3984: . i - the indices into `j` for the start of each local row (indices start with zero)
3985: . j - the column indices for each local row (indices start with zero)
3986: - v - optional values in the matrix

3988:   Level: developer

3990:   Notes:
3991:   The `i`, `j`, and `v` arrays ARE copied by this routine into the internal format used by PETSc;
3992:   thus you CANNOT change the matrix entries by changing the values of `v` after you have
3993:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

3995:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

3997:   A convenience routine for this functionality is `MatCreateMPIAIJWithArrays()`.

3999:   You can update the matrix with new numerical values using `MatUpdateMPIAIJWithArrays()` after this call if the column indices in `j` are sorted.

4001:   If you do **not** use `MatUpdateMPIAIJWithArrays()`, the column indices in `j` do not need to be sorted. If you will use
4002:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

4004:   The format which is used for the sparse matrix input, is equivalent to a
4005:   row-major ordering.. i.e for the following matrix, the input data expected is
4006:   as shown
4007: .vb
4008:         1 0 0
4009:         2 0 3     P0
4010:        -------
4011:         4 5 6     P1

4013:      Process0 [P0] rows_owned=[0,1]
4014:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4015:         j =  {0,0,2}  [size = 3]
4016:         v =  {1,2,3}  [size = 3]

4018:      Process1 [P1] rows_owned=[2]
4019:         i =  {0,3}    [size = nrow+1  = 1+1]
4020:         j =  {0,1,2}  [size = 3]
4021:         v =  {4,5,6}  [size = 3]
4022: .ve

4024: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatCreateAIJ()`,
4025:           `MatCreateSeqAIJWithArrays()`, `MatCreateMPIAIJWithSplitArrays()`, `MatCreateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4026: @*/
4027: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4028: {
4029:   PetscFunctionBegin;
4030:   PetscTryMethod(B, "MatMPIAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4031:   PetscFunctionReturn(PETSC_SUCCESS);
4032: }

4034: /*@
4035:   MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in `MATMPIAIJ` format
4036:   (the default parallel PETSc format).  For good matrix assembly performance
4037:   the user should preallocate the matrix storage by setting the parameters
4038:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4040:   Collective

4042:   Input Parameters:
4043: + B     - the matrix
4044: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4045:            (same value is used for all local rows)
4046: . d_nnz - array containing the number of nonzeros in the various rows of the
4047:            DIAGONAL portion of the local submatrix (possibly different for each row)
4048:            or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
4049:            The size of this array is equal to the number of local rows, i.e 'm'.
4050:            For matrices that will be factored, you must leave room for (and set)
4051:            the diagonal entry even if it is zero.
4052: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4053:            submatrix (same value is used for all local rows).
4054: - o_nnz - array containing the number of nonzeros in the various rows of the
4055:            OFF-DIAGONAL portion of the local submatrix (possibly different for
4056:            each row) or `NULL` (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
4057:            structure. The size of this array is equal to the number
4058:            of local rows, i.e 'm'.

4060:   Example Usage:
4061:   Consider the following 8x8 matrix with 34 non-zero values, that is
4062:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4063:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4064:   as follows

4066: .vb
4067:             1  2  0  |  0  3  0  |  0  4
4068:     Proc0   0  5  6  |  7  0  0  |  8  0
4069:             9  0 10  | 11  0  0  | 12  0
4070:     -------------------------------------
4071:            13  0 14  | 15 16 17  |  0  0
4072:     Proc1   0 18  0  | 19 20 21  |  0  0
4073:             0  0  0  | 22 23  0  | 24  0
4074:     -------------------------------------
4075:     Proc2  25 26 27  |  0  0 28  | 29  0
4076:            30  0  0  | 31 32 33  |  0 34
4077: .ve

4079:   This can be represented as a collection of submatrices as
4080: .vb
4081:       A B C
4082:       D E F
4083:       G H I
4084: .ve

4086:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4087:   owned by proc1, G,H,I are owned by proc2.

4089:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4090:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4091:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4093:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4094:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4095:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4096:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4097:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4098:   matrix, and [DF] as another `MATSEQAIJ` matrix.

4100:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4101:   allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4102:   storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4103:   One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4104:   the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4105:   In this case, the values of `d_nz`, `o_nz` are
4106: .vb
4107:      proc0  dnz = 2, o_nz = 2
4108:      proc1  dnz = 3, o_nz = 2
4109:      proc2  dnz = 1, o_nz = 4
4110: .ve
4111:   We are allocating `m`*(`d_nz`+`o_nz`) storage locations for every proc. This
4112:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4113:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4114:   34 values.

4116:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4117:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4118:   In the above case the values for `d_nnz`, `o_nnz` are
4119: .vb
4120:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4121:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4122:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4123: .ve
4124:   Here the space allocated is sum of all the above values i.e 34, and
4125:   hence pre-allocation is perfect.

4127:   Level: intermediate

4129:   Notes:
4130:   If the *_nnz parameter is given then the *_nz parameter is ignored

4132:   The `MATAIJ` format, also called compressed row storage (CSR), is compatible with standard Fortran
4133:   storage.  The stored row and column indices begin with zero.
4134:   See [Sparse Matrices](sec_matsparse) for details.

4136:   The parallel matrix is partitioned such that the first m0 rows belong to
4137:   process 0, the next m1 rows belong to process 1, the next m2 rows belong
4138:   to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.

4140:   The DIAGONAL portion of the local submatrix of a processor can be defined
4141:   as the submatrix which is obtained by extraction the part corresponding to
4142:   the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
4143:   first row that belongs to the processor, r2 is the last row belonging to
4144:   the this processor, and c1-c2 is range of indices of the local part of a
4145:   vector suitable for applying the matrix to.  This is an mxn matrix.  In the
4146:   common case of a square matrix, the row and column ranges are the same and
4147:   the DIAGONAL part is also square. The remaining portion of the local
4148:   submatrix (mxN) constitute the OFF-DIAGONAL portion.

4150:   If `o_nnz` and `d_nnz` are specified, then `o_nz` and `d_nz` are ignored.

4152:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
4153:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4154:   You can also run with the option `-info` and look for messages with the string
4155:   malloc in them to see if additional memory allocation was needed.

4157: .seealso: [](ch_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MATMPIAIJ`, `MATAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateAIJ()`, `MatMPIAIJSetPreallocationCSR()`,
4158:           `MatGetInfo()`, `PetscSplitOwnership()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4159: @*/
4160: PetscErrorCode MatMPIAIJSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
4161: {
4162:   PetscFunctionBegin;
4165:   PetscTryMethod(B, "MatMPIAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
4166:   PetscFunctionReturn(PETSC_SUCCESS);
4167: }

4169: /*@
4170:   MatCreateMPIAIJWithArrays - creates a `MATMPIAIJ` matrix using arrays that contain in standard
4171:   CSR format for the local rows.

4173:   Collective

4175:   Input Parameters:
4176: + comm - MPI communicator
4177: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
4178: . n    - This value should be the same as the local size used in creating the
4179:          x vector for the matrix-vector product $ y = Ax$. (or `PETSC_DECIDE` to have
4180:          calculated if `N` is given) For square matrices n is almost always `m`.
4181: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
4182: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
4183: . 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
4184: . j    - global column indices
4185: - a    - optional matrix values

4187:   Output Parameter:
4188: . mat - the matrix

4190:   Level: intermediate

4192:   Notes:
4193:   The `i`, `j`, and `a` arrays ARE copied by this routine into the internal format used by PETSc;
4194:   thus you CANNOT change the matrix entries by changing the values of `a[]` after you have
4195:   called this routine. Use `MatCreateMPIAIJWithSplitArrays()` to avoid needing to copy the arrays.

4197:   The `i` and `j` indices are 0 based, and `i` indices are indices corresponding to the local `j` array.

4199:   Once you have created the matrix you can update it with new numerical values using `MatUpdateMPIAIJWithArray()`

4201:   If you do **not** use `MatUpdateMPIAIJWithArray()`, the column indices in `j` do not need to be sorted. If you will use
4202:   `MatUpdateMPIAIJWithArrays()`, the column indices **must** be sorted.

4204:   The format which is used for the sparse matrix input, is equivalent to a
4205:   row-major ordering, i.e., for the following matrix, the input data expected is
4206:   as shown
4207: .vb
4208:         1 0 0
4209:         2 0 3     P0
4210:        -------
4211:         4 5 6     P1

4213:      Process0 [P0] rows_owned=[0,1]
4214:         i =  {0,1,3}  [size = nrow+1  = 2+1]
4215:         j =  {0,0,2}  [size = 3]
4216:         v =  {1,2,3}  [size = 3]

4218:      Process1 [P1] rows_owned=[2]
4219:         i =  {0,3}    [size = nrow+1  = 1+1]
4220:         j =  {0,1,2}  [size = 3]
4221:         v =  {4,5,6}  [size = 3]
4222: .ve

4224: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4225:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4226: @*/
4227: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt i[], const PetscInt j[], const PetscScalar a[], Mat *mat)
4228: {
4229:   PetscFunctionBegin;
4230:   PetscCheck(!i || !i[0], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4231:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4232:   PetscCall(MatCreate(comm, mat));
4233:   PetscCall(MatSetSizes(*mat, m, n, M, N));
4234:   /* PetscCall(MatSetBlockSizes(M,bs,cbs)); */
4235:   PetscCall(MatSetType(*mat, MATMPIAIJ));
4236:   PetscCall(MatMPIAIJSetPreallocationCSR(*mat, i, j, a));
4237:   PetscFunctionReturn(PETSC_SUCCESS);
4238: }

4240: /*@
4241:   MatUpdateMPIAIJWithArrays - updates a `MATMPIAIJ` matrix using arrays that contain in standard
4242:   CSR format for the local rows. Only the numerical values are updated the other arrays must be identical to what was passed
4243:   from `MatCreateMPIAIJWithArrays()`

4245:   Deprecated: Use `MatUpdateMPIAIJWithArray()`

4247:   Collective

4249:   Input Parameters:
4250: + mat - the matrix
4251: . m   - number of local rows (Cannot be `PETSC_DECIDE`)
4252: . n   - This value should be the same as the local size used in creating the
4253:        x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4254:        calculated if N is given) For square matrices n is almost always m.
4255: . M   - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4256: . N   - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4257: . Ii  - row indices; that is Ii[0] = 0, Ii[row] = Ii[row-1] + number of elements in that row of the matrix
4258: . J   - column indices
4259: - v   - matrix values

4261:   Level: deprecated

4263: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4264:           `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArray()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4265: @*/
4266: PetscErrorCode MatUpdateMPIAIJWithArrays(Mat mat, PetscInt m, PetscInt n, PetscInt M, PetscInt N, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4267: {
4268:   PetscInt        nnz, i;
4269:   PetscBool       nooffprocentries;
4270:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4271:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4272:   PetscScalar    *ad, *ao;
4273:   PetscInt        ldi, Iii, md;
4274:   const PetscInt *Adi = Ad->i;
4275:   PetscInt       *ld  = Aij->ld;

4277:   PetscFunctionBegin;
4278:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
4279:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
4280:   PetscCheck(m == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of rows cannot change from call to MatUpdateMPIAIJWithArrays()");
4281:   PetscCheck(n == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Local number of columns cannot change from call to MatUpdateMPIAIJWithArrays()");

4283:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4284:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));

4286:   for (i = 0; i < m; i++) {
4287:     if (PetscDefined(USE_DEBUG)) {
4288:       for (PetscInt j = Ii[i] + 1; j < Ii[i + 1]; ++j) {
4289:         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);
4290:         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);
4291:       }
4292:     }
4293:     nnz = Ii[i + 1] - Ii[i];
4294:     Iii = Ii[i];
4295:     ldi = ld[i];
4296:     md  = Adi[i + 1] - Adi[i];
4297:     PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4298:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4299:     PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4300:     ad += md;
4301:     ao += nnz - md;
4302:   }
4303:   nooffprocentries      = mat->nooffprocentries;
4304:   mat->nooffprocentries = PETSC_TRUE;
4305:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4306:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4307:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4308:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4309:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4310:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4311:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4312:   mat->nooffprocentries = nooffprocentries;
4313:   PetscFunctionReturn(PETSC_SUCCESS);
4314: }

4316: /*@
4317:   MatUpdateMPIAIJWithArray - updates an `MATMPIAIJ` matrix using an array that contains the nonzero values

4319:   Collective

4321:   Input Parameters:
4322: + mat - the matrix
4323: - v   - matrix values, stored by row

4325:   Level: intermediate

4327:   Notes:
4328:   The matrix must have been obtained with `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()`

4330:   The column indices in the call to `MatCreateMPIAIJWithArrays()` or `MatMPIAIJSetPreallocationCSR()` must have been sorted for this call to work correctly

4332: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4333:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithSplitArrays()`, `MatUpdateMPIAIJWithArrays()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
4334: @*/
4335: PetscErrorCode MatUpdateMPIAIJWithArray(Mat mat, const PetscScalar v[])
4336: {
4337:   PetscInt        nnz, i, m;
4338:   PetscBool       nooffprocentries;
4339:   Mat_MPIAIJ     *Aij = (Mat_MPIAIJ *)mat->data;
4340:   Mat_SeqAIJ     *Ad  = (Mat_SeqAIJ *)Aij->A->data;
4341:   Mat_SeqAIJ     *Ao  = (Mat_SeqAIJ *)Aij->B->data;
4342:   PetscScalar    *ad, *ao;
4343:   const PetscInt *Adi = Ad->i, *Adj = Ao->i;
4344:   PetscInt        ldi, Iii, md;
4345:   PetscInt       *ld = Aij->ld;

4347:   PetscFunctionBegin;
4348:   m = mat->rmap->n;

4350:   PetscCall(MatSeqAIJGetArrayWrite(Aij->A, &ad));
4351:   PetscCall(MatSeqAIJGetArrayWrite(Aij->B, &ao));
4352:   Iii = 0;
4353:   for (i = 0; i < m; i++) {
4354:     nnz = Adi[i + 1] - Adi[i] + Adj[i + 1] - Adj[i];
4355:     ldi = ld[i];
4356:     md  = Adi[i + 1] - Adi[i];
4357:     PetscCall(PetscArraycpy(ad, v + Iii + ldi, md));
4358:     ad += md;
4359:     if (ao) {
4360:       PetscCall(PetscArraycpy(ao, v + Iii, ldi));
4361:       PetscCall(PetscArraycpy(ao + ldi, v + Iii + ldi + md, nnz - ldi - md));
4362:       ao += nnz - md;
4363:     }
4364:     Iii += nnz;
4365:   }
4366:   nooffprocentries      = mat->nooffprocentries;
4367:   mat->nooffprocentries = PETSC_TRUE;
4368:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->A, &ad));
4369:   PetscCall(MatSeqAIJRestoreArrayWrite(Aij->B, &ao));
4370:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->A));
4371:   PetscCall(PetscObjectStateIncrease((PetscObject)Aij->B));
4372:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
4373:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
4374:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
4375:   mat->nooffprocentries = nooffprocentries;
4376:   PetscFunctionReturn(PETSC_SUCCESS);
4377: }

4379: /*@
4380:   MatCreateAIJ - Creates a sparse parallel matrix in `MATAIJ` format
4381:   (the default parallel PETSc format).  For good matrix assembly performance
4382:   the user should preallocate the matrix storage by setting the parameters
4383:   `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).

4385:   Collective

4387:   Input Parameters:
4388: + comm  - MPI communicator
4389: . m     - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
4390:           This value should be the same as the local size used in creating the
4391:           y vector for the matrix-vector product y = Ax.
4392: . n     - This value should be the same as the local size used in creating the
4393:           x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
4394:           calculated if N is given) For square matrices n is almost always m.
4395: . M     - number of global rows (or `PETSC_DETERMINE` to have calculated if m is given)
4396: . N     - number of global columns (or `PETSC_DETERMINE` to have calculated if n is given)
4397: . d_nz  - number of nonzeros per row in DIAGONAL portion of local submatrix
4398:           (same value is used for all local rows)
4399: . d_nnz - array containing the number of nonzeros in the various rows of the
4400:           DIAGONAL portion of the local submatrix (possibly different for each row)
4401:           or `NULL`, if `d_nz` is used to specify the nonzero structure.
4402:           The size of this array is equal to the number of local rows, i.e 'm'.
4403: . o_nz  - number of nonzeros per row in the OFF-DIAGONAL portion of local
4404:           submatrix (same value is used for all local rows).
4405: - o_nnz - array containing the number of nonzeros in the various rows of the
4406:           OFF-DIAGONAL portion of the local submatrix (possibly different for
4407:           each row) or `NULL`, if `o_nz` is used to specify the nonzero
4408:           structure. The size of this array is equal to the number
4409:           of local rows, i.e 'm'.

4411:   Output Parameter:
4412: . A - the matrix

4414:   Options Database Keys:
4415: + -mat_no_inode                     - Do not use inodes
4416: . -mat_inode_limit <limit>          - Sets inode limit (max limit=5)
4417: - -matmult_vecscatter_view <viewer> - View the vecscatter (i.e., communication pattern) used in `MatMult()` of sparse parallel matrices.
4418:                                       See viewer types in manual of `MatView()`. Of them, ascii_matlab, draw or binary cause the `VecScatter`
4419:                                       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.

4421:   Level: intermediate

4423:   Notes:
4424:   It is recommended that one use `MatCreateFromOptions()` or the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
4425:   MatXXXXSetPreallocation() paradigm instead of this routine directly.
4426:   [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

4428:   If the *_nnz parameter is given then the *_nz parameter is ignored

4430:   The `m`,`n`,`M`,`N` parameters specify the size of the matrix, and its partitioning across
4431:   processors, while `d_nz`,`d_nnz`,`o_nz`,`o_nnz` parameters specify the approximate
4432:   storage requirements for this matrix.

4434:   If `PETSC_DECIDE` or  `PETSC_DETERMINE` is used for a particular argument on one
4435:   processor than it must be used on all processors that share the object for
4436:   that argument.

4438:   If `m` and `n` are not `PETSC_DECIDE`, then the values determine the `PetscLayout` of the matrix and the ranges returned by
4439:   `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, and `MatGetOwnershipRangesColumn()`.

4441:   The user MUST specify either the local or global matrix dimensions
4442:   (possibly both).

4444:   The parallel matrix is partitioned across processors such that the
4445:   first `m0` rows belong to process 0, the next `m1` rows belong to
4446:   process 1, the next `m2` rows belong to process 2, etc., where
4447:   `m0`, `m1`, `m2`... are the input parameter `m` on each MPI process. I.e., each MPI process stores
4448:   values corresponding to [m x N] submatrix.

4450:   The columns are logically partitioned with the n0 columns belonging
4451:   to 0th partition, the next n1 columns belonging to the next
4452:   partition etc.. where n0,n1,n2... are the input parameter 'n'.

4454:   The DIAGONAL portion of the local submatrix on any given processor
4455:   is the submatrix corresponding to the rows and columns m,n
4456:   corresponding to the given processor. i.e diagonal matrix on
4457:   process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4458:   etc. The remaining portion of the local submatrix [m x (N-n)]
4459:   constitute the OFF-DIAGONAL portion. The example below better
4460:   illustrates this concept. The two matrices, the DIAGONAL portion and
4461:   the OFF-DIAGONAL portion are each stored as `MATSEQAIJ` matrices.

4463:   For a square global matrix we define each processor's diagonal portion
4464:   to be its local rows and the corresponding columns (a square submatrix);
4465:   each processor's off-diagonal portion encompasses the remainder of the
4466:   local matrix (a rectangular submatrix).

4468:   If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.

4470:   When calling this routine with a single process communicator, a matrix of
4471:   type `MATSEQAIJ` is returned.  If a matrix of type `MATMPIAIJ` is desired for this
4472:   type of communicator, use the construction mechanism
4473: .vb
4474:   MatCreate(..., &A);
4475:   MatSetType(A, MATMPIAIJ);
4476:   MatSetSizes(A, m, n, M, N);
4477:   MatMPIAIJSetPreallocation(A, ...);
4478: .ve

4480:   By default, this format uses inodes (identical nodes) when possible.
4481:   We search for consecutive rows with the same nonzero structure, thereby
4482:   reusing matrix information to achieve increased efficiency.

4484:   Example Usage:
4485:   Consider the following 8x8 matrix with 34 non-zero values, that is
4486:   assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4487:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4488:   as follows

4490: .vb
4491:             1  2  0  |  0  3  0  |  0  4
4492:     Proc0   0  5  6  |  7  0  0  |  8  0
4493:             9  0 10  | 11  0  0  | 12  0
4494:     -------------------------------------
4495:            13  0 14  | 15 16 17  |  0  0
4496:     Proc1   0 18  0  | 19 20 21  |  0  0
4497:             0  0  0  | 22 23  0  | 24  0
4498:     -------------------------------------
4499:     Proc2  25 26 27  |  0  0 28  | 29  0
4500:            30  0  0  | 31 32 33  |  0 34
4501: .ve

4503:   This can be represented as a collection of submatrices as

4505: .vb
4506:       A B C
4507:       D E F
4508:       G H I
4509: .ve

4511:   Where the submatrices A,B,C are owned by proc0, D,E,F are
4512:   owned by proc1, G,H,I are owned by proc2.

4514:   The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4515:   The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4516:   The 'M','N' parameters are 8,8, and have the same values on all procs.

4518:   The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4519:   submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4520:   corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4521:   Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4522:   part as `MATSEQAIJ` matrices. For example, proc1 will store [E] as a `MATSEQAIJ`
4523:   matrix, and [DF] as another SeqAIJ matrix.

4525:   When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
4526:   allocated for every row of the local DIAGONAL submatrix, and `o_nz`
4527:   storage locations are allocated for every row of the OFF-DIAGONAL submatrix.
4528:   One way to choose `d_nz` and `o_nz` is to use the maximum number of nonzeros over
4529:   the local rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4530:   In this case, the values of `d_nz`,`o_nz` are
4531: .vb
4532:      proc0  dnz = 2, o_nz = 2
4533:      proc1  dnz = 3, o_nz = 2
4534:      proc2  dnz = 1, o_nz = 4
4535: .ve
4536:   We are allocating m*(`d_nz`+`o_nz`) storage locations for every proc. This
4537:   translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4538:   for proc3. i.e we are using 12+15+10=37 storage locations to store
4539:   34 values.

4541:   When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
4542:   for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4543:   In the above case the values for d_nnz,o_nnz are
4544: .vb
4545:      proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
4546:      proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
4547:      proc2 d_nnz = [1,1]   and o_nnz = [4,4]
4548: .ve
4549:   Here the space allocated is sum of all the above values i.e 34, and
4550:   hence pre-allocation is perfect.

4552: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
4553:           `MATMPIAIJ`, `MatCreateMPIAIJWithArrays()`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`,
4554:           `MatGetOwnershipRangesColumn()`, `PetscLayout`
4555: @*/
4556: 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)
4557: {
4558:   PetscMPIInt size;

4560:   PetscFunctionBegin;
4561:   PetscCall(MatCreate(comm, A));
4562:   PetscCall(MatSetSizes(*A, m, n, M, N));
4563:   PetscCallMPI(MPI_Comm_size(comm, &size));
4564:   if (size > 1) {
4565:     PetscCall(MatSetType(*A, MATMPIAIJ));
4566:     PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
4567:   } else {
4568:     PetscCall(MatSetType(*A, MATSEQAIJ));
4569:     PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
4570:   }
4571:   PetscFunctionReturn(PETSC_SUCCESS);
4572: }

4574: /*MC
4575:     MatMPIAIJGetSeqAIJF90 - Returns the local pieces of this distributed matrix

4577:     Synopsis:
4578:     MatMPIAIJGetSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4580:     Not Collective

4582:     Input Parameter:
4583: .   A - the `MATMPIAIJ` matrix

4585:     Output Parameters:
4586: +   Ad - the diagonal portion of the matrix
4587: .   Ao - the off-diagonal portion of the matrix
4588: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4589: -   ierr - error code

4591:      Level: advanced

4593:     Note:
4594:     Use  `MatMPIAIJRestoreSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4596: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJRestoreSeqAIJF90()`
4597: M*/

4599: /*MC
4600:     MatMPIAIJRestoreSeqAIJF90 - call after `MatMPIAIJGetSeqAIJF90()` when you no longer need access to the matrices and `colmap`

4602:     Synopsis:
4603:     MatMPIAIJRestoreSeqAIJF90(Mat A, Mat Ad, Mat Ao, {PetscInt, pointer :: colmap(:)},integer ierr)

4605:     Not Collective

4607:     Input Parameters:
4608: +   A - the `MATMPIAIJ` matrix
4609: .   Ad - the diagonal portion of the matrix
4610: .   Ao - the off-diagonal portion of the matrix
4611: .   colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
4612: -   ierr - error code

4614:      Level: advanced

4616: .seealso: [](ch_matrices), `Mat`, [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJ()`, `MatMPIAIJGetSeqAIJF90()`
4617: M*/

4619: /*@C
4620:   MatMPIAIJGetSeqAIJ - Returns the local pieces of this distributed matrix

4622:   Not Collective

4624:   Input Parameter:
4625: . A - The `MATMPIAIJ` matrix

4627:   Output Parameters:
4628: + Ad     - The local diagonal block as a `MATSEQAIJ` matrix
4629: . Ao     - The local off-diagonal block as a `MATSEQAIJ` matrix
4630: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix

4632:   Level: intermediate

4634:   Note:
4635:   The rows in `Ad` and `Ao` are in [0, Nr), where Nr is the number of local rows on this process. The columns
4636:   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
4637:   the number of nonzero columns in the local off-diagonal piece of the matrix `A`. The array colmap maps these
4638:   local column numbers to global column numbers in the original matrix.

4640:   Fortran Notes:
4641:   `MatMPIAIJGetSeqAIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatMPIAIJGetSeqAIJF90()`

4643: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatMPIAIJGetSeqAIJF90()`, `MatMPIAIJRestoreSeqAIJF90()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`, `MatCreateAIJ()`, `MATSEQAIJ`
4644: @*/
4645: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
4646: {
4647:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
4648:   PetscBool   flg;

4650:   PetscFunctionBegin;
4651:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &flg));
4652:   PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPIAIJ matrix as input");
4653:   if (Ad) *Ad = a->A;
4654:   if (Ao) *Ao = a->B;
4655:   if (colmap) *colmap = a->garray;
4656:   PetscFunctionReturn(PETSC_SUCCESS);
4657: }

4659: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
4660: {
4661:   PetscInt     m, N, i, rstart, nnz, Ii;
4662:   PetscInt    *indx;
4663:   PetscScalar *values;
4664:   MatType      rootType;

4666:   PetscFunctionBegin;
4667:   PetscCall(MatGetSize(inmat, &m, &N));
4668:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
4669:     PetscInt *dnz, *onz, sum, bs, cbs;

4671:     if (n == PETSC_DECIDE) PetscCall(PetscSplitOwnership(comm, &n, &N));
4672:     /* Check sum(n) = N */
4673:     PetscCallMPI(MPIU_Allreduce(&n, &sum, 1, MPIU_INT, MPI_SUM, comm));
4674:     PetscCheck(sum == N, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Sum of local columns %" PetscInt_FMT " != global columns %" PetscInt_FMT, sum, N);

4676:     PetscCallMPI(MPI_Scan(&m, &rstart, 1, MPIU_INT, MPI_SUM, comm));
4677:     rstart -= m;

4679:     MatPreallocateBegin(comm, m, n, dnz, onz);
4680:     for (i = 0; i < m; i++) {
4681:       PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4682:       PetscCall(MatPreallocateSet(i + rstart, nnz, indx, dnz, onz));
4683:       PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, NULL));
4684:     }

4686:     PetscCall(MatCreate(comm, outmat));
4687:     PetscCall(MatSetSizes(*outmat, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
4688:     PetscCall(MatGetBlockSizes(inmat, &bs, &cbs));
4689:     PetscCall(MatSetBlockSizes(*outmat, bs, cbs));
4690:     PetscCall(MatGetRootType_Private(inmat, &rootType));
4691:     PetscCall(MatSetType(*outmat, rootType));
4692:     PetscCall(MatSeqAIJSetPreallocation(*outmat, 0, dnz));
4693:     PetscCall(MatMPIAIJSetPreallocation(*outmat, 0, dnz, 0, onz));
4694:     MatPreallocateEnd(dnz, onz);
4695:     PetscCall(MatSetOption(*outmat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
4696:   }

4698:   /* numeric phase */
4699:   PetscCall(MatGetOwnershipRange(*outmat, &rstart, NULL));
4700:   for (i = 0; i < m; i++) {
4701:     PetscCall(MatGetRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4702:     Ii = i + rstart;
4703:     PetscCall(MatSetValues(*outmat, 1, &Ii, nnz, indx, values, INSERT_VALUES));
4704:     PetscCall(MatRestoreRow_SeqAIJ(inmat, i, &nnz, &indx, &values));
4705:   }
4706:   PetscCall(MatAssemblyBegin(*outmat, MAT_FINAL_ASSEMBLY));
4707:   PetscCall(MatAssemblyEnd(*outmat, MAT_FINAL_ASSEMBLY));
4708:   PetscFunctionReturn(PETSC_SUCCESS);
4709: }

4711: static PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(void **data)
4712: {
4713:   Mat_Merge_SeqsToMPI *merge = (Mat_Merge_SeqsToMPI *)*data;

4715:   PetscFunctionBegin;
4716:   if (!merge) PetscFunctionReturn(PETSC_SUCCESS);
4717:   PetscCall(PetscFree(merge->id_r));
4718:   PetscCall(PetscFree(merge->len_s));
4719:   PetscCall(PetscFree(merge->len_r));
4720:   PetscCall(PetscFree(merge->bi));
4721:   PetscCall(PetscFree(merge->bj));
4722:   PetscCall(PetscFree(merge->buf_ri[0]));
4723:   PetscCall(PetscFree(merge->buf_ri));
4724:   PetscCall(PetscFree(merge->buf_rj[0]));
4725:   PetscCall(PetscFree(merge->buf_rj));
4726:   PetscCall(PetscFree(merge->coi));
4727:   PetscCall(PetscFree(merge->coj));
4728:   PetscCall(PetscFree(merge->owners_co));
4729:   PetscCall(PetscLayoutDestroy(&merge->rowmap));
4730:   PetscCall(PetscFree(merge));
4731:   PetscFunctionReturn(PETSC_SUCCESS);
4732: }

4734: #include <../src/mat/utils/freespace.h>
4735: #include <petscbt.h>

4737: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat, Mat mpimat)
4738: {
4739:   MPI_Comm             comm;
4740:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4741:   PetscMPIInt          size, rank, taga, *len_s;
4742:   PetscInt             N = mpimat->cmap->N, i, j, *owners, *ai = a->i, *aj, m;
4743:   PetscMPIInt          proc, k;
4744:   PetscInt           **buf_ri, **buf_rj;
4745:   PetscInt             anzi, *bj_i, *bi, *bj, arow, bnzi, nextaj;
4746:   PetscInt             nrows, **buf_ri_k, **nextrow, **nextai;
4747:   MPI_Request         *s_waits, *r_waits;
4748:   MPI_Status          *status;
4749:   const MatScalar     *aa, *a_a;
4750:   MatScalar          **abuf_r, *ba_i;
4751:   Mat_Merge_SeqsToMPI *merge;
4752:   PetscContainer       container;

4754:   PetscFunctionBegin;
4755:   PetscCall(PetscObjectGetComm((PetscObject)mpimat, &comm));
4756:   PetscCall(PetscLogEventBegin(MAT_Seqstompinum, seqmat, 0, 0, 0));

4758:   PetscCallMPI(MPI_Comm_size(comm, &size));
4759:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4761:   PetscCall(PetscObjectQuery((PetscObject)mpimat, "MatMergeSeqsToMPI", (PetscObject *)&container));
4762:   PetscCheck(container, PetscObjectComm((PetscObject)mpimat), PETSC_ERR_PLIB, "Mat not created from MatCreateMPIAIJSumSeqAIJSymbolic");
4763:   PetscCall(PetscContainerGetPointer(container, (void **)&merge));
4764:   PetscCall(MatSeqAIJGetArrayRead(seqmat, &a_a));
4765:   aa = a_a;

4767:   bi     = merge->bi;
4768:   bj     = merge->bj;
4769:   buf_ri = merge->buf_ri;
4770:   buf_rj = merge->buf_rj;

4772:   PetscCall(PetscMalloc1(size, &status));
4773:   owners = merge->rowmap->range;
4774:   len_s  = merge->len_s;

4776:   /* send and recv matrix values */
4777:   PetscCall(PetscObjectGetNewTag((PetscObject)mpimat, &taga));
4778:   PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));

4780:   PetscCall(PetscMalloc1(merge->nsend + 1, &s_waits));
4781:   for (proc = 0, k = 0; proc < size; proc++) {
4782:     if (!len_s[proc]) continue;
4783:     i = owners[proc];
4784:     PetscCallMPI(MPIU_Isend(aa + ai[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
4785:     k++;
4786:   }

4788:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
4789:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
4790:   PetscCall(PetscFree(status));

4792:   PetscCall(PetscFree(s_waits));
4793:   PetscCall(PetscFree(r_waits));

4795:   /* insert mat values of mpimat */
4796:   PetscCall(PetscMalloc1(N, &ba_i));
4797:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

4799:   for (k = 0; k < merge->nrecv; k++) {
4800:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4801:     nrows       = *buf_ri_k[k];
4802:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
4803:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
4804:   }

4806:   /* set values of ba */
4807:   m = merge->rowmap->n;
4808:   for (i = 0; i < m; i++) {
4809:     arow = owners[rank] + i;
4810:     bj_i = bj + bi[i]; /* col indices of the i-th row of mpimat */
4811:     bnzi = bi[i + 1] - bi[i];
4812:     PetscCall(PetscArrayzero(ba_i, bnzi));

4814:     /* add local non-zero vals of this proc's seqmat into ba */
4815:     anzi   = ai[arow + 1] - ai[arow];
4816:     aj     = a->j + ai[arow];
4817:     aa     = a_a + ai[arow];
4818:     nextaj = 0;
4819:     for (j = 0; nextaj < anzi; j++) {
4820:       if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4821:         ba_i[j] += aa[nextaj++];
4822:       }
4823:     }

4825:     /* add received vals into ba */
4826:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
4827:       /* i-th row */
4828:       if (i == *nextrow[k]) {
4829:         anzi   = *(nextai[k] + 1) - *nextai[k];
4830:         aj     = buf_rj[k] + *nextai[k];
4831:         aa     = abuf_r[k] + *nextai[k];
4832:         nextaj = 0;
4833:         for (j = 0; nextaj < anzi; j++) {
4834:           if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4835:             ba_i[j] += aa[nextaj++];
4836:           }
4837:         }
4838:         nextrow[k]++;
4839:         nextai[k]++;
4840:       }
4841:     }
4842:     PetscCall(MatSetValues(mpimat, 1, &arow, bnzi, bj_i, ba_i, INSERT_VALUES));
4843:   }
4844:   PetscCall(MatSeqAIJRestoreArrayRead(seqmat, &a_a));
4845:   PetscCall(MatAssemblyBegin(mpimat, MAT_FINAL_ASSEMBLY));
4846:   PetscCall(MatAssemblyEnd(mpimat, MAT_FINAL_ASSEMBLY));

4848:   PetscCall(PetscFree(abuf_r[0]));
4849:   PetscCall(PetscFree(abuf_r));
4850:   PetscCall(PetscFree(ba_i));
4851:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));
4852:   PetscCall(PetscLogEventEnd(MAT_Seqstompinum, seqmat, 0, 0, 0));
4853:   PetscFunctionReturn(PETSC_SUCCESS);
4854: }

4856: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, Mat *mpimat)
4857: {
4858:   Mat                  B_mpi;
4859:   Mat_SeqAIJ          *a = (Mat_SeqAIJ *)seqmat->data;
4860:   PetscMPIInt          size, rank, tagi, tagj, *len_s, *len_si, *len_ri;
4861:   PetscInt           **buf_rj, **buf_ri, **buf_ri_k;
4862:   PetscInt             M = seqmat->rmap->n, N = seqmat->cmap->n, i, *owners, *ai = a->i, *aj = a->j;
4863:   PetscInt             len, *dnz, *onz, bs, cbs;
4864:   PetscInt             k, anzi, *bi, *bj, *lnk, nlnk, arow, bnzi;
4865:   PetscInt             nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextai;
4866:   MPI_Request         *si_waits, *sj_waits, *ri_waits, *rj_waits;
4867:   MPI_Status          *status;
4868:   PetscFreeSpaceList   free_space = NULL, current_space = NULL;
4869:   PetscBT              lnkbt;
4870:   Mat_Merge_SeqsToMPI *merge;
4871:   PetscContainer       container;

4873:   PetscFunctionBegin;
4874:   PetscCall(PetscLogEventBegin(MAT_Seqstompisym, seqmat, 0, 0, 0));

4876:   /* make sure it is a PETSc comm */
4877:   PetscCall(PetscCommDuplicate(comm, &comm, NULL));
4878:   PetscCallMPI(MPI_Comm_size(comm, &size));
4879:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

4881:   PetscCall(PetscNew(&merge));
4882:   PetscCall(PetscMalloc1(size, &status));

4884:   /* determine row ownership */
4885:   PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
4886:   PetscCall(PetscLayoutSetLocalSize(merge->rowmap, m));
4887:   PetscCall(PetscLayoutSetSize(merge->rowmap, M));
4888:   PetscCall(PetscLayoutSetBlockSize(merge->rowmap, 1));
4889:   PetscCall(PetscLayoutSetUp(merge->rowmap));
4890:   PetscCall(PetscMalloc1(size, &len_si));
4891:   PetscCall(PetscMalloc1(size, &merge->len_s));

4893:   m      = merge->rowmap->n;
4894:   owners = merge->rowmap->range;

4896:   /* determine the number of messages to send, their lengths */
4897:   len_s = merge->len_s;

4899:   len          = 0; /* length of buf_si[] */
4900:   merge->nsend = 0;
4901:   for (PetscMPIInt proc = 0; proc < size; proc++) {
4902:     len_si[proc] = 0;
4903:     if (proc == rank) {
4904:       len_s[proc] = 0;
4905:     } else {
4906:       PetscCall(PetscMPIIntCast(owners[proc + 1] - owners[proc] + 1, &len_si[proc]));
4907:       PetscCall(PetscMPIIntCast(ai[owners[proc + 1]] - ai[owners[proc]], &len_s[proc])); /* num of rows to be sent to [proc] */
4908:     }
4909:     if (len_s[proc]) {
4910:       merge->nsend++;
4911:       nrows = 0;
4912:       for (i = owners[proc]; i < owners[proc + 1]; i++) {
4913:         if (ai[i + 1] > ai[i]) nrows++;
4914:       }
4915:       PetscCall(PetscMPIIntCast(2 * (nrows + 1), &len_si[proc]));
4916:       len += len_si[proc];
4917:     }
4918:   }

4920:   /* determine the number and length of messages to receive for ij-structure */
4921:   PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
4922:   PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));

4924:   /* post the Irecv of j-structure */
4925:   PetscCall(PetscCommGetNewTag(comm, &tagj));
4926:   PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rj_waits));

4928:   /* post the Isend of j-structure */
4929:   PetscCall(PetscMalloc2(merge->nsend, &si_waits, merge->nsend, &sj_waits));

4931:   for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4932:     if (!len_s[proc]) continue;
4933:     i = owners[proc];
4934:     PetscCallMPI(MPIU_Isend(aj + ai[i], len_s[proc], MPIU_INT, proc, tagj, comm, sj_waits + k));
4935:     k++;
4936:   }

4938:   /* receives and sends of j-structure are complete */
4939:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, rj_waits, status));
4940:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, sj_waits, status));

4942:   /* send and recv i-structure */
4943:   PetscCall(PetscCommGetNewTag(comm, &tagi));
4944:   PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &ri_waits));

4946:   PetscCall(PetscMalloc1(len + 1, &buf_s));
4947:   buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4948:   for (PetscMPIInt proc = 0, k = 0; proc < size; proc++) {
4949:     if (!len_s[proc]) continue;
4950:     /* form outgoing message for i-structure:
4951:          buf_si[0]:                 nrows to be sent
4952:                [1:nrows]:           row index (global)
4953:                [nrows+1:2*nrows+1]: i-structure index
4954:     */
4955:     nrows       = len_si[proc] / 2 - 1;
4956:     buf_si_i    = buf_si + nrows + 1;
4957:     buf_si[0]   = nrows;
4958:     buf_si_i[0] = 0;
4959:     nrows       = 0;
4960:     for (i = owners[proc]; i < owners[proc + 1]; i++) {
4961:       anzi = ai[i + 1] - ai[i];
4962:       if (anzi) {
4963:         buf_si_i[nrows + 1] = buf_si_i[nrows] + anzi; /* i-structure */
4964:         buf_si[nrows + 1]   = i - owners[proc];       /* local row index */
4965:         nrows++;
4966:       }
4967:     }
4968:     PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, si_waits + k));
4969:     k++;
4970:     buf_si += len_si[proc];
4971:   }

4973:   if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, ri_waits, status));
4974:   if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, si_waits, status));

4976:   PetscCall(PetscInfo(seqmat, "nsend: %d, nrecv: %d\n", merge->nsend, merge->nrecv));
4977:   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]));

4979:   PetscCall(PetscFree(len_si));
4980:   PetscCall(PetscFree(len_ri));
4981:   PetscCall(PetscFree(rj_waits));
4982:   PetscCall(PetscFree2(si_waits, sj_waits));
4983:   PetscCall(PetscFree(ri_waits));
4984:   PetscCall(PetscFree(buf_s));
4985:   PetscCall(PetscFree(status));

4987:   /* compute a local seq matrix in each processor */
4988:   /* allocate bi array and free space for accumulating nonzero column info */
4989:   PetscCall(PetscMalloc1(m + 1, &bi));
4990:   bi[0] = 0;

4992:   /* create and initialize a linked list */
4993:   nlnk = N + 1;
4994:   PetscCall(PetscLLCreate(N, N, nlnk, lnk, lnkbt));

4996:   /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4997:   len = ai[owners[rank + 1]] - ai[owners[rank]];
4998:   PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(2, len) + 1, &free_space));

5000:   current_space = free_space;

5002:   /* determine symbolic info for each local row */
5003:   PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextai));

5005:   for (k = 0; k < merge->nrecv; k++) {
5006:     buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
5007:     nrows       = *buf_ri_k[k];
5008:     nextrow[k]  = buf_ri_k[k] + 1;           /* next row number of k-th recved i-structure */
5009:     nextai[k]   = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure  */
5010:   }

5012:   MatPreallocateBegin(comm, m, n, dnz, onz);
5013:   len = 0;
5014:   for (i = 0; i < m; i++) {
5015:     bnzi = 0;
5016:     /* add local non-zero cols of this proc's seqmat into lnk */
5017:     arow = owners[rank] + i;
5018:     anzi = ai[arow + 1] - ai[arow];
5019:     aj   = a->j + ai[arow];
5020:     PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5021:     bnzi += nlnk;
5022:     /* add received col data into lnk */
5023:     for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
5024:       if (i == *nextrow[k]) {            /* i-th row */
5025:         anzi = *(nextai[k] + 1) - *nextai[k];
5026:         aj   = buf_rj[k] + *nextai[k];
5027:         PetscCall(PetscLLAddSorted(anzi, aj, N, &nlnk, lnk, lnkbt));
5028:         bnzi += nlnk;
5029:         nextrow[k]++;
5030:         nextai[k]++;
5031:       }
5032:     }
5033:     if (len < bnzi) len = bnzi; /* =max(bnzi) */

5035:     /* if free space is not available, make more free space */
5036:     if (current_space->local_remaining < bnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(bnzi, current_space->total_array_size), &current_space));
5037:     /* copy data into free space, then initialize lnk */
5038:     PetscCall(PetscLLClean(N, N, bnzi, lnk, current_space->array, lnkbt));
5039:     PetscCall(MatPreallocateSet(i + owners[rank], bnzi, current_space->array, dnz, onz));

5041:     current_space->array += bnzi;
5042:     current_space->local_used += bnzi;
5043:     current_space->local_remaining -= bnzi;

5045:     bi[i + 1] = bi[i] + bnzi;
5046:   }

5048:   PetscCall(PetscFree3(buf_ri_k, nextrow, nextai));

5050:   PetscCall(PetscMalloc1(bi[m] + 1, &bj));
5051:   PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
5052:   PetscCall(PetscLLDestroy(lnk, lnkbt));

5054:   /* create symbolic parallel matrix B_mpi */
5055:   PetscCall(MatGetBlockSizes(seqmat, &bs, &cbs));
5056:   PetscCall(MatCreate(comm, &B_mpi));
5057:   if (n == PETSC_DECIDE) {
5058:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, N));
5059:   } else {
5060:     PetscCall(MatSetSizes(B_mpi, m, n, PETSC_DETERMINE, PETSC_DETERMINE));
5061:   }
5062:   PetscCall(MatSetBlockSizes(B_mpi, bs, cbs));
5063:   PetscCall(MatSetType(B_mpi, MATMPIAIJ));
5064:   PetscCall(MatMPIAIJSetPreallocation(B_mpi, 0, dnz, 0, onz));
5065:   MatPreallocateEnd(dnz, onz);
5066:   PetscCall(MatSetOption(B_mpi, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));

5068:   /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
5069:   B_mpi->assembled = PETSC_FALSE;
5070:   merge->bi        = bi;
5071:   merge->bj        = bj;
5072:   merge->buf_ri    = buf_ri;
5073:   merge->buf_rj    = buf_rj;
5074:   merge->coi       = NULL;
5075:   merge->coj       = NULL;
5076:   merge->owners_co = NULL;

5078:   PetscCall(PetscCommDestroy(&comm));

5080:   /* attach the supporting struct to B_mpi for reuse */
5081:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
5082:   PetscCall(PetscContainerSetPointer(container, merge));
5083:   PetscCall(PetscContainerSetCtxDestroy(container, MatDestroy_MPIAIJ_SeqsToMPI));
5084:   PetscCall(PetscObjectCompose((PetscObject)B_mpi, "MatMergeSeqsToMPI", (PetscObject)container));
5085:   PetscCall(PetscContainerDestroy(&container));
5086:   *mpimat = B_mpi;

5088:   PetscCall(PetscLogEventEnd(MAT_Seqstompisym, seqmat, 0, 0, 0));
5089:   PetscFunctionReturn(PETSC_SUCCESS);
5090: }

5092: /*@
5093:   MatCreateMPIAIJSumSeqAIJ - Creates a `MATMPIAIJ` matrix by adding sequential
5094:   matrices from each processor

5096:   Collective

5098:   Input Parameters:
5099: + comm   - the communicators the parallel matrix will live on
5100: . seqmat - the input sequential matrices
5101: . m      - number of local rows (or `PETSC_DECIDE`)
5102: . n      - number of local columns (or `PETSC_DECIDE`)
5103: - scall  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5105:   Output Parameter:
5106: . mpimat - the parallel matrix generated

5108:   Level: advanced

5110:   Note:
5111:   The dimensions of the sequential matrix in each processor MUST be the same.
5112:   The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
5113:   destroyed when `mpimat` is destroyed. Call `PetscObjectQuery()` to access `seqmat`.

5115: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`
5116: @*/
5117: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm, Mat seqmat, PetscInt m, PetscInt n, MatReuse scall, Mat *mpimat)
5118: {
5119:   PetscMPIInt size;

5121:   PetscFunctionBegin;
5122:   PetscCallMPI(MPI_Comm_size(comm, &size));
5123:   if (size == 1) {
5124:     PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5125:     if (scall == MAT_INITIAL_MATRIX) {
5126:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
5127:     } else {
5128:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
5129:     }
5130:     PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5131:     PetscFunctionReturn(PETSC_SUCCESS);
5132:   }
5133:   PetscCall(PetscLogEventBegin(MAT_Seqstompi, seqmat, 0, 0, 0));
5134:   if (scall == MAT_INITIAL_MATRIX) PetscCall(MatCreateMPIAIJSumSeqAIJSymbolic(comm, seqmat, m, n, mpimat));
5135:   PetscCall(MatCreateMPIAIJSumSeqAIJNumeric(seqmat, *mpimat));
5136:   PetscCall(PetscLogEventEnd(MAT_Seqstompi, seqmat, 0, 0, 0));
5137:   PetscFunctionReturn(PETSC_SUCCESS);
5138: }

5140: /*@
5141:   MatAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATAIJ` matrix.

5143:   Not Collective

5145:   Input Parameter:
5146: . A - the matrix

5148:   Output Parameter:
5149: . A_loc - the local sequential matrix generated

5151:   Level: developer

5153:   Notes:
5154:   The matrix is created by taking `A`'s local rows and putting them into a sequential matrix
5155:   with `mlocal` rows and `n` columns. Where `mlocal` is obtained with `MatGetLocalSize()` and
5156:   `n` is the global column count obtained with `MatGetSize()`

5158:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5160:   For parallel matrices this creates an entirely new matrix. If the matrix is sequential it merely increases the reference count.

5162:   Destroy the matrix with `MatDestroy()`

5164: .seealso: [](ch_matrices), `Mat`, `MatMPIAIJGetLocalMat()`
5165: @*/
5166: PetscErrorCode MatAIJGetLocalMat(Mat A, Mat *A_loc)
5167: {
5168:   PetscBool mpi;

5170:   PetscFunctionBegin;
5171:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &mpi));
5172:   if (mpi) {
5173:     PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, A_loc));
5174:   } else {
5175:     *A_loc = A;
5176:     PetscCall(PetscObjectReference((PetscObject)*A_loc));
5177:   }
5178:   PetscFunctionReturn(PETSC_SUCCESS);
5179: }

5181: /*@
5182:   MatMPIAIJGetLocalMat - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix.

5184:   Not Collective

5186:   Input Parameters:
5187: + A     - the matrix
5188: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5190:   Output Parameter:
5191: . A_loc - the local sequential matrix generated

5193:   Level: developer

5195:   Notes:
5196:   The matrix is created by taking all `A`'s local rows and putting them into a sequential
5197:   matrix with `mlocal` rows and `n` columns.`mlocal` is the row count obtained with
5198:   `MatGetLocalSize()` and `n` is the global column count obtained with `MatGetSize()`.

5200:   In other words combines the two parts of a parallel `MATMPIAIJ` matrix on each process to a single matrix.

5202:   When `A` is sequential and `MAT_INITIAL_MATRIX` is requested, the matrix returned is the diagonal part of `A` (which contains the entire matrix),
5203:   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
5204:   then `MatCopy`(Adiag,*`A_loc`,`SAME_NONZERO_PATTERN`) is called to fill `A_loc`. Thus one can preallocate the appropriate sequential matrix `A_loc`
5205:   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.

5207: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMatCondensed()`, `MatMPIAIJGetLocalMatMerge()`
5208: @*/
5209: PetscErrorCode MatMPIAIJGetLocalMat(Mat A, MatReuse scall, Mat *A_loc)
5210: {
5211:   Mat_MPIAIJ        *mpimat = (Mat_MPIAIJ *)A->data;
5212:   Mat_SeqAIJ        *mat, *a, *b;
5213:   PetscInt          *ai, *aj, *bi, *bj, *cmap = mpimat->garray;
5214:   const PetscScalar *aa, *ba, *aav, *bav;
5215:   PetscScalar       *ca, *cam;
5216:   PetscMPIInt        size;
5217:   PetscInt           am = A->rmap->n, i, j, k, cstart = A->cmap->rstart;
5218:   PetscInt          *ci, *cj, col, ncols_d, ncols_o, jo;
5219:   PetscBool          match;

5221:   PetscFunctionBegin;
5222:   PetscCall(PetscStrbeginswith(((PetscObject)A)->type_name, MATMPIAIJ, &match));
5223:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5224:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5225:   if (size == 1) {
5226:     if (scall == MAT_INITIAL_MATRIX) {
5227:       PetscCall(PetscObjectReference((PetscObject)mpimat->A));
5228:       *A_loc = mpimat->A;
5229:     } else if (scall == MAT_REUSE_MATRIX) {
5230:       PetscCall(MatCopy(mpimat->A, *A_loc, SAME_NONZERO_PATTERN));
5231:     }
5232:     PetscFunctionReturn(PETSC_SUCCESS);
5233:   }

5235:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5236:   a  = (Mat_SeqAIJ *)mpimat->A->data;
5237:   b  = (Mat_SeqAIJ *)mpimat->B->data;
5238:   ai = a->i;
5239:   aj = a->j;
5240:   bi = b->i;
5241:   bj = b->j;
5242:   PetscCall(MatSeqAIJGetArrayRead(mpimat->A, &aav));
5243:   PetscCall(MatSeqAIJGetArrayRead(mpimat->B, &bav));
5244:   aa = aav;
5245:   ba = bav;
5246:   if (scall == MAT_INITIAL_MATRIX) {
5247:     PetscCall(PetscMalloc1(1 + am, &ci));
5248:     ci[0] = 0;
5249:     for (i = 0; i < am; i++) ci[i + 1] = ci[i] + (ai[i + 1] - ai[i]) + (bi[i + 1] - bi[i]);
5250:     PetscCall(PetscMalloc1(1 + ci[am], &cj));
5251:     PetscCall(PetscMalloc1(1 + ci[am], &ca));
5252:     k = 0;
5253:     for (i = 0; i < am; i++) {
5254:       ncols_o = bi[i + 1] - bi[i];
5255:       ncols_d = ai[i + 1] - ai[i];
5256:       /* off-diagonal portion of A */
5257:       for (jo = 0; jo < ncols_o; jo++) {
5258:         col = cmap[*bj];
5259:         if (col >= cstart) break;
5260:         cj[k] = col;
5261:         bj++;
5262:         ca[k++] = *ba++;
5263:       }
5264:       /* diagonal portion of A */
5265:       for (j = 0; j < ncols_d; j++) {
5266:         cj[k]   = cstart + *aj++;
5267:         ca[k++] = *aa++;
5268:       }
5269:       /* off-diagonal portion of A */
5270:       for (j = jo; j < ncols_o; j++) {
5271:         cj[k]   = cmap[*bj++];
5272:         ca[k++] = *ba++;
5273:       }
5274:     }
5275:     /* put together the new matrix */
5276:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, A->cmap->N, ci, cj, ca, A_loc));
5277:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5278:     /* Since these are PETSc arrays, change flags to free them as necessary. */
5279:     mat          = (Mat_SeqAIJ *)(*A_loc)->data;
5280:     mat->free_a  = PETSC_TRUE;
5281:     mat->free_ij = PETSC_TRUE;
5282:     mat->nonew   = 0;
5283:   } else if (scall == MAT_REUSE_MATRIX) {
5284:     mat = (Mat_SeqAIJ *)(*A_loc)->data;
5285:     ci  = mat->i;
5286:     cj  = mat->j;
5287:     PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &cam));
5288:     for (i = 0; i < am; i++) {
5289:       /* off-diagonal portion of A */
5290:       ncols_o = bi[i + 1] - bi[i];
5291:       for (jo = 0; jo < ncols_o; jo++) {
5292:         col = cmap[*bj];
5293:         if (col >= cstart) break;
5294:         *cam++ = *ba++;
5295:         bj++;
5296:       }
5297:       /* diagonal portion of A */
5298:       ncols_d = ai[i + 1] - ai[i];
5299:       for (j = 0; j < ncols_d; j++) *cam++ = *aa++;
5300:       /* off-diagonal portion of A */
5301:       for (j = jo; j < ncols_o; j++) {
5302:         *cam++ = *ba++;
5303:         bj++;
5304:       }
5305:     }
5306:     PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &cam));
5307:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5308:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->A, &aav));
5309:   PetscCall(MatSeqAIJRestoreArrayRead(mpimat->B, &bav));
5310:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5311:   PetscFunctionReturn(PETSC_SUCCESS);
5312: }

5314: /*@
5315:   MatMPIAIJGetLocalMatMerge - Creates a `MATSEQAIJ` from a `MATMPIAIJ` matrix by taking all its local rows and putting them into a sequential matrix with
5316:   mlocal rows and n columns. Where n is the sum of the number of columns of the diagonal and off-diagonal part

5318:   Not Collective

5320:   Input Parameters:
5321: + A     - the matrix
5322: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5324:   Output Parameters:
5325: + glob  - sequential `IS` with global indices associated with the columns of the local sequential matrix generated (can be `NULL`)
5326: - A_loc - the local sequential matrix generated

5328:   Level: developer

5330:   Note:
5331:   This is different from `MatMPIAIJGetLocalMat()` since the first columns in the returning matrix are those associated with the diagonal
5332:   part, then those associated with the off-diagonal part (in its local ordering)

5334: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`, `MatMPIAIJGetLocalMatCondensed()`
5335: @*/
5336: PetscErrorCode MatMPIAIJGetLocalMatMerge(Mat A, MatReuse scall, IS *glob, Mat *A_loc)
5337: {
5338:   Mat             Ao, Ad;
5339:   const PetscInt *cmap;
5340:   PetscMPIInt     size;
5341:   PetscErrorCode (*f)(Mat, MatReuse, IS *, Mat *);

5343:   PetscFunctionBegin;
5344:   PetscCall(MatMPIAIJGetSeqAIJ(A, &Ad, &Ao, &cmap));
5345:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
5346:   if (size == 1) {
5347:     if (scall == MAT_INITIAL_MATRIX) {
5348:       PetscCall(PetscObjectReference((PetscObject)Ad));
5349:       *A_loc = Ad;
5350:     } else if (scall == MAT_REUSE_MATRIX) {
5351:       PetscCall(MatCopy(Ad, *A_loc, SAME_NONZERO_PATTERN));
5352:     }
5353:     if (glob) PetscCall(ISCreateStride(PetscObjectComm((PetscObject)Ad), Ad->cmap->n, Ad->cmap->rstart, 1, glob));
5354:     PetscFunctionReturn(PETSC_SUCCESS);
5355:   }
5356:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", &f));
5357:   PetscCall(PetscLogEventBegin(MAT_Getlocalmat, A, 0, 0, 0));
5358:   if (f) {
5359:     PetscCall((*f)(A, scall, glob, A_loc));
5360:   } else {
5361:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)Ad->data;
5362:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)Ao->data;
5363:     Mat_SeqAIJ        *c;
5364:     PetscInt          *ai = a->i, *aj = a->j;
5365:     PetscInt          *bi = b->i, *bj = b->j;
5366:     PetscInt          *ci, *cj;
5367:     const PetscScalar *aa, *ba;
5368:     PetscScalar       *ca;
5369:     PetscInt           i, j, am, dn, on;

5371:     PetscCall(MatGetLocalSize(Ad, &am, &dn));
5372:     PetscCall(MatGetLocalSize(Ao, NULL, &on));
5373:     PetscCall(MatSeqAIJGetArrayRead(Ad, &aa));
5374:     PetscCall(MatSeqAIJGetArrayRead(Ao, &ba));
5375:     if (scall == MAT_INITIAL_MATRIX) {
5376:       PetscInt k;
5377:       PetscCall(PetscMalloc1(1 + am, &ci));
5378:       PetscCall(PetscMalloc1(ai[am] + bi[am], &cj));
5379:       PetscCall(PetscMalloc1(ai[am] + bi[am], &ca));
5380:       ci[0] = 0;
5381:       for (i = 0, k = 0; i < am; i++) {
5382:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5383:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5384:         ci[i + 1]              = ci[i] + ncols_o + ncols_d;
5385:         /* diagonal portion of A */
5386:         for (j = 0; j < ncols_d; j++, k++) {
5387:           cj[k] = *aj++;
5388:           ca[k] = *aa++;
5389:         }
5390:         /* off-diagonal portion of A */
5391:         for (j = 0; j < ncols_o; j++, k++) {
5392:           cj[k] = dn + *bj++;
5393:           ca[k] = *ba++;
5394:         }
5395:       }
5396:       /* put together the new matrix */
5397:       PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, am, dn + on, ci, cj, ca, A_loc));
5398:       /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5399:       /* Since these are PETSc arrays, change flags to free them as necessary. */
5400:       c          = (Mat_SeqAIJ *)(*A_loc)->data;
5401:       c->free_a  = PETSC_TRUE;
5402:       c->free_ij = PETSC_TRUE;
5403:       c->nonew   = 0;
5404:       PetscCall(MatSetType(*A_loc, ((PetscObject)Ad)->type_name));
5405:     } else if (scall == MAT_REUSE_MATRIX) {
5406:       PetscCall(MatSeqAIJGetArrayWrite(*A_loc, &ca));
5407:       for (i = 0; i < am; i++) {
5408:         const PetscInt ncols_d = ai[i + 1] - ai[i];
5409:         const PetscInt ncols_o = bi[i + 1] - bi[i];
5410:         /* diagonal portion of A */
5411:         for (j = 0; j < ncols_d; j++) *ca++ = *aa++;
5412:         /* off-diagonal portion of A */
5413:         for (j = 0; j < ncols_o; j++) *ca++ = *ba++;
5414:       }
5415:       PetscCall(MatSeqAIJRestoreArrayWrite(*A_loc, &ca));
5416:     } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Invalid MatReuse %d", (int)scall);
5417:     PetscCall(MatSeqAIJRestoreArrayRead(Ad, &aa));
5418:     PetscCall(MatSeqAIJRestoreArrayRead(Ao, &aa));
5419:     if (glob) {
5420:       PetscInt cst, *gidx;

5422:       PetscCall(MatGetOwnershipRangeColumn(A, &cst, NULL));
5423:       PetscCall(PetscMalloc1(dn + on, &gidx));
5424:       for (i = 0; i < dn; i++) gidx[i] = cst + i;
5425:       for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
5426:       PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
5427:     }
5428:   }
5429:   PetscCall(PetscLogEventEnd(MAT_Getlocalmat, A, 0, 0, 0));
5430:   PetscFunctionReturn(PETSC_SUCCESS);
5431: }

5433: /*@C
5434:   MatMPIAIJGetLocalMatCondensed - Creates a `MATSEQAIJ` matrix from an `MATMPIAIJ` matrix by taking all its local rows and NON-ZERO columns

5436:   Not Collective

5438:   Input Parameters:
5439: + A     - the matrix
5440: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
5441: . row   - index set of rows to extract (or `NULL`)
5442: - col   - index set of columns to extract (or `NULL`)

5444:   Output Parameter:
5445: . A_loc - the local sequential matrix generated

5447:   Level: developer

5449: .seealso: [](ch_matrices), `Mat`, `MATMPIAIJ`, `MatGetOwnershipRange()`, `MatMPIAIJGetLocalMat()`
5450: @*/
5451: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
5452: {
5453:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5454:   PetscInt    i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
5455:   IS          isrowa, iscola;
5456:   Mat        *aloc;
5457:   PetscBool   match;

5459:   PetscFunctionBegin;
5460:   PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &match));
5461:   PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPIAIJ matrix as input");
5462:   PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5463:   if (!row) {
5464:     start = A->rmap->rstart;
5465:     end   = A->rmap->rend;
5466:     PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
5467:   } else {
5468:     isrowa = *row;
5469:   }
5470:   if (!col) {
5471:     start = A->cmap->rstart;
5472:     cmap  = a->garray;
5473:     nzA   = a->A->cmap->n;
5474:     nzB   = a->B->cmap->n;
5475:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5476:     ncols = 0;
5477:     for (i = 0; i < nzB; i++) {
5478:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5479:       else break;
5480:     }
5481:     imark = i;
5482:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
5483:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
5484:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
5485:   } else {
5486:     iscola = *col;
5487:   }
5488:   if (scall != MAT_INITIAL_MATRIX) {
5489:     PetscCall(PetscMalloc1(1, &aloc));
5490:     aloc[0] = *A_loc;
5491:   }
5492:   PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
5493:   if (!col) { /* attach global id of condensed columns */
5494:     PetscCall(PetscObjectCompose((PetscObject)aloc[0], "_petsc_GetLocalMatCondensed_iscol", (PetscObject)iscola));
5495:   }
5496:   *A_loc = aloc[0];
5497:   PetscCall(PetscFree(aloc));
5498:   if (!row) PetscCall(ISDestroy(&isrowa));
5499:   if (!col) PetscCall(ISDestroy(&iscola));
5500:   PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
5501:   PetscFunctionReturn(PETSC_SUCCESS);
5502: }

5504: /*
5505:  * Create a sequential AIJ matrix based on row indices. a whole column is extracted once a row is matched.
5506:  * Row could be local or remote.The routine is designed to be scalable in memory so that nothing is based
5507:  * on a global size.
5508:  * */
5509: static PetscErrorCode MatCreateSeqSubMatrixWithRows_Private(Mat P, IS rows, Mat *P_oth)
5510: {
5511:   Mat_MPIAIJ            *p  = (Mat_MPIAIJ *)P->data;
5512:   Mat_SeqAIJ            *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data, *p_oth;
5513:   PetscInt               plocalsize, nrows, *ilocal, *oilocal, i, lidx, *nrcols, *nlcols, ncol;
5514:   PetscMPIInt            owner;
5515:   PetscSFNode           *iremote, *oiremote;
5516:   const PetscInt        *lrowindices;
5517:   PetscSF                sf, osf;
5518:   PetscInt               pcstart, *roffsets, *loffsets, *pnnz, j;
5519:   PetscInt               ontotalcols, dntotalcols, ntotalcols, nout;
5520:   MPI_Comm               comm;
5521:   ISLocalToGlobalMapping mapping;
5522:   const PetscScalar     *pd_a, *po_a;

5524:   PetscFunctionBegin;
5525:   PetscCall(PetscObjectGetComm((PetscObject)P, &comm));
5526:   /* plocalsize is the number of roots
5527:    * nrows is the number of leaves
5528:    * */
5529:   PetscCall(MatGetLocalSize(P, &plocalsize, NULL));
5530:   PetscCall(ISGetLocalSize(rows, &nrows));
5531:   PetscCall(PetscCalloc1(nrows, &iremote));
5532:   PetscCall(ISGetIndices(rows, &lrowindices));
5533:   for (i = 0; i < nrows; i++) {
5534:     /* Find a remote index and an owner for a row
5535:      * The row could be local or remote
5536:      * */
5537:     owner = 0;
5538:     lidx  = 0;
5539:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, &lidx));
5540:     iremote[i].index = lidx;
5541:     iremote[i].rank  = owner;
5542:   }
5543:   /* Create SF to communicate how many nonzero columns for each row */
5544:   PetscCall(PetscSFCreate(comm, &sf));
5545:   /* SF will figure out the number of nonzero columns for each row, and their
5546:    * offsets
5547:    * */
5548:   PetscCall(PetscSFSetGraph(sf, plocalsize, nrows, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5549:   PetscCall(PetscSFSetFromOptions(sf));
5550:   PetscCall(PetscSFSetUp(sf));

5552:   PetscCall(PetscCalloc1(2 * (plocalsize + 1), &roffsets));
5553:   PetscCall(PetscCalloc1(2 * plocalsize, &nrcols));
5554:   PetscCall(PetscCalloc1(nrows, &pnnz));
5555:   roffsets[0] = 0;
5556:   roffsets[1] = 0;
5557:   for (i = 0; i < plocalsize; i++) {
5558:     /* diagonal */
5559:     nrcols[i * 2 + 0] = pd->i[i + 1] - pd->i[i];
5560:     /* off-diagonal */
5561:     nrcols[i * 2 + 1] = po->i[i + 1] - po->i[i];
5562:     /* compute offsets so that we relative location for each row */
5563:     roffsets[(i + 1) * 2 + 0] = roffsets[i * 2 + 0] + nrcols[i * 2 + 0];
5564:     roffsets[(i + 1) * 2 + 1] = roffsets[i * 2 + 1] + nrcols[i * 2 + 1];
5565:   }
5566:   PetscCall(PetscCalloc1(2 * nrows, &nlcols));
5567:   PetscCall(PetscCalloc1(2 * nrows, &loffsets));
5568:   /* 'r' means root, and 'l' means leaf */
5569:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5570:   PetscCall(PetscSFBcastBegin(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5571:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, nrcols, nlcols, MPI_REPLACE));
5572:   PetscCall(PetscSFBcastEnd(sf, MPIU_2INT, roffsets, loffsets, MPI_REPLACE));
5573:   PetscCall(PetscSFDestroy(&sf));
5574:   PetscCall(PetscFree(roffsets));
5575:   PetscCall(PetscFree(nrcols));
5576:   dntotalcols = 0;
5577:   ontotalcols = 0;
5578:   ncol        = 0;
5579:   for (i = 0; i < nrows; i++) {
5580:     pnnz[i] = nlcols[i * 2 + 0] + nlcols[i * 2 + 1];
5581:     ncol    = PetscMax(pnnz[i], ncol);
5582:     /* diagonal */
5583:     dntotalcols += nlcols[i * 2 + 0];
5584:     /* off-diagonal */
5585:     ontotalcols += nlcols[i * 2 + 1];
5586:   }
5587:   /* We do not need to figure the right number of columns
5588:    * since all the calculations will be done by going through the raw data
5589:    * */
5590:   PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, nrows, ncol, 0, pnnz, P_oth));
5591:   PetscCall(MatSetUp(*P_oth));
5592:   PetscCall(PetscFree(pnnz));
5593:   p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5594:   /* diagonal */
5595:   PetscCall(PetscCalloc1(dntotalcols, &iremote));
5596:   /* off-diagonal */
5597:   PetscCall(PetscCalloc1(ontotalcols, &oiremote));
5598:   /* diagonal */
5599:   PetscCall(PetscCalloc1(dntotalcols, &ilocal));
5600:   /* off-diagonal */
5601:   PetscCall(PetscCalloc1(ontotalcols, &oilocal));
5602:   dntotalcols = 0;
5603:   ontotalcols = 0;
5604:   ntotalcols  = 0;
5605:   for (i = 0; i < nrows; i++) {
5606:     owner = 0;
5607:     PetscCall(PetscLayoutFindOwnerIndex(P->rmap, lrowindices[i], &owner, NULL));
5608:     /* Set iremote for diag matrix */
5609:     for (j = 0; j < nlcols[i * 2 + 0]; j++) {
5610:       iremote[dntotalcols].index = loffsets[i * 2 + 0] + j;
5611:       iremote[dntotalcols].rank  = owner;
5612:       /* P_oth is seqAIJ so that ilocal need to point to the first part of memory */
5613:       ilocal[dntotalcols++] = ntotalcols++;
5614:     }
5615:     /* off-diagonal */
5616:     for (j = 0; j < nlcols[i * 2 + 1]; j++) {
5617:       oiremote[ontotalcols].index = loffsets[i * 2 + 1] + j;
5618:       oiremote[ontotalcols].rank  = owner;
5619:       oilocal[ontotalcols++]      = ntotalcols++;
5620:     }
5621:   }
5622:   PetscCall(ISRestoreIndices(rows, &lrowindices));
5623:   PetscCall(PetscFree(loffsets));
5624:   PetscCall(PetscFree(nlcols));
5625:   PetscCall(PetscSFCreate(comm, &sf));
5626:   /* P serves as roots and P_oth is leaves
5627:    * Diag matrix
5628:    * */
5629:   PetscCall(PetscSFSetGraph(sf, pd->i[plocalsize], dntotalcols, ilocal, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
5630:   PetscCall(PetscSFSetFromOptions(sf));
5631:   PetscCall(PetscSFSetUp(sf));

5633:   PetscCall(PetscSFCreate(comm, &osf));
5634:   /* off-diagonal */
5635:   PetscCall(PetscSFSetGraph(osf, po->i[plocalsize], ontotalcols, oilocal, PETSC_OWN_POINTER, oiremote, PETSC_OWN_POINTER));
5636:   PetscCall(PetscSFSetFromOptions(osf));
5637:   PetscCall(PetscSFSetUp(osf));
5638:   PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5639:   PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5640:   /* operate on the matrix internal data to save memory */
5641:   PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5642:   PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5643:   PetscCall(MatGetOwnershipRangeColumn(P, &pcstart, NULL));
5644:   /* Convert to global indices for diag matrix */
5645:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] += pcstart;
5646:   PetscCall(PetscSFBcastBegin(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5647:   /* We want P_oth store global indices */
5648:   PetscCall(ISLocalToGlobalMappingCreate(comm, 1, p->B->cmap->n, p->garray, PETSC_COPY_VALUES, &mapping));
5649:   /* Use memory scalable approach */
5650:   PetscCall(ISLocalToGlobalMappingSetType(mapping, ISLOCALTOGLOBALMAPPINGHASH));
5651:   PetscCall(ISLocalToGlobalMappingApply(mapping, po->i[plocalsize], po->j, po->j));
5652:   PetscCall(PetscSFBcastBegin(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5653:   PetscCall(PetscSFBcastEnd(sf, MPIU_INT, pd->j, p_oth->j, MPI_REPLACE));
5654:   /* Convert back to local indices */
5655:   for (i = 0; i < pd->i[plocalsize]; i++) pd->j[i] -= pcstart;
5656:   PetscCall(PetscSFBcastEnd(osf, MPIU_INT, po->j, p_oth->j, MPI_REPLACE));
5657:   nout = 0;
5658:   PetscCall(ISGlobalToLocalMappingApply(mapping, IS_GTOLM_DROP, po->i[plocalsize], po->j, &nout, po->j));
5659:   PetscCheck(nout == po->i[plocalsize], comm, PETSC_ERR_ARG_INCOMP, "n %" PetscInt_FMT " does not equal to nout %" PetscInt_FMT " ", po->i[plocalsize], nout);
5660:   PetscCall(ISLocalToGlobalMappingDestroy(&mapping));
5661:   /* Exchange values */
5662:   PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5663:   PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5664:   PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5665:   PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5666:   /* Stop PETSc from shrinking memory */
5667:   for (i = 0; i < nrows; i++) p_oth->ilen[i] = p_oth->imax[i];
5668:   PetscCall(MatAssemblyBegin(*P_oth, MAT_FINAL_ASSEMBLY));
5669:   PetscCall(MatAssemblyEnd(*P_oth, MAT_FINAL_ASSEMBLY));
5670:   /* Attach PetscSF objects to P_oth so that we can reuse it later */
5671:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "diagsf", (PetscObject)sf));
5672:   PetscCall(PetscObjectCompose((PetscObject)*P_oth, "offdiagsf", (PetscObject)osf));
5673:   PetscCall(PetscSFDestroy(&sf));
5674:   PetscCall(PetscSFDestroy(&osf));
5675:   PetscFunctionReturn(PETSC_SUCCESS);
5676: }

5678: /*
5679:  * Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5680:  * This supports MPIAIJ and MAIJ
5681:  * */
5682: PetscErrorCode MatGetBrowsOfAcols_MPIXAIJ(Mat A, Mat P, PetscInt dof, MatReuse reuse, Mat *P_oth)
5683: {
5684:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *p = (Mat_MPIAIJ *)P->data;
5685:   Mat_SeqAIJ *p_oth;
5686:   IS          rows, map;
5687:   PetscHMapI  hamp;
5688:   PetscInt    i, htsize, *rowindices, off, *mapping, key, count;
5689:   MPI_Comm    comm;
5690:   PetscSF     sf, osf;
5691:   PetscBool   has;

5693:   PetscFunctionBegin;
5694:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5695:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, P, 0, 0));
5696:   /* If it is the first time, create an index set of off-diag nonzero columns of A,
5697:    *  and then create a submatrix (that often is an overlapping matrix)
5698:    * */
5699:   if (reuse == MAT_INITIAL_MATRIX) {
5700:     /* Use a hash table to figure out unique keys */
5701:     PetscCall(PetscHMapICreateWithSize(a->B->cmap->n, &hamp));
5702:     PetscCall(PetscCalloc1(a->B->cmap->n, &mapping));
5703:     count = 0;
5704:     /* Assume that  a->g is sorted, otherwise the following does not make sense */
5705:     for (i = 0; i < a->B->cmap->n; i++) {
5706:       key = a->garray[i] / dof;
5707:       PetscCall(PetscHMapIHas(hamp, key, &has));
5708:       if (!has) {
5709:         mapping[i] = count;
5710:         PetscCall(PetscHMapISet(hamp, key, count++));
5711:       } else {
5712:         /* Current 'i' has the same value the previous step */
5713:         mapping[i] = count - 1;
5714:       }
5715:     }
5716:     PetscCall(ISCreateGeneral(comm, a->B->cmap->n, mapping, PETSC_OWN_POINTER, &map));
5717:     PetscCall(PetscHMapIGetSize(hamp, &htsize));
5718:     PetscCheck(htsize == count, comm, PETSC_ERR_ARG_INCOMP, " Size of hash map %" PetscInt_FMT " is inconsistent with count %" PetscInt_FMT, htsize, count);
5719:     PetscCall(PetscCalloc1(htsize, &rowindices));
5720:     off = 0;
5721:     PetscCall(PetscHMapIGetKeys(hamp, &off, rowindices));
5722:     PetscCall(PetscHMapIDestroy(&hamp));
5723:     PetscCall(PetscSortInt(htsize, rowindices));
5724:     PetscCall(ISCreateGeneral(comm, htsize, rowindices, PETSC_OWN_POINTER, &rows));
5725:     /* In case, the matrix was already created but users want to recreate the matrix */
5726:     PetscCall(MatDestroy(P_oth));
5727:     PetscCall(MatCreateSeqSubMatrixWithRows_Private(P, rows, P_oth));
5728:     PetscCall(PetscObjectCompose((PetscObject)*P_oth, "aoffdiagtopothmapping", (PetscObject)map));
5729:     PetscCall(ISDestroy(&map));
5730:     PetscCall(ISDestroy(&rows));
5731:   } else if (reuse == MAT_REUSE_MATRIX) {
5732:     /* If matrix was already created, we simply update values using SF objects
5733:      * that as attached to the matrix earlier.
5734:      */
5735:     const PetscScalar *pd_a, *po_a;

5737:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "diagsf", (PetscObject *)&sf));
5738:     PetscCall(PetscObjectQuery((PetscObject)*P_oth, "offdiagsf", (PetscObject *)&osf));
5739:     PetscCheck(sf && osf, comm, PETSC_ERR_ARG_NULL, "Matrix is not initialized yet");
5740:     p_oth = (Mat_SeqAIJ *)(*P_oth)->data;
5741:     /* Update values in place */
5742:     PetscCall(MatSeqAIJGetArrayRead(p->A, &pd_a));
5743:     PetscCall(MatSeqAIJGetArrayRead(p->B, &po_a));
5744:     PetscCall(PetscSFBcastBegin(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5745:     PetscCall(PetscSFBcastBegin(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5746:     PetscCall(PetscSFBcastEnd(sf, MPIU_SCALAR, pd_a, p_oth->a, MPI_REPLACE));
5747:     PetscCall(PetscSFBcastEnd(osf, MPIU_SCALAR, po_a, p_oth->a, MPI_REPLACE));
5748:     PetscCall(MatSeqAIJRestoreArrayRead(p->A, &pd_a));
5749:     PetscCall(MatSeqAIJRestoreArrayRead(p->B, &po_a));
5750:   } else SETERRQ(comm, PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown reuse type");
5751:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, P, 0, 0));
5752:   PetscFunctionReturn(PETSC_SUCCESS);
5753: }

5755: /*@C
5756:   MatGetBrowsOfAcols - Returns `IS` that contain rows of `B` that equal to nonzero columns of local `A`

5758:   Collective

5760:   Input Parameters:
5761: + A     - the first matrix in `MATMPIAIJ` format
5762: . B     - the second matrix in `MATMPIAIJ` format
5763: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5765:   Output Parameters:
5766: + rowb  - On input index sets of rows of B to extract (or `NULL`), modified on output
5767: . colb  - On input index sets of columns of B to extract (or `NULL`), modified on output
5768: - B_seq - the sequential matrix generated

5770:   Level: developer

5772: .seealso: `Mat`, `MATMPIAIJ`, `IS`, `MatReuse`
5773: @*/
5774: PetscErrorCode MatGetBrowsOfAcols(Mat A, Mat B, MatReuse scall, IS *rowb, IS *colb, Mat *B_seq)
5775: {
5776:   Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
5777:   PetscInt   *idx, i, start, ncols, nzA, nzB, *cmap, imark;
5778:   IS          isrowb, iscolb;
5779:   Mat        *bseq = NULL;

5781:   PetscFunctionBegin;
5782:   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 ")",
5783:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5784:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAcols, A, B, 0, 0));

5786:   if (scall == MAT_INITIAL_MATRIX) {
5787:     start = A->cmap->rstart;
5788:     cmap  = a->garray;
5789:     nzA   = a->A->cmap->n;
5790:     nzB   = a->B->cmap->n;
5791:     PetscCall(PetscMalloc1(nzA + nzB, &idx));
5792:     ncols = 0;
5793:     for (i = 0; i < nzB; i++) { /* row < local row index */
5794:       if (cmap[i] < start) idx[ncols++] = cmap[i];
5795:       else break;
5796:     }
5797:     imark = i;
5798:     for (i = 0; i < nzA; i++) idx[ncols++] = start + i;   /* local rows */
5799:     for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5800:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &isrowb));
5801:     PetscCall(ISCreateStride(PETSC_COMM_SELF, B->cmap->N, 0, 1, &iscolb));
5802:   } else {
5803:     PetscCheck(rowb && colb, PETSC_COMM_SELF, PETSC_ERR_SUP, "IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5804:     isrowb = *rowb;
5805:     iscolb = *colb;
5806:     PetscCall(PetscMalloc1(1, &bseq));
5807:     bseq[0] = *B_seq;
5808:   }
5809:   PetscCall(MatCreateSubMatrices(B, 1, &isrowb, &iscolb, scall, &bseq));
5810:   *B_seq = bseq[0];
5811:   PetscCall(PetscFree(bseq));
5812:   if (!rowb) {
5813:     PetscCall(ISDestroy(&isrowb));
5814:   } else {
5815:     *rowb = isrowb;
5816:   }
5817:   if (!colb) {
5818:     PetscCall(ISDestroy(&iscolb));
5819:   } else {
5820:     *colb = iscolb;
5821:   }
5822:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAcols, A, B, 0, 0));
5823:   PetscFunctionReturn(PETSC_SUCCESS);
5824: }

5826: /*
5827:     MatGetBrowsOfAoCols_MPIAIJ - Creates a `MATSEQAIJ` matrix by taking rows of B that equal to nonzero columns
5828:     of the OFF-DIAGONAL portion of local A

5830:     Collective

5832:    Input Parameters:
5833: +    A,B - the matrices in `MATMPIAIJ` format
5834: -    scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

5836:    Output Parameter:
5837: +    startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5838: .    startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5839: .    bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5840: -    B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N

5842:     Developer Note:
5843:     This directly accesses information inside the VecScatter associated with the matrix-vector product
5844:      for this matrix. This is not desirable..

5846:     Level: developer

5848: */

5850: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A, Mat B, MatReuse scall, PetscInt **startsj_s, PetscInt **startsj_r, MatScalar **bufa_ptr, Mat *B_oth)
5851: {
5852:   Mat_MPIAIJ        *a = (Mat_MPIAIJ *)A->data;
5853:   VecScatter         ctx;
5854:   MPI_Comm           comm;
5855:   const PetscMPIInt *rprocs, *sprocs;
5856:   PetscMPIInt        nrecvs, nsends;
5857:   const PetscInt    *srow, *rstarts, *sstarts;
5858:   PetscInt          *rowlen, *bufj, *bufJ, ncols = 0, aBn = a->B->cmap->n, row, *b_othi, *b_othj, *rvalues = NULL, *svalues = NULL, *cols, sbs, rbs;
5859:   PetscInt           i, j, k = 0, l, ll, nrows, *rstartsj = NULL, *sstartsj, len;
5860:   PetscScalar       *b_otha, *bufa, *bufA, *vals = NULL;
5861:   MPI_Request       *reqs = NULL, *rwaits = NULL, *swaits = NULL;
5862:   PetscMPIInt        size, tag, rank, nreqs;

5864:   PetscFunctionBegin;
5865:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
5866:   PetscCallMPI(MPI_Comm_size(comm, &size));

5868:   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 ")",
5869:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
5870:   PetscCall(PetscLogEventBegin(MAT_GetBrowsOfAocols, A, B, 0, 0));
5871:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

5873:   if (size == 1) {
5874:     startsj_s = NULL;
5875:     bufa_ptr  = NULL;
5876:     *B_oth    = NULL;
5877:     PetscFunctionReturn(PETSC_SUCCESS);
5878:   }

5880:   ctx = a->Mvctx;
5881:   tag = ((PetscObject)ctx)->tag;

5883:   PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &srow, &sprocs, &sbs));
5884:   /* rprocs[] must be ordered so that indices received from them are ordered in rvalues[], which is key to algorithms used in this subroutine */
5885:   PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL /*indices not needed*/, &rprocs, &rbs));
5886:   PetscCall(PetscMPIIntCast(nsends + nrecvs, &nreqs));
5887:   PetscCall(PetscMalloc1(nreqs, &reqs));
5888:   rwaits = reqs;
5889:   swaits = PetscSafePointerPlusOffset(reqs, nrecvs);

5891:   if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5892:   if (scall == MAT_INITIAL_MATRIX) {
5893:     /* i-array */
5894:     /*  post receives */
5895:     if (nrecvs) PetscCall(PetscMalloc1(rbs * (rstarts[nrecvs] - rstarts[0]), &rvalues)); /* rstarts can be NULL when nrecvs=0 */
5896:     for (i = 0; i < nrecvs; i++) {
5897:       rowlen = rvalues + rstarts[i] * rbs;
5898:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of indices to be received */
5899:       PetscCallMPI(MPIU_Irecv(rowlen, nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5900:     }

5902:     /* pack the outgoing message */
5903:     PetscCall(PetscMalloc2(nsends + 1, &sstartsj, nrecvs + 1, &rstartsj));

5905:     sstartsj[0] = 0;
5906:     rstartsj[0] = 0;
5907:     len         = 0; /* total length of j or a array to be sent */
5908:     if (nsends) {
5909:       k = sstarts[0]; /* ATTENTION: sstarts[0] and rstarts[0] are not necessarily zero */
5910:       PetscCall(PetscMalloc1(sbs * (sstarts[nsends] - sstarts[0]), &svalues));
5911:     }
5912:     for (i = 0; i < nsends; i++) {
5913:       rowlen = svalues + (sstarts[i] - sstarts[0]) * sbs;
5914:       nrows  = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5915:       for (j = 0; j < nrows; j++) {
5916:         row = srow[k] + B->rmap->range[rank]; /* global row idx */
5917:         for (l = 0; l < sbs; l++) {
5918:           PetscCall(MatGetRow_MPIAIJ(B, row + l, &ncols, NULL, NULL)); /* rowlength */

5920:           rowlen[j * sbs + l] = ncols;

5922:           len += ncols;
5923:           PetscCall(MatRestoreRow_MPIAIJ(B, row + l, &ncols, NULL, NULL));
5924:         }
5925:         k++;
5926:       }
5927:       PetscCallMPI(MPIU_Isend(rowlen, nrows * sbs, MPIU_INT, sprocs[i], tag, comm, swaits + i));

5929:       sstartsj[i + 1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5930:     }
5931:     /* recvs and sends of i-array are completed */
5932:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5933:     PetscCall(PetscFree(svalues));

5935:     /* allocate buffers for sending j and a arrays */
5936:     PetscCall(PetscMalloc1(len + 1, &bufj));
5937:     PetscCall(PetscMalloc1(len + 1, &bufa));

5939:     /* create i-array of B_oth */
5940:     PetscCall(PetscMalloc1(aBn + 2, &b_othi));

5942:     b_othi[0] = 0;
5943:     len       = 0; /* total length of j or a array to be received */
5944:     k         = 0;
5945:     for (i = 0; i < nrecvs; i++) {
5946:       rowlen = rvalues + (rstarts[i] - rstarts[0]) * rbs;
5947:       nrows  = (rstarts[i + 1] - rstarts[i]) * rbs; /* num of rows to be received */
5948:       for (j = 0; j < nrows; j++) {
5949:         b_othi[k + 1] = b_othi[k] + rowlen[j];
5950:         PetscCall(PetscIntSumError(rowlen[j], len, &len));
5951:         k++;
5952:       }
5953:       rstartsj[i + 1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5954:     }
5955:     PetscCall(PetscFree(rvalues));

5957:     /* allocate space for j and a arrays of B_oth */
5958:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_othj));
5959:     PetscCall(PetscMalloc1(b_othi[aBn] + 1, &b_otha));

5961:     /* j-array */
5962:     /*  post receives of j-array */
5963:     for (i = 0; i < nrecvs; i++) {
5964:       nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5965:       PetscCallMPI(MPIU_Irecv(b_othj + rstartsj[i], nrows, MPIU_INT, rprocs[i], tag, comm, rwaits + i));
5966:     }

5968:     /* pack the outgoing message j-array */
5969:     if (nsends) k = sstarts[0];
5970:     for (i = 0; i < nsends; i++) {
5971:       nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
5972:       bufJ  = bufj + sstartsj[i];
5973:       for (j = 0; j < nrows; j++) {
5974:         row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5975:         for (ll = 0; ll < sbs; ll++) {
5976:           PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5977:           for (l = 0; l < ncols; l++) *bufJ++ = cols[l];
5978:           PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, &cols, NULL));
5979:         }
5980:       }
5981:       PetscCallMPI(MPIU_Isend(bufj + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_INT, sprocs[i], tag, comm, swaits + i));
5982:     }

5984:     /* recvs and sends of j-array are completed */
5985:     if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
5986:   } else if (scall == MAT_REUSE_MATRIX) {
5987:     sstartsj = *startsj_s;
5988:     rstartsj = *startsj_r;
5989:     bufa     = *bufa_ptr;
5990:     PetscCall(MatSeqAIJGetArrayWrite(*B_oth, &b_otha));
5991:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not possess an object container");

5993:   /* a-array */
5994:   /*  post receives of a-array */
5995:   for (i = 0; i < nrecvs; i++) {
5996:     nrows = rstartsj[i + 1] - rstartsj[i]; /* length of the msg received */
5997:     PetscCallMPI(MPIU_Irecv(b_otha + rstartsj[i], nrows, MPIU_SCALAR, rprocs[i], tag, comm, rwaits + i));
5998:   }

6000:   /* pack the outgoing message a-array */
6001:   if (nsends) k = sstarts[0];
6002:   for (i = 0; i < nsends; i++) {
6003:     nrows = sstarts[i + 1] - sstarts[i]; /* num of block rows */
6004:     bufA  = bufa + sstartsj[i];
6005:     for (j = 0; j < nrows; j++) {
6006:       row = srow[k++] + B->rmap->range[rank]; /* global row idx */
6007:       for (ll = 0; ll < sbs; ll++) {
6008:         PetscCall(MatGetRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6009:         for (l = 0; l < ncols; l++) *bufA++ = vals[l];
6010:         PetscCall(MatRestoreRow_MPIAIJ(B, row + ll, &ncols, NULL, &vals));
6011:       }
6012:     }
6013:     PetscCallMPI(MPIU_Isend(bufa + sstartsj[i], sstartsj[i + 1] - sstartsj[i], MPIU_SCALAR, sprocs[i], tag, comm, swaits + i));
6014:   }
6015:   /* recvs and sends of a-array are completed */
6016:   if (nreqs) PetscCallMPI(MPI_Waitall(nreqs, reqs, MPI_STATUSES_IGNORE));
6017:   PetscCall(PetscFree(reqs));

6019:   if (scall == MAT_INITIAL_MATRIX) {
6020:     Mat_SeqAIJ *b_oth;

6022:     /* put together the new matrix */
6023:     PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, aBn, B->cmap->N, b_othi, b_othj, b_otha, B_oth));

6025:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
6026:     /* Since these are PETSc arrays, change flags to free them as necessary. */
6027:     b_oth          = (Mat_SeqAIJ *)(*B_oth)->data;
6028:     b_oth->free_a  = PETSC_TRUE;
6029:     b_oth->free_ij = PETSC_TRUE;
6030:     b_oth->nonew   = 0;

6032:     PetscCall(PetscFree(bufj));
6033:     if (!startsj_s || !bufa_ptr) {
6034:       PetscCall(PetscFree2(sstartsj, rstartsj));
6035:       PetscCall(PetscFree(bufa_ptr));
6036:     } else {
6037:       *startsj_s = sstartsj;
6038:       *startsj_r = rstartsj;
6039:       *bufa_ptr  = bufa;
6040:     }
6041:   } else if (scall == MAT_REUSE_MATRIX) {
6042:     PetscCall(MatSeqAIJRestoreArrayWrite(*B_oth, &b_otha));
6043:   }

6045:   PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE, &nsends, &sstarts, &srow, &sprocs, &sbs));
6046:   PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE, &nrecvs, &rstarts, NULL, &rprocs, &rbs));
6047:   PetscCall(PetscLogEventEnd(MAT_GetBrowsOfAocols, A, B, 0, 0));
6048:   PetscFunctionReturn(PETSC_SUCCESS);
6049: }

6051: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat, MatType, MatReuse, Mat *);
6052: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat, MatType, MatReuse, Mat *);
6053: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJSELL(Mat, MatType, MatReuse, Mat *);
6054: #if defined(PETSC_HAVE_MKL_SPARSE)
6055: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJMKL(Mat, MatType, MatReuse, Mat *);
6056: #endif
6057: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIBAIJ(Mat, MatType, MatReuse, Mat *);
6058: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat, MatType, MatReuse, Mat *);
6059: #if defined(PETSC_HAVE_ELEMENTAL)
6060: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
6061: #endif
6062: #if defined(PETSC_HAVE_SCALAPACK)
6063: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
6064: #endif
6065: #if defined(PETSC_HAVE_HYPRE)
6066: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
6067: #endif
6068: #if defined(PETSC_HAVE_CUDA)
6069: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
6070: #endif
6071: #if defined(PETSC_HAVE_HIP)
6072: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
6073: #endif
6074: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6075: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat, MatType, MatReuse, Mat *);
6076: #endif
6077: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat, MatType, MatReuse, Mat *);
6078: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
6079: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

6081: /*
6082:     Computes (B'*A')' since computing B*A directly is untenable

6084:                n                       p                          p
6085:         [             ]       [             ]         [                 ]
6086:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
6087:         [             ]       [             ]         [                 ]

6089: */
6090: static PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A, Mat B, Mat C)
6091: {
6092:   Mat At, Bt, Ct;

6094:   PetscFunctionBegin;
6095:   PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
6096:   PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &Bt));
6097:   PetscCall(MatMatMult(Bt, At, MAT_INITIAL_MATRIX, PETSC_CURRENT, &Ct));
6098:   PetscCall(MatDestroy(&At));
6099:   PetscCall(MatDestroy(&Bt));
6100:   PetscCall(MatTransposeSetPrecursor(Ct, C));
6101:   PetscCall(MatTranspose(Ct, MAT_REUSE_MATRIX, &C));
6102:   PetscCall(MatDestroy(&Ct));
6103:   PetscFunctionReturn(PETSC_SUCCESS);
6104: }

6106: static PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A, Mat B, PetscReal fill, Mat C)
6107: {
6108:   PetscBool cisdense;

6110:   PetscFunctionBegin;
6111:   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);
6112:   PetscCall(MatSetSizes(C, A->rmap->n, B->cmap->n, A->rmap->N, B->cmap->N));
6113:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
6114:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATMPIDENSE, MATMPIDENSECUDA, MATMPIDENSEHIP, ""));
6115:   if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
6116:   PetscCall(MatSetUp(C));

6118:   C->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
6119:   PetscFunctionReturn(PETSC_SUCCESS);
6120: }

6122: static PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ_AB(Mat C)
6123: {
6124:   Mat_Product *product = C->product;
6125:   Mat          A = product->A, B = product->B;

6127:   PetscFunctionBegin;
6128:   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 ")",
6129:              A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
6130:   C->ops->matmultsymbolic = MatMatMultSymbolic_MPIDense_MPIAIJ;
6131:   C->ops->productsymbolic = MatProductSymbolic_AB;
6132:   PetscFunctionReturn(PETSC_SUCCESS);
6133: }

6135: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIDense_MPIAIJ(Mat C)
6136: {
6137:   Mat_Product *product = C->product;

6139:   PetscFunctionBegin;
6140:   if (product->type == MATPRODUCT_AB) PetscCall(MatProductSetFromOptions_MPIDense_MPIAIJ_AB(C));
6141:   PetscFunctionReturn(PETSC_SUCCESS);
6142: }

6144: /*
6145:    Merge two sets of sorted nonzeros and return a CSR for the merged (sequential) matrix

6147:   Input Parameters:

6149:     j1,rowBegin1,rowEnd1,jmap1: describe the first set of nonzeros (Set1)
6150:     j2,rowBegin2,rowEnd2,jmap2: describe the second set of nonzeros (Set2)

6152:     mat: both sets' nonzeros are on m rows, where m is the number of local rows of the matrix mat

6154:     For Set1, j1[] contains column indices of the nonzeros.
6155:     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
6156:     respectively (note rowEnd1[k] is not necessarily equal to rwoBegin1[k+1]). Indices in this range of j1[] are sorted,
6157:     but might have repeats. jmap1[t+1] - jmap1[t] is the number of repeats for the t-th unique nonzero in Set1.

6159:     Similar for Set2.

6161:     This routine merges the two sets of nonzeros row by row and removes repeats.

6163:   Output Parameters: (memory is allocated by the caller)

6165:     i[],j[]: the CSR of the merged matrix, which has m rows.
6166:     imap1[]: the k-th unique nonzero in Set1 (k=0,1,...) corresponds to imap1[k]-th unique nonzero in the merged matrix.
6167:     imap2[]: similar to imap1[], but for Set2.
6168:     Note we order nonzeros row-by-row and from left to right.
6169: */
6170: 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[])
6171: {
6172:   PetscInt   r, m; /* Row index of mat */
6173:   PetscCount t, t1, t2, b1, e1, b2, e2;

6175:   PetscFunctionBegin;
6176:   PetscCall(MatGetLocalSize(mat, &m, NULL));
6177:   t1 = t2 = t = 0; /* Count unique nonzeros of in Set1, Set1 and the merged respectively */
6178:   i[0]        = 0;
6179:   for (r = 0; r < m; r++) { /* Do row by row merging */
6180:     b1 = rowBegin1[r];
6181:     e1 = rowEnd1[r];
6182:     b2 = rowBegin2[r];
6183:     e2 = rowEnd2[r];
6184:     while (b1 < e1 && b2 < e2) {
6185:       if (j1[b1] == j2[b2]) { /* Same column index and hence same nonzero */
6186:         j[t]      = j1[b1];
6187:         imap1[t1] = t;
6188:         imap2[t2] = t;
6189:         b1 += jmap1[t1 + 1] - jmap1[t1]; /* Jump to next unique local nonzero */
6190:         b2 += jmap2[t2 + 1] - jmap2[t2]; /* Jump to next unique remote nonzero */
6191:         t1++;
6192:         t2++;
6193:         t++;
6194:       } else if (j1[b1] < j2[b2]) {
6195:         j[t]      = j1[b1];
6196:         imap1[t1] = t;
6197:         b1 += jmap1[t1 + 1] - jmap1[t1];
6198:         t1++;
6199:         t++;
6200:       } else {
6201:         j[t]      = j2[b2];
6202:         imap2[t2] = t;
6203:         b2 += jmap2[t2 + 1] - jmap2[t2];
6204:         t2++;
6205:         t++;
6206:       }
6207:     }
6208:     /* Merge the remaining in either j1[] or j2[] */
6209:     while (b1 < e1) {
6210:       j[t]      = j1[b1];
6211:       imap1[t1] = t;
6212:       b1 += jmap1[t1 + 1] - jmap1[t1];
6213:       t1++;
6214:       t++;
6215:     }
6216:     while (b2 < e2) {
6217:       j[t]      = j2[b2];
6218:       imap2[t2] = t;
6219:       b2 += jmap2[t2 + 1] - jmap2[t2];
6220:       t2++;
6221:       t++;
6222:     }
6223:     PetscCall(PetscIntCast(t, i + r + 1));
6224:   }
6225:   PetscFunctionReturn(PETSC_SUCCESS);
6226: }

6228: /*
6229:   Split nonzeros in a block of local rows into two subsets: those in the diagonal block and those in the off-diagonal block

6231:   Input Parameters:
6232:     mat: an MPI matrix that provides row and column layout information for splitting. Let's say its number of local rows is m.
6233:     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[]
6234:       respectively, along with a permutation array perm[]. Length of the i[],j[],perm[] arrays is n.

6236:       i[] is already sorted, but within a row, j[] is not sorted and might have repeats.
6237:       i[] might contain negative indices at the beginning, which means the corresponding entries should be ignored in the splitting.

6239:   Output Parameters:
6240:     j[],perm[]: the routine needs to sort j[] within each row along with perm[].
6241:     rowBegin[],rowMid[],rowEnd[]: of length m, and the memory is preallocated and zeroed by the caller.
6242:       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,
6243:       and [rowMid[r],rowEnd[r]) point to begin/end entries of row r of the off-diagonal block.

6245:     Aperm[],Ajmap[],Atot,Annz: Arrays are allocated by this routine.
6246:       Atot: number of entries belonging to the diagonal block.
6247:       Annz: number of unique nonzeros belonging to the diagonal block.
6248:       Aperm[Atot] stores values from perm[] for entries belonging to the diagonal block. Length of Aperm[] is Atot, though it may also count
6249:         repeats (i.e., same 'i,j' pair).
6250:       Ajmap[Annz+1] stores the number of repeats of each unique entry belonging to the diagonal block. More precisely, Ajmap[t+1] - Ajmap[t]
6251:         is the number of repeats for the t-th unique entry in the diagonal block. Ajmap[0] is always 0.

6253:       Atot: number of entries belonging to the diagonal block
6254:       Annz: number of unique nonzeros belonging to the diagonal block.

6256:     Bperm[], Bjmap[], Btot, Bnnz are similar but for the off-diagonal block.

6258:     Aperm[],Bperm[],Ajmap[] and Bjmap[] are allocated separately by this routine with PetscMalloc1().
6259: */
6260: 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_)
6261: {
6262:   PetscInt    cstart, cend, rstart, rend, row, col;
6263:   PetscCount  Atot = 0, Btot = 0; /* Total number of nonzeros in the diagonal and off-diagonal blocks */
6264:   PetscCount  Annz = 0, Bnnz = 0; /* Number of unique nonzeros in the diagonal and off-diagonal blocks */
6265:   PetscCount  k, m, p, q, r, s, mid;
6266:   PetscCount *Aperm, *Bperm, *Ajmap, *Bjmap;

6268:   PetscFunctionBegin;
6269:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6270:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6271:   m = rend - rstart;

6273:   /* Skip negative rows */
6274:   for (k = 0; k < n; k++)
6275:     if (i[k] >= 0) break;

6277:   /* Process [k,n): sort and partition each local row into diag and offdiag portions,
6278:      fill rowBegin[], rowMid[], rowEnd[], and count Atot, Btot, Annz, Bnnz.
6279:   */
6280:   while (k < n) {
6281:     row = i[k];
6282:     /* Entries in [k,s) are in one row. Shift diagonal block col indices so that diag is ahead of offdiag after sorting the row */
6283:     for (s = k; s < n; s++)
6284:       if (i[s] != row) break;

6286:     /* Shift diag columns to range of [-PETSC_INT_MAX, -1] */
6287:     for (p = k; p < s; p++) {
6288:       if (j[p] >= cstart && j[p] < cend) j[p] -= PETSC_INT_MAX;
6289:       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]);
6290:     }
6291:     PetscCall(PetscSortIntWithCountArray(s - k, j + k, perm + k));
6292:     PetscCall(PetscSortedIntUpperBound(j, k, s, -1, &mid)); /* Separate [k,s) into [k,mid) for diag and [mid,s) for offdiag */
6293:     rowBegin[row - rstart] = k;
6294:     rowMid[row - rstart]   = mid;
6295:     rowEnd[row - rstart]   = s;

6297:     /* Count nonzeros of this diag/offdiag row, which might have repeats */
6298:     Atot += mid - k;
6299:     Btot += s - mid;

6301:     /* Count unique nonzeros of this diag row */
6302:     for (p = k; p < mid;) {
6303:       col = j[p];
6304:       do {
6305:         j[p] += PETSC_INT_MAX; /* Revert the modified diagonal indices */
6306:         p++;
6307:       } while (p < mid && j[p] == col);
6308:       Annz++;
6309:     }

6311:     /* Count unique nonzeros of this offdiag row */
6312:     for (p = mid; p < s;) {
6313:       col = j[p];
6314:       do {
6315:         p++;
6316:       } while (p < s && j[p] == col);
6317:       Bnnz++;
6318:     }
6319:     k = s;
6320:   }

6322:   /* Allocation according to Atot, Btot, Annz, Bnnz */
6323:   PetscCall(PetscMalloc1(Atot, &Aperm));
6324:   PetscCall(PetscMalloc1(Btot, &Bperm));
6325:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap));
6326:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap));

6328:   /* Re-scan indices and copy diag/offdiag permutation indices to Aperm, Bperm and also fill Ajmap and Bjmap */
6329:   Ajmap[0] = Bjmap[0] = Atot = Btot = Annz = Bnnz = 0;
6330:   for (r = 0; r < m; r++) {
6331:     k   = rowBegin[r];
6332:     mid = rowMid[r];
6333:     s   = rowEnd[r];
6334:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Aperm, Atot), PetscSafePointerPlusOffset(perm, k), mid - k));
6335:     PetscCall(PetscArraycpy(PetscSafePointerPlusOffset(Bperm, Btot), PetscSafePointerPlusOffset(perm, mid), s - mid));
6336:     Atot += mid - k;
6337:     Btot += s - mid;

6339:     /* Scan column indices in this row and find out how many repeats each unique nonzero has */
6340:     for (p = k; p < mid;) {
6341:       col = j[p];
6342:       q   = p;
6343:       do {
6344:         p++;
6345:       } while (p < mid && j[p] == col);
6346:       Ajmap[Annz + 1] = Ajmap[Annz] + (p - q);
6347:       Annz++;
6348:     }

6350:     for (p = mid; p < s;) {
6351:       col = j[p];
6352:       q   = p;
6353:       do {
6354:         p++;
6355:       } while (p < s && j[p] == col);
6356:       Bjmap[Bnnz + 1] = Bjmap[Bnnz] + (p - q);
6357:       Bnnz++;
6358:     }
6359:   }
6360:   /* Output */
6361:   *Aperm_ = Aperm;
6362:   *Annz_  = Annz;
6363:   *Atot_  = Atot;
6364:   *Ajmap_ = Ajmap;
6365:   *Bperm_ = Bperm;
6366:   *Bnnz_  = Bnnz;
6367:   *Btot_  = Btot;
6368:   *Bjmap_ = Bjmap;
6369:   PetscFunctionReturn(PETSC_SUCCESS);
6370: }

6372: /*
6373:   Expand the jmap[] array to make a new one in view of nonzeros in the merged matrix

6375:   Input Parameters:
6376:     nnz1: number of unique nonzeros in a set that was used to produce imap[], jmap[]
6377:     nnz:  number of unique nonzeros in the merged matrix
6378:     imap[nnz1]: i-th nonzero in the set is the imap[i]-th nonzero in the merged matrix
6379:     jmap[nnz1+1]: i-th nonzero in the set has jmap[i+1] - jmap[i] repeats in the set

6381:   Output Parameter: (memory is allocated by the caller)
6382:     jmap_new[nnz+1]: i-th nonzero in the merged matrix has jmap_new[i+1] - jmap_new[i] repeats in the set

6384:   Example:
6385:     nnz1 = 4
6386:     nnz  = 6
6387:     imap = [1,3,4,5]
6388:     jmap = [0,3,5,6,7]
6389:    then,
6390:     jmap_new = [0,0,3,3,5,6,7]
6391: */
6392: static PetscErrorCode ExpandJmap_Internal(PetscCount nnz1, PetscCount nnz, const PetscCount imap[], const PetscCount jmap[], PetscCount jmap_new[])
6393: {
6394:   PetscCount k, p;

6396:   PetscFunctionBegin;
6397:   jmap_new[0] = 0;
6398:   p           = nnz;                /* p loops over jmap_new[] backwards */
6399:   for (k = nnz1 - 1; k >= 0; k--) { /* k loops over imap[] */
6400:     for (; p > imap[k]; p--) jmap_new[p] = jmap[k + 1];
6401:   }
6402:   for (; p >= 0; p--) jmap_new[p] = jmap[0];
6403:   PetscFunctionReturn(PETSC_SUCCESS);
6404: }

6406: static PetscErrorCode MatCOOStructDestroy_MPIAIJ(void **data)
6407: {
6408:   MatCOOStruct_MPIAIJ *coo = (MatCOOStruct_MPIAIJ *)*data;

6410:   PetscFunctionBegin;
6411:   PetscCall(PetscSFDestroy(&coo->sf));
6412:   PetscCall(PetscFree(coo->Aperm1));
6413:   PetscCall(PetscFree(coo->Bperm1));
6414:   PetscCall(PetscFree(coo->Ajmap1));
6415:   PetscCall(PetscFree(coo->Bjmap1));
6416:   PetscCall(PetscFree(coo->Aimap2));
6417:   PetscCall(PetscFree(coo->Bimap2));
6418:   PetscCall(PetscFree(coo->Aperm2));
6419:   PetscCall(PetscFree(coo->Bperm2));
6420:   PetscCall(PetscFree(coo->Ajmap2));
6421:   PetscCall(PetscFree(coo->Bjmap2));
6422:   PetscCall(PetscFree(coo->Cperm1));
6423:   PetscCall(PetscFree2(coo->sendbuf, coo->recvbuf));
6424:   PetscCall(PetscFree(coo));
6425:   PetscFunctionReturn(PETSC_SUCCESS);
6426: }

6428: PetscErrorCode MatSetPreallocationCOO_MPIAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
6429: {
6430:   MPI_Comm             comm;
6431:   PetscMPIInt          rank, size;
6432:   PetscInt             m, n, M, N, rstart, rend, cstart, cend; /* Sizes, indices of row/col, therefore with type PetscInt */
6433:   PetscCount           k, p, q, rem;                           /* Loop variables over coo arrays */
6434:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6435:   PetscContainer       container;
6436:   MatCOOStruct_MPIAIJ *coo;

6438:   PetscFunctionBegin;
6439:   PetscCall(PetscFree(mpiaij->garray));
6440:   PetscCall(VecDestroy(&mpiaij->lvec));
6441: #if defined(PETSC_USE_CTABLE)
6442:   PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
6443: #else
6444:   PetscCall(PetscFree(mpiaij->colmap));
6445: #endif
6446:   PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
6447:   mat->assembled     = PETSC_FALSE;
6448:   mat->was_assembled = PETSC_FALSE;

6450:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
6451:   PetscCallMPI(MPI_Comm_size(comm, &size));
6452:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
6453:   PetscCall(PetscLayoutSetUp(mat->rmap));
6454:   PetscCall(PetscLayoutSetUp(mat->cmap));
6455:   PetscCall(PetscLayoutGetRange(mat->rmap, &rstart, &rend));
6456:   PetscCall(PetscLayoutGetRange(mat->cmap, &cstart, &cend));
6457:   PetscCall(MatGetLocalSize(mat, &m, &n));
6458:   PetscCall(MatGetSize(mat, &M, &N));

6460:   /* Sort (i,j) by row along with a permutation array, so that the to-be-ignored */
6461:   /* entries come first, then local rows, then remote rows.                     */
6462:   PetscCount n1 = coo_n, *perm1;
6463:   PetscInt  *i1 = coo_i, *j1 = coo_j;

6465:   PetscCall(PetscMalloc1(n1, &perm1));
6466:   for (k = 0; k < n1; k++) perm1[k] = k;

6468:   /* Manipulate indices so that entries with negative row or col indices will have smallest
6469:      row indices, local entries will have greater but negative row indices, and remote entries
6470:      will have positive row indices.
6471:   */
6472:   for (k = 0; k < n1; k++) {
6473:     if (i1[k] < 0 || j1[k] < 0) i1[k] = PETSC_INT_MIN;                /* e.g., -2^31, minimal to move them ahead */
6474:     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] */
6475:     else {
6476:       PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_USER_INPUT, "MAT_NO_OFF_PROC_ENTRIES is set but insert to remote rows");
6477:       if (mpiaij->donotstash) i1[k] = PETSC_INT_MIN; /* Ignore offproc entries as if they had negative indices */
6478:     }
6479:   }

6481:   /* Sort by row; after that, [0,k) have ignored entries, [k,rem) have local rows and [rem,n1) have remote rows */
6482:   PetscCall(PetscSortIntWithIntCountArrayPair(n1, i1, j1, perm1));

6484:   /* Advance k to the first entry we need to take care of */
6485:   for (k = 0; k < n1; k++)
6486:     if (i1[k] > PETSC_INT_MIN) break;
6487:   PetscCount i1start = k;

6489:   PetscCall(PetscSortedIntUpperBound(i1, k, n1, rend - 1 - PETSC_INT_MAX, &rem)); /* rem is upper bound of the last local row */
6490:   for (; k < rem; k++) i1[k] += PETSC_INT_MAX;                                    /* Revert row indices of local rows*/

6492:   /*           Send remote rows to their owner                                  */
6493:   /* Find which rows should be sent to which remote ranks*/
6494:   PetscInt        nsend = 0; /* Number of MPI ranks to send data to */
6495:   PetscMPIInt    *sendto;    /* [nsend], storing remote ranks */
6496:   PetscInt       *nentries;  /* [nsend], storing number of entries sent to remote ranks; Assume PetscInt is big enough for this count, and error if not */
6497:   const PetscInt *ranges;
6498:   PetscInt        maxNsend = size >= 128 ? 128 : size; /* Assume max 128 neighbors; realloc when needed */

6500:   PetscCall(PetscLayoutGetRanges(mat->rmap, &ranges));
6501:   PetscCall(PetscMalloc2(maxNsend, &sendto, maxNsend, &nentries));
6502:   for (k = rem; k < n1;) {
6503:     PetscMPIInt owner;
6504:     PetscInt    firstRow, lastRow;

6506:     /* Locate a row range */
6507:     firstRow = i1[k]; /* first row of this owner */
6508:     PetscCall(PetscLayoutFindOwner(mat->rmap, firstRow, &owner));
6509:     lastRow = ranges[owner + 1] - 1; /* last row of this owner */

6511:     /* Find the first index 'p' in [k,n) with i[p] belonging to next owner */
6512:     PetscCall(PetscSortedIntUpperBound(i1, k, n1, lastRow, &p));

6514:     /* All entries in [k,p) belong to this remote owner */
6515:     if (nsend >= maxNsend) { /* Double the remote ranks arrays if not long enough */
6516:       PetscMPIInt *sendto2;
6517:       PetscInt    *nentries2;
6518:       PetscInt     maxNsend2 = (maxNsend <= size / 2) ? maxNsend * 2 : size;

6520:       PetscCall(PetscMalloc2(maxNsend2, &sendto2, maxNsend2, &nentries2));
6521:       PetscCall(PetscArraycpy(sendto2, sendto, maxNsend));
6522:       PetscCall(PetscArraycpy(nentries2, nentries2, maxNsend + 1));
6523:       PetscCall(PetscFree2(sendto, nentries2));
6524:       sendto   = sendto2;
6525:       nentries = nentries2;
6526:       maxNsend = maxNsend2;
6527:     }
6528:     sendto[nsend] = owner;
6529:     PetscCall(PetscIntCast(p - k, &nentries[nsend]));
6530:     nsend++;
6531:     k = p;
6532:   }

6534:   /* Build 1st SF to know offsets on remote to send data */
6535:   PetscSF      sf1;
6536:   PetscInt     nroots = 1, nroots2 = 0;
6537:   PetscInt     nleaves = nsend, nleaves2 = 0;
6538:   PetscInt    *offsets;
6539:   PetscSFNode *iremote;

6541:   PetscCall(PetscSFCreate(comm, &sf1));
6542:   PetscCall(PetscMalloc1(nsend, &iremote));
6543:   PetscCall(PetscMalloc1(nsend, &offsets));
6544:   for (k = 0; k < nsend; k++) {
6545:     iremote[k].rank  = sendto[k];
6546:     iremote[k].index = 0;
6547:     nleaves2 += nentries[k];
6548:     PetscCheck(nleaves2 >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF leaves is too large for PetscInt");
6549:   }
6550:   PetscCall(PetscSFSetGraph(sf1, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
6551:   PetscCall(PetscSFFetchAndOpWithMemTypeBegin(sf1, MPIU_INT, PETSC_MEMTYPE_HOST, &nroots2 /*rootdata*/, PETSC_MEMTYPE_HOST, nentries /*leafdata*/, PETSC_MEMTYPE_HOST, offsets /*leafupdate*/, MPI_SUM));
6552:   PetscCall(PetscSFFetchAndOpEnd(sf1, MPIU_INT, &nroots2, nentries, offsets, MPI_SUM)); /* Would nroots2 overflow, we check offsets[] below */
6553:   PetscCall(PetscSFDestroy(&sf1));
6554:   PetscAssert(nleaves2 == n1 - rem, PETSC_COMM_SELF, PETSC_ERR_PLIB, "nleaves2 %" PetscInt_FMT " != number of remote entries %" PetscCount_FMT, nleaves2, n1 - rem);

6556:   /* Build 2nd SF to send remote COOs to their owner */
6557:   PetscSF sf2;
6558:   nroots  = nroots2;
6559:   nleaves = nleaves2;
6560:   PetscCall(PetscSFCreate(comm, &sf2));
6561:   PetscCall(PetscSFSetFromOptions(sf2));
6562:   PetscCall(PetscMalloc1(nleaves, &iremote));
6563:   p = 0;
6564:   for (k = 0; k < nsend; k++) {
6565:     PetscCheck(offsets[k] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of SF roots is too large for PetscInt");
6566:     for (q = 0; q < nentries[k]; q++, p++) {
6567:       iremote[p].rank = sendto[k];
6568:       PetscCall(PetscIntCast(offsets[k] + q, &iremote[p].index));
6569:     }
6570:   }
6571:   PetscCall(PetscSFSetGraph(sf2, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));

6573:   /* Send the remote COOs to their owner */
6574:   PetscInt    n2 = nroots, *i2, *j2; /* Buffers for received COOs from other ranks, along with a permutation array */
6575:   PetscCount *perm2;                 /* Though PetscInt is enough for remote entries, we use PetscCount here as we want to reuse MatSplitEntries_Internal() */
6576:   PetscCall(PetscMalloc3(n2, &i2, n2, &j2, n2, &perm2));
6577:   PetscAssert(rem == 0 || i1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6578:   PetscAssert(rem == 0 || j1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6579:   PetscInt *i1prem = PetscSafePointerPlusOffset(i1, rem);
6580:   PetscInt *j1prem = PetscSafePointerPlusOffset(j1, rem);
6581:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, i1prem, PETSC_MEMTYPE_HOST, i2, MPI_REPLACE));
6582:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, i1prem, i2, MPI_REPLACE));
6583:   PetscCall(PetscSFReduceWithMemTypeBegin(sf2, MPIU_INT, PETSC_MEMTYPE_HOST, j1prem, PETSC_MEMTYPE_HOST, j2, MPI_REPLACE));
6584:   PetscCall(PetscSFReduceEnd(sf2, MPIU_INT, j1prem, j2, MPI_REPLACE));

6586:   PetscCall(PetscFree(offsets));
6587:   PetscCall(PetscFree2(sendto, nentries));

6589:   /* Sort received COOs by row along with the permutation array     */
6590:   for (k = 0; k < n2; k++) perm2[k] = k;
6591:   PetscCall(PetscSortIntWithIntCountArrayPair(n2, i2, j2, perm2));

6593:   /* sf2 only sends contiguous leafdata to contiguous rootdata. We record the permutation which will be used to fill leafdata */
6594:   PetscCount *Cperm1;
6595:   PetscAssert(rem == 0 || perm1 != NULL, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Cannot add nonzero offset to null");
6596:   PetscCount *perm1prem = PetscSafePointerPlusOffset(perm1, rem);
6597:   PetscCall(PetscMalloc1(nleaves, &Cperm1));
6598:   PetscCall(PetscArraycpy(Cperm1, perm1prem, nleaves));

6600:   /* Support for HYPRE matrices, kind of a hack.
6601:      Swap min column with diagonal so that diagonal values will go first */
6602:   PetscBool hypre;
6603:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", ((PetscObject)mat)->name, &hypre));
6604:   if (hypre) {
6605:     PetscInt *minj;
6606:     PetscBT   hasdiag;

6608:     PetscCall(PetscBTCreate(m, &hasdiag));
6609:     PetscCall(PetscMalloc1(m, &minj));
6610:     for (k = 0; k < m; k++) minj[k] = PETSC_INT_MAX;
6611:     for (k = i1start; k < rem; k++) {
6612:       if (j1[k] < cstart || j1[k] >= cend) continue;
6613:       const PetscInt rindex = i1[k] - rstart;
6614:       if ((j1[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6615:       minj[rindex] = PetscMin(minj[rindex], j1[k]);
6616:     }
6617:     for (k = 0; k < n2; k++) {
6618:       if (j2[k] < cstart || j2[k] >= cend) continue;
6619:       const PetscInt rindex = i2[k] - rstart;
6620:       if ((j2[k] - cstart) == rindex) PetscCall(PetscBTSet(hasdiag, rindex));
6621:       minj[rindex] = PetscMin(minj[rindex], j2[k]);
6622:     }
6623:     for (k = i1start; k < rem; k++) {
6624:       const PetscInt rindex = i1[k] - rstart;
6625:       if (j1[k] < cstart || j1[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6626:       if (j1[k] == minj[rindex]) j1[k] = i1[k] + (cstart - rstart);
6627:       else if ((j1[k] - cstart) == rindex) j1[k] = minj[rindex];
6628:     }
6629:     for (k = 0; k < n2; k++) {
6630:       const PetscInt rindex = i2[k] - rstart;
6631:       if (j2[k] < cstart || j2[k] >= cend || !PetscBTLookup(hasdiag, rindex)) continue;
6632:       if (j2[k] == minj[rindex]) j2[k] = i2[k] + (cstart - rstart);
6633:       else if ((j2[k] - cstart) == rindex) j2[k] = minj[rindex];
6634:     }
6635:     PetscCall(PetscBTDestroy(&hasdiag));
6636:     PetscCall(PetscFree(minj));
6637:   }

6639:   /* Split local COOs and received COOs into diag/offdiag portions */
6640:   PetscCount *rowBegin1, *rowMid1, *rowEnd1;
6641:   PetscCount *Ajmap1, *Aperm1, *Bjmap1, *Bperm1;
6642:   PetscCount  Annz1, Bnnz1, Atot1, Btot1;
6643:   PetscCount *rowBegin2, *rowMid2, *rowEnd2;
6644:   PetscCount *Ajmap2, *Aperm2, *Bjmap2, *Bperm2;
6645:   PetscCount  Annz2, Bnnz2, Atot2, Btot2;

6647:   PetscCall(PetscCalloc3(m, &rowBegin1, m, &rowMid1, m, &rowEnd1));
6648:   PetscCall(PetscCalloc3(m, &rowBegin2, m, &rowMid2, m, &rowEnd2));
6649:   PetscCall(MatSplitEntries_Internal(mat, rem, i1, j1, perm1, rowBegin1, rowMid1, rowEnd1, &Atot1, &Aperm1, &Annz1, &Ajmap1, &Btot1, &Bperm1, &Bnnz1, &Bjmap1));
6650:   PetscCall(MatSplitEntries_Internal(mat, n2, i2, j2, perm2, rowBegin2, rowMid2, rowEnd2, &Atot2, &Aperm2, &Annz2, &Ajmap2, &Btot2, &Bperm2, &Bnnz2, &Bjmap2));

6652:   /* Merge local COOs with received COOs: diag with diag, offdiag with offdiag */
6653:   PetscInt *Ai, *Bi;
6654:   PetscInt *Aj, *Bj;

6656:   PetscCall(PetscMalloc1(m + 1, &Ai));
6657:   PetscCall(PetscMalloc1(m + 1, &Bi));
6658:   PetscCall(PetscMalloc1(Annz1 + Annz2, &Aj)); /* Since local and remote entries might have dups, we might allocate excess memory */
6659:   PetscCall(PetscMalloc1(Bnnz1 + Bnnz2, &Bj));

6661:   PetscCount *Aimap1, *Bimap1, *Aimap2, *Bimap2;
6662:   PetscCall(PetscMalloc1(Annz1, &Aimap1));
6663:   PetscCall(PetscMalloc1(Bnnz1, &Bimap1));
6664:   PetscCall(PetscMalloc1(Annz2, &Aimap2));
6665:   PetscCall(PetscMalloc1(Bnnz2, &Bimap2));

6667:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowBegin1, rowMid1, rowBegin2, rowMid2, Ajmap1, Ajmap2, Aimap1, Aimap2, Ai, Aj));
6668:   PetscCall(MatMergeEntries_Internal(mat, j1, j2, rowMid1, rowEnd1, rowMid2, rowEnd2, Bjmap1, Bjmap2, Bimap1, Bimap2, Bi, Bj));

6670:   /* Expand Ajmap1/Bjmap1 to make them based off nonzeros in A/B, since we     */
6671:   /* expect nonzeros in A/B most likely have local contributing entries        */
6672:   PetscInt    Annz = Ai[m];
6673:   PetscInt    Bnnz = Bi[m];
6674:   PetscCount *Ajmap1_new, *Bjmap1_new;

6676:   PetscCall(PetscMalloc1(Annz + 1, &Ajmap1_new));
6677:   PetscCall(PetscMalloc1(Bnnz + 1, &Bjmap1_new));

6679:   PetscCall(ExpandJmap_Internal(Annz1, Annz, Aimap1, Ajmap1, Ajmap1_new));
6680:   PetscCall(ExpandJmap_Internal(Bnnz1, Bnnz, Bimap1, Bjmap1, Bjmap1_new));

6682:   PetscCall(PetscFree(Aimap1));
6683:   PetscCall(PetscFree(Ajmap1));
6684:   PetscCall(PetscFree(Bimap1));
6685:   PetscCall(PetscFree(Bjmap1));
6686:   PetscCall(PetscFree3(rowBegin1, rowMid1, rowEnd1));
6687:   PetscCall(PetscFree3(rowBegin2, rowMid2, rowEnd2));
6688:   PetscCall(PetscFree(perm1));
6689:   PetscCall(PetscFree3(i2, j2, perm2));

6691:   Ajmap1 = Ajmap1_new;
6692:   Bjmap1 = Bjmap1_new;

6694:   /* Reallocate Aj, Bj once we know actual numbers of unique nonzeros in A and B */
6695:   if (Annz < Annz1 + Annz2) {
6696:     PetscInt *Aj_new;
6697:     PetscCall(PetscMalloc1(Annz, &Aj_new));
6698:     PetscCall(PetscArraycpy(Aj_new, Aj, Annz));
6699:     PetscCall(PetscFree(Aj));
6700:     Aj = Aj_new;
6701:   }

6703:   if (Bnnz < Bnnz1 + Bnnz2) {
6704:     PetscInt *Bj_new;
6705:     PetscCall(PetscMalloc1(Bnnz, &Bj_new));
6706:     PetscCall(PetscArraycpy(Bj_new, Bj, Bnnz));
6707:     PetscCall(PetscFree(Bj));
6708:     Bj = Bj_new;
6709:   }

6711:   /* Create new submatrices for on-process and off-process coupling                  */
6712:   PetscScalar     *Aa, *Ba;
6713:   MatType          rtype;
6714:   Mat_SeqAIJ      *a, *b;
6715:   PetscObjectState state;
6716:   PetscCall(PetscCalloc1(Annz, &Aa)); /* Zero matrix on device */
6717:   PetscCall(PetscCalloc1(Bnnz, &Ba));
6718:   /* make Aj[] local, i.e, based off the start column of the diagonal portion */
6719:   if (cstart) {
6720:     for (k = 0; k < Annz; k++) Aj[k] -= cstart;
6721:   }

6723:   PetscCall(MatGetRootType_Private(mat, &rtype));

6725:   MatSeqXAIJGetOptions_Private(mpiaij->A);
6726:   PetscCall(MatDestroy(&mpiaij->A));
6727:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, Ai, Aj, Aa, &mpiaij->A));
6728:   PetscCall(MatSetBlockSizesFromMats(mpiaij->A, mat, mat));
6729:   MatSeqXAIJRestoreOptions_Private(mpiaij->A);

6731:   MatSeqXAIJGetOptions_Private(mpiaij->B);
6732:   PetscCall(MatDestroy(&mpiaij->B));
6733:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, mat->cmap->N, Bi, Bj, Ba, &mpiaij->B));
6734:   PetscCall(MatSetBlockSizesFromMats(mpiaij->B, mat, mat));
6735:   MatSeqXAIJRestoreOptions_Private(mpiaij->B);

6737:   PetscCall(MatSetUpMultiply_MPIAIJ(mat));
6738:   mat->was_assembled = PETSC_TRUE; // was_assembled in effect means the Mvctx is built; doing so avoids redundant MatSetUpMultiply_MPIAIJ
6739:   state              = mpiaij->A->nonzerostate + mpiaij->B->nonzerostate;
6740:   PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));

6742:   a          = (Mat_SeqAIJ *)mpiaij->A->data;
6743:   b          = (Mat_SeqAIJ *)mpiaij->B->data;
6744:   a->free_a  = PETSC_TRUE;
6745:   a->free_ij = PETSC_TRUE;
6746:   b->free_a  = PETSC_TRUE;
6747:   b->free_ij = PETSC_TRUE;
6748:   a->maxnz   = a->nz;
6749:   b->maxnz   = b->nz;

6751:   /* conversion must happen AFTER multiply setup */
6752:   PetscCall(MatConvert(mpiaij->A, rtype, MAT_INPLACE_MATRIX, &mpiaij->A));
6753:   PetscCall(MatConvert(mpiaij->B, rtype, MAT_INPLACE_MATRIX, &mpiaij->B));
6754:   PetscCall(VecDestroy(&mpiaij->lvec));
6755:   PetscCall(MatCreateVecs(mpiaij->B, &mpiaij->lvec, NULL));

6757:   // Put the COO struct in a container and then attach that to the matrix
6758:   PetscCall(PetscMalloc1(1, &coo));
6759:   coo->n       = coo_n;
6760:   coo->sf      = sf2;
6761:   coo->sendlen = nleaves;
6762:   coo->recvlen = nroots;
6763:   coo->Annz    = Annz;
6764:   coo->Bnnz    = Bnnz;
6765:   coo->Annz2   = Annz2;
6766:   coo->Bnnz2   = Bnnz2;
6767:   coo->Atot1   = Atot1;
6768:   coo->Atot2   = Atot2;
6769:   coo->Btot1   = Btot1;
6770:   coo->Btot2   = Btot2;
6771:   coo->Ajmap1  = Ajmap1;
6772:   coo->Aperm1  = Aperm1;
6773:   coo->Bjmap1  = Bjmap1;
6774:   coo->Bperm1  = Bperm1;
6775:   coo->Aimap2  = Aimap2;
6776:   coo->Ajmap2  = Ajmap2;
6777:   coo->Aperm2  = Aperm2;
6778:   coo->Bimap2  = Bimap2;
6779:   coo->Bjmap2  = Bjmap2;
6780:   coo->Bperm2  = Bperm2;
6781:   coo->Cperm1  = Cperm1;
6782:   // Allocate in preallocation. If not used, it has zero cost on host
6783:   PetscCall(PetscMalloc2(coo->sendlen, &coo->sendbuf, coo->recvlen, &coo->recvbuf));
6784:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
6785:   PetscCall(PetscContainerSetPointer(container, coo));
6786:   PetscCall(PetscContainerSetCtxDestroy(container, MatCOOStructDestroy_MPIAIJ));
6787:   PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
6788:   PetscCall(PetscContainerDestroy(&container));
6789:   PetscFunctionReturn(PETSC_SUCCESS);
6790: }

6792: static PetscErrorCode MatSetValuesCOO_MPIAIJ(Mat mat, const PetscScalar v[], InsertMode imode)
6793: {
6794:   Mat_MPIAIJ          *mpiaij = (Mat_MPIAIJ *)mat->data;
6795:   Mat                  A = mpiaij->A, B = mpiaij->B;
6796:   PetscScalar         *Aa, *Ba;
6797:   PetscScalar         *sendbuf, *recvbuf;
6798:   const PetscCount    *Ajmap1, *Ajmap2, *Aimap2;
6799:   const PetscCount    *Bjmap1, *Bjmap2, *Bimap2;
6800:   const PetscCount    *Aperm1, *Aperm2, *Bperm1, *Bperm2;
6801:   const PetscCount    *Cperm1;
6802:   PetscContainer       container;
6803:   MatCOOStruct_MPIAIJ *coo;

6805:   PetscFunctionBegin;
6806:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
6807:   PetscCheck(container, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
6808:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
6809:   sendbuf = coo->sendbuf;
6810:   recvbuf = coo->recvbuf;
6811:   Ajmap1  = coo->Ajmap1;
6812:   Ajmap2  = coo->Ajmap2;
6813:   Aimap2  = coo->Aimap2;
6814:   Bjmap1  = coo->Bjmap1;
6815:   Bjmap2  = coo->Bjmap2;
6816:   Bimap2  = coo->Bimap2;
6817:   Aperm1  = coo->Aperm1;
6818:   Aperm2  = coo->Aperm2;
6819:   Bperm1  = coo->Bperm1;
6820:   Bperm2  = coo->Bperm2;
6821:   Cperm1  = coo->Cperm1;

6823:   PetscCall(MatSeqAIJGetArray(A, &Aa)); /* Might read and write matrix values */
6824:   PetscCall(MatSeqAIJGetArray(B, &Ba));

6826:   /* Pack entries to be sent to remote */
6827:   for (PetscCount i = 0; i < coo->sendlen; i++) sendbuf[i] = v[Cperm1[i]];

6829:   /* Send remote entries to their owner and overlap the communication with local computation */
6830:   PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_HOST, sendbuf, PETSC_MEMTYPE_HOST, recvbuf, MPI_REPLACE));
6831:   /* Add local entries to A and B */
6832:   for (PetscCount i = 0; i < coo->Annz; i++) { /* All nonzeros in A are either zero'ed or added with a value (i.e., initialized) */
6833:     PetscScalar sum = 0.0;                     /* Do partial summation first to improve numerical stability */
6834:     for (PetscCount k = Ajmap1[i]; k < Ajmap1[i + 1]; k++) sum += v[Aperm1[k]];
6835:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
6836:   }
6837:   for (PetscCount i = 0; i < coo->Bnnz; i++) {
6838:     PetscScalar sum = 0.0;
6839:     for (PetscCount k = Bjmap1[i]; k < Bjmap1[i + 1]; k++) sum += v[Bperm1[k]];
6840:     Ba[i] = (imode == INSERT_VALUES ? 0.0 : Ba[i]) + sum;
6841:   }
6842:   PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, sendbuf, recvbuf, MPI_REPLACE));

6844:   /* Add received remote entries to A and B */
6845:   for (PetscCount i = 0; i < coo->Annz2; i++) {
6846:     for (PetscCount k = Ajmap2[i]; k < Ajmap2[i + 1]; k++) Aa[Aimap2[i]] += recvbuf[Aperm2[k]];
6847:   }
6848:   for (PetscCount i = 0; i < coo->Bnnz2; i++) {
6849:     for (PetscCount k = Bjmap2[i]; k < Bjmap2[i + 1]; k++) Ba[Bimap2[i]] += recvbuf[Bperm2[k]];
6850:   }
6851:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
6852:   PetscCall(MatSeqAIJRestoreArray(B, &Ba));
6853:   PetscFunctionReturn(PETSC_SUCCESS);
6854: }

6856: /*MC
6857:    MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.

6859:    Options Database Keys:
6860: . -mat_type mpiaij - sets the matrix type to `MATMPIAIJ` during a call to `MatSetFromOptions()`

6862:    Level: beginner

6864:    Notes:
6865:    `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
6866:     in this case the values associated with the rows and columns one passes in are set to zero
6867:     in the matrix

6869:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
6870:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

6872: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`, `MATAIJ`, `MatCreateAIJ()`
6873: M*/
6874: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
6875: {
6876:   Mat_MPIAIJ *b;
6877:   PetscMPIInt size;

6879:   PetscFunctionBegin;
6880:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));

6882:   PetscCall(PetscNew(&b));
6883:   B->data       = (void *)b;
6884:   B->ops[0]     = MatOps_Values;
6885:   B->assembled  = PETSC_FALSE;
6886:   B->insertmode = NOT_SET_VALUES;
6887:   b->size       = size;

6889:   PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));

6891:   /* build cache for off array entries formed */
6892:   PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));

6894:   b->donotstash  = PETSC_FALSE;
6895:   b->colmap      = NULL;
6896:   b->garray      = NULL;
6897:   b->roworiented = PETSC_TRUE;

6899:   /* stuff used for matrix vector multiply */
6900:   b->lvec  = NULL;
6901:   b->Mvctx = NULL;

6903:   /* stuff for MatGetRow() */
6904:   b->rowindices   = NULL;
6905:   b->rowvalues    = NULL;
6906:   b->getrowactive = PETSC_FALSE;

6908:   /* flexible pointer used in CUSPARSE classes */
6909:   b->spptr = NULL;

6911:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetUseScalableIncreaseOverlap_C", MatMPIAIJSetUseScalableIncreaseOverlap_MPIAIJ));
6912:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPIAIJ));
6913:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPIAIJ));
6914:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPIAIJ));
6915:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJ));
6916:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_MPIAIJ));
6917:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_MPIAIJ));
6918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocationCSR_C", MatMPIAIJSetPreallocationCSR_MPIAIJ));
6919:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPIAIJ));
6920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijperm_C", MatConvert_MPIAIJ_MPIAIJPERM));
6921:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijsell_C", MatConvert_MPIAIJ_MPIAIJSELL));
6922: #if defined(PETSC_HAVE_CUDA)
6923:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcusparse_C", MatConvert_MPIAIJ_MPIAIJCUSPARSE));
6924: #endif
6925: #if defined(PETSC_HAVE_HIP)
6926:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijhipsparse_C", MatConvert_MPIAIJ_MPIAIJHIPSPARSE));
6927: #endif
6928: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
6929:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijkokkos_C", MatConvert_MPIAIJ_MPIAIJKokkos));
6930: #endif
6931: #if defined(PETSC_HAVE_MKL_SPARSE)
6932:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijmkl_C", MatConvert_MPIAIJ_MPIAIJMKL));
6933: #endif
6934:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpiaijcrl_C", MatConvert_MPIAIJ_MPIAIJCRL));
6935:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpibaij_C", MatConvert_MPIAIJ_MPIBAIJ));
6936:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisbaij_C", MatConvert_MPIAIJ_MPISBAIJ));
6937:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpidense_C", MatConvert_MPIAIJ_MPIDense));
6938: #if defined(PETSC_HAVE_ELEMENTAL)
6939:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_elemental_C", MatConvert_MPIAIJ_Elemental));
6940: #endif
6941: #if defined(PETSC_HAVE_SCALAPACK)
6942:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
6943: #endif
6944:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_is_C", MatConvert_XAIJ_IS));
6945:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_mpisell_C", MatConvert_MPIAIJ_MPISELL));
6946: #if defined(PETSC_HAVE_HYPRE)
6947:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpiaij_hypre_C", MatConvert_AIJ_HYPRE));
6948:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_mpiaij_mpiaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
6949: #endif
6950:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_mpiaij_C", MatProductSetFromOptions_IS_XAIJ));
6951:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpiaij_C", MatProductSetFromOptions_MPIAIJ));
6952:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJ));
6953:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJ));
6954:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJ));
6955:   PetscFunctionReturn(PETSC_SUCCESS);
6956: }

6958: /*@
6959:   MatCreateMPIAIJWithSplitArrays - creates a `MATMPIAIJ` matrix using arrays that contain the "diagonal"
6960:   and "off-diagonal" part of the matrix in CSR format.

6962:   Collective

6964:   Input Parameters:
6965: + comm - MPI communicator
6966: . m    - number of local rows (Cannot be `PETSC_DECIDE`)
6967: . n    - This value should be the same as the local size used in creating the
6968:          x vector for the matrix-vector product $y = Ax$. (or `PETSC_DECIDE` to have
6969:          calculated if `N` is given) For square matrices `n` is almost always `m`.
6970: . M    - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
6971: . N    - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
6972: . 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
6973: . j    - column indices, which must be local, i.e., based off the start column of the diagonal portion
6974: . a    - matrix values
6975: . 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
6976: . oj   - column indices, which must be global, representing global columns in the `MATMPIAIJ` matrix
6977: - oa   - matrix values

6979:   Output Parameter:
6980: . mat - the matrix

6982:   Level: advanced

6984:   Notes:
6985:   The `i`, `j`, and `a` arrays ARE NOT copied by this routine into the internal format used by PETSc (even in Fortran). The user
6986:   must free the arrays once the matrix has been destroyed and not before.

6988:   The `i` and `j` indices are 0 based

6990:   See `MatCreateAIJ()` for the definition of "diagonal" and "off-diagonal" portion of the matrix

6992:   This sets local rows and cannot be used to set off-processor values.

6994:   Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
6995:   legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
6996:   not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
6997:   the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
6998:   keep track of the underlying array. Use `MatSetOption`(A,`MAT_NO_OFF_PROC_ENTRIES`,`PETSC_TRUE`) to disable all
6999:   communication if it is known that only local entries will be set.

7001: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatMPIAIJSetPreallocation()`, `MatMPIAIJSetPreallocationCSR()`,
7002:           `MATMPIAIJ`, `MatCreateAIJ()`, `MatCreateMPIAIJWithArrays()`
7003: @*/
7004: 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)
7005: {
7006:   Mat_MPIAIJ *maij;

7008:   PetscFunctionBegin;
7009:   PetscCheck(m >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "local number of rows (m) cannot be PETSC_DECIDE, or negative");
7010:   PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
7011:   PetscCheck(oi[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "oi (row indices) must start with 0");
7012:   PetscCall(MatCreate(comm, mat));
7013:   PetscCall(MatSetSizes(*mat, m, n, M, N));
7014:   PetscCall(MatSetType(*mat, MATMPIAIJ));
7015:   maij = (Mat_MPIAIJ *)(*mat)->data;

7017:   (*mat)->preallocated = PETSC_TRUE;

7019:   PetscCall(PetscLayoutSetUp((*mat)->rmap));
7020:   PetscCall(PetscLayoutSetUp((*mat)->cmap));

7022:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, n, i, j, a, &maij->A));
7023:   PetscCall(MatCreateSeqAIJWithArrays(PETSC_COMM_SELF, m, (*mat)->cmap->N, oi, oj, oa, &maij->B));

7025:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
7026:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
7027:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
7028:   PetscCall(MatSetOption(*mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
7029:   PetscCall(MatSetOption(*mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
7030:   PetscFunctionReturn(PETSC_SUCCESS);
7031: }

7033: typedef struct {
7034:   Mat       *mp;    /* intermediate products */
7035:   PetscBool *mptmp; /* is the intermediate product temporary ? */
7036:   PetscInt   cp;    /* number of intermediate products */

7038:   /* support for MatGetBrowsOfAoCols_MPIAIJ for P_oth */
7039:   PetscInt    *startsj_s, *startsj_r;
7040:   PetscScalar *bufa;
7041:   Mat          P_oth;

7043:   /* may take advantage of merging product->B */
7044:   Mat Bloc; /* B-local by merging diag and off-diag */

7046:   /* cusparse does not have support to split between symbolic and numeric phases.
7047:      When api_user is true, we don't need to update the numerical values
7048:      of the temporary storage */
7049:   PetscBool reusesym;

7051:   /* support for COO values insertion */
7052:   PetscScalar *coo_v, *coo_w; /* store on-process and off-process COO scalars, and used as MPI recv/send buffers respectively */
7053:   PetscInt   **own;           /* own[i] points to address of on-process COO indices for Mat mp[i] */
7054:   PetscInt   **off;           /* off[i] points to address of off-process COO indices for Mat mp[i] */
7055:   PetscBool    hasoffproc;    /* if true, have off-process values insertion (i.e. AtB or PtAP) */
7056:   PetscSF      sf;            /* used for non-local values insertion and memory malloc */
7057:   PetscMemType mtype;

7059:   /* customization */
7060:   PetscBool abmerge;
7061:   PetscBool P_oth_bind;
7062: } MatMatMPIAIJBACKEND;

7064: static PetscErrorCode MatDestroy_MatMatMPIAIJBACKEND(void *data)
7065: {
7066:   MatMatMPIAIJBACKEND *mmdata = (MatMatMPIAIJBACKEND *)data;
7067:   PetscInt             i;

7069:   PetscFunctionBegin;
7070:   PetscCall(PetscFree2(mmdata->startsj_s, mmdata->startsj_r));
7071:   PetscCall(PetscFree(mmdata->bufa));
7072:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_v));
7073:   PetscCall(PetscSFFree(mmdata->sf, mmdata->mtype, mmdata->coo_w));
7074:   PetscCall(MatDestroy(&mmdata->P_oth));
7075:   PetscCall(MatDestroy(&mmdata->Bloc));
7076:   PetscCall(PetscSFDestroy(&mmdata->sf));
7077:   for (i = 0; i < mmdata->cp; i++) PetscCall(MatDestroy(&mmdata->mp[i]));
7078:   PetscCall(PetscFree2(mmdata->mp, mmdata->mptmp));
7079:   PetscCall(PetscFree(mmdata->own[0]));
7080:   PetscCall(PetscFree(mmdata->own));
7081:   PetscCall(PetscFree(mmdata->off[0]));
7082:   PetscCall(PetscFree(mmdata->off));
7083:   PetscCall(PetscFree(mmdata));
7084:   PetscFunctionReturn(PETSC_SUCCESS);
7085: }

7087: /* Copy selected n entries with indices in idx[] of A to v[].
7088:    If idx is NULL, copy the whole data array of A to v[]
7089:  */
7090: static PetscErrorCode MatSeqAIJCopySubArray(Mat A, PetscInt n, const PetscInt idx[], PetscScalar v[])
7091: {
7092:   PetscErrorCode (*f)(Mat, PetscInt, const PetscInt[], PetscScalar[]);

7094:   PetscFunctionBegin;
7095:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatSeqAIJCopySubArray_C", &f));
7096:   if (f) {
7097:     PetscCall((*f)(A, n, idx, v));
7098:   } else {
7099:     const PetscScalar *vv;

7101:     PetscCall(MatSeqAIJGetArrayRead(A, &vv));
7102:     if (n && idx) {
7103:       PetscScalar    *w  = v;
7104:       const PetscInt *oi = idx;
7105:       PetscInt        j;

7107:       for (j = 0; j < n; j++) *w++ = vv[*oi++];
7108:     } else {
7109:       PetscCall(PetscArraycpy(v, vv, n));
7110:     }
7111:     PetscCall(MatSeqAIJRestoreArrayRead(A, &vv));
7112:   }
7113:   PetscFunctionReturn(PETSC_SUCCESS);
7114: }

7116: static PetscErrorCode MatProductNumeric_MPIAIJBACKEND(Mat C)
7117: {
7118:   MatMatMPIAIJBACKEND *mmdata;
7119:   PetscInt             i, n_d, n_o;

7121:   PetscFunctionBegin;
7122:   MatCheckProduct(C, 1);
7123:   PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
7124:   mmdata = (MatMatMPIAIJBACKEND *)C->product->data;
7125:   if (!mmdata->reusesym) { /* update temporary matrices */
7126:     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));
7127:     if (mmdata->Bloc) PetscCall(MatMPIAIJGetLocalMatMerge(C->product->B, MAT_REUSE_MATRIX, NULL, &mmdata->Bloc));
7128:   }
7129:   mmdata->reusesym = PETSC_FALSE;

7131:   for (i = 0; i < mmdata->cp; i++) {
7132:     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]);
7133:     PetscCall((*mmdata->mp[i]->ops->productnumeric)(mmdata->mp[i]));
7134:   }
7135:   for (i = 0, n_d = 0, n_o = 0; i < mmdata->cp; i++) {
7136:     PetscInt noff;

7138:     PetscCall(PetscIntCast(mmdata->off[i + 1] - mmdata->off[i], &noff));
7139:     if (mmdata->mptmp[i]) continue;
7140:     if (noff) {
7141:       PetscInt nown;

7143:       PetscCall(PetscIntCast(mmdata->own[i + 1] - mmdata->own[i], &nown));
7144:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], noff, mmdata->off[i], mmdata->coo_w + n_o));
7145:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], nown, mmdata->own[i], mmdata->coo_v + n_d));
7146:       n_o += noff;
7147:       n_d += nown;
7148:     } else {
7149:       Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mmdata->mp[i]->data;

7151:       PetscCall(MatSeqAIJCopySubArray(mmdata->mp[i], mm->nz, NULL, mmdata->coo_v + n_d));
7152:       n_d += mm->nz;
7153:     }
7154:   }
7155:   if (mmdata->hasoffproc) { /* offprocess insertion */
7156:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7157:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_SCALAR, mmdata->coo_w, mmdata->coo_v + n_d));
7158:   }
7159:   PetscCall(MatSetValuesCOO(C, mmdata->coo_v, INSERT_VALUES));
7160:   PetscFunctionReturn(PETSC_SUCCESS);
7161: }

7163: /* Support for Pt * A, A * P, or Pt * A * P */
7164: #define MAX_NUMBER_INTERMEDIATE 4
7165: PetscErrorCode MatProductSymbolic_MPIAIJBACKEND(Mat C)
7166: {
7167:   Mat_Product           *product = C->product;
7168:   Mat                    A, P, mp[MAX_NUMBER_INTERMEDIATE]; /* A, P and a series of intermediate matrices */
7169:   Mat_MPIAIJ            *a, *p;
7170:   MatMatMPIAIJBACKEND   *mmdata;
7171:   ISLocalToGlobalMapping P_oth_l2g = NULL;
7172:   IS                     glob      = NULL;
7173:   const char            *prefix;
7174:   char                   pprefix[256];
7175:   const PetscInt        *globidx, *P_oth_idx;
7176:   PetscInt               i, j, cp, m, n, M, N, *coo_i, *coo_j;
7177:   PetscCount             ncoo, ncoo_d, ncoo_o, ncoo_oown;
7178:   PetscInt               cmapt[MAX_NUMBER_INTERMEDIATE], rmapt[MAX_NUMBER_INTERMEDIATE]; /* col/row map type for each Mat in mp[]. */
7179:                                                                                          /* type-0: consecutive, start from 0; type-1: consecutive with */
7180:                                                                                          /* a base offset; type-2: sparse with a local to global map table */
7181:   const PetscInt *cmapa[MAX_NUMBER_INTERMEDIATE], *rmapa[MAX_NUMBER_INTERMEDIATE];       /* col/row local to global map array (table) for type-2 map type */

7183:   MatProductType ptype;
7184:   PetscBool      mptmp[MAX_NUMBER_INTERMEDIATE], hasoffproc = PETSC_FALSE, iscuda, iship, iskokk;
7185:   PetscMPIInt    size;

7187:   PetscFunctionBegin;
7188:   MatCheckProduct(C, 1);
7189:   PetscCheck(!product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
7190:   ptype = product->type;
7191:   if (product->A->symmetric == PETSC_BOOL3_TRUE && ptype == MATPRODUCT_AtB) {
7192:     ptype                                          = MATPRODUCT_AB;
7193:     product->symbolic_used_the_fact_A_is_symmetric = PETSC_TRUE;
7194:   }
7195:   switch (ptype) {
7196:   case MATPRODUCT_AB:
7197:     A          = product->A;
7198:     P          = product->B;
7199:     m          = A->rmap->n;
7200:     n          = P->cmap->n;
7201:     M          = A->rmap->N;
7202:     N          = P->cmap->N;
7203:     hasoffproc = PETSC_FALSE; /* will not scatter mat product values to other processes */
7204:     break;
7205:   case MATPRODUCT_AtB:
7206:     P          = product->A;
7207:     A          = product->B;
7208:     m          = P->cmap->n;
7209:     n          = A->cmap->n;
7210:     M          = P->cmap->N;
7211:     N          = A->cmap->N;
7212:     hasoffproc = PETSC_TRUE;
7213:     break;
7214:   case MATPRODUCT_PtAP:
7215:     A          = product->A;
7216:     P          = product->B;
7217:     m          = P->cmap->n;
7218:     n          = P->cmap->n;
7219:     M          = P->cmap->N;
7220:     N          = P->cmap->N;
7221:     hasoffproc = PETSC_TRUE;
7222:     break;
7223:   default:
7224:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7225:   }
7226:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)C), &size));
7227:   if (size == 1) hasoffproc = PETSC_FALSE;

7229:   /* defaults */
7230:   for (i = 0; i < MAX_NUMBER_INTERMEDIATE; i++) {
7231:     mp[i]    = NULL;
7232:     mptmp[i] = PETSC_FALSE;
7233:     rmapt[i] = -1;
7234:     cmapt[i] = -1;
7235:     rmapa[i] = NULL;
7236:     cmapa[i] = NULL;
7237:   }

7239:   /* customization */
7240:   PetscCall(PetscNew(&mmdata));
7241:   mmdata->reusesym = product->api_user;
7242:   if (ptype == MATPRODUCT_AB) {
7243:     if (product->api_user) {
7244:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
7245:       PetscCall(PetscOptionsBool("-matmatmult_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7246:       PetscCall(PetscOptionsBool("-matmatmult_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7247:       PetscOptionsEnd();
7248:     } else {
7249:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
7250:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_mergeB", "Merge product->B local matrices", "MatMatMult", mmdata->abmerge, &mmdata->abmerge, NULL));
7251:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7252:       PetscOptionsEnd();
7253:     }
7254:   } else if (ptype == MATPRODUCT_PtAP) {
7255:     if (product->api_user) {
7256:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
7257:       PetscCall(PetscOptionsBool("-matptap_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7258:       PetscOptionsEnd();
7259:     } else {
7260:       PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
7261:       PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_pothbind", "Bind P_oth to CPU", "MatBindToCPU", mmdata->P_oth_bind, &mmdata->P_oth_bind, NULL));
7262:       PetscOptionsEnd();
7263:     }
7264:   }
7265:   a = (Mat_MPIAIJ *)A->data;
7266:   p = (Mat_MPIAIJ *)P->data;
7267:   PetscCall(MatSetSizes(C, m, n, M, N));
7268:   PetscCall(PetscLayoutSetUp(C->rmap));
7269:   PetscCall(PetscLayoutSetUp(C->cmap));
7270:   PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
7271:   PetscCall(MatGetOptionsPrefix(C, &prefix));

7273:   cp = 0;
7274:   switch (ptype) {
7275:   case MATPRODUCT_AB: /* A * P */
7276:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));

7278:     /* A_diag * P_local (merged or not) */
7279:     if (mmdata->abmerge) { /* P's diagonal and off-diag blocks are merged to one matrix, then multiplied by A_diag */
7280:       /* P is product->B */
7281:       PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7282:       PetscCall(MatProductCreate(a->A, mmdata->Bloc, 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:       PetscCall(ISGetIndices(glob, &globidx));
7292:       rmapt[cp] = 1;
7293:       cmapt[cp] = 2;
7294:       cmapa[cp] = globidx;
7295:       mptmp[cp] = PETSC_FALSE;
7296:       cp++;
7297:     } else { /* A_diag * P_diag and A_diag * P_off */
7298:       PetscCall(MatProductCreate(a->A, p->A, NULL, &mp[cp]));
7299:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7300:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7301:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7302:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7303:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7304:       mp[cp]->product->api_user = product->api_user;
7305:       PetscCall(MatProductSetFromOptions(mp[cp]));
7306:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7307:       rmapt[cp] = 1;
7308:       cmapt[cp] = 1;
7309:       mptmp[cp] = PETSC_FALSE;
7310:       cp++;
7311:       PetscCall(MatProductCreate(a->A, p->B, NULL, &mp[cp]));
7312:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7313:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7314:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7315:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7316:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7317:       mp[cp]->product->api_user = product->api_user;
7318:       PetscCall(MatProductSetFromOptions(mp[cp]));
7319:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7320:       rmapt[cp] = 1;
7321:       cmapt[cp] = 2;
7322:       cmapa[cp] = p->garray;
7323:       mptmp[cp] = PETSC_FALSE;
7324:       cp++;
7325:     }

7327:     /* A_off * P_other */
7328:     if (mmdata->P_oth) {
7329:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g)); /* make P_oth use local col ids */
7330:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7331:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7332:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7333:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7334:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7335:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7336:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7337:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7338:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7339:       mp[cp]->product->api_user = product->api_user;
7340:       PetscCall(MatProductSetFromOptions(mp[cp]));
7341:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7342:       rmapt[cp] = 1;
7343:       cmapt[cp] = 2;
7344:       cmapa[cp] = P_oth_idx;
7345:       mptmp[cp] = PETSC_FALSE;
7346:       cp++;
7347:     }
7348:     break;

7350:   case MATPRODUCT_AtB: /* (P^t * A): P_diag * A_loc + P_off * A_loc */
7351:     /* A is product->B */
7352:     PetscCall(MatMPIAIJGetLocalMatMerge(A, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7353:     if (A == P) { /* when A==P, we can take advantage of the already merged mmdata->Bloc */
7354:       PetscCall(MatProductCreate(mmdata->Bloc, mmdata->Bloc, NULL, &mp[cp]));
7355:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7356:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7357:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7358:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7359:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7360:       mp[cp]->product->api_user = product->api_user;
7361:       PetscCall(MatProductSetFromOptions(mp[cp]));
7362:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7363:       PetscCall(ISGetIndices(glob, &globidx));
7364:       rmapt[cp] = 2;
7365:       rmapa[cp] = globidx;
7366:       cmapt[cp] = 2;
7367:       cmapa[cp] = globidx;
7368:       mptmp[cp] = PETSC_FALSE;
7369:       cp++;
7370:     } else {
7371:       PetscCall(MatProductCreate(p->A, mmdata->Bloc, NULL, &mp[cp]));
7372:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7373:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7374:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7375:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7376:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7377:       mp[cp]->product->api_user = product->api_user;
7378:       PetscCall(MatProductSetFromOptions(mp[cp]));
7379:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7380:       PetscCall(ISGetIndices(glob, &globidx));
7381:       rmapt[cp] = 1;
7382:       cmapt[cp] = 2;
7383:       cmapa[cp] = globidx;
7384:       mptmp[cp] = PETSC_FALSE;
7385:       cp++;
7386:       PetscCall(MatProductCreate(p->B, mmdata->Bloc, NULL, &mp[cp]));
7387:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7388:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7389:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7390:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7391:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7392:       mp[cp]->product->api_user = product->api_user;
7393:       PetscCall(MatProductSetFromOptions(mp[cp]));
7394:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7395:       rmapt[cp] = 2;
7396:       rmapa[cp] = p->garray;
7397:       cmapt[cp] = 2;
7398:       cmapa[cp] = globidx;
7399:       mptmp[cp] = PETSC_FALSE;
7400:       cp++;
7401:     }
7402:     break;
7403:   case MATPRODUCT_PtAP:
7404:     PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &mmdata->startsj_s, &mmdata->startsj_r, &mmdata->bufa, &mmdata->P_oth));
7405:     /* P is product->B */
7406:     PetscCall(MatMPIAIJGetLocalMatMerge(P, MAT_INITIAL_MATRIX, &glob, &mmdata->Bloc));
7407:     PetscCall(MatProductCreate(a->A, mmdata->Bloc, NULL, &mp[cp]));
7408:     PetscCall(MatProductSetType(mp[cp], MATPRODUCT_PtAP));
7409:     PetscCall(MatProductSetFill(mp[cp], product->fill));
7410:     PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7411:     PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7412:     PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7413:     mp[cp]->product->api_user = product->api_user;
7414:     PetscCall(MatProductSetFromOptions(mp[cp]));
7415:     PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7416:     PetscCall(ISGetIndices(glob, &globidx));
7417:     rmapt[cp] = 2;
7418:     rmapa[cp] = globidx;
7419:     cmapt[cp] = 2;
7420:     cmapa[cp] = globidx;
7421:     mptmp[cp] = PETSC_FALSE;
7422:     cp++;
7423:     if (mmdata->P_oth) {
7424:       PetscCall(MatSeqAIJCompactOutExtraColumns_SeqAIJ(mmdata->P_oth, &P_oth_l2g));
7425:       PetscCall(ISLocalToGlobalMappingGetIndices(P_oth_l2g, &P_oth_idx));
7426:       PetscCall(MatSetType(mmdata->P_oth, ((PetscObject)a->B)->type_name));
7427:       PetscCall(MatBindToCPU(mmdata->P_oth, mmdata->P_oth_bind));
7428:       PetscCall(MatProductCreate(a->B, mmdata->P_oth, NULL, &mp[cp]));
7429:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AB));
7430:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7431:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7432:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7433:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7434:       mp[cp]->product->api_user = product->api_user;
7435:       PetscCall(MatProductSetFromOptions(mp[cp]));
7436:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7437:       mptmp[cp] = PETSC_TRUE;
7438:       cp++;
7439:       PetscCall(MatProductCreate(mmdata->Bloc, mp[1], NULL, &mp[cp]));
7440:       PetscCall(MatProductSetType(mp[cp], MATPRODUCT_AtB));
7441:       PetscCall(MatProductSetFill(mp[cp], product->fill));
7442:       PetscCall(PetscSNPrintf(pprefix, sizeof(pprefix), "backend_p%" PetscInt_FMT "_", cp));
7443:       PetscCall(MatSetOptionsPrefix(mp[cp], prefix));
7444:       PetscCall(MatAppendOptionsPrefix(mp[cp], pprefix));
7445:       mp[cp]->product->api_user = product->api_user;
7446:       PetscCall(MatProductSetFromOptions(mp[cp]));
7447:       PetscCall((*mp[cp]->ops->productsymbolic)(mp[cp]));
7448:       rmapt[cp] = 2;
7449:       rmapa[cp] = globidx;
7450:       cmapt[cp] = 2;
7451:       cmapa[cp] = P_oth_idx;
7452:       mptmp[cp] = PETSC_FALSE;
7453:       cp++;
7454:     }
7455:     break;
7456:   default:
7457:     SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
7458:   }
7459:   /* sanity check */
7460:   if (size > 1)
7461:     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);

7463:   PetscCall(PetscMalloc2(cp, &mmdata->mp, cp, &mmdata->mptmp));
7464:   for (i = 0; i < cp; i++) {
7465:     mmdata->mp[i]    = mp[i];
7466:     mmdata->mptmp[i] = mptmp[i];
7467:   }
7468:   mmdata->cp             = cp;
7469:   C->product->data       = mmdata;
7470:   C->product->destroy    = MatDestroy_MatMatMPIAIJBACKEND;
7471:   C->ops->productnumeric = MatProductNumeric_MPIAIJBACKEND;

7473:   /* memory type */
7474:   mmdata->mtype = PETSC_MEMTYPE_HOST;
7475:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iscuda, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, ""));
7476:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iship, MATSEQAIJHIPSPARSE, MATMPIAIJHIPSPARSE, ""));
7477:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &iskokk, MATSEQAIJKOKKOS, MATMPIAIJKOKKOS, ""));
7478:   if (iscuda) mmdata->mtype = PETSC_MEMTYPE_CUDA;
7479:   else if (iship) mmdata->mtype = PETSC_MEMTYPE_HIP;
7480:   else if (iskokk) mmdata->mtype = PETSC_MEMTYPE_KOKKOS;

7482:   /* prepare coo coordinates for values insertion */

7484:   /* count total nonzeros of those intermediate seqaij Mats
7485:     ncoo_d:    # of nonzeros of matrices that do not have offproc entries
7486:     ncoo_o:    # of nonzeros (of matrices that might have offproc entries) that will be inserted to remote procs
7487:     ncoo_oown: # of nonzeros (of matrices that might have offproc entries) that will be inserted locally
7488:   */
7489:   for (cp = 0, ncoo_d = 0, ncoo_o = 0, ncoo_oown = 0; cp < mmdata->cp; cp++) {
7490:     Mat_SeqAIJ *mm = (Mat_SeqAIJ *)mp[cp]->data;
7491:     if (mptmp[cp]) continue;
7492:     if (rmapt[cp] == 2 && hasoffproc) { /* the rows need to be scatter to all processes (might include self) */
7493:       const PetscInt *rmap = rmapa[cp];
7494:       const PetscInt  mr   = mp[cp]->rmap->n;
7495:       const PetscInt  rs   = C->rmap->rstart;
7496:       const PetscInt  re   = C->rmap->rend;
7497:       const PetscInt *ii   = mm->i;
7498:       for (i = 0; i < mr; i++) {
7499:         const PetscInt gr = rmap[i];
7500:         const PetscInt nz = ii[i + 1] - ii[i];
7501:         if (gr < rs || gr >= re) ncoo_o += nz; /* this row is offproc */
7502:         else ncoo_oown += nz;                  /* this row is local */
7503:       }
7504:     } else ncoo_d += mm->nz;
7505:   }

7507:   /*
7508:     ncoo: total number of nonzeros (including those inserted by remote procs) belonging to this proc

7510:     ncoo = ncoo_d + ncoo_oown + ncoo2, which ncoo2 is number of nonzeros inserted to me by other procs.

7512:     off[0] points to a big index array, which is shared by off[1,2,...]. Similarly, for own[0].

7514:     off[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert to others
7515:     own[p]: points to the segment for matrix mp[p], storing location of nonzeros that mp[p] will insert locally
7516:     so, off[p+1]-off[p] is the number of nonzeros that mp[p] will send to others.

7518:     coo_i/j/v[]: [ncoo] row/col/val of nonzeros belonging to this proc.
7519:     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.
7520:   */
7521:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->off)); /* +1 to make a csr-like data structure */
7522:   PetscCall(PetscCalloc1(mmdata->cp + 1, &mmdata->own));

7524:   /* gather (i,j) of nonzeros inserted by remote procs */
7525:   if (hasoffproc) {
7526:     PetscSF  msf;
7527:     PetscInt ncoo2, *coo_i2, *coo_j2;

7529:     PetscCall(PetscMalloc1(ncoo_o, &mmdata->off[0]));
7530:     PetscCall(PetscMalloc1(ncoo_oown, &mmdata->own[0]));
7531:     PetscCall(PetscMalloc2(ncoo_o, &coo_i, ncoo_o, &coo_j)); /* to collect (i,j) of entries to be sent to others */

7533:     for (cp = 0, ncoo_o = 0; cp < mmdata->cp; cp++) {
7534:       Mat_SeqAIJ *mm     = (Mat_SeqAIJ *)mp[cp]->data;
7535:       PetscInt   *idxoff = mmdata->off[cp];
7536:       PetscInt   *idxown = mmdata->own[cp];
7537:       if (!mptmp[cp] && rmapt[cp] == 2) { /* row map is sparse */
7538:         const PetscInt *rmap = rmapa[cp];
7539:         const PetscInt *cmap = cmapa[cp];
7540:         const PetscInt *ii   = mm->i;
7541:         PetscInt       *coi  = coo_i + ncoo_o;
7542:         PetscInt       *coj  = coo_j + ncoo_o;
7543:         const PetscInt  mr   = mp[cp]->rmap->n;
7544:         const PetscInt  rs   = C->rmap->rstart;
7545:         const PetscInt  re   = C->rmap->rend;
7546:         const PetscInt  cs   = C->cmap->rstart;
7547:         for (i = 0; i < mr; i++) {
7548:           const PetscInt *jj = mm->j + ii[i];
7549:           const PetscInt  gr = rmap[i];
7550:           const PetscInt  nz = ii[i + 1] - ii[i];
7551:           if (gr < rs || gr >= re) { /* this is an offproc row */
7552:             for (j = ii[i]; j < ii[i + 1]; j++) {
7553:               *coi++    = gr;
7554:               *idxoff++ = j;
7555:             }
7556:             if (!cmapt[cp]) { /* already global */
7557:               for (j = 0; j < nz; j++) *coj++ = jj[j];
7558:             } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7559:               for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7560:             } else { /* offdiag */
7561:               for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7562:             }
7563:             ncoo_o += nz;
7564:           } else { /* this is a local row */
7565:             for (j = ii[i]; j < ii[i + 1]; j++) *idxown++ = j;
7566:           }
7567:         }
7568:       }
7569:       mmdata->off[cp + 1] = idxoff;
7570:       mmdata->own[cp + 1] = idxown;
7571:     }

7573:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7574:     PetscInt incoo_o;
7575:     PetscCall(PetscIntCast(ncoo_o, &incoo_o));
7576:     PetscCall(PetscSFSetGraphLayout(mmdata->sf, C->rmap, incoo_o /*nleaves*/, NULL /*ilocal*/, PETSC_OWN_POINTER, coo_i));
7577:     PetscCall(PetscSFGetMultiSF(mmdata->sf, &msf));
7578:     PetscCall(PetscSFGetGraph(msf, &ncoo2 /*nroots*/, NULL, NULL, NULL));
7579:     ncoo = ncoo_d + ncoo_oown + ncoo2;
7580:     PetscCall(PetscMalloc2(ncoo, &coo_i2, ncoo, &coo_j2));
7581:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown)); /* put (i,j) of remote nonzeros at back */
7582:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_i, coo_i2 + ncoo_d + ncoo_oown));
7583:     PetscCall(PetscSFGatherBegin(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7584:     PetscCall(PetscSFGatherEnd(mmdata->sf, MPIU_INT, coo_j, coo_j2 + ncoo_d + ncoo_oown));
7585:     PetscCall(PetscFree2(coo_i, coo_j));
7586:     /* allocate MPI send buffer to collect nonzero values to be sent to remote procs */
7587:     PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo_o * sizeof(PetscScalar), (void **)&mmdata->coo_w));
7588:     coo_i = coo_i2;
7589:     coo_j = coo_j2;
7590:   } else { /* no offproc values insertion */
7591:     ncoo = ncoo_d;
7592:     PetscCall(PetscMalloc2(ncoo, &coo_i, ncoo, &coo_j));

7594:     PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)C), &mmdata->sf));
7595:     PetscCall(PetscSFSetGraph(mmdata->sf, 0, 0, NULL, PETSC_OWN_POINTER, NULL, PETSC_OWN_POINTER));
7596:     PetscCall(PetscSFSetUp(mmdata->sf));
7597:   }
7598:   mmdata->hasoffproc = hasoffproc;

7600:   /* gather (i,j) of nonzeros inserted locally */
7601:   for (cp = 0, ncoo_d = 0; cp < mmdata->cp; cp++) {
7602:     Mat_SeqAIJ     *mm   = (Mat_SeqAIJ *)mp[cp]->data;
7603:     PetscInt       *coi  = coo_i + ncoo_d;
7604:     PetscInt       *coj  = coo_j + ncoo_d;
7605:     const PetscInt *jj   = mm->j;
7606:     const PetscInt *ii   = mm->i;
7607:     const PetscInt *cmap = cmapa[cp];
7608:     const PetscInt *rmap = rmapa[cp];
7609:     const PetscInt  mr   = mp[cp]->rmap->n;
7610:     const PetscInt  rs   = C->rmap->rstart;
7611:     const PetscInt  re   = C->rmap->rend;
7612:     const PetscInt  cs   = C->cmap->rstart;

7614:     if (mptmp[cp]) continue;
7615:     if (rmapt[cp] == 1) { /* consecutive rows */
7616:       /* fill coo_i */
7617:       for (i = 0; i < mr; i++) {
7618:         const PetscInt gr = i + rs;
7619:         for (j = ii[i]; j < ii[i + 1]; j++) coi[j] = gr;
7620:       }
7621:       /* fill coo_j */
7622:       if (!cmapt[cp]) { /* type-0, already global */
7623:         PetscCall(PetscArraycpy(coj, jj, mm->nz));
7624:       } else if (cmapt[cp] == 1) {                        /* type-1, local to global for consecutive columns of C */
7625:         for (j = 0; j < mm->nz; j++) coj[j] = jj[j] + cs; /* lid + col start */
7626:       } else {                                            /* type-2, local to global for sparse columns */
7627:         for (j = 0; j < mm->nz; j++) coj[j] = cmap[jj[j]];
7628:       }
7629:       ncoo_d += mm->nz;
7630:     } else if (rmapt[cp] == 2) { /* sparse rows */
7631:       for (i = 0; i < mr; i++) {
7632:         const PetscInt *jj = mm->j + ii[i];
7633:         const PetscInt  gr = rmap[i];
7634:         const PetscInt  nz = ii[i + 1] - ii[i];
7635:         if (gr >= rs && gr < re) { /* local rows */
7636:           for (j = ii[i]; j < ii[i + 1]; j++) *coi++ = gr;
7637:           if (!cmapt[cp]) { /* type-0, already global */
7638:             for (j = 0; j < nz; j++) *coj++ = jj[j];
7639:           } else if (cmapt[cp] == 1) { /* local to global for owned columns of C */
7640:             for (j = 0; j < nz; j++) *coj++ = jj[j] + cs;
7641:           } else { /* type-2, local to global for sparse columns */
7642:             for (j = 0; j < nz; j++) *coj++ = cmap[jj[j]];
7643:           }
7644:           ncoo_d += nz;
7645:         }
7646:       }
7647:     }
7648:   }
7649:   if (glob) PetscCall(ISRestoreIndices(glob, &globidx));
7650:   PetscCall(ISDestroy(&glob));
7651:   if (P_oth_l2g) PetscCall(ISLocalToGlobalMappingRestoreIndices(P_oth_l2g, &P_oth_idx));
7652:   PetscCall(ISLocalToGlobalMappingDestroy(&P_oth_l2g));
7653:   /* allocate an array to store all nonzeros (inserted locally or remotely) belonging to this proc */
7654:   PetscCall(PetscSFMalloc(mmdata->sf, mmdata->mtype, ncoo * sizeof(PetscScalar), (void **)&mmdata->coo_v));

7656:   /* preallocate with COO data */
7657:   PetscCall(MatSetPreallocationCOO(C, ncoo, coo_i, coo_j));
7658:   PetscCall(PetscFree2(coo_i, coo_j));
7659:   PetscFunctionReturn(PETSC_SUCCESS);
7660: }

7662: PetscErrorCode MatProductSetFromOptions_MPIAIJBACKEND(Mat mat)
7663: {
7664:   Mat_Product *product = mat->product;
7665: #if defined(PETSC_HAVE_DEVICE)
7666:   PetscBool match  = PETSC_FALSE;
7667:   PetscBool usecpu = PETSC_FALSE;
7668: #else
7669:   PetscBool match = PETSC_TRUE;
7670: #endif

7672:   PetscFunctionBegin;
7673:   MatCheckProduct(mat, 1);
7674: #if defined(PETSC_HAVE_DEVICE)
7675:   if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
7676:   if (match) { /* we can always fallback to the CPU if requested */
7677:     switch (product->type) {
7678:     case MATPRODUCT_AB:
7679:       if (product->api_user) {
7680:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
7681:         PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7682:         PetscOptionsEnd();
7683:       } else {
7684:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
7685:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
7686:         PetscOptionsEnd();
7687:       }
7688:       break;
7689:     case MATPRODUCT_AtB:
7690:       if (product->api_user) {
7691:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
7692:         PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7693:         PetscOptionsEnd();
7694:       } else {
7695:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
7696:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
7697:         PetscOptionsEnd();
7698:       }
7699:       break;
7700:     case MATPRODUCT_PtAP:
7701:       if (product->api_user) {
7702:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
7703:         PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7704:         PetscOptionsEnd();
7705:       } else {
7706:         PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
7707:         PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
7708:         PetscOptionsEnd();
7709:       }
7710:       break;
7711:     default:
7712:       break;
7713:     }
7714:     match = (PetscBool)!usecpu;
7715:   }
7716: #endif
7717:   if (match) {
7718:     switch (product->type) {
7719:     case MATPRODUCT_AB:
7720:     case MATPRODUCT_AtB:
7721:     case MATPRODUCT_PtAP:
7722:       mat->ops->productsymbolic = MatProductSymbolic_MPIAIJBACKEND;
7723:       break;
7724:     default:
7725:       break;
7726:     }
7727:   }
7728:   /* fallback to MPIAIJ ops */
7729:   if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
7730:   PetscFunctionReturn(PETSC_SUCCESS);
7731: }

7733: /*
7734:    Produces a set of block column indices of the matrix row, one for each block represented in the original row

7736:    n - the number of block indices in cc[]
7737:    cc - the block indices (must be large enough to contain the indices)
7738: */
7739: static inline PetscErrorCode MatCollapseRow(Mat Amat, PetscInt row, PetscInt bs, PetscInt *n, PetscInt *cc)
7740: {
7741:   PetscInt        cnt = -1, nidx, j;
7742:   const PetscInt *idx;

7744:   PetscFunctionBegin;
7745:   PetscCall(MatGetRow(Amat, row, &nidx, &idx, NULL));
7746:   if (nidx) {
7747:     cnt     = 0;
7748:     cc[cnt] = idx[0] / bs;
7749:     for (j = 1; j < nidx; j++) {
7750:       if (cc[cnt] < idx[j] / bs) cc[++cnt] = idx[j] / bs;
7751:     }
7752:   }
7753:   PetscCall(MatRestoreRow(Amat, row, &nidx, &idx, NULL));
7754:   *n = cnt + 1;
7755:   PetscFunctionReturn(PETSC_SUCCESS);
7756: }

7758: /*
7759:     Produces a set of block column indices of the matrix block row, one for each block represented in the original set of rows

7761:     ncollapsed - the number of block indices
7762:     collapsed - the block indices (must be large enough to contain the indices)
7763: */
7764: static inline PetscErrorCode MatCollapseRows(Mat Amat, PetscInt start, PetscInt bs, PetscInt *w0, PetscInt *w1, PetscInt *w2, PetscInt *ncollapsed, PetscInt **collapsed)
7765: {
7766:   PetscInt i, nprev, *cprev = w0, ncur = 0, *ccur = w1, *merged = w2, *cprevtmp;

7768:   PetscFunctionBegin;
7769:   PetscCall(MatCollapseRow(Amat, start, bs, &nprev, cprev));
7770:   for (i = start + 1; i < start + bs; i++) {
7771:     PetscCall(MatCollapseRow(Amat, i, bs, &ncur, ccur));
7772:     PetscCall(PetscMergeIntArray(nprev, cprev, ncur, ccur, &nprev, &merged));
7773:     cprevtmp = cprev;
7774:     cprev    = merged;
7775:     merged   = cprevtmp;
7776:   }
7777:   *ncollapsed = nprev;
7778:   if (collapsed) *collapsed = cprev;
7779:   PetscFunctionReturn(PETSC_SUCCESS);
7780: }

7782: /*
7783:  MatCreateGraph_Simple_AIJ - create simple scalar matrix (graph) from potentially blocked matrix

7785:  Input Parameter:
7786:  . Amat - matrix
7787:  - symmetrize - make the result symmetric
7788:  + scale - scale with diagonal

7790:  Output Parameter:
7791:  . a_Gmat - output scalar graph >= 0

7793: */
7794: PETSC_INTERN PetscErrorCode MatCreateGraph_Simple_AIJ(Mat Amat, PetscBool symmetrize, PetscBool scale, PetscReal filter, PetscInt index_size, PetscInt index[], Mat *a_Gmat)
7795: {
7796:   PetscInt  Istart, Iend, Ii, jj, kk, ncols, nloc, NN, MM, bs;
7797:   MPI_Comm  comm;
7798:   Mat       Gmat;
7799:   PetscBool ismpiaij, isseqaij;
7800:   Mat       a, b, c;
7801:   MatType   jtype;

7803:   PetscFunctionBegin;
7804:   PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
7805:   PetscCall(MatGetOwnershipRange(Amat, &Istart, &Iend));
7806:   PetscCall(MatGetSize(Amat, &MM, &NN));
7807:   PetscCall(MatGetBlockSize(Amat, &bs));
7808:   nloc = (Iend - Istart) / bs;

7810:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATSEQAIJ, &isseqaij));
7811:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)Amat, MATMPIAIJ, &ismpiaij));
7812:   PetscCheck(isseqaij || ismpiaij, comm, PETSC_ERR_USER, "Require (MPI)AIJ matrix type");

7814:   /* TODO GPU: these calls are potentially expensive if matrices are large and we want to use the GPU */
7815:   /* A solution consists in providing a new API, MatAIJGetCollapsedAIJ, and each class can provide a fast
7816:      implementation */
7817:   if (bs > 1) {
7818:     PetscCall(MatGetType(Amat, &jtype));
7819:     PetscCall(MatCreate(comm, &Gmat));
7820:     PetscCall(MatSetType(Gmat, jtype));
7821:     PetscCall(MatSetSizes(Gmat, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE));
7822:     PetscCall(MatSetBlockSizes(Gmat, 1, 1));
7823:     if (isseqaij || ((Mat_MPIAIJ *)Amat->data)->garray) {
7824:       PetscInt  *d_nnz, *o_nnz;
7825:       MatScalar *aa, val, *AA;
7826:       PetscInt  *aj, *ai, *AJ, nc, nmax = 0;

7828:       if (isseqaij) {
7829:         a = Amat;
7830:         b = NULL;
7831:       } else {
7832:         Mat_MPIAIJ *d = (Mat_MPIAIJ *)Amat->data;
7833:         a             = d->A;
7834:         b             = d->B;
7835:       }
7836:       PetscCall(PetscInfo(Amat, "New bs>1 Graph. nloc=%" PetscInt_FMT "\n", nloc));
7837:       PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7838:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
7839:         PetscInt       *nnz = (c == a) ? d_nnz : o_nnz;
7840:         const PetscInt *cols1, *cols2;

7842:         for (PetscInt brow = 0, nc1, nc2, ok = 1; brow < nloc * bs; brow += bs) { // block rows
7843:           PetscCall(MatGetRow(c, brow, &nc2, &cols2, NULL));
7844:           nnz[brow / bs] = nc2 / bs;
7845:           if (nc2 % bs) ok = 0;
7846:           if (nnz[brow / bs] > nmax) nmax = nnz[brow / bs];
7847:           for (PetscInt ii = 1; ii < bs; ii++) { // check for non-dense blocks
7848:             PetscCall(MatGetRow(c, brow + ii, &nc1, &cols1, NULL));
7849:             if (nc1 != nc2) ok = 0;
7850:             else {
7851:               for (PetscInt jj = 0; jj < nc1 && ok == 1; jj++) {
7852:                 if (cols1[jj] != cols2[jj]) ok = 0;
7853:                 if (cols1[jj] % bs != jj % bs) ok = 0;
7854:               }
7855:             }
7856:             PetscCall(MatRestoreRow(c, brow + ii, &nc1, &cols1, NULL));
7857:           }
7858:           PetscCall(MatRestoreRow(c, brow, &nc2, &cols2, NULL));
7859:           if (!ok) {
7860:             PetscCall(PetscFree2(d_nnz, o_nnz));
7861:             PetscCall(PetscInfo(Amat, "Found sparse blocks - revert to slow method\n"));
7862:             goto old_bs;
7863:           }
7864:         }
7865:       }
7866:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
7867:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
7868:       PetscCall(PetscFree2(d_nnz, o_nnz));
7869:       PetscCall(PetscMalloc2(nmax, &AA, nmax, &AJ));
7870:       // diag
7871:       for (PetscInt brow = 0, n, grow; brow < nloc * bs; brow += bs) { // block rows
7872:         Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)a->data;

7874:         ai = aseq->i;
7875:         n  = ai[brow + 1] - ai[brow];
7876:         aj = aseq->j + ai[brow];
7877:         for (PetscInt k = 0; k < n; k += bs) {   // block columns
7878:           AJ[k / bs] = aj[k] / bs + Istart / bs; // diag starts at (Istart,Istart)
7879:           val        = 0;
7880:           if (index_size == 0) {
7881:             for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7882:               aa = aseq->a + ai[brow + ii] + k;
7883:               for (PetscInt jj = 0; jj < bs; jj++) {    // columns in block
7884:                 val += PetscAbs(PetscRealPart(aa[jj])); // a sort of norm
7885:               }
7886:             }
7887:           } else {                                            // use (index,index) value if provided
7888:             for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7889:               PetscInt ii = index[iii];
7890:               aa          = aseq->a + ai[brow + ii] + k;
7891:               for (PetscInt jjj = 0; jjj < index_size; jjj++) { // columns in block
7892:                 PetscInt jj = index[jjj];
7893:                 val += PetscAbs(PetscRealPart(aa[jj]));
7894:               }
7895:             }
7896:           }
7897:           PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7898:           AA[k / bs] = val;
7899:         }
7900:         grow = Istart / bs + brow / bs;
7901:         PetscCall(MatSetValues(Gmat, 1, &grow, n / bs, AJ, AA, ADD_VALUES));
7902:       }
7903:       // off-diag
7904:       if (ismpiaij) {
7905:         Mat_MPIAIJ        *aij = (Mat_MPIAIJ *)Amat->data;
7906:         const PetscScalar *vals;
7907:         const PetscInt    *cols, *garray = aij->garray;

7909:         PetscCheck(garray, PETSC_COMM_SELF, PETSC_ERR_USER, "No garray ?");
7910:         for (PetscInt brow = 0, grow; brow < nloc * bs; brow += bs) { // block rows
7911:           PetscCall(MatGetRow(b, brow, &ncols, &cols, NULL));
7912:           for (PetscInt k = 0, cidx = 0; k < ncols; k += bs, cidx++) {
7913:             PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs >= nmax");
7914:             AA[k / bs] = 0;
7915:             AJ[cidx]   = garray[cols[k]] / bs;
7916:           }
7917:           nc = ncols / bs;
7918:           PetscCall(MatRestoreRow(b, brow, &ncols, &cols, NULL));
7919:           if (index_size == 0) {
7920:             for (PetscInt ii = 0; ii < bs; ii++) { // rows in block
7921:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7922:               for (PetscInt k = 0; k < ncols; k += bs) {
7923:                 for (PetscInt jj = 0; jj < bs; jj++) { // cols in block
7924:                   PetscAssert(k / bs < nmax, comm, PETSC_ERR_USER, "k / bs (%" PetscInt_FMT ") >= nmax (%" PetscInt_FMT ")", k / bs, nmax);
7925:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7926:                 }
7927:               }
7928:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7929:             }
7930:           } else {                                            // use (index,index) value if provided
7931:             for (PetscInt iii = 0; iii < index_size; iii++) { // rows in block
7932:               PetscInt ii = index[iii];
7933:               PetscCall(MatGetRow(b, brow + ii, &ncols, &cols, &vals));
7934:               for (PetscInt k = 0; k < ncols; k += bs) {
7935:                 for (PetscInt jjj = 0; jjj < index_size; jjj++) { // cols in block
7936:                   PetscInt jj = index[jjj];
7937:                   AA[k / bs] += PetscAbs(PetscRealPart(vals[k + jj]));
7938:                 }
7939:               }
7940:               PetscCall(MatRestoreRow(b, brow + ii, &ncols, &cols, &vals));
7941:             }
7942:           }
7943:           grow = Istart / bs + brow / bs;
7944:           PetscCall(MatSetValues(Gmat, 1, &grow, nc, AJ, AA, ADD_VALUES));
7945:         }
7946:       }
7947:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
7948:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
7949:       PetscCall(PetscFree2(AA, AJ));
7950:     } else {
7951:       const PetscScalar *vals;
7952:       const PetscInt    *idx;
7953:       PetscInt          *d_nnz, *o_nnz, *w0, *w1, *w2;
7954:     old_bs:
7955:       /*
7956:        Determine the preallocation needed for the scalar matrix derived from the vector matrix.
7957:        */
7958:       PetscCall(PetscInfo(Amat, "OLD bs>1 CreateGraph\n"));
7959:       PetscCall(PetscMalloc2(nloc, &d_nnz, (isseqaij ? 0 : nloc), &o_nnz));
7960:       if (isseqaij) {
7961:         PetscInt max_d_nnz;

7963:         /*
7964:          Determine exact preallocation count for (sequential) scalar matrix
7965:          */
7966:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Amat, &max_d_nnz));
7967:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7968:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7969:         for (Ii = 0, jj = 0; Ii < Iend; Ii += bs, jj++) PetscCall(MatCollapseRows(Amat, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7970:         PetscCall(PetscFree3(w0, w1, w2));
7971:       } else if (ismpiaij) {
7972:         Mat             Daij, Oaij;
7973:         const PetscInt *garray;
7974:         PetscInt        max_d_nnz;

7976:         PetscCall(MatMPIAIJGetSeqAIJ(Amat, &Daij, &Oaij, &garray));
7977:         /*
7978:          Determine exact preallocation count for diagonal block portion of scalar matrix
7979:          */
7980:         PetscCall(MatSeqAIJGetMaxRowNonzeros(Daij, &max_d_nnz));
7981:         max_d_nnz = PetscMin(nloc, bs * max_d_nnz);
7982:         PetscCall(PetscMalloc3(max_d_nnz, &w0, max_d_nnz, &w1, max_d_nnz, &w2));
7983:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) PetscCall(MatCollapseRows(Daij, Ii, bs, w0, w1, w2, &d_nnz[jj], NULL));
7984:         PetscCall(PetscFree3(w0, w1, w2));
7985:         /*
7986:          Over estimate (usually grossly over), preallocation count for off-diagonal portion of scalar matrix
7987:          */
7988:         for (Ii = 0, jj = 0; Ii < Iend - Istart; Ii += bs, jj++) {
7989:           o_nnz[jj] = 0;
7990:           for (kk = 0; kk < bs; kk++) { /* rows that get collapsed to a single row */
7991:             PetscCall(MatGetRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7992:             o_nnz[jj] += ncols;
7993:             PetscCall(MatRestoreRow(Oaij, Ii + kk, &ncols, NULL, NULL));
7994:           }
7995:           if (o_nnz[jj] > (NN / bs - nloc)) o_nnz[jj] = NN / bs - nloc;
7996:         }
7997:       } else SETERRQ(comm, PETSC_ERR_USER, "Require AIJ matrix type");
7998:       /* get scalar copy (norms) of matrix */
7999:       PetscCall(MatSeqAIJSetPreallocation(Gmat, 0, d_nnz));
8000:       PetscCall(MatMPIAIJSetPreallocation(Gmat, 0, d_nnz, 0, o_nnz));
8001:       PetscCall(PetscFree2(d_nnz, o_nnz));
8002:       for (Ii = Istart; Ii < Iend; Ii++) {
8003:         PetscInt dest_row = Ii / bs;

8005:         PetscCall(MatGetRow(Amat, Ii, &ncols, &idx, &vals));
8006:         for (jj = 0; jj < ncols; jj++) {
8007:           PetscInt    dest_col = idx[jj] / bs;
8008:           PetscScalar sv       = PetscAbs(PetscRealPart(vals[jj]));

8010:           PetscCall(MatSetValues(Gmat, 1, &dest_row, 1, &dest_col, &sv, ADD_VALUES));
8011:         }
8012:         PetscCall(MatRestoreRow(Amat, Ii, &ncols, &idx, &vals));
8013:       }
8014:       PetscCall(MatAssemblyBegin(Gmat, MAT_FINAL_ASSEMBLY));
8015:       PetscCall(MatAssemblyEnd(Gmat, MAT_FINAL_ASSEMBLY));
8016:     }
8017:   } else {
8018:     if (symmetrize || filter >= 0 || scale) PetscCall(MatDuplicate(Amat, MAT_COPY_VALUES, &Gmat));
8019:     else {
8020:       Gmat = Amat;
8021:       PetscCall(PetscObjectReference((PetscObject)Gmat));
8022:     }
8023:     if (isseqaij) {
8024:       a = Gmat;
8025:       b = NULL;
8026:     } else {
8027:       Mat_MPIAIJ *d = (Mat_MPIAIJ *)Gmat->data;
8028:       a             = d->A;
8029:       b             = d->B;
8030:     }
8031:     if (filter >= 0 || scale) {
8032:       /* take absolute value of each entry */
8033:       for (c = a, kk = 0; c && kk < 2; c = b, kk++) {
8034:         MatInfo      info;
8035:         PetscScalar *avals;

8037:         PetscCall(MatGetInfo(c, MAT_LOCAL, &info));
8038:         PetscCall(MatSeqAIJGetArray(c, &avals));
8039:         for (int jj = 0; jj < info.nz_used; jj++) avals[jj] = PetscAbsScalar(avals[jj]);
8040:         PetscCall(MatSeqAIJRestoreArray(c, &avals));
8041:       }
8042:     }
8043:   }
8044:   if (symmetrize) {
8045:     PetscBool isset, issym;

8047:     PetscCall(MatIsSymmetricKnown(Amat, &isset, &issym));
8048:     if (!isset || !issym) {
8049:       Mat matTrans;

8051:       PetscCall(MatTranspose(Gmat, MAT_INITIAL_MATRIX, &matTrans));
8052:       PetscCall(MatAXPY(Gmat, 1.0, matTrans, Gmat->structurally_symmetric == PETSC_BOOL3_TRUE ? SAME_NONZERO_PATTERN : DIFFERENT_NONZERO_PATTERN));
8053:       PetscCall(MatDestroy(&matTrans));
8054:     }
8055:     PetscCall(MatSetOption(Gmat, MAT_SYMMETRIC, PETSC_TRUE));
8056:   } else if (Amat != Gmat) PetscCall(MatPropagateSymmetryOptions(Amat, Gmat));
8057:   if (scale) {
8058:     /* scale c for all diagonal values = 1 or -1 */
8059:     Vec diag;

8061:     PetscCall(MatCreateVecs(Gmat, &diag, NULL));
8062:     PetscCall(MatGetDiagonal(Gmat, diag));
8063:     PetscCall(VecReciprocal(diag));
8064:     PetscCall(VecSqrtAbs(diag));
8065:     PetscCall(MatDiagonalScale(Gmat, diag, diag));
8066:     PetscCall(VecDestroy(&diag));
8067:   }
8068:   PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_graph_view"));
8069:   if (filter >= 0) {
8070:     PetscCall(MatFilter(Gmat, filter, PETSC_TRUE, PETSC_TRUE));
8071:     PetscCall(MatViewFromOptions(Gmat, NULL, "-mat_filter_graph_view"));
8072:   }
8073:   *a_Gmat = Gmat;
8074:   PetscFunctionReturn(PETSC_SUCCESS);
8075: }

8077: /*
8078:     Special version for direct calls from Fortran
8079: */

8081: /* Change these macros so can be used in void function */
8082: /* Identical to PetscCallVoid, except it assigns to *_ierr */
8083: #undef PetscCall
8084: #define PetscCall(...) \
8085:   do { \
8086:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
8087:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
8088:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
8089:       return; \
8090:     } \
8091:   } while (0)

8093: #undef SETERRQ
8094: #define SETERRQ(comm, ierr, ...) \
8095:   do { \
8096:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
8097:     return; \
8098:   } while (0)

8100: #if defined(PETSC_HAVE_FORTRAN_CAPS)
8101:   #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
8102: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8103:   #define matsetvaluesmpiaij_ matsetvaluesmpiaij
8104: #else
8105: #endif
8106: PETSC_EXTERN void matsetvaluesmpiaij_(Mat *mmat, PetscInt *mm, const PetscInt im[], PetscInt *mn, const PetscInt in[], const PetscScalar v[], InsertMode *maddv, PetscErrorCode *_ierr)
8107: {
8108:   Mat         mat = *mmat;
8109:   PetscInt    m = *mm, n = *mn;
8110:   InsertMode  addv = *maddv;
8111:   Mat_MPIAIJ *aij  = (Mat_MPIAIJ *)mat->data;
8112:   PetscScalar value;

8114:   MatCheckPreallocated(mat, 1);
8115:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
8116:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
8117:   {
8118:     PetscInt  i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
8119:     PetscInt  cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
8120:     PetscBool roworiented = aij->roworiented;

8122:     /* Some Variables required in the macro */
8123:     Mat         A     = aij->A;
8124:     Mat_SeqAIJ *a     = (Mat_SeqAIJ *)A->data;
8125:     PetscInt   *aimax = a->imax, *ai = a->i, *ailen = a->ilen, *aj = a->j;
8126:     MatScalar  *aa;
8127:     PetscBool   ignorezeroentries = ((a->ignorezeroentries && (addv == ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
8128:     Mat         B                 = aij->B;
8129:     Mat_SeqAIJ *b                 = (Mat_SeqAIJ *)B->data;
8130:     PetscInt   *bimax = b->imax, *bi = b->i, *bilen = b->ilen, *bj = b->j, bm = aij->B->rmap->n, am = aij->A->rmap->n;
8131:     MatScalar  *ba;
8132:     /* This variable below is only for the PETSC_HAVE_VIENNACL or PETSC_HAVE_CUDA cases, but we define it in all cases because we
8133:      * cannot use "#if defined" inside a macro. */
8134:     PETSC_UNUSED PetscBool inserted = PETSC_FALSE;

8136:     PetscInt  *rp1, *rp2, ii, nrow1, nrow2, _i, rmax1, rmax2, N, low1, high1, low2, high2, t, lastcol1, lastcol2;
8137:     PetscInt   nonew = a->nonew;
8138:     MatScalar *ap1, *ap2;

8140:     PetscFunctionBegin;
8141:     PetscCall(MatSeqAIJGetArray(A, &aa));
8142:     PetscCall(MatSeqAIJGetArray(B, &ba));
8143:     for (i = 0; i < m; i++) {
8144:       if (im[i] < 0) continue;
8145:       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);
8146:       if (im[i] >= rstart && im[i] < rend) {
8147:         row      = im[i] - rstart;
8148:         lastcol1 = -1;
8149:         rp1      = aj + ai[row];
8150:         ap1      = aa + ai[row];
8151:         rmax1    = aimax[row];
8152:         nrow1    = ailen[row];
8153:         low1     = 0;
8154:         high1    = nrow1;
8155:         lastcol2 = -1;
8156:         rp2      = bj + bi[row];
8157:         ap2      = ba + bi[row];
8158:         rmax2    = bimax[row];
8159:         nrow2    = bilen[row];
8160:         low2     = 0;
8161:         high2    = nrow2;

8163:         for (j = 0; j < n; j++) {
8164:           if (roworiented) value = v[i * n + j];
8165:           else value = v[i + j * m];
8166:           if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES) && im[i] != in[j]) continue;
8167:           if (in[j] >= cstart && in[j] < cend) {
8168:             col = in[j] - cstart;
8169:             MatSetValues_SeqAIJ_A_Private(row, col, value, addv, im[i], in[j]);
8170:           } else if (in[j] < 0) continue;
8171:           else if (PetscUnlikelyDebug(in[j] >= mat->cmap->N)) {
8172:             SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
8173:           } else {
8174:             if (mat->was_assembled) {
8175:               if (!aij->colmap) PetscCall(MatCreateColmap_MPIAIJ_Private(mat));
8176: #if defined(PETSC_USE_CTABLE)
8177:               PetscCall(PetscHMapIGetWithDefault(aij->colmap, in[j] + 1, 0, &col));
8178:               col--;
8179: #else
8180:               col = aij->colmap[in[j]] - 1;
8181: #endif
8182:               if (col < 0 && !((Mat_SeqAIJ *)aij->A->data)->nonew) {
8183:                 PetscCall(MatDisAssemble_MPIAIJ(mat, PETSC_FALSE));
8184:                 col = in[j];
8185:                 /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
8186:                 B        = aij->B;
8187:                 b        = (Mat_SeqAIJ *)B->data;
8188:                 bimax    = b->imax;
8189:                 bi       = b->i;
8190:                 bilen    = b->ilen;
8191:                 bj       = b->j;
8192:                 rp2      = bj + bi[row];
8193:                 ap2      = ba + bi[row];
8194:                 rmax2    = bimax[row];
8195:                 nrow2    = bilen[row];
8196:                 low2     = 0;
8197:                 high2    = nrow2;
8198:                 bm       = aij->B->rmap->n;
8199:                 ba       = b->a;
8200:                 inserted = PETSC_FALSE;
8201:               }
8202:             } else col = in[j];
8203:             MatSetValues_SeqAIJ_B_Private(row, col, value, addv, im[i], in[j]);
8204:           }
8205:         }
8206:       } else if (!aij->donotstash) {
8207:         if (roworiented) {
8208:           PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8209:         } else {
8210:           PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
8211:         }
8212:       }
8213:     }
8214:     PetscCall(MatSeqAIJRestoreArray(A, &aa));
8215:     PetscCall(MatSeqAIJRestoreArray(B, &ba));
8216:   }
8217:   PetscFunctionReturnVoid();
8218: }

8220: /* Undefining these here since they were redefined from their original definition above! No
8221:  * other PETSc functions should be defined past this point, as it is impossible to recover the
8222:  * original definitions */
8223: #undef PetscCall
8224: #undef SETERRQ