Actual source code: aij.c

  1: /*
  2:     Defines the basic matrix operations for the AIJ (compressed row)
  3:   matrix storage format.
  4: */

  6: #include <../src/mat/impls/aij/seq/aij.h>
  7: #include <petscblaslapack.h>
  8: #include <petscbt.h>
  9: #include <petsc/private/kernels/blocktranspose.h>

 11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
 12: #define TYPE AIJ
 13: #define TYPE_BS
 14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
 15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
 16: #undef TYPE
 17: #undef TYPE_BS

 19: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
 20: {
 21:   PetscBool flg;
 22:   char      type[256];

 24:   PetscFunctionBegin;
 25:   PetscObjectOptionsBegin((PetscObject)A);
 26:   PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
 27:   if (flg) PetscCall(MatSeqAIJSetType(A, type));
 28:   PetscOptionsEnd();
 29:   PetscFunctionReturn(PETSC_SUCCESS);
 30: }

 32: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
 33: {
 34:   PetscInt    i, m, n;
 35:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

 37:   PetscFunctionBegin;
 38:   PetscCall(MatGetSize(A, &m, &n));
 39:   PetscCall(PetscArrayzero(reductions, n));
 40:   if (type == NORM_2) {
 41:     for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
 42:   } else if (type == NORM_1) {
 43:     for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
 44:   } else if (type == NORM_INFINITY) {
 45:     for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
 46:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
 47:     for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
 48:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
 49:     for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
 50:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");

 52:   if (type == NORM_2) {
 53:     for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
 54:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
 55:     for (i = 0; i < n; i++) reductions[i] /= m;
 56:   }
 57:   PetscFunctionReturn(PETSC_SUCCESS);
 58: }

 60: static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
 61: {
 62:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
 63:   PetscInt        i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
 64:   const PetscInt *jj = a->j, *ii = a->i;
 65:   PetscInt       *rows;

 67:   PetscFunctionBegin;
 68:   for (i = 0; i < m; i++) {
 69:     if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
 70:   }
 71:   PetscCall(PetscMalloc1(cnt, &rows));
 72:   cnt = 0;
 73:   for (i = 0; i < m; i++) {
 74:     if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
 75:       rows[cnt] = i;
 76:       cnt++;
 77:     }
 78:   }
 79:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
 80:   PetscFunctionReturn(PETSC_SUCCESS);
 81: }

 83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
 84: {
 85:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
 86:   const MatScalar *aa;
 87:   PetscInt         i, m = A->rmap->n, cnt = 0;
 88:   const PetscInt  *ii = a->i, *jj = a->j, *diag;
 89:   PetscInt        *rows;

 91:   PetscFunctionBegin;
 92:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
 93:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
 94:   diag = a->diag;
 95:   for (i = 0; i < m; i++) {
 96:     if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
 97:   }
 98:   PetscCall(PetscMalloc1(cnt, &rows));
 99:   cnt = 0;
100:   for (i = 0; i < m; i++) {
101:     if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
102:   }
103:   *nrows = cnt;
104:   *zrows = rows;
105:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
106:   PetscFunctionReturn(PETSC_SUCCESS);
107: }

109: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
110: {
111:   PetscInt nrows, *rows;

113:   PetscFunctionBegin;
114:   *zrows = NULL;
115:   PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
116:   PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
117:   PetscFunctionReturn(PETSC_SUCCESS);
118: }

120: static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
121: {
122:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
123:   const MatScalar *aa;
124:   PetscInt         m = A->rmap->n, cnt = 0;
125:   const PetscInt  *ii;
126:   PetscInt         n, i, j, *rows;

128:   PetscFunctionBegin;
129:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
130:   *keptrows = NULL;
131:   ii        = a->i;
132:   for (i = 0; i < m; i++) {
133:     n = ii[i + 1] - ii[i];
134:     if (!n) {
135:       cnt++;
136:       goto ok1;
137:     }
138:     for (j = ii[i]; j < ii[i + 1]; j++) {
139:       if (aa[j] != 0.0) goto ok1;
140:     }
141:     cnt++;
142:   ok1:;
143:   }
144:   if (!cnt) {
145:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
146:     PetscFunctionReturn(PETSC_SUCCESS);
147:   }
148:   PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
149:   cnt = 0;
150:   for (i = 0; i < m; i++) {
151:     n = ii[i + 1] - ii[i];
152:     if (!n) continue;
153:     for (j = ii[i]; j < ii[i + 1]; j++) {
154:       if (aa[j] != 0.0) {
155:         rows[cnt++] = i;
156:         break;
157:       }
158:     }
159:   }
160:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
161:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
162:   PetscFunctionReturn(PETSC_SUCCESS);
163: }

165: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
166: {
167:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ *)Y->data;
168:   PetscInt           i, m = Y->rmap->n;
169:   const PetscInt    *diag;
170:   MatScalar         *aa;
171:   const PetscScalar *v;
172:   PetscBool          missing;

174:   PetscFunctionBegin;
175:   if (Y->assembled) {
176:     PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL));
177:     if (!missing) {
178:       diag = aij->diag;
179:       PetscCall(VecGetArrayRead(D, &v));
180:       PetscCall(MatSeqAIJGetArray(Y, &aa));
181:       if (is == INSERT_VALUES) {
182:         for (i = 0; i < m; i++) aa[diag[i]] = v[i];
183:       } else {
184:         for (i = 0; i < m; i++) aa[diag[i]] += v[i];
185:       }
186:       PetscCall(MatSeqAIJRestoreArray(Y, &aa));
187:       PetscCall(VecRestoreArrayRead(D, &v));
188:       PetscFunctionReturn(PETSC_SUCCESS);
189:     }
190:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
191:   }
192:   PetscCall(MatDiagonalSet_Default(Y, D, is));
193:   PetscFunctionReturn(PETSC_SUCCESS);
194: }

196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
197: {
198:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199:   PetscInt    i, ishift;

201:   PetscFunctionBegin;
202:   if (m) *m = A->rmap->n;
203:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
204:   ishift = 0;
205:   if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
206:     PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
207:   } else if (oshift == 1) {
208:     PetscInt *tia;
209:     PetscInt  nz = a->i[A->rmap->n];
210:     /* malloc space and  add 1 to i and j indices */
211:     PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
212:     for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
213:     *ia = tia;
214:     if (ja) {
215:       PetscInt *tja;
216:       PetscCall(PetscMalloc1(nz + 1, &tja));
217:       for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
218:       *ja = tja;
219:     }
220:   } else {
221:     *ia = a->i;
222:     if (ja) *ja = a->j;
223:   }
224:   PetscFunctionReturn(PETSC_SUCCESS);
225: }

227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
228: {
229:   PetscFunctionBegin;
230:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
231:   if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
232:     PetscCall(PetscFree(*ia));
233:     if (ja) PetscCall(PetscFree(*ja));
234:   }
235:   PetscFunctionReturn(PETSC_SUCCESS);
236: }

238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
239: {
240:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
241:   PetscInt    i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
242:   PetscInt    nz = a->i[m], row, *jj, mr, col;

244:   PetscFunctionBegin;
245:   *nn = n;
246:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
247:   if (symmetric) {
248:     PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja));
249:   } else {
250:     PetscCall(PetscCalloc1(n, &collengths));
251:     PetscCall(PetscMalloc1(n + 1, &cia));
252:     PetscCall(PetscMalloc1(nz, &cja));
253:     jj = a->j;
254:     for (i = 0; i < nz; i++) collengths[jj[i]]++;
255:     cia[0] = oshift;
256:     for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
257:     PetscCall(PetscArrayzero(collengths, n));
258:     jj = a->j;
259:     for (row = 0; row < m; row++) {
260:       mr = a->i[row + 1] - a->i[row];
261:       for (i = 0; i < mr; i++) {
262:         col = *jj++;

264:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
265:       }
266:     }
267:     PetscCall(PetscFree(collengths));
268:     *ia = cia;
269:     *ja = cja;
270:   }
271:   PetscFunctionReturn(PETSC_SUCCESS);
272: }

274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
275: {
276:   PetscFunctionBegin;
277:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);

279:   PetscCall(PetscFree(*ia));
280:   PetscCall(PetscFree(*ja));
281:   PetscFunctionReturn(PETSC_SUCCESS);
282: }

284: /*
285:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
286:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
287:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
288: */
289: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
290: {
291:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
292:   PetscInt        i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
293:   PetscInt        nz = a->i[m], row, mr, col, tmp;
294:   PetscInt       *cspidx;
295:   const PetscInt *jj;

297:   PetscFunctionBegin;
298:   *nn = n;
299:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);

301:   PetscCall(PetscCalloc1(n, &collengths));
302:   PetscCall(PetscMalloc1(n + 1, &cia));
303:   PetscCall(PetscMalloc1(nz, &cja));
304:   PetscCall(PetscMalloc1(nz, &cspidx));
305:   jj = a->j;
306:   for (i = 0; i < nz; i++) collengths[jj[i]]++;
307:   cia[0] = oshift;
308:   for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
309:   PetscCall(PetscArrayzero(collengths, n));
310:   jj = a->j;
311:   for (row = 0; row < m; row++) {
312:     mr = a->i[row + 1] - a->i[row];
313:     for (i = 0; i < mr; i++) {
314:       col         = *jj++;
315:       tmp         = cia[col] + collengths[col]++ - oshift;
316:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
317:       cja[tmp]    = row + oshift;
318:     }
319:   }
320:   PetscCall(PetscFree(collengths));
321:   *ia    = cia;
322:   *ja    = cja;
323:   *spidx = cspidx;
324:   PetscFunctionReturn(PETSC_SUCCESS);
325: }

327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
328: {
329:   PetscFunctionBegin;
330:   PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
331:   PetscCall(PetscFree(*spidx));
332:   PetscFunctionReturn(PETSC_SUCCESS);
333: }

335: static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
336: {
337:   Mat_SeqAIJ  *a  = (Mat_SeqAIJ *)A->data;
338:   PetscInt    *ai = a->i;
339:   PetscScalar *aa;

341:   PetscFunctionBegin;
342:   PetscCall(MatSeqAIJGetArray(A, &aa));
343:   PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
344:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
345:   PetscFunctionReturn(PETSC_SUCCESS);
346: }

348: /*
349:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

351:       -   a single row of values is set with each call
352:       -   no row or column indices are negative or (in error) larger than the number of rows or columns
353:       -   the values are always added to the matrix, not set
354:       -   no new locations are introduced in the nonzero structure of the matrix

356:      This does NOT assume the global column indices are sorted

358: */

360: #include <petsc/private/isimpl.h>
361: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
362: {
363:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
364:   PetscInt        low, high, t, row, nrow, i, col, l;
365:   const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j;
366:   PetscInt        lastcol = -1;
367:   MatScalar      *ap, value, *aa;
368:   const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices;

370:   PetscFunctionBegin;
371:   PetscCall(MatSeqAIJGetArray(A, &aa));
372:   row  = ridx[im[0]];
373:   rp   = aj + ai[row];
374:   ap   = aa + ai[row];
375:   nrow = ailen[row];
376:   low  = 0;
377:   high = nrow;
378:   for (l = 0; l < n; l++) { /* loop over added columns */
379:     col   = cidx[in[l]];
380:     value = v[l];

382:     if (col <= lastcol) low = 0;
383:     else high = nrow;
384:     lastcol = col;
385:     while (high - low > 5) {
386:       t = (low + high) / 2;
387:       if (rp[t] > col) high = t;
388:       else low = t;
389:     }
390:     for (i = low; i < high; i++) {
391:       if (rp[i] == col) {
392:         ap[i] += value;
393:         low = i + 1;
394:         break;
395:       }
396:     }
397:   }
398:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
399:   return PETSC_SUCCESS;
400: }

402: PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
403: {
404:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
405:   PetscInt   *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
406:   PetscInt   *imax = a->imax, *ai = a->i, *ailen = a->ilen;
407:   PetscInt   *aj = a->j, nonew = a->nonew, lastcol = -1;
408:   MatScalar  *ap = NULL, value = 0.0, *aa;
409:   PetscBool   ignorezeroentries = a->ignorezeroentries;
410:   PetscBool   roworiented       = a->roworiented;

412:   PetscFunctionBegin;
413:   PetscCall(MatSeqAIJGetArray(A, &aa));
414:   for (k = 0; k < m; k++) { /* loop over added rows */
415:     row = im[k];
416:     if (row < 0) continue;
417:     PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
418:     rp = PetscSafePointerPlusOffset(aj, ai[row]);
419:     if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]);
420:     rmax = imax[row];
421:     nrow = ailen[row];
422:     low  = 0;
423:     high = nrow;
424:     for (l = 0; l < n; l++) { /* loop over added columns */
425:       if (in[l] < 0) continue;
426:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
427:       col = in[l];
428:       if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
429:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

431:       if (col <= lastcol) low = 0;
432:       else high = nrow;
433:       lastcol = col;
434:       while (high - low > 5) {
435:         t = (low + high) / 2;
436:         if (rp[t] > col) high = t;
437:         else low = t;
438:       }
439:       for (i = low; i < high; i++) {
440:         if (rp[i] > col) break;
441:         if (rp[i] == col) {
442:           if (!A->structure_only) {
443:             if (is == ADD_VALUES) {
444:               ap[i] += value;
445:               (void)PetscLogFlops(1.0);
446:             } else ap[i] = value;
447:           }
448:           low = i + 1;
449:           goto noinsert;
450:         }
451:       }
452:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
453:       if (nonew == 1) goto noinsert;
454:       PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col);
455:       if (A->structure_only) {
456:         MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
457:       } else {
458:         MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
459:       }
460:       N = nrow++ - 1;
461:       a->nz++;
462:       high++;
463:       /* shift up all the later entries in this row */
464:       PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
465:       rp[i] = col;
466:       if (!A->structure_only) {
467:         PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1));
468:         ap[i] = value;
469:       }
470:       low = i + 1;
471:     noinsert:;
472:     }
473:     ailen[row] = nrow;
474:   }
475:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
476:   PetscFunctionReturn(PETSC_SUCCESS);
477: }

479: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
480: {
481:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
482:   PetscInt   *rp, k, row;
483:   PetscInt   *ai = a->i;
484:   PetscInt   *aj = a->j;
485:   MatScalar  *aa, *ap;

487:   PetscFunctionBegin;
488:   PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
489:   PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);

491:   PetscCall(MatSeqAIJGetArray(A, &aa));
492:   for (k = 0; k < m; k++) { /* loop over added rows */
493:     row = im[k];
494:     rp  = aj + ai[row];
495:     ap  = PetscSafePointerPlusOffset(aa, ai[row]);

497:     PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
498:     if (!A->structure_only) {
499:       if (v) {
500:         PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
501:         v += n;
502:       } else {
503:         PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
504:       }
505:     }
506:     a->ilen[row]  = n;
507:     a->imax[row]  = n;
508:     a->i[row + 1] = a->i[row] + n;
509:     a->nz += n;
510:   }
511:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
512:   PetscFunctionReturn(PETSC_SUCCESS);
513: }

515: /*@
516:   MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

518:   Input Parameters:
519: + A       - the `MATSEQAIJ` matrix
520: - nztotal - bound on the number of nonzeros

522:   Level: advanced

524:   Notes:
525:   This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
526:   Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
527:   as always with multiple matrix assemblies.

529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
530: @*/
531: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
532: {
533:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

535:   PetscFunctionBegin;
536:   PetscCall(PetscLayoutSetUp(A->rmap));
537:   PetscCall(PetscLayoutSetUp(A->cmap));
538:   a->maxnz = nztotal;
539:   if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
540:   if (!a->ilen) {
541:     PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
542:   } else {
543:     PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
544:   }

546:   /* allocate the matrix space */
547:   PetscCall(PetscShmgetAllocateArray(A->rmap->n + 1, sizeof(PetscInt), (void **)&a->i));
548:   PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscInt), (void **)&a->j));
549:   a->free_ij = PETSC_TRUE;
550:   if (A->structure_only) {
551:     a->free_a = PETSC_FALSE;
552:   } else {
553:     PetscCall(PetscShmgetAllocateArray(nztotal, sizeof(PetscScalar), (void **)&a->a));
554:     a->free_a = PETSC_TRUE;
555:   }
556:   a->i[0]           = 0;
557:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
558:   A->preallocated   = PETSC_TRUE;
559:   PetscFunctionReturn(PETSC_SUCCESS);
560: }

562: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
563: {
564:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
565:   PetscInt   *rp, k, row;
566:   PetscInt   *ai = a->i, *ailen = a->ilen;
567:   PetscInt   *aj = a->j;
568:   MatScalar  *aa, *ap;

570:   PetscFunctionBegin;
571:   PetscCall(MatSeqAIJGetArray(A, &aa));
572:   for (k = 0; k < m; k++) { /* loop over added rows */
573:     row = im[k];
574:     PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
575:     rp = aj + ai[row];
576:     ap = aa + ai[row];
577:     if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
578:     if (!A->structure_only) {
579:       if (v) {
580:         PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
581:         v += n;
582:       } else {
583:         PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
584:       }
585:     }
586:     ailen[row] = n;
587:     a->nz += n;
588:   }
589:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
590:   PetscFunctionReturn(PETSC_SUCCESS);
591: }

593: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
594: {
595:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
596:   PetscInt        *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
597:   PetscInt        *ai = a->i, *ailen = a->ilen;
598:   const MatScalar *ap, *aa;

600:   PetscFunctionBegin;
601:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
602:   for (k = 0; k < m; k++) { /* loop over rows */
603:     row = im[k];
604:     if (row < 0) {
605:       v += n;
606:       continue;
607:     } /* negative row */
608:     PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
609:     rp   = PetscSafePointerPlusOffset(aj, ai[row]);
610:     ap   = PetscSafePointerPlusOffset(aa, ai[row]);
611:     nrow = ailen[row];
612:     for (l = 0; l < n; l++) { /* loop over columns */
613:       if (in[l] < 0) {
614:         v++;
615:         continue;
616:       } /* negative column */
617:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
618:       col  = in[l];
619:       high = nrow;
620:       low  = 0; /* assume unsorted */
621:       while (high - low > 5) {
622:         t = (low + high) / 2;
623:         if (rp[t] > col) high = t;
624:         else low = t;
625:       }
626:       for (i = low; i < high; i++) {
627:         if (rp[i] > col) break;
628:         if (rp[i] == col) {
629:           *v++ = ap[i];
630:           goto finished;
631:         }
632:       }
633:       *v++ = 0.0;
634:     finished:;
635:     }
636:   }
637:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
638:   PetscFunctionReturn(PETSC_SUCCESS);
639: }

641: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
642: {
643:   Mat_SeqAIJ        *A = (Mat_SeqAIJ *)mat->data;
644:   const PetscScalar *av;
645:   PetscInt           header[4], M, N, m, nz, i;
646:   PetscInt          *rowlens;

648:   PetscFunctionBegin;
649:   PetscCall(PetscViewerSetUp(viewer));

651:   M  = mat->rmap->N;
652:   N  = mat->cmap->N;
653:   m  = mat->rmap->n;
654:   nz = A->nz;

656:   /* write matrix header */
657:   header[0] = MAT_FILE_CLASSID;
658:   header[1] = M;
659:   header[2] = N;
660:   header[3] = nz;
661:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

663:   /* fill in and store row lengths */
664:   PetscCall(PetscMalloc1(m, &rowlens));
665:   for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
666:   if (PetscDefined(USE_DEBUG)) {
667:     PetscInt mnz = 0;

669:     for (i = 0; i < m; i++) mnz += rowlens[i];
670:     PetscCheck(nz == mnz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row lens %" PetscInt_FMT " do not sum to nz %" PetscInt_FMT, mnz, nz);
671:   }
672:   PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673:   PetscCall(PetscFree(rowlens));
674:   /* store column indices */
675:   PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676:   /* store nonzero values */
677:   PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678:   PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679:   PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));

681:   /* write block size option to the viewer's .info file */
682:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683:   PetscFunctionReturn(PETSC_SUCCESS);
684: }

686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689:   PetscInt    i, k, m = A->rmap->N;

691:   PetscFunctionBegin;
692:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693:   for (i = 0; i < m; i++) {
694:     PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695:     for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697:   }
698:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699:   PetscFunctionReturn(PETSC_SUCCESS);
700: }

702: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);

704: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
705: {
706:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
707:   const PetscScalar *av;
708:   PetscInt           i, j, m = A->rmap->n;
709:   const char        *name;
710:   PetscViewerFormat  format;

712:   PetscFunctionBegin;
713:   if (A->structure_only) {
714:     PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
715:     PetscFunctionReturn(PETSC_SUCCESS);
716:   }

718:   PetscCall(PetscViewerGetFormat(viewer, &format));
719:   // By petsc's rule, even PETSC_VIEWER_ASCII_INFO_DETAIL doesn't print matrix entries
720:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);

722:   /* trigger copy to CPU if needed */
723:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
724:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
725:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
726:     PetscInt nofinalvalue = 0;
727:     if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
728:       /* Need a dummy value to ensure the dimension of the matrix. */
729:       nofinalvalue = 1;
730:     }
731:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
732:     PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
733:     PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
734: #if defined(PETSC_USE_COMPLEX)
735:     PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
736: #else
737:     PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
738: #endif
739:     PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));

741:     for (i = 0; i < m; i++) {
742:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
743: #if defined(PETSC_USE_COMPLEX)
744:         PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
745: #else
746:         PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
747: #endif
748:       }
749:     }
750:     if (nofinalvalue) {
751: #if defined(PETSC_USE_COMPLEX)
752:       PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
753: #else
754:       PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT "  %18.16e\n", m, A->cmap->n, 0.0));
755: #endif
756:     }
757:     PetscCall(PetscObjectGetName((PetscObject)A, &name));
758:     PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
759:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
760:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
761:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
762:     for (i = 0; i < m; i++) {
763:       PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
764:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
765: #if defined(PETSC_USE_COMPLEX)
766:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
767:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
768:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
769:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
770:         } else if (PetscRealPart(a->a[j]) != 0.0) {
771:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
772:         }
773: #else
774:         if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
775: #endif
776:       }
777:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
778:     }
779:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
780:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
781:     PetscInt nzd = 0, fshift = 1, *sptr;
782:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
783:     PetscCall(PetscMalloc1(m + 1, &sptr));
784:     for (i = 0; i < m; i++) {
785:       sptr[i] = nzd + 1;
786:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
787:         if (a->j[j] >= i) {
788: #if defined(PETSC_USE_COMPLEX)
789:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
790: #else
791:           if (a->a[j] != 0.0) nzd++;
792: #endif
793:         }
794:       }
795:     }
796:     sptr[m] = nzd + 1;
797:     PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
798:     for (i = 0; i < m + 1; i += 6) {
799:       if (i + 4 < m) {
800:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5]));
801:       } else if (i + 3 < m) {
802:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4]));
803:       } else if (i + 2 < m) {
804:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
805:       } else if (i + 1 < m) {
806:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
807:       } else if (i < m) {
808:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
809:       } else {
810:         PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
811:       }
812:     }
813:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
814:     PetscCall(PetscFree(sptr));
815:     for (i = 0; i < m; i++) {
816:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
817:         if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
818:       }
819:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
820:     }
821:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
822:     for (i = 0; i < m; i++) {
823:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
824:         if (a->j[j] >= i) {
825: #if defined(PETSC_USE_COMPLEX)
826:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
827: #else
828:           if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
829: #endif
830:         }
831:       }
832:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
833:     }
834:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
835:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
836:     PetscInt    cnt = 0, jcnt;
837:     PetscScalar value;
838: #if defined(PETSC_USE_COMPLEX)
839:     PetscBool realonly = PETSC_TRUE;

841:     for (i = 0; i < a->i[m]; i++) {
842:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
843:         realonly = PETSC_FALSE;
844:         break;
845:       }
846:     }
847: #endif

849:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
850:     for (i = 0; i < m; i++) {
851:       jcnt = 0;
852:       for (j = 0; j < A->cmap->n; j++) {
853:         if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
854:           value = a->a[cnt++];
855:           jcnt++;
856:         } else {
857:           value = 0.0;
858:         }
859: #if defined(PETSC_USE_COMPLEX)
860:         if (realonly) {
861:           PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
862:         } else {
863:           PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
864:         }
865: #else
866:         PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
867: #endif
868:       }
869:       PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
870:     }
871:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
872:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
873:     PetscInt fshift = 1;
874:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
875: #if defined(PETSC_USE_COMPLEX)
876:     PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
877: #else
878:     PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
879: #endif
880:     PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
881:     for (i = 0; i < m; i++) {
882:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
883: #if defined(PETSC_USE_COMPLEX)
884:         PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
885: #else
886:         PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
887: #endif
888:       }
889:     }
890:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
891:   } else {
892:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
893:     if (A->factortype) {
894:       for (i = 0; i < m; i++) {
895:         PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
896:         /* L part */
897:         for (j = a->i[i]; j < a->i[i + 1]; j++) {
898: #if defined(PETSC_USE_COMPLEX)
899:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
900:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
901:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
902:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
903:           } else {
904:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
905:           }
906: #else
907:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
908: #endif
909:         }
910:         /* diagonal */
911:         j = a->diag[i];
912: #if defined(PETSC_USE_COMPLEX)
913:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
914:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)PetscImaginaryPart(1 / a->a[j])));
915:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
916:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1 / a->a[j]), (double)(-PetscImaginaryPart(1 / a->a[j]))));
917:         } else {
918:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1 / a->a[j])));
919:         }
920: #else
921:         PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1 / a->a[j])));
922: #endif

924:         /* U part */
925:         for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
926: #if defined(PETSC_USE_COMPLEX)
927:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
928:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
929:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
930:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
931:           } else {
932:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
933:           }
934: #else
935:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
936: #endif
937:         }
938:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
939:       }
940:     } else {
941:       for (i = 0; i < m; i++) {
942:         PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
943:         for (j = a->i[i]; j < a->i[i + 1]; j++) {
944: #if defined(PETSC_USE_COMPLEX)
945:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
946:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
947:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
948:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
949:           } else {
950:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
951:           }
952: #else
953:           PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
954: #endif
955:         }
956:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
957:       }
958:     }
959:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
960:   }
961:   PetscCall(PetscViewerFlush(viewer));
962:   PetscFunctionReturn(PETSC_SUCCESS);
963: }

965: #include <petscdraw.h>
966: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
967: {
968:   Mat                A = (Mat)Aa;
969:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
970:   PetscInt           i, j, m = A->rmap->n;
971:   int                color;
972:   PetscReal          xl, yl, xr, yr, x_l, x_r, y_l, y_r;
973:   PetscViewer        viewer;
974:   PetscViewerFormat  format;
975:   const PetscScalar *aa;

977:   PetscFunctionBegin;
978:   PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
979:   PetscCall(PetscViewerGetFormat(viewer, &format));
980:   PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));

982:   /* loop over matrix elements drawing boxes */
983:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
984:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
985:     PetscDrawCollectiveBegin(draw);
986:     /* Blue for negative, Cyan for zero and  Red for positive */
987:     color = PETSC_DRAW_BLUE;
988:     for (i = 0; i < m; i++) {
989:       y_l = m - i - 1.0;
990:       y_r = y_l + 1.0;
991:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
992:         x_l = a->j[j];
993:         x_r = x_l + 1.0;
994:         if (PetscRealPart(aa[j]) >= 0.) continue;
995:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
996:       }
997:     }
998:     color = PETSC_DRAW_CYAN;
999:     for (i = 0; i < m; i++) {
1000:       y_l = m - i - 1.0;
1001:       y_r = y_l + 1.0;
1002:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1003:         x_l = a->j[j];
1004:         x_r = x_l + 1.0;
1005:         if (aa[j] != 0.) continue;
1006:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1007:       }
1008:     }
1009:     color = PETSC_DRAW_RED;
1010:     for (i = 0; i < m; i++) {
1011:       y_l = m - i - 1.0;
1012:       y_r = y_l + 1.0;
1013:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1014:         x_l = a->j[j];
1015:         x_r = x_l + 1.0;
1016:         if (PetscRealPart(aa[j]) <= 0.) continue;
1017:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1018:       }
1019:     }
1020:     PetscDrawCollectiveEnd(draw);
1021:   } else {
1022:     /* use contour shading to indicate magnitude of values */
1023:     /* first determine max of all nonzero values */
1024:     PetscReal minv = 0.0, maxv = 0.0;
1025:     PetscInt  nz = a->nz, count = 0;
1026:     PetscDraw popup;

1028:     for (i = 0; i < nz; i++) {
1029:       if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1030:     }
1031:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
1032:     PetscCall(PetscDrawGetPopup(draw, &popup));
1033:     PetscCall(PetscDrawScalePopup(popup, minv, maxv));

1035:     PetscDrawCollectiveBegin(draw);
1036:     for (i = 0; i < m; i++) {
1037:       y_l = m - i - 1.0;
1038:       y_r = y_l + 1.0;
1039:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1040:         x_l   = a->j[j];
1041:         x_r   = x_l + 1.0;
1042:         color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1043:         PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1044:         count++;
1045:       }
1046:     }
1047:     PetscDrawCollectiveEnd(draw);
1048:   }
1049:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1050:   PetscFunctionReturn(PETSC_SUCCESS);
1051: }

1053: #include <petscdraw.h>
1054: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1055: {
1056:   PetscDraw draw;
1057:   PetscReal xr, yr, xl, yl, h, w;
1058:   PetscBool isnull;

1060:   PetscFunctionBegin;
1061:   PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1062:   PetscCall(PetscDrawIsNull(draw, &isnull));
1063:   if (isnull) PetscFunctionReturn(PETSC_SUCCESS);

1065:   xr = A->cmap->n;
1066:   yr = A->rmap->n;
1067:   h  = yr / 10.0;
1068:   w  = xr / 10.0;
1069:   xr += w;
1070:   yr += h;
1071:   xl = -w;
1072:   yl = -h;
1073:   PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1074:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1075:   PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1076:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1077:   PetscCall(PetscDrawSave(draw));
1078:   PetscFunctionReturn(PETSC_SUCCESS);
1079: }

1081: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1082: {
1083:   PetscBool iascii, isbinary, isdraw;

1085:   PetscFunctionBegin;
1086:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1087:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1088:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1089:   if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1090:   else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1091:   else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1092:   PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1093:   PetscFunctionReturn(PETSC_SUCCESS);
1094: }

1096: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1097: {
1098:   Mat_SeqAIJ *a      = (Mat_SeqAIJ *)A->data;
1099:   PetscInt    fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1100:   PetscInt    m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1101:   MatScalar  *aa    = a->a, *ap;
1102:   PetscReal   ratio = 0.6;

1104:   PetscFunctionBegin;
1105:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1106:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1107:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1108:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1109:     PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1110:     PetscFunctionReturn(PETSC_SUCCESS);
1111:   }

1113:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1114:   for (i = 1; i < m; i++) {
1115:     /* move each row back by the amount of empty slots (fshift) before it*/
1116:     fshift += imax[i - 1] - ailen[i - 1];
1117:     rmax = PetscMax(rmax, ailen[i]);
1118:     if (fshift) {
1119:       ip = aj + ai[i];
1120:       ap = aa + ai[i];
1121:       N  = ailen[i];
1122:       PetscCall(PetscArraymove(ip - fshift, ip, N));
1123:       if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1124:     }
1125:     ai[i] = ai[i - 1] + ailen[i - 1];
1126:   }
1127:   if (m) {
1128:     fshift += imax[m - 1] - ailen[m - 1];
1129:     ai[m] = ai[m - 1] + ailen[m - 1];
1130:   }
1131:   /* reset ilen and imax for each row */
1132:   a->nonzerorowcnt = 0;
1133:   if (A->structure_only) {
1134:     PetscCall(PetscFree(a->imax));
1135:     PetscCall(PetscFree(a->ilen));
1136:   } else { /* !A->structure_only */
1137:     for (i = 0; i < m; i++) {
1138:       ailen[i] = imax[i] = ai[i + 1] - ai[i];
1139:       a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1140:     }
1141:   }
1142:   a->nz = ai[m];
1143:   PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);
1144:   PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1145:   a->diagonaldense = PETSC_TRUE;
1146:   n                = PetscMin(A->rmap->n, A->cmap->n);
1147:   for (i = 0; i < n; i++) {
1148:     if (a->diag[i] >= ai[i + 1]) {
1149:       a->diagonaldense = PETSC_FALSE;
1150:       break;
1151:     }
1152:   }
1153:   PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz));
1154:   PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1155:   PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));

1157:   A->info.mallocs += a->reallocs;
1158:   a->reallocs         = 0;
1159:   A->info.nz_unneeded = (PetscReal)fshift;
1160:   a->rmax             = rmax;

1162:   if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1163:   PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1164:   PetscFunctionReturn(PETSC_SUCCESS);
1165: }

1167: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1168: {
1169:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1170:   PetscInt    i, nz = a->nz;
1171:   MatScalar  *aa;

1173:   PetscFunctionBegin;
1174:   PetscCall(MatSeqAIJGetArray(A, &aa));
1175:   for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1176:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1177:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1178:   PetscFunctionReturn(PETSC_SUCCESS);
1179: }

1181: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1182: {
1183:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1184:   PetscInt    i, nz = a->nz;
1185:   MatScalar  *aa;

1187:   PetscFunctionBegin;
1188:   PetscCall(MatSeqAIJGetArray(A, &aa));
1189:   for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1190:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
1191:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1192:   PetscFunctionReturn(PETSC_SUCCESS);
1193: }

1195: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1196: {
1197:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1198:   MatScalar  *aa;

1200:   PetscFunctionBegin;
1201:   PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1202:   PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1203:   PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1204:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
1205:   PetscFunctionReturn(PETSC_SUCCESS);
1206: }

1208: static PetscErrorCode MatReset_SeqAIJ(Mat A)
1209: {
1210:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1212:   PetscFunctionBegin;
1213:   if (A->hash_active) {
1214:     A->ops[0] = a->cops;
1215:     PetscCall(PetscHMapIJVDestroy(&a->ht));
1216:     PetscCall(PetscFree(a->dnz));
1217:     A->hash_active = PETSC_FALSE;
1218:   }

1220:   PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1221:   PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1222:   PetscCall(ISDestroy(&a->row));
1223:   PetscCall(ISDestroy(&a->col));
1224:   PetscCall(PetscFree(a->diag));
1225:   PetscCall(PetscFree(a->ibdiag));
1226:   PetscCall(PetscFree(a->imax));
1227:   PetscCall(PetscFree(a->ilen));
1228:   PetscCall(PetscFree(a->ipre));
1229:   PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1230:   PetscCall(PetscFree(a->solve_work));
1231:   PetscCall(ISDestroy(&a->icol));
1232:   PetscCall(PetscFree(a->saved_values));
1233:   PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1234:   PetscCall(MatDestroy_SeqAIJ_Inode(A));
1235:   PetscFunctionReturn(PETSC_SUCCESS);
1236: }

1238: static PetscErrorCode MatResetHash_SeqAIJ(Mat A)
1239: {
1240:   PetscFunctionBegin;
1241:   PetscCall(MatReset_SeqAIJ(A));
1242:   PetscCall(MatCreate_SeqAIJ_Inode(A));
1243:   PetscCall(MatSetUp_Seq_Hash(A));
1244:   A->nonzerostate++;
1245:   PetscFunctionReturn(PETSC_SUCCESS);
1246: }

1248: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1249: {
1250:   PetscFunctionBegin;
1251:   PetscCall(MatReset_SeqAIJ(A));
1252:   PetscCall(PetscFree(A->data));

1254:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1255:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1256:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
1257:      users reusing the matrix object with different data to incur in obscure segmentation faults
1258:      due to different matrix sizes */
1259:   PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));

1261:   PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1262:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1263:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1264:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1265:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1266:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1267:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1268:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1269:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1270:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1271: #if defined(PETSC_HAVE_MKL_SPARSE)
1272:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1273: #endif
1274: #if defined(PETSC_HAVE_CUDA)
1275:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1276:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1277:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1278: #endif
1279: #if defined(PETSC_HAVE_HIP)
1280:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1281:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1282:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1283: #endif
1284: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1285:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1286: #endif
1287:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1288: #if defined(PETSC_HAVE_ELEMENTAL)
1289:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1290: #endif
1291: #if defined(PETSC_HAVE_SCALAPACK)
1292:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1293: #endif
1294: #if defined(PETSC_HAVE_HYPRE)
1295:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1296:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1297: #endif
1298:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1299:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1300:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1301:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1302:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1303:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1304:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1305:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetHash_C", NULL));
1306:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1307:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1308:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1309:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1310:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1311:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1312:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1313:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1314:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1315:   /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1316:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1317:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1318:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1319:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1320:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1321:   PetscFunctionReturn(PETSC_SUCCESS);
1322: }

1324: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1325: {
1326:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

1328:   PetscFunctionBegin;
1329:   switch (op) {
1330:   case MAT_ROW_ORIENTED:
1331:     a->roworiented = flg;
1332:     break;
1333:   case MAT_KEEP_NONZERO_PATTERN:
1334:     a->keepnonzeropattern = flg;
1335:     break;
1336:   case MAT_NEW_NONZERO_LOCATIONS:
1337:     a->nonew = (flg ? 0 : 1);
1338:     break;
1339:   case MAT_NEW_NONZERO_LOCATION_ERR:
1340:     a->nonew = (flg ? -1 : 0);
1341:     break;
1342:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1343:     a->nonew = (flg ? -2 : 0);
1344:     break;
1345:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1346:     a->nounused = (flg ? -1 : 0);
1347:     break;
1348:   case MAT_IGNORE_ZERO_ENTRIES:
1349:     a->ignorezeroentries = flg;
1350:     break;
1351:   case MAT_SPD:
1352:   case MAT_SYMMETRIC:
1353:   case MAT_STRUCTURALLY_SYMMETRIC:
1354:   case MAT_HERMITIAN:
1355:   case MAT_SYMMETRY_ETERNAL:
1356:   case MAT_STRUCTURE_ONLY:
1357:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1358:   case MAT_SPD_ETERNAL:
1359:     /* if the diagonal matrix is square it inherits some of the properties above */
1360:     break;
1361:   case MAT_FORCE_DIAGONAL_ENTRIES:
1362:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1363:   case MAT_USE_HASH_TABLE:
1364:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1365:     break;
1366:   case MAT_USE_INODES:
1367:     PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1368:     break;
1369:   case MAT_SUBMAT_SINGLEIS:
1370:     A->submat_singleis = flg;
1371:     break;
1372:   case MAT_SORTED_FULL:
1373:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1374:     else A->ops->setvalues = MatSetValues_SeqAIJ;
1375:     break;
1376:   case MAT_FORM_EXPLICIT_TRANSPOSE:
1377:     A->form_explicit_transpose = flg;
1378:     break;
1379:   default:
1380:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1381:   }
1382:   PetscFunctionReturn(PETSC_SUCCESS);
1383: }

1385: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1386: {
1387:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1388:   PetscInt           i, j, n, *ai = a->i, *aj = a->j;
1389:   PetscScalar       *x;
1390:   const PetscScalar *aa;

1392:   PetscFunctionBegin;
1393:   PetscCall(VecGetLocalSize(v, &n));
1394:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1395:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1396:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1397:     PetscInt *diag = a->diag;
1398:     PetscCall(VecGetArrayWrite(v, &x));
1399:     for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1400:     PetscCall(VecRestoreArrayWrite(v, &x));
1401:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1402:     PetscFunctionReturn(PETSC_SUCCESS);
1403:   }

1405:   PetscCall(VecGetArrayWrite(v, &x));
1406:   for (i = 0; i < n; i++) {
1407:     x[i] = 0.0;
1408:     for (j = ai[i]; j < ai[i + 1]; j++) {
1409:       if (aj[j] == i) {
1410:         x[i] = aa[j];
1411:         break;
1412:       }
1413:     }
1414:   }
1415:   PetscCall(VecRestoreArrayWrite(v, &x));
1416:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1417:   PetscFunctionReturn(PETSC_SUCCESS);
1418: }

1420: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1421: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1422: {
1423:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1424:   const MatScalar   *aa;
1425:   PetscScalar       *y;
1426:   const PetscScalar *x;
1427:   PetscInt           m = A->rmap->n;
1428: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1429:   const MatScalar  *v;
1430:   PetscScalar       alpha;
1431:   PetscInt          n, i, j;
1432:   const PetscInt   *idx, *ii, *ridx = NULL;
1433:   Mat_CompressedRow cprow    = a->compressedrow;
1434:   PetscBool         usecprow = cprow.use;
1435: #endif

1437:   PetscFunctionBegin;
1438:   if (zz != yy) PetscCall(VecCopy(zz, yy));
1439:   PetscCall(VecGetArrayRead(xx, &x));
1440:   PetscCall(VecGetArray(yy, &y));
1441:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));

1443: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1444:   fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1445: #else
1446:   if (usecprow) {
1447:     m    = cprow.nrows;
1448:     ii   = cprow.i;
1449:     ridx = cprow.rindex;
1450:   } else {
1451:     ii = a->i;
1452:   }
1453:   for (i = 0; i < m; i++) {
1454:     idx = a->j + ii[i];
1455:     v   = aa + ii[i];
1456:     n   = ii[i + 1] - ii[i];
1457:     if (usecprow) {
1458:       alpha = x[ridx[i]];
1459:     } else {
1460:       alpha = x[i];
1461:     }
1462:     for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1463:   }
1464: #endif
1465:   PetscCall(PetscLogFlops(2.0 * a->nz));
1466:   PetscCall(VecRestoreArrayRead(xx, &x));
1467:   PetscCall(VecRestoreArray(yy, &y));
1468:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1469:   PetscFunctionReturn(PETSC_SUCCESS);
1470: }

1472: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1473: {
1474:   PetscFunctionBegin;
1475:   PetscCall(VecSet(yy, 0.0));
1476:   PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1477:   PetscFunctionReturn(PETSC_SUCCESS);
1478: }

1480: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1482: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1483: {
1484:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1485:   PetscScalar       *y;
1486:   const PetscScalar *x;
1487:   const MatScalar   *a_a;
1488:   PetscInt           m = A->rmap->n;
1489:   const PetscInt    *ii, *ridx = NULL;
1490:   PetscBool          usecprow = a->compressedrow.use;

1492: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1493:   #pragma disjoint(*x, *y, *aa)
1494: #endif

1496:   PetscFunctionBegin;
1497:   if (a->inode.use && a->inode.checked) {
1498:     PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1499:     PetscFunctionReturn(PETSC_SUCCESS);
1500:   }
1501:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1502:   PetscCall(VecGetArrayRead(xx, &x));
1503:   PetscCall(VecGetArray(yy, &y));
1504:   ii = a->i;
1505:   if (usecprow) { /* use compressed row format */
1506:     PetscCall(PetscArrayzero(y, m));
1507:     m    = a->compressedrow.nrows;
1508:     ii   = a->compressedrow.i;
1509:     ridx = a->compressedrow.rindex;
1510:     PetscPragmaUseOMPKernels(parallel for)
1511:     for (PetscInt i = 0; i < m; i++) {
1512:       PetscInt           n   = ii[i + 1] - ii[i];
1513:       const PetscInt    *aj  = a->j + ii[i];
1514:       const PetscScalar *aa  = a_a + ii[i];
1515:       PetscScalar        sum = 0.0;
1516:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1517:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1518:       y[*ridx++] = sum;
1519:     }
1520:   } else { /* do not use compressed row format */
1521: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1522:     fortranmultaij_(&m, x, ii, a->j, a_a, y);
1523: #else
1524:     PetscPragmaUseOMPKernels(parallel for)
1525:     for (PetscInt i = 0; i < m; i++) {
1526:       PetscInt           n   = ii[i + 1] - ii[i];
1527:       const PetscInt    *aj  = a->j + ii[i];
1528:       const PetscScalar *aa  = a_a + ii[i];
1529:       PetscScalar        sum = 0.0;
1530:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1531:       y[i] = sum;
1532:     }
1533: #endif
1534:   }
1535:   PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1536:   PetscCall(VecRestoreArrayRead(xx, &x));
1537:   PetscCall(VecRestoreArray(yy, &y));
1538:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1539:   PetscFunctionReturn(PETSC_SUCCESS);
1540: }

1542: // HACK!!!!! Used by src/mat/tests/ex170.c
1543: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1544: {
1545:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1546:   PetscScalar       *y;
1547:   const PetscScalar *x;
1548:   const MatScalar   *aa, *a_a;
1549:   PetscInt           m = A->rmap->n;
1550:   const PetscInt    *aj, *ii, *ridx   = NULL;
1551:   PetscInt           n, i, nonzerorow = 0;
1552:   PetscScalar        sum;
1553:   PetscBool          usecprow = a->compressedrow.use;

1555: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1556:   #pragma disjoint(*x, *y, *aa)
1557: #endif

1559:   PetscFunctionBegin;
1560:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1561:   PetscCall(VecGetArrayRead(xx, &x));
1562:   PetscCall(VecGetArray(yy, &y));
1563:   if (usecprow) { /* use compressed row format */
1564:     m    = a->compressedrow.nrows;
1565:     ii   = a->compressedrow.i;
1566:     ridx = a->compressedrow.rindex;
1567:     for (i = 0; i < m; i++) {
1568:       n   = ii[i + 1] - ii[i];
1569:       aj  = a->j + ii[i];
1570:       aa  = a_a + ii[i];
1571:       sum = 0.0;
1572:       nonzerorow += (n > 0);
1573:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1574:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1575:       y[*ridx++] = sum;
1576:     }
1577:   } else { /* do not use compressed row format */
1578:     ii = a->i;
1579:     for (i = 0; i < m; i++) {
1580:       n   = ii[i + 1] - ii[i];
1581:       aj  = a->j + ii[i];
1582:       aa  = a_a + ii[i];
1583:       sum = 0.0;
1584:       nonzerorow += (n > 0);
1585:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1586:       y[i] = sum;
1587:     }
1588:   }
1589:   PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1590:   PetscCall(VecRestoreArrayRead(xx, &x));
1591:   PetscCall(VecRestoreArray(yy, &y));
1592:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1593:   PetscFunctionReturn(PETSC_SUCCESS);
1594: }

1596: // HACK!!!!! Used by src/mat/tests/ex170.c
1597: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1598: {
1599:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1600:   PetscScalar       *y, *z;
1601:   const PetscScalar *x;
1602:   const MatScalar   *aa, *a_a;
1603:   PetscInt           m = A->rmap->n, *aj, *ii;
1604:   PetscInt           n, i, *ridx = NULL;
1605:   PetscScalar        sum;
1606:   PetscBool          usecprow = a->compressedrow.use;

1608:   PetscFunctionBegin;
1609:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1610:   PetscCall(VecGetArrayRead(xx, &x));
1611:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1612:   if (usecprow) { /* use compressed row format */
1613:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1614:     m    = a->compressedrow.nrows;
1615:     ii   = a->compressedrow.i;
1616:     ridx = a->compressedrow.rindex;
1617:     for (i = 0; i < m; i++) {
1618:       n   = ii[i + 1] - ii[i];
1619:       aj  = a->j + ii[i];
1620:       aa  = a_a + ii[i];
1621:       sum = y[*ridx];
1622:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1623:       z[*ridx++] = sum;
1624:     }
1625:   } else { /* do not use compressed row format */
1626:     ii = a->i;
1627:     for (i = 0; i < m; i++) {
1628:       n   = ii[i + 1] - ii[i];
1629:       aj  = a->j + ii[i];
1630:       aa  = a_a + ii[i];
1631:       sum = y[i];
1632:       PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1633:       z[i] = sum;
1634:     }
1635:   }
1636:   PetscCall(PetscLogFlops(2.0 * a->nz));
1637:   PetscCall(VecRestoreArrayRead(xx, &x));
1638:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1639:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1640:   PetscFunctionReturn(PETSC_SUCCESS);
1641: }

1643: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1644: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1645: {
1646:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1647:   PetscScalar       *y, *z;
1648:   const PetscScalar *x;
1649:   const MatScalar   *a_a;
1650:   const PetscInt    *ii, *ridx = NULL;
1651:   PetscInt           m        = A->rmap->n;
1652:   PetscBool          usecprow = a->compressedrow.use;

1654:   PetscFunctionBegin;
1655:   if (a->inode.use && a->inode.checked) {
1656:     PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1657:     PetscFunctionReturn(PETSC_SUCCESS);
1658:   }
1659:   PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1660:   PetscCall(VecGetArrayRead(xx, &x));
1661:   PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1662:   if (usecprow) { /* use compressed row format */
1663:     if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1664:     m    = a->compressedrow.nrows;
1665:     ii   = a->compressedrow.i;
1666:     ridx = a->compressedrow.rindex;
1667:     for (PetscInt i = 0; i < m; i++) {
1668:       PetscInt           n   = ii[i + 1] - ii[i];
1669:       const PetscInt    *aj  = a->j + ii[i];
1670:       const PetscScalar *aa  = a_a + ii[i];
1671:       PetscScalar        sum = y[*ridx];
1672:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1673:       z[*ridx++] = sum;
1674:     }
1675:   } else { /* do not use compressed row format */
1676:     ii = a->i;
1677: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1678:     fortranmultaddaij_(&m, x, ii, a->j, a_a, y, z);
1679: #else
1680:     PetscPragmaUseOMPKernels(parallel for)
1681:     for (PetscInt i = 0; i < m; i++) {
1682:       PetscInt           n   = ii[i + 1] - ii[i];
1683:       const PetscInt    *aj  = a->j + ii[i];
1684:       const PetscScalar *aa  = a_a + ii[i];
1685:       PetscScalar        sum = y[i];
1686:       PetscSparseDensePlusDot(sum, x, aa, aj, n);
1687:       z[i] = sum;
1688:     }
1689: #endif
1690:   }
1691:   PetscCall(PetscLogFlops(2.0 * a->nz));
1692:   PetscCall(VecRestoreArrayRead(xx, &x));
1693:   PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1694:   PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1695:   PetscFunctionReturn(PETSC_SUCCESS);
1696: }

1698: /*
1699:      Adds diagonal pointers to sparse matrix nonzero structure.
1700: */
1701: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1702: {
1703:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1704:   PetscInt    i, j, m = A->rmap->n;
1705:   PetscBool   alreadySet = PETSC_TRUE;

1707:   PetscFunctionBegin;
1708:   if (!a->diag) {
1709:     PetscCall(PetscMalloc1(m, &a->diag));
1710:     alreadySet = PETSC_FALSE;
1711:   }
1712:   for (i = 0; i < A->rmap->n; i++) {
1713:     /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1714:     if (alreadySet) {
1715:       PetscInt pos = a->diag[i];
1716:       if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1717:     }

1719:     a->diag[i] = a->i[i + 1];
1720:     for (j = a->i[i]; j < a->i[i + 1]; j++) {
1721:       if (a->j[j] == i) {
1722:         a->diag[i] = j;
1723:         break;
1724:       }
1725:     }
1726:   }
1727:   PetscFunctionReturn(PETSC_SUCCESS);
1728: }

1730: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1731: {
1732:   Mat_SeqAIJ     *a    = (Mat_SeqAIJ *)A->data;
1733:   const PetscInt *diag = (const PetscInt *)a->diag;
1734:   const PetscInt *ii   = (const PetscInt *)a->i;
1735:   PetscInt        i, *mdiag = NULL;
1736:   PetscInt        cnt = 0; /* how many diagonals are missing */

1738:   PetscFunctionBegin;
1739:   if (!A->preallocated || !a->nz) {
1740:     PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1741:     PetscCall(MatShift_Basic(A, v));
1742:     PetscFunctionReturn(PETSC_SUCCESS);
1743:   }

1745:   if (a->diagonaldense) {
1746:     cnt = 0;
1747:   } else {
1748:     PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1749:     for (i = 0; i < A->rmap->n; i++) {
1750:       if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1751:         cnt++;
1752:         mdiag[i] = 1;
1753:       }
1754:     }
1755:   }
1756:   if (!cnt) {
1757:     PetscCall(MatShift_Basic(A, v));
1758:   } else {
1759:     PetscScalar       *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1760:     PetscInt          *oldj = a->j, *oldi = a->i;
1761:     PetscBool          free_a = a->free_a, free_ij = a->free_ij;
1762:     const PetscScalar *Aa;

1764:     PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1765:     PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));

1767:     a->a = NULL;
1768:     a->j = NULL;
1769:     a->i = NULL;
1770:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1771:     for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1772:     PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));

1774:     /* copy old values into new matrix data structure */
1775:     for (i = 0; i < A->rmap->n; i++) {
1776:       PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1777:       if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1778:     }
1779:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1780:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1781:     if (free_a) PetscCall(PetscShmgetDeallocateArray((void **)&olda));
1782:     if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldj));
1783:     if (free_ij) PetscCall(PetscShmgetDeallocateArray((void **)&oldi));
1784:   }
1785:   PetscCall(PetscFree(mdiag));
1786:   a->diagonaldense = PETSC_TRUE;
1787:   PetscFunctionReturn(PETSC_SUCCESS);
1788: }

1790: /*
1791:      Checks for missing diagonals
1792: */
1793: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1794: {
1795:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1796:   PetscInt   *diag, *ii = a->i, i;

1798:   PetscFunctionBegin;
1799:   *missing = PETSC_FALSE;
1800:   if (A->rmap->n > 0 && !ii) {
1801:     *missing = PETSC_TRUE;
1802:     if (d) *d = 0;
1803:     PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1804:   } else {
1805:     PetscInt n;
1806:     n    = PetscMin(A->rmap->n, A->cmap->n);
1807:     diag = a->diag;
1808:     for (i = 0; i < n; i++) {
1809:       if (diag[i] >= ii[i + 1]) {
1810:         *missing = PETSC_TRUE;
1811:         if (d) *d = i;
1812:         PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1813:         break;
1814:       }
1815:     }
1816:   }
1817:   PetscFunctionReturn(PETSC_SUCCESS);
1818: }

1820: #include <petscblaslapack.h>
1821: #include <petsc/private/kernels/blockinvert.h>

1823: /*
1824:     Note that values is allocated externally by the PC and then passed into this routine
1825: */
1826: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1827: {
1828:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1829:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;
1830:   const PetscReal shift = 0.0;
1831:   PetscInt        ipvt[5];
1832:   PetscCount      flops = 0;
1833:   PetscScalar     work[25], *v_work;

1835:   PetscFunctionBegin;
1836:   allowzeropivot = PetscNot(A->erroriffailure);
1837:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1838:   PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1839:   for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1840:   PetscCall(PetscMalloc1(bsizemax, &indx));
1841:   if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1842:   ncnt = 0;
1843:   for (i = 0; i < nblocks; i++) {
1844:     for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1845:     PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1846:     switch (bsizes[i]) {
1847:     case 1:
1848:       *diag = 1.0 / (*diag);
1849:       break;
1850:     case 2:
1851:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1852:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1853:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1854:       break;
1855:     case 3:
1856:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1857:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1858:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1859:       break;
1860:     case 4:
1861:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1862:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1863:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1864:       break;
1865:     case 5:
1866:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1867:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1868:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1869:       break;
1870:     case 6:
1871:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1872:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1873:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1874:       break;
1875:     case 7:
1876:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1877:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1878:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1879:       break;
1880:     default:
1881:       PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1882:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1883:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1884:     }
1885:     ncnt += bsizes[i];
1886:     diag += bsizes[i] * bsizes[i];
1887:     flops += 2 * PetscPowInt64(bsizes[i], 3) / 3;
1888:   }
1889:   PetscCall(PetscLogFlops(flops));
1890:   if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1891:   PetscCall(PetscFree(indx));
1892:   PetscFunctionReturn(PETSC_SUCCESS);
1893: }

1895: /*
1896:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1897: */
1898: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1899: {
1900:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
1901:   PetscInt         i, *diag, m = A->rmap->n;
1902:   const MatScalar *v;
1903:   PetscScalar     *idiag, *mdiag;

1905:   PetscFunctionBegin;
1906:   if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1907:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
1908:   diag = a->diag;
1909:   if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }

1911:   mdiag = a->mdiag;
1912:   idiag = a->idiag;
1913:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
1914:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1915:     for (i = 0; i < m; i++) {
1916:       mdiag[i] = v[diag[i]];
1917:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1918:         if (PetscRealPart(fshift)) {
1919:           PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1920:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1921:           A->factorerror_zeropivot_value = 0.0;
1922:           A->factorerror_zeropivot_row   = i;
1923:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1924:       }
1925:       idiag[i] = 1.0 / v[diag[i]];
1926:     }
1927:     PetscCall(PetscLogFlops(m));
1928:   } else {
1929:     for (i = 0; i < m; i++) {
1930:       mdiag[i] = v[diag[i]];
1931:       idiag[i] = omega / (fshift + v[diag[i]]);
1932:     }
1933:     PetscCall(PetscLogFlops(2.0 * m));
1934:   }
1935:   a->idiagvalid = PETSC_TRUE;
1936:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1937:   PetscFunctionReturn(PETSC_SUCCESS);
1938: }

1940: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1941: {
1942:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
1943:   PetscScalar       *x, d, sum, *t, scale;
1944:   const MatScalar   *v, *idiag = NULL, *mdiag, *aa;
1945:   const PetscScalar *b, *bs, *xb, *ts;
1946:   PetscInt           n, m = A->rmap->n, i;
1947:   const PetscInt    *idx, *diag;

1949:   PetscFunctionBegin;
1950:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1951:     PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1952:     PetscFunctionReturn(PETSC_SUCCESS);
1953:   }
1954:   its = its * lits;

1956:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1957:   if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1958:   a->fshift = fshift;
1959:   a->omega  = omega;

1961:   diag  = a->diag;
1962:   t     = a->ssor_work;
1963:   idiag = a->idiag;
1964:   mdiag = a->mdiag;

1966:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1967:   PetscCall(VecGetArray(xx, &x));
1968:   PetscCall(VecGetArrayRead(bb, &b));
1969:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1970:   if (flag == SOR_APPLY_UPPER) {
1971:     /* apply (U + D/omega) to the vector */
1972:     bs = b;
1973:     for (i = 0; i < m; i++) {
1974:       d   = fshift + mdiag[i];
1975:       n   = a->i[i + 1] - diag[i] - 1;
1976:       idx = a->j + diag[i] + 1;
1977:       v   = aa + diag[i] + 1;
1978:       sum = b[i] * d / omega;
1979:       PetscSparseDensePlusDot(sum, bs, v, idx, n);
1980:       x[i] = sum;
1981:     }
1982:     PetscCall(VecRestoreArray(xx, &x));
1983:     PetscCall(VecRestoreArrayRead(bb, &b));
1984:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1985:     PetscCall(PetscLogFlops(a->nz));
1986:     PetscFunctionReturn(PETSC_SUCCESS);
1987:   }

1989:   PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1990:   if (flag & SOR_EISENSTAT) {
1991:     /* Let  A = L + U + D; where L is lower triangular,
1992:     U is upper triangular, E = D/omega; This routine applies

1994:             (L + E)^{-1} A (U + E)^{-1}

1996:     to a vector efficiently using Eisenstat's trick.
1997:     */
1998:     scale = (2.0 / omega) - 1.0;

2000:     /*  x = (E + U)^{-1} b */
2001:     for (i = m - 1; i >= 0; i--) {
2002:       n   = a->i[i + 1] - diag[i] - 1;
2003:       idx = a->j + diag[i] + 1;
2004:       v   = aa + diag[i] + 1;
2005:       sum = b[i];
2006:       PetscSparseDenseMinusDot(sum, x, v, idx, n);
2007:       x[i] = sum * idiag[i];
2008:     }

2010:     /*  t = b - (2*E - D)x */
2011:     v = aa;
2012:     for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];

2014:     /*  t = (E + L)^{-1}t */
2015:     ts   = t;
2016:     diag = a->diag;
2017:     for (i = 0; i < m; i++) {
2018:       n   = diag[i] - a->i[i];
2019:       idx = a->j + a->i[i];
2020:       v   = aa + a->i[i];
2021:       sum = t[i];
2022:       PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2023:       t[i] = sum * idiag[i];
2024:       /*  x = x + t */
2025:       x[i] += t[i];
2026:     }

2028:     PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2029:     PetscCall(VecRestoreArray(xx, &x));
2030:     PetscCall(VecRestoreArrayRead(bb, &b));
2031:     PetscFunctionReturn(PETSC_SUCCESS);
2032:   }
2033:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2034:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2035:       for (i = 0; i < m; i++) {
2036:         n   = diag[i] - a->i[i];
2037:         idx = a->j + a->i[i];
2038:         v   = aa + a->i[i];
2039:         sum = b[i];
2040:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2041:         t[i] = sum;
2042:         x[i] = sum * idiag[i];
2043:       }
2044:       xb = t;
2045:       PetscCall(PetscLogFlops(a->nz));
2046:     } else xb = b;
2047:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2048:       for (i = m - 1; i >= 0; i--) {
2049:         n   = a->i[i + 1] - diag[i] - 1;
2050:         idx = a->j + diag[i] + 1;
2051:         v   = aa + diag[i] + 1;
2052:         sum = xb[i];
2053:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2054:         if (xb == b) {
2055:           x[i] = sum * idiag[i];
2056:         } else {
2057:           x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2058:         }
2059:       }
2060:       PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2061:     }
2062:     its--;
2063:   }
2064:   while (its--) {
2065:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2066:       for (i = 0; i < m; i++) {
2067:         /* lower */
2068:         n   = diag[i] - a->i[i];
2069:         idx = a->j + a->i[i];
2070:         v   = aa + a->i[i];
2071:         sum = b[i];
2072:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2073:         t[i] = sum; /* save application of the lower-triangular part */
2074:         /* upper */
2075:         n   = a->i[i + 1] - diag[i] - 1;
2076:         idx = a->j + diag[i] + 1;
2077:         v   = aa + diag[i] + 1;
2078:         PetscSparseDenseMinusDot(sum, x, v, idx, n);
2079:         x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2080:       }
2081:       xb = t;
2082:       PetscCall(PetscLogFlops(2.0 * a->nz));
2083:     } else xb = b;
2084:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2085:       for (i = m - 1; i >= 0; i--) {
2086:         sum = xb[i];
2087:         if (xb == b) {
2088:           /* whole matrix (no checkpointing available) */
2089:           n   = a->i[i + 1] - a->i[i];
2090:           idx = a->j + a->i[i];
2091:           v   = aa + a->i[i];
2092:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2093:           x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2094:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2095:           n   = a->i[i + 1] - diag[i] - 1;
2096:           idx = a->j + diag[i] + 1;
2097:           v   = aa + diag[i] + 1;
2098:           PetscSparseDenseMinusDot(sum, x, v, idx, n);
2099:           x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2100:         }
2101:       }
2102:       if (xb == b) {
2103:         PetscCall(PetscLogFlops(2.0 * a->nz));
2104:       } else {
2105:         PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2106:       }
2107:     }
2108:   }
2109:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2110:   PetscCall(VecRestoreArray(xx, &x));
2111:   PetscCall(VecRestoreArrayRead(bb, &b));
2112:   PetscFunctionReturn(PETSC_SUCCESS);
2113: }

2115: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2116: {
2117:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2119:   PetscFunctionBegin;
2120:   info->block_size   = 1.0;
2121:   info->nz_allocated = a->maxnz;
2122:   info->nz_used      = a->nz;
2123:   info->nz_unneeded  = (a->maxnz - a->nz);
2124:   info->assemblies   = A->num_ass;
2125:   info->mallocs      = A->info.mallocs;
2126:   info->memory       = 0; /* REVIEW ME */
2127:   if (A->factortype) {
2128:     info->fill_ratio_given  = A->info.fill_ratio_given;
2129:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2130:     info->factor_mallocs    = A->info.factor_mallocs;
2131:   } else {
2132:     info->fill_ratio_given  = 0;
2133:     info->fill_ratio_needed = 0;
2134:     info->factor_mallocs    = 0;
2135:   }
2136:   PetscFunctionReturn(PETSC_SUCCESS);
2137: }

2139: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2140: {
2141:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2142:   PetscInt           i, m = A->rmap->n - 1;
2143:   const PetscScalar *xx;
2144:   PetscScalar       *bb, *aa;
2145:   PetscInt           d = 0;

2147:   PetscFunctionBegin;
2148:   if (x && b) {
2149:     PetscCall(VecGetArrayRead(x, &xx));
2150:     PetscCall(VecGetArray(b, &bb));
2151:     for (i = 0; i < N; i++) {
2152:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2153:       if (rows[i] >= A->cmap->n) continue;
2154:       bb[rows[i]] = diag * xx[rows[i]];
2155:     }
2156:     PetscCall(VecRestoreArrayRead(x, &xx));
2157:     PetscCall(VecRestoreArray(b, &bb));
2158:   }

2160:   PetscCall(MatSeqAIJGetArray(A, &aa));
2161:   if (a->keepnonzeropattern) {
2162:     for (i = 0; i < N; i++) {
2163:       PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2164:       PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2165:     }
2166:     if (diag != 0.0) {
2167:       for (i = 0; i < N; i++) {
2168:         d = rows[i];
2169:         if (rows[i] >= A->cmap->n) continue;
2170:         PetscCheck(a->diag[d] < a->i[d + 1], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in the zeroed row %" PetscInt_FMT, d);
2171:       }
2172:       for (i = 0; i < N; i++) {
2173:         if (rows[i] >= A->cmap->n) continue;
2174:         aa[a->diag[rows[i]]] = diag;
2175:       }
2176:     }
2177:   } else {
2178:     if (diag != 0.0) {
2179:       for (i = 0; i < N; i++) {
2180:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2181:         if (a->ilen[rows[i]] > 0) {
2182:           if (rows[i] >= A->cmap->n) {
2183:             a->ilen[rows[i]] = 0;
2184:           } else {
2185:             a->ilen[rows[i]]    = 1;
2186:             aa[a->i[rows[i]]]   = diag;
2187:             a->j[a->i[rows[i]]] = rows[i];
2188:           }
2189:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2190:           PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2191:         }
2192:       }
2193:     } else {
2194:       for (i = 0; i < N; i++) {
2195:         PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2196:         a->ilen[rows[i]] = 0;
2197:       }
2198:     }
2199:     A->nonzerostate++;
2200:   }
2201:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2202:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2203:   PetscFunctionReturn(PETSC_SUCCESS);
2204: }

2206: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2207: {
2208:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2209:   PetscInt           i, j, m = A->rmap->n - 1, d = 0;
2210:   PetscBool          missing, *zeroed, vecs = PETSC_FALSE;
2211:   const PetscScalar *xx;
2212:   PetscScalar       *bb, *aa;

2214:   PetscFunctionBegin;
2215:   if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2216:   PetscCall(MatSeqAIJGetArray(A, &aa));
2217:   if (x && b) {
2218:     PetscCall(VecGetArrayRead(x, &xx));
2219:     PetscCall(VecGetArray(b, &bb));
2220:     vecs = PETSC_TRUE;
2221:   }
2222:   PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2223:   for (i = 0; i < N; i++) {
2224:     PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2225:     PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));

2227:     zeroed[rows[i]] = PETSC_TRUE;
2228:   }
2229:   for (i = 0; i < A->rmap->n; i++) {
2230:     if (!zeroed[i]) {
2231:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
2232:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2233:           if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2234:           aa[j] = 0.0;
2235:         }
2236:       }
2237:     } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2238:   }
2239:   if (x && b) {
2240:     PetscCall(VecRestoreArrayRead(x, &xx));
2241:     PetscCall(VecRestoreArray(b, &bb));
2242:   }
2243:   PetscCall(PetscFree(zeroed));
2244:   if (diag != 0.0) {
2245:     PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2246:     if (missing) {
2247:       for (i = 0; i < N; i++) {
2248:         if (rows[i] >= A->cmap->N) continue;
2249:         PetscCheck(!a->nonew || rows[i] < d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in row %" PetscInt_FMT " (%" PetscInt_FMT ")", d, rows[i]);
2250:         PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2251:       }
2252:     } else {
2253:       for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2254:     }
2255:   }
2256:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
2257:   PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2258:   PetscFunctionReturn(PETSC_SUCCESS);
2259: }

2261: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2262: {
2263:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2264:   const PetscScalar *aa;

2266:   PetscFunctionBegin;
2267:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2268:   *nz = a->i[row + 1] - a->i[row];
2269:   if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2270:   if (idx) {
2271:     if (*nz && a->j) *idx = a->j + a->i[row];
2272:     else *idx = NULL;
2273:   }
2274:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2275:   PetscFunctionReturn(PETSC_SUCCESS);
2276: }

2278: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2279: {
2280:   PetscFunctionBegin;
2281:   PetscFunctionReturn(PETSC_SUCCESS);
2282: }

2284: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2285: {
2286:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
2287:   const MatScalar *v;
2288:   PetscReal        sum = 0.0;
2289:   PetscInt         i, j;

2291:   PetscFunctionBegin;
2292:   PetscCall(MatSeqAIJGetArrayRead(A, &v));
2293:   if (type == NORM_FROBENIUS) {
2294: #if defined(PETSC_USE_REAL___FP16)
2295:     PetscBLASInt one = 1, nz = a->nz;
2296:     PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2297: #else
2298:     for (i = 0; i < a->nz; i++) {
2299:       sum += PetscRealPart(PetscConj(*v) * (*v));
2300:       v++;
2301:     }
2302:     *nrm = PetscSqrtReal(sum);
2303: #endif
2304:     PetscCall(PetscLogFlops(2.0 * a->nz));
2305:   } else if (type == NORM_1) {
2306:     PetscReal *tmp;
2307:     PetscInt  *jj = a->j;
2308:     PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2309:     *nrm = 0.0;
2310:     for (j = 0; j < a->nz; j++) {
2311:       tmp[*jj++] += PetscAbsScalar(*v);
2312:       v++;
2313:     }
2314:     for (j = 0; j < A->cmap->n; j++) {
2315:       if (tmp[j] > *nrm) *nrm = tmp[j];
2316:     }
2317:     PetscCall(PetscFree(tmp));
2318:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2319:   } else if (type == NORM_INFINITY) {
2320:     *nrm = 0.0;
2321:     for (j = 0; j < A->rmap->n; j++) {
2322:       const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2323:       sum                   = 0.0;
2324:       for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2325:         sum += PetscAbsScalar(*v2);
2326:         v2++;
2327:       }
2328:       if (sum > *nrm) *nrm = sum;
2329:     }
2330:     PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2331:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2332:   PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2333:   PetscFunctionReturn(PETSC_SUCCESS);
2334: }

2336: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2337: {
2338:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2339:   PetscInt        *adx, *bdx, *aii, *bii, *aptr, *bptr;
2340:   const MatScalar *va, *vb;
2341:   PetscInt         ma, na, mb, nb, i;

2343:   PetscFunctionBegin;
2344:   PetscCall(MatGetSize(A, &ma, &na));
2345:   PetscCall(MatGetSize(B, &mb, &nb));
2346:   if (ma != nb || na != mb) {
2347:     *f = PETSC_FALSE;
2348:     PetscFunctionReturn(PETSC_SUCCESS);
2349:   }
2350:   PetscCall(MatSeqAIJGetArrayRead(A, &va));
2351:   PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2352:   aii = aij->i;
2353:   bii = bij->i;
2354:   adx = aij->j;
2355:   bdx = bij->j;
2356:   PetscCall(PetscMalloc1(ma, &aptr));
2357:   PetscCall(PetscMalloc1(mb, &bptr));
2358:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
2359:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

2361:   *f = PETSC_TRUE;
2362:   for (i = 0; i < ma; i++) {
2363:     while (aptr[i] < aii[i + 1]) {
2364:       PetscInt    idc, idr;
2365:       PetscScalar vc, vr;
2366:       /* column/row index/value */
2367:       idc = adx[aptr[i]];
2368:       idr = bdx[bptr[idc]];
2369:       vc  = va[aptr[i]];
2370:       vr  = vb[bptr[idc]];
2371:       if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2372:         *f = PETSC_FALSE;
2373:         goto done;
2374:       } else {
2375:         aptr[i]++;
2376:         if (B || i != idc) bptr[idc]++;
2377:       }
2378:     }
2379:   }
2380: done:
2381:   PetscCall(PetscFree(aptr));
2382:   PetscCall(PetscFree(bptr));
2383:   PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2384:   PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2385:   PetscFunctionReturn(PETSC_SUCCESS);
2386: }

2388: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2389: {
2390:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2391:   PetscInt   *adx, *bdx, *aii, *bii, *aptr, *bptr;
2392:   MatScalar  *va, *vb;
2393:   PetscInt    ma, na, mb, nb, i;

2395:   PetscFunctionBegin;
2396:   PetscCall(MatGetSize(A, &ma, &na));
2397:   PetscCall(MatGetSize(B, &mb, &nb));
2398:   if (ma != nb || na != mb) {
2399:     *f = PETSC_FALSE;
2400:     PetscFunctionReturn(PETSC_SUCCESS);
2401:   }
2402:   aii = aij->i;
2403:   bii = bij->i;
2404:   adx = aij->j;
2405:   bdx = bij->j;
2406:   va  = aij->a;
2407:   vb  = bij->a;
2408:   PetscCall(PetscMalloc1(ma, &aptr));
2409:   PetscCall(PetscMalloc1(mb, &bptr));
2410:   for (i = 0; i < ma; i++) aptr[i] = aii[i];
2411:   for (i = 0; i < mb; i++) bptr[i] = bii[i];

2413:   *f = PETSC_TRUE;
2414:   for (i = 0; i < ma; i++) {
2415:     while (aptr[i] < aii[i + 1]) {
2416:       PetscInt    idc, idr;
2417:       PetscScalar vc, vr;
2418:       /* column/row index/value */
2419:       idc = adx[aptr[i]];
2420:       idr = bdx[bptr[idc]];
2421:       vc  = va[aptr[i]];
2422:       vr  = vb[bptr[idc]];
2423:       if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2424:         *f = PETSC_FALSE;
2425:         goto done;
2426:       } else {
2427:         aptr[i]++;
2428:         if (B || i != idc) bptr[idc]++;
2429:       }
2430:     }
2431:   }
2432: done:
2433:   PetscCall(PetscFree(aptr));
2434:   PetscCall(PetscFree(bptr));
2435:   PetscFunctionReturn(PETSC_SUCCESS);
2436: }

2438: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2439: {
2440:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2441:   const PetscScalar *l, *r;
2442:   PetscScalar        x;
2443:   MatScalar         *v;
2444:   PetscInt           i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2445:   const PetscInt    *jj;

2447:   PetscFunctionBegin;
2448:   if (ll) {
2449:     /* The local size is used so that VecMPI can be passed to this routine
2450:        by MatDiagonalScale_MPIAIJ */
2451:     PetscCall(VecGetLocalSize(ll, &m));
2452:     PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2453:     PetscCall(VecGetArrayRead(ll, &l));
2454:     PetscCall(MatSeqAIJGetArray(A, &v));
2455:     for (i = 0; i < m; i++) {
2456:       x = l[i];
2457:       M = a->i[i + 1] - a->i[i];
2458:       for (j = 0; j < M; j++) (*v++) *= x;
2459:     }
2460:     PetscCall(VecRestoreArrayRead(ll, &l));
2461:     PetscCall(PetscLogFlops(nz));
2462:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2463:   }
2464:   if (rr) {
2465:     PetscCall(VecGetLocalSize(rr, &n));
2466:     PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2467:     PetscCall(VecGetArrayRead(rr, &r));
2468:     PetscCall(MatSeqAIJGetArray(A, &v));
2469:     jj = a->j;
2470:     for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2471:     PetscCall(MatSeqAIJRestoreArray(A, &v));
2472:     PetscCall(VecRestoreArrayRead(rr, &r));
2473:     PetscCall(PetscLogFlops(nz));
2474:   }
2475:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
2476:   PetscFunctionReturn(PETSC_SUCCESS);
2477: }

2479: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2480: {
2481:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *c;
2482:   PetscInt          *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2483:   PetscInt           row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2484:   const PetscInt    *irow, *icol;
2485:   const PetscScalar *aa;
2486:   PetscInt           nrows, ncols;
2487:   PetscInt          *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2488:   MatScalar         *a_new, *mat_a, *c_a;
2489:   Mat                C;
2490:   PetscBool          stride;

2492:   PetscFunctionBegin;
2493:   PetscCall(ISGetIndices(isrow, &irow));
2494:   PetscCall(ISGetLocalSize(isrow, &nrows));
2495:   PetscCall(ISGetLocalSize(iscol, &ncols));

2497:   PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2498:   if (stride) {
2499:     PetscCall(ISStrideGetInfo(iscol, &first, &step));
2500:   } else {
2501:     first = 0;
2502:     step  = 0;
2503:   }
2504:   if (stride && step == 1) {
2505:     /* special case of contiguous rows */
2506:     PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2507:     /* loop over new rows determining lens and starting points */
2508:     for (i = 0; i < nrows; i++) {
2509:       kstart    = ai[irow[i]];
2510:       kend      = kstart + ailen[irow[i]];
2511:       starts[i] = kstart;
2512:       for (k = kstart; k < kend; k++) {
2513:         if (aj[k] >= first) {
2514:           starts[i] = k;
2515:           break;
2516:         }
2517:       }
2518:       sum = 0;
2519:       while (k < kend) {
2520:         if (aj[k++] >= first + ncols) break;
2521:         sum++;
2522:       }
2523:       lens[i] = sum;
2524:     }
2525:     /* create submatrix */
2526:     if (scall == MAT_REUSE_MATRIX) {
2527:       PetscInt n_cols, n_rows;
2528:       PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2529:       PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2530:       PetscCall(MatZeroEntries(*B));
2531:       C = *B;
2532:     } else {
2533:       PetscInt rbs, cbs;
2534:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2535:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2536:       PetscCall(ISGetBlockSize(isrow, &rbs));
2537:       PetscCall(ISGetBlockSize(iscol, &cbs));
2538:       PetscCall(MatSetBlockSizes(C, rbs, cbs));
2539:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2540:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2541:     }
2542:     c = (Mat_SeqAIJ *)C->data;

2544:     /* loop over rows inserting into submatrix */
2545:     PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2546:     j_new = c->j;
2547:     i_new = c->i;
2548:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2549:     for (i = 0; i < nrows; i++) {
2550:       ii    = starts[i];
2551:       lensi = lens[i];
2552:       if (lensi) {
2553:         for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2554:         PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2555:         a_new += lensi;
2556:       }
2557:       i_new[i + 1] = i_new[i] + lensi;
2558:       c->ilen[i]   = lensi;
2559:     }
2560:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2561:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2562:     PetscCall(PetscFree2(lens, starts));
2563:   } else {
2564:     PetscCall(ISGetIndices(iscol, &icol));
2565:     PetscCall(PetscCalloc1(oldcols, &smap));
2566:     PetscCall(PetscMalloc1(1 + nrows, &lens));
2567:     for (i = 0; i < ncols; i++) {
2568:       PetscCheck(icol[i] < oldcols, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Requesting column beyond largest column icol[%" PetscInt_FMT "] %" PetscInt_FMT " >= A->cmap->n %" PetscInt_FMT, i, icol[i], oldcols);
2569:       smap[icol[i]] = i + 1;
2570:     }

2572:     /* determine lens of each row */
2573:     for (i = 0; i < nrows; i++) {
2574:       kstart  = ai[irow[i]];
2575:       kend    = kstart + a->ilen[irow[i]];
2576:       lens[i] = 0;
2577:       for (k = kstart; k < kend; k++) {
2578:         if (smap[aj[k]]) lens[i]++;
2579:       }
2580:     }
2581:     /* Create and fill new matrix */
2582:     if (scall == MAT_REUSE_MATRIX) {
2583:       PetscBool equal;

2585:       c = (Mat_SeqAIJ *)((*B)->data);
2586:       PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2587:       PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2588:       PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2589:       PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2590:       C = *B;
2591:     } else {
2592:       PetscInt rbs, cbs;
2593:       PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2594:       PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2595:       PetscCall(ISGetBlockSize(isrow, &rbs));
2596:       PetscCall(ISGetBlockSize(iscol, &cbs));
2597:       if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2598:       PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2599:       PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2600:     }
2601:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));

2603:     c = (Mat_SeqAIJ *)C->data;
2604:     PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2605:     for (i = 0; i < nrows; i++) {
2606:       row      = irow[i];
2607:       kstart   = ai[row];
2608:       kend     = kstart + a->ilen[row];
2609:       mat_i    = c->i[i];
2610:       mat_j    = PetscSafePointerPlusOffset(c->j, mat_i);
2611:       mat_a    = PetscSafePointerPlusOffset(c_a, mat_i);
2612:       mat_ilen = c->ilen + i;
2613:       for (k = kstart; k < kend; k++) {
2614:         if ((tcol = smap[a->j[k]])) {
2615:           *mat_j++ = tcol - 1;
2616:           *mat_a++ = aa[k];
2617:           (*mat_ilen)++;
2618:         }
2619:       }
2620:     }
2621:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2622:     /* Free work space */
2623:     PetscCall(ISRestoreIndices(iscol, &icol));
2624:     PetscCall(PetscFree(smap));
2625:     PetscCall(PetscFree(lens));
2626:     /* sort */
2627:     for (i = 0; i < nrows; i++) {
2628:       PetscInt ilen;

2630:       mat_i = c->i[i];
2631:       mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2632:       mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2633:       ilen  = c->ilen[i];
2634:       PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2635:     }
2636:     PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2637:   }
2638: #if defined(PETSC_HAVE_DEVICE)
2639:   PetscCall(MatBindToCPU(C, A->boundtocpu));
2640: #endif
2641:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2642:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));

2644:   PetscCall(ISRestoreIndices(isrow, &irow));
2645:   *B = C;
2646:   PetscFunctionReturn(PETSC_SUCCESS);
2647: }

2649: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2650: {
2651:   Mat B;

2653:   PetscFunctionBegin;
2654:   if (scall == MAT_INITIAL_MATRIX) {
2655:     PetscCall(MatCreate(subComm, &B));
2656:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2657:     PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2658:     PetscCall(MatSetType(B, MATSEQAIJ));
2659:     PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2660:     *subMat = B;
2661:   } else {
2662:     PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2663:   }
2664:   PetscFunctionReturn(PETSC_SUCCESS);
2665: }

2667: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2668: {
2669:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2670:   Mat         outA;
2671:   PetscBool   row_identity, col_identity;

2673:   PetscFunctionBegin;
2674:   PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");

2676:   PetscCall(ISIdentity(row, &row_identity));
2677:   PetscCall(ISIdentity(col, &col_identity));

2679:   outA             = inA;
2680:   outA->factortype = MAT_FACTOR_LU;
2681:   PetscCall(PetscFree(inA->solvertype));
2682:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));

2684:   PetscCall(PetscObjectReference((PetscObject)row));
2685:   PetscCall(ISDestroy(&a->row));

2687:   a->row = row;

2689:   PetscCall(PetscObjectReference((PetscObject)col));
2690:   PetscCall(ISDestroy(&a->col));

2692:   a->col = col;

2694:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2695:   PetscCall(ISDestroy(&a->icol));
2696:   PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));

2698:   if (!a->solve_work) { /* this matrix may have been factored before */
2699:     PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2700:   }

2702:   PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2703:   if (row_identity && col_identity) {
2704:     PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2705:   } else {
2706:     PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2707:   }
2708:   PetscFunctionReturn(PETSC_SUCCESS);
2709: }

2711: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2712: {
2713:   Mat_SeqAIJ  *a = (Mat_SeqAIJ *)inA->data;
2714:   PetscScalar *v;
2715:   PetscBLASInt one = 1, bnz;

2717:   PetscFunctionBegin;
2718:   PetscCall(MatSeqAIJGetArray(inA, &v));
2719:   PetscCall(PetscBLASIntCast(a->nz, &bnz));
2720:   PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2721:   PetscCall(PetscLogFlops(a->nz));
2722:   PetscCall(MatSeqAIJRestoreArray(inA, &v));
2723:   PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2724:   PetscFunctionReturn(PETSC_SUCCESS);
2725: }

2727: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2728: {
2729:   PetscInt i;

2731:   PetscFunctionBegin;
2732:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2733:     PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));

2735:     for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2736:     PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));

2738:     if (submatj->rbuf1) {
2739:       PetscCall(PetscFree(submatj->rbuf1[0]));
2740:       PetscCall(PetscFree(submatj->rbuf1));
2741:     }

2743:     for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2744:     PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2745:     PetscCall(PetscFree(submatj->pa));
2746:   }

2748: #if defined(PETSC_USE_CTABLE)
2749:   PetscCall(PetscHMapIDestroy(&submatj->rmap));
2750:   if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2751:   PetscCall(PetscFree(submatj->rmap_loc));
2752: #else
2753:   PetscCall(PetscFree(submatj->rmap));
2754: #endif

2756:   if (!submatj->allcolumns) {
2757: #if defined(PETSC_USE_CTABLE)
2758:     PetscCall(PetscHMapIDestroy(&submatj->cmap));
2759: #else
2760:     PetscCall(PetscFree(submatj->cmap));
2761: #endif
2762:   }
2763:   PetscCall(PetscFree(submatj->row2proc));

2765:   PetscCall(PetscFree(submatj));
2766:   PetscFunctionReturn(PETSC_SUCCESS);
2767: }

2769: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2770: {
2771:   Mat_SeqAIJ  *c       = (Mat_SeqAIJ *)C->data;
2772:   Mat_SubSppt *submatj = c->submatis1;

2774:   PetscFunctionBegin;
2775:   PetscCall((*submatj->destroy)(C));
2776:   PetscCall(MatDestroySubMatrix_Private(submatj));
2777:   PetscFunctionReturn(PETSC_SUCCESS);
2778: }

2780: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2781: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2782: {
2783:   PetscInt     i;
2784:   Mat          C;
2785:   Mat_SeqAIJ  *c;
2786:   Mat_SubSppt *submatj;

2788:   PetscFunctionBegin;
2789:   for (i = 0; i < n; i++) {
2790:     C       = (*mat)[i];
2791:     c       = (Mat_SeqAIJ *)C->data;
2792:     submatj = c->submatis1;
2793:     if (submatj) {
2794:       if (--((PetscObject)C)->refct <= 0) {
2795:         PetscCall(PetscFree(C->factorprefix));
2796:         PetscCall((*submatj->destroy)(C));
2797:         PetscCall(MatDestroySubMatrix_Private(submatj));
2798:         PetscCall(PetscFree(C->defaultvectype));
2799:         PetscCall(PetscFree(C->defaultrandtype));
2800:         PetscCall(PetscLayoutDestroy(&C->rmap));
2801:         PetscCall(PetscLayoutDestroy(&C->cmap));
2802:         PetscCall(PetscHeaderDestroy(&C));
2803:       }
2804:     } else {
2805:       PetscCall(MatDestroy(&C));
2806:     }
2807:   }

2809:   /* Destroy Dummy submatrices created for reuse */
2810:   PetscCall(MatDestroySubMatrices_Dummy(n, mat));

2812:   PetscCall(PetscFree(*mat));
2813:   PetscFunctionReturn(PETSC_SUCCESS);
2814: }

2816: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2817: {
2818:   PetscInt i;

2820:   PetscFunctionBegin;
2821:   if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));

2823:   for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2824:   PetscFunctionReturn(PETSC_SUCCESS);
2825: }

2827: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2828: {
2829:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2830:   PetscInt        row, i, j, k, l, ll, m, n, *nidx, isz, val;
2831:   const PetscInt *idx;
2832:   PetscInt        start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2833:   PetscBT         table;

2835:   PetscFunctionBegin;
2836:   m  = A->rmap->n / bs;
2837:   ai = a->i;
2838:   aj = a->j;

2840:   PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");

2842:   PetscCall(PetscMalloc1(m + 1, &nidx));
2843:   PetscCall(PetscBTCreate(m, &table));

2845:   for (i = 0; i < is_max; i++) {
2846:     /* Initialize the two local arrays */
2847:     isz = 0;
2848:     PetscCall(PetscBTMemzero(m, table));

2850:     /* Extract the indices, assume there can be duplicate entries */
2851:     PetscCall(ISGetIndices(is[i], &idx));
2852:     PetscCall(ISGetLocalSize(is[i], &n));

2854:     if (bs > 1) {
2855:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2856:       for (j = 0; j < n; ++j) {
2857:         if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2858:       }
2859:       PetscCall(ISRestoreIndices(is[i], &idx));
2860:       PetscCall(ISDestroy(&is[i]));

2862:       k = 0;
2863:       for (j = 0; j < ov; j++) { /* for each overlap */
2864:         n = isz;
2865:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2866:           for (ll = 0; ll < bs; ll++) {
2867:             row   = bs * nidx[k] + ll;
2868:             start = ai[row];
2869:             end   = ai[row + 1];
2870:             for (l = start; l < end; l++) {
2871:               val = aj[l] / bs;
2872:               if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2873:             }
2874:           }
2875:         }
2876:       }
2877:       PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, is + i));
2878:     } else {
2879:       /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2880:       for (j = 0; j < n; ++j) {
2881:         if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2882:       }
2883:       PetscCall(ISRestoreIndices(is[i], &idx));
2884:       PetscCall(ISDestroy(&is[i]));

2886:       k = 0;
2887:       for (j = 0; j < ov; j++) { /* for each overlap */
2888:         n = isz;
2889:         for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2890:           row   = nidx[k];
2891:           start = ai[row];
2892:           end   = ai[row + 1];
2893:           for (l = start; l < end; l++) {
2894:             val = aj[l];
2895:             if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2896:           }
2897:         }
2898:       }
2899:       PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, is + i));
2900:     }
2901:   }
2902:   PetscCall(PetscBTDestroy(&table));
2903:   PetscCall(PetscFree(nidx));
2904:   PetscFunctionReturn(PETSC_SUCCESS);
2905: }

2907: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2908: {
2909:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
2910:   PetscInt        i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2911:   const PetscInt *row, *col;
2912:   PetscInt       *cnew, j, *lens;
2913:   IS              icolp, irowp;
2914:   PetscInt       *cwork = NULL;
2915:   PetscScalar    *vwork = NULL;

2917:   PetscFunctionBegin;
2918:   PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2919:   PetscCall(ISGetIndices(irowp, &row));
2920:   PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2921:   PetscCall(ISGetIndices(icolp, &col));

2923:   /* determine lengths of permuted rows */
2924:   PetscCall(PetscMalloc1(m + 1, &lens));
2925:   for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2926:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2927:   PetscCall(MatSetSizes(*B, m, n, m, n));
2928:   PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2929:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2930:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2931:   PetscCall(PetscFree(lens));

2933:   PetscCall(PetscMalloc1(n, &cnew));
2934:   for (i = 0; i < m; i++) {
2935:     PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2936:     for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2937:     PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2938:     PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2939:   }
2940:   PetscCall(PetscFree(cnew));

2942:   (*B)->assembled = PETSC_FALSE;

2944: #if defined(PETSC_HAVE_DEVICE)
2945:   PetscCall(MatBindToCPU(*B, A->boundtocpu));
2946: #endif
2947:   PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2948:   PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2949:   PetscCall(ISRestoreIndices(irowp, &row));
2950:   PetscCall(ISRestoreIndices(icolp, &col));
2951:   PetscCall(ISDestroy(&irowp));
2952:   PetscCall(ISDestroy(&icolp));
2953:   if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2954:   PetscFunctionReturn(PETSC_SUCCESS);
2955: }

2957: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2958: {
2959:   PetscFunctionBegin;
2960:   /* If the two matrices have the same copy implementation, use fast copy. */
2961:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2962:     Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
2963:     Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
2964:     const PetscScalar *aa;

2966:     PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2967:     PetscCheck(a->i[A->rmap->n] == b->i[B->rmap->n], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzeros in two matrices are different %" PetscInt_FMT " != %" PetscInt_FMT, a->i[A->rmap->n], b->i[B->rmap->n]);
2968:     PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2969:     PetscCall(PetscObjectStateIncrease((PetscObject)B));
2970:     PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2971:   } else {
2972:     PetscCall(MatCopy_Basic(A, B, str));
2973:   }
2974:   PetscFunctionReturn(PETSC_SUCCESS);
2975: }

2977: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2978: {
2979:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

2981:   PetscFunctionBegin;
2982:   *array = a->a;
2983:   PetscFunctionReturn(PETSC_SUCCESS);
2984: }

2986: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2987: {
2988:   PetscFunctionBegin;
2989:   *array = NULL;
2990:   PetscFunctionReturn(PETSC_SUCCESS);
2991: }

2993: /*
2994:    Computes the number of nonzeros per row needed for preallocation when X and Y
2995:    have different nonzero structure.
2996: */
2997: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2998: {
2999:   PetscInt i, j, k, nzx, nzy;

3001:   PetscFunctionBegin;
3002:   /* Set the number of nonzeros in the new matrix */
3003:   for (i = 0; i < m; i++) {
3004:     const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
3005:     nzx    = xi[i + 1] - xi[i];
3006:     nzy    = yi[i + 1] - yi[i];
3007:     nnz[i] = 0;
3008:     for (j = 0, k = 0; j < nzx; j++) {                  /* Point in X */
3009:       for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
3010:       if (k < nzy && yjj[k] == xjj[j]) k++;             /* Skip duplicate */
3011:       nnz[i]++;
3012:     }
3013:     for (; k < nzy; k++) nnz[i]++;
3014:   }
3015:   PetscFunctionReturn(PETSC_SUCCESS);
3016: }

3018: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3019: {
3020:   PetscInt    m = Y->rmap->N;
3021:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3022:   Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;

3024:   PetscFunctionBegin;
3025:   /* Set the number of nonzeros in the new matrix */
3026:   PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3027:   PetscFunctionReturn(PETSC_SUCCESS);
3028: }

3030: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3031: {
3032:   Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;

3034:   PetscFunctionBegin;
3035:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3036:     PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3037:     if (e) {
3038:       PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3039:       if (e) {
3040:         PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3041:         if (e) str = SAME_NONZERO_PATTERN;
3042:       }
3043:     }
3044:     if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3045:   }
3046:   if (str == SAME_NONZERO_PATTERN) {
3047:     const PetscScalar *xa;
3048:     PetscScalar       *ya, alpha = a;
3049:     PetscBLASInt       one = 1, bnz;

3051:     PetscCall(PetscBLASIntCast(x->nz, &bnz));
3052:     PetscCall(MatSeqAIJGetArray(Y, &ya));
3053:     PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3054:     PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3055:     PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3056:     PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3057:     PetscCall(PetscLogFlops(2.0 * bnz));
3058:     PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3059:     PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3060:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3061:     PetscCall(MatAXPY_Basic(Y, a, X, str));
3062:   } else {
3063:     Mat       B;
3064:     PetscInt *nnz;
3065:     PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3066:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3067:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3068:     PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3069:     PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3070:     PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3071:     PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3072:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3073:     PetscCall(MatHeaderMerge(Y, &B));
3074:     PetscCall(MatSeqAIJCheckInode(Y));
3075:     PetscCall(PetscFree(nnz));
3076:   }
3077:   PetscFunctionReturn(PETSC_SUCCESS);
3078: }

3080: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3081: {
3082: #if defined(PETSC_USE_COMPLEX)
3083:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
3084:   PetscInt     i, nz;
3085:   PetscScalar *a;

3087:   PetscFunctionBegin;
3088:   nz = aij->nz;
3089:   PetscCall(MatSeqAIJGetArray(mat, &a));
3090:   for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3091:   PetscCall(MatSeqAIJRestoreArray(mat, &a));
3092: #else
3093:   PetscFunctionBegin;
3094: #endif
3095:   PetscFunctionReturn(PETSC_SUCCESS);
3096: }

3098: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3099: {
3100:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3101:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3102:   PetscReal        atmp;
3103:   PetscScalar     *x;
3104:   const MatScalar *aa, *av;

3106:   PetscFunctionBegin;
3107:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3108:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3109:   aa = av;
3110:   ai = a->i;
3111:   aj = a->j;

3113:   PetscCall(VecSet(v, 0.0));
3114:   PetscCall(VecGetArrayWrite(v, &x));
3115:   PetscCall(VecGetLocalSize(v, &n));
3116:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3117:   for (i = 0; i < m; i++) {
3118:     ncols = ai[1] - ai[0];
3119:     ai++;
3120:     for (j = 0; j < ncols; j++) {
3121:       atmp = PetscAbsScalar(*aa);
3122:       if (PetscAbsScalar(x[i]) < atmp) {
3123:         x[i] = atmp;
3124:         if (idx) idx[i] = *aj;
3125:       }
3126:       aa++;
3127:       aj++;
3128:     }
3129:   }
3130:   PetscCall(VecRestoreArrayWrite(v, &x));
3131:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3132:   PetscFunctionReturn(PETSC_SUCCESS);
3133: }

3135: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3136: {
3137:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3138:   PetscInt         i, j, m = A->rmap->n, *ai, ncols, n;
3139:   PetscScalar     *x;
3140:   const MatScalar *aa, *av;

3142:   PetscFunctionBegin;
3143:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3144:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3145:   aa = av;
3146:   ai = a->i;

3148:   PetscCall(VecSet(v, 0.0));
3149:   PetscCall(VecGetArrayWrite(v, &x));
3150:   PetscCall(VecGetLocalSize(v, &n));
3151:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3152:   for (i = 0; i < m; i++) {
3153:     ncols = ai[1] - ai[0];
3154:     ai++;
3155:     for (j = 0; j < ncols; j++) {
3156:       x[i] += PetscAbsScalar(*aa);
3157:       aa++;
3158:     }
3159:   }
3160:   PetscCall(VecRestoreArrayWrite(v, &x));
3161:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3162:   PetscFunctionReturn(PETSC_SUCCESS);
3163: }

3165: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3166: {
3167:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3168:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3169:   PetscScalar     *x;
3170:   const MatScalar *aa, *av;

3172:   PetscFunctionBegin;
3173:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3174:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3175:   aa = av;
3176:   ai = a->i;
3177:   aj = a->j;

3179:   PetscCall(VecSet(v, 0.0));
3180:   PetscCall(VecGetArrayWrite(v, &x));
3181:   PetscCall(VecGetLocalSize(v, &n));
3182:   PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3183:   for (i = 0; i < m; i++) {
3184:     ncols = ai[1] - ai[0];
3185:     ai++;
3186:     if (ncols == A->cmap->n) { /* row is dense */
3187:       x[i] = *aa;
3188:       if (idx) idx[i] = 0;
3189:     } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3190:       x[i] = 0.0;
3191:       if (idx) {
3192:         for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3193:           if (aj[j] > j) {
3194:             idx[i] = j;
3195:             break;
3196:           }
3197:         }
3198:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3199:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3200:       }
3201:     }
3202:     for (j = 0; j < ncols; j++) {
3203:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3204:         x[i] = *aa;
3205:         if (idx) idx[i] = *aj;
3206:       }
3207:       aa++;
3208:       aj++;
3209:     }
3210:   }
3211:   PetscCall(VecRestoreArrayWrite(v, &x));
3212:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3213:   PetscFunctionReturn(PETSC_SUCCESS);
3214: }

3216: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3217: {
3218:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3219:   PetscInt         i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3220:   PetscScalar     *x;
3221:   const MatScalar *aa, *av;

3223:   PetscFunctionBegin;
3224:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3225:   aa = av;
3226:   ai = a->i;
3227:   aj = a->j;

3229:   PetscCall(VecSet(v, 0.0));
3230:   PetscCall(VecGetArrayWrite(v, &x));
3231:   PetscCall(VecGetLocalSize(v, &n));
3232:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3233:   for (i = 0; i < m; i++) {
3234:     ncols = ai[1] - ai[0];
3235:     ai++;
3236:     if (ncols == A->cmap->n) { /* row is dense */
3237:       x[i] = *aa;
3238:       if (idx) idx[i] = 0;
3239:     } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3240:       x[i] = 0.0;
3241:       if (idx) { /* find first implicit 0.0 in the row */
3242:         for (j = 0; j < ncols; j++) {
3243:           if (aj[j] > j) {
3244:             idx[i] = j;
3245:             break;
3246:           }
3247:         }
3248:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3249:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3250:       }
3251:     }
3252:     for (j = 0; j < ncols; j++) {
3253:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3254:         x[i] = *aa;
3255:         if (idx) idx[i] = *aj;
3256:       }
3257:       aa++;
3258:       aj++;
3259:     }
3260:   }
3261:   PetscCall(VecRestoreArrayWrite(v, &x));
3262:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3263:   PetscFunctionReturn(PETSC_SUCCESS);
3264: }

3266: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3267: {
3268:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data;
3269:   PetscInt         i, j, m = A->rmap->n, ncols, n;
3270:   const PetscInt  *ai, *aj;
3271:   PetscScalar     *x;
3272:   const MatScalar *aa, *av;

3274:   PetscFunctionBegin;
3275:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3276:   PetscCall(MatSeqAIJGetArrayRead(A, &av));
3277:   aa = av;
3278:   ai = a->i;
3279:   aj = a->j;

3281:   PetscCall(VecSet(v, 0.0));
3282:   PetscCall(VecGetArrayWrite(v, &x));
3283:   PetscCall(VecGetLocalSize(v, &n));
3284:   PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3285:   for (i = 0; i < m; i++) {
3286:     ncols = ai[1] - ai[0];
3287:     ai++;
3288:     if (ncols == A->cmap->n) { /* row is dense */
3289:       x[i] = *aa;
3290:       if (idx) idx[i] = 0;
3291:     } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3292:       x[i] = 0.0;
3293:       if (idx) { /* find first implicit 0.0 in the row */
3294:         for (j = 0; j < ncols; j++) {
3295:           if (aj[j] > j) {
3296:             idx[i] = j;
3297:             break;
3298:           }
3299:         }
3300:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
3301:         if (j == ncols && j < A->cmap->n) idx[i] = j;
3302:       }
3303:     }
3304:     for (j = 0; j < ncols; j++) {
3305:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3306:         x[i] = *aa;
3307:         if (idx) idx[i] = *aj;
3308:       }
3309:       aa++;
3310:       aj++;
3311:     }
3312:   }
3313:   PetscCall(VecRestoreArrayWrite(v, &x));
3314:   PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3315:   PetscFunctionReturn(PETSC_SUCCESS);
3316: }

3318: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3319: {
3320:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data;
3321:   PetscInt        i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3322:   MatScalar      *diag, work[25], *v_work;
3323:   const PetscReal shift = 0.0;
3324:   PetscBool       allowzeropivot, zeropivotdetected = PETSC_FALSE;

3326:   PetscFunctionBegin;
3327:   allowzeropivot = PetscNot(A->erroriffailure);
3328:   if (a->ibdiagvalid) {
3329:     if (values) *values = a->ibdiag;
3330:     PetscFunctionReturn(PETSC_SUCCESS);
3331:   }
3332:   PetscCall(MatMarkDiagonal_SeqAIJ(A));
3333:   if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3334:   diag = a->ibdiag;
3335:   if (values) *values = a->ibdiag;
3336:   /* factor and invert each block */
3337:   switch (bs) {
3338:   case 1:
3339:     for (i = 0; i < mbs; i++) {
3340:       PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3341:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3342:         if (allowzeropivot) {
3343:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3344:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3345:           A->factorerror_zeropivot_row   = i;
3346:           PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3347:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
3348:       }
3349:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3350:     }
3351:     break;
3352:   case 2:
3353:     for (i = 0; i < mbs; i++) {
3354:       ij[0] = 2 * i;
3355:       ij[1] = 2 * i + 1;
3356:       PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3357:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3358:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3359:       PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3360:       diag += 4;
3361:     }
3362:     break;
3363:   case 3:
3364:     for (i = 0; i < mbs; i++) {
3365:       ij[0] = 3 * i;
3366:       ij[1] = 3 * i + 1;
3367:       ij[2] = 3 * i + 2;
3368:       PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3369:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3370:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3371:       PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3372:       diag += 9;
3373:     }
3374:     break;
3375:   case 4:
3376:     for (i = 0; i < mbs; i++) {
3377:       ij[0] = 4 * i;
3378:       ij[1] = 4 * i + 1;
3379:       ij[2] = 4 * i + 2;
3380:       ij[3] = 4 * i + 3;
3381:       PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3382:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3383:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3384:       PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3385:       diag += 16;
3386:     }
3387:     break;
3388:   case 5:
3389:     for (i = 0; i < mbs; i++) {
3390:       ij[0] = 5 * i;
3391:       ij[1] = 5 * i + 1;
3392:       ij[2] = 5 * i + 2;
3393:       ij[3] = 5 * i + 3;
3394:       ij[4] = 5 * i + 4;
3395:       PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3396:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3397:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3398:       PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3399:       diag += 25;
3400:     }
3401:     break;
3402:   case 6:
3403:     for (i = 0; i < mbs; i++) {
3404:       ij[0] = 6 * i;
3405:       ij[1] = 6 * i + 1;
3406:       ij[2] = 6 * i + 2;
3407:       ij[3] = 6 * i + 3;
3408:       ij[4] = 6 * i + 4;
3409:       ij[5] = 6 * i + 5;
3410:       PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3411:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3412:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3413:       PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3414:       diag += 36;
3415:     }
3416:     break;
3417:   case 7:
3418:     for (i = 0; i < mbs; i++) {
3419:       ij[0] = 7 * i;
3420:       ij[1] = 7 * i + 1;
3421:       ij[2] = 7 * i + 2;
3422:       ij[3] = 7 * i + 3;
3423:       ij[4] = 7 * i + 4;
3424:       ij[5] = 7 * i + 5;
3425:       ij[6] = 7 * i + 6;
3426:       PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3427:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3428:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3429:       PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3430:       diag += 49;
3431:     }
3432:     break;
3433:   default:
3434:     PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3435:     for (i = 0; i < mbs; i++) {
3436:       for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3437:       PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3438:       PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3439:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3440:       PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3441:       diag += bs2;
3442:     }
3443:     PetscCall(PetscFree3(v_work, v_pivots, IJ));
3444:   }
3445:   a->ibdiagvalid = PETSC_TRUE;
3446:   PetscFunctionReturn(PETSC_SUCCESS);
3447: }

3449: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3450: {
3451:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3452:   PetscScalar a, *aa;
3453:   PetscInt    m, n, i, j, col;

3455:   PetscFunctionBegin;
3456:   if (!x->assembled) {
3457:     PetscCall(MatGetSize(x, &m, &n));
3458:     for (i = 0; i < m; i++) {
3459:       for (j = 0; j < aij->imax[i]; j++) {
3460:         PetscCall(PetscRandomGetValue(rctx, &a));
3461:         col = (PetscInt)(n * PetscRealPart(a));
3462:         PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3463:       }
3464:     }
3465:   } else {
3466:     PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3467:     for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3468:     PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3469:   }
3470:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3471:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3472:   PetscFunctionReturn(PETSC_SUCCESS);
3473: }

3475: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3476: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3477: {
3478:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3479:   PetscScalar a;
3480:   PetscInt    m, n, i, j, col, nskip;

3482:   PetscFunctionBegin;
3483:   nskip = high - low;
3484:   PetscCall(MatGetSize(x, &m, &n));
3485:   n -= nskip; /* shrink number of columns where nonzeros can be set */
3486:   for (i = 0; i < m; i++) {
3487:     for (j = 0; j < aij->imax[i]; j++) {
3488:       PetscCall(PetscRandomGetValue(rctx, &a));
3489:       col = (PetscInt)(n * PetscRealPart(a));
3490:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3491:       PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3492:     }
3493:   }
3494:   PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3495:   PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3496:   PetscFunctionReturn(PETSC_SUCCESS);
3497: }

3499: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3500:                                        MatGetRow_SeqAIJ,
3501:                                        MatRestoreRow_SeqAIJ,
3502:                                        MatMult_SeqAIJ,
3503:                                        /*  4*/ MatMultAdd_SeqAIJ,
3504:                                        MatMultTranspose_SeqAIJ,
3505:                                        MatMultTransposeAdd_SeqAIJ,
3506:                                        NULL,
3507:                                        NULL,
3508:                                        NULL,
3509:                                        /* 10*/ NULL,
3510:                                        MatLUFactor_SeqAIJ,
3511:                                        NULL,
3512:                                        MatSOR_SeqAIJ,
3513:                                        MatTranspose_SeqAIJ,
3514:                                        /*1 5*/ MatGetInfo_SeqAIJ,
3515:                                        MatEqual_SeqAIJ,
3516:                                        MatGetDiagonal_SeqAIJ,
3517:                                        MatDiagonalScale_SeqAIJ,
3518:                                        MatNorm_SeqAIJ,
3519:                                        /* 20*/ NULL,
3520:                                        MatAssemblyEnd_SeqAIJ,
3521:                                        MatSetOption_SeqAIJ,
3522:                                        MatZeroEntries_SeqAIJ,
3523:                                        /* 24*/ MatZeroRows_SeqAIJ,
3524:                                        NULL,
3525:                                        NULL,
3526:                                        NULL,
3527:                                        NULL,
3528:                                        /* 29*/ MatSetUp_Seq_Hash,
3529:                                        NULL,
3530:                                        NULL,
3531:                                        NULL,
3532:                                        NULL,
3533:                                        /* 34*/ MatDuplicate_SeqAIJ,
3534:                                        NULL,
3535:                                        NULL,
3536:                                        MatILUFactor_SeqAIJ,
3537:                                        NULL,
3538:                                        /* 39*/ MatAXPY_SeqAIJ,
3539:                                        MatCreateSubMatrices_SeqAIJ,
3540:                                        MatIncreaseOverlap_SeqAIJ,
3541:                                        MatGetValues_SeqAIJ,
3542:                                        MatCopy_SeqAIJ,
3543:                                        /* 44*/ MatGetRowMax_SeqAIJ,
3544:                                        MatScale_SeqAIJ,
3545:                                        MatShift_SeqAIJ,
3546:                                        MatDiagonalSet_SeqAIJ,
3547:                                        MatZeroRowsColumns_SeqAIJ,
3548:                                        /* 49*/ MatSetRandom_SeqAIJ,
3549:                                        MatGetRowIJ_SeqAIJ,
3550:                                        MatRestoreRowIJ_SeqAIJ,
3551:                                        MatGetColumnIJ_SeqAIJ,
3552:                                        MatRestoreColumnIJ_SeqAIJ,
3553:                                        /* 54*/ MatFDColoringCreate_SeqXAIJ,
3554:                                        NULL,
3555:                                        NULL,
3556:                                        MatPermute_SeqAIJ,
3557:                                        NULL,
3558:                                        /* 59*/ NULL,
3559:                                        MatDestroy_SeqAIJ,
3560:                                        MatView_SeqAIJ,
3561:                                        NULL,
3562:                                        NULL,
3563:                                        /* 64*/ NULL,
3564:                                        MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3565:                                        NULL,
3566:                                        NULL,
3567:                                        NULL,
3568:                                        /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3569:                                        MatGetRowMinAbs_SeqAIJ,
3570:                                        NULL,
3571:                                        NULL,
3572:                                        NULL,
3573:                                        /* 74*/ NULL,
3574:                                        MatFDColoringApply_AIJ,
3575:                                        NULL,
3576:                                        NULL,
3577:                                        NULL,
3578:                                        /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3579:                                        NULL,
3580:                                        NULL,
3581:                                        NULL,
3582:                                        MatLoad_SeqAIJ,
3583:                                        /* 84*/ NULL,
3584:                                        NULL,
3585:                                        NULL,
3586:                                        NULL,
3587:                                        NULL,
3588:                                        /* 89*/ NULL,
3589:                                        NULL,
3590:                                        MatMatMultNumeric_SeqAIJ_SeqAIJ,
3591:                                        NULL,
3592:                                        NULL,
3593:                                        /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3594:                                        NULL,
3595:                                        NULL,
3596:                                        MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3597:                                        NULL,
3598:                                        /* 99*/ MatProductSetFromOptions_SeqAIJ,
3599:                                        NULL,
3600:                                        NULL,
3601:                                        MatConjugate_SeqAIJ,
3602:                                        NULL,
3603:                                        /*104*/ MatSetValuesRow_SeqAIJ,
3604:                                        MatRealPart_SeqAIJ,
3605:                                        MatImaginaryPart_SeqAIJ,
3606:                                        NULL,
3607:                                        NULL,
3608:                                        /*109*/ MatMatSolve_SeqAIJ,
3609:                                        NULL,
3610:                                        MatGetRowMin_SeqAIJ,
3611:                                        NULL,
3612:                                        MatMissingDiagonal_SeqAIJ,
3613:                                        /*114*/ NULL,
3614:                                        NULL,
3615:                                        NULL,
3616:                                        NULL,
3617:                                        NULL,
3618:                                        /*119*/ NULL,
3619:                                        NULL,
3620:                                        NULL,
3621:                                        NULL,
3622:                                        MatGetMultiProcBlock_SeqAIJ,
3623:                                        /*124*/ MatFindNonzeroRows_SeqAIJ,
3624:                                        MatGetColumnReductions_SeqAIJ,
3625:                                        MatInvertBlockDiagonal_SeqAIJ,
3626:                                        MatInvertVariableBlockDiagonal_SeqAIJ,
3627:                                        NULL,
3628:                                        /*129*/ NULL,
3629:                                        NULL,
3630:                                        NULL,
3631:                                        MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3632:                                        MatTransposeColoringCreate_SeqAIJ,
3633:                                        /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3634:                                        MatTransColoringApplyDenToSp_SeqAIJ,
3635:                                        NULL,
3636:                                        NULL,
3637:                                        MatRARtNumeric_SeqAIJ_SeqAIJ,
3638:                                        /*139*/ NULL,
3639:                                        NULL,
3640:                                        NULL,
3641:                                        MatFDColoringSetUp_SeqXAIJ,
3642:                                        MatFindOffBlockDiagonalEntries_SeqAIJ,
3643:                                        MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3644:                                        /*145*/ MatDestroySubMatrices_SeqAIJ,
3645:                                        NULL,
3646:                                        NULL,
3647:                                        MatCreateGraph_Simple_AIJ,
3648:                                        NULL,
3649:                                        /*150*/ MatTransposeSymbolic_SeqAIJ,
3650:                                        MatEliminateZeros_SeqAIJ,
3651:                                        MatGetRowSumAbs_SeqAIJ,
3652:                                        NULL,
3653:                                        NULL,
3654:                                        /*155*/ NULL,
3655:                                        MatCopyHashToXAIJ_Seq_Hash};

3657: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3658: {
3659:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3660:   PetscInt    i, nz, n;

3662:   PetscFunctionBegin;
3663:   nz = aij->maxnz;
3664:   n  = mat->rmap->n;
3665:   for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3666:   aij->nz = nz;
3667:   for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3668:   PetscFunctionReturn(PETSC_SUCCESS);
3669: }

3671: /*
3672:  * Given a sparse matrix with global column indices, compact it by using a local column space.
3673:  * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3674:  */
3675: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3676: {
3677:   Mat_SeqAIJ   *aij = (Mat_SeqAIJ *)mat->data;
3678:   PetscHMapI    gid1_lid1;
3679:   PetscHashIter tpos;
3680:   PetscInt      gid, lid, i, ec, nz = aij->nz;
3681:   PetscInt     *garray, *jj = aij->j;

3683:   PetscFunctionBegin;
3685:   PetscAssertPointer(mapping, 2);
3686:   /* use a table */
3687:   PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3688:   ec = 0;
3689:   for (i = 0; i < nz; i++) {
3690:     PetscInt data, gid1 = jj[i] + 1;
3691:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3692:     if (!data) {
3693:       /* one based table */
3694:       PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3695:     }
3696:   }
3697:   /* form array of columns we need */
3698:   PetscCall(PetscMalloc1(ec, &garray));
3699:   PetscHashIterBegin(gid1_lid1, tpos);
3700:   while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3701:     PetscHashIterGetKey(gid1_lid1, tpos, gid);
3702:     PetscHashIterGetVal(gid1_lid1, tpos, lid);
3703:     PetscHashIterNext(gid1_lid1, tpos);
3704:     gid--;
3705:     lid--;
3706:     garray[lid] = gid;
3707:   }
3708:   PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3709:   PetscCall(PetscHMapIClear(gid1_lid1));
3710:   for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3711:   /* compact out the extra columns in B */
3712:   for (i = 0; i < nz; i++) {
3713:     PetscInt gid1 = jj[i] + 1;
3714:     PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3715:     lid--;
3716:     jj[i] = lid;
3717:   }
3718:   PetscCall(PetscLayoutDestroy(&mat->cmap));
3719:   PetscCall(PetscHMapIDestroy(&gid1_lid1));
3720:   PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3721:   PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3722:   PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3723:   PetscFunctionReturn(PETSC_SUCCESS);
3724: }

3726: /*@
3727:   MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3728:   in the matrix.

3730:   Input Parameters:
3731: + mat     - the `MATSEQAIJ` matrix
3732: - indices - the column indices

3734:   Level: advanced

3736:   Notes:
3737:   This can be called if you have precomputed the nonzero structure of the
3738:   matrix and want to provide it to the matrix object to improve the performance
3739:   of the `MatSetValues()` operation.

3741:   You MUST have set the correct numbers of nonzeros per row in the call to
3742:   `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.

3744:   MUST be called before any calls to `MatSetValues()`

3746:   The indices should start with zero, not one.

3748: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3749: @*/
3750: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3751: {
3752:   PetscFunctionBegin;
3754:   PetscAssertPointer(indices, 2);
3755:   PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3756:   PetscFunctionReturn(PETSC_SUCCESS);
3757: }

3759: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3760: {
3761:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3762:   size_t      nz  = aij->i[mat->rmap->n];

3764:   PetscFunctionBegin;
3765:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3767:   /* allocate space for values if not already there */
3768:   if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }

3770:   /* copy values over */
3771:   PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3772:   PetscFunctionReturn(PETSC_SUCCESS);
3773: }

3775: /*@
3776:   MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3777:   nonlinear portion.

3779:   Logically Collect

3781:   Input Parameter:
3782: . mat - the matrix (currently only `MATAIJ` matrices support this option)

3784:   Level: advanced

3786:   Example Usage:
3787: .vb
3788:     Using SNES
3789:     Create Jacobian matrix
3790:     Set linear terms into matrix
3791:     Apply boundary conditions to matrix, at this time matrix must have
3792:       final nonzero structure (i.e. setting the nonlinear terms and applying
3793:       boundary conditions again will not change the nonzero structure
3794:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3795:     MatStoreValues(mat);
3796:     Call SNESSetJacobian() with matrix
3797:     In your Jacobian routine
3798:       MatRetrieveValues(mat);
3799:       Set nonlinear terms in matrix

3801:     Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3802:     // build linear portion of Jacobian
3803:     MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3804:     MatStoreValues(mat);
3805:     loop over nonlinear iterations
3806:        MatRetrieveValues(mat);
3807:        // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3808:        // call MatAssemblyBegin/End() on matrix
3809:        Solve linear system with Jacobian
3810:     endloop
3811: .ve

3813:   Notes:
3814:   Matrix must already be assembled before calling this routine
3815:   Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3816:   calling this routine.

3818:   When this is called multiple times it overwrites the previous set of stored values
3819:   and does not allocated additional space.

3821: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3822: @*/
3823: PetscErrorCode MatStoreValues(Mat mat)
3824: {
3825:   PetscFunctionBegin;
3827:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3828:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3829:   PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3830:   PetscFunctionReturn(PETSC_SUCCESS);
3831: }

3833: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3834: {
3835:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3836:   PetscInt    nz  = aij->i[mat->rmap->n];

3838:   PetscFunctionBegin;
3839:   PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3840:   PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3841:   /* copy values over */
3842:   PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3843:   PetscFunctionReturn(PETSC_SUCCESS);
3844: }

3846: /*@
3847:   MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`

3849:   Logically Collect

3851:   Input Parameter:
3852: . mat - the matrix (currently only `MATAIJ` matrices support this option)

3854:   Level: advanced

3856: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3857: @*/
3858: PetscErrorCode MatRetrieveValues(Mat mat)
3859: {
3860:   PetscFunctionBegin;
3862:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3863:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3864:   PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3865:   PetscFunctionReturn(PETSC_SUCCESS);
3866: }

3868: /*@
3869:   MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3870:   (the default parallel PETSc format).  For good matrix assembly performance
3871:   the user should preallocate the matrix storage by setting the parameter `nz`
3872:   (or the array `nnz`).

3874:   Collective

3876:   Input Parameters:
3877: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3878: . m    - number of rows
3879: . n    - number of columns
3880: . nz   - number of nonzeros per row (same for all rows)
3881: - nnz  - array containing the number of nonzeros in the various rows
3882:          (possibly different for each row) or NULL

3884:   Output Parameter:
3885: . A - the matrix

3887:   Options Database Keys:
3888: + -mat_no_inode            - Do not use inodes
3889: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3891:   Level: intermediate

3893:   Notes:
3894:   It is recommend to use `MatCreateFromOptions()` instead of this routine

3896:   If `nnz` is given then `nz` is ignored

3898:   The `MATSEQAIJ` format, also called
3899:   compressed row storage, is fully compatible with standard Fortran
3900:   storage.  That is, the stored row and column indices can begin at
3901:   either one (as in Fortran) or zero.

3903:   Specify the preallocated storage with either `nz` or `nnz` (not both).
3904:   Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3905:   allocation.

3907:   By default, this format uses inodes (identical nodes) when possible, to
3908:   improve numerical efficiency of matrix-vector products and solves. We
3909:   search for consecutive rows with the same nonzero structure, thereby
3910:   reusing matrix information to achieve increased efficiency.

3912: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3913: @*/
3914: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3915: {
3916:   PetscFunctionBegin;
3917:   PetscCall(MatCreate(comm, A));
3918:   PetscCall(MatSetSizes(*A, m, n, m, n));
3919:   PetscCall(MatSetType(*A, MATSEQAIJ));
3920:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3921:   PetscFunctionReturn(PETSC_SUCCESS);
3922: }

3924: /*@
3925:   MatSeqAIJSetPreallocation - For good matrix assembly performance
3926:   the user should preallocate the matrix storage by setting the parameter nz
3927:   (or the array nnz).  By setting these parameters accurately, performance
3928:   during matrix assembly can be increased by more than a factor of 50.

3930:   Collective

3932:   Input Parameters:
3933: + B   - The matrix
3934: . nz  - number of nonzeros per row (same for all rows)
3935: - nnz - array containing the number of nonzeros in the various rows
3936:          (possibly different for each row) or NULL

3938:   Options Database Keys:
3939: + -mat_no_inode            - Do not use inodes
3940: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3942:   Level: intermediate

3944:   Notes:
3945:   If `nnz` is given then `nz` is ignored

3947:   The `MATSEQAIJ` format also called
3948:   compressed row storage, is fully compatible with standard Fortran
3949:   storage.  That is, the stored row and column indices can begin at
3950:   either one (as in Fortran) or zero.  See the users' manual for details.

3952:   Specify the preallocated storage with either `nz` or `nnz` (not both).
3953:   Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3954:   allocation.

3956:   You can call `MatGetInfo()` to get information on how effective the preallocation was;
3957:   for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3958:   You can also run with the option -info and look for messages with the string
3959:   malloc in them to see if additional memory allocation was needed.

3961:   Developer Notes:
3962:   Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3963:   entries or columns indices

3965:   By default, this format uses inodes (identical nodes) when possible, to
3966:   improve numerical efficiency of matrix-vector products and solves. We
3967:   search for consecutive rows with the same nonzero structure, thereby
3968:   reusing matrix information to achieve increased efficiency.

3970: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3971:           `MatSeqAIJSetTotalPreallocation()`
3972: @*/
3973: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3974: {
3975:   PetscFunctionBegin;
3978:   PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3979:   PetscFunctionReturn(PETSC_SUCCESS);
3980: }

3982: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3983: {
3984:   Mat_SeqAIJ *b              = (Mat_SeqAIJ *)B->data;
3985:   PetscBool   skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3986:   PetscInt    i;

3988:   PetscFunctionBegin;
3989:   if (B->hash_active) {
3990:     B->ops[0] = b->cops;
3991:     PetscCall(PetscHMapIJVDestroy(&b->ht));
3992:     PetscCall(PetscFree(b->dnz));
3993:     B->hash_active = PETSC_FALSE;
3994:   }
3995:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3996:   if (nz == MAT_SKIP_ALLOCATION) {
3997:     skipallocation = PETSC_TRUE;
3998:     nz             = 0;
3999:   }
4000:   PetscCall(PetscLayoutSetUp(B->rmap));
4001:   PetscCall(PetscLayoutSetUp(B->cmap));

4003:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
4004:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
4005:   if (nnz) {
4006:     for (i = 0; i < B->rmap->n; i++) {
4007:       PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
4008:       PetscCheck(nnz[i] <= B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], B->cmap->n);
4009:     }
4010:   }

4012:   B->preallocated = PETSC_TRUE;
4013:   if (!skipallocation) {
4014:     if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
4015:     if (!b->ilen) {
4016:       /* b->ilen will count nonzeros in each row so far. */
4017:       PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
4018:     } else {
4019:       PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4020:     }
4021:     if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4022:     if (!nnz) {
4023:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4024:       else if (nz < 0) nz = 1;
4025:       nz = PetscMin(nz, B->cmap->n);
4026:       for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4027:       PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4028:     } else {
4029:       PetscInt64 nz64 = 0;
4030:       for (i = 0; i < B->rmap->n; i++) {
4031:         b->imax[i] = nnz[i];
4032:         nz64 += nnz[i];
4033:       }
4034:       PetscCall(PetscIntCast(nz64, &nz));
4035:     }

4037:     /* allocate the matrix space */
4038:     PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4039:     PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscInt), (void **)&b->j));
4040:     PetscCall(PetscShmgetAllocateArray(B->rmap->n + 1, sizeof(PetscInt), (void **)&b->i));
4041:     b->free_ij = PETSC_TRUE;
4042:     if (B->structure_only) {
4043:       b->free_a = PETSC_FALSE;
4044:     } else {
4045:       PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)&b->a));
4046:       b->free_a = PETSC_TRUE;
4047:     }
4048:     b->i[0] = 0;
4049:     for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4050:   } else {
4051:     b->free_a  = PETSC_FALSE;
4052:     b->free_ij = PETSC_FALSE;
4053:   }

4055:   if (b->ipre && nnz != b->ipre && b->imax) {
4056:     /* reserve user-requested sparsity */
4057:     PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4058:   }

4060:   b->nz               = 0;
4061:   b->maxnz            = nz;
4062:   B->info.nz_unneeded = (double)b->maxnz;
4063:   if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4064:   B->was_assembled = PETSC_FALSE;
4065:   B->assembled     = PETSC_FALSE;
4066:   /* We simply deem preallocation has changed nonzero state. Updating the state
4067:      will give clients (like AIJKokkos) a chance to know something has happened.
4068:   */
4069:   B->nonzerostate++;
4070:   PetscFunctionReturn(PETSC_SUCCESS);
4071: }

4073: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4074: {
4075:   Mat_SeqAIJ *a;
4076:   PetscInt    i;
4077:   PetscBool   skipreset;

4079:   PetscFunctionBegin;

4082:   /* Check local size. If zero, then return */
4083:   if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);

4085:   a = (Mat_SeqAIJ *)A->data;
4086:   /* if no saved info, we error out */
4087:   PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");

4089:   PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");

4091:   PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4092:   if (!skipreset) {
4093:     PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4094:     PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4095:     a->i[0] = 0;
4096:     for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4097:     A->preallocated     = PETSC_TRUE;
4098:     a->nz               = 0;
4099:     a->maxnz            = a->i[A->rmap->n];
4100:     A->info.nz_unneeded = (double)a->maxnz;
4101:     A->was_assembled    = PETSC_FALSE;
4102:     A->assembled        = PETSC_FALSE;
4103:   }
4104:   PetscFunctionReturn(PETSC_SUCCESS);
4105: }

4107: /*@
4108:   MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.

4110:   Input Parameters:
4111: + B - the matrix
4112: . i - the indices into `j` for the start of each row (indices start with zero)
4113: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4114: - v - optional values in the matrix, use `NULL` if not provided

4116:   Level: developer

4118:   Notes:
4119:   The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`

4121:   This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4122:   structure will be the union of all the previous nonzero structures.

4124:   Developer Notes:
4125:   An optimization could be added to the implementation where it checks if the `i`, and `j` are identical to the current `i` and `j` and
4126:   then just copies the `v` values directly with `PetscMemcpy()`.

4128:   This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.

4130: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4131: @*/
4132: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4133: {
4134:   PetscFunctionBegin;
4137:   PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4138:   PetscFunctionReturn(PETSC_SUCCESS);
4139: }

4141: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4142: {
4143:   PetscInt  i;
4144:   PetscInt  m, n;
4145:   PetscInt  nz;
4146:   PetscInt *nnz;

4148:   PetscFunctionBegin;
4149:   PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);

4151:   PetscCall(PetscLayoutSetUp(B->rmap));
4152:   PetscCall(PetscLayoutSetUp(B->cmap));

4154:   PetscCall(MatGetSize(B, &m, &n));
4155:   PetscCall(PetscMalloc1(m + 1, &nnz));
4156:   for (i = 0; i < m; i++) {
4157:     nz = Ii[i + 1] - Ii[i];
4158:     PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4159:     nnz[i] = nz;
4160:   }
4161:   PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4162:   PetscCall(PetscFree(nnz));

4164:   for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES));

4166:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4167:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));

4169:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4170:   PetscFunctionReturn(PETSC_SUCCESS);
4171: }

4173: /*@
4174:   MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.

4176:   Input Parameters:
4177: + A     - left-hand side matrix
4178: . B     - right-hand side matrix
4179: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

4181:   Output Parameter:
4182: . C - Kronecker product of `A` and `B`

4184:   Level: intermediate

4186:   Note:
4187:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.

4189: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4190: @*/
4191: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4192: {
4193:   PetscFunctionBegin;
4198:   PetscAssertPointer(C, 4);
4199:   if (reuse == MAT_REUSE_MATRIX) {
4202:   }
4203:   PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4204:   PetscFunctionReturn(PETSC_SUCCESS);
4205: }

4207: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4208: {
4209:   Mat                newmat;
4210:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
4211:   Mat_SeqAIJ        *b = (Mat_SeqAIJ *)B->data;
4212:   PetscScalar       *v;
4213:   const PetscScalar *aa, *ba;
4214:   PetscInt          *i, *j, m, n, p, q, nnz = 0, am = A->rmap->n, bm = B->rmap->n, an = A->cmap->n, bn = B->cmap->n;
4215:   PetscBool          flg;

4217:   PetscFunctionBegin;
4218:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4219:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4220:   PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4221:   PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4222:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4223:   PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4224:   PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4225:   if (reuse == MAT_INITIAL_MATRIX) {
4226:     PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4227:     PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4228:     PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4229:     PetscCall(MatSetType(newmat, MATAIJ));
4230:     i[0] = 0;
4231:     for (m = 0; m < am; ++m) {
4232:       for (p = 0; p < bm; ++p) {
4233:         i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4234:         for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4235:           for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4236:         }
4237:       }
4238:     }
4239:     PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4240:     *C = newmat;
4241:     PetscCall(PetscFree2(i, j));
4242:     nnz = 0;
4243:   }
4244:   PetscCall(MatSeqAIJGetArray(*C, &v));
4245:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4246:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4247:   for (m = 0; m < am; ++m) {
4248:     for (p = 0; p < bm; ++p) {
4249:       for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4250:         for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4251:       }
4252:     }
4253:   }
4254:   PetscCall(MatSeqAIJRestoreArray(*C, &v));
4255:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4256:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4257:   PetscFunctionReturn(PETSC_SUCCESS);
4258: }

4260: #include <../src/mat/impls/dense/seq/dense.h>
4261: #include <petsc/private/kernels/petscaxpy.h>

4263: /*
4264:     Computes (B'*A')' since computing B*A directly is untenable

4266:                n                       p                          p
4267:         [             ]       [             ]         [                 ]
4268:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
4269:         [             ]       [             ]         [                 ]

4271: */
4272: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4273: {
4274:   Mat_SeqDense      *sub_a = (Mat_SeqDense *)A->data;
4275:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ *)B->data;
4276:   Mat_SeqDense      *sub_c = (Mat_SeqDense *)C->data;
4277:   PetscInt           i, j, n, m, q, p;
4278:   const PetscInt    *ii, *idx;
4279:   const PetscScalar *b, *a, *a_q;
4280:   PetscScalar       *c, *c_q;
4281:   PetscInt           clda = sub_c->lda;
4282:   PetscInt           alda = sub_a->lda;

4284:   PetscFunctionBegin;
4285:   m = A->rmap->n;
4286:   n = A->cmap->n;
4287:   p = B->cmap->n;
4288:   a = sub_a->v;
4289:   b = sub_b->a;
4290:   c = sub_c->v;
4291:   if (clda == m) {
4292:     PetscCall(PetscArrayzero(c, m * p));
4293:   } else {
4294:     for (j = 0; j < p; j++)
4295:       for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4296:   }
4297:   ii  = sub_b->i;
4298:   idx = sub_b->j;
4299:   for (i = 0; i < n; i++) {
4300:     q = ii[i + 1] - ii[i];
4301:     while (q-- > 0) {
4302:       c_q = c + clda * (*idx);
4303:       a_q = a + alda * i;
4304:       PetscKernelAXPY(c_q, *b, a_q, m);
4305:       idx++;
4306:       b++;
4307:     }
4308:   }
4309:   PetscFunctionReturn(PETSC_SUCCESS);
4310: }

4312: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4313: {
4314:   PetscInt  m = A->rmap->n, n = B->cmap->n;
4315:   PetscBool cisdense;

4317:   PetscFunctionBegin;
4318:   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);
4319:   PetscCall(MatSetSizes(C, m, n, m, n));
4320:   PetscCall(MatSetBlockSizesFromMats(C, A, B));
4321:   PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4322:   if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4323:   PetscCall(MatSetUp(C));

4325:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4326:   PetscFunctionReturn(PETSC_SUCCESS);
4327: }

4329: /*MC
4330:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4331:    based on compressed sparse row format.

4333:    Options Database Key:
4334: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4336:    Level: beginner

4338:    Notes:
4339:     `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4340:     in this case the values associated with the rows and columns one passes in are set to zero
4341:     in the matrix

4343:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4344:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

4346:   Developer Note:
4347:     It would be nice if all matrix formats supported passing `NULL` in for the numerical values

4349: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4350: M*/

4352: /*MC
4353:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4355:    This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4356:    and `MATMPIAIJ` otherwise.  As a result, for single process communicators,
4357:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4358:    for communicators controlling multiple processes.  It is recommended that you call both of
4359:    the above preallocation routines for simplicity.

4361:    Options Database Key:
4362: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`

4364:   Level: beginner

4366:    Note:
4367:    Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4368:    enough exist.

4370: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4371: M*/

4373: /*MC
4374:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4376:    Options Database Key:
4377: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`

4379:   Level: beginner

4381:    Note:
4382:    This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4383:    and `MATMPIAIJCRL` otherwise.  As a result, for single process communicators,
4384:    `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4385:    for communicators controlling multiple processes.  It is recommended that you call both of
4386:    the above preallocation routines for simplicity.

4388: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4389: M*/

4391: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4392: #if defined(PETSC_HAVE_ELEMENTAL)
4393: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4394: #endif
4395: #if defined(PETSC_HAVE_SCALAPACK)
4396: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4397: #endif
4398: #if defined(PETSC_HAVE_HYPRE)
4399: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4400: #endif

4402: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4403: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4404: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4406: /*@C
4407:   MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored

4409:   Not Collective

4411:   Input Parameter:
4412: . A - a `MATSEQAIJ` matrix

4414:   Output Parameter:
4415: . array - pointer to the data

4417:   Level: intermediate

4419:   Fortran Notes:
4420:   `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`

4422: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4423: @*/
4424: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar *array[])
4425: {
4426:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4428:   PetscFunctionBegin;
4429:   if (aij->ops->getarray) {
4430:     PetscCall((*aij->ops->getarray)(A, array));
4431:   } else {
4432:     *array = aij->a;
4433:   }
4434:   PetscFunctionReturn(PETSC_SUCCESS);
4435: }

4437: /*@C
4438:   MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`

4440:   Not Collective

4442:   Input Parameters:
4443: + A     - a `MATSEQAIJ` matrix
4444: - array - pointer to the data

4446:   Level: intermediate

4448:   Fortran Notes:
4449:   `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`

4451: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4452: @*/
4453: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar *array[])
4454: {
4455:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4457:   PetscFunctionBegin;
4458:   if (aij->ops->restorearray) {
4459:     PetscCall((*aij->ops->restorearray)(A, array));
4460:   } else {
4461:     *array = NULL;
4462:   }
4463:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4464:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4465:   PetscFunctionReturn(PETSC_SUCCESS);
4466: }

4468: /*@C
4469:   MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4471:   Not Collective; No Fortran Support

4473:   Input Parameter:
4474: . A - a `MATSEQAIJ` matrix

4476:   Output Parameter:
4477: . array - pointer to the data

4479:   Level: intermediate

4481: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4482: @*/
4483: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar *array[])
4484: {
4485:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4487:   PetscFunctionBegin;
4488:   if (aij->ops->getarrayread) {
4489:     PetscCall((*aij->ops->getarrayread)(A, array));
4490:   } else {
4491:     *array = aij->a;
4492:   }
4493:   PetscFunctionReturn(PETSC_SUCCESS);
4494: }

4496: /*@C
4497:   MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`

4499:   Not Collective; No Fortran Support

4501:   Input Parameter:
4502: . A - a `MATSEQAIJ` matrix

4504:   Output Parameter:
4505: . array - pointer to the data

4507:   Level: intermediate

4509: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4510: @*/
4511: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar *array[])
4512: {
4513:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4515:   PetscFunctionBegin;
4516:   if (aij->ops->restorearrayread) {
4517:     PetscCall((*aij->ops->restorearrayread)(A, array));
4518:   } else {
4519:     *array = NULL;
4520:   }
4521:   PetscFunctionReturn(PETSC_SUCCESS);
4522: }

4524: /*@C
4525:   MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored

4527:   Not Collective; No Fortran Support

4529:   Input Parameter:
4530: . A - a `MATSEQAIJ` matrix

4532:   Output Parameter:
4533: . array - pointer to the data

4535:   Level: intermediate

4537: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4538: @*/
4539: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar *array[])
4540: {
4541:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4543:   PetscFunctionBegin;
4544:   if (aij->ops->getarraywrite) {
4545:     PetscCall((*aij->ops->getarraywrite)(A, array));
4546:   } else {
4547:     *array = aij->a;
4548:   }
4549:   PetscCall(MatSeqAIJInvalidateDiagonal(A));
4550:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
4551:   PetscFunctionReturn(PETSC_SUCCESS);
4552: }

4554: /*@C
4555:   MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4557:   Not Collective; No Fortran Support

4559:   Input Parameter:
4560: . A - a MATSEQAIJ matrix

4562:   Output Parameter:
4563: . array - pointer to the data

4565:   Level: intermediate

4567: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4568: @*/
4569: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar *array[])
4570: {
4571:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4573:   PetscFunctionBegin;
4574:   if (aij->ops->restorearraywrite) {
4575:     PetscCall((*aij->ops->restorearraywrite)(A, array));
4576:   } else {
4577:     *array = NULL;
4578:   }
4579:   PetscFunctionReturn(PETSC_SUCCESS);
4580: }

4582: /*@C
4583:   MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix

4585:   Not Collective; No Fortran Support

4587:   Input Parameter:
4588: . mat - a matrix of type `MATSEQAIJ` or its subclasses

4590:   Output Parameters:
4591: + i     - row map array of the matrix
4592: . j     - column index array of the matrix
4593: . a     - data array of the matrix
4594: - mtype - memory type of the arrays

4596:   Level: developer

4598:   Notes:
4599:   Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4600:   If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.

4602:   One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4603:   If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.

4605: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4606: @*/
4607: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt *i[], const PetscInt *j[], PetscScalar *a[], PetscMemType *mtype)
4608: {
4609:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;

4611:   PetscFunctionBegin;
4612:   PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4613:   if (aij->ops->getcsrandmemtype) {
4614:     PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4615:   } else {
4616:     if (i) *i = aij->i;
4617:     if (j) *j = aij->j;
4618:     if (a) *a = aij->a;
4619:     if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4620:   }
4621:   PetscFunctionReturn(PETSC_SUCCESS);
4622: }

4624: /*@
4625:   MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4627:   Not Collective

4629:   Input Parameter:
4630: . A - a `MATSEQAIJ` matrix

4632:   Output Parameter:
4633: . nz - the maximum number of nonzeros in any row

4635:   Level: intermediate

4637: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4638: @*/
4639: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4640: {
4641:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;

4643:   PetscFunctionBegin;
4644:   *nz = aij->rmax;
4645:   PetscFunctionReturn(PETSC_SUCCESS);
4646: }

4648: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void **data)
4649: {
4650:   MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)*data;

4652:   PetscFunctionBegin;
4653:   PetscCall(PetscFree(coo->perm));
4654:   PetscCall(PetscFree(coo->jmap));
4655:   PetscCall(PetscFree(coo));
4656:   PetscFunctionReturn(PETSC_SUCCESS);
4657: }

4659: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4660: {
4661:   MPI_Comm             comm;
4662:   PetscInt            *i, *j;
4663:   PetscInt             M, N, row, iprev;
4664:   PetscCount           k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4665:   PetscInt            *Ai;                             /* Change to PetscCount once we use it for row pointers */
4666:   PetscInt            *Aj;
4667:   PetscScalar         *Aa;
4668:   Mat_SeqAIJ          *seqaij = (Mat_SeqAIJ *)mat->data;
4669:   MatType              rtype;
4670:   PetscCount          *perm, *jmap;
4671:   MatCOOStruct_SeqAIJ *coo;
4672:   PetscBool            isorted;
4673:   PetscBool            hypre;
4674:   const char          *name;

4676:   PetscFunctionBegin;
4677:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4678:   PetscCall(MatGetSize(mat, &M, &N));
4679:   i = coo_i;
4680:   j = coo_j;
4681:   PetscCall(PetscMalloc1(coo_n, &perm));

4683:   /* Ignore entries with negative row or col indices; at the same time, check if i[] is already sorted (e.g., MatConvert_AlJ_HYPRE results in this case) */
4684:   isorted = PETSC_TRUE;
4685:   iprev   = PETSC_INT_MIN;
4686:   for (k = 0; k < coo_n; k++) {
4687:     if (j[k] < 0) i[k] = -1;
4688:     if (isorted) {
4689:       if (i[k] < iprev) isorted = PETSC_FALSE;
4690:       else iprev = i[k];
4691:     }
4692:     perm[k] = k;
4693:   }

4695:   /* Sort by row if not already */
4696:   if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));

4698:   /* Advance k to the first row with a non-negative index */
4699:   for (k = 0; k < coo_n; k++)
4700:     if (i[k] >= 0) break;
4701:   nneg = k;
4702:   PetscCall(PetscMalloc1(coo_n - nneg + 1, &jmap)); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */
4703:   nnz = 0;                                          /* Total number of unique nonzeros to be counted */
4704:   jmap++;                                           /* Inc jmap by 1 for convenience */

4706:   PetscCall(PetscShmgetAllocateArray(M + 1, sizeof(PetscInt), (void **)&Ai)); /* CSR of A */
4707:   PetscCall(PetscArrayzero(Ai, M + 1));
4708:   PetscCall(PetscShmgetAllocateArray(coo_n - nneg, sizeof(PetscInt), (void **)&Aj)); /* We have at most coo_n-nneg unique nonzeros */

4710:   PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4711:   PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));

4713:   /* In each row, sort by column, then unique column indices to get row length */
4714:   Ai++;  /* Inc by 1 for convenience */
4715:   q = 0; /* q-th unique nonzero, with q starting from 0 */
4716:   while (k < coo_n) {
4717:     PetscBool strictly_sorted; // this row is strictly sorted?
4718:     PetscInt  jprev;

4720:     /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4721:     row             = i[k];
4722:     start           = k;
4723:     jprev           = PETSC_INT_MIN;
4724:     strictly_sorted = PETSC_TRUE;
4725:     while (k < coo_n && i[k] == row) {
4726:       if (strictly_sorted) {
4727:         if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4728:         else jprev = j[k];
4729:       }
4730:       k++;
4731:     }
4732:     end = k;

4734:     /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4735:     if (hypre) {
4736:       PetscInt  minj    = PETSC_INT_MAX;
4737:       PetscBool hasdiag = PETSC_FALSE;

4739:       if (strictly_sorted) { // fast path to swap the first and the diag
4740:         PetscCount tmp;
4741:         for (p = start; p < end; p++) {
4742:           if (j[p] == row && p != start) {
4743:             j[p]        = j[start]; // swap j[], so that the diagonal value will go first (manipulated by perm[])
4744:             j[start]    = row;
4745:             tmp         = perm[start];
4746:             perm[start] = perm[p]; // also swap perm[] so we can save the call to PetscSortIntWithCountArray() below
4747:             perm[p]     = tmp;
4748:             break;
4749:           }
4750:         }
4751:       } else {
4752:         for (p = start; p < end; p++) {
4753:           hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4754:           minj    = PetscMin(minj, j[p]);
4755:         }

4757:         if (hasdiag) {
4758:           for (p = start; p < end; p++) {
4759:             if (j[p] == minj) j[p] = row;
4760:             else if (j[p] == row) j[p] = minj;
4761:           }
4762:         }
4763:       }
4764:     }
4765:     // sort by columns in a row. perm[] indicates their original order
4766:     if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));

4768:     if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4769:       for (p = start; p < end; p++, q++) {
4770:         Aj[q]   = j[p];
4771:         jmap[q] = 1;
4772:       }
4773:       PetscCall(PetscIntCast(end - start, Ai + row));
4774:       nnz += Ai[row]; // q is already advanced
4775:     } else {
4776:       /* Find number of unique col entries in this row */
4777:       Aj[q]   = j[start]; /* Log the first nonzero in this row */
4778:       jmap[q] = 1;        /* Number of repeats of this nonzero entry */
4779:       Ai[row] = 1;
4780:       nnz++;

4782:       for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4783:         if (j[p] != j[p - 1]) {           /* Meet a new nonzero */
4784:           q++;
4785:           jmap[q] = 1;
4786:           Aj[q]   = j[p];
4787:           Ai[row]++;
4788:           nnz++;
4789:         } else {
4790:           jmap[q]++;
4791:         }
4792:       }
4793:       q++; /* Move to next row and thus next unique nonzero */
4794:     }
4795:   }

4797:   Ai--; /* Back to the beginning of Ai[] */
4798:   for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4799:   jmap--; // Back to the beginning of jmap[]
4800:   jmap[0] = 0;
4801:   for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];

4803:   if (nnz < coo_n - nneg) { /* Reallocate with actual number of unique nonzeros */
4804:     PetscCount *jmap_new;
4805:     PetscInt   *Aj_new;

4807:     PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4808:     PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4809:     PetscCall(PetscFree(jmap));
4810:     jmap = jmap_new;

4812:     PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscInt), (void **)&Aj_new));
4813:     PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4814:     PetscCall(PetscShmgetDeallocateArray((void **)&Aj));
4815:     Aj = Aj_new;
4816:   }

4818:   if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4819:     PetscCount *perm_new;

4821:     PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4822:     PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4823:     PetscCall(PetscFree(perm));
4824:     perm = perm_new;
4825:   }

4827:   PetscCall(MatGetRootType_Private(mat, &rtype));
4828:   PetscCall(PetscShmgetAllocateArray(nnz, sizeof(PetscScalar), (void **)&Aa));
4829:   PetscCall(PetscArrayzero(Aa, nnz));
4830:   PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));

4832:   seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */

4834:   // Put the COO struct in a container and then attach that to the matrix
4835:   PetscCall(PetscMalloc1(1, &coo));
4836:   PetscCall(PetscIntCast(nnz, &coo->nz));
4837:   coo->n    = coo_n;
4838:   coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4839:   coo->jmap = jmap;         // of length nnz+1
4840:   coo->perm = perm;
4841:   PetscCall(PetscObjectContainerCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", coo, MatCOOStructDestroy_SeqAIJ));
4842:   PetscFunctionReturn(PETSC_SUCCESS);
4843: }

4845: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4846: {
4847:   Mat_SeqAIJ          *aseq = (Mat_SeqAIJ *)A->data;
4848:   PetscCount           i, j, Annz = aseq->nz;
4849:   PetscCount          *perm, *jmap;
4850:   PetscScalar         *Aa;
4851:   PetscContainer       container;
4852:   MatCOOStruct_SeqAIJ *coo;

4854:   PetscFunctionBegin;
4855:   PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4856:   PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4857:   PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4858:   perm = coo->perm;
4859:   jmap = coo->jmap;
4860:   PetscCall(MatSeqAIJGetArray(A, &Aa));
4861:   for (i = 0; i < Annz; i++) {
4862:     PetscScalar sum = 0.0;
4863:     for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4864:     Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4865:   }
4866:   PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4867:   PetscFunctionReturn(PETSC_SUCCESS);
4868: }

4870: #if defined(PETSC_HAVE_CUDA)
4871: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4872: #endif
4873: #if defined(PETSC_HAVE_HIP)
4874: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4875: #endif
4876: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4877: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4878: #endif

4880: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4881: {
4882:   Mat_SeqAIJ *b;
4883:   PetscMPIInt size;

4885:   PetscFunctionBegin;
4886:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4887:   PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");

4889:   PetscCall(PetscNew(&b));

4891:   B->data   = (void *)b;
4892:   B->ops[0] = MatOps_Values;
4893:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4895:   b->row                = NULL;
4896:   b->col                = NULL;
4897:   b->icol               = NULL;
4898:   b->reallocs           = 0;
4899:   b->ignorezeroentries  = PETSC_FALSE;
4900:   b->roworiented        = PETSC_TRUE;
4901:   b->nonew              = 0;
4902:   b->diag               = NULL;
4903:   b->solve_work         = NULL;
4904:   B->spptr              = NULL;
4905:   b->saved_values       = NULL;
4906:   b->idiag              = NULL;
4907:   b->mdiag              = NULL;
4908:   b->ssor_work          = NULL;
4909:   b->omega              = 1.0;
4910:   b->fshift             = 0.0;
4911:   b->idiagvalid         = PETSC_FALSE;
4912:   b->ibdiagvalid        = PETSC_FALSE;
4913:   b->keepnonzeropattern = PETSC_FALSE;

4915:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4916: #if defined(PETSC_HAVE_MATLAB)
4917:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4918:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4919: #endif
4920:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4921:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4922:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4923:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4924:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4925:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4926:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4927: #if defined(PETSC_HAVE_MKL_SPARSE)
4928:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4929: #endif
4930: #if defined(PETSC_HAVE_CUDA)
4931:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4932:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4933:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4934: #endif
4935: #if defined(PETSC_HAVE_HIP)
4936:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4937:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4938:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4939: #endif
4940: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4941:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4942: #endif
4943:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4944: #if defined(PETSC_HAVE_ELEMENTAL)
4945:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4946: #endif
4947: #if defined(PETSC_HAVE_SCALAPACK)
4948:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4949: #endif
4950: #if defined(PETSC_HAVE_HYPRE)
4951:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4952:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4953: #endif
4954:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4955:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4956:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4957:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4958:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4959:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4960:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4961:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetHash_C", MatResetHash_SeqAIJ));
4962:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4963:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4964:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4965:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4966:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4967:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4968:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4969:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4970:   PetscCall(MatCreate_SeqAIJ_Inode(B));
4971:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4972:   PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4973:   PetscFunctionReturn(PETSC_SUCCESS);
4974: }

4976: /*
4977:     Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4978: */
4979: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4980: {
4981:   Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4982:   PetscInt    m = A->rmap->n, i;

4984:   PetscFunctionBegin;
4985:   PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");

4987:   C->factortype    = A->factortype;
4988:   c->row           = NULL;
4989:   c->col           = NULL;
4990:   c->icol          = NULL;
4991:   c->reallocs      = 0;
4992:   c->diagonaldense = a->diagonaldense;

4994:   C->assembled = A->assembled;

4996:   if (A->preallocated) {
4997:     PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4998:     PetscCall(PetscLayoutReference(A->cmap, &C->cmap));

5000:     if (!A->hash_active) {
5001:       PetscCall(PetscMalloc1(m, &c->imax));
5002:       PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
5003:       PetscCall(PetscMalloc1(m, &c->ilen));
5004:       PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));

5006:       /* allocate the matrix space */
5007:       if (mallocmatspace) {
5008:         PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscScalar), (void **)&c->a));
5009:         PetscCall(PetscShmgetAllocateArray(a->i[m], sizeof(PetscInt), (void **)&c->j));
5010:         PetscCall(PetscShmgetAllocateArray(m + 1, sizeof(PetscInt), (void **)&c->i));
5011:         PetscCall(PetscArraycpy(c->i, a->i, m + 1));
5012:         c->free_a  = PETSC_TRUE;
5013:         c->free_ij = PETSC_TRUE;
5014:         if (m > 0) {
5015:           PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
5016:           if (cpvalues == MAT_COPY_VALUES) {
5017:             const PetscScalar *aa;

5019:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5020:             PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5021:             PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5022:           } else {
5023:             PetscCall(PetscArrayzero(c->a, a->i[m]));
5024:           }
5025:         }
5026:       }
5027:       C->preallocated = PETSC_TRUE;
5028:     } else {
5029:       PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5030:       PetscCall(MatSetUp(C));
5031:     }

5033:     c->ignorezeroentries = a->ignorezeroentries;
5034:     c->roworiented       = a->roworiented;
5035:     c->nonew             = a->nonew;
5036:     if (a->diag) {
5037:       PetscCall(PetscMalloc1(m + 1, &c->diag));
5038:       PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5039:     } else c->diag = NULL;

5041:     c->solve_work         = NULL;
5042:     c->saved_values       = NULL;
5043:     c->idiag              = NULL;
5044:     c->ssor_work          = NULL;
5045:     c->keepnonzeropattern = a->keepnonzeropattern;

5047:     c->rmax  = a->rmax;
5048:     c->nz    = a->nz;
5049:     c->maxnz = a->nz; /* Since we allocate exactly the right amount */

5051:     c->compressedrow.use   = a->compressedrow.use;
5052:     c->compressedrow.nrows = a->compressedrow.nrows;
5053:     if (a->compressedrow.use) {
5054:       i = a->compressedrow.nrows;
5055:       PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5056:       PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5057:       PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5058:     } else {
5059:       c->compressedrow.use    = PETSC_FALSE;
5060:       c->compressedrow.i      = NULL;
5061:       c->compressedrow.rindex = NULL;
5062:     }
5063:     c->nonzerorowcnt = a->nonzerorowcnt;
5064:     C->nonzerostate  = A->nonzerostate;

5066:     PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5067:   }
5068:   PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5069:   PetscFunctionReturn(PETSC_SUCCESS);
5070: }

5072: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5073: {
5074:   PetscFunctionBegin;
5075:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5076:   PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5077:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5078:   PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5079:   PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5080:   PetscFunctionReturn(PETSC_SUCCESS);
5081: }

5083: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5084: {
5085:   PetscBool isbinary, ishdf5;

5087:   PetscFunctionBegin;
5090:   /* force binary viewer to load .info file if it has not yet done so */
5091:   PetscCall(PetscViewerSetUp(viewer));
5092:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5093:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5094:   if (isbinary) {
5095:     PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5096:   } else if (ishdf5) {
5097: #if defined(PETSC_HAVE_HDF5)
5098:     PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5099: #else
5100:     SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5101: #endif
5102:   } else {
5103:     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);
5104:   }
5105:   PetscFunctionReturn(PETSC_SUCCESS);
5106: }

5108: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5109: {
5110:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5111:   PetscInt    header[4], *rowlens, M, N, nz, sum, rows, cols, i;

5113:   PetscFunctionBegin;
5114:   PetscCall(PetscViewerSetUp(viewer));

5116:   /* read in matrix header */
5117:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5118:   PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5119:   M  = header[1];
5120:   N  = header[2];
5121:   nz = header[3];
5122:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5123:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5124:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");

5126:   /* set block sizes from the viewer's .info file */
5127:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5128:   /* set local and global sizes if not set already */
5129:   if (mat->rmap->n < 0) mat->rmap->n = M;
5130:   if (mat->cmap->n < 0) mat->cmap->n = N;
5131:   if (mat->rmap->N < 0) mat->rmap->N = M;
5132:   if (mat->cmap->N < 0) mat->cmap->N = N;
5133:   PetscCall(PetscLayoutSetUp(mat->rmap));
5134:   PetscCall(PetscLayoutSetUp(mat->cmap));

5136:   /* check if the matrix sizes are correct */
5137:   PetscCall(MatGetSize(mat, &rows, &cols));
5138:   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);

5140:   /* read in row lengths */
5141:   PetscCall(PetscMalloc1(M, &rowlens));
5142:   PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5143:   /* check if sum(rowlens) is same as nz */
5144:   sum = 0;
5145:   for (i = 0; i < M; i++) sum += rowlens[i];
5146:   PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
5147:   /* preallocate and check sizes */
5148:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5149:   PetscCall(MatGetSize(mat, &rows, &cols));
5150:   PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5151:   /* store row lengths */
5152:   PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5153:   PetscCall(PetscFree(rowlens));

5155:   /* fill in "i" row pointers */
5156:   a->i[0] = 0;
5157:   for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5158:   /* read in "j" column indices */
5159:   PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5160:   /* read in "a" nonzero values */
5161:   PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));

5163:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5164:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5165:   PetscFunctionReturn(PETSC_SUCCESS);
5166: }

5168: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5169: {
5170:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5171:   const PetscScalar *aa, *ba;
5172: #if defined(PETSC_USE_COMPLEX)
5173:   PetscInt k;
5174: #endif

5176:   PetscFunctionBegin;
5177:   /* If the  matrix dimensions are not equal,or no of nonzeros */
5178:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5179:     *flg = PETSC_FALSE;
5180:     PetscFunctionReturn(PETSC_SUCCESS);
5181:   }

5183:   /* if the a->i are the same */
5184:   PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5185:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5187:   /* if a->j are the same */
5188:   PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5189:   if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);

5191:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5192:   PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5193:   /* if a->a are the same */
5194: #if defined(PETSC_USE_COMPLEX)
5195:   for (k = 0; k < a->nz; k++) {
5196:     if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5197:       *flg = PETSC_FALSE;
5198:       PetscFunctionReturn(PETSC_SUCCESS);
5199:     }
5200:   }
5201: #else
5202:   PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5203: #endif
5204:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5205:   PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5206:   PetscFunctionReturn(PETSC_SUCCESS);
5207: }

5209: /*@
5210:   MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5211:   provided by the user.

5213:   Collective

5215:   Input Parameters:
5216: + comm - must be an MPI communicator of size 1
5217: . m    - number of rows
5218: . n    - number of columns
5219: . i    - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5220: . j    - column indices
5221: - a    - matrix values

5223:   Output Parameter:
5224: . mat - the matrix

5226:   Level: intermediate

5228:   Notes:
5229:   The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5230:   once the matrix is destroyed and not before

5232:   You cannot set new nonzero locations into this matrix, that will generate an error.

5234:   The `i` and `j` indices are 0 based

5236:   The format which is used for the sparse matrix input, is equivalent to a
5237:   row-major ordering.. i.e for the following matrix, the input data expected is
5238:   as shown
5239: .vb
5240:         1 0 0
5241:         2 0 3
5242:         4 5 6

5244:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
5245:         j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
5246:         v =  {1,2,3,4,5,6}  [size = 6]
5247: .ve

5249: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5250: @*/
5251: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5252: {
5253:   PetscInt    ii;
5254:   Mat_SeqAIJ *aij;
5255:   PetscInt    jj;

5257:   PetscFunctionBegin;
5258:   PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5259:   PetscCall(MatCreate(comm, mat));
5260:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5261:   /* PetscCall(MatSetBlockSizes(*mat,,)); */
5262:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5263:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5264:   aij = (Mat_SeqAIJ *)(*mat)->data;
5265:   PetscCall(PetscMalloc1(m, &aij->imax));
5266:   PetscCall(PetscMalloc1(m, &aij->ilen));

5268:   aij->i       = i;
5269:   aij->j       = j;
5270:   aij->a       = a;
5271:   aij->nonew   = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5272:   aij->free_a  = PETSC_FALSE;
5273:   aij->free_ij = PETSC_FALSE;

5275:   for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5276:     aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5277:     if (PetscDefined(USE_DEBUG)) {
5278:       PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]);
5279:       for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5280:         PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii);
5281:         PetscCheck(j[jj] != j[jj - 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", jj - i[ii], j[jj], ii);
5282:       }
5283:     }
5284:   }
5285:   if (PetscDefined(USE_DEBUG)) {
5286:     for (ii = 0; ii < aij->i[m]; ii++) {
5287:       PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5288:       PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT " last column = %" PetscInt_FMT, ii, j[ii], n - 1);
5289:     }
5290:   }

5292:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5293:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5294:   PetscFunctionReturn(PETSC_SUCCESS);
5295: }

5297: /*@
5298:   MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5299:   provided by the user.

5301:   Collective

5303:   Input Parameters:
5304: + comm - must be an MPI communicator of size 1
5305: . m    - number of rows
5306: . n    - number of columns
5307: . i    - row indices
5308: . j    - column indices
5309: . a    - matrix values
5310: . nz   - number of nonzeros
5311: - idx  - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`

5313:   Output Parameter:
5314: . mat - the matrix

5316:   Level: intermediate

5318:   Example:
5319:   For the following matrix, the input data expected is as shown (using 0 based indexing)
5320: .vb
5321:         1 0 0
5322:         2 0 3
5323:         4 5 6

5325:         i =  {0,1,1,2,2,2}
5326:         j =  {0,0,2,0,1,2}
5327:         v =  {1,2,3,4,5,6}
5328: .ve

5330:   Note:
5331:   Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5332:   and are particularly useful in iterative applications.

5334: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5335: @*/
5336: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5337: {
5338:   PetscInt ii, *nnz, one = 1, row, col;

5340:   PetscFunctionBegin;
5341:   PetscCall(PetscCalloc1(m, &nnz));
5342:   for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5343:   PetscCall(MatCreate(comm, mat));
5344:   PetscCall(MatSetSizes(*mat, m, n, m, n));
5345:   PetscCall(MatSetType(*mat, MATSEQAIJ));
5346:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5347:   for (ii = 0; ii < nz; ii++) {
5348:     if (idx) {
5349:       row = i[ii] - 1;
5350:       col = j[ii] - 1;
5351:     } else {
5352:       row = i[ii];
5353:       col = j[ii];
5354:     }
5355:     PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5356:   }
5357:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5358:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5359:   PetscCall(PetscFree(nnz));
5360:   PetscFunctionReturn(PETSC_SUCCESS);
5361: }

5363: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5364: {
5365:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;

5367:   PetscFunctionBegin;
5368:   a->idiagvalid  = PETSC_FALSE;
5369:   a->ibdiagvalid = PETSC_FALSE;

5371:   PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5372:   PetscFunctionReturn(PETSC_SUCCESS);
5373: }

5375: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5376: {
5377:   PetscFunctionBegin;
5378:   PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5379:   PetscFunctionReturn(PETSC_SUCCESS);
5380: }

5382: /*
5383:  Permute A into C's *local* index space using rowemb,colemb.
5384:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5385:  of [0,m), colemb is in [0,n).
5386:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5387:  */
5388: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5389: {
5390:   /* If making this function public, change the error returned in this function away from _PLIB. */
5391:   Mat_SeqAIJ     *Baij;
5392:   PetscBool       seqaij;
5393:   PetscInt        m, n, *nz, i, j, count;
5394:   PetscScalar     v;
5395:   const PetscInt *rowindices, *colindices;

5397:   PetscFunctionBegin;
5398:   if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5399:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5400:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5401:   PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5402:   if (rowemb) {
5403:     PetscCall(ISGetLocalSize(rowemb, &m));
5404:     PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n);
5405:   } else {
5406:     PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5407:   }
5408:   if (colemb) {
5409:     PetscCall(ISGetLocalSize(colemb, &n));
5410:     PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n);
5411:   } else {
5412:     PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5413:   }

5415:   Baij = (Mat_SeqAIJ *)B->data;
5416:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
5417:     PetscCall(PetscMalloc1(B->rmap->n, &nz));
5418:     for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5419:     PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5420:     PetscCall(PetscFree(nz));
5421:   }
5422:   if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5423:   count      = 0;
5424:   rowindices = NULL;
5425:   colindices = NULL;
5426:   if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5427:   if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5428:   for (i = 0; i < B->rmap->n; i++) {
5429:     PetscInt row;
5430:     row = i;
5431:     if (rowindices) row = rowindices[i];
5432:     for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5433:       PetscInt col;
5434:       col = Baij->j[count];
5435:       if (colindices) col = colindices[col];
5436:       v = Baij->a[count];
5437:       PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5438:       ++count;
5439:     }
5440:   }
5441:   /* FIXME: set C's nonzerostate correctly. */
5442:   /* Assembly for C is necessary. */
5443:   C->preallocated  = PETSC_TRUE;
5444:   C->assembled     = PETSC_TRUE;
5445:   C->was_assembled = PETSC_FALSE;
5446:   PetscFunctionReturn(PETSC_SUCCESS);
5447: }

5449: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5450: {
5451:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5452:   MatScalar  *aa = a->a;
5453:   PetscInt    m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5454:   PetscInt   *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;

5456:   PetscFunctionBegin;
5457:   PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5458:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5459:   for (i = 1; i <= m; i++) {
5460:     /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5461:     for (k = ai[i - 1]; k < ai[i]; k++) {
5462:       if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5463:       else {
5464:         if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5465:         aa[k - fshift] = aa[k];
5466:         aj[k - fshift] = aj[k];
5467:       }
5468:     }
5469:     ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5470:     fshift_prev = fshift;
5471:     /* reset ilen and imax for each row */
5472:     ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5473:     a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5474:     rmax = PetscMax(rmax, ailen[i - 1]);
5475:   }
5476:   if (fshift) {
5477:     if (m) {
5478:       ai[m] -= fshift;
5479:       a->nz = ai[m];
5480:     }
5481:     PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz));
5482:     A->nonzerostate++;
5483:     A->info.nz_unneeded += (PetscReal)fshift;
5484:     a->rmax = rmax;
5485:     if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5486:     PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5487:     PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5488:   }
5489:   PetscFunctionReturn(PETSC_SUCCESS);
5490: }

5492: PetscFunctionList MatSeqAIJList = NULL;

5494: /*@
5495:   MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype

5497:   Collective

5499:   Input Parameters:
5500: + mat    - the matrix object
5501: - matype - matrix type

5503:   Options Database Key:
5504: . -mat_seqaij_type  <method> - for example seqaijcrl

5506:   Level: intermediate

5508: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5509: @*/
5510: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5511: {
5512:   PetscBool sametype;
5513:   PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);

5515:   PetscFunctionBegin;
5517:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5518:   if (sametype) PetscFunctionReturn(PETSC_SUCCESS);

5520:   PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5521:   PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5522:   PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5523:   PetscFunctionReturn(PETSC_SUCCESS);
5524: }

5526: /*@C
5527:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices

5529:   Not Collective, No Fortran Support

5531:   Input Parameters:
5532: + sname    - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5533: - function - routine to convert to subtype

5535:   Level: advanced

5537:   Notes:
5538:   `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.

5540:   Then, your matrix can be chosen with the procedural interface at runtime via the option
5541: $     -mat_seqaij_type my_mat

5543: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5544: @*/
5545: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5546: {
5547:   PetscFunctionBegin;
5548:   PetscCall(MatInitializePackage());
5549:   PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5550:   PetscFunctionReturn(PETSC_SUCCESS);
5551: }

5553: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5555: /*@C
5556:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`

5558:   Not Collective

5560:   Level: advanced

5562:   Note:
5563:   This registers the versions of `MATSEQAIJ` for GPUs

5565: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5566: @*/
5567: PetscErrorCode MatSeqAIJRegisterAll(void)
5568: {
5569:   PetscFunctionBegin;
5570:   if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5571:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5573:   PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5574:   PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5575:   PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5576: #if defined(PETSC_HAVE_MKL_SPARSE)
5577:   PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5578: #endif
5579: #if defined(PETSC_HAVE_CUDA)
5580:   PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5581: #endif
5582: #if defined(PETSC_HAVE_HIP)
5583:   PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5584: #endif
5585: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5586:   PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5587: #endif
5588: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5589:   PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5590: #endif
5591:   PetscFunctionReturn(PETSC_SUCCESS);
5592: }

5594: /*
5595:     Special version for direct calls from Fortran
5596: */
5597: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5598:   #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5599: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5600:   #define matsetvaluesseqaij_ matsetvaluesseqaij
5601: #endif

5603: /* Change these macros so can be used in void function */

5605: /* Change these macros so can be used in void function */
5606: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5607: #undef PetscCall
5608: #define PetscCall(...) \
5609:   do { \
5610:     PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5611:     if (PetscUnlikely(ierr_msv_mpiaij)) { \
5612:       *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5613:       return; \
5614:     } \
5615:   } while (0)

5617: #undef SETERRQ
5618: #define SETERRQ(comm, ierr, ...) \
5619:   do { \
5620:     *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5621:     return; \
5622:   } while (0)

5624: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5625: {
5626:   Mat         A = *AA;
5627:   PetscInt    m = *mm, n = *nn;
5628:   InsertMode  is = *isis;
5629:   Mat_SeqAIJ *a  = (Mat_SeqAIJ *)A->data;
5630:   PetscInt   *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5631:   PetscInt   *imax, *ai, *ailen;
5632:   PetscInt   *aj, nonew = a->nonew, lastcol = -1;
5633:   MatScalar  *ap, value, *aa;
5634:   PetscBool   ignorezeroentries = a->ignorezeroentries;
5635:   PetscBool   roworiented       = a->roworiented;

5637:   PetscFunctionBegin;
5638:   MatCheckPreallocated(A, 1);
5639:   imax  = a->imax;
5640:   ai    = a->i;
5641:   ailen = a->ilen;
5642:   aj    = a->j;
5643:   aa    = a->a;

5645:   for (k = 0; k < m; k++) { /* loop over added rows */
5646:     row = im[k];
5647:     if (row < 0) continue;
5648:     PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5649:     rp   = aj + ai[row];
5650:     ap   = aa + ai[row];
5651:     rmax = imax[row];
5652:     nrow = ailen[row];
5653:     low  = 0;
5654:     high = nrow;
5655:     for (l = 0; l < n; l++) { /* loop over added columns */
5656:       if (in[l] < 0) continue;
5657:       PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5658:       col = in[l];
5659:       if (roworiented) value = v[l + k * n];
5660:       else value = v[k + l * m];

5662:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5664:       if (col <= lastcol) low = 0;
5665:       else high = nrow;
5666:       lastcol = col;
5667:       while (high - low > 5) {
5668:         t = (low + high) / 2;
5669:         if (rp[t] > col) high = t;
5670:         else low = t;
5671:       }
5672:       for (i = low; i < high; i++) {
5673:         if (rp[i] > col) break;
5674:         if (rp[i] == col) {
5675:           if (is == ADD_VALUES) ap[i] += value;
5676:           else ap[i] = value;
5677:           goto noinsert;
5678:         }
5679:       }
5680:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5681:       if (nonew == 1) goto noinsert;
5682:       PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5683:       MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5684:       N = nrow++ - 1;
5685:       a->nz++;
5686:       high++;
5687:       /* shift up all the later entries in this row */
5688:       for (ii = N; ii >= i; ii--) {
5689:         rp[ii + 1] = rp[ii];
5690:         ap[ii + 1] = ap[ii];
5691:       }
5692:       rp[i] = col;
5693:       ap[i] = value;
5694:     noinsert:;
5695:       low = i + 1;
5696:     }
5697:     ailen[row] = nrow;
5698:   }
5699:   PetscFunctionReturnVoid();
5700: }
5701: /* Undefining these here since they were redefined from their original definition above! No
5702:  * other PETSc functions should be defined past this point, as it is impossible to recover the
5703:  * original definitions */
5704: #undef PetscCall
5705: #undef SETERRQ