Actual source code: aij.c

petsc-master 2020-09-19
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  1: /*
  2:     Defines the basic matrix operations for the AIJ (compressed row)
  3:   matrix storage format.
  4: */


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

 12: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
 13: {
 14:   PetscErrorCode       ierr;
 15:   PetscBool            flg;
 16:   char                 type[256];

 19:   PetscObjectOptionsBegin((PetscObject)A);
 20:   PetscOptionsFList("-mat_seqaij_type","Matrix SeqAIJ type","MatSeqAIJSetType",MatSeqAIJList,"seqaij",type,256,&flg);
 21:   if (flg) {
 22:     MatSeqAIJSetType(A,type);
 23:   }
 24:   PetscOptionsEnd();
 25:   return(0);
 26: }

 28: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
 29: {
 31:   PetscInt       i,m,n;
 32:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

 35:   MatGetSize(A,&m,&n);
 36:   PetscArrayzero(norms,n);
 37:   if (type == NORM_2) {
 38:     for (i=0; i<aij->i[m]; i++) {
 39:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
 40:     }
 41:   } else if (type == NORM_1) {
 42:     for (i=0; i<aij->i[m]; i++) {
 43:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
 44:     }
 45:   } else if (type == NORM_INFINITY) {
 46:     for (i=0; i<aij->i[m]; i++) {
 47:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
 48:     }
 49:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

 51:   if (type == NORM_2) {
 52:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
 53:   }
 54:   return(0);
 55: }

 57: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
 58: {
 59:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 60:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
 61:   const PetscInt  *jj = a->j,*ii = a->i;
 62:   PetscInt        *rows;
 63:   PetscErrorCode  ierr;

 66:   for (i=0; i<m; i++) {
 67:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
 68:       cnt++;
 69:     }
 70:   }
 71:   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:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
 80:   return(0);
 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 = a->a;
 87:   PetscInt        i,m=A->rmap->n,cnt = 0;
 88:   const PetscInt  *ii = a->i,*jj = a->j,*diag;
 89:   PetscInt        *rows;
 90:   PetscErrorCode  ierr;

 93:   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)) {
 97:       cnt++;
 98:     }
 99:   }
100:   PetscMalloc1(cnt,&rows);
101:   cnt  = 0;
102:   for (i=0; i<m; i++) {
103:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
104:       rows[cnt++] = i;
105:     }
106:   }
107:   *nrows = cnt;
108:   *zrows = rows;
109:   return(0);
110: }

112: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
113: {
114:   PetscInt       nrows,*rows;

118:   *zrows = NULL;
119:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
120:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
121:   return(0);
122: }

124: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
125: {
126:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
127:   const MatScalar *aa;
128:   PetscInt        m=A->rmap->n,cnt = 0;
129:   const PetscInt  *ii;
130:   PetscInt        n,i,j,*rows;
131:   PetscErrorCode  ierr;

134:   *keptrows = NULL;
135:   ii        = a->i;
136:   for (i=0; i<m; i++) {
137:     n = ii[i+1] - ii[i];
138:     if (!n) {
139:       cnt++;
140:       goto ok1;
141:     }
142:     aa = a->a + ii[i];
143:     for (j=0; j<n; j++) {
144:       if (aa[j] != 0.0) goto ok1;
145:     }
146:     cnt++;
147: ok1:;
148:   }
149:   if (!cnt) return(0);
150:   PetscMalloc1(A->rmap->n-cnt,&rows);
151:   cnt  = 0;
152:   for (i=0; i<m; i++) {
153:     n = ii[i+1] - ii[i];
154:     if (!n) continue;
155:     aa = a->a + ii[i];
156:     for (j=0; j<n; j++) {
157:       if (aa[j] != 0.0) {
158:         rows[cnt++] = i;
159:         break;
160:       }
161:     }
162:   }
163:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
164:   return(0);
165: }

167: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
168: {
169:   PetscErrorCode    ierr;
170:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
171:   PetscInt          i,m = Y->rmap->n;
172:   const PetscInt    *diag;
173:   MatScalar         *aa = aij->a;
174:   const PetscScalar *v;
175:   PetscBool         missing;
176: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
177:   PetscBool         inserted = PETSC_FALSE;
178: #endif

181:   if (Y->assembled) {
182:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
183:     if (!missing) {
184:       diag = aij->diag;
185:       VecGetArrayRead(D,&v);
186:       if (is == INSERT_VALUES) {
187: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
188:         inserted = PETSC_TRUE;
189: #endif
190:         for (i=0; i<m; i++) {
191:           aa[diag[i]] = v[i];
192:         }
193:       } else {
194:         for (i=0; i<m; i++) {
195: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
196:           if (v[i] != 0.0) inserted = PETSC_TRUE;
197: #endif
198:           aa[diag[i]] += v[i];
199:         }
200:       }
201: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
202:       if (inserted) Y->offloadmask = PETSC_OFFLOAD_CPU;
203: #endif
204:       VecRestoreArrayRead(D,&v);
205:       return(0);
206:     }
207:     MatSeqAIJInvalidateDiagonal(Y);
208:   }
209:   MatDiagonalSet_Default(Y,D,is);
210:   return(0);
211: }

213: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
214: {
215:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
217:   PetscInt       i,ishift;

220:   *m = A->rmap->n;
221:   if (!ia) return(0);
222:   ishift = 0;
223:   if (symmetric && !A->structurally_symmetric) {
224:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
225:   } else if (oshift == 1) {
226:     PetscInt *tia;
227:     PetscInt nz = a->i[A->rmap->n];
228:     /* malloc space and  add 1 to i and j indices */
229:     PetscMalloc1(A->rmap->n+1,&tia);
230:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
231:     *ia = tia;
232:     if (ja) {
233:       PetscInt *tja;
234:       PetscMalloc1(nz+1,&tja);
235:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
236:       *ja = tja;
237:     }
238:   } else {
239:     *ia = a->i;
240:     if (ja) *ja = a->j;
241:   }
242:   return(0);
243: }

245: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
246: {

250:   if (!ia) return(0);
251:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
252:     PetscFree(*ia);
253:     if (ja) {PetscFree(*ja);}
254:   }
255:   return(0);
256: }

258: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
259: {
260:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
262:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
263:   PetscInt       nz = a->i[m],row,*jj,mr,col;

266:   *nn = n;
267:   if (!ia) return(0);
268:   if (symmetric) {
269:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
270:   } else {
271:     PetscCalloc1(n,&collengths);
272:     PetscMalloc1(n+1,&cia);
273:     PetscMalloc1(nz,&cja);
274:     jj   = a->j;
275:     for (i=0; i<nz; i++) {
276:       collengths[jj[i]]++;
277:     }
278:     cia[0] = oshift;
279:     for (i=0; i<n; i++) {
280:       cia[i+1] = cia[i] + collengths[i];
281:     }
282:     PetscArrayzero(collengths,n);
283:     jj   = a->j;
284:     for (row=0; row<m; row++) {
285:       mr = a->i[row+1] - a->i[row];
286:       for (i=0; i<mr; i++) {
287:         col = *jj++;

289:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
290:       }
291:     }
292:     PetscFree(collengths);
293:     *ia  = cia; *ja = cja;
294:   }
295:   return(0);
296: }

298: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
299: {

303:   if (!ia) return(0);

305:   PetscFree(*ia);
306:   PetscFree(*ja);
307:   return(0);
308: }

310: /*
311:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
312:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
313:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
314: */
315: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
316: {
317:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
319:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
320:   PetscInt       nz = a->i[m],row,mr,col,tmp;
321:   PetscInt       *cspidx;
322:   const PetscInt *jj;

325:   *nn = n;
326:   if (!ia) return(0);

328:   PetscCalloc1(n,&collengths);
329:   PetscMalloc1(n+1,&cia);
330:   PetscMalloc1(nz,&cja);
331:   PetscMalloc1(nz,&cspidx);
332:   jj   = a->j;
333:   for (i=0; i<nz; i++) {
334:     collengths[jj[i]]++;
335:   }
336:   cia[0] = oshift;
337:   for (i=0; i<n; i++) {
338:     cia[i+1] = cia[i] + collengths[i];
339:   }
340:   PetscArrayzero(collengths,n);
341:   jj   = a->j;
342:   for (row=0; row<m; row++) {
343:     mr = a->i[row+1] - a->i[row];
344:     for (i=0; i<mr; i++) {
345:       col         = *jj++;
346:       tmp         = cia[col] + collengths[col]++ - oshift;
347:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
348:       cja[tmp]    = row + oshift;
349:     }
350:   }
351:   PetscFree(collengths);
352:   *ia    = cia;
353:   *ja    = cja;
354:   *spidx = cspidx;
355:   return(0);
356: }

358: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
359: {

363:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
364:   PetscFree(*spidx);
365:   return(0);
366: }

368: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
369: {
370:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
371:   PetscInt       *ai = a->i;

375:   PetscArraycpy(a->a+ai[row],v,ai[row+1]-ai[row]);
376: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
377:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && ai[row+1]-ai[row]) A->offloadmask = PETSC_OFFLOAD_CPU;
378: #endif
379:   return(0);
380: }

382: /*
383:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

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

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

392: */

394: #include <petsc/private/isimpl.h>
395: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
396: {
397:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
398:   PetscInt       low,high,t,row,nrow,i,col,l;
399:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
400:   PetscInt       lastcol = -1;
401:   MatScalar      *ap,value,*aa = a->a;
402:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

404:   row  = ridx[im[0]];
405:   rp   = aj + ai[row];
406:   ap   = aa + ai[row];
407:   nrow = ailen[row];
408:   low  = 0;
409:   high = nrow;
410:   for (l=0; l<n; l++) { /* loop over added columns */
411:     col = cidx[in[l]];
412:     value = v[l];

414:     if (col <= lastcol) low = 0;
415:     else high = nrow;
416:     lastcol = col;
417:     while (high-low > 5) {
418:       t = (low+high)/2;
419:       if (rp[t] > col) high = t;
420:       else low = t;
421:     }
422:     for (i=low; i<high; i++) {
423:       if (rp[i] == col) {
424:         ap[i] += value;
425:         low = i + 1;
426:         break;
427:       }
428:     }
429:   }
430: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
431:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
432: #endif
433:   return 0;
434: }

436: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
437: {
438:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
439:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
440:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
442:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
443:   MatScalar      *ap=NULL,value=0.0,*aa = a->a;
444:   PetscBool      ignorezeroentries = a->ignorezeroentries;
445:   PetscBool      roworiented       = a->roworiented;
446: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
447:   PetscBool      inserted          = PETSC_FALSE;
448: #endif

451:   for (k=0; k<m; k++) { /* loop over added rows */
452:     row = im[k];
453:     if (row < 0) continue;
454:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
455:     rp   = aj + ai[row];
456:     if (!A->structure_only) ap = aa + ai[row];
457:     rmax = imax[row]; nrow = ailen[row];
458:     low  = 0;
459:     high = nrow;
460:     for (l=0; l<n; l++) { /* loop over added columns */
461:       if (in[l] < 0) continue;
462:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
463:       col = in[l];
464:       if (v && !A->structure_only) value = roworiented ? v[l + k*n] : v[k + l*m];
465:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

467:       if (col <= lastcol) low = 0;
468:       else high = nrow;
469:       lastcol = col;
470:       while (high-low > 5) {
471:         t = (low+high)/2;
472:         if (rp[t] > col) high = t;
473:         else low = t;
474:       }
475:       for (i=low; i<high; i++) {
476:         if (rp[i] > col) break;
477:         if (rp[i] == col) {
478:           if (!A->structure_only) {
479:             if (is == ADD_VALUES) {
480:               ap[i] += value;
481:               (void)PetscLogFlops(1.0);
482:             }
483:             else ap[i] = value;
484: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
485:             inserted = PETSC_TRUE;
486: #endif
487:           }
488:           low = i + 1;
489:           goto noinsert;
490:         }
491:       }
492:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
493:       if (nonew == 1) goto noinsert;
494:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
495:       if (A->structure_only) {
496:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
497:       } else {
498:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
499:       }
500:       N = nrow++ - 1; a->nz++; high++;
501:       /* shift up all the later entries in this row */
502:       PetscArraymove(rp+i+1,rp+i,N-i+1);
503:       rp[i] = col;
504:       if (!A->structure_only){
505:         PetscArraymove(ap+i+1,ap+i,N-i+1);
506:         ap[i] = value;
507:       }
508:       low = i + 1;
509:       A->nonzerostate++;
510: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
511:       inserted = PETSC_TRUE;
512: #endif
513: noinsert:;
514:     }
515:     ailen[row] = nrow;
516:   }
517: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
518:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && inserted) A->offloadmask = PETSC_OFFLOAD_CPU;
519: #endif
520:   return(0);
521: }


524: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
525: {
526:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
527:   PetscInt       *rp,k,row;
528:   PetscInt       *ai = a->i;
530:   PetscInt       *aj = a->j;
531:   MatScalar      *aa = a->a,*ap;

534:   if (A->was_assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot call on assembled matrix.");
535:   if (m*n+a->nz > a->maxnz) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Number of entries in matrix will be larger than maximum nonzeros allocated for %D in MatSeqAIJSetTotalPreallocation()",a->maxnz);
536:   for (k=0; k<m; k++) { /* loop over added rows */
537:     row  = im[k];
538:     rp   = aj + ai[row];
539:     ap   = aa + ai[row];

541:     PetscMemcpy(rp,in,n*sizeof(PetscInt));
542:     if (!A->structure_only) {
543:       if (v) {
544:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
545:         v   += n;
546:       } else {
547:         PetscMemzero(ap,n*sizeof(PetscScalar));
548:       }
549:     }
550:     a->ilen[row] = n;
551:     a->imax[row] = n;
552:     a->i[row+1]  = a->i[row]+n;
553:     a->nz       += n;
554:   }
555: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
556:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
557: #endif
558:   return(0);
559: }

561: /*@
562:     MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

564:   Input Parameters:
565: +  A - the SeqAIJ matrix
566: -  nztotal - bound on the number of nonzeros

568:   Level: advanced

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

575: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
576: @*/

578: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
579: {
581:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

584:   PetscLayoutSetUp(A->rmap);
585:   PetscLayoutSetUp(A->cmap);
586:   a->maxnz  = nztotal;
587:   if (!a->imax) {
588:     PetscMalloc1(A->rmap->n,&a->imax);
589:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
590:   }
591:   if (!a->ilen) {
592:     PetscMalloc1(A->rmap->n,&a->ilen);
593:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
594:   } else {
595:     PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
596:   }

598:   /* allocate the matrix space */
599:   if (A->structure_only) {
600:     PetscMalloc1(nztotal,&a->j);
601:     PetscMalloc1(A->rmap->n+1,&a->i);
602:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
603:   } else {
604:     PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
605:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
606:   }
607:   a->i[0] = 0;
608:   if (A->structure_only) {
609:     a->singlemalloc = PETSC_FALSE;
610:     a->free_a       = PETSC_FALSE;
611:   } else {
612:     a->singlemalloc = PETSC_TRUE;
613:     a->free_a       = PETSC_TRUE;
614:   }
615:   a->free_ij         = PETSC_TRUE;
616:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
617:   A->preallocated   = PETSC_TRUE;
618:   return(0);
619: }

621: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
622: {
623:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
624:   PetscInt       *rp,k,row;
625:   PetscInt       *ai = a->i,*ailen = a->ilen;
627:   PetscInt       *aj = a->j;
628:   MatScalar      *aa = a->a,*ap;

631:   for (k=0; k<m; k++) { /* loop over added rows */
632:     row  = im[k];
633:     if (PetscUnlikelyDebug(n > a->imax[row])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Preallocation for row %D does not match number of columns provided",n);
634:     rp   = aj + ai[row];
635:     ap   = aa + ai[row];
636:     if (!A->was_assembled) {
637:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
638:     }
639:     if (!A->structure_only) {
640:       if (v) {
641:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
642:         v   += n;
643:       } else {
644:         PetscMemzero(ap,n*sizeof(PetscScalar));
645:       }
646:     }
647:     ailen[row] = n;
648:     a->nz      += n;
649:   }
650: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
651:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
652: #endif
653:   return(0);
654: }


657: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
658: {
659:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
660:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
661:   PetscInt   *ai = a->i,*ailen = a->ilen;
662:   MatScalar  *ap,*aa = a->a;

665:   for (k=0; k<m; k++) { /* loop over rows */
666:     row = im[k];
667:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
668:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
669:     rp   = aj + ai[row]; ap = aa + ai[row];
670:     nrow = ailen[row];
671:     for (l=0; l<n; l++) { /* loop over columns */
672:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
673:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
674:       col  = in[l];
675:       high = nrow; low = 0; /* assume unsorted */
676:       while (high-low > 5) {
677:         t = (low+high)/2;
678:         if (rp[t] > col) high = t;
679:         else low = t;
680:       }
681:       for (i=low; i<high; i++) {
682:         if (rp[i] > col) break;
683:         if (rp[i] == col) {
684:           *v++ = ap[i];
685:           goto finished;
686:         }
687:       }
688:       *v++ = 0.0;
689: finished:;
690:     }
691:   }
692:   return(0);
693: }

695: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
696: {
697:   Mat_SeqAIJ     *A = (Mat_SeqAIJ*)mat->data;
698:   PetscInt       header[4],M,N,m,nz,i;
699:   PetscInt       *rowlens;

703:   PetscViewerSetUp(viewer);

705:   M  = mat->rmap->N;
706:   N  = mat->cmap->N;
707:   m  = mat->rmap->n;
708:   nz = A->nz;

710:   /* write matrix header */
711:   header[0] = MAT_FILE_CLASSID;
712:   header[1] = M; header[2] = N; header[3] = nz;
713:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

715:   /* fill in and store row lengths */
716:   PetscMalloc1(m,&rowlens);
717:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
718:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
719:   PetscFree(rowlens);
720:   /* store column indices */
721:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
722:   /* store nonzero values */
723:   PetscViewerBinaryWrite(viewer,A->a,nz,PETSC_SCALAR);

725:   /* write block size option to the viewer's .info file */
726:   MatView_Binary_BlockSizes(mat,viewer);
727:   return(0);
728: }

730: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
731: {
733:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
734:   PetscInt       i,k,m=A->rmap->N;

737:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
738:   for (i=0; i<m; i++) {
739:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
740:     for (k=a->i[i]; k<a->i[i+1]; k++) {
741:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
742:     }
743:     PetscViewerASCIIPrintf(viewer,"\n");
744:   }
745:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
746:   return(0);
747: }

749: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

751: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
752: {
753:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
754:   PetscErrorCode    ierr;
755:   PetscInt          i,j,m = A->rmap->n;
756:   const char        *name;
757:   PetscViewerFormat format;

760:   if (A->structure_only) {
761:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
762:     return(0);
763:   }

765:   PetscViewerGetFormat(viewer,&format);
766:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
767:     PetscInt nofinalvalue = 0;
768:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
769:       /* Need a dummy value to ensure the dimension of the matrix. */
770:       nofinalvalue = 1;
771:     }
772:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
773:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
774:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
775: #if defined(PETSC_USE_COMPLEX)
776:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
777: #else
778:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
779: #endif
780:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

782:     for (i=0; i<m; i++) {
783:       for (j=a->i[i]; j<a->i[i+1]; j++) {
784: #if defined(PETSC_USE_COMPLEX)
785:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
786: #else
787:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
788: #endif
789:       }
790:     }
791:     if (nofinalvalue) {
792: #if defined(PETSC_USE_COMPLEX)
793:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
794: #else
795:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
796: #endif
797:     }
798:     PetscObjectGetName((PetscObject)A,&name);
799:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
800:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
801:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
802:     return(0);
803:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
804:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
805:     for (i=0; i<m; i++) {
806:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
807:       for (j=a->i[i]; j<a->i[i+1]; j++) {
808: #if defined(PETSC_USE_COMPLEX)
809:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
810:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
811:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
812:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
813:         } else if (PetscRealPart(a->a[j]) != 0.0) {
814:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
815:         }
816: #else
817:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
818: #endif
819:       }
820:       PetscViewerASCIIPrintf(viewer,"\n");
821:     }
822:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
823:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
824:     PetscInt nzd=0,fshift=1,*sptr;
825:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
826:     PetscMalloc1(m+1,&sptr);
827:     for (i=0; i<m; i++) {
828:       sptr[i] = nzd+1;
829:       for (j=a->i[i]; j<a->i[i+1]; j++) {
830:         if (a->j[j] >= i) {
831: #if defined(PETSC_USE_COMPLEX)
832:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
833: #else
834:           if (a->a[j] != 0.0) nzd++;
835: #endif
836:         }
837:       }
838:     }
839:     sptr[m] = nzd+1;
840:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
841:     for (i=0; i<m+1; i+=6) {
842:       if (i+4<m) {
843:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
844:       } else if (i+3<m) {
845:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
846:       } else if (i+2<m) {
847:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
848:       } else if (i+1<m) {
849:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
850:       } else if (i<m) {
851:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
852:       } else {
853:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
854:       }
855:     }
856:     PetscViewerASCIIPrintf(viewer,"\n");
857:     PetscFree(sptr);
858:     for (i=0; i<m; i++) {
859:       for (j=a->i[i]; j<a->i[i+1]; j++) {
860:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
861:       }
862:       PetscViewerASCIIPrintf(viewer,"\n");
863:     }
864:     PetscViewerASCIIPrintf(viewer,"\n");
865:     for (i=0; i<m; i++) {
866:       for (j=a->i[i]; j<a->i[i+1]; j++) {
867:         if (a->j[j] >= i) {
868: #if defined(PETSC_USE_COMPLEX)
869:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
870:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
871:           }
872: #else
873:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
874: #endif
875:         }
876:       }
877:       PetscViewerASCIIPrintf(viewer,"\n");
878:     }
879:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
880:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
881:     PetscInt    cnt = 0,jcnt;
882:     PetscScalar value;
883: #if defined(PETSC_USE_COMPLEX)
884:     PetscBool   realonly = PETSC_TRUE;

886:     for (i=0; i<a->i[m]; i++) {
887:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
888:         realonly = PETSC_FALSE;
889:         break;
890:       }
891:     }
892: #endif

894:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
895:     for (i=0; i<m; i++) {
896:       jcnt = 0;
897:       for (j=0; j<A->cmap->n; j++) {
898:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
899:           value = a->a[cnt++];
900:           jcnt++;
901:         } else {
902:           value = 0.0;
903:         }
904: #if defined(PETSC_USE_COMPLEX)
905:         if (realonly) {
906:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
907:         } else {
908:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
909:         }
910: #else
911:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
912: #endif
913:       }
914:       PetscViewerASCIIPrintf(viewer,"\n");
915:     }
916:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
917:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
918:     PetscInt fshift=1;
919:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
920: #if defined(PETSC_USE_COMPLEX)
921:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
922: #else
923:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
924: #endif
925:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
926:     for (i=0; i<m; i++) {
927:       for (j=a->i[i]; j<a->i[i+1]; j++) {
928: #if defined(PETSC_USE_COMPLEX)
929:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
930: #else
931:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
932: #endif
933:       }
934:     }
935:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
936:   } else {
937:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
938:     if (A->factortype) {
939:       for (i=0; i<m; i++) {
940:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
941:         /* L part */
942:         for (j=a->i[i]; j<a->i[i+1]; j++) {
943: #if defined(PETSC_USE_COMPLEX)
944:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
945:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
946:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
947:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
948:           } else {
949:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
950:           }
951: #else
952:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
953: #endif
954:         }
955:         /* diagonal */
956:         j = a->diag[i];
957: #if defined(PETSC_USE_COMPLEX)
958:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
959:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
960:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
961:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
962:         } else {
963:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
964:         }
965: #else
966:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
967: #endif

969:         /* U part */
970:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
971: #if defined(PETSC_USE_COMPLEX)
972:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
973:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
974:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
975:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
976:           } else {
977:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
978:           }
979: #else
980:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
981: #endif
982:         }
983:         PetscViewerASCIIPrintf(viewer,"\n");
984:       }
985:     } else {
986:       for (i=0; i<m; i++) {
987:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
988:         for (j=a->i[i]; j<a->i[i+1]; j++) {
989: #if defined(PETSC_USE_COMPLEX)
990:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
991:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
992:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
993:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
994:           } else {
995:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
996:           }
997: #else
998:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
999: #endif
1000:         }
1001:         PetscViewerASCIIPrintf(viewer,"\n");
1002:       }
1003:     }
1004:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1005:   }
1006:   PetscViewerFlush(viewer);
1007:   return(0);
1008: }

1010: #include <petscdraw.h>
1011: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1012: {
1013:   Mat               A  = (Mat) Aa;
1014:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1015:   PetscErrorCode    ierr;
1016:   PetscInt          i,j,m = A->rmap->n;
1017:   int               color;
1018:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1019:   PetscViewer       viewer;
1020:   PetscViewerFormat format;

1023:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1024:   PetscViewerGetFormat(viewer,&format);
1025:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

1027:   /* loop over matrix elements drawing boxes */

1029:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1030:     PetscDrawCollectiveBegin(draw);
1031:     /* Blue for negative, Cyan for zero and  Red for positive */
1032:     color = PETSC_DRAW_BLUE;
1033:     for (i=0; i<m; i++) {
1034:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1035:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1036:         x_l = a->j[j]; x_r = x_l + 1.0;
1037:         if (PetscRealPart(a->a[j]) >=  0.) continue;
1038:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1039:       }
1040:     }
1041:     color = PETSC_DRAW_CYAN;
1042:     for (i=0; i<m; i++) {
1043:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1044:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1045:         x_l = a->j[j]; x_r = x_l + 1.0;
1046:         if (a->a[j] !=  0.) continue;
1047:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1048:       }
1049:     }
1050:     color = PETSC_DRAW_RED;
1051:     for (i=0; i<m; i++) {
1052:       y_l = m - i - 1.0; y_r = y_l + 1.0;
1053:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1054:         x_l = a->j[j]; x_r = x_l + 1.0;
1055:         if (PetscRealPart(a->a[j]) <=  0.) continue;
1056:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1057:       }
1058:     }
1059:     PetscDrawCollectiveEnd(draw);
1060:   } else {
1061:     /* use contour shading to indicate magnitude of values */
1062:     /* first determine max of all nonzero values */
1063:     PetscReal minv = 0.0, maxv = 0.0;
1064:     PetscInt  nz = a->nz, count = 0;
1065:     PetscDraw popup;

1067:     for (i=0; i<nz; i++) {
1068:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
1069:     }
1070:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
1071:     PetscDrawGetPopup(draw,&popup);
1072:     PetscDrawScalePopup(popup,minv,maxv);

1074:     PetscDrawCollectiveBegin(draw);
1075:     for (i=0; i<m; i++) {
1076:       y_l = m - i - 1.0;
1077:       y_r = y_l + 1.0;
1078:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1079:         x_l = a->j[j];
1080:         x_r = x_l + 1.0;
1081:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
1082:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1083:         count++;
1084:       }
1085:     }
1086:     PetscDrawCollectiveEnd(draw);
1087:   }
1088:   return(0);
1089: }

1091: #include <petscdraw.h>
1092: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
1093: {
1095:   PetscDraw      draw;
1096:   PetscReal      xr,yr,xl,yl,h,w;
1097:   PetscBool      isnull;

1100:   PetscViewerDrawGetDraw(viewer,0,&draw);
1101:   PetscDrawIsNull(draw,&isnull);
1102:   if (isnull) return(0);

1104:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
1105:   xr  += w;          yr += h;         xl = -w;     yl = -h;
1106:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1107:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1108:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
1109:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1110:   PetscDrawSave(draw);
1111:   return(0);
1112: }

1114: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
1115: {
1117:   PetscBool      iascii,isbinary,isdraw;

1120:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1121:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1122:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1123:   if (iascii) {
1124:     MatView_SeqAIJ_ASCII(A,viewer);
1125:   } else if (isbinary) {
1126:     MatView_SeqAIJ_Binary(A,viewer);
1127:   } else if (isdraw) {
1128:     MatView_SeqAIJ_Draw(A,viewer);
1129:   }
1130:   MatView_SeqAIJ_Inode(A,viewer);
1131:   return(0);
1132: }

1134: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
1135: {
1136:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1138:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
1139:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
1140:   MatScalar      *aa    = a->a,*ap;
1141:   PetscReal      ratio  = 0.6;

1144:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1145:   MatSeqAIJInvalidateDiagonal(A);
1146:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) return(0);

1148:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1149:   for (i=1; i<m; i++) {
1150:     /* move each row back by the amount of empty slots (fshift) before it*/
1151:     fshift += imax[i-1] - ailen[i-1];
1152:     rmax    = PetscMax(rmax,ailen[i]);
1153:     if (fshift) {
1154:       ip = aj + ai[i];
1155:       ap = aa + ai[i];
1156:       N  = ailen[i];
1157:       PetscArraymove(ip-fshift,ip,N);
1158:       if (!A->structure_only) {
1159:         PetscArraymove(ap-fshift,ap,N);
1160:       }
1161:     }
1162:     ai[i] = ai[i-1] + ailen[i-1];
1163:   }
1164:   if (m) {
1165:     fshift += imax[m-1] - ailen[m-1];
1166:     ai[m]   = ai[m-1] + ailen[m-1];
1167:   }

1169:   /* reset ilen and imax for each row */
1170:   a->nonzerorowcnt = 0;
1171:   if (A->structure_only) {
1172:     PetscFree(a->imax);
1173:     PetscFree(a->ilen);
1174:   } else { /* !A->structure_only */
1175:     for (i=0; i<m; i++) {
1176:       ailen[i] = imax[i] = ai[i+1] - ai[i];
1177:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1178:     }
1179:   }
1180:   a->nz = ai[m];
1181:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

1183:   MatMarkDiagonal_SeqAIJ(A);
1184:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1185:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1186:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

1188:   A->info.mallocs    += a->reallocs;
1189:   a->reallocs         = 0;
1190:   A->info.nz_unneeded = (PetscReal)fshift;
1191:   a->rmax             = rmax;

1193:   if (!A->structure_only) {
1194:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1195:   }
1196:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1197:   return(0);
1198: }

1200: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1201: {
1202:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1203:   PetscInt       i,nz = a->nz;
1204:   MatScalar      *aa = a->a;

1208:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1209:   MatSeqAIJInvalidateDiagonal(A);
1210: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1211:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1212: #endif
1213:   return(0);
1214: }

1216: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1217: {
1218:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1219:   PetscInt       i,nz = a->nz;
1220:   MatScalar      *aa = a->a;

1224:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1225:   MatSeqAIJInvalidateDiagonal(A);
1226: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1227:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1228: #endif
1229:   return(0);
1230: }

1232: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1233: {
1234:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1238:   PetscArrayzero(a->a,a->i[A->rmap->n]);
1239:   MatSeqAIJInvalidateDiagonal(A);
1240: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1241:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1242: #endif
1243:   return(0);
1244: }

1246: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1247: {
1248:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1252: #if defined(PETSC_USE_LOG)
1253:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1254: #endif
1255:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1256:   ISDestroy(&a->row);
1257:   ISDestroy(&a->col);
1258:   PetscFree(a->diag);
1259:   PetscFree(a->ibdiag);
1260:   PetscFree(a->imax);
1261:   PetscFree(a->ilen);
1262:   PetscFree(a->ipre);
1263:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1264:   PetscFree(a->solve_work);
1265:   ISDestroy(&a->icol);
1266:   PetscFree(a->saved_values);
1267:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

1269:   MatDestroy_SeqAIJ_Inode(A);
1270:   PetscFree(A->data);

1272:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1273:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1274:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
1275:      users reusing the matrix object with different data to incur in obscure segmentation faults
1276:      due to different matrix sizes */
1277:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

1279:   PetscObjectChangeTypeName((PetscObject)A,NULL);
1280:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1281:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1282:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1283:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1284:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1285:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);

1287: #if defined(PETSC_HAVE_CUDA)
1288:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
1289:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
1290: #endif
1291:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
1292: #if defined(PETSC_HAVE_ELEMENTAL)
1293:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1294: #endif
1295: #if defined(PETSC_HAVE_SCALAPACK)
1296:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
1297: #endif
1298: #if defined(PETSC_HAVE_HYPRE)
1299:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1300:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
1301: #endif
1302:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1303:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
1304:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
1305:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1306:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1307:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1308:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1309:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1310:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
1311:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
1312:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
1313:   return(0);
1314: }

1316: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1317: {
1318:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1322:   switch (op) {
1323:   case MAT_ROW_ORIENTED:
1324:     a->roworiented = flg;
1325:     break;
1326:   case MAT_KEEP_NONZERO_PATTERN:
1327:     a->keepnonzeropattern = flg;
1328:     break;
1329:   case MAT_NEW_NONZERO_LOCATIONS:
1330:     a->nonew = (flg ? 0 : 1);
1331:     break;
1332:   case MAT_NEW_NONZERO_LOCATION_ERR:
1333:     a->nonew = (flg ? -1 : 0);
1334:     break;
1335:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1336:     a->nonew = (flg ? -2 : 0);
1337:     break;
1338:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1339:     a->nounused = (flg ? -1 : 0);
1340:     break;
1341:   case MAT_IGNORE_ZERO_ENTRIES:
1342:     a->ignorezeroentries = flg;
1343:     break;
1344:   case MAT_SPD:
1345:   case MAT_SYMMETRIC:
1346:   case MAT_STRUCTURALLY_SYMMETRIC:
1347:   case MAT_HERMITIAN:
1348:   case MAT_SYMMETRY_ETERNAL:
1349:   case MAT_STRUCTURE_ONLY:
1350:     /* These options are handled directly by MatSetOption() */
1351:     break;
1352:   case MAT_NEW_DIAGONALS:
1353:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1354:   case MAT_USE_HASH_TABLE:
1355:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1356:     break;
1357:   case MAT_USE_INODES:
1358:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1359:     break;
1360:   case MAT_SUBMAT_SINGLEIS:
1361:     A->submat_singleis = flg;
1362:     break;
1363:   case MAT_SORTED_FULL:
1364:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1365:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
1366:     break;
1367:   default:
1368:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1369:   }
1370:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1371:   return(0);
1372: }

1374: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1375: {
1376:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1378:   PetscInt       i,j,n,*ai=a->i,*aj=a->j;
1379:   PetscScalar    *aa=a->a,*x;

1382:   VecGetLocalSize(v,&n);
1383:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1385:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1386:     PetscInt *diag=a->diag;
1387:     VecGetArrayWrite(v,&x);
1388:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1389:     VecRestoreArrayWrite(v,&x);
1390:     return(0);
1391:   }

1393:   VecGetArrayWrite(v,&x);
1394:   for (i=0; i<n; i++) {
1395:     x[i] = 0.0;
1396:     for (j=ai[i]; j<ai[i+1]; j++) {
1397:       if (aj[j] == i) {
1398:         x[i] = aa[j];
1399:         break;
1400:       }
1401:     }
1402:   }
1403:   VecRestoreArrayWrite(v,&x);
1404:   return(0);
1405: }

1407: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1408: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1409: {
1410:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1411:   PetscScalar       *y;
1412:   const PetscScalar *x;
1413:   PetscErrorCode    ierr;
1414:   PetscInt          m = A->rmap->n;
1415: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1416:   const MatScalar   *v;
1417:   PetscScalar       alpha;
1418:   PetscInt          n,i,j;
1419:   const PetscInt    *idx,*ii,*ridx=NULL;
1420:   Mat_CompressedRow cprow    = a->compressedrow;
1421:   PetscBool         usecprow = cprow.use;
1422: #endif

1425:   if (zz != yy) {VecCopy(zz,yy);}
1426:   VecGetArrayRead(xx,&x);
1427:   VecGetArray(yy,&y);

1429: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1430:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1431: #else
1432:   if (usecprow) {
1433:     m    = cprow.nrows;
1434:     ii   = cprow.i;
1435:     ridx = cprow.rindex;
1436:   } else {
1437:     ii = a->i;
1438:   }
1439:   for (i=0; i<m; i++) {
1440:     idx = a->j + ii[i];
1441:     v   = a->a + ii[i];
1442:     n   = ii[i+1] - ii[i];
1443:     if (usecprow) {
1444:       alpha = x[ridx[i]];
1445:     } else {
1446:       alpha = x[i];
1447:     }
1448:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1449:   }
1450: #endif
1451:   PetscLogFlops(2.0*a->nz);
1452:   VecRestoreArrayRead(xx,&x);
1453:   VecRestoreArray(yy,&y);
1454:   return(0);
1455: }

1457: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1458: {

1462:   VecSet(yy,0.0);
1463:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1464:   return(0);
1465: }

1467: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1469: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1470: {
1471:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1472:   PetscScalar       *y;
1473:   const PetscScalar *x;
1474:   const MatScalar   *aa;
1475:   PetscErrorCode    ierr;
1476:   PetscInt          m=A->rmap->n;
1477:   const PetscInt    *aj,*ii,*ridx=NULL;
1478:   PetscInt          n,i;
1479:   PetscScalar       sum;
1480:   PetscBool         usecprow=a->compressedrow.use;

1482: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1483: #pragma disjoint(*x,*y,*aa)
1484: #endif

1487:   VecGetArrayRead(xx,&x);
1488:   VecGetArray(yy,&y);
1489:   ii   = a->i;
1490:   if (usecprow) { /* use compressed row format */
1491:     PetscArrayzero(y,m);
1492:     m    = a->compressedrow.nrows;
1493:     ii   = a->compressedrow.i;
1494:     ridx = a->compressedrow.rindex;
1495:     for (i=0; i<m; i++) {
1496:       n           = ii[i+1] - ii[i];
1497:       aj          = a->j + ii[i];
1498:       aa          = a->a + ii[i];
1499:       sum         = 0.0;
1500:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1501:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1502:       y[*ridx++] = sum;
1503:     }
1504:   } else { /* do not use compressed row format */
1505: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1506:     aj   = a->j;
1507:     aa   = a->a;
1508:     fortranmultaij_(&m,x,ii,aj,aa,y);
1509: #else
1510:     for (i=0; i<m; i++) {
1511:       n           = ii[i+1] - ii[i];
1512:       aj          = a->j + ii[i];
1513:       aa          = a->a + ii[i];
1514:       sum         = 0.0;
1515:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1516:       y[i] = sum;
1517:     }
1518: #endif
1519:   }
1520:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1521:   VecRestoreArrayRead(xx,&x);
1522:   VecRestoreArray(yy,&y);
1523:   return(0);
1524: }

1526: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1527: {
1528:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1529:   PetscScalar       *y;
1530:   const PetscScalar *x;
1531:   const MatScalar   *aa;
1532:   PetscErrorCode    ierr;
1533:   PetscInt          m=A->rmap->n;
1534:   const PetscInt    *aj,*ii,*ridx=NULL;
1535:   PetscInt          n,i,nonzerorow=0;
1536:   PetscScalar       sum;
1537:   PetscBool         usecprow=a->compressedrow.use;

1539: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1540: #pragma disjoint(*x,*y,*aa)
1541: #endif

1544:   VecGetArrayRead(xx,&x);
1545:   VecGetArray(yy,&y);
1546:   if (usecprow) { /* use compressed row format */
1547:     m    = a->compressedrow.nrows;
1548:     ii   = a->compressedrow.i;
1549:     ridx = a->compressedrow.rindex;
1550:     for (i=0; i<m; i++) {
1551:       n           = ii[i+1] - ii[i];
1552:       aj          = a->j + ii[i];
1553:       aa          = a->a + ii[i];
1554:       sum         = 0.0;
1555:       nonzerorow += (n>0);
1556:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1557:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1558:       y[*ridx++] = sum;
1559:     }
1560:   } else { /* do not use compressed row format */
1561:     ii = a->i;
1562:     for (i=0; i<m; i++) {
1563:       n           = ii[i+1] - ii[i];
1564:       aj          = a->j + ii[i];
1565:       aa          = a->a + ii[i];
1566:       sum         = 0.0;
1567:       nonzerorow += (n>0);
1568:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1569:       y[i] = sum;
1570:     }
1571:   }
1572:   PetscLogFlops(2.0*a->nz - nonzerorow);
1573:   VecRestoreArrayRead(xx,&x);
1574:   VecRestoreArray(yy,&y);
1575:   return(0);
1576: }

1578: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1579: {
1580:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1581:   PetscScalar       *y,*z;
1582:   const PetscScalar *x;
1583:   const MatScalar   *aa;
1584:   PetscErrorCode    ierr;
1585:   PetscInt          m = A->rmap->n,*aj,*ii;
1586:   PetscInt          n,i,*ridx=NULL;
1587:   PetscScalar       sum;
1588:   PetscBool         usecprow=a->compressedrow.use;

1591:   VecGetArrayRead(xx,&x);
1592:   VecGetArrayPair(yy,zz,&y,&z);
1593:   if (usecprow) { /* use compressed row format */
1594:     if (zz != yy) {
1595:       PetscArraycpy(z,y,m);
1596:     }
1597:     m    = a->compressedrow.nrows;
1598:     ii   = a->compressedrow.i;
1599:     ridx = a->compressedrow.rindex;
1600:     for (i=0; i<m; i++) {
1601:       n   = ii[i+1] - ii[i];
1602:       aj  = a->j + ii[i];
1603:       aa  = a->a + ii[i];
1604:       sum = y[*ridx];
1605:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1606:       z[*ridx++] = sum;
1607:     }
1608:   } else { /* do not use compressed row format */
1609:     ii = a->i;
1610:     for (i=0; i<m; i++) {
1611:       n   = ii[i+1] - ii[i];
1612:       aj  = a->j + ii[i];
1613:       aa  = a->a + ii[i];
1614:       sum = y[i];
1615:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1616:       z[i] = sum;
1617:     }
1618:   }
1619:   PetscLogFlops(2.0*a->nz);
1620:   VecRestoreArrayRead(xx,&x);
1621:   VecRestoreArrayPair(yy,zz,&y,&z);
1622:   return(0);
1623: }

1625: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1626: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1627: {
1628:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1629:   PetscScalar       *y,*z;
1630:   const PetscScalar *x;
1631:   const MatScalar   *aa;
1632:   PetscErrorCode    ierr;
1633:   const PetscInt    *aj,*ii,*ridx=NULL;
1634:   PetscInt          m = A->rmap->n,n,i;
1635:   PetscScalar       sum;
1636:   PetscBool         usecprow=a->compressedrow.use;

1639:   VecGetArrayRead(xx,&x);
1640:   VecGetArrayPair(yy,zz,&y,&z);
1641:   if (usecprow) { /* use compressed row format */
1642:     if (zz != yy) {
1643:       PetscArraycpy(z,y,m);
1644:     }
1645:     m    = a->compressedrow.nrows;
1646:     ii   = a->compressedrow.i;
1647:     ridx = a->compressedrow.rindex;
1648:     for (i=0; i<m; i++) {
1649:       n   = ii[i+1] - ii[i];
1650:       aj  = a->j + ii[i];
1651:       aa  = a->a + ii[i];
1652:       sum = y[*ridx];
1653:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1654:       z[*ridx++] = sum;
1655:     }
1656:   } else { /* do not use compressed row format */
1657:     ii = a->i;
1658: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1659:     aj = a->j;
1660:     aa = a->a;
1661:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1662: #else
1663:     for (i=0; i<m; i++) {
1664:       n   = ii[i+1] - ii[i];
1665:       aj  = a->j + ii[i];
1666:       aa  = a->a + ii[i];
1667:       sum = y[i];
1668:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1669:       z[i] = sum;
1670:     }
1671: #endif
1672:   }
1673:   PetscLogFlops(2.0*a->nz);
1674:   VecRestoreArrayRead(xx,&x);
1675:   VecRestoreArrayPair(yy,zz,&y,&z);
1676:   return(0);
1677: }

1679: /*
1680:      Adds diagonal pointers to sparse matrix structure.
1681: */
1682: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1683: {
1684:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1686:   PetscInt       i,j,m = A->rmap->n;

1689:   if (!a->diag) {
1690:     PetscMalloc1(m,&a->diag);
1691:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1692:   }
1693:   for (i=0; i<A->rmap->n; i++) {
1694:     a->diag[i] = a->i[i+1];
1695:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1696:       if (a->j[j] == i) {
1697:         a->diag[i] = j;
1698:         break;
1699:       }
1700:     }
1701:   }
1702:   return(0);
1703: }

1705: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
1706: {
1707:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1708:   const PetscInt    *diag = (const PetscInt*)a->diag;
1709:   const PetscInt    *ii = (const PetscInt*) a->i;
1710:   PetscInt          i,*mdiag = NULL;
1711:   PetscErrorCode    ierr;
1712:   PetscInt          cnt = 0; /* how many diagonals are missing */

1715:   if (!A->preallocated || !a->nz) {
1716:     MatSeqAIJSetPreallocation(A,1,NULL);
1717:     MatShift_Basic(A,v);
1718:     return(0);
1719:   }

1721:   if (a->diagonaldense) {
1722:     cnt = 0;
1723:   } else {
1724:     PetscCalloc1(A->rmap->n,&mdiag);
1725:     for (i=0; i<A->rmap->n; i++) {
1726:       if (diag[i] >= ii[i+1]) {
1727:         cnt++;
1728:         mdiag[i] = 1;
1729:       }
1730:     }
1731:   }
1732:   if (!cnt) {
1733:     MatShift_Basic(A,v);
1734:   } else {
1735:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
1736:     PetscInt    *oldj = a->j, *oldi = a->i;
1737:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

1739:     a->a = NULL;
1740:     a->j = NULL;
1741:     a->i = NULL;
1742:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1743:     for (i=0; i<A->rmap->n; i++) {
1744:       a->imax[i] += mdiag[i];
1745:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
1746:     }
1747:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

1749:     /* copy old values into new matrix data structure */
1750:     for (i=0; i<A->rmap->n; i++) {
1751:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
1752:       if (i < A->cmap->n) {
1753:         MatSetValue(A,i,i,v,ADD_VALUES);
1754:       }
1755:     }
1756:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1757:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1758:     if (singlemalloc) {
1759:       PetscFree3(olda,oldj,oldi);
1760:     } else {
1761:       if (free_a)  {PetscFree(olda);}
1762:       if (free_ij) {PetscFree(oldj);}
1763:       if (free_ij) {PetscFree(oldi);}
1764:     }
1765:   }
1766:   PetscFree(mdiag);
1767:   a->diagonaldense = PETSC_TRUE;
1768:   return(0);
1769: }

1771: /*
1772:      Checks for missing diagonals
1773: */
1774: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1775: {
1776:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1777:   PetscInt       *diag,*ii = a->i,i;

1781:   *missing = PETSC_FALSE;
1782:   if (A->rmap->n > 0 && !ii) {
1783:     *missing = PETSC_TRUE;
1784:     if (d) *d = 0;
1785:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1786:   } else {
1787:     PetscInt n;
1788:     n = PetscMin(A->rmap->n, A->cmap->n);
1789:     diag = a->diag;
1790:     for (i=0; i<n; i++) {
1791:       if (diag[i] >= ii[i+1]) {
1792:         *missing = PETSC_TRUE;
1793:         if (d) *d = i;
1794:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1795:         break;
1796:       }
1797:     }
1798:   }
1799:   return(0);
1800: }

1802: #include <petscblaslapack.h>
1803: #include <petsc/private/kernels/blockinvert.h>

1805: /*
1806:     Note that values is allocated externally by the PC and then passed into this routine
1807: */
1808: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1809: {
1810:   PetscErrorCode  ierr;
1811:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1812:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
1813:   const PetscReal shift = 0.0;
1814:   PetscInt        ipvt[5];
1815:   PetscScalar     work[25],*v_work;

1818:   allowzeropivot = PetscNot(A->erroriffailure);
1819:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1820:   if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1821:   for (i=0; i<nblocks; i++) {
1822:     bsizemax = PetscMax(bsizemax,bsizes[i]);
1823:   }
1824:   PetscMalloc1(bsizemax,&indx);
1825:   if (bsizemax > 7) {
1826:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1827:   }
1828:   ncnt = 0;
1829:   for (i=0; i<nblocks; i++) {
1830:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1831:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1832:     switch (bsizes[i]) {
1833:     case 1:
1834:       *diag = 1.0/(*diag);
1835:       break;
1836:     case 2:
1837:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1838:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1839:       PetscKernel_A_gets_transpose_A_2(diag);
1840:       break;
1841:     case 3:
1842:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1843:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1844:       PetscKernel_A_gets_transpose_A_3(diag);
1845:       break;
1846:     case 4:
1847:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1848:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1849:       PetscKernel_A_gets_transpose_A_4(diag);
1850:       break;
1851:     case 5:
1852:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1853:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1854:       PetscKernel_A_gets_transpose_A_5(diag);
1855:       break;
1856:     case 6:
1857:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1858:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1859:       PetscKernel_A_gets_transpose_A_6(diag);
1860:       break;
1861:     case 7:
1862:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1863:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1864:       PetscKernel_A_gets_transpose_A_7(diag);
1865:       break;
1866:     default:
1867:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1868:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1869:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1870:     }
1871:     ncnt   += bsizes[i];
1872:     diag += bsizes[i]*bsizes[i];
1873:   }
1874:   if (bsizemax > 7) {
1875:     PetscFree2(v_work,v_pivots);
1876:   }
1877:   PetscFree(indx);
1878:   return(0);
1879: }

1881: /*
1882:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1883: */
1884: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1885: {
1886:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1888:   PetscInt       i,*diag,m = A->rmap->n;
1889:   MatScalar      *v = a->a;
1890:   PetscScalar    *idiag,*mdiag;

1893:   if (a->idiagvalid) return(0);
1894:   MatMarkDiagonal_SeqAIJ(A);
1895:   diag = a->diag;
1896:   if (!a->idiag) {
1897:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1898:     PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1899:     v    = a->a;
1900:   }
1901:   mdiag = a->mdiag;
1902:   idiag = a->idiag;

1904:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1905:     for (i=0; i<m; i++) {
1906:       mdiag[i] = v[diag[i]];
1907:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1908:         if (PetscRealPart(fshift)) {
1909:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
1910:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1911:           A->factorerror_zeropivot_value = 0.0;
1912:           A->factorerror_zeropivot_row   = i;
1913:         } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1914:       }
1915:       idiag[i] = 1.0/v[diag[i]];
1916:     }
1917:     PetscLogFlops(m);
1918:   } else {
1919:     for (i=0; i<m; i++) {
1920:       mdiag[i] = v[diag[i]];
1921:       idiag[i] = omega/(fshift + v[diag[i]]);
1922:     }
1923:     PetscLogFlops(2.0*m);
1924:   }
1925:   a->idiagvalid = PETSC_TRUE;
1926:   return(0);
1927: }

1929: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1930: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1931: {
1932:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1933:   PetscScalar       *x,d,sum,*t,scale;
1934:   const MatScalar   *v,*idiag=NULL,*mdiag;
1935:   const PetscScalar *b, *bs,*xb, *ts;
1936:   PetscErrorCode    ierr;
1937:   PetscInt          n,m = A->rmap->n,i;
1938:   const PetscInt    *idx,*diag;

1941:   its = its*lits;

1943:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1944:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1945:   a->fshift = fshift;
1946:   a->omega  = omega;

1948:   diag  = a->diag;
1949:   t     = a->ssor_work;
1950:   idiag = a->idiag;
1951:   mdiag = a->mdiag;

1953:   VecGetArray(xx,&x);
1954:   VecGetArrayRead(bb,&b);
1955:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1956:   if (flag == SOR_APPLY_UPPER) {
1957:     /* apply (U + D/omega) to the vector */
1958:     bs = b;
1959:     for (i=0; i<m; i++) {
1960:       d   = fshift + mdiag[i];
1961:       n   = a->i[i+1] - diag[i] - 1;
1962:       idx = a->j + diag[i] + 1;
1963:       v   = a->a + diag[i] + 1;
1964:       sum = b[i]*d/omega;
1965:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
1966:       x[i] = sum;
1967:     }
1968:     VecRestoreArray(xx,&x);
1969:     VecRestoreArrayRead(bb,&b);
1970:     PetscLogFlops(a->nz);
1971:     return(0);
1972:   }

1974:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1975:   else if (flag & SOR_EISENSTAT) {
1976:     /* Let  A = L + U + D; where L is lower triangular,
1977:     U is upper triangular, E = D/omega; This routine applies

1979:             (L + E)^{-1} A (U + E)^{-1}

1981:     to a vector efficiently using Eisenstat's trick.
1982:     */
1983:     scale = (2.0/omega) - 1.0;

1985:     /*  x = (E + U)^{-1} b */
1986:     for (i=m-1; i>=0; i--) {
1987:       n   = a->i[i+1] - diag[i] - 1;
1988:       idx = a->j + diag[i] + 1;
1989:       v   = a->a + diag[i] + 1;
1990:       sum = b[i];
1991:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1992:       x[i] = sum*idiag[i];
1993:     }

1995:     /*  t = b - (2*E - D)x */
1996:     v = a->a;
1997:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

1999:     /*  t = (E + L)^{-1}t */
2000:     ts   = t;
2001:     diag = a->diag;
2002:     for (i=0; i<m; i++) {
2003:       n   = diag[i] - a->i[i];
2004:       idx = a->j + a->i[i];
2005:       v   = a->a + a->i[i];
2006:       sum = t[i];
2007:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
2008:       t[i] = sum*idiag[i];
2009:       /*  x = x + t */
2010:       x[i] += t[i];
2011:     }

2013:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
2014:     VecRestoreArray(xx,&x);
2015:     VecRestoreArrayRead(bb,&b);
2016:     return(0);
2017:   }
2018:   if (flag & SOR_ZERO_INITIAL_GUESS) {
2019:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2020:       for (i=0; i<m; i++) {
2021:         n   = diag[i] - a->i[i];
2022:         idx = a->j + a->i[i];
2023:         v   = a->a + a->i[i];
2024:         sum = b[i];
2025:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2026:         t[i] = sum;
2027:         x[i] = sum*idiag[i];
2028:       }
2029:       xb   = t;
2030:       PetscLogFlops(a->nz);
2031:     } else xb = b;
2032:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2033:       for (i=m-1; i>=0; i--) {
2034:         n   = a->i[i+1] - diag[i] - 1;
2035:         idx = a->j + diag[i] + 1;
2036:         v   = a->a + diag[i] + 1;
2037:         sum = xb[i];
2038:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2039:         if (xb == b) {
2040:           x[i] = sum*idiag[i];
2041:         } else {
2042:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
2043:         }
2044:       }
2045:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2046:     }
2047:     its--;
2048:   }
2049:   while (its--) {
2050:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2051:       for (i=0; i<m; i++) {
2052:         /* lower */
2053:         n   = diag[i] - a->i[i];
2054:         idx = a->j + a->i[i];
2055:         v   = a->a + a->i[i];
2056:         sum = b[i];
2057:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2058:         t[i] = sum;             /* save application of the lower-triangular part */
2059:         /* upper */
2060:         n   = a->i[i+1] - diag[i] - 1;
2061:         idx = a->j + diag[i] + 1;
2062:         v   = a->a + diag[i] + 1;
2063:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
2064:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
2065:       }
2066:       xb   = t;
2067:       PetscLogFlops(2.0*a->nz);
2068:     } else xb = b;
2069:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2070:       for (i=m-1; i>=0; i--) {
2071:         sum = xb[i];
2072:         if (xb == b) {
2073:           /* whole matrix (no checkpointing available) */
2074:           n   = a->i[i+1] - a->i[i];
2075:           idx = a->j + a->i[i];
2076:           v   = a->a + a->i[i];
2077:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
2078:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
2079:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2080:           n   = a->i[i+1] - diag[i] - 1;
2081:           idx = a->j + diag[i] + 1;
2082:           v   = a->a + diag[i] + 1;
2083:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
2084:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
2085:         }
2086:       }
2087:       if (xb == b) {
2088:         PetscLogFlops(2.0*a->nz);
2089:       } else {
2090:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2091:       }
2092:     }
2093:   }
2094:   VecRestoreArray(xx,&x);
2095:   VecRestoreArrayRead(bb,&b);
2096:   return(0);
2097: }


2100: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
2101: {
2102:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2105:   info->block_size   = 1.0;
2106:   info->nz_allocated = a->maxnz;
2107:   info->nz_used      = a->nz;
2108:   info->nz_unneeded  = (a->maxnz - a->nz);
2109:   info->assemblies   = A->num_ass;
2110:   info->mallocs      = A->info.mallocs;
2111:   info->memory       = ((PetscObject)A)->mem;
2112:   if (A->factortype) {
2113:     info->fill_ratio_given  = A->info.fill_ratio_given;
2114:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2115:     info->factor_mallocs    = A->info.factor_mallocs;
2116:   } else {
2117:     info->fill_ratio_given  = 0;
2118:     info->fill_ratio_needed = 0;
2119:     info->factor_mallocs    = 0;
2120:   }
2121:   return(0);
2122: }

2124: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2125: {
2126:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2127:   PetscInt          i,m = A->rmap->n - 1;
2128:   PetscErrorCode    ierr;
2129:   const PetscScalar *xx;
2130:   PetscScalar       *bb;
2131:   PetscInt          d = 0;

2134:   if (x && b) {
2135:     VecGetArrayRead(x,&xx);
2136:     VecGetArray(b,&bb);
2137:     for (i=0; i<N; i++) {
2138:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2139:       if (rows[i] >= A->cmap->n) continue;
2140:       bb[rows[i]] = diag*xx[rows[i]];
2141:     }
2142:     VecRestoreArrayRead(x,&xx);
2143:     VecRestoreArray(b,&bb);
2144:   }

2146:   if (a->keepnonzeropattern) {
2147:     for (i=0; i<N; i++) {
2148:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2149:       PetscArrayzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]);
2150:     }
2151:     if (diag != 0.0) {
2152:       for (i=0; i<N; i++) {
2153:         d = rows[i];
2154:         if (rows[i] >= A->cmap->n) continue;
2155:         if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
2156:       }
2157:       for (i=0; i<N; i++) {
2158:         if (rows[i] >= A->cmap->n) continue;
2159:         a->a[a->diag[rows[i]]] = diag;
2160:       }
2161:     }
2162:   } else {
2163:     if (diag != 0.0) {
2164:       for (i=0; i<N; i++) {
2165:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2166:         if (a->ilen[rows[i]] > 0) {
2167:           if (rows[i] >= A->cmap->n) {
2168:             a->ilen[rows[i]] = 0;
2169:           } else {
2170:             a->ilen[rows[i]]    = 1;
2171:             a->a[a->i[rows[i]]] = diag;
2172:             a->j[a->i[rows[i]]] = rows[i];
2173:           }
2174:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2175:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2176:         }
2177:       }
2178:     } else {
2179:       for (i=0; i<N; i++) {
2180:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2181:         a->ilen[rows[i]] = 0;
2182:       }
2183:     }
2184:     A->nonzerostate++;
2185:   }
2186: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2187:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2188: #endif
2189:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2190:   return(0);
2191: }

2193: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2194: {
2195:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2196:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
2197:   PetscErrorCode    ierr;
2198:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
2199:   const PetscScalar *xx;
2200:   PetscScalar       *bb;

2203:   if (x && b) {
2204:     VecGetArrayRead(x,&xx);
2205:     VecGetArray(b,&bb);
2206:     vecs = PETSC_TRUE;
2207:   }
2208:   PetscCalloc1(A->rmap->n,&zeroed);
2209:   for (i=0; i<N; i++) {
2210:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2211:     PetscArrayzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]);

2213:     zeroed[rows[i]] = PETSC_TRUE;
2214:   }
2215:   for (i=0; i<A->rmap->n; i++) {
2216:     if (!zeroed[i]) {
2217:       for (j=a->i[i]; j<a->i[i+1]; j++) {
2218:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2219:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
2220:           a->a[j] = 0.0;
2221:         }
2222:       }
2223:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2224:   }
2225:   if (x && b) {
2226:     VecRestoreArrayRead(x,&xx);
2227:     VecRestoreArray(b,&bb);
2228:   }
2229:   PetscFree(zeroed);
2230:   if (diag != 0.0) {
2231:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2232:     if (missing) {
2233:       for (i=0; i<N; i++) {
2234:         if (rows[i] >= A->cmap->N) continue;
2235:         if (a->nonew && rows[i] >= d) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D (%D)",d,rows[i]);
2236:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2237:       }
2238:     } else {
2239:       for (i=0; i<N; i++) {
2240:         a->a[a->diag[rows[i]]] = diag;
2241:       }
2242:     }
2243:   }
2244: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2245:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2246: #endif
2247:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2248:   return(0);
2249: }

2251: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2252: {
2253:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2254:   PetscInt   *itmp;

2257:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

2259:   *nz = a->i[row+1] - a->i[row];
2260:   if (v) *v = a->a + a->i[row];
2261:   if (idx) {
2262:     itmp = a->j + a->i[row];
2263:     if (*nz) *idx = itmp;
2264:     else *idx = NULL;
2265:   }
2266:   return(0);
2267: }

2269: /* remove this function? */
2270: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2271: {
2273:   return(0);
2274: }

2276: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2277: {
2278:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
2279:   MatScalar      *v  = a->a;
2280:   PetscReal      sum = 0.0;
2282:   PetscInt       i,j;

2285:   if (type == NORM_FROBENIUS) {
2286: #if defined(PETSC_USE_REAL___FP16)
2287:     PetscBLASInt one = 1,nz = a->nz;
2288:     *nrm = BLASnrm2_(&nz,v,&one);
2289: #else
2290:     for (i=0; i<a->nz; i++) {
2291:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2292:     }
2293:     *nrm = PetscSqrtReal(sum);
2294: #endif
2295:     PetscLogFlops(2.0*a->nz);
2296:   } else if (type == NORM_1) {
2297:     PetscReal *tmp;
2298:     PetscInt  *jj = a->j;
2299:     PetscCalloc1(A->cmap->n+1,&tmp);
2300:     *nrm = 0.0;
2301:     for (j=0; j<a->nz; j++) {
2302:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
2303:     }
2304:     for (j=0; j<A->cmap->n; j++) {
2305:       if (tmp[j] > *nrm) *nrm = tmp[j];
2306:     }
2307:     PetscFree(tmp);
2308:     PetscLogFlops(PetscMax(a->nz-1,0));
2309:   } else if (type == NORM_INFINITY) {
2310:     *nrm = 0.0;
2311:     for (j=0; j<A->rmap->n; j++) {
2312:       v   = a->a + a->i[j];
2313:       sum = 0.0;
2314:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2315:         sum += PetscAbsScalar(*v); v++;
2316:       }
2317:       if (sum > *nrm) *nrm = sum;
2318:     }
2319:     PetscLogFlops(PetscMax(a->nz-1,0));
2320:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2321:   return(0);
2322: }

2324: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2325: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2326: {
2328:   PetscInt       i,j,anzj;
2329:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
2330:   PetscInt       an=A->cmap->N,am=A->rmap->N;
2331:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

2334:   /* Allocate space for symbolic transpose info and work array */
2335:   PetscCalloc1(an+1,&ati);
2336:   PetscMalloc1(ai[am],&atj);
2337:   PetscMalloc1(an,&atfill);

2339:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
2340:   /* Note: offset by 1 for fast conversion into csr format. */
2341:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2342:   /* Form ati for csr format of A^T. */
2343:   for (i=0;i<an;i++) ati[i+1] += ati[i];

2345:   /* Copy ati into atfill so we have locations of the next free space in atj */
2346:   PetscArraycpy(atfill,ati,an);

2348:   /* Walk through A row-wise and mark nonzero entries of A^T. */
2349:   for (i=0;i<am;i++) {
2350:     anzj = ai[i+1] - ai[i];
2351:     for (j=0;j<anzj;j++) {
2352:       atj[atfill[*aj]] = i;
2353:       atfill[*aj++]   += 1;
2354:     }
2355:   }

2357:   /* Clean up temporary space and complete requests. */
2358:   PetscFree(atfill);
2359:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2360:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2361:   MatSetType(*B,((PetscObject)A)->type_name);

2363:   b          = (Mat_SeqAIJ*)((*B)->data);
2364:   b->free_a  = PETSC_FALSE;
2365:   b->free_ij = PETSC_TRUE;
2366:   b->nonew   = 0;
2367:   return(0);
2368: }

2370: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2371: {
2372:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2373:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2374:   MatScalar      *va,*vb;
2376:   PetscInt       ma,na,mb,nb, i;

2379:   MatGetSize(A,&ma,&na);
2380:   MatGetSize(B,&mb,&nb);
2381:   if (ma!=nb || na!=mb) {
2382:     *f = PETSC_FALSE;
2383:     return(0);
2384:   }
2385:   aii  = aij->i; bii = bij->i;
2386:   adx  = aij->j; bdx = bij->j;
2387:   va   = aij->a; vb = bij->a;
2388:   PetscMalloc1(ma,&aptr);
2389:   PetscMalloc1(mb,&bptr);
2390:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2391:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2393:   *f = PETSC_TRUE;
2394:   for (i=0; i<ma; i++) {
2395:     while (aptr[i]<aii[i+1]) {
2396:       PetscInt    idc,idr;
2397:       PetscScalar vc,vr;
2398:       /* column/row index/value */
2399:       idc = adx[aptr[i]];
2400:       idr = bdx[bptr[idc]];
2401:       vc  = va[aptr[i]];
2402:       vr  = vb[bptr[idc]];
2403:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2404:         *f = PETSC_FALSE;
2405:         goto done;
2406:       } else {
2407:         aptr[i]++;
2408:         if (B || i!=idc) bptr[idc]++;
2409:       }
2410:     }
2411:   }
2412: done:
2413:   PetscFree(aptr);
2414:   PetscFree(bptr);
2415:   return(0);
2416: }

2418: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2419: {
2420:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2421:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2422:   MatScalar      *va,*vb;
2424:   PetscInt       ma,na,mb,nb, i;

2427:   MatGetSize(A,&ma,&na);
2428:   MatGetSize(B,&mb,&nb);
2429:   if (ma!=nb || na!=mb) {
2430:     *f = PETSC_FALSE;
2431:     return(0);
2432:   }
2433:   aii  = aij->i; bii = bij->i;
2434:   adx  = aij->j; bdx = bij->j;
2435:   va   = aij->a; vb = bij->a;
2436:   PetscMalloc1(ma,&aptr);
2437:   PetscMalloc1(mb,&bptr);
2438:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2439:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2441:   *f = PETSC_TRUE;
2442:   for (i=0; i<ma; i++) {
2443:     while (aptr[i]<aii[i+1]) {
2444:       PetscInt    idc,idr;
2445:       PetscScalar vc,vr;
2446:       /* column/row index/value */
2447:       idc = adx[aptr[i]];
2448:       idr = bdx[bptr[idc]];
2449:       vc  = va[aptr[i]];
2450:       vr  = vb[bptr[idc]];
2451:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2452:         *f = PETSC_FALSE;
2453:         goto done;
2454:       } else {
2455:         aptr[i]++;
2456:         if (B || i!=idc) bptr[idc]++;
2457:       }
2458:     }
2459:   }
2460: done:
2461:   PetscFree(aptr);
2462:   PetscFree(bptr);
2463:   return(0);
2464: }

2466: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2467: {

2471:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2472:   return(0);
2473: }

2475: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2476: {

2480:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2481:   return(0);
2482: }

2484: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2485: {
2486:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2487:   const PetscScalar *l,*r;
2488:   PetscScalar       x;
2489:   MatScalar         *v;
2490:   PetscErrorCode    ierr;
2491:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2492:   const PetscInt    *jj;

2495:   if (ll) {
2496:     /* The local size is used so that VecMPI can be passed to this routine
2497:        by MatDiagonalScale_MPIAIJ */
2498:     VecGetLocalSize(ll,&m);
2499:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2500:     VecGetArrayRead(ll,&l);
2501:     v    = a->a;
2502:     for (i=0; i<m; i++) {
2503:       x = l[i];
2504:       M = a->i[i+1] - a->i[i];
2505:       for (j=0; j<M; j++) (*v++) *= x;
2506:     }
2507:     VecRestoreArrayRead(ll,&l);
2508:     PetscLogFlops(nz);
2509:   }
2510:   if (rr) {
2511:     VecGetLocalSize(rr,&n);
2512:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2513:     VecGetArrayRead(rr,&r);
2514:     v    = a->a; jj = a->j;
2515:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2516:     VecRestoreArrayRead(rr,&r);
2517:     PetscLogFlops(nz);
2518:   }
2519:   MatSeqAIJInvalidateDiagonal(A);
2520: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2521:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2522: #endif
2523:   return(0);
2524: }

2526: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2527: {
2528:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2530:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2531:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2532:   const PetscInt *irow,*icol;
2533:   PetscInt       nrows,ncols;
2534:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2535:   MatScalar      *a_new,*mat_a;
2536:   Mat            C;
2537:   PetscBool      stride;


2541:   ISGetIndices(isrow,&irow);
2542:   ISGetLocalSize(isrow,&nrows);
2543:   ISGetLocalSize(iscol,&ncols);

2545:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2546:   if (stride) {
2547:     ISStrideGetInfo(iscol,&first,&step);
2548:   } else {
2549:     first = 0;
2550:     step  = 0;
2551:   }
2552:   if (stride && step == 1) {
2553:     /* special case of contiguous rows */
2554:     PetscMalloc2(nrows,&lens,nrows,&starts);
2555:     /* loop over new rows determining lens and starting points */
2556:     for (i=0; i<nrows; i++) {
2557:       kstart = ai[irow[i]];
2558:       kend   = kstart + ailen[irow[i]];
2559:       starts[i] = kstart;
2560:       for (k=kstart; k<kend; k++) {
2561:         if (aj[k] >= first) {
2562:           starts[i] = k;
2563:           break;
2564:         }
2565:       }
2566:       sum = 0;
2567:       while (k < kend) {
2568:         if (aj[k++] >= first+ncols) break;
2569:         sum++;
2570:       }
2571:       lens[i] = sum;
2572:     }
2573:     /* create submatrix */
2574:     if (scall == MAT_REUSE_MATRIX) {
2575:       PetscInt n_cols,n_rows;
2576:       MatGetSize(*B,&n_rows,&n_cols);
2577:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2578:       MatZeroEntries(*B);
2579:       C    = *B;
2580:     } else {
2581:       PetscInt rbs,cbs;
2582:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2583:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2584:       ISGetBlockSize(isrow,&rbs);
2585:       ISGetBlockSize(iscol,&cbs);
2586:       MatSetBlockSizes(C,rbs,cbs);
2587:       MatSetType(C,((PetscObject)A)->type_name);
2588:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2589:     }
2590:     c = (Mat_SeqAIJ*)C->data;

2592:     /* loop over rows inserting into submatrix */
2593:     a_new = c->a;
2594:     j_new = c->j;
2595:     i_new = c->i;

2597:     for (i=0; i<nrows; i++) {
2598:       ii    = starts[i];
2599:       lensi = lens[i];
2600:       for (k=0; k<lensi; k++) {
2601:         *j_new++ = aj[ii+k] - first;
2602:       }
2603:       PetscArraycpy(a_new,a->a + starts[i],lensi);
2604:       a_new     += lensi;
2605:       i_new[i+1] = i_new[i] + lensi;
2606:       c->ilen[i] = lensi;
2607:     }
2608:     PetscFree2(lens,starts);
2609:   } else {
2610:     ISGetIndices(iscol,&icol);
2611:     PetscCalloc1(oldcols,&smap);
2612:     PetscMalloc1(1+nrows,&lens);
2613:     for (i=0; i<ncols; i++) {
2614:       if (PetscUnlikelyDebug(icol[i] >= oldcols)) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D >= A->cmap->n %D",i,icol[i],oldcols);
2615:       smap[icol[i]] = i+1;
2616:     }

2618:     /* determine lens of each row */
2619:     for (i=0; i<nrows; i++) {
2620:       kstart  = ai[irow[i]];
2621:       kend    = kstart + a->ilen[irow[i]];
2622:       lens[i] = 0;
2623:       for (k=kstart; k<kend; k++) {
2624:         if (smap[aj[k]]) {
2625:           lens[i]++;
2626:         }
2627:       }
2628:     }
2629:     /* Create and fill new matrix */
2630:     if (scall == MAT_REUSE_MATRIX) {
2631:       PetscBool equal;

2633:       c = (Mat_SeqAIJ*)((*B)->data);
2634:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2635:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
2636:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2637:       PetscArrayzero(c->ilen,(*B)->rmap->n);
2638:       C    = *B;
2639:     } else {
2640:       PetscInt rbs,cbs;
2641:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2642:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2643:       ISGetBlockSize(isrow,&rbs);
2644:       ISGetBlockSize(iscol,&cbs);
2645:       MatSetBlockSizes(C,rbs,cbs);
2646:       MatSetType(C,((PetscObject)A)->type_name);
2647:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2648:     }
2649:     c = (Mat_SeqAIJ*)(C->data);
2650:     for (i=0; i<nrows; i++) {
2651:       row      = irow[i];
2652:       kstart   = ai[row];
2653:       kend     = kstart + a->ilen[row];
2654:       mat_i    = c->i[i];
2655:       mat_j    = c->j + mat_i;
2656:       mat_a    = c->a + mat_i;
2657:       mat_ilen = c->ilen + i;
2658:       for (k=kstart; k<kend; k++) {
2659:         if ((tcol=smap[a->j[k]])) {
2660:           *mat_j++ = tcol - 1;
2661:           *mat_a++ = a->a[k];
2662:           (*mat_ilen)++;

2664:         }
2665:       }
2666:     }
2667:     /* Free work space */
2668:     ISRestoreIndices(iscol,&icol);
2669:     PetscFree(smap);
2670:     PetscFree(lens);
2671:     /* sort */
2672:     for (i = 0; i < nrows; i++) {
2673:       PetscInt ilen;

2675:       mat_i = c->i[i];
2676:       mat_j = c->j + mat_i;
2677:       mat_a = c->a + mat_i;
2678:       ilen  = c->ilen[i];
2679:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2680:     }
2681:   }
2682: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2683:   MatBindToCPU(C,A->boundtocpu);
2684: #endif
2685:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2686:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2688:   ISRestoreIndices(isrow,&irow);
2689:   *B   = C;
2690:   return(0);
2691: }

2693: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2694: {
2696:   Mat            B;

2699:   if (scall == MAT_INITIAL_MATRIX) {
2700:     MatCreate(subComm,&B);
2701:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2702:     MatSetBlockSizesFromMats(B,mat,mat);
2703:     MatSetType(B,MATSEQAIJ);
2704:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2705:     *subMat = B;
2706:   } else {
2707:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2708:   }
2709:   return(0);
2710: }

2712: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2713: {
2714:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2716:   Mat            outA;
2717:   PetscBool      row_identity,col_identity;

2720:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2722:   ISIdentity(row,&row_identity);
2723:   ISIdentity(col,&col_identity);

2725:   outA             = inA;
2726:   outA->factortype = MAT_FACTOR_LU;
2727:   PetscFree(inA->solvertype);
2728:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

2730:   PetscObjectReference((PetscObject)row);
2731:   ISDestroy(&a->row);

2733:   a->row = row;

2735:   PetscObjectReference((PetscObject)col);
2736:   ISDestroy(&a->col);

2738:   a->col = col;

2740:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2741:   ISDestroy(&a->icol);
2742:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2743:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

2745:   if (!a->solve_work) { /* this matrix may have been factored before */
2746:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2747:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2748:   }

2750:   MatMarkDiagonal_SeqAIJ(inA);
2751:   if (row_identity && col_identity) {
2752:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2753:   } else {
2754:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2755:   }
2756:   return(0);
2757: }

2759: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2760: {
2761:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
2762:   PetscScalar    oalpha = alpha;
2764:   PetscBLASInt   one = 1,bnz;

2767:   PetscBLASIntCast(a->nz,&bnz);
2768:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2769:   PetscLogFlops(a->nz);
2770:   MatSeqAIJInvalidateDiagonal(inA);
2771: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2772:   if (inA->offloadmask != PETSC_OFFLOAD_UNALLOCATED) inA->offloadmask = PETSC_OFFLOAD_CPU;
2773: #endif
2774:   return(0);
2775: }

2777: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2778: {
2780:   PetscInt       i;

2783:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2784:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

2786:     for (i=0; i<submatj->nrqr; ++i) {
2787:       PetscFree(submatj->sbuf2[i]);
2788:     }
2789:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

2791:     if (submatj->rbuf1) {
2792:       PetscFree(submatj->rbuf1[0]);
2793:       PetscFree(submatj->rbuf1);
2794:     }

2796:     for (i=0; i<submatj->nrqs; ++i) {
2797:       PetscFree(submatj->rbuf3[i]);
2798:     }
2799:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2800:     PetscFree(submatj->pa);
2801:   }

2803: #if defined(PETSC_USE_CTABLE)
2804:   PetscTableDestroy((PetscTable*)&submatj->rmap);
2805:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2806:   PetscFree(submatj->rmap_loc);
2807: #else
2808:   PetscFree(submatj->rmap);
2809: #endif

2811:   if (!submatj->allcolumns) {
2812: #if defined(PETSC_USE_CTABLE)
2813:     PetscTableDestroy((PetscTable*)&submatj->cmap);
2814: #else
2815:     PetscFree(submatj->cmap);
2816: #endif
2817:   }
2818:   PetscFree(submatj->row2proc);

2820:   PetscFree(submatj);
2821:   return(0);
2822: }

2824: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2825: {
2827:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
2828:   Mat_SubSppt    *submatj = c->submatis1;

2831:   (*submatj->destroy)(C);
2832:   MatDestroySubMatrix_Private(submatj);
2833:   return(0);
2834: }

2836: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2837: {
2839:   PetscInt       i;
2840:   Mat            C;
2841:   Mat_SeqAIJ     *c;
2842:   Mat_SubSppt    *submatj;

2845:   for (i=0; i<n; i++) {
2846:     C       = (*mat)[i];
2847:     c       = (Mat_SeqAIJ*)C->data;
2848:     submatj = c->submatis1;
2849:     if (submatj) {
2850:       if (--((PetscObject)C)->refct <= 0) {
2851:         (*submatj->destroy)(C);
2852:         MatDestroySubMatrix_Private(submatj);
2853:         PetscFree(C->defaultvectype);
2854:         PetscLayoutDestroy(&C->rmap);
2855:         PetscLayoutDestroy(&C->cmap);
2856:         PetscHeaderDestroy(&C);
2857:       }
2858:     } else {
2859:       MatDestroy(&C);
2860:     }
2861:   }

2863:   /* Destroy Dummy submatrices created for reuse */
2864:   MatDestroySubMatrices_Dummy(n,mat);

2866:   PetscFree(*mat);
2867:   return(0);
2868: }

2870: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2871: {
2873:   PetscInt       i;

2876:   if (scall == MAT_INITIAL_MATRIX) {
2877:     PetscCalloc1(n+1,B);
2878:   }

2880:   for (i=0; i<n; i++) {
2881:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2882:   }
2883:   return(0);
2884: }

2886: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2887: {
2888:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2890:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2891:   const PetscInt *idx;
2892:   PetscInt       start,end,*ai,*aj;
2893:   PetscBT        table;

2896:   m  = A->rmap->n;
2897:   ai = a->i;
2898:   aj = a->j;

2900:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2902:   PetscMalloc1(m+1,&nidx);
2903:   PetscBTCreate(m,&table);

2905:   for (i=0; i<is_max; i++) {
2906:     /* Initialize the two local arrays */
2907:     isz  = 0;
2908:     PetscBTMemzero(m,table);

2910:     /* Extract the indices, assume there can be duplicate entries */
2911:     ISGetIndices(is[i],&idx);
2912:     ISGetLocalSize(is[i],&n);

2914:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2915:     for (j=0; j<n; ++j) {
2916:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2917:     }
2918:     ISRestoreIndices(is[i],&idx);
2919:     ISDestroy(&is[i]);

2921:     k = 0;
2922:     for (j=0; j<ov; j++) { /* for each overlap */
2923:       n = isz;
2924:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2925:         row   = nidx[k];
2926:         start = ai[row];
2927:         end   = ai[row+1];
2928:         for (l = start; l<end; l++) {
2929:           val = aj[l];
2930:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2931:         }
2932:       }
2933:     }
2934:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2935:   }
2936:   PetscBTDestroy(&table);
2937:   PetscFree(nidx);
2938:   return(0);
2939: }

2941: /* -------------------------------------------------------------- */
2942: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2943: {
2944:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2946:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2947:   const PetscInt *row,*col;
2948:   PetscInt       *cnew,j,*lens;
2949:   IS             icolp,irowp;
2950:   PetscInt       *cwork = NULL;
2951:   PetscScalar    *vwork = NULL;

2954:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2955:   ISGetIndices(irowp,&row);
2956:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2957:   ISGetIndices(icolp,&col);

2959:   /* determine lengths of permuted rows */
2960:   PetscMalloc1(m+1,&lens);
2961:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2962:   MatCreate(PetscObjectComm((PetscObject)A),B);
2963:   MatSetSizes(*B,m,n,m,n);
2964:   MatSetBlockSizesFromMats(*B,A,A);
2965:   MatSetType(*B,((PetscObject)A)->type_name);
2966:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2967:   PetscFree(lens);

2969:   PetscMalloc1(n,&cnew);
2970:   for (i=0; i<m; i++) {
2971:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2972:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2973:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2974:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2975:   }
2976:   PetscFree(cnew);

2978:   (*B)->assembled = PETSC_FALSE;

2980: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2981:   MatBindToCPU(*B,A->boundtocpu);
2982: #endif
2983:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2984:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2985:   ISRestoreIndices(irowp,&row);
2986:   ISRestoreIndices(icolp,&col);
2987:   ISDestroy(&irowp);
2988:   ISDestroy(&icolp);
2989:   if (rowp == colp) {
2990:     if (A->symmetric) {
2991:       MatSetOption(*B,MAT_SYMMETRIC,PETSC_TRUE);
2992:     }
2993:     if (A->hermitian) {
2994:       MatSetOption(*B,MAT_HERMITIAN,PETSC_TRUE);
2995:     }
2996:   }
2997:   return(0);
2998: }

3000: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
3001: {

3005:   /* If the two matrices have the same copy implementation, use fast copy. */
3006:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
3007:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3008:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

3010:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different %D != %D",a->i[A->rmap->n],b->i[B->rmap->n]);
3011:     PetscArraycpy(b->a,a->a,a->i[A->rmap->n]);
3012:     PetscObjectStateIncrease((PetscObject)B);
3013:   } else {
3014:     MatCopy_Basic(A,B,str);
3015:   }
3016:   return(0);
3017: }

3019: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
3020: {

3024:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
3025:   return(0);
3026: }

3028: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
3029: {
3030:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

3033:   *array = a->a;
3034:   return(0);
3035: }

3037: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
3038: {
3040:   *array = NULL;
3041:   return(0);
3042: }

3044: /*
3045:    Computes the number of nonzeros per row needed for preallocation when X and Y
3046:    have different nonzero structure.
3047: */
3048: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
3049: {
3050:   PetscInt       i,j,k,nzx,nzy;

3053:   /* Set the number of nonzeros in the new matrix */
3054:   for (i=0; i<m; i++) {
3055:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
3056:     nzx = xi[i+1] - xi[i];
3057:     nzy = yi[i+1] - yi[i];
3058:     nnz[i] = 0;
3059:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
3060:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
3061:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
3062:       nnz[i]++;
3063:     }
3064:     for (; k<nzy; k++) nnz[i]++;
3065:   }
3066:   return(0);
3067: }

3069: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
3070: {
3071:   PetscInt       m = Y->rmap->N;
3072:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
3073:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

3077:   /* Set the number of nonzeros in the new matrix */
3078:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
3079:   return(0);
3080: }

3082: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
3083: {
3085:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;

3088:   if (str == DIFFERENT_NONZERO_PATTERN) {
3089:     if (x->nz == y->nz) {
3090:       PetscBool e;
3091:       PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
3092:       if (e) {
3093:         PetscArraycmp(x->j,y->j,y->nz,&e);
3094:         if (e) {
3095:           str = SAME_NONZERO_PATTERN;
3096:         }
3097:       }
3098:     }
3099:   }
3100:   if (str == SAME_NONZERO_PATTERN) {
3101:     PetscScalar  alpha = a;
3102:     PetscBLASInt one = 1,bnz;

3104:     PetscBLASIntCast(x->nz,&bnz);
3105:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
3106:     MatSeqAIJInvalidateDiagonal(Y);
3107:     PetscObjectStateIncrease((PetscObject)Y);
3108:     /* the MatAXPY_Basic* subroutines calls MatAssembly, so the matrix on the GPU will be updated */
3109: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
3110:     if (Y->offloadmask != PETSC_OFFLOAD_UNALLOCATED) {
3111:       Y->offloadmask = PETSC_OFFLOAD_CPU;
3112:     }
3113: #endif
3114:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3115:     MatAXPY_Basic(Y,a,X,str);
3116:   } else {
3117:     Mat      B;
3118:     PetscInt *nnz;
3119:     PetscMalloc1(Y->rmap->N,&nnz);
3120:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
3121:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
3122:     MatSetLayouts(B,Y->rmap,Y->cmap);
3123:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
3124:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
3125:     MatSeqAIJSetPreallocation(B,0,nnz);
3126:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
3127:     MatHeaderReplace(Y,&B);
3128:     PetscFree(nnz);
3129:   }
3130:   return(0);
3131: }

3133: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
3134: {
3135: #if defined(PETSC_USE_COMPLEX)
3136:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
3137:   PetscInt    i,nz;
3138:   PetscScalar *a;

3141:   nz = aij->nz;
3142:   a  = aij->a;
3143:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
3144: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
3145:   if (mat->offloadmask != PETSC_OFFLOAD_UNALLOCATED) mat->offloadmask = PETSC_OFFLOAD_CPU;
3146: #endif
3147: #else
3149: #endif
3150:   return(0);
3151: }

3153: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3154: {
3155:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3157:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3158:   PetscReal      atmp;
3159:   PetscScalar    *x;
3160:   MatScalar      *aa;

3163:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3164:   aa = a->a;
3165:   ai = a->i;
3166:   aj = a->j;

3168:   VecSet(v,0.0);
3169:   VecGetArray(v,&x);
3170:   VecGetLocalSize(v,&n);
3171:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3172:   for (i=0; i<m; i++) {
3173:     ncols = ai[1] - ai[0]; ai++;
3174:     x[i]  = 0.0;
3175:     for (j=0; j<ncols; j++) {
3176:       atmp = PetscAbsScalar(*aa);
3177:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3178:       aa++; aj++;
3179:     }
3180:   }
3181:   VecRestoreArray(v,&x);
3182:   return(0);
3183: }

3185: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3186: {
3187:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3189:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3190:   PetscScalar    *x;
3191:   MatScalar      *aa;

3194:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3195:   aa = a->a;
3196:   ai = a->i;
3197:   aj = a->j;

3199:   VecSet(v,0.0);
3200:   VecGetArray(v,&x);
3201:   VecGetLocalSize(v,&n);
3202:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3203:   for (i=0; i<m; i++) {
3204:     ncols = ai[1] - ai[0]; ai++;
3205:     if (ncols == A->cmap->n) { /* row is dense */
3206:       x[i] = *aa; if (idx) idx[i] = 0;
3207:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
3208:       x[i] = 0.0;
3209:       if (idx) {
3210:         idx[i] = 0; /* in case ncols is zero */
3211:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
3212:           if (aj[j] > j) {
3213:             idx[i] = j;
3214:             break;
3215:           }
3216:         }
3217:       }
3218:     }
3219:     for (j=0; j<ncols; j++) {
3220:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3221:       aa++; aj++;
3222:     }
3223:   }
3224:   VecRestoreArray(v,&x);
3225:   return(0);
3226: }

3228: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3229: {
3230:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3232:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3233:   PetscReal      atmp;
3234:   PetscScalar    *x;
3235:   MatScalar      *aa;

3238:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3239:   aa = a->a;
3240:   ai = a->i;
3241:   aj = a->j;

3243:   VecSet(v,0.0);
3244:   VecGetArray(v,&x);
3245:   VecGetLocalSize(v,&n);
3246:   if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", A->rmap->n, n);
3247:   for (i=0; i<m; i++) {
3248:     ncols = ai[1] - ai[0]; ai++;
3249:     if (ncols) {
3250:       /* Get first nonzero */
3251:       for (j = 0; j < ncols; j++) {
3252:         atmp = PetscAbsScalar(aa[j]);
3253:         if (atmp > 1.0e-12) {
3254:           x[i] = atmp;
3255:           if (idx) idx[i] = aj[j];
3256:           break;
3257:         }
3258:       }
3259:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
3260:     } else {
3261:       x[i] = 0.0; if (idx) idx[i] = 0;
3262:     }
3263:     for (j = 0; j < ncols; j++) {
3264:       atmp = PetscAbsScalar(*aa);
3265:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3266:       aa++; aj++;
3267:     }
3268:   }
3269:   VecRestoreArray(v,&x);
3270:   return(0);
3271: }

3273: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3274: {
3275:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3276:   PetscErrorCode  ierr;
3277:   PetscInt        i,j,m = A->rmap->n,ncols,n;
3278:   const PetscInt  *ai,*aj;
3279:   PetscScalar     *x;
3280:   const MatScalar *aa;

3283:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3284:   aa = a->a;
3285:   ai = a->i;
3286:   aj = a->j;

3288:   VecSet(v,0.0);
3289:   VecGetArray(v,&x);
3290:   VecGetLocalSize(v,&n);
3291:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3292:   for (i=0; i<m; i++) {
3293:     ncols = ai[1] - ai[0]; ai++;
3294:     if (ncols == A->cmap->n) { /* row is dense */
3295:       x[i] = *aa; if (idx) idx[i] = 0;
3296:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
3297:       x[i] = 0.0;
3298:       if (idx) {   /* find first implicit 0.0 in the row */
3299:         idx[i] = 0; /* in case ncols is zero */
3300:         for (j=0; j<ncols; j++) {
3301:           if (aj[j] > j) {
3302:             idx[i] = j;
3303:             break;
3304:           }
3305:         }
3306:       }
3307:     }
3308:     for (j=0; j<ncols; j++) {
3309:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3310:       aa++; aj++;
3311:     }
3312:   }
3313:   VecRestoreArray(v,&x);
3314:   return(0);
3315: }

3317: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3318: {
3319:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
3320:   PetscErrorCode  ierr;
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;

3327:   allowzeropivot = PetscNot(A->erroriffailure);
3328:   if (a->ibdiagvalid) {
3329:     if (values) *values = a->ibdiag;
3330:     return(0);
3331:   }
3332:   MatMarkDiagonal_SeqAIJ(A);
3333:   if (!a->ibdiag) {
3334:     PetscMalloc1(bs2*mbs,&a->ibdiag);
3335:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3336:   }
3337:   diag = a->ibdiag;
3338:   if (values) *values = a->ibdiag;
3339:   /* factor and invert each block */
3340:   switch (bs) {
3341:   case 1:
3342:     for (i=0; i<mbs; i++) {
3343:       MatGetValues(A,1,&i,1,&i,diag+i);
3344:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3345:         if (allowzeropivot) {
3346:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3347:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3348:           A->factorerror_zeropivot_row   = i;
3349:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3350:         } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3351:       }
3352:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3353:     }
3354:     break;
3355:   case 2:
3356:     for (i=0; i<mbs; i++) {
3357:       ij[0] = 2*i; ij[1] = 2*i + 1;
3358:       MatGetValues(A,2,ij,2,ij,diag);
3359:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3360:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3361:       PetscKernel_A_gets_transpose_A_2(diag);
3362:       diag += 4;
3363:     }
3364:     break;
3365:   case 3:
3366:     for (i=0; i<mbs; i++) {
3367:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3368:       MatGetValues(A,3,ij,3,ij,diag);
3369:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3370:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3371:       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; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3378:       MatGetValues(A,4,ij,4,ij,diag);
3379:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3380:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3381:       PetscKernel_A_gets_transpose_A_4(diag);
3382:       diag += 16;
3383:     }
3384:     break;
3385:   case 5:
3386:     for (i=0; i<mbs; i++) {
3387:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3388:       MatGetValues(A,5,ij,5,ij,diag);
3389:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3390:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3391:       PetscKernel_A_gets_transpose_A_5(diag);
3392:       diag += 25;
3393:     }
3394:     break;
3395:   case 6:
3396:     for (i=0; i<mbs; i++) {
3397:       ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
3398:       MatGetValues(A,6,ij,6,ij,diag);
3399:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3400:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3401:       PetscKernel_A_gets_transpose_A_6(diag);
3402:       diag += 36;
3403:     }
3404:     break;
3405:   case 7:
3406:     for (i=0; i<mbs; i++) {
3407:       ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
3408:       MatGetValues(A,7,ij,7,ij,diag);
3409:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3410:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3411:       PetscKernel_A_gets_transpose_A_7(diag);
3412:       diag += 49;
3413:     }
3414:     break;
3415:   default:
3416:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3417:     for (i=0; i<mbs; i++) {
3418:       for (j=0; j<bs; j++) {
3419:         IJ[j] = bs*i + j;
3420:       }
3421:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3422:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3423:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3424:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3425:       diag += bs2;
3426:     }
3427:     PetscFree3(v_work,v_pivots,IJ);
3428:   }
3429:   a->ibdiagvalid = PETSC_TRUE;
3430:   return(0);
3431: }

3433: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3434: {
3436:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3437:   PetscScalar    a;
3438:   PetscInt       m,n,i,j,col;

3441:   if (!x->assembled) {
3442:     MatGetSize(x,&m,&n);
3443:     for (i=0; i<m; i++) {
3444:       for (j=0; j<aij->imax[i]; j++) {
3445:         PetscRandomGetValue(rctx,&a);
3446:         col  = (PetscInt)(n*PetscRealPart(a));
3447:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3448:       }
3449:     }
3450:   } else {
3451:     for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3452:   }
3453:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3454:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3455:   return(0);
3456: }

3458: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3459: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3460: {
3462:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3463:   PetscScalar    a;
3464:   PetscInt       m,n,i,j,col,nskip;

3467:   nskip = high - low;
3468:   MatGetSize(x,&m,&n);
3469:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
3470:   for (i=0; i<m; i++) {
3471:     for (j=0; j<aij->imax[i]; j++) {
3472:       PetscRandomGetValue(rctx,&a);
3473:       col  = (PetscInt)(n*PetscRealPart(a));
3474:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3475:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3476:     }
3477:   }
3478:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3479:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3480:   return(0);
3481: }


3484: /* -------------------------------------------------------------------*/
3485: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3486:                                         MatGetRow_SeqAIJ,
3487:                                         MatRestoreRow_SeqAIJ,
3488:                                         MatMult_SeqAIJ,
3489:                                 /*  4*/ MatMultAdd_SeqAIJ,
3490:                                         MatMultTranspose_SeqAIJ,
3491:                                         MatMultTransposeAdd_SeqAIJ,
3492:                                         NULL,
3493:                                         NULL,
3494:                                         NULL,
3495:                                 /* 10*/ NULL,
3496:                                         MatLUFactor_SeqAIJ,
3497:                                         NULL,
3498:                                         MatSOR_SeqAIJ,
3499:                                         MatTranspose_SeqAIJ,
3500:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3501:                                         MatEqual_SeqAIJ,
3502:                                         MatGetDiagonal_SeqAIJ,
3503:                                         MatDiagonalScale_SeqAIJ,
3504:                                         MatNorm_SeqAIJ,
3505:                                 /* 20*/ NULL,
3506:                                         MatAssemblyEnd_SeqAIJ,
3507:                                         MatSetOption_SeqAIJ,
3508:                                         MatZeroEntries_SeqAIJ,
3509:                                 /* 24*/ MatZeroRows_SeqAIJ,
3510:                                         NULL,
3511:                                         NULL,
3512:                                         NULL,
3513:                                         NULL,
3514:                                 /* 29*/ MatSetUp_SeqAIJ,
3515:                                         NULL,
3516:                                         NULL,
3517:                                         NULL,
3518:                                         NULL,
3519:                                 /* 34*/ MatDuplicate_SeqAIJ,
3520:                                         NULL,
3521:                                         NULL,
3522:                                         MatILUFactor_SeqAIJ,
3523:                                         NULL,
3524:                                 /* 39*/ MatAXPY_SeqAIJ,
3525:                                         MatCreateSubMatrices_SeqAIJ,
3526:                                         MatIncreaseOverlap_SeqAIJ,
3527:                                         MatGetValues_SeqAIJ,
3528:                                         MatCopy_SeqAIJ,
3529:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3530:                                         MatScale_SeqAIJ,
3531:                                         MatShift_SeqAIJ,
3532:                                         MatDiagonalSet_SeqAIJ,
3533:                                         MatZeroRowsColumns_SeqAIJ,
3534:                                 /* 49*/ MatSetRandom_SeqAIJ,
3535:                                         MatGetRowIJ_SeqAIJ,
3536:                                         MatRestoreRowIJ_SeqAIJ,
3537:                                         MatGetColumnIJ_SeqAIJ,
3538:                                         MatRestoreColumnIJ_SeqAIJ,
3539:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
3540:                                         NULL,
3541:                                         NULL,
3542:                                         MatPermute_SeqAIJ,
3543:                                         NULL,
3544:                                 /* 59*/ NULL,
3545:                                         MatDestroy_SeqAIJ,
3546:                                         MatView_SeqAIJ,
3547:                                         NULL,
3548:                                         NULL,
3549:                                 /* 64*/ NULL,
3550:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3551:                                         NULL,
3552:                                         NULL,
3553:                                         NULL,
3554:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3555:                                         MatGetRowMinAbs_SeqAIJ,
3556:                                         NULL,
3557:                                         NULL,
3558:                                         NULL,
3559:                                 /* 74*/ NULL,
3560:                                         MatFDColoringApply_AIJ,
3561:                                         NULL,
3562:                                         NULL,
3563:                                         NULL,
3564:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3565:                                         NULL,
3566:                                         NULL,
3567:                                         NULL,
3568:                                         MatLoad_SeqAIJ,
3569:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3570:                                         MatIsHermitian_SeqAIJ,
3571:                                         NULL,
3572:                                         NULL,
3573:                                         NULL,
3574:                                 /* 89*/ NULL,
3575:                                         NULL,
3576:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3577:                                         NULL,
3578:                                         NULL,
3579:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3580:                                         NULL,
3581:                                         NULL,
3582:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3583:                                         NULL,
3584:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
3585:                                         NULL,
3586:                                         NULL,
3587:                                         MatConjugate_SeqAIJ,
3588:                                         NULL,
3589:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3590:                                         MatRealPart_SeqAIJ,
3591:                                         MatImaginaryPart_SeqAIJ,
3592:                                         NULL,
3593:                                         NULL,
3594:                                 /*109*/ MatMatSolve_SeqAIJ,
3595:                                         NULL,
3596:                                         MatGetRowMin_SeqAIJ,
3597:                                         NULL,
3598:                                         MatMissingDiagonal_SeqAIJ,
3599:                                 /*114*/ NULL,
3600:                                         NULL,
3601:                                         NULL,
3602:                                         NULL,
3603:                                         NULL,
3604:                                 /*119*/ NULL,
3605:                                         NULL,
3606:                                         NULL,
3607:                                         NULL,
3608:                                         MatGetMultiProcBlock_SeqAIJ,
3609:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3610:                                         MatGetColumnNorms_SeqAIJ,
3611:                                         MatInvertBlockDiagonal_SeqAIJ,
3612:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
3613:                                         NULL,
3614:                                 /*129*/ NULL,
3615:                                         NULL,
3616:                                         NULL,
3617:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3618:                                         MatTransposeColoringCreate_SeqAIJ,
3619:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3620:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3621:                                         NULL,
3622:                                         NULL,
3623:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3624:                                  /*139*/NULL,
3625:                                         NULL,
3626:                                         NULL,
3627:                                         MatFDColoringSetUp_SeqXAIJ,
3628:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
3629:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3630:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
3631:                                         NULL,
3632:                                         NULL
3633: };

3635: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3636: {
3637:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3638:   PetscInt   i,nz,n;

3641:   nz = aij->maxnz;
3642:   n  = mat->rmap->n;
3643:   for (i=0; i<nz; i++) {
3644:     aij->j[i] = indices[i];
3645:   }
3646:   aij->nz = nz;
3647:   for (i=0; i<n; i++) {
3648:     aij->ilen[i] = aij->imax[i];
3649:   }
3650:   return(0);
3651: }

3653: /*
3654:  * When a sparse matrix has many zero columns, we should compact them out to save the space
3655:  * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3656:  * */
3657: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3658: {
3659:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3660:   PetscTable         gid1_lid1;
3661:   PetscTablePosition tpos;
3662:   PetscInt           gid,lid,i,j,ncols,ec;
3663:   PetscInt           *garray;
3664:   PetscErrorCode  ierr;

3669:   /* use a table */
3670:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3671:   ec = 0;
3672:   for (i=0; i<mat->rmap->n; i++) {
3673:     ncols = aij->i[i+1] - aij->i[i];
3674:     for (j=0; j<ncols; j++) {
3675:       PetscInt data,gid1 = aij->j[aij->i[i] + j] + 1;
3676:       PetscTableFind(gid1_lid1,gid1,&data);
3677:       if (!data) {
3678:         /* one based table */
3679:         PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3680:       }
3681:     }
3682:   }
3683:   /* form array of columns we need */
3684:   PetscMalloc1(ec+1,&garray);
3685:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
3686:   while (tpos) {
3687:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3688:     gid--;
3689:     lid--;
3690:     garray[lid] = gid;
3691:   }
3692:   PetscSortInt(ec,garray); /* sort, and rebuild */
3693:   PetscTableRemoveAll(gid1_lid1);
3694:   for (i=0; i<ec; i++) {
3695:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3696:   }
3697:   /* compact out the extra columns in B */
3698:   for (i=0; i<mat->rmap->n; i++) {
3699:         ncols = aij->i[i+1] - aij->i[i];
3700:     for (j=0; j<ncols; j++) {
3701:       PetscInt gid1 = aij->j[aij->i[i] + j] + 1;
3702:       PetscTableFind(gid1_lid1,gid1,&lid);
3703:       lid--;
3704:       aij->j[aij->i[i] + j] = lid;
3705:     }
3706:   }
3707:   PetscLayoutDestroy(&mat->cmap);
3708:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
3709:   PetscTableDestroy(&gid1_lid1);
3710:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3711:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3712:   return(0);
3713: }

3715: /*@
3716:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3717:        in the matrix.

3719:   Input Parameters:
3720: +  mat - the SeqAIJ matrix
3721: -  indices - the column indices

3723:   Level: advanced

3725:   Notes:
3726:     This can be called if you have precomputed the nonzero structure of the
3727:   matrix and want to provide it to the matrix object to improve the performance
3728:   of the MatSetValues() operation.

3730:     You MUST have set the correct numbers of nonzeros per row in the call to
3731:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3733:     MUST be called before any calls to MatSetValues();

3735:     The indices should start with zero, not one.

3737: @*/
3738: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3739: {

3745:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3746:   return(0);
3747: }

3749: /* ----------------------------------------------------------------------------------------*/

3751: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3752: {
3753:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3755:   size_t         nz = aij->i[mat->rmap->n];

3758:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3760:   /* allocate space for values if not already there */
3761:   if (!aij->saved_values) {
3762:     PetscMalloc1(nz+1,&aij->saved_values);
3763:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3764:   }

3766:   /* copy values over */
3767:   PetscArraycpy(aij->saved_values,aij->a,nz);
3768:   return(0);
3769: }

3771: /*@
3772:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3773:        example, reuse of the linear part of a Jacobian, while recomputing the
3774:        nonlinear portion.

3776:    Collect on Mat

3778:   Input Parameters:
3779: .  mat - the matrix (currently only AIJ matrices support this option)

3781:   Level: advanced

3783:   Common Usage, with SNESSolve():
3784: $    Create Jacobian matrix
3785: $    Set linear terms into matrix
3786: $    Apply boundary conditions to matrix, at this time matrix must have
3787: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3788: $      boundary conditions again will not change the nonzero structure
3789: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3790: $    MatStoreValues(mat);
3791: $    Call SNESSetJacobian() with matrix
3792: $    In your Jacobian routine
3793: $      MatRetrieveValues(mat);
3794: $      Set nonlinear terms in matrix

3796:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3797: $    // build linear portion of Jacobian
3798: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3799: $    MatStoreValues(mat);
3800: $    loop over nonlinear iterations
3801: $       MatRetrieveValues(mat);
3802: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3803: $       // call MatAssemblyBegin/End() on matrix
3804: $       Solve linear system with Jacobian
3805: $    endloop

3807:   Notes:
3808:     Matrix must already be assemblied before calling this routine
3809:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3810:     calling this routine.

3812:     When this is called multiple times it overwrites the previous set of stored values
3813:     and does not allocated additional space.

3815: .seealso: MatRetrieveValues()

3817: @*/
3818: PetscErrorCode  MatStoreValues(Mat mat)
3819: {

3824:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3825:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3826:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3827:   return(0);
3828: }

3830: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3831: {
3832:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3834:   PetscInt       nz = aij->i[mat->rmap->n];

3837:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3838:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3839:   /* copy values over */
3840:   PetscArraycpy(aij->a,aij->saved_values,nz);
3841:   return(0);
3842: }

3844: /*@
3845:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3846:        example, reuse of the linear part of a Jacobian, while recomputing the
3847:        nonlinear portion.

3849:    Collect on Mat

3851:   Input Parameters:
3852: .  mat - the matrix (currently only AIJ matrices support this option)

3854:   Level: advanced

3856: .seealso: MatStoreValues()

3858: @*/
3859: PetscErrorCode  MatRetrieveValues(Mat mat)
3860: {

3865:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3866:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3867:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3868:   return(0);
3869: }


3872: /* --------------------------------------------------------------------------------*/
3873: /*@C
3874:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3875:    (the default parallel PETSc format).  For good matrix assembly performance
3876:    the user should preallocate the matrix storage by setting the parameter nz
3877:    (or the array nnz).  By setting these parameters accurately, performance
3878:    during matrix assembly can be increased by more than a factor of 50.

3880:    Collective

3882:    Input Parameters:
3883: +  comm - MPI communicator, set to PETSC_COMM_SELF
3884: .  m - number of rows
3885: .  n - number of columns
3886: .  nz - number of nonzeros per row (same for all rows)
3887: -  nnz - array containing the number of nonzeros in the various rows
3888:          (possibly different for each row) or NULL

3890:    Output Parameter:
3891: .  A - the matrix

3893:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3894:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
3895:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3897:    Notes:
3898:    If nnz is given then nz is ignored

3900:    The AIJ format (also called the Yale sparse matrix format or
3901:    compressed row storage), is fully compatible with standard Fortran 77
3902:    storage.  That is, the stored row and column indices can begin at
3903:    either one (as in Fortran) or zero.  See the users' manual for details.

3905:    Specify the preallocated storage with either nz or nnz (not both).
3906:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3907:    allocation.  For large problems you MUST preallocate memory or you
3908:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3910:    By default, this format uses inodes (identical nodes) when possible, to
3911:    improve numerical efficiency of matrix-vector products and solves. We
3912:    search for consecutive rows with the same nonzero structure, thereby
3913:    reusing matrix information to achieve increased efficiency.

3915:    Options Database Keys:
3916: +  -mat_no_inode  - Do not use inodes
3917: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3919:    Level: intermediate

3921: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3923: @*/
3924: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3925: {

3929:   MatCreate(comm,A);
3930:   MatSetSizes(*A,m,n,m,n);
3931:   MatSetType(*A,MATSEQAIJ);
3932:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3933:   return(0);
3934: }

3936: /*@C
3937:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3938:    the user should preallocate the matrix storage by setting the parameter nz
3939:    (or the array nnz).  By setting these parameters accurately, performance
3940:    during matrix assembly can be increased by more than a factor of 50.

3942:    Collective

3944:    Input Parameters:
3945: +  B - The matrix
3946: .  nz - number of nonzeros per row (same for all rows)
3947: -  nnz - array containing the number of nonzeros in the various rows
3948:          (possibly different for each row) or NULL

3950:    Notes:
3951:      If nnz is given then nz is ignored

3953:     The AIJ format (also called the Yale sparse matrix format or
3954:    compressed row storage), is fully compatible with standard Fortran 77
3955:    storage.  That is, the stored row and column indices can begin at
3956:    either one (as in Fortran) or zero.  See the users' manual for details.

3958:    Specify the preallocated storage with either nz or nnz (not both).
3959:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3960:    allocation.  For large problems you MUST preallocate memory or you
3961:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3963:    You can call MatGetInfo() to get information on how effective the preallocation was;
3964:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3965:    You can also run with the option -info and look for messages with the string
3966:    malloc in them to see if additional memory allocation was needed.

3968:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3969:    entries or columns indices

3971:    By default, this format uses inodes (identical nodes) when possible, to
3972:    improve numerical efficiency of matrix-vector products and solves. We
3973:    search for consecutive rows with the same nonzero structure, thereby
3974:    reusing matrix information to achieve increased efficiency.

3976:    Options Database Keys:
3977: +  -mat_no_inode  - Do not use inodes
3978: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3980:    Level: intermediate

3982: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
3983:           MatSeqAIJSetTotalPreallocation()

3985: @*/
3986: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3987: {

3993:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3994:   return(0);
3995: }

3997: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3998: {
3999:   Mat_SeqAIJ     *b;
4000:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
4002:   PetscInt       i;

4005:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
4006:   if (nz == MAT_SKIP_ALLOCATION) {
4007:     skipallocation = PETSC_TRUE;
4008:     nz             = 0;
4009:   }
4010:   PetscLayoutSetUp(B->rmap);
4011:   PetscLayoutSetUp(B->cmap);

4013:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
4014:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
4015:   if (PetscUnlikelyDebug(nnz)) {
4016:     for (i=0; i<B->rmap->n; i++) {
4017:       if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
4018:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
4019:     }
4020:   }

4022:   B->preallocated = PETSC_TRUE;

4024:   b = (Mat_SeqAIJ*)B->data;

4026:   if (!skipallocation) {
4027:     if (!b->imax) {
4028:       PetscMalloc1(B->rmap->n,&b->imax);
4029:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4030:     }
4031:     if (!b->ilen) {
4032:       /* b->ilen will count nonzeros in each row so far. */
4033:       PetscCalloc1(B->rmap->n,&b->ilen);
4034:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4035:     } else {
4036:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
4037:     }
4038:     if (!b->ipre) {
4039:       PetscMalloc1(B->rmap->n,&b->ipre);
4040:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4041:     }
4042:     if (!nnz) {
4043:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4044:       else if (nz < 0) nz = 1;
4045:       nz = PetscMin(nz,B->cmap->n);
4046:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
4047:       nz = nz*B->rmap->n;
4048:     } else {
4049:       PetscInt64 nz64 = 0;
4050:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
4051:       PetscIntCast(nz64,&nz);
4052:     }

4054:     /* allocate the matrix space */
4055:     /* FIXME: should B's old memory be unlogged? */
4056:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
4057:     if (B->structure_only) {
4058:       PetscMalloc1(nz,&b->j);
4059:       PetscMalloc1(B->rmap->n+1,&b->i);
4060:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
4061:     } else {
4062:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
4063:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
4064:     }
4065:     b->i[0] = 0;
4066:     for (i=1; i<B->rmap->n+1; i++) {
4067:       b->i[i] = b->i[i-1] + b->imax[i-1];
4068:     }
4069:     if (B->structure_only) {
4070:       b->singlemalloc = PETSC_FALSE;
4071:       b->free_a       = PETSC_FALSE;
4072:     } else {
4073:       b->singlemalloc = PETSC_TRUE;
4074:       b->free_a       = PETSC_TRUE;
4075:     }
4076:     b->free_ij      = PETSC_TRUE;
4077:   } else {
4078:     b->free_a  = PETSC_FALSE;
4079:     b->free_ij = PETSC_FALSE;
4080:   }

4082:   if (b->ipre && nnz != b->ipre  && b->imax) {
4083:     /* reserve user-requested sparsity */
4084:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
4085:   }


4088:   b->nz               = 0;
4089:   b->maxnz            = nz;
4090:   B->info.nz_unneeded = (double)b->maxnz;
4091:   if (realalloc) {
4092:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
4093:   }
4094:   B->was_assembled = PETSC_FALSE;
4095:   B->assembled     = PETSC_FALSE;
4096:   return(0);
4097: }


4100: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4101: {
4102:   Mat_SeqAIJ     *a;
4103:   PetscInt       i;


4109:   /* Check local size. If zero, then return */
4110:   if (!A->rmap->n) return(0);

4112:   a = (Mat_SeqAIJ*)A->data;
4113:   /* if no saved info, we error out */
4114:   if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");

4116:   if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");

4118:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
4119:   PetscArrayzero(a->ilen,A->rmap->n);
4120:   a->i[0] = 0;
4121:   for (i=1; i<A->rmap->n+1; i++) {
4122:     a->i[i] = a->i[i-1] + a->imax[i-1];
4123:   }
4124:   A->preallocated     = PETSC_TRUE;
4125:   a->nz               = 0;
4126:   a->maxnz            = a->i[A->rmap->n];
4127:   A->info.nz_unneeded = (double)a->maxnz;
4128:   A->was_assembled    = PETSC_FALSE;
4129:   A->assembled        = PETSC_FALSE;
4130:   return(0);
4131: }

4133: /*@
4134:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

4136:    Input Parameters:
4137: +  B - the matrix
4138: .  i - the indices into j for the start of each row (starts with zero)
4139: .  j - the column indices for each row (starts with zero) these must be sorted for each row
4140: -  v - optional values in the matrix

4142:    Level: developer

4144:    Notes:
4145:       The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

4147:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4148:       structure will be the union of all the previous nonzero structures.

4150:     Developer Notes:
4151:       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
4152:       then just copies the v values directly with PetscMemcpy().

4154:       This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.

4156: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
4157: @*/
4158: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
4159: {

4165:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
4166:   return(0);
4167: }

4169: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
4170: {
4171:   PetscInt       i;
4172:   PetscInt       m,n;
4173:   PetscInt       nz;
4174:   PetscInt       *nnz;

4178:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

4180:   PetscLayoutSetUp(B->rmap);
4181:   PetscLayoutSetUp(B->cmap);

4183:   MatGetSize(B, &m, &n);
4184:   PetscMalloc1(m+1, &nnz);
4185:   for (i = 0; i < m; i++) {
4186:     nz     = Ii[i+1]- Ii[i];
4187:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
4188:     nnz[i] = nz;
4189:   }
4190:   MatSeqAIJSetPreallocation(B, 0, nnz);
4191:   PetscFree(nnz);

4193:   for (i = 0; i < m; i++) {
4194:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
4195:   }

4197:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4198:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

4200:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4201:   return(0);
4202: }

4204: #include <../src/mat/impls/dense/seq/dense.h>
4205: #include <petsc/private/kernels/petscaxpy.h>

4207: /*
4208:     Computes (B'*A')' since computing B*A directly is untenable

4210:                n                       p                          p
4211:         [             ]       [             ]         [                 ]
4212:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
4213:         [             ]       [             ]         [                 ]

4215: */
4216: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4217: {
4218:   PetscErrorCode    ierr;
4219:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
4220:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
4221:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
4222:   PetscInt          i,j,n,m,q,p;
4223:   const PetscInt    *ii,*idx;
4224:   const PetscScalar *b,*a,*a_q;
4225:   PetscScalar       *c,*c_q;
4226:   PetscInt          clda = sub_c->lda;
4227:   PetscInt          alda = sub_a->lda;

4230:   m    = A->rmap->n;
4231:   n    = A->cmap->n;
4232:   p    = B->cmap->n;
4233:   a    = sub_a->v;
4234:   b    = sub_b->a;
4235:   c    = sub_c->v;
4236:   if (clda == m) {
4237:     PetscArrayzero(c,m*p);
4238:   } else {
4239:     for (j=0;j<p;j++)
4240:       for (i=0;i<m;i++)
4241:         c[j*clda + i] = 0.0;
4242:   }
4243:   ii  = sub_b->i;
4244:   idx = sub_b->j;
4245:   for (i=0; i<n; i++) {
4246:     q = ii[i+1] - ii[i];
4247:     while (q-->0) {
4248:       c_q = c + clda*(*idx);
4249:       a_q = a + alda*i;
4250:       PetscKernelAXPY(c_q,*b,a_q,m);
4251:       idx++;
4252:       b++;
4253:     }
4254:   }
4255:   return(0);
4256: }

4258: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
4259: {
4261:   PetscInt       m=A->rmap->n,n=B->cmap->n;
4262:   PetscBool      cisdense;

4265:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
4266:   MatSetSizes(C,m,n,m,n);
4267:   MatSetBlockSizesFromMats(C,A,B);
4268:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
4269:   if (!cisdense) {
4270:     MatSetType(C,MATDENSE);
4271:   }
4272:   MatSetUp(C);

4274:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4275:   return(0);
4276: }

4278: /* ----------------------------------------------------------------*/
4279: /*MC
4280:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4281:    based on compressed sparse row format.

4283:    Options Database Keys:
4284: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4286:    Level: beginner

4288:    Notes:
4289:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
4290:     in this case the values associated with the rows and columns one passes in are set to zero
4291:     in the matrix

4293:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
4294:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

4296:   Developer Notes:
4297:     It would be nice if all matrix formats supported passing NULL in for the numerical values

4299: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4300: M*/

4302: /*MC
4303:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4305:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4306:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
4307:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
4308:   for communicators controlling multiple processes.  It is recommended that you call both of
4309:   the above preallocation routines for simplicity.

4311:    Options Database Keys:
4312: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

4314:   Developer Notes:
4315:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4316:    enough exist.

4318:   Level: beginner

4320: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4321: M*/

4323: /*MC
4324:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4326:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4327:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
4328:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4329:   for communicators controlling multiple processes.  It is recommended that you call both of
4330:   the above preallocation routines for simplicity.

4332:    Options Database Keys:
4333: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

4335:   Level: beginner

4337: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4338: M*/

4340: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4341: #if defined(PETSC_HAVE_ELEMENTAL)
4342: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4343: #endif
4344: #if defined(PETSC_HAVE_SCALAPACK)
4345: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
4346: #endif
4347: #if defined(PETSC_HAVE_HYPRE)
4348: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4349: #endif
4350: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);

4352: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4353: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4354: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4356: /*@C
4357:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

4359:    Not Collective

4361:    Input Parameter:
4362: .  mat - a MATSEQAIJ matrix

4364:    Output Parameter:
4365: .   array - pointer to the data

4367:    Level: intermediate

4369: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4370: @*/
4371: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
4372: {

4376:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4377:   return(0);
4378: }

4380: /*@C
4381:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

4383:    Not Collective

4385:    Input Parameter:
4386: .  mat - a MATSEQAIJ matrix

4388:    Output Parameter:
4389: .   array - pointer to the data

4391:    Level: intermediate

4393: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
4394: @*/
4395: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
4396: {
4397: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4398:   PetscOffloadMask oval;
4399: #endif

4403: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4404:   oval = A->offloadmask;
4405: #endif
4406:   MatSeqAIJGetArray(A,(PetscScalar**)array);
4407: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4408:   if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
4409: #endif
4410:   return(0);
4411: }

4413: /*@C
4414:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4416:    Not Collective

4418:    Input Parameter:
4419: .  mat - a MATSEQAIJ matrix

4421:    Output Parameter:
4422: .   array - pointer to the data

4424:    Level: intermediate

4426: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
4427: @*/
4428: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
4429: {
4430: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4431:   PetscOffloadMask oval;
4432: #endif

4436: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4437:   oval = A->offloadmask;
4438: #endif
4439:   MatSeqAIJRestoreArray(A,(PetscScalar**)array);
4440: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4441:   A->offloadmask = oval;
4442: #endif
4443:   return(0);
4444: }

4446: /*@C
4447:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4449:    Not Collective

4451:    Input Parameter:
4452: .  mat - a MATSEQAIJ matrix

4454:    Output Parameter:
4455: .   nz - the maximum number of nonzeros in any row

4457:    Level: intermediate

4459: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4460: @*/
4461: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4462: {
4463:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

4466:   *nz = aij->rmax;
4467:   return(0);
4468: }

4470: /*@C
4471:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

4473:    Not Collective

4475:    Input Parameters:
4476: +  mat - a MATSEQAIJ matrix
4477: -  array - pointer to the data

4479:    Level: intermediate

4481: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4482: @*/
4483: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4484: {

4488:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4489:   return(0);
4490: }

4492: #if defined(PETSC_HAVE_CUDA)
4493: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4494: #endif

4496: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4497: {
4498:   Mat_SeqAIJ     *b;
4500:   PetscMPIInt    size;

4503:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4504:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

4506:   PetscNewLog(B,&b);

4508:   B->data = (void*)b;

4510:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4511:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4513:   b->row                = NULL;
4514:   b->col                = NULL;
4515:   b->icol               = NULL;
4516:   b->reallocs           = 0;
4517:   b->ignorezeroentries  = PETSC_FALSE;
4518:   b->roworiented        = PETSC_TRUE;
4519:   b->nonew              = 0;
4520:   b->diag               = NULL;
4521:   b->solve_work         = NULL;
4522:   B->spptr              = NULL;
4523:   b->saved_values       = NULL;
4524:   b->idiag              = NULL;
4525:   b->mdiag              = NULL;
4526:   b->ssor_work          = NULL;
4527:   b->omega              = 1.0;
4528:   b->fshift             = 0.0;
4529:   b->idiagvalid         = PETSC_FALSE;
4530:   b->ibdiagvalid        = PETSC_FALSE;
4531:   b->keepnonzeropattern = PETSC_FALSE;

4533:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4534:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4535:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

4537: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4538:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4539:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4540: #endif

4542:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4543:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4544:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4545:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4546:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4547:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4548:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4549: #if defined(PETSC_HAVE_MKL_SPARSE)
4550:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4551: #endif
4552: #if defined(PETSC_HAVE_CUDA)
4553:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4554:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4555: #endif
4556:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4557: #if defined(PETSC_HAVE_ELEMENTAL)
4558:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4559: #endif
4560: #if defined(PETSC_HAVE_SCALAPACK)
4561:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
4562: #endif
4563: #if defined(PETSC_HAVE_HYPRE)
4564:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4565:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
4566: #endif
4567:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4568:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4569:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4570:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4571:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4572:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4573:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4574:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4575:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4576:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
4577:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
4578:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4579:   MatCreate_SeqAIJ_Inode(B);
4580:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4581:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4582:   return(0);
4583: }

4585: /*
4586:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4587: */
4588: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4589: {
4590:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
4592:   PetscInt       m = A->rmap->n,i;

4595:   if (!A->assembled && cpvalues!=MAT_DO_NOT_COPY_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot duplicate unassembled matrix");

4597:   C->factortype = A->factortype;
4598:   c->row        = NULL;
4599:   c->col        = NULL;
4600:   c->icol       = NULL;
4601:   c->reallocs   = 0;

4603:   C->assembled = PETSC_TRUE;

4605:   PetscLayoutReference(A->rmap,&C->rmap);
4606:   PetscLayoutReference(A->cmap,&C->cmap);

4608:   PetscMalloc1(m,&c->imax);
4609:   PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
4610:   PetscMalloc1(m,&c->ilen);
4611:   PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
4612:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

4614:   /* allocate the matrix space */
4615:   if (mallocmatspace) {
4616:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4617:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4619:     c->singlemalloc = PETSC_TRUE;

4621:     PetscArraycpy(c->i,a->i,m+1);
4622:     if (m > 0) {
4623:       PetscArraycpy(c->j,a->j,a->i[m]);
4624:       if (cpvalues == MAT_COPY_VALUES) {
4625:         PetscArraycpy(c->a,a->a,a->i[m]);
4626:       } else {
4627:         PetscArrayzero(c->a,a->i[m]);
4628:       }
4629:     }
4630:   }

4632:   c->ignorezeroentries = a->ignorezeroentries;
4633:   c->roworiented       = a->roworiented;
4634:   c->nonew             = a->nonew;
4635:   if (a->diag) {
4636:     PetscMalloc1(m+1,&c->diag);
4637:     PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
4638:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4639:   } else c->diag = NULL;

4641:   c->solve_work         = NULL;
4642:   c->saved_values       = NULL;
4643:   c->idiag              = NULL;
4644:   c->ssor_work          = NULL;
4645:   c->keepnonzeropattern = a->keepnonzeropattern;
4646:   c->free_a             = PETSC_TRUE;
4647:   c->free_ij            = PETSC_TRUE;

4649:   c->rmax         = a->rmax;
4650:   c->nz           = a->nz;
4651:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4652:   C->preallocated = PETSC_TRUE;

4654:   c->compressedrow.use   = a->compressedrow.use;
4655:   c->compressedrow.nrows = a->compressedrow.nrows;
4656:   if (a->compressedrow.use) {
4657:     i    = a->compressedrow.nrows;
4658:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4659:     PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
4660:     PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
4661:   } else {
4662:     c->compressedrow.use    = PETSC_FALSE;
4663:     c->compressedrow.i      = NULL;
4664:     c->compressedrow.rindex = NULL;
4665:   }
4666:   c->nonzerorowcnt = a->nonzerorowcnt;
4667:   C->nonzerostate  = A->nonzerostate;

4669:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4670:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4671:   return(0);
4672: }

4674: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4675: {

4679:   MatCreate(PetscObjectComm((PetscObject)A),B);
4680:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4681:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4682:     MatSetBlockSizesFromMats(*B,A,A);
4683:   }
4684:   MatSetType(*B,((PetscObject)A)->type_name);
4685:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4686:   return(0);
4687: }

4689: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4690: {
4691:   PetscBool      isbinary, ishdf5;

4697:   /* force binary viewer to load .info file if it has not yet done so */
4698:   PetscViewerSetUp(viewer);
4699:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4700:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
4701:   if (isbinary) {
4702:     MatLoad_SeqAIJ_Binary(newMat,viewer);
4703:   } else if (ishdf5) {
4704: #if defined(PETSC_HAVE_HDF5)
4705:     MatLoad_AIJ_HDF5(newMat,viewer);
4706: #else
4707:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4708: #endif
4709:   } else {
4710:     SETERRQ2(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
4711:   }
4712:   return(0);
4713: }

4715: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4716: {
4717:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
4719:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

4722:   PetscViewerSetUp(viewer);

4724:   /* read in matrix header */
4725:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
4726:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
4727:   M = header[1]; N = header[2]; nz = header[3];
4728:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
4729:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
4730:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");

4732:   /* set block sizes from the viewer's .info file */
4733:   MatLoad_Binary_BlockSizes(mat,viewer);
4734:   /* set local and global sizes if not set already */
4735:   if (mat->rmap->n < 0) mat->rmap->n = M;
4736:   if (mat->cmap->n < 0) mat->cmap->n = N;
4737:   if (mat->rmap->N < 0) mat->rmap->N = M;
4738:   if (mat->cmap->N < 0) mat->cmap->N = N;
4739:   PetscLayoutSetUp(mat->rmap);
4740:   PetscLayoutSetUp(mat->cmap);

4742:   /* check if the matrix sizes are correct */
4743:   MatGetSize(mat,&rows,&cols);
4744:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);

4746:   /* read in row lengths */
4747:   PetscMalloc1(M,&rowlens);
4748:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
4749:   /* check if sum(rowlens) is same as nz */
4750:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
4751:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);
4752:   /* preallocate and check sizes */
4753:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
4754:   MatGetSize(mat,&rows,&cols);
4755:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
4756:   /* store row lengths */
4757:   PetscArraycpy(a->ilen,rowlens,M);
4758:   PetscFree(rowlens);

4760:   /* fill in "i" row pointers */
4761:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
4762:   /* read in "j" column indices */
4763:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
4764:   /* read in "a" nonzero values */
4765:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

4767:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
4768:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
4769:   return(0);
4770: }

4772: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4773: {
4774:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4776: #if defined(PETSC_USE_COMPLEX)
4777:   PetscInt k;
4778: #endif

4781:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4782:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4783:     *flg = PETSC_FALSE;
4784:     return(0);
4785:   }

4787:   /* if the a->i are the same */
4788:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
4789:   if (!*flg) return(0);

4791:   /* if a->j are the same */
4792:   PetscArraycmp(a->j,b->j,a->nz,flg);
4793:   if (!*flg) return(0);

4795:   /* if a->a are the same */
4796: #if defined(PETSC_USE_COMPLEX)
4797:   for (k=0; k<a->nz; k++) {
4798:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4799:       *flg = PETSC_FALSE;
4800:       return(0);
4801:     }
4802:   }
4803: #else
4804:   PetscArraycmp(a->a,b->a,a->nz,flg);
4805: #endif
4806:   return(0);
4807: }

4809: /*@
4810:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4811:               provided by the user.

4813:       Collective

4815:    Input Parameters:
4816: +   comm - must be an MPI communicator of size 1
4817: .   m - number of rows
4818: .   n - number of columns
4819: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4820: .   j - column indices
4821: -   a - matrix values

4823:    Output Parameter:
4824: .   mat - the matrix

4826:    Level: intermediate

4828:    Notes:
4829:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4830:     once the matrix is destroyed and not before

4832:        You cannot set new nonzero locations into this matrix, that will generate an error.

4834:        The i and j indices are 0 based

4836:        The format which is used for the sparse matrix input, is equivalent to a
4837:     row-major ordering.. i.e for the following matrix, the input data expected is
4838:     as shown

4840: $        1 0 0
4841: $        2 0 3
4842: $        4 5 6
4843: $
4844: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4845: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
4846: $        v =  {1,2,3,4,5,6}  [size = 6]


4849: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4851: @*/
4852: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4853: {
4855:   PetscInt       ii;
4856:   Mat_SeqAIJ     *aij;
4857:   PetscInt jj;

4860:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4861:   MatCreate(comm,mat);
4862:   MatSetSizes(*mat,m,n,m,n);
4863:   /* MatSetBlockSizes(*mat,,); */
4864:   MatSetType(*mat,MATSEQAIJ);
4865:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
4866:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4867:   PetscMalloc1(m,&aij->imax);
4868:   PetscMalloc1(m,&aij->ilen);

4870:   aij->i            = i;
4871:   aij->j            = j;
4872:   aij->a            = a;
4873:   aij->singlemalloc = PETSC_FALSE;
4874:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4875:   aij->free_a       = PETSC_FALSE;
4876:   aij->free_ij      = PETSC_FALSE;

4878:   for (ii=0; ii<m; ii++) {
4879:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4880:     if (PetscDefined(USE_DEBUG)) {
4881:       if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
4882:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4883:         if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4884:         if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4885:       }
4886:     }
4887:   }
4888:   if (PetscDefined(USE_DEBUG)) {
4889:     for (ii=0; ii<aij->i[m]; ii++) {
4890:       if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4891:       if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
4892:     }
4893:   }

4895:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4896:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4897:   return(0);
4898: }
4899: /*@C
4900:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4901:               provided by the user.

4903:       Collective

4905:    Input Parameters:
4906: +   comm - must be an MPI communicator of size 1
4907: .   m   - number of rows
4908: .   n   - number of columns
4909: .   i   - row indices
4910: .   j   - column indices
4911: .   a   - matrix values
4912: .   nz  - number of nonzeros
4913: -   idx - 0 or 1 based

4915:    Output Parameter:
4916: .   mat - the matrix

4918:    Level: intermediate

4920:    Notes:
4921:        The i and j indices are 0 based

4923:        The format which is used for the sparse matrix input, is equivalent to a
4924:     row-major ordering.. i.e for the following matrix, the input data expected is
4925:     as shown:

4927:         1 0 0
4928:         2 0 3
4929:         4 5 6

4931:         i =  {0,1,1,2,2,2}
4932:         j =  {0,0,2,0,1,2}
4933:         v =  {1,2,3,4,5,6}


4936: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4938: @*/
4939: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4940: {
4942:   PetscInt       ii, *nnz, one = 1,row,col;


4946:   PetscCalloc1(m,&nnz);
4947:   for (ii = 0; ii < nz; ii++) {
4948:     nnz[i[ii] - !!idx] += 1;
4949:   }
4950:   MatCreate(comm,mat);
4951:   MatSetSizes(*mat,m,n,m,n);
4952:   MatSetType(*mat,MATSEQAIJ);
4953:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4954:   for (ii = 0; ii < nz; ii++) {
4955:     if (idx) {
4956:       row = i[ii] - 1;
4957:       col = j[ii] - 1;
4958:     } else {
4959:       row = i[ii];
4960:       col = j[ii];
4961:     }
4962:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4963:   }
4964:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4965:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4966:   PetscFree(nnz);
4967:   return(0);
4968: }

4970: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4971: {
4972:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

4976:   a->idiagvalid  = PETSC_FALSE;
4977:   a->ibdiagvalid = PETSC_FALSE;

4979:   MatSeqAIJInvalidateDiagonal_Inode(A);
4980:   return(0);
4981: }

4983: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4984: {
4986:   PetscMPIInt    size;

4989:   MPI_Comm_size(comm,&size);
4990:   if (size == 1) {
4991:     if (scall == MAT_INITIAL_MATRIX) {
4992:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4993:     } else {
4994:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4995:     }
4996:   } else {
4997:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4998:   }
4999:   return(0);
5000: }

5002: /*
5003:  Permute A into C's *local* index space using rowemb,colemb.
5004:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5005:  of [0,m), colemb is in [0,n).
5006:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5007:  */
5008: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
5009: {
5010:   /* If making this function public, change the error returned in this function away from _PLIB. */
5012:   Mat_SeqAIJ     *Baij;
5013:   PetscBool      seqaij;
5014:   PetscInt       m,n,*nz,i,j,count;
5015:   PetscScalar    v;
5016:   const PetscInt *rowindices,*colindices;

5019:   if (!B) return(0);
5020:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5021:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
5022:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
5023:   if (rowemb) {
5024:     ISGetLocalSize(rowemb,&m);
5025:     if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
5026:   } else {
5027:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
5028:   }
5029:   if (colemb) {
5030:     ISGetLocalSize(colemb,&n);
5031:     if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
5032:   } else {
5033:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
5034:   }

5036:   Baij = (Mat_SeqAIJ*)(B->data);
5037:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
5038:     PetscMalloc1(B->rmap->n,&nz);
5039:     for (i=0; i<B->rmap->n; i++) {
5040:       nz[i] = Baij->i[i+1] - Baij->i[i];
5041:     }
5042:     MatSeqAIJSetPreallocation(C,0,nz);
5043:     PetscFree(nz);
5044:   }
5045:   if (pattern == SUBSET_NONZERO_PATTERN) {
5046:     MatZeroEntries(C);
5047:   }
5048:   count = 0;
5049:   rowindices = NULL;
5050:   colindices = NULL;
5051:   if (rowemb) {
5052:     ISGetIndices(rowemb,&rowindices);
5053:   }
5054:   if (colemb) {
5055:     ISGetIndices(colemb,&colindices);
5056:   }
5057:   for (i=0; i<B->rmap->n; i++) {
5058:     PetscInt row;
5059:     row = i;
5060:     if (rowindices) row = rowindices[i];
5061:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
5062:       PetscInt col;
5063:       col  = Baij->j[count];
5064:       if (colindices) col = colindices[col];
5065:       v    = Baij->a[count];
5066:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
5067:       ++count;
5068:     }
5069:   }
5070:   /* FIXME: set C's nonzerostate correctly. */
5071:   /* Assembly for C is necessary. */
5072:   C->preallocated = PETSC_TRUE;
5073:   C->assembled     = PETSC_TRUE;
5074:   C->was_assembled = PETSC_FALSE;
5075:   return(0);
5076: }

5078: PetscFunctionList MatSeqAIJList = NULL;

5080: /*@C
5081:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

5083:    Collective on Mat

5085:    Input Parameters:
5086: +  mat      - the matrix object
5087: -  matype   - matrix type

5089:    Options Database Key:
5090: .  -mat_seqai_type  <method> - for example seqaijcrl


5093:   Level: intermediate

5095: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
5096: @*/
5097: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
5098: {
5099:   PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
5100:   PetscBool      sametype;

5104:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
5105:   if (sametype) return(0);

5107:    PetscFunctionListFind(MatSeqAIJList,matype,&r);
5108:   if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
5109:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
5110:   return(0);
5111: }


5114: /*@C
5115:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

5117:    Not Collective

5119:    Input Parameters:
5120: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
5121: -  function - routine to convert to subtype

5123:    Notes:
5124:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.


5127:    Then, your matrix can be chosen with the procedural interface at runtime via the option
5128: $     -mat_seqaij_type my_mat

5130:    Level: advanced

5132: .seealso: MatSeqAIJRegisterAll()


5135:   Level: advanced
5136: @*/
5137: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
5138: {

5142:   MatInitializePackage();
5143:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
5144:   return(0);
5145: }

5147: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5149: /*@C
5150:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

5152:   Not Collective

5154:   Level: advanced

5156:   Developers Note: CUSP and CUSPARSE do not yet support the  MatConvert_SeqAIJ..() paradigm and thus cannot be registered here

5158: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
5159: @*/
5160: PetscErrorCode  MatSeqAIJRegisterAll(void)
5161: {

5165:   if (MatSeqAIJRegisterAllCalled) return(0);
5166:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5168:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
5169:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
5170:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
5171: #if defined(PETSC_HAVE_MKL_SPARSE)
5172:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
5173: #endif
5174: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5175:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
5176: #endif
5177:   return(0);
5178: }

5180: /*
5181:     Special version for direct calls from Fortran
5182: */
5183: #include <petsc/private/fortranimpl.h>
5184: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5185: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5186: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5187: #define matsetvaluesseqaij_ matsetvaluesseqaij
5188: #endif

5190: /* Change these macros so can be used in void function */
5191: #undef CHKERRQ
5192: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
5193: #undef SETERRQ2
5194: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5195: #undef SETERRQ3
5196: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

5198: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
5199: {
5200:   Mat            A  = *AA;
5201:   PetscInt       m  = *mm, n = *nn;
5202:   InsertMode     is = *isis;
5203:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
5204:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
5205:   PetscInt       *imax,*ai,*ailen;
5207:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
5208:   MatScalar      *ap,value,*aa;
5209:   PetscBool      ignorezeroentries = a->ignorezeroentries;
5210:   PetscBool      roworiented       = a->roworiented;

5213:   MatCheckPreallocated(A,1);
5214:   imax  = a->imax;
5215:   ai    = a->i;
5216:   ailen = a->ilen;
5217:   aj    = a->j;
5218:   aa    = a->a;

5220:   for (k=0; k<m; k++) { /* loop over added rows */
5221:     row = im[k];
5222:     if (row < 0) continue;
5223:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
5224:     rp   = aj + ai[row]; ap = aa + ai[row];
5225:     rmax = imax[row]; nrow = ailen[row];
5226:     low  = 0;
5227:     high = nrow;
5228:     for (l=0; l<n; l++) { /* loop over added columns */
5229:       if (in[l] < 0) continue;
5230:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
5231:       col = in[l];
5232:       if (roworiented) value = v[l + k*n];
5233:       else value = v[k + l*m];

5235:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5237:       if (col <= lastcol) low = 0;
5238:       else high = nrow;
5239:       lastcol = col;
5240:       while (high-low > 5) {
5241:         t = (low+high)/2;
5242:         if (rp[t] > col) high = t;
5243:         else             low  = t;
5244:       }
5245:       for (i=low; i<high; i++) {
5246:         if (rp[i] > col) break;
5247:         if (rp[i] == col) {
5248:           if (is == ADD_VALUES) ap[i] += value;
5249:           else                  ap[i] = value;
5250:           goto noinsert;
5251:         }
5252:       }
5253:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5254:       if (nonew == 1) goto noinsert;
5255:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5256:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5257:       N = nrow++ - 1; a->nz++; high++;
5258:       /* shift up all the later entries in this row */
5259:       for (ii=N; ii>=i; ii--) {
5260:         rp[ii+1] = rp[ii];
5261:         ap[ii+1] = ap[ii];
5262:       }
5263:       rp[i] = col;
5264:       ap[i] = value;
5265:       A->nonzerostate++;
5266: noinsert:;
5267:       low = i + 1;
5268:     }
5269:     ailen[row] = nrow;
5270:   }
5271:   PetscFunctionReturnVoid();
5272: }