Actual source code: aij.c

petsc-master 2020-07-04
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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 = 0;
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: }

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

533:   for (k=0; k<m; k++) { /* loop over added rows */
534:     row  = im[k];
535:     rp   = aj + ai[row];
536:     ap   = aa + ai[row];
537:     if (!A->was_assembled) {
538:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
539:     }
540:     if (!A->structure_only) {
541:       if (v) {
542:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
543:         v   += n;
544:       } else {
545:         PetscMemzero(ap,n*sizeof(PetscScalar));
546:       }
547:     }
548:     ailen[row] = n;
549:     a->nz      += n;
550:   }
551: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
552:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
553: #endif
554:   return(0);
555: }


558: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
559: {
560:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
561:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
562:   PetscInt   *ai = a->i,*ailen = a->ilen;
563:   MatScalar  *ap,*aa = a->a;

566:   for (k=0; k<m; k++) { /* loop over rows */
567:     row = im[k];
568:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
569:     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);
570:     rp   = aj + ai[row]; ap = aa + ai[row];
571:     nrow = ailen[row];
572:     for (l=0; l<n; l++) { /* loop over columns */
573:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
574:       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);
575:       col  = in[l];
576:       high = nrow; low = 0; /* assume unsorted */
577:       while (high-low > 5) {
578:         t = (low+high)/2;
579:         if (rp[t] > col) high = t;
580:         else low = t;
581:       }
582:       for (i=low; i<high; i++) {
583:         if (rp[i] > col) break;
584:         if (rp[i] == col) {
585:           *v++ = ap[i];
586:           goto finished;
587:         }
588:       }
589:       *v++ = 0.0;
590: finished:;
591:     }
592:   }
593:   return(0);
594: }

596: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
597: {
598:   Mat_SeqAIJ     *A = (Mat_SeqAIJ*)mat->data;
599:   PetscInt       header[4],M,N,m,nz,i;
600:   PetscInt       *rowlens;

604:   PetscViewerSetUp(viewer);

606:   M  = mat->rmap->N;
607:   N  = mat->cmap->N;
608:   m  = mat->rmap->n;
609:   nz = A->nz;

611:   /* write matrix header */
612:   header[0] = MAT_FILE_CLASSID;
613:   header[1] = M; header[2] = N; header[3] = nz;
614:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

616:   /* fill in and store row lengths */
617:   PetscMalloc1(m,&rowlens);
618:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
619:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
620:   PetscFree(rowlens);
621:   /* store column indices */
622:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
623:   /* store nonzero values */
624:   PetscViewerBinaryWrite(viewer,A->a,nz,PETSC_SCALAR);

626:   /* write block size option to the viewer's .info file */
627:   MatView_Binary_BlockSizes(mat,viewer);
628:   return(0);
629: }

631: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
632: {
634:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
635:   PetscInt       i,k,m=A->rmap->N;

638:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
639:   for (i=0; i<m; i++) {
640:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
641:     for (k=a->i[i]; k<a->i[i+1]; k++) {
642:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
643:     }
644:     PetscViewerASCIIPrintf(viewer,"\n");
645:   }
646:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
647:   return(0);
648: }

650: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

652: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
653: {
654:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
655:   PetscErrorCode    ierr;
656:   PetscInt          i,j,m = A->rmap->n;
657:   const char        *name;
658:   PetscViewerFormat format;

661:   if (A->structure_only) {
662:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
663:     return(0);
664:   }

666:   PetscViewerGetFormat(viewer,&format);
667:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
668:     PetscInt nofinalvalue = 0;
669:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
670:       /* Need a dummy value to ensure the dimension of the matrix. */
671:       nofinalvalue = 1;
672:     }
673:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
674:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
675:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
676: #if defined(PETSC_USE_COMPLEX)
677:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
678: #else
679:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
680: #endif
681:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

683:     for (i=0; i<m; i++) {
684:       for (j=a->i[i]; j<a->i[i+1]; j++) {
685: #if defined(PETSC_USE_COMPLEX)
686:         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]));
687: #else
688:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
689: #endif
690:       }
691:     }
692:     if (nofinalvalue) {
693: #if defined(PETSC_USE_COMPLEX)
694:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
695: #else
696:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
697: #endif
698:     }
699:     PetscObjectGetName((PetscObject)A,&name);
700:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
701:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
702:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
703:     return(0);
704:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
705:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
706:     for (i=0; i<m; i++) {
707:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
708:       for (j=a->i[i]; j<a->i[i+1]; j++) {
709: #if defined(PETSC_USE_COMPLEX)
710:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
711:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
712:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
713:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
714:         } else if (PetscRealPart(a->a[j]) != 0.0) {
715:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
716:         }
717: #else
718:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
719: #endif
720:       }
721:       PetscViewerASCIIPrintf(viewer,"\n");
722:     }
723:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
724:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
725:     PetscInt nzd=0,fshift=1,*sptr;
726:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
727:     PetscMalloc1(m+1,&sptr);
728:     for (i=0; i<m; i++) {
729:       sptr[i] = nzd+1;
730:       for (j=a->i[i]; j<a->i[i+1]; j++) {
731:         if (a->j[j] >= i) {
732: #if defined(PETSC_USE_COMPLEX)
733:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
734: #else
735:           if (a->a[j] != 0.0) nzd++;
736: #endif
737:         }
738:       }
739:     }
740:     sptr[m] = nzd+1;
741:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
742:     for (i=0; i<m+1; i+=6) {
743:       if (i+4<m) {
744:         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]);
745:       } else if (i+3<m) {
746:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
747:       } else if (i+2<m) {
748:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
749:       } else if (i+1<m) {
750:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
751:       } else if (i<m) {
752:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
753:       } else {
754:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
755:       }
756:     }
757:     PetscViewerASCIIPrintf(viewer,"\n");
758:     PetscFree(sptr);
759:     for (i=0; i<m; i++) {
760:       for (j=a->i[i]; j<a->i[i+1]; j++) {
761:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
762:       }
763:       PetscViewerASCIIPrintf(viewer,"\n");
764:     }
765:     PetscViewerASCIIPrintf(viewer,"\n");
766:     for (i=0; i<m; i++) {
767:       for (j=a->i[i]; j<a->i[i+1]; j++) {
768:         if (a->j[j] >= i) {
769: #if defined(PETSC_USE_COMPLEX)
770:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
771:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
772:           }
773: #else
774:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
775: #endif
776:         }
777:       }
778:       PetscViewerASCIIPrintf(viewer,"\n");
779:     }
780:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
781:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
782:     PetscInt    cnt = 0,jcnt;
783:     PetscScalar value;
784: #if defined(PETSC_USE_COMPLEX)
785:     PetscBool   realonly = PETSC_TRUE;

787:     for (i=0; i<a->i[m]; i++) {
788:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
789:         realonly = PETSC_FALSE;
790:         break;
791:       }
792:     }
793: #endif

795:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
796:     for (i=0; i<m; i++) {
797:       jcnt = 0;
798:       for (j=0; j<A->cmap->n; j++) {
799:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
800:           value = a->a[cnt++];
801:           jcnt++;
802:         } else {
803:           value = 0.0;
804:         }
805: #if defined(PETSC_USE_COMPLEX)
806:         if (realonly) {
807:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
808:         } else {
809:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
810:         }
811: #else
812:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
813: #endif
814:       }
815:       PetscViewerASCIIPrintf(viewer,"\n");
816:     }
817:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
818:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
819:     PetscInt fshift=1;
820:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
821: #if defined(PETSC_USE_COMPLEX)
822:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
823: #else
824:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
825: #endif
826:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
827:     for (i=0; i<m; i++) {
828:       for (j=a->i[i]; j<a->i[i+1]; j++) {
829: #if defined(PETSC_USE_COMPLEX)
830:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
831: #else
832:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
833: #endif
834:       }
835:     }
836:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
837:   } else {
838:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
839:     if (A->factortype) {
840:       for (i=0; i<m; i++) {
841:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
842:         /* L part */
843:         for (j=a->i[i]; j<a->i[i+1]; j++) {
844: #if defined(PETSC_USE_COMPLEX)
845:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
846:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
847:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
848:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
849:           } else {
850:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
851:           }
852: #else
853:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
854: #endif
855:         }
856:         /* diagonal */
857:         j = a->diag[i];
858: #if defined(PETSC_USE_COMPLEX)
859:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
860:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
861:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
862:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
863:         } else {
864:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
865:         }
866: #else
867:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
868: #endif

870:         /* U part */
871:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
872: #if defined(PETSC_USE_COMPLEX)
873:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
874:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
875:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
876:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
877:           } else {
878:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
879:           }
880: #else
881:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
882: #endif
883:         }
884:         PetscViewerASCIIPrintf(viewer,"\n");
885:       }
886:     } else {
887:       for (i=0; i<m; i++) {
888:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
889:         for (j=a->i[i]; j<a->i[i+1]; j++) {
890: #if defined(PETSC_USE_COMPLEX)
891:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
892:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
893:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
894:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
895:           } else {
896:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
897:           }
898: #else
899:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
900: #endif
901:         }
902:         PetscViewerASCIIPrintf(viewer,"\n");
903:       }
904:     }
905:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
906:   }
907:   PetscViewerFlush(viewer);
908:   return(0);
909: }

911:  #include <petscdraw.h>
912: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
913: {
914:   Mat               A  = (Mat) Aa;
915:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
916:   PetscErrorCode    ierr;
917:   PetscInt          i,j,m = A->rmap->n;
918:   int               color;
919:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
920:   PetscViewer       viewer;
921:   PetscViewerFormat format;

924:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
925:   PetscViewerGetFormat(viewer,&format);
926:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

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

930:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
931:     PetscDrawCollectiveBegin(draw);
932:     /* Blue for negative, Cyan for zero and  Red for positive */
933:     color = PETSC_DRAW_BLUE;
934:     for (i=0; i<m; i++) {
935:       y_l = m - i - 1.0; y_r = y_l + 1.0;
936:       for (j=a->i[i]; j<a->i[i+1]; j++) {
937:         x_l = a->j[j]; x_r = x_l + 1.0;
938:         if (PetscRealPart(a->a[j]) >=  0.) continue;
939:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
940:       }
941:     }
942:     color = PETSC_DRAW_CYAN;
943:     for (i=0; i<m; i++) {
944:       y_l = m - i - 1.0; y_r = y_l + 1.0;
945:       for (j=a->i[i]; j<a->i[i+1]; j++) {
946:         x_l = a->j[j]; x_r = x_l + 1.0;
947:         if (a->a[j] !=  0.) continue;
948:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
949:       }
950:     }
951:     color = PETSC_DRAW_RED;
952:     for (i=0; i<m; i++) {
953:       y_l = m - i - 1.0; y_r = y_l + 1.0;
954:       for (j=a->i[i]; j<a->i[i+1]; j++) {
955:         x_l = a->j[j]; x_r = x_l + 1.0;
956:         if (PetscRealPart(a->a[j]) <=  0.) continue;
957:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
958:       }
959:     }
960:     PetscDrawCollectiveEnd(draw);
961:   } else {
962:     /* use contour shading to indicate magnitude of values */
963:     /* first determine max of all nonzero values */
964:     PetscReal minv = 0.0, maxv = 0.0;
965:     PetscInt  nz = a->nz, count = 0;
966:     PetscDraw popup;

968:     for (i=0; i<nz; i++) {
969:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
970:     }
971:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
972:     PetscDrawGetPopup(draw,&popup);
973:     PetscDrawScalePopup(popup,minv,maxv);

975:     PetscDrawCollectiveBegin(draw);
976:     for (i=0; i<m; i++) {
977:       y_l = m - i - 1.0;
978:       y_r = y_l + 1.0;
979:       for (j=a->i[i]; j<a->i[i+1]; j++) {
980:         x_l = a->j[j];
981:         x_r = x_l + 1.0;
982:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
983:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
984:         count++;
985:       }
986:     }
987:     PetscDrawCollectiveEnd(draw);
988:   }
989:   return(0);
990: }

992:  #include <petscdraw.h>
993: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
994: {
996:   PetscDraw      draw;
997:   PetscReal      xr,yr,xl,yl,h,w;
998:   PetscBool      isnull;

1001:   PetscViewerDrawGetDraw(viewer,0,&draw);
1002:   PetscDrawIsNull(draw,&isnull);
1003:   if (isnull) return(0);

1005:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
1006:   xr  += w;          yr += h;         xl = -w;     yl = -h;
1007:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1008:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1009:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
1010:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1011:   PetscDrawSave(draw);
1012:   return(0);
1013: }

1015: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
1016: {
1018:   PetscBool      iascii,isbinary,isdraw;

1021:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1022:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1023:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1024:   if (iascii) {
1025:     MatView_SeqAIJ_ASCII(A,viewer);
1026:   } else if (isbinary) {
1027:     MatView_SeqAIJ_Binary(A,viewer);
1028:   } else if (isdraw) {
1029:     MatView_SeqAIJ_Draw(A,viewer);
1030:   }
1031:   MatView_SeqAIJ_Inode(A,viewer);
1032:   return(0);
1033: }

1035: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
1036: {
1037:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1039:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
1040:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
1041:   MatScalar      *aa    = a->a,*ap;
1042:   PetscReal      ratio  = 0.6;

1045:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1046:   MatSeqAIJInvalidateDiagonal(A);
1047:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) return(0);

1049:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1050:   for (i=1; i<m; i++) {
1051:     /* move each row back by the amount of empty slots (fshift) before it*/
1052:     fshift += imax[i-1] - ailen[i-1];
1053:     rmax    = PetscMax(rmax,ailen[i]);
1054:     if (fshift) {
1055:       ip = aj + ai[i];
1056:       ap = aa + ai[i];
1057:       N  = ailen[i];
1058:       PetscArraymove(ip-fshift,ip,N);
1059:       if (!A->structure_only) {
1060:         PetscArraymove(ap-fshift,ap,N);
1061:       }
1062:     }
1063:     ai[i] = ai[i-1] + ailen[i-1];
1064:   }
1065:   if (m) {
1066:     fshift += imax[m-1] - ailen[m-1];
1067:     ai[m]   = ai[m-1] + ailen[m-1];
1068:   }

1070:   /* reset ilen and imax for each row */
1071:   a->nonzerorowcnt = 0;
1072:   if (A->structure_only) {
1073:     PetscFree(a->imax);
1074:     PetscFree(a->ilen);
1075:   } else { /* !A->structure_only */
1076:     for (i=0; i<m; i++) {
1077:       ailen[i] = imax[i] = ai[i+1] - ai[i];
1078:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1079:     }
1080:   }
1081:   a->nz = ai[m];
1082:   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);

1084:   MatMarkDiagonal_SeqAIJ(A);
1085:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1086:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1087:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

1089:   A->info.mallocs    += a->reallocs;
1090:   a->reallocs         = 0;
1091:   A->info.nz_unneeded = (PetscReal)fshift;
1092:   a->rmax             = rmax;

1094:   if (!A->structure_only) {
1095:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1096:   }
1097:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1098:   return(0);
1099: }

1101: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1102: {
1103:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1104:   PetscInt       i,nz = a->nz;
1105:   MatScalar      *aa = a->a;

1109:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1110:   MatSeqAIJInvalidateDiagonal(A);
1111: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1112:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1113: #endif
1114:   return(0);
1115: }

1117: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1118: {
1119:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1120:   PetscInt       i,nz = a->nz;
1121:   MatScalar      *aa = a->a;

1125:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1126:   MatSeqAIJInvalidateDiagonal(A);
1127: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1128:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1129: #endif
1130:   return(0);
1131: }

1133: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1134: {
1135:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1139:   PetscArrayzero(a->a,a->i[A->rmap->n]);
1140:   MatSeqAIJInvalidateDiagonal(A);
1141: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
1142:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1143: #endif
1144:   return(0);
1145: }

1147: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1148: {
1149:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1153: #if defined(PETSC_USE_LOG)
1154:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1155: #endif
1156:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1157:   ISDestroy(&a->row);
1158:   ISDestroy(&a->col);
1159:   PetscFree(a->diag);
1160:   PetscFree(a->ibdiag);
1161:   PetscFree(a->imax);
1162:   PetscFree(a->ilen);
1163:   PetscFree(a->ipre);
1164:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1165:   PetscFree(a->solve_work);
1166:   ISDestroy(&a->icol);
1167:   PetscFree(a->saved_values);
1168:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

1170:   MatDestroy_SeqAIJ_Inode(A);
1171:   PetscFree(A->data);

1173:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1174:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1175:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
1176:      users reusing the matrix object with different data to incur in obscure segmentation faults
1177:      due to different matrix sizes */
1178:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

1180:   PetscObjectChangeTypeName((PetscObject)A,0);
1181:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1182:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1183:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1184:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1185:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1186:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);

1188: #if defined(PETSC_HAVE_CUDA)
1189:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
1190:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
1191: #endif
1192:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
1193: #if defined(PETSC_HAVE_ELEMENTAL)
1194:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1195: #endif
1196: #if defined(PETSC_HAVE_SCALAPACK)
1197:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
1198: #endif
1199: #if defined(PETSC_HAVE_HYPRE)
1200:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1201:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
1202: #endif
1203:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1204:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
1205:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
1206:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1207:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1208:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1209:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1210:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1211:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
1212:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
1213:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
1214:   return(0);
1215: }

1217: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1218: {
1219:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1223:   switch (op) {
1224:   case MAT_ROW_ORIENTED:
1225:     a->roworiented = flg;
1226:     break;
1227:   case MAT_KEEP_NONZERO_PATTERN:
1228:     a->keepnonzeropattern = flg;
1229:     break;
1230:   case MAT_NEW_NONZERO_LOCATIONS:
1231:     a->nonew = (flg ? 0 : 1);
1232:     break;
1233:   case MAT_NEW_NONZERO_LOCATION_ERR:
1234:     a->nonew = (flg ? -1 : 0);
1235:     break;
1236:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1237:     a->nonew = (flg ? -2 : 0);
1238:     break;
1239:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1240:     a->nounused = (flg ? -1 : 0);
1241:     break;
1242:   case MAT_IGNORE_ZERO_ENTRIES:
1243:     a->ignorezeroentries = flg;
1244:     break;
1245:   case MAT_SPD:
1246:   case MAT_SYMMETRIC:
1247:   case MAT_STRUCTURALLY_SYMMETRIC:
1248:   case MAT_HERMITIAN:
1249:   case MAT_SYMMETRY_ETERNAL:
1250:   case MAT_STRUCTURE_ONLY:
1251:     /* These options are handled directly by MatSetOption() */
1252:     break;
1253:   case MAT_NEW_DIAGONALS:
1254:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1255:   case MAT_USE_HASH_TABLE:
1256:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1257:     break;
1258:   case MAT_USE_INODES:
1259:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1260:     break;
1261:   case MAT_SUBMAT_SINGLEIS:
1262:     A->submat_singleis = flg;
1263:     break;
1264:   case MAT_SORTED_FULL:
1265:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1266:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
1267:     break;
1268:   default:
1269:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1270:   }
1271:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1272:   return(0);
1273: }

1275: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1276: {
1277:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1279:   PetscInt       i,j,n,*ai=a->i,*aj=a->j;
1280:   PetscScalar    *aa=a->a,*x;

1283:   VecGetLocalSize(v,&n);
1284:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1286:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1287:     PetscInt *diag=a->diag;
1288:     VecGetArrayWrite(v,&x);
1289:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1290:     VecRestoreArrayWrite(v,&x);
1291:     return(0);
1292:   }

1294:   VecGetArrayWrite(v,&x);
1295:   for (i=0; i<n; i++) {
1296:     x[i] = 0.0;
1297:     for (j=ai[i]; j<ai[i+1]; j++) {
1298:       if (aj[j] == i) {
1299:         x[i] = aa[j];
1300:         break;
1301:       }
1302:     }
1303:   }
1304:   VecRestoreArrayWrite(v,&x);
1305:   return(0);
1306: }

1308:  #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1309: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1310: {
1311:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1312:   PetscScalar       *y;
1313:   const PetscScalar *x;
1314:   PetscErrorCode    ierr;
1315:   PetscInt          m = A->rmap->n;
1316: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1317:   const MatScalar   *v;
1318:   PetscScalar       alpha;
1319:   PetscInt          n,i,j;
1320:   const PetscInt    *idx,*ii,*ridx=NULL;
1321:   Mat_CompressedRow cprow    = a->compressedrow;
1322:   PetscBool         usecprow = cprow.use;
1323: #endif

1326:   if (zz != yy) {VecCopy(zz,yy);}
1327:   VecGetArrayRead(xx,&x);
1328:   VecGetArray(yy,&y);

1330: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1331:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1332: #else
1333:   if (usecprow) {
1334:     m    = cprow.nrows;
1335:     ii   = cprow.i;
1336:     ridx = cprow.rindex;
1337:   } else {
1338:     ii = a->i;
1339:   }
1340:   for (i=0; i<m; i++) {
1341:     idx = a->j + ii[i];
1342:     v   = a->a + ii[i];
1343:     n   = ii[i+1] - ii[i];
1344:     if (usecprow) {
1345:       alpha = x[ridx[i]];
1346:     } else {
1347:       alpha = x[i];
1348:     }
1349:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1350:   }
1351: #endif
1352:   PetscLogFlops(2.0*a->nz);
1353:   VecRestoreArrayRead(xx,&x);
1354:   VecRestoreArray(yy,&y);
1355:   return(0);
1356: }

1358: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1359: {

1363:   VecSet(yy,0.0);
1364:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1365:   return(0);
1366: }

1368:  #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

1370: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1371: {
1372:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1373:   PetscScalar       *y;
1374:   const PetscScalar *x;
1375:   const MatScalar   *aa;
1376:   PetscErrorCode    ierr;
1377:   PetscInt          m=A->rmap->n;
1378:   const PetscInt    *aj,*ii,*ridx=NULL;
1379:   PetscInt          n,i;
1380:   PetscScalar       sum;
1381:   PetscBool         usecprow=a->compressedrow.use;

1383: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1384: #pragma disjoint(*x,*y,*aa)
1385: #endif

1388:   VecGetArrayRead(xx,&x);
1389:   VecGetArray(yy,&y);
1390:   ii   = a->i;
1391:   if (usecprow) { /* use compressed row format */
1392:     PetscArrayzero(y,m);
1393:     m    = a->compressedrow.nrows;
1394:     ii   = a->compressedrow.i;
1395:     ridx = a->compressedrow.rindex;
1396:     for (i=0; i<m; i++) {
1397:       n           = ii[i+1] - ii[i];
1398:       aj          = a->j + ii[i];
1399:       aa          = a->a + ii[i];
1400:       sum         = 0.0;
1401:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1402:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1403:       y[*ridx++] = sum;
1404:     }
1405:   } else { /* do not use compressed row format */
1406: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1407:     aj   = a->j;
1408:     aa   = a->a;
1409:     fortranmultaij_(&m,x,ii,aj,aa,y);
1410: #else
1411:     for (i=0; i<m; i++) {
1412:       n           = ii[i+1] - ii[i];
1413:       aj          = a->j + ii[i];
1414:       aa          = a->a + ii[i];
1415:       sum         = 0.0;
1416:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1417:       y[i] = sum;
1418:     }
1419: #endif
1420:   }
1421:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1422:   VecRestoreArrayRead(xx,&x);
1423:   VecRestoreArray(yy,&y);
1424:   return(0);
1425: }

1427: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1428: {
1429:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1430:   PetscScalar       *y;
1431:   const PetscScalar *x;
1432:   const MatScalar   *aa;
1433:   PetscErrorCode    ierr;
1434:   PetscInt          m=A->rmap->n;
1435:   const PetscInt    *aj,*ii,*ridx=NULL;
1436:   PetscInt          n,i,nonzerorow=0;
1437:   PetscScalar       sum;
1438:   PetscBool         usecprow=a->compressedrow.use;

1440: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1441: #pragma disjoint(*x,*y,*aa)
1442: #endif

1445:   VecGetArrayRead(xx,&x);
1446:   VecGetArray(yy,&y);
1447:   if (usecprow) { /* use compressed row format */
1448:     m    = a->compressedrow.nrows;
1449:     ii   = a->compressedrow.i;
1450:     ridx = a->compressedrow.rindex;
1451:     for (i=0; i<m; i++) {
1452:       n           = ii[i+1] - ii[i];
1453:       aj          = a->j + ii[i];
1454:       aa          = a->a + ii[i];
1455:       sum         = 0.0;
1456:       nonzerorow += (n>0);
1457:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1458:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1459:       y[*ridx++] = sum;
1460:     }
1461:   } else { /* do not use compressed row format */
1462:     ii = a->i;
1463:     for (i=0; i<m; i++) {
1464:       n           = ii[i+1] - ii[i];
1465:       aj          = a->j + ii[i];
1466:       aa          = a->a + ii[i];
1467:       sum         = 0.0;
1468:       nonzerorow += (n>0);
1469:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1470:       y[i] = sum;
1471:     }
1472:   }
1473:   PetscLogFlops(2.0*a->nz - nonzerorow);
1474:   VecRestoreArrayRead(xx,&x);
1475:   VecRestoreArray(yy,&y);
1476:   return(0);
1477: }

1479: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1480: {
1481:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1482:   PetscScalar       *y,*z;
1483:   const PetscScalar *x;
1484:   const MatScalar   *aa;
1485:   PetscErrorCode    ierr;
1486:   PetscInt          m = A->rmap->n,*aj,*ii;
1487:   PetscInt          n,i,*ridx=NULL;
1488:   PetscScalar       sum;
1489:   PetscBool         usecprow=a->compressedrow.use;

1492:   VecGetArrayRead(xx,&x);
1493:   VecGetArrayPair(yy,zz,&y,&z);
1494:   if (usecprow) { /* use compressed row format */
1495:     if (zz != yy) {
1496:       PetscArraycpy(z,y,m);
1497:     }
1498:     m    = a->compressedrow.nrows;
1499:     ii   = a->compressedrow.i;
1500:     ridx = a->compressedrow.rindex;
1501:     for (i=0; i<m; i++) {
1502:       n   = ii[i+1] - ii[i];
1503:       aj  = a->j + ii[i];
1504:       aa  = a->a + ii[i];
1505:       sum = y[*ridx];
1506:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1507:       z[*ridx++] = sum;
1508:     }
1509:   } else { /* do not use compressed row format */
1510:     ii = a->i;
1511:     for (i=0; i<m; i++) {
1512:       n   = ii[i+1] - ii[i];
1513:       aj  = a->j + ii[i];
1514:       aa  = a->a + ii[i];
1515:       sum = y[i];
1516:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1517:       z[i] = sum;
1518:     }
1519:   }
1520:   PetscLogFlops(2.0*a->nz);
1521:   VecRestoreArrayRead(xx,&x);
1522:   VecRestoreArrayPair(yy,zz,&y,&z);
1523:   return(0);
1524: }

1526:  #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1527: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1528: {
1529:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1530:   PetscScalar       *y,*z;
1531:   const PetscScalar *x;
1532:   const MatScalar   *aa;
1533:   PetscErrorCode    ierr;
1534:   const PetscInt    *aj,*ii,*ridx=NULL;
1535:   PetscInt          m = A->rmap->n,n,i;
1536:   PetscScalar       sum;
1537:   PetscBool         usecprow=a->compressedrow.use;

1540:   VecGetArrayRead(xx,&x);
1541:   VecGetArrayPair(yy,zz,&y,&z);
1542:   if (usecprow) { /* use compressed row format */
1543:     if (zz != yy) {
1544:       PetscArraycpy(z,y,m);
1545:     }
1546:     m    = a->compressedrow.nrows;
1547:     ii   = a->compressedrow.i;
1548:     ridx = a->compressedrow.rindex;
1549:     for (i=0; i<m; i++) {
1550:       n   = ii[i+1] - ii[i];
1551:       aj  = a->j + ii[i];
1552:       aa  = a->a + ii[i];
1553:       sum = y[*ridx];
1554:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1555:       z[*ridx++] = sum;
1556:     }
1557:   } else { /* do not use compressed row format */
1558:     ii = a->i;
1559: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1560:     aj = a->j;
1561:     aa = a->a;
1562:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1563: #else
1564:     for (i=0; i<m; i++) {
1565:       n   = ii[i+1] - ii[i];
1566:       aj  = a->j + ii[i];
1567:       aa  = a->a + ii[i];
1568:       sum = y[i];
1569:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1570:       z[i] = sum;
1571:     }
1572: #endif
1573:   }
1574:   PetscLogFlops(2.0*a->nz);
1575:   VecRestoreArrayRead(xx,&x);
1576:   VecRestoreArrayPair(yy,zz,&y,&z);
1577:   return(0);
1578: }

1580: /*
1581:      Adds diagonal pointers to sparse matrix structure.
1582: */
1583: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1584: {
1585:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1587:   PetscInt       i,j,m = A->rmap->n;

1590:   if (!a->diag) {
1591:     PetscMalloc1(m,&a->diag);
1592:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1593:   }
1594:   for (i=0; i<A->rmap->n; i++) {
1595:     a->diag[i] = a->i[i+1];
1596:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1597:       if (a->j[j] == i) {
1598:         a->diag[i] = j;
1599:         break;
1600:       }
1601:     }
1602:   }
1603:   return(0);
1604: }

1606: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
1607: {
1608:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1609:   const PetscInt    *diag = (const PetscInt*)a->diag;
1610:   const PetscInt    *ii = (const PetscInt*) a->i;
1611:   PetscInt          i,*mdiag = NULL;
1612:   PetscErrorCode    ierr;
1613:   PetscInt          cnt = 0; /* how many diagonals are missing */

1616:   if (!A->preallocated || !a->nz) {
1617:     MatSeqAIJSetPreallocation(A,1,NULL);
1618:     MatShift_Basic(A,v);
1619:     return(0);
1620:   }

1622:   if (a->diagonaldense) {
1623:     cnt = 0;
1624:   } else {
1625:     PetscCalloc1(A->rmap->n,&mdiag);
1626:     for (i=0; i<A->rmap->n; i++) {
1627:       if (diag[i] >= ii[i+1]) {
1628:         cnt++;
1629:         mdiag[i] = 1;
1630:       }
1631:     }
1632:   }
1633:   if (!cnt) {
1634:     MatShift_Basic(A,v);
1635:   } else {
1636:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
1637:     PetscInt    *oldj = a->j, *oldi = a->i;
1638:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

1640:     a->a = NULL;
1641:     a->j = NULL;
1642:     a->i = NULL;
1643:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1644:     for (i=0; i<A->rmap->n; i++) {
1645:       a->imax[i] += mdiag[i];
1646:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
1647:     }
1648:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

1650:     /* copy old values into new matrix data structure */
1651:     for (i=0; i<A->rmap->n; i++) {
1652:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
1653:       if (i < A->cmap->n) {
1654:         MatSetValue(A,i,i,v,ADD_VALUES);
1655:       }
1656:     }
1657:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1658:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1659:     if (singlemalloc) {
1660:       PetscFree3(olda,oldj,oldi);
1661:     } else {
1662:       if (free_a)  {PetscFree(olda);}
1663:       if (free_ij) {PetscFree(oldj);}
1664:       if (free_ij) {PetscFree(oldi);}
1665:     }
1666:   }
1667:   PetscFree(mdiag);
1668:   a->diagonaldense = PETSC_TRUE;
1669:   return(0);
1670: }

1672: /*
1673:      Checks for missing diagonals
1674: */
1675: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1676: {
1677:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1678:   PetscInt       *diag,*ii = a->i,i;

1682:   *missing = PETSC_FALSE;
1683:   if (A->rmap->n > 0 && !ii) {
1684:     *missing = PETSC_TRUE;
1685:     if (d) *d = 0;
1686:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1687:   } else {
1688:     PetscInt n;
1689:     n = PetscMin(A->rmap->n, A->cmap->n);
1690:     diag = a->diag;
1691:     for (i=0; i<n; i++) {
1692:       if (diag[i] >= ii[i+1]) {
1693:         *missing = PETSC_TRUE;
1694:         if (d) *d = i;
1695:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1696:         break;
1697:       }
1698:     }
1699:   }
1700:   return(0);
1701: }

1703:  #include <petscblaslapack.h>
1704:  #include <petsc/private/kernels/blockinvert.h>

1706: /*
1707:     Note that values is allocated externally by the PC and then passed into this routine
1708: */
1709: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1710: {
1711:   PetscErrorCode  ierr;
1712:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1713:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
1714:   const PetscReal shift = 0.0;
1715:   PetscInt        ipvt[5];
1716:   PetscScalar     work[25],*v_work;

1719:   allowzeropivot = PetscNot(A->erroriffailure);
1720:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1721:   if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1722:   for (i=0; i<nblocks; i++) {
1723:     bsizemax = PetscMax(bsizemax,bsizes[i]);
1724:   }
1725:   PetscMalloc1(bsizemax,&indx);
1726:   if (bsizemax > 7) {
1727:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1728:   }
1729:   ncnt = 0;
1730:   for (i=0; i<nblocks; i++) {
1731:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1732:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1733:     switch (bsizes[i]) {
1734:     case 1:
1735:       *diag = 1.0/(*diag);
1736:       break;
1737:     case 2:
1738:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1739:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1740:       PetscKernel_A_gets_transpose_A_2(diag);
1741:       break;
1742:     case 3:
1743:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1744:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1745:       PetscKernel_A_gets_transpose_A_3(diag);
1746:       break;
1747:     case 4:
1748:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1749:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1750:       PetscKernel_A_gets_transpose_A_4(diag);
1751:       break;
1752:     case 5:
1753:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1754:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1755:       PetscKernel_A_gets_transpose_A_5(diag);
1756:       break;
1757:     case 6:
1758:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1759:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1760:       PetscKernel_A_gets_transpose_A_6(diag);
1761:       break;
1762:     case 7:
1763:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1764:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1765:       PetscKernel_A_gets_transpose_A_7(diag);
1766:       break;
1767:     default:
1768:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1769:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1770:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1771:     }
1772:     ncnt   += bsizes[i];
1773:     diag += bsizes[i]*bsizes[i];
1774:   }
1775:   if (bsizemax > 7) {
1776:     PetscFree2(v_work,v_pivots);
1777:   }
1778:   PetscFree(indx);
1779:   return(0);
1780: }

1782: /*
1783:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1784: */
1785: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1786: {
1787:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1789:   PetscInt       i,*diag,m = A->rmap->n;
1790:   MatScalar      *v = a->a;
1791:   PetscScalar    *idiag,*mdiag;

1794:   if (a->idiagvalid) return(0);
1795:   MatMarkDiagonal_SeqAIJ(A);
1796:   diag = a->diag;
1797:   if (!a->idiag) {
1798:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1799:     PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1800:     v    = a->a;
1801:   }
1802:   mdiag = a->mdiag;
1803:   idiag = a->idiag;

1805:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1806:     for (i=0; i<m; i++) {
1807:       mdiag[i] = v[diag[i]];
1808:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1809:         if (PetscRealPart(fshift)) {
1810:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
1811:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1812:           A->factorerror_zeropivot_value = 0.0;
1813:           A->factorerror_zeropivot_row   = i;
1814:         } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1815:       }
1816:       idiag[i] = 1.0/v[diag[i]];
1817:     }
1818:     PetscLogFlops(m);
1819:   } else {
1820:     for (i=0; i<m; i++) {
1821:       mdiag[i] = v[diag[i]];
1822:       idiag[i] = omega/(fshift + v[diag[i]]);
1823:     }
1824:     PetscLogFlops(2.0*m);
1825:   }
1826:   a->idiagvalid = PETSC_TRUE;
1827:   return(0);
1828: }

1830:  #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1831: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1832: {
1833:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1834:   PetscScalar       *x,d,sum,*t,scale;
1835:   const MatScalar   *v,*idiag=0,*mdiag;
1836:   const PetscScalar *b, *bs,*xb, *ts;
1837:   PetscErrorCode    ierr;
1838:   PetscInt          n,m = A->rmap->n,i;
1839:   const PetscInt    *idx,*diag;

1842:   its = its*lits;

1844:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1845:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1846:   a->fshift = fshift;
1847:   a->omega  = omega;

1849:   diag  = a->diag;
1850:   t     = a->ssor_work;
1851:   idiag = a->idiag;
1852:   mdiag = a->mdiag;

1854:   VecGetArray(xx,&x);
1855:   VecGetArrayRead(bb,&b);
1856:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1857:   if (flag == SOR_APPLY_UPPER) {
1858:     /* apply (U + D/omega) to the vector */
1859:     bs = b;
1860:     for (i=0; i<m; i++) {
1861:       d   = fshift + mdiag[i];
1862:       n   = a->i[i+1] - diag[i] - 1;
1863:       idx = a->j + diag[i] + 1;
1864:       v   = a->a + diag[i] + 1;
1865:       sum = b[i]*d/omega;
1866:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
1867:       x[i] = sum;
1868:     }
1869:     VecRestoreArray(xx,&x);
1870:     VecRestoreArrayRead(bb,&b);
1871:     PetscLogFlops(a->nz);
1872:     return(0);
1873:   }

1875:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1876:   else if (flag & SOR_EISENSTAT) {
1877:     /* Let  A = L + U + D; where L is lower triangular,
1878:     U is upper triangular, E = D/omega; This routine applies

1880:             (L + E)^{-1} A (U + E)^{-1}

1882:     to a vector efficiently using Eisenstat's trick.
1883:     */
1884:     scale = (2.0/omega) - 1.0;

1886:     /*  x = (E + U)^{-1} b */
1887:     for (i=m-1; i>=0; i--) {
1888:       n   = a->i[i+1] - diag[i] - 1;
1889:       idx = a->j + diag[i] + 1;
1890:       v   = a->a + diag[i] + 1;
1891:       sum = b[i];
1892:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1893:       x[i] = sum*idiag[i];
1894:     }

1896:     /*  t = b - (2*E - D)x */
1897:     v = a->a;
1898:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

1900:     /*  t = (E + L)^{-1}t */
1901:     ts   = t;
1902:     diag = a->diag;
1903:     for (i=0; i<m; i++) {
1904:       n   = diag[i] - a->i[i];
1905:       idx = a->j + a->i[i];
1906:       v   = a->a + a->i[i];
1907:       sum = t[i];
1908:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1909:       t[i] = sum*idiag[i];
1910:       /*  x = x + t */
1911:       x[i] += t[i];
1912:     }

1914:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1915:     VecRestoreArray(xx,&x);
1916:     VecRestoreArrayRead(bb,&b);
1917:     return(0);
1918:   }
1919:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1920:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1921:       for (i=0; i<m; i++) {
1922:         n   = diag[i] - a->i[i];
1923:         idx = a->j + a->i[i];
1924:         v   = a->a + a->i[i];
1925:         sum = b[i];
1926:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1927:         t[i] = sum;
1928:         x[i] = sum*idiag[i];
1929:       }
1930:       xb   = t;
1931:       PetscLogFlops(a->nz);
1932:     } else xb = b;
1933:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1934:       for (i=m-1; i>=0; i--) {
1935:         n   = a->i[i+1] - diag[i] - 1;
1936:         idx = a->j + diag[i] + 1;
1937:         v   = a->a + diag[i] + 1;
1938:         sum = xb[i];
1939:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1940:         if (xb == b) {
1941:           x[i] = sum*idiag[i];
1942:         } else {
1943:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
1944:         }
1945:       }
1946:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1947:     }
1948:     its--;
1949:   }
1950:   while (its--) {
1951:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1952:       for (i=0; i<m; i++) {
1953:         /* lower */
1954:         n   = diag[i] - a->i[i];
1955:         idx = a->j + a->i[i];
1956:         v   = a->a + a->i[i];
1957:         sum = b[i];
1958:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1959:         t[i] = sum;             /* save application of the lower-triangular part */
1960:         /* upper */
1961:         n   = a->i[i+1] - diag[i] - 1;
1962:         idx = a->j + diag[i] + 1;
1963:         v   = a->a + diag[i] + 1;
1964:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1965:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1966:       }
1967:       xb   = t;
1968:       PetscLogFlops(2.0*a->nz);
1969:     } else xb = b;
1970:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1971:       for (i=m-1; i>=0; i--) {
1972:         sum = xb[i];
1973:         if (xb == b) {
1974:           /* whole matrix (no checkpointing available) */
1975:           n   = a->i[i+1] - a->i[i];
1976:           idx = a->j + a->i[i];
1977:           v   = a->a + a->i[i];
1978:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
1979:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1980:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1981:           n   = a->i[i+1] - diag[i] - 1;
1982:           idx = a->j + diag[i] + 1;
1983:           v   = a->a + diag[i] + 1;
1984:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
1985:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
1986:         }
1987:       }
1988:       if (xb == b) {
1989:         PetscLogFlops(2.0*a->nz);
1990:       } else {
1991:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1992:       }
1993:     }
1994:   }
1995:   VecRestoreArray(xx,&x);
1996:   VecRestoreArrayRead(bb,&b);
1997:   return(0);
1998: }


2001: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
2002: {
2003:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2006:   info->block_size   = 1.0;
2007:   info->nz_allocated = a->maxnz;
2008:   info->nz_used      = a->nz;
2009:   info->nz_unneeded  = (a->maxnz - a->nz);
2010:   info->assemblies   = A->num_ass;
2011:   info->mallocs      = A->info.mallocs;
2012:   info->memory       = ((PetscObject)A)->mem;
2013:   if (A->factortype) {
2014:     info->fill_ratio_given  = A->info.fill_ratio_given;
2015:     info->fill_ratio_needed = A->info.fill_ratio_needed;
2016:     info->factor_mallocs    = A->info.factor_mallocs;
2017:   } else {
2018:     info->fill_ratio_given  = 0;
2019:     info->fill_ratio_needed = 0;
2020:     info->factor_mallocs    = 0;
2021:   }
2022:   return(0);
2023: }

2025: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2026: {
2027:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2028:   PetscInt          i,m = A->rmap->n - 1;
2029:   PetscErrorCode    ierr;
2030:   const PetscScalar *xx;
2031:   PetscScalar       *bb;
2032:   PetscInt          d = 0;

2035:   if (x && b) {
2036:     VecGetArrayRead(x,&xx);
2037:     VecGetArray(b,&bb);
2038:     for (i=0; i<N; i++) {
2039:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2040:       if (rows[i] >= A->cmap->n) continue;
2041:       bb[rows[i]] = diag*xx[rows[i]];
2042:     }
2043:     VecRestoreArrayRead(x,&xx);
2044:     VecRestoreArray(b,&bb);
2045:   }

2047:   if (a->keepnonzeropattern) {
2048:     for (i=0; i<N; i++) {
2049:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2050:       PetscArrayzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]);
2051:     }
2052:     if (diag != 0.0) {
2053:       for (i=0; i<N; i++) {
2054:         d = rows[i];
2055:         if (rows[i] >= A->cmap->n) continue;
2056:         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);
2057:       }
2058:       for (i=0; i<N; i++) {
2059:         if (rows[i] >= A->cmap->n) continue;
2060:         a->a[a->diag[rows[i]]] = diag;
2061:       }
2062:     }
2063:   } else {
2064:     if (diag != 0.0) {
2065:       for (i=0; i<N; i++) {
2066:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2067:         if (a->ilen[rows[i]] > 0) {
2068:           if (rows[i] >= A->cmap->n) {
2069:             a->ilen[rows[i]] = 0;
2070:           } else {
2071:             a->ilen[rows[i]]    = 1;
2072:             a->a[a->i[rows[i]]] = diag;
2073:             a->j[a->i[rows[i]]] = rows[i];
2074:           }
2075:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2076:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2077:         }
2078:       }
2079:     } else {
2080:       for (i=0; i<N; i++) {
2081:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2082:         a->ilen[rows[i]] = 0;
2083:       }
2084:     }
2085:     A->nonzerostate++;
2086:   }
2087: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2088:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2089: #endif
2090:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2091:   return(0);
2092: }

2094: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2095: {
2096:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2097:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
2098:   PetscErrorCode    ierr;
2099:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
2100:   const PetscScalar *xx;
2101:   PetscScalar       *bb;

2104:   if (x && b) {
2105:     VecGetArrayRead(x,&xx);
2106:     VecGetArray(b,&bb);
2107:     vecs = PETSC_TRUE;
2108:   }
2109:   PetscCalloc1(A->rmap->n,&zeroed);
2110:   for (i=0; i<N; i++) {
2111:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2112:     PetscArrayzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]);

2114:     zeroed[rows[i]] = PETSC_TRUE;
2115:   }
2116:   for (i=0; i<A->rmap->n; i++) {
2117:     if (!zeroed[i]) {
2118:       for (j=a->i[i]; j<a->i[i+1]; j++) {
2119:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2120:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
2121:           a->a[j] = 0.0;
2122:         }
2123:       }
2124:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2125:   }
2126:   if (x && b) {
2127:     VecRestoreArrayRead(x,&xx);
2128:     VecRestoreArray(b,&bb);
2129:   }
2130:   PetscFree(zeroed);
2131:   if (diag != 0.0) {
2132:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2133:     if (missing) {
2134:       for (i=0; i<N; i++) {
2135:         if (rows[i] >= A->cmap->N) continue;
2136:         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]);
2137:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2138:       }
2139:     } else {
2140:       for (i=0; i<N; i++) {
2141:         a->a[a->diag[rows[i]]] = diag;
2142:       }
2143:     }
2144:   }
2145: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2146:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2147: #endif
2148:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2149:   return(0);
2150: }

2152: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2153: {
2154:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2155:   PetscInt   *itmp;

2158:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

2160:   *nz = a->i[row+1] - a->i[row];
2161:   if (v) *v = a->a + a->i[row];
2162:   if (idx) {
2163:     itmp = a->j + a->i[row];
2164:     if (*nz) *idx = itmp;
2165:     else *idx = 0;
2166:   }
2167:   return(0);
2168: }

2170: /* remove this function? */
2171: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2172: {
2174:   return(0);
2175: }

2177: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2178: {
2179:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
2180:   MatScalar      *v  = a->a;
2181:   PetscReal      sum = 0.0;
2183:   PetscInt       i,j;

2186:   if (type == NORM_FROBENIUS) {
2187: #if defined(PETSC_USE_REAL___FP16)
2188:     PetscBLASInt one = 1,nz = a->nz;
2189:     *nrm = BLASnrm2_(&nz,v,&one);
2190: #else
2191:     for (i=0; i<a->nz; i++) {
2192:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2193:     }
2194:     *nrm = PetscSqrtReal(sum);
2195: #endif
2196:     PetscLogFlops(2*a->nz);
2197:   } else if (type == NORM_1) {
2198:     PetscReal *tmp;
2199:     PetscInt  *jj = a->j;
2200:     PetscCalloc1(A->cmap->n+1,&tmp);
2201:     *nrm = 0.0;
2202:     for (j=0; j<a->nz; j++) {
2203:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
2204:     }
2205:     for (j=0; j<A->cmap->n; j++) {
2206:       if (tmp[j] > *nrm) *nrm = tmp[j];
2207:     }
2208:     PetscFree(tmp);
2209:     PetscLogFlops(PetscMax(a->nz-1,0));
2210:   } else if (type == NORM_INFINITY) {
2211:     *nrm = 0.0;
2212:     for (j=0; j<A->rmap->n; j++) {
2213:       v   = a->a + a->i[j];
2214:       sum = 0.0;
2215:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2216:         sum += PetscAbsScalar(*v); v++;
2217:       }
2218:       if (sum > *nrm) *nrm = sum;
2219:     }
2220:     PetscLogFlops(PetscMax(a->nz-1,0));
2221:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2222:   return(0);
2223: }

2225: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2226: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2227: {
2229:   PetscInt       i,j,anzj;
2230:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
2231:   PetscInt       an=A->cmap->N,am=A->rmap->N;
2232:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

2235:   /* Allocate space for symbolic transpose info and work array */
2236:   PetscCalloc1(an+1,&ati);
2237:   PetscMalloc1(ai[am],&atj);
2238:   PetscMalloc1(an,&atfill);

2240:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
2241:   /* Note: offset by 1 for fast conversion into csr format. */
2242:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2243:   /* Form ati for csr format of A^T. */
2244:   for (i=0;i<an;i++) ati[i+1] += ati[i];

2246:   /* Copy ati into atfill so we have locations of the next free space in atj */
2247:   PetscArraycpy(atfill,ati,an);

2249:   /* Walk through A row-wise and mark nonzero entries of A^T. */
2250:   for (i=0;i<am;i++) {
2251:     anzj = ai[i+1] - ai[i];
2252:     for (j=0;j<anzj;j++) {
2253:       atj[atfill[*aj]] = i;
2254:       atfill[*aj++]   += 1;
2255:     }
2256:   }

2258:   /* Clean up temporary space and complete requests. */
2259:   PetscFree(atfill);
2260:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2261:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2262:   MatSetType(*B,((PetscObject)A)->type_name);

2264:   b          = (Mat_SeqAIJ*)((*B)->data);
2265:   b->free_a  = PETSC_FALSE;
2266:   b->free_ij = PETSC_TRUE;
2267:   b->nonew   = 0;
2268:   return(0);
2269: }

2271: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2272: {
2273:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2274:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2275:   MatScalar      *va,*vb;
2277:   PetscInt       ma,na,mb,nb, i;

2280:   MatGetSize(A,&ma,&na);
2281:   MatGetSize(B,&mb,&nb);
2282:   if (ma!=nb || na!=mb) {
2283:     *f = PETSC_FALSE;
2284:     return(0);
2285:   }
2286:   aii  = aij->i; bii = bij->i;
2287:   adx  = aij->j; bdx = bij->j;
2288:   va   = aij->a; vb = bij->a;
2289:   PetscMalloc1(ma,&aptr);
2290:   PetscMalloc1(mb,&bptr);
2291:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2292:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2294:   *f = PETSC_TRUE;
2295:   for (i=0; i<ma; i++) {
2296:     while (aptr[i]<aii[i+1]) {
2297:       PetscInt    idc,idr;
2298:       PetscScalar vc,vr;
2299:       /* column/row index/value */
2300:       idc = adx[aptr[i]];
2301:       idr = bdx[bptr[idc]];
2302:       vc  = va[aptr[i]];
2303:       vr  = vb[bptr[idc]];
2304:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2305:         *f = PETSC_FALSE;
2306:         goto done;
2307:       } else {
2308:         aptr[i]++;
2309:         if (B || i!=idc) bptr[idc]++;
2310:       }
2311:     }
2312:   }
2313: done:
2314:   PetscFree(aptr);
2315:   PetscFree(bptr);
2316:   return(0);
2317: }

2319: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
2320: {
2321:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2322:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
2323:   MatScalar      *va,*vb;
2325:   PetscInt       ma,na,mb,nb, i;

2328:   MatGetSize(A,&ma,&na);
2329:   MatGetSize(B,&mb,&nb);
2330:   if (ma!=nb || na!=mb) {
2331:     *f = PETSC_FALSE;
2332:     return(0);
2333:   }
2334:   aii  = aij->i; bii = bij->i;
2335:   adx  = aij->j; bdx = bij->j;
2336:   va   = aij->a; vb = bij->a;
2337:   PetscMalloc1(ma,&aptr);
2338:   PetscMalloc1(mb,&bptr);
2339:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2340:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2342:   *f = PETSC_TRUE;
2343:   for (i=0; i<ma; i++) {
2344:     while (aptr[i]<aii[i+1]) {
2345:       PetscInt    idc,idr;
2346:       PetscScalar vc,vr;
2347:       /* column/row index/value */
2348:       idc = adx[aptr[i]];
2349:       idr = bdx[bptr[idc]];
2350:       vc  = va[aptr[i]];
2351:       vr  = vb[bptr[idc]];
2352:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2353:         *f = PETSC_FALSE;
2354:         goto done;
2355:       } else {
2356:         aptr[i]++;
2357:         if (B || i!=idc) bptr[idc]++;
2358:       }
2359:     }
2360:   }
2361: done:
2362:   PetscFree(aptr);
2363:   PetscFree(bptr);
2364:   return(0);
2365: }

2367: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2368: {

2372:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2373:   return(0);
2374: }

2376: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2377: {

2381:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2382:   return(0);
2383: }

2385: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2386: {
2387:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
2388:   const PetscScalar *l,*r;
2389:   PetscScalar       x;
2390:   MatScalar         *v;
2391:   PetscErrorCode    ierr;
2392:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2393:   const PetscInt    *jj;

2396:   if (ll) {
2397:     /* The local size is used so that VecMPI can be passed to this routine
2398:        by MatDiagonalScale_MPIAIJ */
2399:     VecGetLocalSize(ll,&m);
2400:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2401:     VecGetArrayRead(ll,&l);
2402:     v    = a->a;
2403:     for (i=0; i<m; i++) {
2404:       x = l[i];
2405:       M = a->i[i+1] - a->i[i];
2406:       for (j=0; j<M; j++) (*v++) *= x;
2407:     }
2408:     VecRestoreArrayRead(ll,&l);
2409:     PetscLogFlops(nz);
2410:   }
2411:   if (rr) {
2412:     VecGetLocalSize(rr,&n);
2413:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2414:     VecGetArrayRead(rr,&r);
2415:     v    = a->a; jj = a->j;
2416:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2417:     VecRestoreArrayRead(rr,&r);
2418:     PetscLogFlops(nz);
2419:   }
2420:   MatSeqAIJInvalidateDiagonal(A);
2421: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2422:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2423: #endif
2424:   return(0);
2425: }

2427: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2428: {
2429:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2431:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2432:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2433:   const PetscInt *irow,*icol;
2434:   PetscInt       nrows,ncols;
2435:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2436:   MatScalar      *a_new,*mat_a;
2437:   Mat            C;
2438:   PetscBool      stride;


2442:   ISGetIndices(isrow,&irow);
2443:   ISGetLocalSize(isrow,&nrows);
2444:   ISGetLocalSize(iscol,&ncols);

2446:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2447:   if (stride) {
2448:     ISStrideGetInfo(iscol,&first,&step);
2449:   } else {
2450:     first = 0;
2451:     step  = 0;
2452:   }
2453:   if (stride && step == 1) {
2454:     /* special case of contiguous rows */
2455:     PetscMalloc2(nrows,&lens,nrows,&starts);
2456:     /* loop over new rows determining lens and starting points */
2457:     for (i=0; i<nrows; i++) {
2458:       kstart = ai[irow[i]];
2459:       kend   = kstart + ailen[irow[i]];
2460:       starts[i] = kstart;
2461:       for (k=kstart; k<kend; k++) {
2462:         if (aj[k] >= first) {
2463:           starts[i] = k;
2464:           break;
2465:         }
2466:       }
2467:       sum = 0;
2468:       while (k < kend) {
2469:         if (aj[k++] >= first+ncols) break;
2470:         sum++;
2471:       }
2472:       lens[i] = sum;
2473:     }
2474:     /* create submatrix */
2475:     if (scall == MAT_REUSE_MATRIX) {
2476:       PetscInt n_cols,n_rows;
2477:       MatGetSize(*B,&n_rows,&n_cols);
2478:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2479:       MatZeroEntries(*B);
2480:       C    = *B;
2481:     } else {
2482:       PetscInt rbs,cbs;
2483:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2484:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2485:       ISGetBlockSize(isrow,&rbs);
2486:       ISGetBlockSize(iscol,&cbs);
2487:       MatSetBlockSizes(C,rbs,cbs);
2488:       MatSetType(C,((PetscObject)A)->type_name);
2489:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2490:     }
2491:     c = (Mat_SeqAIJ*)C->data;

2493:     /* loop over rows inserting into submatrix */
2494:     a_new = c->a;
2495:     j_new = c->j;
2496:     i_new = c->i;

2498:     for (i=0; i<nrows; i++) {
2499:       ii    = starts[i];
2500:       lensi = lens[i];
2501:       for (k=0; k<lensi; k++) {
2502:         *j_new++ = aj[ii+k] - first;
2503:       }
2504:       PetscArraycpy(a_new,a->a + starts[i],lensi);
2505:       a_new     += lensi;
2506:       i_new[i+1] = i_new[i] + lensi;
2507:       c->ilen[i] = lensi;
2508:     }
2509:     PetscFree2(lens,starts);
2510:   } else {
2511:     ISGetIndices(iscol,&icol);
2512:     PetscCalloc1(oldcols,&smap);
2513:     PetscMalloc1(1+nrows,&lens);
2514:     for (i=0; i<ncols; i++) {
2515:       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);
2516:       smap[icol[i]] = i+1;
2517:     }

2519:     /* determine lens of each row */
2520:     for (i=0; i<nrows; i++) {
2521:       kstart  = ai[irow[i]];
2522:       kend    = kstart + a->ilen[irow[i]];
2523:       lens[i] = 0;
2524:       for (k=kstart; k<kend; k++) {
2525:         if (smap[aj[k]]) {
2526:           lens[i]++;
2527:         }
2528:       }
2529:     }
2530:     /* Create and fill new matrix */
2531:     if (scall == MAT_REUSE_MATRIX) {
2532:       PetscBool equal;

2534:       c = (Mat_SeqAIJ*)((*B)->data);
2535:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2536:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
2537:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2538:       PetscArrayzero(c->ilen,(*B)->rmap->n);
2539:       C    = *B;
2540:     } else {
2541:       PetscInt rbs,cbs;
2542:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2543:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2544:       ISGetBlockSize(isrow,&rbs);
2545:       ISGetBlockSize(iscol,&cbs);
2546:       MatSetBlockSizes(C,rbs,cbs);
2547:       MatSetType(C,((PetscObject)A)->type_name);
2548:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2549:     }
2550:     c = (Mat_SeqAIJ*)(C->data);
2551:     for (i=0; i<nrows; i++) {
2552:       row      = irow[i];
2553:       kstart   = ai[row];
2554:       kend     = kstart + a->ilen[row];
2555:       mat_i    = c->i[i];
2556:       mat_j    = c->j + mat_i;
2557:       mat_a    = c->a + mat_i;
2558:       mat_ilen = c->ilen + i;
2559:       for (k=kstart; k<kend; k++) {
2560:         if ((tcol=smap[a->j[k]])) {
2561:           *mat_j++ = tcol - 1;
2562:           *mat_a++ = a->a[k];
2563:           (*mat_ilen)++;

2565:         }
2566:       }
2567:     }
2568:     /* Free work space */
2569:     ISRestoreIndices(iscol,&icol);
2570:     PetscFree(smap);
2571:     PetscFree(lens);
2572:     /* sort */
2573:     for (i = 0; i < nrows; i++) {
2574:       PetscInt ilen;

2576:       mat_i = c->i[i];
2577:       mat_j = c->j + mat_i;
2578:       mat_a = c->a + mat_i;
2579:       ilen  = c->ilen[i];
2580:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2581:     }
2582:   }
2583: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2584:   MatBindToCPU(C,A->boundtocpu);
2585: #endif
2586:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2587:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2589:   ISRestoreIndices(isrow,&irow);
2590:   *B   = C;
2591:   return(0);
2592: }

2594: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2595: {
2597:   Mat            B;

2600:   if (scall == MAT_INITIAL_MATRIX) {
2601:     MatCreate(subComm,&B);
2602:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2603:     MatSetBlockSizesFromMats(B,mat,mat);
2604:     MatSetType(B,MATSEQAIJ);
2605:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2606:     *subMat = B;
2607:   } else {
2608:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2609:   }
2610:   return(0);
2611: }

2613: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2614: {
2615:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2617:   Mat            outA;
2618:   PetscBool      row_identity,col_identity;

2621:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2623:   ISIdentity(row,&row_identity);
2624:   ISIdentity(col,&col_identity);

2626:   outA             = inA;
2627:   outA->factortype = MAT_FACTOR_LU;
2628:   PetscFree(inA->solvertype);
2629:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

2631:   PetscObjectReference((PetscObject)row);
2632:   ISDestroy(&a->row);

2634:   a->row = row;

2636:   PetscObjectReference((PetscObject)col);
2637:   ISDestroy(&a->col);

2639:   a->col = col;

2641:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2642:   ISDestroy(&a->icol);
2643:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2644:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

2646:   if (!a->solve_work) { /* this matrix may have been factored before */
2647:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2648:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2649:   }

2651:   MatMarkDiagonal_SeqAIJ(inA);
2652:   if (row_identity && col_identity) {
2653:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2654:   } else {
2655:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2656:   }
2657:   return(0);
2658: }

2660: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2661: {
2662:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
2663:   PetscScalar    oalpha = alpha;
2665:   PetscBLASInt   one = 1,bnz;

2668:   PetscBLASIntCast(a->nz,&bnz);
2669:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2670:   PetscLogFlops(a->nz);
2671:   MatSeqAIJInvalidateDiagonal(inA);
2672: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2673:   if (inA->offloadmask != PETSC_OFFLOAD_UNALLOCATED) inA->offloadmask = PETSC_OFFLOAD_CPU;
2674: #endif
2675:   return(0);
2676: }

2678: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2679: {
2681:   PetscInt       i;

2684:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2685:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

2687:     for (i=0; i<submatj->nrqr; ++i) {
2688:       PetscFree(submatj->sbuf2[i]);
2689:     }
2690:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

2692:     if (submatj->rbuf1) {
2693:       PetscFree(submatj->rbuf1[0]);
2694:       PetscFree(submatj->rbuf1);
2695:     }

2697:     for (i=0; i<submatj->nrqs; ++i) {
2698:       PetscFree(submatj->rbuf3[i]);
2699:     }
2700:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2701:     PetscFree(submatj->pa);
2702:   }

2704: #if defined(PETSC_USE_CTABLE)
2705:   PetscTableDestroy((PetscTable*)&submatj->rmap);
2706:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2707:   PetscFree(submatj->rmap_loc);
2708: #else
2709:   PetscFree(submatj->rmap);
2710: #endif

2712:   if (!submatj->allcolumns) {
2713: #if defined(PETSC_USE_CTABLE)
2714:     PetscTableDestroy((PetscTable*)&submatj->cmap);
2715: #else
2716:     PetscFree(submatj->cmap);
2717: #endif
2718:   }
2719:   PetscFree(submatj->row2proc);

2721:   PetscFree(submatj);
2722:   return(0);
2723: }

2725: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2726: {
2728:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
2729:   Mat_SubSppt    *submatj = c->submatis1;

2732:   (*submatj->destroy)(C);
2733:   MatDestroySubMatrix_Private(submatj);
2734:   return(0);
2735: }

2737: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2738: {
2740:   PetscInt       i;
2741:   Mat            C;
2742:   Mat_SeqAIJ     *c;
2743:   Mat_SubSppt    *submatj;

2746:   for (i=0; i<n; i++) {
2747:     C       = (*mat)[i];
2748:     c       = (Mat_SeqAIJ*)C->data;
2749:     submatj = c->submatis1;
2750:     if (submatj) {
2751:       if (--((PetscObject)C)->refct <= 0) {
2752:         (*submatj->destroy)(C);
2753:         MatDestroySubMatrix_Private(submatj);
2754:         PetscFree(C->defaultvectype);
2755:         PetscLayoutDestroy(&C->rmap);
2756:         PetscLayoutDestroy(&C->cmap);
2757:         PetscHeaderDestroy(&C);
2758:       }
2759:     } else {
2760:       MatDestroy(&C);
2761:     }
2762:   }

2764:   /* Destroy Dummy submatrices created for reuse */
2765:   MatDestroySubMatrices_Dummy(n,mat);

2767:   PetscFree(*mat);
2768:   return(0);
2769: }

2771: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2772: {
2774:   PetscInt       i;

2777:   if (scall == MAT_INITIAL_MATRIX) {
2778:     PetscCalloc1(n+1,B);
2779:   }

2781:   for (i=0; i<n; i++) {
2782:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2783:   }
2784:   return(0);
2785: }

2787: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2788: {
2789:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2791:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2792:   const PetscInt *idx;
2793:   PetscInt       start,end,*ai,*aj;
2794:   PetscBT        table;

2797:   m  = A->rmap->n;
2798:   ai = a->i;
2799:   aj = a->j;

2801:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2803:   PetscMalloc1(m+1,&nidx);
2804:   PetscBTCreate(m,&table);

2806:   for (i=0; i<is_max; i++) {
2807:     /* Initialize the two local arrays */
2808:     isz  = 0;
2809:     PetscBTMemzero(m,table);

2811:     /* Extract the indices, assume there can be duplicate entries */
2812:     ISGetIndices(is[i],&idx);
2813:     ISGetLocalSize(is[i],&n);

2815:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2816:     for (j=0; j<n; ++j) {
2817:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2818:     }
2819:     ISRestoreIndices(is[i],&idx);
2820:     ISDestroy(&is[i]);

2822:     k = 0;
2823:     for (j=0; j<ov; j++) { /* for each overlap */
2824:       n = isz;
2825:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2826:         row   = nidx[k];
2827:         start = ai[row];
2828:         end   = ai[row+1];
2829:         for (l = start; l<end; l++) {
2830:           val = aj[l];
2831:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2832:         }
2833:       }
2834:     }
2835:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2836:   }
2837:   PetscBTDestroy(&table);
2838:   PetscFree(nidx);
2839:   return(0);
2840: }

2842: /* -------------------------------------------------------------- */
2843: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2844: {
2845:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2847:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2848:   const PetscInt *row,*col;
2849:   PetscInt       *cnew,j,*lens;
2850:   IS             icolp,irowp;
2851:   PetscInt       *cwork = NULL;
2852:   PetscScalar    *vwork = NULL;

2855:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2856:   ISGetIndices(irowp,&row);
2857:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2858:   ISGetIndices(icolp,&col);

2860:   /* determine lengths of permuted rows */
2861:   PetscMalloc1(m+1,&lens);
2862:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2863:   MatCreate(PetscObjectComm((PetscObject)A),B);
2864:   MatSetSizes(*B,m,n,m,n);
2865:   MatSetBlockSizesFromMats(*B,A,A);
2866:   MatSetType(*B,((PetscObject)A)->type_name);
2867:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2868:   PetscFree(lens);

2870:   PetscMalloc1(n,&cnew);
2871:   for (i=0; i<m; i++) {
2872:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2873:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2874:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2875:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2876:   }
2877:   PetscFree(cnew);

2879:   (*B)->assembled = PETSC_FALSE;

2881: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2882:   MatBindToCPU(*B,A->boundtocpu);
2883: #endif
2884:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2885:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2886:   ISRestoreIndices(irowp,&row);
2887:   ISRestoreIndices(icolp,&col);
2888:   ISDestroy(&irowp);
2889:   ISDestroy(&icolp);
2890:   if (rowp == colp) {
2891:     if (A->symmetric) {
2892:       MatSetOption(*B,MAT_SYMMETRIC,PETSC_TRUE);
2893:     }
2894:     if (A->hermitian) {
2895:       MatSetOption(*B,MAT_HERMITIAN,PETSC_TRUE);
2896:     }
2897:   }
2898:   return(0);
2899: }

2901: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2902: {

2906:   /* If the two matrices have the same copy implementation, use fast copy. */
2907:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2908:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2909:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2911:     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]);
2912:     PetscArraycpy(b->a,a->a,a->i[A->rmap->n]);
2913:     PetscObjectStateIncrease((PetscObject)B);
2914:   } else {
2915:     MatCopy_Basic(A,B,str);
2916:   }
2917:   return(0);
2918: }

2920: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2921: {

2925:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2926:   return(0);
2927: }

2929: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2930: {
2931:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2934:   *array = a->a;
2935:   return(0);
2936: }

2938: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2939: {
2941:   *array = NULL;
2942:   return(0);
2943: }

2945: /*
2946:    Computes the number of nonzeros per row needed for preallocation when X and Y
2947:    have different nonzero structure.
2948: */
2949: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2950: {
2951:   PetscInt       i,j,k,nzx,nzy;

2954:   /* Set the number of nonzeros in the new matrix */
2955:   for (i=0; i<m; i++) {
2956:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2957:     nzx = xi[i+1] - xi[i];
2958:     nzy = yi[i+1] - yi[i];
2959:     nnz[i] = 0;
2960:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2961:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2962:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
2963:       nnz[i]++;
2964:     }
2965:     for (; k<nzy; k++) nnz[i]++;
2966:   }
2967:   return(0);
2968: }

2970: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2971: {
2972:   PetscInt       m = Y->rmap->N;
2973:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
2974:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

2978:   /* Set the number of nonzeros in the new matrix */
2979:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2980:   return(0);
2981: }

2983: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2984: {
2986:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2987:   PetscBLASInt   one=1,bnz;

2990:   PetscBLASIntCast(x->nz,&bnz);
2991:   if (str == SAME_NONZERO_PATTERN) {
2992:     PetscScalar alpha = a;
2993:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2994:     MatSeqAIJInvalidateDiagonal(Y);
2995:     PetscObjectStateIncrease((PetscObject)Y);
2996:     /* the MatAXPY_Basic* subroutines calls MatAssembly, so the matrix on the GPU
2997:        will be updated */
2998: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
2999:     if (Y->offloadmask != PETSC_OFFLOAD_UNALLOCATED) {
3000:       Y->offloadmask = PETSC_OFFLOAD_CPU;
3001:     }
3002: #endif
3003:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3004:     MatAXPY_Basic(Y,a,X,str);
3005:   } else {
3006:     Mat      B;
3007:     PetscInt *nnz;
3008:     PetscMalloc1(Y->rmap->N,&nnz);
3009:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
3010:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
3011:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
3012:     MatSetBlockSizesFromMats(B,Y,Y);
3013:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
3014:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
3015:     MatSeqAIJSetPreallocation(B,0,nnz);
3016:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
3017:     MatHeaderReplace(Y,&B);
3018:     PetscFree(nnz);
3019:   }
3020:   return(0);
3021: }

3023: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
3024: {
3025: #if defined(PETSC_USE_COMPLEX)
3026:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
3027:   PetscInt    i,nz;
3028:   PetscScalar *a;

3031:   nz = aij->nz;
3032:   a  = aij->a;
3033:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
3034: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
3035:   if (mat->offloadmask != PETSC_OFFLOAD_UNALLOCATED) mat->offloadmask = PETSC_OFFLOAD_CPU;
3036: #endif
3037: #else
3039: #endif
3040:   return(0);
3041: }

3043: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3044: {
3045:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3047:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3048:   PetscReal      atmp;
3049:   PetscScalar    *x;
3050:   MatScalar      *aa;

3053:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3054:   aa = a->a;
3055:   ai = a->i;
3056:   aj = a->j;

3058:   VecSet(v,0.0);
3059:   VecGetArray(v,&x);
3060:   VecGetLocalSize(v,&n);
3061:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3062:   for (i=0; i<m; i++) {
3063:     ncols = ai[1] - ai[0]; ai++;
3064:     x[i]  = 0.0;
3065:     for (j=0; j<ncols; j++) {
3066:       atmp = PetscAbsScalar(*aa);
3067:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3068:       aa++; aj++;
3069:     }
3070:   }
3071:   VecRestoreArray(v,&x);
3072:   return(0);
3073: }

3075: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3076: {
3077:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3079:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3080:   PetscScalar    *x;
3081:   MatScalar      *aa;

3084:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3085:   aa = a->a;
3086:   ai = a->i;
3087:   aj = a->j;

3089:   VecSet(v,0.0);
3090:   VecGetArray(v,&x);
3091:   VecGetLocalSize(v,&n);
3092:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3093:   for (i=0; i<m; i++) {
3094:     ncols = ai[1] - ai[0]; ai++;
3095:     if (ncols == A->cmap->n) { /* row is dense */
3096:       x[i] = *aa; if (idx) idx[i] = 0;
3097:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
3098:       x[i] = 0.0;
3099:       if (idx) {
3100:         idx[i] = 0; /* in case ncols is zero */
3101:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
3102:           if (aj[j] > j) {
3103:             idx[i] = j;
3104:             break;
3105:           }
3106:         }
3107:       }
3108:     }
3109:     for (j=0; j<ncols; j++) {
3110:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3111:       aa++; aj++;
3112:     }
3113:   }
3114:   VecRestoreArray(v,&x);
3115:   return(0);
3116: }

3118: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3119: {
3120:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3122:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3123:   PetscReal      atmp;
3124:   PetscScalar    *x;
3125:   MatScalar      *aa;

3128:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3129:   aa = a->a;
3130:   ai = a->i;
3131:   aj = a->j;

3133:   VecSet(v,0.0);
3134:   VecGetArray(v,&x);
3135:   VecGetLocalSize(v,&n);
3136:   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);
3137:   for (i=0; i<m; i++) {
3138:     ncols = ai[1] - ai[0]; ai++;
3139:     if (ncols) {
3140:       /* Get first nonzero */
3141:       for (j = 0; j < ncols; j++) {
3142:         atmp = PetscAbsScalar(aa[j]);
3143:         if (atmp > 1.0e-12) {
3144:           x[i] = atmp;
3145:           if (idx) idx[i] = aj[j];
3146:           break;
3147:         }
3148:       }
3149:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
3150:     } else {
3151:       x[i] = 0.0; if (idx) idx[i] = 0;
3152:     }
3153:     for (j = 0; j < ncols; j++) {
3154:       atmp = PetscAbsScalar(*aa);
3155:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3156:       aa++; aj++;
3157:     }
3158:   }
3159:   VecRestoreArray(v,&x);
3160:   return(0);
3161: }

3163: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3164: {
3165:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3166:   PetscErrorCode  ierr;
3167:   PetscInt        i,j,m = A->rmap->n,ncols,n;
3168:   const PetscInt  *ai,*aj;
3169:   PetscScalar     *x;
3170:   const MatScalar *aa;

3173:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3174:   aa = a->a;
3175:   ai = a->i;
3176:   aj = a->j;

3178:   VecSet(v,0.0);
3179:   VecGetArray(v,&x);
3180:   VecGetLocalSize(v,&n);
3181:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3182:   for (i=0; i<m; i++) {
3183:     ncols = ai[1] - ai[0]; ai++;
3184:     if (ncols == A->cmap->n) { /* row is dense */
3185:       x[i] = *aa; if (idx) idx[i] = 0;
3186:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
3187:       x[i] = 0.0;
3188:       if (idx) {   /* find first implicit 0.0 in the row */
3189:         idx[i] = 0; /* in case ncols is zero */
3190:         for (j=0; j<ncols; j++) {
3191:           if (aj[j] > j) {
3192:             idx[i] = j;
3193:             break;
3194:           }
3195:         }
3196:       }
3197:     }
3198:     for (j=0; j<ncols; j++) {
3199:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3200:       aa++; aj++;
3201:     }
3202:   }
3203:   VecRestoreArray(v,&x);
3204:   return(0);
3205: }

3207: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3208: {
3209:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
3210:   PetscErrorCode  ierr;
3211:   PetscInt        i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
3212:   MatScalar       *diag,work[25],*v_work;
3213:   const PetscReal shift = 0.0;
3214:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;

3217:   allowzeropivot = PetscNot(A->erroriffailure);
3218:   if (a->ibdiagvalid) {
3219:     if (values) *values = a->ibdiag;
3220:     return(0);
3221:   }
3222:   MatMarkDiagonal_SeqAIJ(A);
3223:   if (!a->ibdiag) {
3224:     PetscMalloc1(bs2*mbs,&a->ibdiag);
3225:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3226:   }
3227:   diag = a->ibdiag;
3228:   if (values) *values = a->ibdiag;
3229:   /* factor and invert each block */
3230:   switch (bs) {
3231:   case 1:
3232:     for (i=0; i<mbs; i++) {
3233:       MatGetValues(A,1,&i,1,&i,diag+i);
3234:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3235:         if (allowzeropivot) {
3236:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3237:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3238:           A->factorerror_zeropivot_row   = i;
3239:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3240:         } 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);
3241:       }
3242:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3243:     }
3244:     break;
3245:   case 2:
3246:     for (i=0; i<mbs; i++) {
3247:       ij[0] = 2*i; ij[1] = 2*i + 1;
3248:       MatGetValues(A,2,ij,2,ij,diag);
3249:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3250:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3251:       PetscKernel_A_gets_transpose_A_2(diag);
3252:       diag += 4;
3253:     }
3254:     break;
3255:   case 3:
3256:     for (i=0; i<mbs; i++) {
3257:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3258:       MatGetValues(A,3,ij,3,ij,diag);
3259:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3260:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3261:       PetscKernel_A_gets_transpose_A_3(diag);
3262:       diag += 9;
3263:     }
3264:     break;
3265:   case 4:
3266:     for (i=0; i<mbs; i++) {
3267:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3268:       MatGetValues(A,4,ij,4,ij,diag);
3269:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3270:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3271:       PetscKernel_A_gets_transpose_A_4(diag);
3272:       diag += 16;
3273:     }
3274:     break;
3275:   case 5:
3276:     for (i=0; i<mbs; i++) {
3277:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3278:       MatGetValues(A,5,ij,5,ij,diag);
3279:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3280:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3281:       PetscKernel_A_gets_transpose_A_5(diag);
3282:       diag += 25;
3283:     }
3284:     break;
3285:   case 6:
3286:     for (i=0; i<mbs; i++) {
3287:       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;
3288:       MatGetValues(A,6,ij,6,ij,diag);
3289:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3290:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3291:       PetscKernel_A_gets_transpose_A_6(diag);
3292:       diag += 36;
3293:     }
3294:     break;
3295:   case 7:
3296:     for (i=0; i<mbs; i++) {
3297:       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;
3298:       MatGetValues(A,7,ij,7,ij,diag);
3299:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3300:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3301:       PetscKernel_A_gets_transpose_A_7(diag);
3302:       diag += 49;
3303:     }
3304:     break;
3305:   default:
3306:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3307:     for (i=0; i<mbs; i++) {
3308:       for (j=0; j<bs; j++) {
3309:         IJ[j] = bs*i + j;
3310:       }
3311:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3312:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3313:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3314:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3315:       diag += bs2;
3316:     }
3317:     PetscFree3(v_work,v_pivots,IJ);
3318:   }
3319:   a->ibdiagvalid = PETSC_TRUE;
3320:   return(0);
3321: }

3323: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3324: {
3326:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3327:   PetscScalar    a;
3328:   PetscInt       m,n,i,j,col;

3331:   if (!x->assembled) {
3332:     MatGetSize(x,&m,&n);
3333:     for (i=0; i<m; i++) {
3334:       for (j=0; j<aij->imax[i]; j++) {
3335:         PetscRandomGetValue(rctx,&a);
3336:         col  = (PetscInt)(n*PetscRealPart(a));
3337:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3338:       }
3339:     }
3340:   } else {
3341:     for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3342:   }
3343:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3344:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3345:   return(0);
3346: }

3348: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3349: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3350: {
3352:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3353:   PetscScalar    a;
3354:   PetscInt       m,n,i,j,col,nskip;

3357:   nskip = high - low;
3358:   MatGetSize(x,&m,&n);
3359:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
3360:   for (i=0; i<m; i++) {
3361:     for (j=0; j<aij->imax[i]; j++) {
3362:       PetscRandomGetValue(rctx,&a);
3363:       col  = (PetscInt)(n*PetscRealPart(a));
3364:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3365:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3366:     }
3367:   }
3368:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3369:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3370:   return(0);
3371: }


3374: /* -------------------------------------------------------------------*/
3375: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3376:                                         MatGetRow_SeqAIJ,
3377:                                         MatRestoreRow_SeqAIJ,
3378:                                         MatMult_SeqAIJ,
3379:                                 /*  4*/ MatMultAdd_SeqAIJ,
3380:                                         MatMultTranspose_SeqAIJ,
3381:                                         MatMultTransposeAdd_SeqAIJ,
3382:                                         0,
3383:                                         0,
3384:                                         0,
3385:                                 /* 10*/ 0,
3386:                                         MatLUFactor_SeqAIJ,
3387:                                         0,
3388:                                         MatSOR_SeqAIJ,
3389:                                         MatTranspose_SeqAIJ,
3390:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3391:                                         MatEqual_SeqAIJ,
3392:                                         MatGetDiagonal_SeqAIJ,
3393:                                         MatDiagonalScale_SeqAIJ,
3394:                                         MatNorm_SeqAIJ,
3395:                                 /* 20*/ 0,
3396:                                         MatAssemblyEnd_SeqAIJ,
3397:                                         MatSetOption_SeqAIJ,
3398:                                         MatZeroEntries_SeqAIJ,
3399:                                 /* 24*/ MatZeroRows_SeqAIJ,
3400:                                         0,
3401:                                         0,
3402:                                         0,
3403:                                         0,
3404:                                 /* 29*/ MatSetUp_SeqAIJ,
3405:                                         0,
3406:                                         0,
3407:                                         0,
3408:                                         0,
3409:                                 /* 34*/ MatDuplicate_SeqAIJ,
3410:                                         0,
3411:                                         0,
3412:                                         MatILUFactor_SeqAIJ,
3413:                                         0,
3414:                                 /* 39*/ MatAXPY_SeqAIJ,
3415:                                         MatCreateSubMatrices_SeqAIJ,
3416:                                         MatIncreaseOverlap_SeqAIJ,
3417:                                         MatGetValues_SeqAIJ,
3418:                                         MatCopy_SeqAIJ,
3419:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3420:                                         MatScale_SeqAIJ,
3421:                                         MatShift_SeqAIJ,
3422:                                         MatDiagonalSet_SeqAIJ,
3423:                                         MatZeroRowsColumns_SeqAIJ,
3424:                                 /* 49*/ MatSetRandom_SeqAIJ,
3425:                                         MatGetRowIJ_SeqAIJ,
3426:                                         MatRestoreRowIJ_SeqAIJ,
3427:                                         MatGetColumnIJ_SeqAIJ,
3428:                                         MatRestoreColumnIJ_SeqAIJ,
3429:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
3430:                                         0,
3431:                                         0,
3432:                                         MatPermute_SeqAIJ,
3433:                                         0,
3434:                                 /* 59*/ 0,
3435:                                         MatDestroy_SeqAIJ,
3436:                                         MatView_SeqAIJ,
3437:                                         0,
3438:                                         0,
3439:                                 /* 64*/ 0,
3440:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3441:                                         0,
3442:                                         0,
3443:                                         0,
3444:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3445:                                         MatGetRowMinAbs_SeqAIJ,
3446:                                         0,
3447:                                         0,
3448:                                         0,
3449:                                 /* 74*/ 0,
3450:                                         MatFDColoringApply_AIJ,
3451:                                         0,
3452:                                         0,
3453:                                         0,
3454:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3455:                                         0,
3456:                                         0,
3457:                                         0,
3458:                                         MatLoad_SeqAIJ,
3459:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3460:                                         MatIsHermitian_SeqAIJ,
3461:                                         0,
3462:                                         0,
3463:                                         0,
3464:                                 /* 89*/ 0,
3465:                                         0,
3466:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3467:                                         0,
3468:                                         0,
3469:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3470:                                         0,
3471:                                         0,
3472:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3473:                                         0,
3474:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
3475:                                         0,
3476:                                         0,
3477:                                         MatConjugate_SeqAIJ,
3478:                                         0,
3479:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3480:                                         MatRealPart_SeqAIJ,
3481:                                         MatImaginaryPart_SeqAIJ,
3482:                                         0,
3483:                                         0,
3484:                                 /*109*/ MatMatSolve_SeqAIJ,
3485:                                         0,
3486:                                         MatGetRowMin_SeqAIJ,
3487:                                         0,
3488:                                         MatMissingDiagonal_SeqAIJ,
3489:                                 /*114*/ 0,
3490:                                         0,
3491:                                         0,
3492:                                         0,
3493:                                         0,
3494:                                 /*119*/ 0,
3495:                                         0,
3496:                                         0,
3497:                                         0,
3498:                                         MatGetMultiProcBlock_SeqAIJ,
3499:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3500:                                         MatGetColumnNorms_SeqAIJ,
3501:                                         MatInvertBlockDiagonal_SeqAIJ,
3502:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
3503:                                         0,
3504:                                 /*129*/ 0,
3505:                                         0,
3506:                                         0,
3507:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3508:                                         MatTransposeColoringCreate_SeqAIJ,
3509:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3510:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3511:                                         0,
3512:                                         0,
3513:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3514:                                  /*139*/0,
3515:                                         0,
3516:                                         0,
3517:                                         MatFDColoringSetUp_SeqXAIJ,
3518:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
3519:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3520:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
3521:                                         0,
3522:                                         0
3523: };

3525: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3526: {
3527:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3528:   PetscInt   i,nz,n;

3531:   nz = aij->maxnz;
3532:   n  = mat->rmap->n;
3533:   for (i=0; i<nz; i++) {
3534:     aij->j[i] = indices[i];
3535:   }
3536:   aij->nz = nz;
3537:   for (i=0; i<n; i++) {
3538:     aij->ilen[i] = aij->imax[i];
3539:   }
3540:   return(0);
3541: }

3543: /*
3544:  * When a sparse matrix has many zero columns, we should compact them out to save the space
3545:  * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3546:  * */
3547: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3548: {
3549:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3550:   PetscTable         gid1_lid1;
3551:   PetscTablePosition tpos;
3552:   PetscInt           gid,lid,i,j,ncols,ec;
3553:   PetscInt           *garray;
3554:   PetscErrorCode  ierr;

3559:   /* use a table */
3560:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3561:   ec = 0;
3562:   for (i=0; i<mat->rmap->n; i++) {
3563:     ncols = aij->i[i+1] - aij->i[i];
3564:     for (j=0; j<ncols; j++) {
3565:       PetscInt data,gid1 = aij->j[aij->i[i] + j] + 1;
3566:       PetscTableFind(gid1_lid1,gid1,&data);
3567:       if (!data) {
3568:         /* one based table */
3569:         PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3570:       }
3571:     }
3572:   }
3573:   /* form array of columns we need */
3574:   PetscMalloc1(ec+1,&garray);
3575:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
3576:   while (tpos) {
3577:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3578:     gid--;
3579:     lid--;
3580:     garray[lid] = gid;
3581:   }
3582:   PetscSortInt(ec,garray); /* sort, and rebuild */
3583:   PetscTableRemoveAll(gid1_lid1);
3584:   for (i=0; i<ec; i++) {
3585:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3586:   }
3587:   /* compact out the extra columns in B */
3588:   for (i=0; i<mat->rmap->n; i++) {
3589:         ncols = aij->i[i+1] - aij->i[i];
3590:     for (j=0; j<ncols; j++) {
3591:       PetscInt gid1 = aij->j[aij->i[i] + j] + 1;
3592:       PetscTableFind(gid1_lid1,gid1,&lid);
3593:       lid--;
3594:       aij->j[aij->i[i] + j] = lid;
3595:     }
3596:   }
3597:   PetscLayoutDestroy(&mat->cmap);
3598:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
3599:   PetscTableDestroy(&gid1_lid1);
3600:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3601:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3602:   return(0);
3603: }

3605: /*@
3606:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3607:        in the matrix.

3609:   Input Parameters:
3610: +  mat - the SeqAIJ matrix
3611: -  indices - the column indices

3613:   Level: advanced

3615:   Notes:
3616:     This can be called if you have precomputed the nonzero structure of the
3617:   matrix and want to provide it to the matrix object to improve the performance
3618:   of the MatSetValues() operation.

3620:     You MUST have set the correct numbers of nonzeros per row in the call to
3621:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3623:     MUST be called before any calls to MatSetValues();

3625:     The indices should start with zero, not one.

3627: @*/
3628: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3629: {

3635:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3636:   return(0);
3637: }

3639: /* ----------------------------------------------------------------------------------------*/

3641: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3642: {
3643:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3645:   size_t         nz = aij->i[mat->rmap->n];

3648:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3650:   /* allocate space for values if not already there */
3651:   if (!aij->saved_values) {
3652:     PetscMalloc1(nz+1,&aij->saved_values);
3653:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3654:   }

3656:   /* copy values over */
3657:   PetscArraycpy(aij->saved_values,aij->a,nz);
3658:   return(0);
3659: }

3661: /*@
3662:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3663:        example, reuse of the linear part of a Jacobian, while recomputing the
3664:        nonlinear portion.

3666:    Collect on Mat

3668:   Input Parameters:
3669: .  mat - the matrix (currently only AIJ matrices support this option)

3671:   Level: advanced

3673:   Common Usage, with SNESSolve():
3674: $    Create Jacobian matrix
3675: $    Set linear terms into matrix
3676: $    Apply boundary conditions to matrix, at this time matrix must have
3677: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3678: $      boundary conditions again will not change the nonzero structure
3679: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3680: $    MatStoreValues(mat);
3681: $    Call SNESSetJacobian() with matrix
3682: $    In your Jacobian routine
3683: $      MatRetrieveValues(mat);
3684: $      Set nonlinear terms in matrix

3686:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3687: $    // build linear portion of Jacobian
3688: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3689: $    MatStoreValues(mat);
3690: $    loop over nonlinear iterations
3691: $       MatRetrieveValues(mat);
3692: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3693: $       // call MatAssemblyBegin/End() on matrix
3694: $       Solve linear system with Jacobian
3695: $    endloop

3697:   Notes:
3698:     Matrix must already be assemblied before calling this routine
3699:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3700:     calling this routine.

3702:     When this is called multiple times it overwrites the previous set of stored values
3703:     and does not allocated additional space.

3705: .seealso: MatRetrieveValues()

3707: @*/
3708: PetscErrorCode  MatStoreValues(Mat mat)
3709: {

3714:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3715:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3716:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3717:   return(0);
3718: }

3720: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3721: {
3722:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3724:   PetscInt       nz = aij->i[mat->rmap->n];

3727:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3728:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3729:   /* copy values over */
3730:   PetscArraycpy(aij->a,aij->saved_values,nz);
3731:   return(0);
3732: }

3734: /*@
3735:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3736:        example, reuse of the linear part of a Jacobian, while recomputing the
3737:        nonlinear portion.

3739:    Collect on Mat

3741:   Input Parameters:
3742: .  mat - the matrix (currently only AIJ matrices support this option)

3744:   Level: advanced

3746: .seealso: MatStoreValues()

3748: @*/
3749: PetscErrorCode  MatRetrieveValues(Mat mat)
3750: {

3755:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3756:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3757:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3758:   return(0);
3759: }


3762: /* --------------------------------------------------------------------------------*/
3763: /*@C
3764:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3765:    (the default parallel PETSc format).  For good matrix assembly performance
3766:    the user should preallocate the matrix storage by setting the parameter nz
3767:    (or the array nnz).  By setting these parameters accurately, performance
3768:    during matrix assembly can be increased by more than a factor of 50.

3770:    Collective

3772:    Input Parameters:
3773: +  comm - MPI communicator, set to PETSC_COMM_SELF
3774: .  m - number of rows
3775: .  n - number of columns
3776: .  nz - number of nonzeros per row (same for all rows)
3777: -  nnz - array containing the number of nonzeros in the various rows
3778:          (possibly different for each row) or NULL

3780:    Output Parameter:
3781: .  A - the matrix

3783:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3784:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
3785:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3787:    Notes:
3788:    If nnz is given then nz is ignored

3790:    The AIJ format (also called the Yale sparse matrix format or
3791:    compressed row storage), is fully compatible with standard Fortran 77
3792:    storage.  That is, the stored row and column indices can begin at
3793:    either one (as in Fortran) or zero.  See the users' manual for details.

3795:    Specify the preallocated storage with either nz or nnz (not both).
3796:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3797:    allocation.  For large problems you MUST preallocate memory or you
3798:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3800:    By default, this format uses inodes (identical nodes) when possible, to
3801:    improve numerical efficiency of matrix-vector products and solves. We
3802:    search for consecutive rows with the same nonzero structure, thereby
3803:    reusing matrix information to achieve increased efficiency.

3805:    Options Database Keys:
3806: +  -mat_no_inode  - Do not use inodes
3807: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3809:    Level: intermediate

3811: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3813: @*/
3814: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3815: {

3819:   MatCreate(comm,A);
3820:   MatSetSizes(*A,m,n,m,n);
3821:   MatSetType(*A,MATSEQAIJ);
3822:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3823:   return(0);
3824: }

3826: /*@C
3827:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3828:    the user should preallocate the matrix storage by setting the parameter nz
3829:    (or the array nnz).  By setting these parameters accurately, performance
3830:    during matrix assembly can be increased by more than a factor of 50.

3832:    Collective

3834:    Input Parameters:
3835: +  B - The matrix
3836: .  nz - number of nonzeros per row (same for all rows)
3837: -  nnz - array containing the number of nonzeros in the various rows
3838:          (possibly different for each row) or NULL

3840:    Notes:
3841:      If nnz is given then nz is ignored

3843:     The AIJ format (also called the Yale sparse matrix format or
3844:    compressed row storage), is fully compatible with standard Fortran 77
3845:    storage.  That is, the stored row and column indices can begin at
3846:    either one (as in Fortran) or zero.  See the users' manual for details.

3848:    Specify the preallocated storage with either nz or nnz (not both).
3849:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3850:    allocation.  For large problems you MUST preallocate memory or you
3851:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3853:    You can call MatGetInfo() to get information on how effective the preallocation was;
3854:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3855:    You can also run with the option -info and look for messages with the string
3856:    malloc in them to see if additional memory allocation was needed.

3858:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3859:    entries or columns indices

3861:    By default, this format uses inodes (identical nodes) when possible, to
3862:    improve numerical efficiency of matrix-vector products and solves. We
3863:    search for consecutive rows with the same nonzero structure, thereby
3864:    reusing matrix information to achieve increased efficiency.

3866:    Options Database Keys:
3867: +  -mat_no_inode  - Do not use inodes
3868: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3870:    Level: intermediate

3872: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3874: @*/
3875: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3876: {

3882:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3883:   return(0);
3884: }

3886: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3887: {
3888:   Mat_SeqAIJ     *b;
3889:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3891:   PetscInt       i;

3894:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3895:   if (nz == MAT_SKIP_ALLOCATION) {
3896:     skipallocation = PETSC_TRUE;
3897:     nz             = 0;
3898:   }
3899:   PetscLayoutSetUp(B->rmap);
3900:   PetscLayoutSetUp(B->cmap);

3902:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3903:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3904:   if (PetscUnlikelyDebug(nnz)) {
3905:     for (i=0; i<B->rmap->n; i++) {
3906:       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]);
3907:       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);
3908:     }
3909:   }

3911:   B->preallocated = PETSC_TRUE;

3913:   b = (Mat_SeqAIJ*)B->data;

3915:   if (!skipallocation) {
3916:     if (!b->imax) {
3917:       PetscMalloc1(B->rmap->n,&b->imax);
3918:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3919:     }
3920:     if (!b->ilen) {
3921:       /* b->ilen will count nonzeros in each row so far. */
3922:       PetscCalloc1(B->rmap->n,&b->ilen);
3923:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3924:     } else {
3925:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
3926:     }
3927:     if (!b->ipre) {
3928:       PetscMalloc1(B->rmap->n,&b->ipre);
3929:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3930:     }
3931:     if (!nnz) {
3932:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3933:       else if (nz < 0) nz = 1;
3934:       nz = PetscMin(nz,B->cmap->n);
3935:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3936:       nz = nz*B->rmap->n;
3937:     } else {
3938:       PetscInt64 nz64 = 0;
3939:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
3940:       PetscIntCast(nz64,&nz);
3941:     }

3943:     /* allocate the matrix space */
3944:     /* FIXME: should B's old memory be unlogged? */
3945:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3946:     if (B->structure_only) {
3947:       PetscMalloc1(nz,&b->j);
3948:       PetscMalloc1(B->rmap->n+1,&b->i);
3949:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
3950:     } else {
3951:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3952:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3953:     }
3954:     b->i[0] = 0;
3955:     for (i=1; i<B->rmap->n+1; i++) {
3956:       b->i[i] = b->i[i-1] + b->imax[i-1];
3957:     }
3958:     if (B->structure_only) {
3959:       b->singlemalloc = PETSC_FALSE;
3960:       b->free_a       = PETSC_FALSE;
3961:     } else {
3962:       b->singlemalloc = PETSC_TRUE;
3963:       b->free_a       = PETSC_TRUE;
3964:     }
3965:     b->free_ij      = PETSC_TRUE;
3966:   } else {
3967:     b->free_a  = PETSC_FALSE;
3968:     b->free_ij = PETSC_FALSE;
3969:   }

3971:   if (b->ipre && nnz != b->ipre  && b->imax) {
3972:     /* reserve user-requested sparsity */
3973:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
3974:   }


3977:   b->nz               = 0;
3978:   b->maxnz            = nz;
3979:   B->info.nz_unneeded = (double)b->maxnz;
3980:   if (realalloc) {
3981:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3982:   }
3983:   B->was_assembled = PETSC_FALSE;
3984:   B->assembled     = PETSC_FALSE;
3985:   return(0);
3986: }


3989: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
3990: {
3991:   Mat_SeqAIJ     *a;
3992:   PetscInt       i;


3998:   /* Check local size. If zero, then return */
3999:   if (!A->rmap->n) return(0);

4001:   a = (Mat_SeqAIJ*)A->data;
4002:   /* if no saved info, we error out */
4003:   if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");

4005:   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");

4007:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
4008:   PetscArrayzero(a->ilen,A->rmap->n);
4009:   a->i[0] = 0;
4010:   for (i=1; i<A->rmap->n+1; i++) {
4011:     a->i[i] = a->i[i-1] + a->imax[i-1];
4012:   }
4013:   A->preallocated     = PETSC_TRUE;
4014:   a->nz               = 0;
4015:   a->maxnz            = a->i[A->rmap->n];
4016:   A->info.nz_unneeded = (double)a->maxnz;
4017:   A->was_assembled    = PETSC_FALSE;
4018:   A->assembled        = PETSC_FALSE;
4019:   return(0);
4020: }

4022: /*@
4023:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

4025:    Input Parameters:
4026: +  B - the matrix
4027: .  i - the indices into j for the start of each row (starts with zero)
4028: .  j - the column indices for each row (starts with zero) these must be sorted for each row
4029: -  v - optional values in the matrix

4031:    Level: developer

4033:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

4035: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ
4036: @*/
4037: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
4038: {

4044:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
4045:   return(0);
4046: }

4048: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
4049: {
4050:   PetscInt       i;
4051:   PetscInt       m,n;
4052:   PetscInt       nz;
4053:   PetscInt       *nnz, nz_max = 0;

4057:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

4059:   PetscLayoutSetUp(B->rmap);
4060:   PetscLayoutSetUp(B->cmap);

4062:   MatGetSize(B, &m, &n);
4063:   PetscMalloc1(m+1, &nnz);
4064:   for (i = 0; i < m; i++) {
4065:     nz     = Ii[i+1]- Ii[i];
4066:     nz_max = PetscMax(nz_max, nz);
4067:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
4068:     nnz[i] = nz;
4069:   }
4070:   MatSeqAIJSetPreallocation(B, 0, nnz);
4071:   PetscFree(nnz);

4073:   for (i = 0; i < m; i++) {
4074:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
4075:   }

4077:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4078:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

4080:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4081:   return(0);
4082: }

4084:  #include <../src/mat/impls/dense/seq/dense.h>
4085:  #include <petsc/private/kernels/petscaxpy.h>

4087: /*
4088:     Computes (B'*A')' since computing B*A directly is untenable

4090:                n                       p                          p
4091:         (              )       (              )         (                  )
4092:       m (      A       )  *  n (       B      )   =   m (         C        )
4093:         (              )       (              )         (                  )

4095: */
4096: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4097: {
4098:   PetscErrorCode    ierr;
4099:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
4100:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
4101:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
4102:   PetscInt          i,j,n,m,q,p;
4103:   const PetscInt    *ii,*idx;
4104:   const PetscScalar *b,*a,*a_q;
4105:   PetscScalar       *c,*c_q;
4106:   PetscInt          clda = sub_c->lda;
4107:   PetscInt          alda = sub_a->lda;

4110:   m    = A->rmap->n;
4111:   n    = A->cmap->n;
4112:   p    = B->cmap->n;
4113:   a    = sub_a->v;
4114:   b    = sub_b->a;
4115:   c    = sub_c->v;
4116:   if (clda == m) {
4117:     PetscArrayzero(c,m*p);
4118:   } else {
4119:     for (j=0;j<p;j++)
4120:       for (i=0;i<m;i++)
4121:         c[j*clda + i] = 0.0;
4122:   }
4123:   ii  = sub_b->i;
4124:   idx = sub_b->j;
4125:   for (i=0; i<n; i++) {
4126:     q = ii[i+1] - ii[i];
4127:     while (q-->0) {
4128:       c_q = c + clda*(*idx);
4129:       a_q = a + alda*i;
4130:       PetscKernelAXPY(c_q,*b,a_q,m);
4131:       idx++;
4132:       b++;
4133:     }
4134:   }
4135:   return(0);
4136: }

4138: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
4139: {
4141:   PetscInt       m=A->rmap->n,n=B->cmap->n;
4142:   PetscBool      cisdense;

4145:   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);
4146:   MatSetSizes(C,m,n,m,n);
4147:   MatSetBlockSizesFromMats(C,A,B);
4148:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
4149:   if (!cisdense) {
4150:     MatSetType(C,MATDENSE);
4151:   }
4152:   MatSetUp(C);

4154:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4155:   return(0);
4156: }

4158: /* ----------------------------------------------------------------*/
4159: /*MC
4160:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4161:    based on compressed sparse row format.

4163:    Options Database Keys:
4164: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

4166:    Level: beginner

4168:    Notes:
4169:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
4170:     in this case the values associated with the rows and columns one passes in are set to zero
4171:     in the matrix

4173:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
4174:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

4176:   Developer Notes:
4177:     It would be nice if all matrix formats supported passing NULL in for the numerical values

4179: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4180: M*/

4182: /*MC
4183:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

4185:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4186:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
4187:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
4188:   for communicators controlling multiple processes.  It is recommended that you call both of
4189:   the above preallocation routines for simplicity.

4191:    Options Database Keys:
4192: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

4194:   Developer Notes:
4195:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4196:    enough exist.

4198:   Level: beginner

4200: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4201: M*/

4203: /*MC
4204:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

4206:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4207:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
4208:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4209:   for communicators controlling multiple processes.  It is recommended that you call both of
4210:   the above preallocation routines for simplicity.

4212:    Options Database Keys:
4213: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

4215:   Level: beginner

4217: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4218: M*/

4220: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4221: #if defined(PETSC_HAVE_ELEMENTAL)
4222: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4223: #endif
4224: #if defined(PETSC_HAVE_SCALAPACK)
4225: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
4226: #endif
4227: #if defined(PETSC_HAVE_HYPRE)
4228: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4229: #endif
4230: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);

4232: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4233: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4234: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

4236: /*@C
4237:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

4239:    Not Collective

4241:    Input Parameter:
4242: .  mat - a MATSEQAIJ matrix

4244:    Output Parameter:
4245: .   array - pointer to the data

4247:    Level: intermediate

4249: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4250: @*/
4251: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
4252: {

4256:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4257:   return(0);
4258: }

4260: /*@C
4261:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

4263:    Not Collective

4265:    Input Parameter:
4266: .  mat - a MATSEQAIJ matrix

4268:    Output Parameter:
4269: .   array - pointer to the data

4271:    Level: intermediate

4273: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
4274: @*/
4275: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
4276: {
4277: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4278:   PetscOffloadMask oval;
4279: #endif

4283: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4284:   oval = A->offloadmask;
4285: #endif
4286:   MatSeqAIJGetArray(A,(PetscScalar**)array);
4287: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4288:   if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
4289: #endif
4290:   return(0);
4291: }

4293: /*@C
4294:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

4296:    Not Collective

4298:    Input Parameter:
4299: .  mat - a MATSEQAIJ matrix

4301:    Output Parameter:
4302: .   array - pointer to the data

4304:    Level: intermediate

4306: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
4307: @*/
4308: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
4309: {
4310: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4311:   PetscOffloadMask oval;
4312: #endif

4316: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4317:   oval = A->offloadmask;
4318: #endif
4319:   MatSeqAIJRestoreArray(A,(PetscScalar**)array);
4320: #if defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_VIENNACL)
4321:   A->offloadmask = oval;
4322: #endif
4323:   return(0);
4324: }

4326: /*@C
4327:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

4329:    Not Collective

4331:    Input Parameter:
4332: .  mat - a MATSEQAIJ matrix

4334:    Output Parameter:
4335: .   nz - the maximum number of nonzeros in any row

4337:    Level: intermediate

4339: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4340: @*/
4341: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4342: {
4343:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

4346:   *nz = aij->rmax;
4347:   return(0);
4348: }

4350: /*@C
4351:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

4353:    Not Collective

4355:    Input Parameters:
4356: +  mat - a MATSEQAIJ matrix
4357: -  array - pointer to the data

4359:    Level: intermediate

4361: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4362: @*/
4363: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4364: {

4368:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4369:   return(0);
4370: }

4372: #if defined(PETSC_HAVE_CUDA)
4373: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4374: #endif

4376: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4377: {
4378:   Mat_SeqAIJ     *b;
4380:   PetscMPIInt    size;

4383:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4384:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

4386:   PetscNewLog(B,&b);

4388:   B->data = (void*)b;

4390:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4391:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

4393:   b->row                = 0;
4394:   b->col                = 0;
4395:   b->icol               = 0;
4396:   b->reallocs           = 0;
4397:   b->ignorezeroentries  = PETSC_FALSE;
4398:   b->roworiented        = PETSC_TRUE;
4399:   b->nonew              = 0;
4400:   b->diag               = 0;
4401:   b->solve_work         = 0;
4402:   B->spptr              = 0;
4403:   b->saved_values       = 0;
4404:   b->idiag              = 0;
4405:   b->mdiag              = 0;
4406:   b->ssor_work          = 0;
4407:   b->omega              = 1.0;
4408:   b->fshift             = 0.0;
4409:   b->idiagvalid         = PETSC_FALSE;
4410:   b->ibdiagvalid        = PETSC_FALSE;
4411:   b->keepnonzeropattern = PETSC_FALSE;

4413:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4414:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4415:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

4417: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4418:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4419:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4420: #endif

4422:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4423:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4424:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4425:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4426:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4427:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4428:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4429: #if defined(PETSC_HAVE_MKL_SPARSE)
4430:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4431: #endif
4432: #if defined(PETSC_HAVE_CUDA)
4433:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4434:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4435: #endif
4436:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4437: #if defined(PETSC_HAVE_ELEMENTAL)
4438:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4439: #endif
4440: #if defined(PETSC_HAVE_SCALAPACK)
4441:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
4442: #endif
4443: #if defined(PETSC_HAVE_HYPRE)
4444:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4445:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
4446: #endif
4447:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4448:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4449:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4450:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4451:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4452:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4453:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4454:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4455:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4456:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
4457:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
4458:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4459:   MatCreate_SeqAIJ_Inode(B);
4460:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4461:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4462:   return(0);
4463: }

4465: /*
4466:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4467: */
4468: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4469: {
4470:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
4472:   PetscInt       m = A->rmap->n,i;

4475:   c = (Mat_SeqAIJ*)C->data;

4477:   C->factortype = A->factortype;
4478:   c->row        = 0;
4479:   c->col        = 0;
4480:   c->icol       = 0;
4481:   c->reallocs   = 0;

4483:   C->assembled = PETSC_TRUE;

4485:   PetscLayoutReference(A->rmap,&C->rmap);
4486:   PetscLayoutReference(A->cmap,&C->cmap);

4488:   PetscMalloc1(m,&c->imax);
4489:   PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
4490:   PetscMalloc1(m,&c->ilen);
4491:   PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
4492:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

4494:   /* allocate the matrix space */
4495:   if (mallocmatspace) {
4496:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4497:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4499:     c->singlemalloc = PETSC_TRUE;

4501:     PetscArraycpy(c->i,a->i,m+1);
4502:     if (m > 0) {
4503:       PetscArraycpy(c->j,a->j,a->i[m]);
4504:       if (cpvalues == MAT_COPY_VALUES) {
4505:         PetscArraycpy(c->a,a->a,a->i[m]);
4506:       } else {
4507:         PetscArrayzero(c->a,a->i[m]);
4508:       }
4509:     }
4510:   }

4512:   c->ignorezeroentries = a->ignorezeroentries;
4513:   c->roworiented       = a->roworiented;
4514:   c->nonew             = a->nonew;
4515:   if (a->diag) {
4516:     PetscMalloc1(m+1,&c->diag);
4517:     PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
4518:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4519:   } else c->diag = NULL;

4521:   c->solve_work         = 0;
4522:   c->saved_values       = 0;
4523:   c->idiag              = 0;
4524:   c->ssor_work          = 0;
4525:   c->keepnonzeropattern = a->keepnonzeropattern;
4526:   c->free_a             = PETSC_TRUE;
4527:   c->free_ij            = PETSC_TRUE;

4529:   c->rmax         = a->rmax;
4530:   c->nz           = a->nz;
4531:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4532:   C->preallocated = PETSC_TRUE;

4534:   c->compressedrow.use   = a->compressedrow.use;
4535:   c->compressedrow.nrows = a->compressedrow.nrows;
4536:   if (a->compressedrow.use) {
4537:     i    = a->compressedrow.nrows;
4538:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4539:     PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
4540:     PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
4541:   } else {
4542:     c->compressedrow.use    = PETSC_FALSE;
4543:     c->compressedrow.i      = NULL;
4544:     c->compressedrow.rindex = NULL;
4545:   }
4546:   c->nonzerorowcnt = a->nonzerorowcnt;
4547:   C->nonzerostate  = A->nonzerostate;

4549:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4550:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4551:   return(0);
4552: }

4554: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4555: {

4559:   MatCreate(PetscObjectComm((PetscObject)A),B);
4560:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4561:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4562:     MatSetBlockSizesFromMats(*B,A,A);
4563:   }
4564:   MatSetType(*B,((PetscObject)A)->type_name);
4565:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4566:   return(0);
4567: }

4569: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4570: {
4571:   PetscBool      isbinary, ishdf5;

4577:   /* force binary viewer to load .info file if it has not yet done so */
4578:   PetscViewerSetUp(viewer);
4579:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4580:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
4581:   if (isbinary) {
4582:     MatLoad_SeqAIJ_Binary(newMat,viewer);
4583:   } else if (ishdf5) {
4584: #if defined(PETSC_HAVE_HDF5)
4585:     MatLoad_AIJ_HDF5(newMat,viewer);
4586: #else
4587:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4588: #endif
4589:   } else {
4590:     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);
4591:   }
4592:   return(0);
4593: }

4595: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4596: {
4597:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
4599:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

4602:   PetscViewerSetUp(viewer);

4604:   /* read in matrix header */
4605:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
4606:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
4607:   M = header[1]; N = header[2]; nz = header[3];
4608:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
4609:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
4610:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");

4612:   /* set block sizes from the viewer's .info file */
4613:   MatLoad_Binary_BlockSizes(mat,viewer);
4614:   /* set local and global sizes if not set already */
4615:   if (mat->rmap->n < 0) mat->rmap->n = M;
4616:   if (mat->cmap->n < 0) mat->cmap->n = N;
4617:   if (mat->rmap->N < 0) mat->rmap->N = M;
4618:   if (mat->cmap->N < 0) mat->cmap->N = N;
4619:   PetscLayoutSetUp(mat->rmap);
4620:   PetscLayoutSetUp(mat->cmap);

4622:   /* check if the matrix sizes are correct */
4623:   MatGetSize(mat,&rows,&cols);
4624:   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);

4626:   /* read in row lengths */
4627:   PetscMalloc1(M,&rowlens);
4628:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
4629:   /* check if sum(rowlens) is same as nz */
4630:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
4631:   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);
4632:   /* preallocate and check sizes */
4633:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
4634:   MatGetSize(mat,&rows,&cols);
4635:   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);
4636:   /* store row lengths */
4637:   PetscArraycpy(a->ilen,rowlens,M);
4638:   PetscFree(rowlens);

4640:   /* fill in "i" row pointers */
4641:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
4642:   /* read in "j" column indices */
4643:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
4644:   /* read in "a" nonzero values */
4645:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

4647:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
4648:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
4649:   return(0);
4650: }

4652: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4653: {
4654:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4656: #if defined(PETSC_USE_COMPLEX)
4657:   PetscInt k;
4658: #endif

4661:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4662:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4663:     *flg = PETSC_FALSE;
4664:     return(0);
4665:   }

4667:   /* if the a->i are the same */
4668:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
4669:   if (!*flg) return(0);

4671:   /* if a->j are the same */
4672:   PetscArraycmp(a->j,b->j,a->nz,flg);
4673:   if (!*flg) return(0);

4675:   /* if a->a are the same */
4676: #if defined(PETSC_USE_COMPLEX)
4677:   for (k=0; k<a->nz; k++) {
4678:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4679:       *flg = PETSC_FALSE;
4680:       return(0);
4681:     }
4682:   }
4683: #else
4684:   PetscArraycmp(a->a,b->a,a->nz,flg);
4685: #endif
4686:   return(0);
4687: }

4689: /*@
4690:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4691:               provided by the user.

4693:       Collective

4695:    Input Parameters:
4696: +   comm - must be an MPI communicator of size 1
4697: .   m - number of rows
4698: .   n - number of columns
4699: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4700: .   j - column indices
4701: -   a - matrix values

4703:    Output Parameter:
4704: .   mat - the matrix

4706:    Level: intermediate

4708:    Notes:
4709:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4710:     once the matrix is destroyed and not before

4712:        You cannot set new nonzero locations into this matrix, that will generate an error.

4714:        The i and j indices are 0 based

4716:        The format which is used for the sparse matrix input, is equivalent to a
4717:     row-major ordering.. i.e for the following matrix, the input data expected is
4718:     as shown

4720: $        1 0 0
4721: $        2 0 3
4722: $        4 5 6
4723: $
4724: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4725: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
4726: $        v =  {1,2,3,4,5,6}  [size = 6]


4729: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4731: @*/
4732: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4733: {
4735:   PetscInt       ii;
4736:   Mat_SeqAIJ     *aij;
4737:   PetscInt jj;

4740:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4741:   MatCreate(comm,mat);
4742:   MatSetSizes(*mat,m,n,m,n);
4743:   /* MatSetBlockSizes(*mat,,); */
4744:   MatSetType(*mat,MATSEQAIJ);
4745:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4746:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4747:   PetscMalloc1(m,&aij->imax);
4748:   PetscMalloc1(m,&aij->ilen);

4750:   aij->i            = i;
4751:   aij->j            = j;
4752:   aij->a            = a;
4753:   aij->singlemalloc = PETSC_FALSE;
4754:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4755:   aij->free_a       = PETSC_FALSE;
4756:   aij->free_ij      = PETSC_FALSE;

4758:   for (ii=0; ii<m; ii++) {
4759:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4760:     if (PetscDefined(USE_DEBUG)) {
4761:       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]);
4762:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4763:         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);
4764:         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);
4765:       }
4766:     }
4767:   }
4768:   if (PetscDefined(USE_DEBUG)) {
4769:     for (ii=0; ii<aij->i[m]; ii++) {
4770:       if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4771:       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]);
4772:     }
4773:   }

4775:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4776:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4777:   return(0);
4778: }
4779: /*@C
4780:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4781:               provided by the user.

4783:       Collective

4785:    Input Parameters:
4786: +   comm - must be an MPI communicator of size 1
4787: .   m   - number of rows
4788: .   n   - number of columns
4789: .   i   - row indices
4790: .   j   - column indices
4791: .   a   - matrix values
4792: .   nz  - number of nonzeros
4793: -   idx - 0 or 1 based

4795:    Output Parameter:
4796: .   mat - the matrix

4798:    Level: intermediate

4800:    Notes:
4801:        The i and j indices are 0 based

4803:        The format which is used for the sparse matrix input, is equivalent to a
4804:     row-major ordering.. i.e for the following matrix, the input data expected is
4805:     as shown:

4807:         1 0 0
4808:         2 0 3
4809:         4 5 6

4811:         i =  {0,1,1,2,2,2}
4812:         j =  {0,0,2,0,1,2}
4813:         v =  {1,2,3,4,5,6}


4816: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4818: @*/
4819: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4820: {
4822:   PetscInt       ii, *nnz, one = 1,row,col;


4826:   PetscCalloc1(m,&nnz);
4827:   for (ii = 0; ii < nz; ii++) {
4828:     nnz[i[ii] - !!idx] += 1;
4829:   }
4830:   MatCreate(comm,mat);
4831:   MatSetSizes(*mat,m,n,m,n);
4832:   MatSetType(*mat,MATSEQAIJ);
4833:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4834:   for (ii = 0; ii < nz; ii++) {
4835:     if (idx) {
4836:       row = i[ii] - 1;
4837:       col = j[ii] - 1;
4838:     } else {
4839:       row = i[ii];
4840:       col = j[ii];
4841:     }
4842:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4843:   }
4844:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4845:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4846:   PetscFree(nnz);
4847:   return(0);
4848: }

4850: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4851: {
4852:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

4856:   a->idiagvalid  = PETSC_FALSE;
4857:   a->ibdiagvalid = PETSC_FALSE;

4859:   MatSeqAIJInvalidateDiagonal_Inode(A);
4860:   return(0);
4861: }

4863: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4864: {
4866:   PetscMPIInt    size;

4869:   MPI_Comm_size(comm,&size);
4870:   if (size == 1) {
4871:     if (scall == MAT_INITIAL_MATRIX) {
4872:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4873:     } else {
4874:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4875:     }
4876:   } else {
4877:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4878:   }
4879:   return(0);
4880: }

4882: /*
4883:  Permute A into C's *local* index space using rowemb,colemb.
4884:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4885:  of [0,m), colemb is in [0,n).
4886:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4887:  */
4888: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4889: {
4890:   /* If making this function public, change the error returned in this function away from _PLIB. */
4892:   Mat_SeqAIJ     *Baij;
4893:   PetscBool      seqaij;
4894:   PetscInt       m,n,*nz,i,j,count;
4895:   PetscScalar    v;
4896:   const PetscInt *rowindices,*colindices;

4899:   if (!B) return(0);
4900:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4901:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4902:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4903:   if (rowemb) {
4904:     ISGetLocalSize(rowemb,&m);
4905:     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);
4906:   } else {
4907:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4908:   }
4909:   if (colemb) {
4910:     ISGetLocalSize(colemb,&n);
4911:     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);
4912:   } else {
4913:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4914:   }

4916:   Baij = (Mat_SeqAIJ*)(B->data);
4917:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
4918:     PetscMalloc1(B->rmap->n,&nz);
4919:     for (i=0; i<B->rmap->n; i++) {
4920:       nz[i] = Baij->i[i+1] - Baij->i[i];
4921:     }
4922:     MatSeqAIJSetPreallocation(C,0,nz);
4923:     PetscFree(nz);
4924:   }
4925:   if (pattern == SUBSET_NONZERO_PATTERN) {
4926:     MatZeroEntries(C);
4927:   }
4928:   count = 0;
4929:   rowindices = NULL;
4930:   colindices = NULL;
4931:   if (rowemb) {
4932:     ISGetIndices(rowemb,&rowindices);
4933:   }
4934:   if (colemb) {
4935:     ISGetIndices(colemb,&colindices);
4936:   }
4937:   for (i=0; i<B->rmap->n; i++) {
4938:     PetscInt row;
4939:     row = i;
4940:     if (rowindices) row = rowindices[i];
4941:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4942:       PetscInt col;
4943:       col  = Baij->j[count];
4944:       if (colindices) col = colindices[col];
4945:       v    = Baij->a[count];
4946:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4947:       ++count;
4948:     }
4949:   }
4950:   /* FIXME: set C's nonzerostate correctly. */
4951:   /* Assembly for C is necessary. */
4952:   C->preallocated = PETSC_TRUE;
4953:   C->assembled     = PETSC_TRUE;
4954:   C->was_assembled = PETSC_FALSE;
4955:   return(0);
4956: }

4958: PetscFunctionList MatSeqAIJList = NULL;

4960: /*@C
4961:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

4963:    Collective on Mat

4965:    Input Parameters:
4966: +  mat      - the matrix object
4967: -  matype   - matrix type

4969:    Options Database Key:
4970: .  -mat_seqai_type  <method> - for example seqaijcrl


4973:   Level: intermediate

4975: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
4976: @*/
4977: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
4978: {
4979:   PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
4980:   PetscBool      sametype;

4984:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
4985:   if (sametype) return(0);

4987:    PetscFunctionListFind(MatSeqAIJList,matype,&r);
4988:   if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
4989:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
4990:   return(0);
4991: }


4994: /*@C
4995:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

4997:    Not Collective

4999:    Input Parameters:
5000: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
5001: -  function - routine to convert to subtype

5003:    Notes:
5004:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.


5007:    Then, your matrix can be chosen with the procedural interface at runtime via the option
5008: $     -mat_seqaij_type my_mat

5010:    Level: advanced

5012: .seealso: MatSeqAIJRegisterAll()


5015:   Level: advanced
5016: @*/
5017: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
5018: {

5022:   MatInitializePackage();
5023:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
5024:   return(0);
5025: }

5027: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

5029: /*@C
5030:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

5032:   Not Collective

5034:   Level: advanced

5036:   Developers Note: CUSP and CUSPARSE do not yet support the  MatConvert_SeqAIJ..() paradigm and thus cannot be registered here

5038: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
5039: @*/
5040: PetscErrorCode  MatSeqAIJRegisterAll(void)
5041: {

5045:   if (MatSeqAIJRegisterAllCalled) return(0);
5046:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

5048:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
5049:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
5050:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
5051: #if defined(PETSC_HAVE_MKL_SPARSE)
5052:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
5053: #endif
5054: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5055:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
5056: #endif
5057:   return(0);
5058: }

5060: /*
5061:     Special version for direct calls from Fortran
5062: */
5063:  #include <petsc/private/fortranimpl.h>
5064: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5065: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5066: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5067: #define matsetvaluesseqaij_ matsetvaluesseqaij
5068: #endif

5070: /* Change these macros so can be used in void function */
5071: #undef CHKERRQ
5072: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
5073: #undef SETERRQ2
5074: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5075: #undef SETERRQ3
5076: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

5078: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
5079: {
5080:   Mat            A  = *AA;
5081:   PetscInt       m  = *mm, n = *nn;
5082:   InsertMode     is = *isis;
5083:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
5084:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
5085:   PetscInt       *imax,*ai,*ailen;
5087:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
5088:   MatScalar      *ap,value,*aa;
5089:   PetscBool      ignorezeroentries = a->ignorezeroentries;
5090:   PetscBool      roworiented       = a->roworiented;

5093:   MatCheckPreallocated(A,1);
5094:   imax  = a->imax;
5095:   ai    = a->i;
5096:   ailen = a->ilen;
5097:   aj    = a->j;
5098:   aa    = a->a;

5100:   for (k=0; k<m; k++) { /* loop over added rows */
5101:     row = im[k];
5102:     if (row < 0) continue;
5103:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
5104:     rp   = aj + ai[row]; ap = aa + ai[row];
5105:     rmax = imax[row]; nrow = ailen[row];
5106:     low  = 0;
5107:     high = nrow;
5108:     for (l=0; l<n; l++) { /* loop over added columns */
5109:       if (in[l] < 0) continue;
5110:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
5111:       col = in[l];
5112:       if (roworiented) value = v[l + k*n];
5113:       else value = v[k + l*m];

5115:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

5117:       if (col <= lastcol) low = 0;
5118:       else high = nrow;
5119:       lastcol = col;
5120:       while (high-low > 5) {
5121:         t = (low+high)/2;
5122:         if (rp[t] > col) high = t;
5123:         else             low  = t;
5124:       }
5125:       for (i=low; i<high; i++) {
5126:         if (rp[i] > col) break;
5127:         if (rp[i] == col) {
5128:           if (is == ADD_VALUES) ap[i] += value;
5129:           else                  ap[i] = value;
5130:           goto noinsert;
5131:         }
5132:       }
5133:       if (value == 0.0 && ignorezeroentries) goto noinsert;
5134:       if (nonew == 1) goto noinsert;
5135:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5136:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5137:       N = nrow++ - 1; a->nz++; high++;
5138:       /* shift up all the later entries in this row */
5139:       for (ii=N; ii>=i; ii--) {
5140:         rp[ii+1] = rp[ii];
5141:         ap[ii+1] = ap[ii];
5142:       }
5143:       rp[i] = col;
5144:       ap[i] = value;
5145:       A->nonzerostate++;
5146: noinsert:;
5147:       low = i + 1;
5148:     }
5149:     ailen[row] = nrow;
5150:   }
5151:   PetscFunctionReturnVoid();
5152: }