Actual source code: mpibaij.c

petsc-3.15.0 2021-04-05
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  1: #include <../src/mat/impls/baij/mpi/mpibaij.h>

  3: #include <petsc/private/hashseti.h>
  4: #include <petscblaslapack.h>
  5: #include <petscsf.h>

  7: #if defined(PETSC_HAVE_HYPRE)
  8: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat,MatType,MatReuse,Mat*);
  9: #endif

 11: PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A,Vec v,PetscInt idx[])
 12: {
 13:   Mat_MPIBAIJ       *a = (Mat_MPIBAIJ*)A->data;
 14:   PetscErrorCode    ierr;
 15:   PetscInt          i,*idxb = NULL,m = A->rmap->n,bs = A->cmap->bs;
 16:   PetscScalar       *va,*vv;
 17:   Vec               vB,vA;
 18:   const PetscScalar *vb;

 21:   VecCreateSeq(PETSC_COMM_SELF,m,&vA);
 22:   MatGetRowMaxAbs(a->A,vA,idx);

 24:   VecGetArrayWrite(vA,&va);
 25:   if (idx) {
 26:     for (i=0; i<m; i++) {
 27:       if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
 28:     }
 29:   }

 31:   VecCreateSeq(PETSC_COMM_SELF,m,&vB);
 32:   PetscMalloc1(m,&idxb);
 33:   MatGetRowMaxAbs(a->B,vB,idxb);

 35:   VecGetArrayWrite(v,&vv);
 36:   VecGetArrayRead(vB,&vb);
 37:   for (i=0; i<m; i++) {
 38:     if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
 39:       vv[i] = vb[i];
 40:       if (idx) idx[i] = bs*a->garray[idxb[i]/bs] + (idxb[i] % bs);
 41:     } else {
 42:       vv[i] = va[i];
 43:       if (idx && PetscAbsScalar(va[i]) == PetscAbsScalar(vb[i]) && idxb[i] != -1 && idx[i] > bs*a->garray[idxb[i]/bs] + (idxb[i] % bs))
 44:         idx[i] = bs*a->garray[idxb[i]/bs] + (idxb[i] % bs);
 45:     }
 46:   }
 47:   VecRestoreArrayWrite(vA,&vv);
 48:   VecRestoreArrayWrite(vA,&va);
 49:   VecRestoreArrayRead(vB,&vb);
 50:   PetscFree(idxb);
 51:   VecDestroy(&vA);
 52:   VecDestroy(&vB);
 53:   return(0);
 54: }

 56: PetscErrorCode  MatStoreValues_MPIBAIJ(Mat mat)
 57: {
 58:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;

 62:   MatStoreValues(aij->A);
 63:   MatStoreValues(aij->B);
 64:   return(0);
 65: }

 67: PetscErrorCode  MatRetrieveValues_MPIBAIJ(Mat mat)
 68: {
 69:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;

 73:   MatRetrieveValues(aij->A);
 74:   MatRetrieveValues(aij->B);
 75:   return(0);
 76: }

 78: /*
 79:      Local utility routine that creates a mapping from the global column
 80:    number to the local number in the off-diagonal part of the local
 81:    storage of the matrix.  This is done in a non scalable way since the
 82:    length of colmap equals the global matrix length.
 83: */
 84: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
 85: {
 86:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
 87:   Mat_SeqBAIJ    *B    = (Mat_SeqBAIJ*)baij->B->data;
 89:   PetscInt       nbs = B->nbs,i,bs=mat->rmap->bs;

 92: #if defined(PETSC_USE_CTABLE)
 93:   PetscTableCreate(baij->nbs,baij->Nbs+1,&baij->colmap);
 94:   for (i=0; i<nbs; i++) {
 95:     PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1,INSERT_VALUES);
 96:   }
 97: #else
 98:   PetscCalloc1(baij->Nbs+1,&baij->colmap);
 99:   PetscLogObjectMemory((PetscObject)mat,baij->Nbs*sizeof(PetscInt));
100:   for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
101: #endif
102:   return(0);
103: }

105: #define  MatSetValues_SeqBAIJ_A_Private(row,col,value,addv,orow,ocol)       \
106:   { \
107:     brow = row/bs;  \
108:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
109:     rmax = aimax[brow]; nrow = ailen[brow]; \
110:     bcol = col/bs; \
111:     ridx = row % bs; cidx = col % bs; \
112:     low  = 0; high = nrow; \
113:     while (high-low > 3) { \
114:       t = (low+high)/2; \
115:       if (rp[t] > bcol) high = t; \
116:       else              low  = t; \
117:     } \
118:     for (_i=low; _i<high; _i++) { \
119:       if (rp[_i] > bcol) break; \
120:       if (rp[_i] == bcol) { \
121:         bap = ap +  bs2*_i + bs*cidx + ridx; \
122:         if (addv == ADD_VALUES) *bap += value;  \
123:         else                    *bap  = value;  \
124:         goto a_noinsert; \
125:       } \
126:     } \
127:     if (a->nonew == 1) goto a_noinsert; \
128:     if (a->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at global row/column (%D, %D) into matrix", orow, ocol); \
129:     MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
130:     N = nrow++ - 1;  \
131:     /* shift up all the later entries in this row */ \
132:     PetscArraymove(rp+_i+1,rp+_i,N-_i+1);\
133:     PetscArraymove(ap+bs2*(_i+1),ap+bs2*_i,bs2*(N-_i+1)); \
134:     PetscArrayzero(ap+bs2*_i,bs2);  \
135:     rp[_i]                      = bcol;  \
136:     ap[bs2*_i + bs*cidx + ridx] = value;  \
137: a_noinsert:; \
138:     ailen[brow] = nrow; \
139:   }

141: #define  MatSetValues_SeqBAIJ_B_Private(row,col,value,addv,orow,ocol)       \
142:   { \
143:     brow = row/bs;  \
144:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
145:     rmax = bimax[brow]; nrow = bilen[brow]; \
146:     bcol = col/bs; \
147:     ridx = row % bs; cidx = col % bs; \
148:     low  = 0; high = nrow; \
149:     while (high-low > 3) { \
150:       t = (low+high)/2; \
151:       if (rp[t] > bcol) high = t; \
152:       else              low  = t; \
153:     } \
154:     for (_i=low; _i<high; _i++) { \
155:       if (rp[_i] > bcol) break; \
156:       if (rp[_i] == bcol) { \
157:         bap = ap +  bs2*_i + bs*cidx + ridx; \
158:         if (addv == ADD_VALUES) *bap += value;  \
159:         else                    *bap  = value;  \
160:         goto b_noinsert; \
161:       } \
162:     } \
163:     if (b->nonew == 1) goto b_noinsert; \
164:     if (b->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at global row/column  (%D, %D) into matrix", orow, ocol); \
165:     MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
166:     N = nrow++ - 1;  \
167:     /* shift up all the later entries in this row */ \
168:     PetscArraymove(rp+_i+1,rp+_i,N-_i+1);\
169:     PetscArraymove(ap+bs2*(_i+1),ap+bs2*_i,bs2*(N-_i+1));\
170:     PetscArrayzero(ap+bs2*_i,bs2);  \
171:     rp[_i]                      = bcol;  \
172:     ap[bs2*_i + bs*cidx + ridx] = value;  \
173: b_noinsert:; \
174:     bilen[brow] = nrow; \
175:   }

177: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
178: {
179:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
180:   MatScalar      value;
181:   PetscBool      roworiented = baij->roworiented;
183:   PetscInt       i,j,row,col;
184:   PetscInt       rstart_orig=mat->rmap->rstart;
185:   PetscInt       rend_orig  =mat->rmap->rend,cstart_orig=mat->cmap->rstart;
186:   PetscInt       cend_orig  =mat->cmap->rend,bs=mat->rmap->bs;

188:   /* Some Variables required in the macro */
189:   Mat         A     = baij->A;
190:   Mat_SeqBAIJ *a    = (Mat_SeqBAIJ*)(A)->data;
191:   PetscInt    *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
192:   MatScalar   *aa   =a->a;

194:   Mat         B     = baij->B;
195:   Mat_SeqBAIJ *b    = (Mat_SeqBAIJ*)(B)->data;
196:   PetscInt    *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
197:   MatScalar   *ba   =b->a;

199:   PetscInt  *rp,ii,nrow,_i,rmax,N,brow,bcol;
200:   PetscInt  low,high,t,ridx,cidx,bs2=a->bs2;
201:   MatScalar *ap,*bap;

204:   for (i=0; i<m; i++) {
205:     if (im[i] < 0) continue;
206:     if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
207:     if (im[i] >= rstart_orig && im[i] < rend_orig) {
208:       row = im[i] - rstart_orig;
209:       for (j=0; j<n; j++) {
210:         if (in[j] >= cstart_orig && in[j] < cend_orig) {
211:           col = in[j] - cstart_orig;
212:           if (roworiented) value = v[i*n+j];
213:           else             value = v[i+j*m];
214:           MatSetValues_SeqBAIJ_A_Private(row,col,value,addv,im[i],in[j]);
215:         } else if (in[j] < 0) continue;
216:         else if (PetscUnlikely(in[j] >= mat->cmap->N)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);
217:         else {
218:           if (mat->was_assembled) {
219:             if (!baij->colmap) {
220:               MatCreateColmap_MPIBAIJ_Private(mat);
221:             }
222: #if defined(PETSC_USE_CTABLE)
223:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
224:             col  = col - 1;
225: #else
226:             col = baij->colmap[in[j]/bs] - 1;
227: #endif
228:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
229:               MatDisAssemble_MPIBAIJ(mat);
230:               col  =  in[j];
231:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
232:               B    = baij->B;
233:               b    = (Mat_SeqBAIJ*)(B)->data;
234:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
235:               ba   =b->a;
236:             } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", im[i], in[j]);
237:             else col += in[j]%bs;
238:           } else col = in[j];
239:           if (roworiented) value = v[i*n+j];
240:           else             value = v[i+j*m];
241:           MatSetValues_SeqBAIJ_B_Private(row,col,value,addv,im[i],in[j]);
242:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
243:         }
244:       }
245:     } else {
246:       if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
247:       if (!baij->donotstash) {
248:         mat->assembled = PETSC_FALSE;
249:         if (roworiented) {
250:           MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
251:         } else {
252:           MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
253:         }
254:       }
255:     }
256:   }
257:   return(0);
258: }

260: PETSC_STATIC_INLINE PetscErrorCode MatSetValuesBlocked_SeqBAIJ_Inlined(Mat A,PetscInt row,PetscInt col,const PetscScalar v[],InsertMode is,PetscInt orow,PetscInt ocol)
261: {
262:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ*)A->data;
263:   PetscInt          *rp,low,high,t,ii,jj,nrow,i,rmax,N;
264:   PetscInt          *imax=a->imax,*ai=a->i,*ailen=a->ilen;
265:   PetscErrorCode    ierr;
266:   PetscInt          *aj        =a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap->bs;
267:   PetscBool         roworiented=a->roworiented;
268:   const PetscScalar *value     = v;
269:   MatScalar         *ap,*aa = a->a,*bap;

272:   rp   = aj + ai[row];
273:   ap   = aa + bs2*ai[row];
274:   rmax = imax[row];
275:   nrow = ailen[row];
276:   value = v;
277:   low = 0;
278:   high = nrow;
279:   while (high-low > 7) {
280:     t = (low+high)/2;
281:     if (rp[t] > col) high = t;
282:     else             low  = t;
283:   }
284:   for (i=low; i<high; i++) {
285:     if (rp[i] > col) break;
286:     if (rp[i] == col) {
287:       bap = ap +  bs2*i;
288:       if (roworiented) {
289:         if (is == ADD_VALUES) {
290:           for (ii=0; ii<bs; ii++) {
291:             for (jj=ii; jj<bs2; jj+=bs) {
292:               bap[jj] += *value++;
293:             }
294:           }
295:         } else {
296:           for (ii=0; ii<bs; ii++) {
297:             for (jj=ii; jj<bs2; jj+=bs) {
298:               bap[jj] = *value++;
299:             }
300:           }
301:         }
302:       } else {
303:         if (is == ADD_VALUES) {
304:           for (ii=0; ii<bs; ii++,value+=bs) {
305:             for (jj=0; jj<bs; jj++) {
306:               bap[jj] += value[jj];
307:             }
308:             bap += bs;
309:           }
310:         } else {
311:           for (ii=0; ii<bs; ii++,value+=bs) {
312:             for (jj=0; jj<bs; jj++) {
313:               bap[jj]  = value[jj];
314:             }
315:             bap += bs;
316:           }
317:         }
318:       }
319:       goto noinsert2;
320:     }
321:   }
322:   if (nonew == 1) goto noinsert2;
323:   if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new global block indexed nonzero block (%D, %D) in the matrix", orow, ocol);
324:   MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
325:   N = nrow++ - 1; high++;
326:   /* shift up all the later entries in this row */
327:   PetscArraymove(rp+i+1,rp+i,N-i+1);
328:   PetscArraymove(ap+bs2*(i+1),ap+bs2*i,bs2*(N-i+1));
329:   rp[i] = col;
330:   bap   = ap +  bs2*i;
331:   if (roworiented) {
332:     for (ii=0; ii<bs; ii++) {
333:       for (jj=ii; jj<bs2; jj+=bs) {
334:         bap[jj] = *value++;
335:       }
336:     }
337:   } else {
338:     for (ii=0; ii<bs; ii++) {
339:       for (jj=0; jj<bs; jj++) {
340:         *bap++ = *value++;
341:       }
342:     }
343:   }
344:   noinsert2:;
345:   ailen[row] = nrow;
346:   return(0);
347: }

349: /*
350:     This routine should be optimized so that the block copy at ** Here a copy is required ** below is not needed
351:     by passing additional stride information into the MatSetValuesBlocked_SeqBAIJ_Inlined() routine
352: */
353: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
354: {
355:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
356:   const PetscScalar *value;
357:   MatScalar         *barray     = baij->barray;
358:   PetscBool         roworiented = baij->roworiented;
359:   PetscErrorCode    ierr;
360:   PetscInt          i,j,ii,jj,row,col,rstart=baij->rstartbs;
361:   PetscInt          rend=baij->rendbs,cstart=baij->cstartbs,stepval;
362:   PetscInt          cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

365:   if (!barray) {
366:     PetscMalloc1(bs2,&barray);
367:     baij->barray = barray;
368:   }

370:   if (roworiented) stepval = (n-1)*bs;
371:   else stepval = (m-1)*bs;

373:   for (i=0; i<m; i++) {
374:     if (im[i] < 0) continue;
375:     if (PetscUnlikelyDebug(im[i] >= baij->Mbs)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Block indexed row too large %D max %D",im[i],baij->Mbs-1);
376:     if (im[i] >= rstart && im[i] < rend) {
377:       row = im[i] - rstart;
378:       for (j=0; j<n; j++) {
379:         /* If NumCol = 1 then a copy is not required */
380:         if ((roworiented) && (n == 1)) {
381:           barray = (MatScalar*)v + i*bs2;
382:         } else if ((!roworiented) && (m == 1)) {
383:           barray = (MatScalar*)v + j*bs2;
384:         } else { /* Here a copy is required */
385:           if (roworiented) {
386:             value = v + (i*(stepval+bs) + j)*bs;
387:           } else {
388:             value = v + (j*(stepval+bs) + i)*bs;
389:           }
390:           for (ii=0; ii<bs; ii++,value+=bs+stepval) {
391:             for (jj=0; jj<bs; jj++) barray[jj] = value[jj];
392:             barray += bs;
393:           }
394:           barray -= bs2;
395:         }

397:         if (in[j] >= cstart && in[j] < cend) {
398:           col  = in[j] - cstart;
399:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A,row,col,barray,addv,im[i],in[j]);
400:         } else if (in[j] < 0) continue;
401:         else if (PetscUnlikelyDebug(in[j] >= baij->Nbs)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Block indexed column too large %D max %D",in[j],baij->Nbs-1);
402:         else {
403:           if (mat->was_assembled) {
404:             if (!baij->colmap) {
405:               MatCreateColmap_MPIBAIJ_Private(mat);
406:             }

408: #if defined(PETSC_USE_DEBUG)
409: #if defined(PETSC_USE_CTABLE)
410:             { PetscInt data;
411:               PetscTableFind(baij->colmap,in[j]+1,&data);
412:               if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
413:             }
414: #else
415:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
416: #endif
417: #endif
418: #if defined(PETSC_USE_CTABLE)
419:             PetscTableFind(baij->colmap,in[j]+1,&col);
420:             col  = (col - 1)/bs;
421: #else
422:             col = (baij->colmap[in[j]] - 1)/bs;
423: #endif
424:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
425:               MatDisAssemble_MPIBAIJ(mat);
426:               col  =  in[j];
427:             } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new blocked indexed nonzero block (%D, %D) into matrix",im[i],in[j]);
428:           } else col = in[j];
429:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B,row,col,barray,addv,im[i],in[j]);
430:         }
431:       }
432:     } else {
433:       if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process block indexed row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
434:       if (!baij->donotstash) {
435:         if (roworiented) {
436:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
437:         } else {
438:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
439:         }
440:       }
441:     }
442:   }
443:   return(0);
444: }

446: #define HASH_KEY 0.6180339887
447: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
448: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
449: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
450: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
451: {
452:   Mat_MPIBAIJ    *baij       = (Mat_MPIBAIJ*)mat->data;
453:   PetscBool      roworiented = baij->roworiented;
455:   PetscInt       i,j,row,col;
456:   PetscInt       rstart_orig=mat->rmap->rstart;
457:   PetscInt       rend_orig  =mat->rmap->rend,Nbs=baij->Nbs;
458:   PetscInt       h1,key,size=baij->ht_size,bs=mat->rmap->bs,*HT=baij->ht,idx;
459:   PetscReal      tmp;
460:   MatScalar      **HD = baij->hd,value;
461:   PetscInt       total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;

464:   for (i=0; i<m; i++) {
465:     if (PetscDefined(USE_DEBUG)) {
466:       if (im[i] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
467:       if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
468:     }
469:     row = im[i];
470:     if (row >= rstart_orig && row < rend_orig) {
471:       for (j=0; j<n; j++) {
472:         col = in[j];
473:         if (roworiented) value = v[i*n+j];
474:         else             value = v[i+j*m];
475:         /* Look up PetscInto the Hash Table */
476:         key = (row/bs)*Nbs+(col/bs)+1;
477:         h1  = HASH(size,key,tmp);


480:         idx = h1;
481:         if (PetscDefined(USE_DEBUG)) {
482:           insert_ct++;
483:           total_ct++;
484:           if (HT[idx] != key) {
485:             for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
486:             if (idx == size) {
487:               for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
488:               if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
489:             }
490:           }
491:         } else if (HT[idx] != key) {
492:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
493:           if (idx == size) {
494:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
495:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
496:           }
497:         }
498:         /* A HASH table entry is found, so insert the values at the correct address */
499:         if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
500:         else                    *(HD[idx]+ (col % bs)*bs + (row % bs))  = value;
501:       }
502:     } else if (!baij->donotstash) {
503:       if (roworiented) {
504:         MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
505:       } else {
506:         MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
507:       }
508:     }
509:   }
510:   if (PetscDefined(USE_DEBUG)) {
511:     baij->ht_total_ct  += total_ct;
512:     baij->ht_insert_ct += insert_ct;
513:   }
514:   return(0);
515: }

517: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
518: {
519:   Mat_MPIBAIJ       *baij       = (Mat_MPIBAIJ*)mat->data;
520:   PetscBool         roworiented = baij->roworiented;
521:   PetscErrorCode    ierr;
522:   PetscInt          i,j,ii,jj,row,col;
523:   PetscInt          rstart=baij->rstartbs;
524:   PetscInt          rend  =mat->rmap->rend,stepval,bs=mat->rmap->bs,bs2=baij->bs2,nbs2=n*bs2;
525:   PetscInt          h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
526:   PetscReal         tmp;
527:   MatScalar         **HD = baij->hd,*baij_a;
528:   const PetscScalar *v_t,*value;
529:   PetscInt          total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;

532:   if (roworiented) stepval = (n-1)*bs;
533:   else stepval = (m-1)*bs;

535:   for (i=0; i<m; i++) {
536:     if (PetscDefined(USE_DEBUG)) {
537:       if (im[i] < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
538:       if (im[i] >= baij->Mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],baij->Mbs-1);
539:     }
540:     row = im[i];
541:     v_t = v + i*nbs2;
542:     if (row >= rstart && row < rend) {
543:       for (j=0; j<n; j++) {
544:         col = in[j];

546:         /* Look up into the Hash Table */
547:         key = row*Nbs+col+1;
548:         h1  = HASH(size,key,tmp);

550:         idx = h1;
551:         if (PetscDefined(USE_DEBUG)) {
552:           total_ct++;
553:           insert_ct++;
554:           if (HT[idx] != key) {
555:             for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
556:             if (idx == size) {
557:               for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
558:               if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
559:             }
560:           }
561:         } else if (HT[idx] != key) {
562:           for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
563:           if (idx == size) {
564:             for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
565:             if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
566:           }
567:         }
568:         baij_a = HD[idx];
569:         if (roworiented) {
570:           /*value = v + i*(stepval+bs)*bs + j*bs;*/
571:           /* value = v + (i*(stepval+bs)+j)*bs; */
572:           value = v_t;
573:           v_t  += bs;
574:           if (addv == ADD_VALUES) {
575:             for (ii=0; ii<bs; ii++,value+=stepval) {
576:               for (jj=ii; jj<bs2; jj+=bs) {
577:                 baij_a[jj] += *value++;
578:               }
579:             }
580:           } else {
581:             for (ii=0; ii<bs; ii++,value+=stepval) {
582:               for (jj=ii; jj<bs2; jj+=bs) {
583:                 baij_a[jj] = *value++;
584:               }
585:             }
586:           }
587:         } else {
588:           value = v + j*(stepval+bs)*bs + i*bs;
589:           if (addv == ADD_VALUES) {
590:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
591:               for (jj=0; jj<bs; jj++) {
592:                 baij_a[jj] += *value++;
593:               }
594:             }
595:           } else {
596:             for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
597:               for (jj=0; jj<bs; jj++) {
598:                 baij_a[jj] = *value++;
599:               }
600:             }
601:           }
602:         }
603:       }
604:     } else {
605:       if (!baij->donotstash) {
606:         if (roworiented) {
607:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
608:         } else {
609:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
610:         }
611:       }
612:     }
613:   }
614:   if (PetscDefined(USE_DEBUG)) {
615:     baij->ht_total_ct  += total_ct;
616:     baij->ht_insert_ct += insert_ct;
617:   }
618:   return(0);
619: }

621: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
622: {
623:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
625:   PetscInt       bs       = mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
626:   PetscInt       bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;

629:   for (i=0; i<m; i++) {
630:     if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
631:     if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
632:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
633:       row = idxm[i] - bsrstart;
634:       for (j=0; j<n; j++) {
635:         if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
636:         if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
637:         if (idxn[j] >= bscstart && idxn[j] < bscend) {
638:           col  = idxn[j] - bscstart;
639:           MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
640:         } else {
641:           if (!baij->colmap) {
642:             MatCreateColmap_MPIBAIJ_Private(mat);
643:           }
644: #if defined(PETSC_USE_CTABLE)
645:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
646:           data--;
647: #else
648:           data = baij->colmap[idxn[j]/bs]-1;
649: #endif
650:           if ((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
651:           else {
652:             col  = data + idxn[j]%bs;
653:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
654:           }
655:         }
656:       }
657:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
658:   }
659:   return(0);
660: }

662: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
663: {
664:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
665:   Mat_SeqBAIJ    *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
667:   PetscInt       i,j,bs2=baij->bs2,bs=baij->A->rmap->bs,nz,row,col;
668:   PetscReal      sum = 0.0;
669:   MatScalar      *v;

672:   if (baij->size == 1) {
673:      MatNorm(baij->A,type,nrm);
674:   } else {
675:     if (type == NORM_FROBENIUS) {
676:       v  = amat->a;
677:       nz = amat->nz*bs2;
678:       for (i=0; i<nz; i++) {
679:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
680:       }
681:       v  = bmat->a;
682:       nz = bmat->nz*bs2;
683:       for (i=0; i<nz; i++) {
684:         sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
685:       }
686:       MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
687:       *nrm = PetscSqrtReal(*nrm);
688:     } else if (type == NORM_1) { /* max column sum */
689:       PetscReal *tmp,*tmp2;
690:       PetscInt  *jj,*garray=baij->garray,cstart=baij->rstartbs;
691:       PetscCalloc1(mat->cmap->N,&tmp);
692:       PetscMalloc1(mat->cmap->N,&tmp2);
693:       v    = amat->a; jj = amat->j;
694:       for (i=0; i<amat->nz; i++) {
695:         for (j=0; j<bs; j++) {
696:           col = bs*(cstart + *jj) + j; /* column index */
697:           for (row=0; row<bs; row++) {
698:             tmp[col] += PetscAbsScalar(*v);  v++;
699:           }
700:         }
701:         jj++;
702:       }
703:       v = bmat->a; jj = bmat->j;
704:       for (i=0; i<bmat->nz; i++) {
705:         for (j=0; j<bs; j++) {
706:           col = bs*garray[*jj] + j;
707:           for (row=0; row<bs; row++) {
708:             tmp[col] += PetscAbsScalar(*v); v++;
709:           }
710:         }
711:         jj++;
712:       }
713:       MPIU_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
714:       *nrm = 0.0;
715:       for (j=0; j<mat->cmap->N; j++) {
716:         if (tmp2[j] > *nrm) *nrm = tmp2[j];
717:       }
718:       PetscFree(tmp);
719:       PetscFree(tmp2);
720:     } else if (type == NORM_INFINITY) { /* max row sum */
721:       PetscReal *sums;
722:       PetscMalloc1(bs,&sums);
723:       sum  = 0.0;
724:       for (j=0; j<amat->mbs; j++) {
725:         for (row=0; row<bs; row++) sums[row] = 0.0;
726:         v  = amat->a + bs2*amat->i[j];
727:         nz = amat->i[j+1]-amat->i[j];
728:         for (i=0; i<nz; i++) {
729:           for (col=0; col<bs; col++) {
730:             for (row=0; row<bs; row++) {
731:               sums[row] += PetscAbsScalar(*v); v++;
732:             }
733:           }
734:         }
735:         v  = bmat->a + bs2*bmat->i[j];
736:         nz = bmat->i[j+1]-bmat->i[j];
737:         for (i=0; i<nz; i++) {
738:           for (col=0; col<bs; col++) {
739:             for (row=0; row<bs; row++) {
740:               sums[row] += PetscAbsScalar(*v); v++;
741:             }
742:           }
743:         }
744:         for (row=0; row<bs; row++) {
745:           if (sums[row] > sum) sum = sums[row];
746:         }
747:       }
748:       MPIU_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
749:       PetscFree(sums);
750:     } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for this norm yet");
751:   }
752:   return(0);
753: }

755: /*
756:   Creates the hash table, and sets the table
757:   This table is created only once.
758:   If new entried need to be added to the matrix
759:   then the hash table has to be destroyed and
760:   recreated.
761: */
762: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
763: {
764:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
765:   Mat            A     = baij->A,B=baij->B;
766:   Mat_SeqBAIJ    *a    = (Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)B->data;
767:   PetscInt       i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
769:   PetscInt       ht_size,bs2=baij->bs2,rstart=baij->rstartbs;
770:   PetscInt       cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
771:   PetscInt       *HT,key;
772:   MatScalar      **HD;
773:   PetscReal      tmp;
774: #if defined(PETSC_USE_INFO)
775:   PetscInt ct=0,max=0;
776: #endif

779:   if (baij->ht) return(0);

781:   baij->ht_size = (PetscInt)(factor*nz);
782:   ht_size       = baij->ht_size;

784:   /* Allocate Memory for Hash Table */
785:   PetscCalloc2(ht_size,&baij->hd,ht_size,&baij->ht);
786:   HD   = baij->hd;
787:   HT   = baij->ht;

789:   /* Loop Over A */
790:   for (i=0; i<a->mbs; i++) {
791:     for (j=ai[i]; j<ai[i+1]; j++) {
792:       row = i+rstart;
793:       col = aj[j]+cstart;

795:       key = row*Nbs + col + 1;
796:       h1  = HASH(ht_size,key,tmp);
797:       for (k=0; k<ht_size; k++) {
798:         if (!HT[(h1+k)%ht_size]) {
799:           HT[(h1+k)%ht_size] = key;
800:           HD[(h1+k)%ht_size] = a->a + j*bs2;
801:           break;
802: #if defined(PETSC_USE_INFO)
803:         } else {
804:           ct++;
805: #endif
806:         }
807:       }
808: #if defined(PETSC_USE_INFO)
809:       if (k> max) max = k;
810: #endif
811:     }
812:   }
813:   /* Loop Over B */
814:   for (i=0; i<b->mbs; i++) {
815:     for (j=bi[i]; j<bi[i+1]; j++) {
816:       row = i+rstart;
817:       col = garray[bj[j]];
818:       key = row*Nbs + col + 1;
819:       h1  = HASH(ht_size,key,tmp);
820:       for (k=0; k<ht_size; k++) {
821:         if (!HT[(h1+k)%ht_size]) {
822:           HT[(h1+k)%ht_size] = key;
823:           HD[(h1+k)%ht_size] = b->a + j*bs2;
824:           break;
825: #if defined(PETSC_USE_INFO)
826:         } else {
827:           ct++;
828: #endif
829:         }
830:       }
831: #if defined(PETSC_USE_INFO)
832:       if (k> max) max = k;
833: #endif
834:     }
835:   }

837:   /* Print Summary */
838: #if defined(PETSC_USE_INFO)
839:   for (i=0,j=0; i<ht_size; i++) {
840:     if (HT[i]) j++;
841:   }
842:   PetscInfo2(mat,"Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
843: #endif
844:   return(0);
845: }

847: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
848: {
849:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
851:   PetscInt       nstash,reallocs;

854:   if (baij->donotstash || mat->nooffprocentries) return(0);

856:   MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
857:   MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
858:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
859:   PetscInfo2(mat,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
860:   MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
861:   PetscInfo2(mat,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
862:   return(0);
863: }

865: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
866: {
867:   Mat_MPIBAIJ    *baij=(Mat_MPIBAIJ*)mat->data;
868:   Mat_SeqBAIJ    *a   =(Mat_SeqBAIJ*)baij->A->data;
870:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
871:   PetscInt       *row,*col;
872:   PetscBool      r1,r2,r3,other_disassembled;
873:   MatScalar      *val;
874:   PetscMPIInt    n;

877:   /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
878:   if (!baij->donotstash && !mat->nooffprocentries) {
879:     while (1) {
880:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
881:       if (!flg) break;

883:       for (i=0; i<n;) {
884:         /* Now identify the consecutive vals belonging to the same row */
885:         for (j=i,rstart=row[j]; j<n; j++) {
886:           if (row[j] != rstart) break;
887:         }
888:         if (j < n) ncols = j-i;
889:         else       ncols = n-i;
890:         /* Now assemble all these values with a single function call */
891:         MatSetValues_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i,mat->insertmode);
892:         i    = j;
893:       }
894:     }
895:     MatStashScatterEnd_Private(&mat->stash);
896:     /* Now process the block-stash. Since the values are stashed column-oriented,
897:        set the roworiented flag to column oriented, and after MatSetValues()
898:        restore the original flags */
899:     r1 = baij->roworiented;
900:     r2 = a->roworiented;
901:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;

903:     baij->roworiented = PETSC_FALSE;
904:     a->roworiented    = PETSC_FALSE;

906:     (((Mat_SeqBAIJ*)baij->B->data))->roworiented = PETSC_FALSE; /* b->roworiented */
907:     while (1) {
908:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
909:       if (!flg) break;

911:       for (i=0; i<n;) {
912:         /* Now identify the consecutive vals belonging to the same row */
913:         for (j=i,rstart=row[j]; j<n; j++) {
914:           if (row[j] != rstart) break;
915:         }
916:         if (j < n) ncols = j-i;
917:         else       ncols = n-i;
918:         MatSetValuesBlocked_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,mat->insertmode);
919:         i    = j;
920:       }
921:     }
922:     MatStashScatterEnd_Private(&mat->bstash);

924:     baij->roworiented = r1;
925:     a->roworiented    = r2;

927:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented = r3; /* b->roworiented */
928:   }

930:   MatAssemblyBegin(baij->A,mode);
931:   MatAssemblyEnd(baij->A,mode);

933:   /* determine if any processor has disassembled, if so we must
934:      also disassemble ourselfs, in order that we may reassemble. */
935:   /*
936:      if nonzero structure of submatrix B cannot change then we know that
937:      no processor disassembled thus we can skip this stuff
938:   */
939:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
940:     MPIU_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
941:     if (mat->was_assembled && !other_disassembled) {
942:       MatDisAssemble_MPIBAIJ(mat);
943:     }
944:   }

946:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
947:     MatSetUpMultiply_MPIBAIJ(mat);
948:   }
949:   MatAssemblyBegin(baij->B,mode);
950:   MatAssemblyEnd(baij->B,mode);

952: #if defined(PETSC_USE_INFO)
953:   if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
954:     PetscInfo1(mat,"Average Hash Table Search in MatSetValues = %5.2f\n",(double)((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);

956:     baij->ht_total_ct  = 0;
957:     baij->ht_insert_ct = 0;
958:   }
959: #endif
960:   if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
961:     MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);

963:     mat->ops->setvalues        = MatSetValues_MPIBAIJ_HT;
964:     mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
965:   }

967:   PetscFree2(baij->rowvalues,baij->rowindices);

969:   baij->rowvalues = NULL;

971:   /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
972:   if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
973:     PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
974:     MPIU_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
975:   }
976:   return(0);
977: }

979: extern PetscErrorCode MatView_SeqBAIJ(Mat,PetscViewer);
980: #include <petscdraw.h>
981: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
982: {
983:   Mat_MPIBAIJ       *baij = (Mat_MPIBAIJ*)mat->data;
984:   PetscErrorCode    ierr;
985:   PetscMPIInt       rank = baij->rank;
986:   PetscInt          bs   = mat->rmap->bs;
987:   PetscBool         iascii,isdraw;
988:   PetscViewer       sviewer;
989:   PetscViewerFormat format;

992:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
993:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
994:   if (iascii) {
995:     PetscViewerGetFormat(viewer,&format);
996:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
997:       MatInfo info;
998:       MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
999:       MatGetInfo(mat,MAT_LOCAL,&info);
1000:       PetscViewerASCIIPushSynchronized(viewer);
1001:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %g\n",
1002:                                                 rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,mat->rmap->bs,(double)info.memory);
1003:       MatGetInfo(baij->A,MAT_LOCAL,&info);
1004:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1005:       MatGetInfo(baij->B,MAT_LOCAL,&info);
1006:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1007:       PetscViewerFlush(viewer);
1008:       PetscViewerASCIIPopSynchronized(viewer);
1009:       PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1010:       VecScatterView(baij->Mvctx,viewer);
1011:       return(0);
1012:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
1013:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1014:       return(0);
1015:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1016:       return(0);
1017:     }
1018:   }

1020:   if (isdraw) {
1021:     PetscDraw draw;
1022:     PetscBool isnull;
1023:     PetscViewerDrawGetDraw(viewer,0,&draw);
1024:     PetscDrawIsNull(draw,&isnull);
1025:     if (isnull) return(0);
1026:   }

1028:   {
1029:     /* assemble the entire matrix onto first processor. */
1030:     Mat         A;
1031:     Mat_SeqBAIJ *Aloc;
1032:     PetscInt    M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
1033:     MatScalar   *a;
1034:     const char  *matname;

1036:     /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
1037:     /* Perhaps this should be the type of mat? */
1038:     MatCreate(PetscObjectComm((PetscObject)mat),&A);
1039:     if (!rank) {
1040:       MatSetSizes(A,M,N,M,N);
1041:     } else {
1042:       MatSetSizes(A,0,0,M,N);
1043:     }
1044:     MatSetType(A,MATMPIBAIJ);
1045:     MatMPIBAIJSetPreallocation(A,mat->rmap->bs,0,NULL,0,NULL);
1046:     MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
1047:     PetscLogObjectParent((PetscObject)mat,(PetscObject)A);

1049:     /* copy over the A part */
1050:     Aloc = (Mat_SeqBAIJ*)baij->A->data;
1051:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1052:     PetscMalloc1(bs,&rvals);

1054:     for (i=0; i<mbs; i++) {
1055:       rvals[0] = bs*(baij->rstartbs + i);
1056:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1057:       for (j=ai[i]; j<ai[i+1]; j++) {
1058:         col = (baij->cstartbs+aj[j])*bs;
1059:         for (k=0; k<bs; k++) {
1060:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
1061:           col++; a += bs;
1062:         }
1063:       }
1064:     }
1065:     /* copy over the B part */
1066:     Aloc = (Mat_SeqBAIJ*)baij->B->data;
1067:     ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1068:     for (i=0; i<mbs; i++) {
1069:       rvals[0] = bs*(baij->rstartbs + i);
1070:       for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1071:       for (j=ai[i]; j<ai[i+1]; j++) {
1072:         col = baij->garray[aj[j]]*bs;
1073:         for (k=0; k<bs; k++) {
1074:           MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
1075:           col++; a += bs;
1076:         }
1077:       }
1078:     }
1079:     PetscFree(rvals);
1080:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1081:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1082:     /*
1083:        Everyone has to call to draw the matrix since the graphics waits are
1084:        synchronized across all processors that share the PetscDraw object
1085:     */
1086:     PetscViewerGetSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
1087:     PetscObjectGetName((PetscObject)mat,&matname);
1088:     if (!rank) {
1089:       PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,matname);
1090:       MatView_SeqBAIJ(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1091:     }
1092:     PetscViewerRestoreSubViewer(viewer,PETSC_COMM_SELF,&sviewer);
1093:     PetscViewerFlush(viewer);
1094:     MatDestroy(&A);
1095:   }
1096:   return(0);
1097: }

1099: /* Used for both MPIBAIJ and MPISBAIJ matrices */
1100: PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
1101: {
1102:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)mat->data;
1103:   Mat_SeqBAIJ    *A   = (Mat_SeqBAIJ*)aij->A->data;
1104:   Mat_SeqBAIJ    *B   = (Mat_SeqBAIJ*)aij->B->data;
1105:   const PetscInt *garray = aij->garray;
1106:   PetscInt       header[4],M,N,m,rs,cs,bs,nz,cnt,i,j,ja,jb,k,l;
1107:   PetscInt       *rowlens,*colidxs;
1108:   PetscScalar    *matvals;

1112:   PetscViewerSetUp(viewer);

1114:   M  = mat->rmap->N;
1115:   N  = mat->cmap->N;
1116:   m  = mat->rmap->n;
1117:   rs = mat->rmap->rstart;
1118:   cs = mat->cmap->rstart;
1119:   bs = mat->rmap->bs;
1120:   nz = bs*bs*(A->nz + B->nz);

1122:   /* write matrix header */
1123:   header[0] = MAT_FILE_CLASSID;
1124:   header[1] = M; header[2] = N; header[3] = nz;
1125:   MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1126:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

1128:   /* fill in and store row lengths */
1129:   PetscMalloc1(m,&rowlens);
1130:   for (cnt=0, i=0; i<A->mbs; i++)
1131:     for (j=0; j<bs; j++)
1132:       rowlens[cnt++] = bs*(A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i]);
1133:   PetscViewerBinaryWriteAll(viewer,rowlens,m,rs,M,PETSC_INT);
1134:   PetscFree(rowlens);

1136:   /* fill in and store column indices */
1137:   PetscMalloc1(nz,&colidxs);
1138:   for (cnt=0, i=0; i<A->mbs; i++) {
1139:     for (k=0; k<bs; k++) {
1140:       for (jb=B->i[i]; jb<B->i[i+1]; jb++) {
1141:         if (garray[B->j[jb]] > cs/bs) break;
1142:         for (l=0; l<bs; l++)
1143:           colidxs[cnt++] = bs*garray[B->j[jb]] + l;
1144:       }
1145:       for (ja=A->i[i]; ja<A->i[i+1]; ja++)
1146:         for (l=0; l<bs; l++)
1147:           colidxs[cnt++] = bs*A->j[ja] + l + cs;
1148:       for (; jb<B->i[i+1]; jb++)
1149:         for (l=0; l<bs; l++)
1150:           colidxs[cnt++] = bs*garray[B->j[jb]] + l;
1151:     }
1152:   }
1153:   if (cnt != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,nz);
1154:   PetscViewerBinaryWriteAll(viewer,colidxs,nz,PETSC_DECIDE,PETSC_DECIDE,PETSC_INT);
1155:   PetscFree(colidxs);

1157:   /* fill in and store nonzero values */
1158:   PetscMalloc1(nz,&matvals);
1159:   for (cnt=0, i=0; i<A->mbs; i++) {
1160:     for (k=0; k<bs; k++) {
1161:       for (jb=B->i[i]; jb<B->i[i+1]; jb++) {
1162:         if (garray[B->j[jb]] > cs/bs) break;
1163:         for (l=0; l<bs; l++)
1164:           matvals[cnt++] = B->a[bs*(bs*jb + l) + k];
1165:       }
1166:       for (ja=A->i[i]; ja<A->i[i+1]; ja++)
1167:         for (l=0; l<bs; l++)
1168:           matvals[cnt++] = A->a[bs*(bs*ja + l) + k];
1169:       for (; jb<B->i[i+1]; jb++)
1170:         for (l=0; l<bs; l++)
1171:           matvals[cnt++] = B->a[bs*(bs*jb + l) + k];
1172:     }
1173:   }
1174:   PetscViewerBinaryWriteAll(viewer,matvals,nz,PETSC_DECIDE,PETSC_DECIDE,PETSC_SCALAR);
1175:   PetscFree(matvals);

1177:   /* write block size option to the viewer's .info file */
1178:   MatView_Binary_BlockSizes(mat,viewer);
1179:   return(0);
1180: }

1182: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1183: {
1185:   PetscBool      iascii,isdraw,issocket,isbinary;

1188:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1189:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1190:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
1191:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1192:   if (iascii || isdraw || issocket) {
1193:     MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1194:   } else if (isbinary) {
1195:     MatView_MPIBAIJ_Binary(mat,viewer);
1196:   }
1197:   return(0);
1198: }

1200: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1201: {
1202:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;

1206: #if defined(PETSC_USE_LOG)
1207:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap->N,mat->cmap->N);
1208: #endif
1209:   MatStashDestroy_Private(&mat->stash);
1210:   MatStashDestroy_Private(&mat->bstash);
1211:   MatDestroy(&baij->A);
1212:   MatDestroy(&baij->B);
1213: #if defined(PETSC_USE_CTABLE)
1214:   PetscTableDestroy(&baij->colmap);
1215: #else
1216:   PetscFree(baij->colmap);
1217: #endif
1218:   PetscFree(baij->garray);
1219:   VecDestroy(&baij->lvec);
1220:   VecScatterDestroy(&baij->Mvctx);
1221:   PetscFree2(baij->rowvalues,baij->rowindices);
1222:   PetscFree(baij->barray);
1223:   PetscFree2(baij->hd,baij->ht);
1224:   PetscFree(baij->rangebs);
1225:   PetscFree(mat->data);

1227:   PetscObjectChangeTypeName((PetscObject)mat,NULL);
1228:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
1229:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
1230:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C",NULL);
1231:   PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C",NULL);
1232:   PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
1233:   PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C",NULL);
1234:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpisbaij_C",NULL);
1235:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpibstrm_C",NULL);
1236: #if defined(PETSC_HAVE_HYPRE)
1237:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_hypre_C",NULL);
1238: #endif
1239:   PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_is_C",NULL);
1240:   return(0);
1241: }

1243: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1244: {
1245:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1247:   PetscInt       nt;

1250:   VecGetLocalSize(xx,&nt);
1251:   if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1252:   VecGetLocalSize(yy,&nt);
1253:   if (nt != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1254:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1255:   (*a->A->ops->mult)(a->A,xx,yy);
1256:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1257:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1258:   return(0);
1259: }

1261: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1262: {
1263:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1267:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1268:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
1269:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1270:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1271:   return(0);
1272: }

1274: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1275: {
1276:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1280:   /* do nondiagonal part */
1281:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1282:   /* do local part */
1283:   (*a->A->ops->multtranspose)(a->A,xx,yy);
1284:   /* add partial results together */
1285:   VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1286:   VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1287:   return(0);
1288: }

1290: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1291: {
1292:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1296:   /* do nondiagonal part */
1297:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1298:   /* do local part */
1299:   (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1300:   /* add partial results together */
1301:   VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1302:   VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1303:   return(0);
1304: }

1306: /*
1307:   This only works correctly for square matrices where the subblock A->A is the
1308:    diagonal block
1309: */
1310: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1311: {
1312:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1316:   if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1317:   MatGetDiagonal(a->A,v);
1318:   return(0);
1319: }

1321: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
1322: {
1323:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1327:   MatScale(a->A,aa);
1328:   MatScale(a->B,aa);
1329:   return(0);
1330: }

1332: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1333: {
1334:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
1335:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1337:   PetscInt       bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1338:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
1339:   PetscInt       *cmap,*idx_p,cstart = mat->cstartbs;

1342:   if (row < brstart || row >= brend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local rows");
1343:   if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
1344:   mat->getrowactive = PETSC_TRUE;

1346:   if (!mat->rowvalues && (idx || v)) {
1347:     /*
1348:         allocate enough space to hold information from the longest row.
1349:     */
1350:     Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1351:     PetscInt    max = 1,mbs = mat->mbs,tmp;
1352:     for (i=0; i<mbs; i++) {
1353:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1354:       if (max < tmp) max = tmp;
1355:     }
1356:     PetscMalloc2(max*bs2,&mat->rowvalues,max*bs2,&mat->rowindices);
1357:   }
1358:   lrow = row - brstart;

1360:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1361:   if (!v)   {pvA = NULL; pvB = NULL;}
1362:   if (!idx) {pcA = NULL; if (!v) pcB = NULL;}
1363:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1364:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1365:   nztot = nzA + nzB;

1367:   cmap = mat->garray;
1368:   if (v  || idx) {
1369:     if (nztot) {
1370:       /* Sort by increasing column numbers, assuming A and B already sorted */
1371:       PetscInt imark = -1;
1372:       if (v) {
1373:         *v = v_p = mat->rowvalues;
1374:         for (i=0; i<nzB; i++) {
1375:           if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
1376:           else break;
1377:         }
1378:         imark = i;
1379:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1380:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1381:       }
1382:       if (idx) {
1383:         *idx = idx_p = mat->rowindices;
1384:         if (imark > -1) {
1385:           for (i=0; i<imark; i++) {
1386:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1387:           }
1388:         } else {
1389:           for (i=0; i<nzB; i++) {
1390:             if (cmap[cworkB[i]/bs] < cstart) idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1391:             else break;
1392:           }
1393:           imark = i;
1394:         }
1395:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1396:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1397:       }
1398:     } else {
1399:       if (idx) *idx = NULL;
1400:       if (v)   *v   = NULL;
1401:     }
1402:   }
1403:   *nz  = nztot;
1404:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1405:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1406:   return(0);
1407: }

1409: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1410: {
1411:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;

1414:   if (!baij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1415:   baij->getrowactive = PETSC_FALSE;
1416:   return(0);
1417: }

1419: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1420: {
1421:   Mat_MPIBAIJ    *l = (Mat_MPIBAIJ*)A->data;

1425:   MatZeroEntries(l->A);
1426:   MatZeroEntries(l->B);
1427:   return(0);
1428: }

1430: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1431: {
1432:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)matin->data;
1433:   Mat            A  = a->A,B = a->B;
1435:   PetscLogDouble isend[5],irecv[5];

1438:   info->block_size = (PetscReal)matin->rmap->bs;

1440:   MatGetInfo(A,MAT_LOCAL,info);

1442:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1443:   isend[3] = info->memory;  isend[4] = info->mallocs;

1445:   MatGetInfo(B,MAT_LOCAL,info);

1447:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1448:   isend[3] += info->memory;  isend[4] += info->mallocs;

1450:   if (flag == MAT_LOCAL) {
1451:     info->nz_used      = isend[0];
1452:     info->nz_allocated = isend[1];
1453:     info->nz_unneeded  = isend[2];
1454:     info->memory       = isend[3];
1455:     info->mallocs      = isend[4];
1456:   } else if (flag == MAT_GLOBAL_MAX) {
1457:     MPIU_Allreduce(isend,irecv,5,MPIU_PETSCLOGDOUBLE,MPI_MAX,PetscObjectComm((PetscObject)matin));

1459:     info->nz_used      = irecv[0];
1460:     info->nz_allocated = irecv[1];
1461:     info->nz_unneeded  = irecv[2];
1462:     info->memory       = irecv[3];
1463:     info->mallocs      = irecv[4];
1464:   } else if (flag == MAT_GLOBAL_SUM) {
1465:     MPIU_Allreduce(isend,irecv,5,MPIU_PETSCLOGDOUBLE,MPI_SUM,PetscObjectComm((PetscObject)matin));

1467:     info->nz_used      = irecv[0];
1468:     info->nz_allocated = irecv[1];
1469:     info->nz_unneeded  = irecv[2];
1470:     info->memory       = irecv[3];
1471:     info->mallocs      = irecv[4];
1472:   } else SETERRQ1(PetscObjectComm((PetscObject)matin),PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1473:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1474:   info->fill_ratio_needed = 0;
1475:   info->factor_mallocs    = 0;
1476:   return(0);
1477: }

1479: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op,PetscBool flg)
1480: {
1481:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1485:   switch (op) {
1486:   case MAT_NEW_NONZERO_LOCATIONS:
1487:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1488:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1489:   case MAT_KEEP_NONZERO_PATTERN:
1490:   case MAT_NEW_NONZERO_LOCATION_ERR:
1491:     MatCheckPreallocated(A,1);
1492:     MatSetOption(a->A,op,flg);
1493:     MatSetOption(a->B,op,flg);
1494:     break;
1495:   case MAT_ROW_ORIENTED:
1496:     MatCheckPreallocated(A,1);
1497:     a->roworiented = flg;

1499:     MatSetOption(a->A,op,flg);
1500:     MatSetOption(a->B,op,flg);
1501:     break;
1502:   case MAT_FORCE_DIAGONAL_ENTRIES:
1503:   case MAT_SORTED_FULL:
1504:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1505:     break;
1506:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1507:     a->donotstash = flg;
1508:     break;
1509:   case MAT_USE_HASH_TABLE:
1510:     a->ht_flag = flg;
1511:     a->ht_fact = 1.39;
1512:     break;
1513:   case MAT_SYMMETRIC:
1514:   case MAT_STRUCTURALLY_SYMMETRIC:
1515:   case MAT_HERMITIAN:
1516:   case MAT_SUBMAT_SINGLEIS:
1517:   case MAT_SYMMETRY_ETERNAL:
1518:     MatCheckPreallocated(A,1);
1519:     MatSetOption(a->A,op,flg);
1520:     break;
1521:   default:
1522:     SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"unknown option %d",op);
1523:   }
1524:   return(0);
1525: }

1527: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,MatReuse reuse,Mat *matout)
1528: {
1529:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)A->data;
1530:   Mat_SeqBAIJ    *Aloc;
1531:   Mat            B;
1533:   PetscInt       M =A->rmap->N,N=A->cmap->N,*ai,*aj,i,*rvals,j,k,col;
1534:   PetscInt       bs=A->rmap->bs,mbs=baij->mbs;
1535:   MatScalar      *a;

1538:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1539:     MatCreate(PetscObjectComm((PetscObject)A),&B);
1540:     MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
1541:     MatSetType(B,((PetscObject)A)->type_name);
1542:     /* Do not know preallocation information, but must set block size */
1543:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,PETSC_DECIDE,NULL,PETSC_DECIDE,NULL);
1544:   } else {
1545:     B = *matout;
1546:   }

1548:   /* copy over the A part */
1549:   Aloc = (Mat_SeqBAIJ*)baij->A->data;
1550:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1551:   PetscMalloc1(bs,&rvals);

1553:   for (i=0; i<mbs; i++) {
1554:     rvals[0] = bs*(baij->rstartbs + i);
1555:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1556:     for (j=ai[i]; j<ai[i+1]; j++) {
1557:       col = (baij->cstartbs+aj[j])*bs;
1558:       for (k=0; k<bs; k++) {
1559:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);

1561:         col++; a += bs;
1562:       }
1563:     }
1564:   }
1565:   /* copy over the B part */
1566:   Aloc = (Mat_SeqBAIJ*)baij->B->data;
1567:   ai   = Aloc->i; aj = Aloc->j; a = Aloc->a;
1568:   for (i=0; i<mbs; i++) {
1569:     rvals[0] = bs*(baij->rstartbs + i);
1570:     for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1571:     for (j=ai[i]; j<ai[i+1]; j++) {
1572:       col = baij->garray[aj[j]]*bs;
1573:       for (k=0; k<bs; k++) {
1574:         MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);
1575:         col++;
1576:         a += bs;
1577:       }
1578:     }
1579:   }
1580:   PetscFree(rvals);
1581:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1582:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

1584:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) *matout = B;
1585:   else {
1586:     MatHeaderMerge(A,&B);
1587:   }
1588:   return(0);
1589: }

1591: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1592: {
1593:   Mat_MPIBAIJ    *baij = (Mat_MPIBAIJ*)mat->data;
1594:   Mat            a     = baij->A,b = baij->B;
1596:   PetscInt       s1,s2,s3;

1599:   MatGetLocalSize(mat,&s2,&s3);
1600:   if (rr) {
1601:     VecGetLocalSize(rr,&s1);
1602:     if (s1!=s3) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1603:     /* Overlap communication with computation. */
1604:     VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1605:   }
1606:   if (ll) {
1607:     VecGetLocalSize(ll,&s1);
1608:     if (s1!=s2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1609:     (*b->ops->diagonalscale)(b,ll,NULL);
1610:   }
1611:   /* scale  the diagonal block */
1612:   (*a->ops->diagonalscale)(a,ll,rr);

1614:   if (rr) {
1615:     /* Do a scatter end and then right scale the off-diagonal block */
1616:     VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1617:     (*b->ops->diagonalscale)(b,NULL,baij->lvec);
1618:   }
1619:   return(0);
1620: }

1622: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1623: {
1624:   Mat_MPIBAIJ   *l      = (Mat_MPIBAIJ *) A->data;
1625:   PetscInt      *lrows;
1626:   PetscInt       r, len;
1627:   PetscBool      cong;

1631:   /* get locally owned rows */
1632:   MatZeroRowsMapLocal_Private(A,N,rows,&len,&lrows);
1633:   /* fix right hand side if needed */
1634:   if (x && b) {
1635:     const PetscScalar *xx;
1636:     PetscScalar       *bb;

1638:     VecGetArrayRead(x,&xx);
1639:     VecGetArray(b,&bb);
1640:     for (r = 0; r < len; ++r) bb[lrows[r]] = diag*xx[lrows[r]];
1641:     VecRestoreArrayRead(x,&xx);
1642:     VecRestoreArray(b,&bb);
1643:   }

1645:   /* actually zap the local rows */
1646:   /*
1647:         Zero the required rows. If the "diagonal block" of the matrix
1648:      is square and the user wishes to set the diagonal we use separate
1649:      code so that MatSetValues() is not called for each diagonal allocating
1650:      new memory, thus calling lots of mallocs and slowing things down.

1652:   */
1653:   /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1654:   MatZeroRows_SeqBAIJ(l->B,len,lrows,0.0,NULL,NULL);
1655:   MatHasCongruentLayouts(A,&cong);
1656:   if ((diag != 0.0) && cong) {
1657:     MatZeroRows_SeqBAIJ(l->A,len,lrows,diag,NULL,NULL);
1658:   } else if (diag != 0.0) {
1659:     MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,NULL,NULL);
1660:     if (((Mat_SeqBAIJ*)l->A->data)->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1661:        MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1662:     for (r = 0; r < len; ++r) {
1663:       const PetscInt row = lrows[r] + A->rmap->rstart;
1664:       MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1665:     }
1666:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1667:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1668:   } else {
1669:     MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,NULL,NULL);
1670:   }
1671:   PetscFree(lrows);

1673:   /* only change matrix nonzero state if pattern was allowed to be changed */
1674:   if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
1675:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1676:     MPIU_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
1677:   }
1678:   return(0);
1679: }

1681: PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1682: {
1683:   Mat_MPIBAIJ       *l = (Mat_MPIBAIJ*)A->data;
1684:   PetscErrorCode    ierr;
1685:   PetscMPIInt       n = A->rmap->n,p = 0;
1686:   PetscInt          i,j,k,r,len = 0,row,col,count;
1687:   PetscInt          *lrows,*owners = A->rmap->range;
1688:   PetscSFNode       *rrows;
1689:   PetscSF           sf;
1690:   const PetscScalar *xx;
1691:   PetscScalar       *bb,*mask;
1692:   Vec               xmask,lmask;
1693:   Mat_SeqBAIJ       *baij = (Mat_SeqBAIJ*)l->B->data;
1694:   PetscInt           bs = A->rmap->bs, bs2 = baij->bs2;
1695:   PetscScalar       *aa;

1698:   /* Create SF where leaves are input rows and roots are owned rows */
1699:   PetscMalloc1(n, &lrows);
1700:   for (r = 0; r < n; ++r) lrows[r] = -1;
1701:   PetscMalloc1(N, &rrows);
1702:   for (r = 0; r < N; ++r) {
1703:     const PetscInt idx   = rows[r];
1704:     if (idx < 0 || A->rmap->N <= idx) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range [0,%D)",idx,A->rmap->N);
1705:     if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
1706:       PetscLayoutFindOwner(A->rmap,idx,&p);
1707:     }
1708:     rrows[r].rank  = p;
1709:     rrows[r].index = rows[r] - owners[p];
1710:   }
1711:   PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
1712:   PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
1713:   /* Collect flags for rows to be zeroed */
1714:   PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1715:   PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1716:   PetscSFDestroy(&sf);
1717:   /* Compress and put in row numbers */
1718:   for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
1719:   /* zero diagonal part of matrix */
1720:   MatZeroRowsColumns(l->A,len,lrows,diag,x,b);
1721:   /* handle off diagonal part of matrix */
1722:   MatCreateVecs(A,&xmask,NULL);
1723:   VecDuplicate(l->lvec,&lmask);
1724:   VecGetArray(xmask,&bb);
1725:   for (i=0; i<len; i++) bb[lrows[i]] = 1;
1726:   VecRestoreArray(xmask,&bb);
1727:   VecScatterBegin(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
1728:   VecScatterEnd(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
1729:   VecDestroy(&xmask);
1730:   if (x) {
1731:     VecScatterBegin(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
1732:     VecScatterEnd(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
1733:     VecGetArrayRead(l->lvec,&xx);
1734:     VecGetArray(b,&bb);
1735:   }
1736:   VecGetArray(lmask,&mask);
1737:   /* remove zeroed rows of off diagonal matrix */
1738:   for (i = 0; i < len; ++i) {
1739:     row   = lrows[i];
1740:     count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1741:     aa    = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
1742:     for (k = 0; k < count; ++k) {
1743:       aa[0] = 0.0;
1744:       aa   += bs;
1745:     }
1746:   }
1747:   /* loop over all elements of off process part of matrix zeroing removed columns*/
1748:   for (i = 0; i < l->B->rmap->N; ++i) {
1749:     row = i/bs;
1750:     for (j = baij->i[row]; j < baij->i[row+1]; ++j) {
1751:       for (k = 0; k < bs; ++k) {
1752:         col = bs*baij->j[j] + k;
1753:         if (PetscAbsScalar(mask[col])) {
1754:           aa = ((MatScalar*)(baij->a)) + j*bs2 + (i%bs) + bs*k;
1755:           if (x) bb[i] -= aa[0]*xx[col];
1756:           aa[0] = 0.0;
1757:         }
1758:       }
1759:     }
1760:   }
1761:   if (x) {
1762:     VecRestoreArray(b,&bb);
1763:     VecRestoreArrayRead(l->lvec,&xx);
1764:   }
1765:   VecRestoreArray(lmask,&mask);
1766:   VecDestroy(&lmask);
1767:   PetscFree(lrows);

1769:   /* only change matrix nonzero state if pattern was allowed to be changed */
1770:   if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
1771:     PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1772:     MPIU_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
1773:   }
1774:   return(0);
1775: }

1777: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1778: {
1779:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1783:   MatSetUnfactored(a->A);
1784:   return(0);
1785: }

1787: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat*);

1789: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscBool  *flag)
1790: {
1791:   Mat_MPIBAIJ    *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1792:   Mat            a,b,c,d;
1793:   PetscBool      flg;

1797:   a = matA->A; b = matA->B;
1798:   c = matB->A; d = matB->B;

1800:   MatEqual(a,c,&flg);
1801:   if (flg) {
1802:     MatEqual(b,d,&flg);
1803:   }
1804:   MPIU_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1805:   return(0);
1806: }

1808: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1809: {
1811:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
1812:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;

1815:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1816:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1817:     MatCopy_Basic(A,B,str);
1818:   } else {
1819:     MatCopy(a->A,b->A,str);
1820:     MatCopy(a->B,b->B,str);
1821:   }
1822:   PetscObjectStateIncrease((PetscObject)B);
1823:   return(0);
1824: }

1826: PetscErrorCode MatSetUp_MPIBAIJ(Mat A)
1827: {

1831:   MatMPIBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,NULL,PETSC_DEFAULT,NULL);
1832:   return(0);
1833: }

1835: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y,const PetscInt *yltog,Mat X,const PetscInt *xltog,PetscInt *nnz)
1836: {
1838:   PetscInt       bs = Y->rmap->bs,m = Y->rmap->N/bs;
1839:   Mat_SeqBAIJ    *x = (Mat_SeqBAIJ*)X->data;
1840:   Mat_SeqBAIJ    *y = (Mat_SeqBAIJ*)Y->data;

1843:   MatAXPYGetPreallocation_MPIX_private(m,x->i,x->j,xltog,y->i,y->j,yltog,nnz);
1844:   return(0);
1845: }

1847: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1848: {
1850:   Mat_MPIBAIJ    *xx=(Mat_MPIBAIJ*)X->data,*yy=(Mat_MPIBAIJ*)Y->data;
1851:   PetscBLASInt   bnz,one=1;
1852:   Mat_SeqBAIJ    *x,*y;
1853:   PetscInt       bs2 = Y->rmap->bs*Y->rmap->bs;

1856:   if (str == SAME_NONZERO_PATTERN) {
1857:     PetscScalar alpha = a;
1858:     x    = (Mat_SeqBAIJ*)xx->A->data;
1859:     y    = (Mat_SeqBAIJ*)yy->A->data;
1860:     PetscBLASIntCast(x->nz*bs2,&bnz);
1861:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
1862:     x    = (Mat_SeqBAIJ*)xx->B->data;
1863:     y    = (Mat_SeqBAIJ*)yy->B->data;
1864:     PetscBLASIntCast(x->nz*bs2,&bnz);
1865:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
1866:     PetscObjectStateIncrease((PetscObject)Y);
1867:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1868:     MatAXPY_Basic(Y,a,X,str);
1869:   } else {
1870:     Mat      B;
1871:     PetscInt *nnz_d,*nnz_o,bs=Y->rmap->bs;
1872:     PetscMalloc1(yy->A->rmap->N,&nnz_d);
1873:     PetscMalloc1(yy->B->rmap->N,&nnz_o);
1874:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
1875:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
1876:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
1877:     MatSetBlockSizesFromMats(B,Y,Y);
1878:     MatSetType(B,MATMPIBAIJ);
1879:     MatAXPYGetPreallocation_SeqBAIJ(yy->A,xx->A,nnz_d);
1880:     MatAXPYGetPreallocation_MPIBAIJ(yy->B,yy->garray,xx->B,xx->garray,nnz_o);
1881:     MatMPIBAIJSetPreallocation(B,bs,0,nnz_d,0,nnz_o);
1882:     /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
1883:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
1884:     MatHeaderReplace(Y,&B);
1885:     PetscFree(nnz_d);
1886:     PetscFree(nnz_o);
1887:   }
1888:   return(0);
1889: }

1891: PetscErrorCode MatConjugate_MPIBAIJ(Mat mat)
1892: {
1893: #if defined(PETSC_USE_COMPLEX)
1895:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)mat->data;

1898:   MatConjugate_SeqBAIJ(a->A);
1899:   MatConjugate_SeqBAIJ(a->B);
1900: #else
1902: #endif
1903:   return(0);
1904: }

1906: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
1907: {
1908:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1912:   MatRealPart(a->A);
1913:   MatRealPart(a->B);
1914:   return(0);
1915: }

1917: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
1918: {
1919:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

1923:   MatImaginaryPart(a->A);
1924:   MatImaginaryPart(a->B);
1925:   return(0);
1926: }

1928: PetscErrorCode MatCreateSubMatrix_MPIBAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
1929: {
1931:   IS             iscol_local;
1932:   PetscInt       csize;

1935:   ISGetLocalSize(iscol,&csize);
1936:   if (call == MAT_REUSE_MATRIX) {
1937:     PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
1938:     if (!iscol_local) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
1939:   } else {
1940:     ISAllGather(iscol,&iscol_local);
1941:   }
1942:   MatCreateSubMatrix_MPIBAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
1943:   if (call == MAT_INITIAL_MATRIX) {
1944:     PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
1945:     ISDestroy(&iscol_local);
1946:   }
1947:   return(0);
1948: }

1950: /*
1951:   Not great since it makes two copies of the submatrix, first an SeqBAIJ
1952:   in local and then by concatenating the local matrices the end result.
1953:   Writing it directly would be much like MatCreateSubMatrices_MPIBAIJ().
1954:   This routine is used for BAIJ and SBAIJ matrices (unfortunate dependency).
1955: */
1956: PetscErrorCode MatCreateSubMatrix_MPIBAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
1957: {
1959:   PetscMPIInt    rank,size;
1960:   PetscInt       i,m,n,rstart,row,rend,nz,*cwork,j,bs;
1961:   PetscInt       *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
1962:   Mat            M,Mreuse;
1963:   MatScalar      *vwork,*aa;
1964:   MPI_Comm       comm;
1965:   IS             isrow_new, iscol_new;
1966:   Mat_SeqBAIJ    *aij;

1969:   PetscObjectGetComm((PetscObject)mat,&comm);
1970:   MPI_Comm_rank(comm,&rank);
1971:   MPI_Comm_size(comm,&size);
1972:   /* The compression and expansion should be avoided. Doesn't point
1973:      out errors, might change the indices, hence buggey */
1974:   ISCompressIndicesGeneral(mat->rmap->N,mat->rmap->n,mat->rmap->bs,1,&isrow,&isrow_new);
1975:   ISCompressIndicesGeneral(mat->cmap->N,mat->cmap->n,mat->cmap->bs,1,&iscol,&iscol_new);

1977:   if (call ==  MAT_REUSE_MATRIX) {
1978:     PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
1979:     if (!Mreuse) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
1980:     MatCreateSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_REUSE_MATRIX,&Mreuse);
1981:   } else {
1982:     MatCreateSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_INITIAL_MATRIX,&Mreuse);
1983:   }
1984:   ISDestroy(&isrow_new);
1985:   ISDestroy(&iscol_new);
1986:   /*
1987:       m - number of local rows
1988:       n - number of columns (same on all processors)
1989:       rstart - first row in new global matrix generated
1990:   */
1991:   MatGetBlockSize(mat,&bs);
1992:   MatGetSize(Mreuse,&m,&n);
1993:   m    = m/bs;
1994:   n    = n/bs;

1996:   if (call == MAT_INITIAL_MATRIX) {
1997:     aij = (Mat_SeqBAIJ*)(Mreuse)->data;
1998:     ii  = aij->i;
1999:     jj  = aij->j;

2001:     /*
2002:         Determine the number of non-zeros in the diagonal and off-diagonal
2003:         portions of the matrix in order to do correct preallocation
2004:     */

2006:     /* first get start and end of "diagonal" columns */
2007:     if (csize == PETSC_DECIDE) {
2008:       ISGetSize(isrow,&mglobal);
2009:       if (mglobal == n*bs) { /* square matrix */
2010:         nlocal = m;
2011:       } else {
2012:         nlocal = n/size + ((n % size) > rank);
2013:       }
2014:     } else {
2015:       nlocal = csize/bs;
2016:     }
2017:     MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
2018:     rstart = rend - nlocal;
2019:     if (rank == size - 1 && rend != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);

2021:     /* next, compute all the lengths */
2022:     PetscMalloc2(m+1,&dlens,m+1,&olens);
2023:     for (i=0; i<m; i++) {
2024:       jend = ii[i+1] - ii[i];
2025:       olen = 0;
2026:       dlen = 0;
2027:       for (j=0; j<jend; j++) {
2028:         if (*jj < rstart || *jj >= rend) olen++;
2029:         else dlen++;
2030:         jj++;
2031:       }
2032:       olens[i] = olen;
2033:       dlens[i] = dlen;
2034:     }
2035:     MatCreate(comm,&M);
2036:     MatSetSizes(M,bs*m,bs*nlocal,PETSC_DECIDE,bs*n);
2037:     MatSetType(M,((PetscObject)mat)->type_name);
2038:     MatMPIBAIJSetPreallocation(M,bs,0,dlens,0,olens);
2039:     MatMPISBAIJSetPreallocation(M,bs,0,dlens,0,olens);
2040:     PetscFree2(dlens,olens);
2041:   } else {
2042:     PetscInt ml,nl;

2044:     M    = *newmat;
2045:     MatGetLocalSize(M,&ml,&nl);
2046:     if (ml != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
2047:     MatZeroEntries(M);
2048:     /*
2049:          The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2050:        rather than the slower MatSetValues().
2051:     */
2052:     M->was_assembled = PETSC_TRUE;
2053:     M->assembled     = PETSC_FALSE;
2054:   }
2055:   MatSetOption(M,MAT_ROW_ORIENTED,PETSC_FALSE);
2056:   MatGetOwnershipRange(M,&rstart,&rend);
2057:   aij  = (Mat_SeqBAIJ*)(Mreuse)->data;
2058:   ii   = aij->i;
2059:   jj   = aij->j;
2060:   aa   = aij->a;
2061:   for (i=0; i<m; i++) {
2062:     row   = rstart/bs + i;
2063:     nz    = ii[i+1] - ii[i];
2064:     cwork = jj;     jj += nz;
2065:     vwork = aa;     aa += nz*bs*bs;
2066:     MatSetValuesBlocked_MPIBAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
2067:   }

2069:   MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
2070:   MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
2071:   *newmat = M;

2073:   /* save submatrix used in processor for next request */
2074:   if (call ==  MAT_INITIAL_MATRIX) {
2075:     PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
2076:     PetscObjectDereference((PetscObject)Mreuse);
2077:   }
2078:   return(0);
2079: }

2081: PetscErrorCode MatPermute_MPIBAIJ(Mat A,IS rowp,IS colp,Mat *B)
2082: {
2083:   MPI_Comm       comm,pcomm;
2084:   PetscInt       clocal_size,nrows;
2085:   const PetscInt *rows;
2086:   PetscMPIInt    size;
2087:   IS             crowp,lcolp;

2091:   PetscObjectGetComm((PetscObject)A,&comm);
2092:   /* make a collective version of 'rowp' */
2093:   PetscObjectGetComm((PetscObject)rowp,&pcomm);
2094:   if (pcomm==comm) {
2095:     crowp = rowp;
2096:   } else {
2097:     ISGetSize(rowp,&nrows);
2098:     ISGetIndices(rowp,&rows);
2099:     ISCreateGeneral(comm,nrows,rows,PETSC_COPY_VALUES,&crowp);
2100:     ISRestoreIndices(rowp,&rows);
2101:   }
2102:   ISSetPermutation(crowp);
2103:   /* make a local version of 'colp' */
2104:   PetscObjectGetComm((PetscObject)colp,&pcomm);
2105:   MPI_Comm_size(pcomm,&size);
2106:   if (size==1) {
2107:     lcolp = colp;
2108:   } else {
2109:     ISAllGather(colp,&lcolp);
2110:   }
2111:   ISSetPermutation(lcolp);
2112:   /* now we just get the submatrix */
2113:   MatGetLocalSize(A,NULL,&clocal_size);
2114:   MatCreateSubMatrix_MPIBAIJ_Private(A,crowp,lcolp,clocal_size,MAT_INITIAL_MATRIX,B);
2115:   /* clean up */
2116:   if (pcomm!=comm) {
2117:     ISDestroy(&crowp);
2118:   }
2119:   if (size>1) {
2120:     ISDestroy(&lcolp);
2121:   }
2122:   return(0);
2123: }

2125: PetscErrorCode  MatGetGhosts_MPIBAIJ(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
2126: {
2127:   Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*) mat->data;
2128:   Mat_SeqBAIJ *B    = (Mat_SeqBAIJ*)baij->B->data;

2131:   if (nghosts) *nghosts = B->nbs;
2132:   if (ghosts) *ghosts = baij->garray;
2133:   return(0);
2134: }

2136: PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A,Mat *newmat)
2137: {
2138:   Mat            B;
2139:   Mat_MPIBAIJ    *a  = (Mat_MPIBAIJ*)A->data;
2140:   Mat_SeqBAIJ    *ad = (Mat_SeqBAIJ*)a->A->data,*bd = (Mat_SeqBAIJ*)a->B->data;
2141:   Mat_SeqAIJ     *b;
2143:   PetscMPIInt    size,rank,*recvcounts = NULL,*displs = NULL;
2144:   PetscInt       sendcount,i,*rstarts = A->rmap->range,n,cnt,j,bs = A->rmap->bs;
2145:   PetscInt       m,*garray = a->garray,*lens,*jsendbuf,*a_jsendbuf,*b_jsendbuf;

2148:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
2149:   MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);

2151:   /* ----------------------------------------------------------------
2152:      Tell every processor the number of nonzeros per row
2153:   */
2154:   PetscMalloc1(A->rmap->N/bs,&lens);
2155:   for (i=A->rmap->rstart/bs; i<A->rmap->rend/bs; i++) {
2156:     lens[i] = ad->i[i-A->rmap->rstart/bs+1] - ad->i[i-A->rmap->rstart/bs] + bd->i[i-A->rmap->rstart/bs+1] - bd->i[i-A->rmap->rstart/bs];
2157:   }
2158:   PetscMalloc1(2*size,&recvcounts);
2159:   displs    = recvcounts + size;
2160:   for (i=0; i<size; i++) {
2161:     recvcounts[i] = A->rmap->range[i+1]/bs - A->rmap->range[i]/bs;
2162:     displs[i]     = A->rmap->range[i]/bs;
2163:   }
2164: #if defined(PETSC_HAVE_MPI_IN_PLACE)
2165:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2166: #else
2167:   sendcount = A->rmap->rend/bs - A->rmap->rstart/bs;
2168:   MPI_Allgatherv(lens+A->rmap->rstart/bs,sendcount,MPIU_INT,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2169: #endif
2170:   /* ---------------------------------------------------------------
2171:      Create the sequential matrix of the same type as the local block diagonal
2172:   */
2173:   MatCreate(PETSC_COMM_SELF,&B);
2174:   MatSetSizes(B,A->rmap->N/bs,A->cmap->N/bs,PETSC_DETERMINE,PETSC_DETERMINE);
2175:   MatSetType(B,MATSEQAIJ);
2176:   MatSeqAIJSetPreallocation(B,0,lens);
2177:   b    = (Mat_SeqAIJ*)B->data;

2179:   /*--------------------------------------------------------------------
2180:     Copy my part of matrix column indices over
2181:   */
2182:   sendcount  = ad->nz + bd->nz;
2183:   jsendbuf   = b->j + b->i[rstarts[rank]/bs];
2184:   a_jsendbuf = ad->j;
2185:   b_jsendbuf = bd->j;
2186:   n          = A->rmap->rend/bs - A->rmap->rstart/bs;
2187:   cnt        = 0;
2188:   for (i=0; i<n; i++) {

2190:     /* put in lower diagonal portion */
2191:     m = bd->i[i+1] - bd->i[i];
2192:     while (m > 0) {
2193:       /* is it above diagonal (in bd (compressed) numbering) */
2194:       if (garray[*b_jsendbuf] > A->rmap->rstart/bs + i) break;
2195:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
2196:       m--;
2197:     }

2199:     /* put in diagonal portion */
2200:     for (j=ad->i[i]; j<ad->i[i+1]; j++) {
2201:       jsendbuf[cnt++] = A->rmap->rstart/bs + *a_jsendbuf++;
2202:     }

2204:     /* put in upper diagonal portion */
2205:     while (m-- > 0) {
2206:       jsendbuf[cnt++] = garray[*b_jsendbuf++];
2207:     }
2208:   }
2209:   if (cnt != sendcount) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Corrupted PETSc matrix: nz given %D actual nz %D",sendcount,cnt);

2211:   /*--------------------------------------------------------------------
2212:     Gather all column indices to all processors
2213:   */
2214:   for (i=0; i<size; i++) {
2215:     recvcounts[i] = 0;
2216:     for (j=A->rmap->range[i]/bs; j<A->rmap->range[i+1]/bs; j++) {
2217:       recvcounts[i] += lens[j];
2218:     }
2219:   }
2220:   displs[0] = 0;
2221:   for (i=1; i<size; i++) {
2222:     displs[i] = displs[i-1] + recvcounts[i-1];
2223:   }
2224: #if defined(PETSC_HAVE_MPI_IN_PLACE)
2225:   MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2226: #else
2227:   MPI_Allgatherv(jsendbuf,sendcount,MPIU_INT,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2228: #endif
2229:   /*--------------------------------------------------------------------
2230:     Assemble the matrix into useable form (note numerical values not yet set)
2231:   */
2232:   /* set the b->ilen (length of each row) values */
2233:   PetscArraycpy(b->ilen,lens,A->rmap->N/bs);
2234:   /* set the b->i indices */
2235:   b->i[0] = 0;
2236:   for (i=1; i<=A->rmap->N/bs; i++) {
2237:     b->i[i] = b->i[i-1] + lens[i-1];
2238:   }
2239:   PetscFree(lens);
2240:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2241:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2242:   PetscFree(recvcounts);

2244:   if (A->symmetric) {
2245:     MatSetOption(B,MAT_SYMMETRIC,PETSC_TRUE);
2246:   } else if (A->hermitian) {
2247:     MatSetOption(B,MAT_HERMITIAN,PETSC_TRUE);
2248:   } else if (A->structurally_symmetric) {
2249:     MatSetOption(B,MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
2250:   }
2251:   *newmat = B;
2252:   return(0);
2253: }

2255: PetscErrorCode MatSOR_MPIBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2256: {
2257:   Mat_MPIBAIJ    *mat = (Mat_MPIBAIJ*)matin->data;
2259:   Vec            bb1 = NULL;

2262:   if (flag == SOR_APPLY_UPPER) {
2263:     (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2264:     return(0);
2265:   }

2267:   if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) {
2268:     VecDuplicate(bb,&bb1);
2269:   }

2271:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2272:     if (flag & SOR_ZERO_INITIAL_GUESS) {
2273:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2274:       its--;
2275:     }

2277:     while (its--) {
2278:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2279:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

2281:       /* update rhs: bb1 = bb - B*x */
2282:       VecScale(mat->lvec,-1.0);
2283:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

2285:       /* local sweep */
2286:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
2287:     }
2288:   } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2289:     if (flag & SOR_ZERO_INITIAL_GUESS) {
2290:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2291:       its--;
2292:     }
2293:     while (its--) {
2294:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2295:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

2297:       /* update rhs: bb1 = bb - B*x */
2298:       VecScale(mat->lvec,-1.0);
2299:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

2301:       /* local sweep */
2302:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
2303:     }
2304:   } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2305:     if (flag & SOR_ZERO_INITIAL_GUESS) {
2306:       (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2307:       its--;
2308:     }
2309:     while (its--) {
2310:       VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2311:       VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);

2313:       /* update rhs: bb1 = bb - B*x */
2314:       VecScale(mat->lvec,-1.0);
2315:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);

2317:       /* local sweep */
2318:       (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
2319:     }
2320:   } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel version of SOR requested not supported");

2322:   VecDestroy(&bb1);
2323:   return(0);
2324: }

2326: PetscErrorCode MatGetColumnNorms_MPIBAIJ(Mat A,NormType type,PetscReal *norms)
2327: {
2329:   Mat_MPIBAIJ    *aij = (Mat_MPIBAIJ*)A->data;
2330:   PetscInt       N,i,*garray = aij->garray;
2331:   PetscInt       ib,jb,bs = A->rmap->bs;
2332:   Mat_SeqBAIJ    *a_aij = (Mat_SeqBAIJ*) aij->A->data;
2333:   MatScalar      *a_val = a_aij->a;
2334:   Mat_SeqBAIJ    *b_aij = (Mat_SeqBAIJ*) aij->B->data;
2335:   MatScalar      *b_val = b_aij->a;
2336:   PetscReal      *work;

2339:   MatGetSize(A,NULL,&N);
2340:   PetscCalloc1(N,&work);
2341:   if (type == NORM_2) {
2342:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2343:       for (jb=0; jb<bs; jb++) {
2344:         for (ib=0; ib<bs; ib++) {
2345:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2346:           a_val++;
2347:         }
2348:       }
2349:     }
2350:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2351:       for (jb=0; jb<bs; jb++) {
2352:         for (ib=0; ib<bs; ib++) {
2353:           work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2354:           b_val++;
2355:         }
2356:       }
2357:     }
2358:   } else if (type == NORM_1) {
2359:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2360:       for (jb=0; jb<bs; jb++) {
2361:         for (ib=0; ib<bs; ib++) {
2362:           work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2363:           a_val++;
2364:         }
2365:       }
2366:     }
2367:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2368:       for (jb=0; jb<bs; jb++) {
2369:        for (ib=0; ib<bs; ib++) {
2370:           work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2371:           b_val++;
2372:         }
2373:       }
2374:     }
2375:   } else if (type == NORM_INFINITY) {
2376:     for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2377:       for (jb=0; jb<bs; jb++) {
2378:         for (ib=0; ib<bs; ib++) {
2379:           int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2380:           work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2381:           a_val++;
2382:         }
2383:       }
2384:     }
2385:     for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2386:       for (jb=0; jb<bs; jb++) {
2387:         for (ib=0; ib<bs; ib++) {
2388:           int col = garray[b_aij->j[i]] * bs + jb;
2389:           work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2390:           b_val++;
2391:         }
2392:       }
2393:     }
2394:   } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Unknown NormType");
2395:   if (type == NORM_INFINITY) {
2396:     MPIU_Allreduce(work,norms,N,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
2397:   } else {
2398:     MPIU_Allreduce(work,norms,N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
2399:   }
2400:   PetscFree(work);
2401:   if (type == NORM_2) {
2402:     for (i=0; i<N; i++) norms[i] = PetscSqrtReal(norms[i]);
2403:   }
2404:   return(0);
2405: }

2407: PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A,const PetscScalar **values)
2408: {
2409:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*) A->data;

2413:   MatInvertBlockDiagonal(a->A,values);
2414:   A->factorerrortype             = a->A->factorerrortype;
2415:   A->factorerror_zeropivot_value = a->A->factorerror_zeropivot_value;
2416:   A->factorerror_zeropivot_row   = a->A->factorerror_zeropivot_row;
2417:   return(0);
2418: }

2420: PetscErrorCode MatShift_MPIBAIJ(Mat Y,PetscScalar a)
2421: {
2423:   Mat_MPIBAIJ    *maij = (Mat_MPIBAIJ*)Y->data;
2424:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ*)maij->A->data;

2427:   if (!Y->preallocated) {
2428:     MatMPIBAIJSetPreallocation(Y,Y->rmap->bs,1,NULL,0,NULL);
2429:   } else if (!aij->nz) {
2430:     PetscInt nonew = aij->nonew;
2431:     MatSeqBAIJSetPreallocation(maij->A,Y->rmap->bs,1,NULL);
2432:     aij->nonew = nonew;
2433:   }
2434:   MatShift_Basic(Y,a);
2435:   return(0);
2436: }

2438: PetscErrorCode MatMissingDiagonal_MPIBAIJ(Mat A,PetscBool  *missing,PetscInt *d)
2439: {
2440:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;

2444:   if (A->rmap->n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only works for square matrices");
2445:   MatMissingDiagonal(a->A,missing,d);
2446:   if (d) {
2447:     PetscInt rstart;
2448:     MatGetOwnershipRange(A,&rstart,NULL);
2449:     *d += rstart/A->rmap->bs;

2451:   }
2452:   return(0);
2453: }

2455: PetscErrorCode  MatGetDiagonalBlock_MPIBAIJ(Mat A,Mat *a)
2456: {
2458:   *a = ((Mat_MPIBAIJ*)A->data)->A;
2459:   return(0);
2460: }

2462: /* -------------------------------------------------------------------*/
2463: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2464:                                        MatGetRow_MPIBAIJ,
2465:                                        MatRestoreRow_MPIBAIJ,
2466:                                        MatMult_MPIBAIJ,
2467:                                 /* 4*/ MatMultAdd_MPIBAIJ,
2468:                                        MatMultTranspose_MPIBAIJ,
2469:                                        MatMultTransposeAdd_MPIBAIJ,
2470:                                        NULL,
2471:                                        NULL,
2472:                                        NULL,
2473:                                 /*10*/ NULL,
2474:                                        NULL,
2475:                                        NULL,
2476:                                        MatSOR_MPIBAIJ,
2477:                                        MatTranspose_MPIBAIJ,
2478:                                 /*15*/ MatGetInfo_MPIBAIJ,
2479:                                        MatEqual_MPIBAIJ,
2480:                                        MatGetDiagonal_MPIBAIJ,
2481:                                        MatDiagonalScale_MPIBAIJ,
2482:                                        MatNorm_MPIBAIJ,
2483:                                 /*20*/ MatAssemblyBegin_MPIBAIJ,
2484:                                        MatAssemblyEnd_MPIBAIJ,
2485:                                        MatSetOption_MPIBAIJ,
2486:                                        MatZeroEntries_MPIBAIJ,
2487:                                 /*24*/ MatZeroRows_MPIBAIJ,
2488:                                        NULL,
2489:                                        NULL,
2490:                                        NULL,
2491:                                        NULL,
2492:                                 /*29*/ MatSetUp_MPIBAIJ,
2493:                                        NULL,
2494:                                        NULL,
2495:                                        MatGetDiagonalBlock_MPIBAIJ,
2496:                                        NULL,
2497:                                 /*34*/ MatDuplicate_MPIBAIJ,
2498:                                        NULL,
2499:                                        NULL,
2500:                                        NULL,
2501:                                        NULL,
2502:                                 /*39*/ MatAXPY_MPIBAIJ,
2503:                                        MatCreateSubMatrices_MPIBAIJ,
2504:                                        MatIncreaseOverlap_MPIBAIJ,
2505:                                        MatGetValues_MPIBAIJ,
2506:                                        MatCopy_MPIBAIJ,
2507:                                 /*44*/ NULL,
2508:                                        MatScale_MPIBAIJ,
2509:                                        MatShift_MPIBAIJ,
2510:                                        NULL,
2511:                                        MatZeroRowsColumns_MPIBAIJ,
2512:                                 /*49*/ NULL,
2513:                                        NULL,
2514:                                        NULL,
2515:                                        NULL,
2516:                                        NULL,
2517:                                 /*54*/ MatFDColoringCreate_MPIXAIJ,
2518:                                        NULL,
2519:                                        MatSetUnfactored_MPIBAIJ,
2520:                                        MatPermute_MPIBAIJ,
2521:                                        MatSetValuesBlocked_MPIBAIJ,
2522:                                 /*59*/ MatCreateSubMatrix_MPIBAIJ,
2523:                                        MatDestroy_MPIBAIJ,
2524:                                        MatView_MPIBAIJ,
2525:                                        NULL,
2526:                                        NULL,
2527:                                 /*64*/ NULL,
2528:                                        NULL,
2529:                                        NULL,
2530:                                        NULL,
2531:                                        NULL,
2532:                                 /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2533:                                        NULL,
2534:                                        NULL,
2535:                                        NULL,
2536:                                        NULL,
2537:                                 /*74*/ NULL,
2538:                                        MatFDColoringApply_BAIJ,
2539:                                        NULL,
2540:                                        NULL,
2541:                                        NULL,
2542:                                 /*79*/ NULL,
2543:                                        NULL,
2544:                                        NULL,
2545:                                        NULL,
2546:                                        MatLoad_MPIBAIJ,
2547:                                 /*84*/ NULL,
2548:                                        NULL,
2549:                                        NULL,
2550:                                        NULL,
2551:                                        NULL,
2552:                                 /*89*/ NULL,
2553:                                        NULL,
2554:                                        NULL,
2555:                                        NULL,
2556:                                        NULL,
2557:                                 /*94*/ NULL,
2558:                                        NULL,
2559:                                        NULL,
2560:                                        NULL,
2561:                                        NULL,
2562:                                 /*99*/ NULL,
2563:                                        NULL,
2564:                                        NULL,
2565:                                        MatConjugate_MPIBAIJ,
2566:                                        NULL,
2567:                                 /*104*/NULL,
2568:                                        MatRealPart_MPIBAIJ,
2569:                                        MatImaginaryPart_MPIBAIJ,
2570:                                        NULL,
2571:                                        NULL,
2572:                                 /*109*/NULL,
2573:                                        NULL,
2574:                                        NULL,
2575:                                        NULL,
2576:                                        MatMissingDiagonal_MPIBAIJ,
2577:                                 /*114*/MatGetSeqNonzeroStructure_MPIBAIJ,
2578:                                        NULL,
2579:                                        MatGetGhosts_MPIBAIJ,
2580:                                        NULL,
2581:                                        NULL,
2582:                                 /*119*/NULL,
2583:                                        NULL,
2584:                                        NULL,
2585:                                        NULL,
2586:                                        MatGetMultiProcBlock_MPIBAIJ,
2587:                                 /*124*/NULL,
2588:                                        MatGetColumnNorms_MPIBAIJ,
2589:                                        MatInvertBlockDiagonal_MPIBAIJ,
2590:                                        NULL,
2591:                                        NULL,
2592:                                /*129*/ NULL,
2593:                                        NULL,
2594:                                        NULL,
2595:                                        NULL,
2596:                                        NULL,
2597:                                /*134*/ NULL,
2598:                                        NULL,
2599:                                        NULL,
2600:                                        NULL,
2601:                                        NULL,
2602:                                /*139*/ MatSetBlockSizes_Default,
2603:                                        NULL,
2604:                                        NULL,
2605:                                        MatFDColoringSetUp_MPIXAIJ,
2606:                                        NULL,
2607:                                 /*144*/MatCreateMPIMatConcatenateSeqMat_MPIBAIJ
2608: };


2611: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat,MatType,MatReuse,Mat*);
2612: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);

2614: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
2615: {
2616:   PetscInt       m,rstart,cstart,cend;
2617:   PetscInt       i,j,dlen,olen,nz,nz_max=0,*d_nnz=NULL,*o_nnz=NULL;
2618:   const PetscInt *JJ    =NULL;
2619:   PetscScalar    *values=NULL;
2620:   PetscBool      roworiented = ((Mat_MPIBAIJ*)B->data)->roworiented;
2622:   PetscBool      nooffprocentries;

2625:   PetscLayoutSetBlockSize(B->rmap,bs);
2626:   PetscLayoutSetBlockSize(B->cmap,bs);
2627:   PetscLayoutSetUp(B->rmap);
2628:   PetscLayoutSetUp(B->cmap);
2629:   PetscLayoutGetBlockSize(B->rmap,&bs);
2630:   m      = B->rmap->n/bs;
2631:   rstart = B->rmap->rstart/bs;
2632:   cstart = B->cmap->rstart/bs;
2633:   cend   = B->cmap->rend/bs;

2635:   if (ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"ii[0] must be 0 but it is %D",ii[0]);
2636:   PetscMalloc2(m,&d_nnz,m,&o_nnz);
2637:   for (i=0; i<m; i++) {
2638:     nz = ii[i+1] - ii[i];
2639:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative number of columns %D",i,nz);
2640:     nz_max = PetscMax(nz_max,nz);
2641:     dlen   = 0;
2642:     olen   = 0;
2643:     JJ     = jj + ii[i];
2644:     for (j=0; j<nz; j++) {
2645:       if (*JJ < cstart || *JJ >= cend) olen++;
2646:       else dlen++;
2647:       JJ++;
2648:     }
2649:     d_nnz[i] = dlen;
2650:     o_nnz[i] = olen;
2651:   }
2652:   MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2653:   PetscFree2(d_nnz,o_nnz);

2655:   values = (PetscScalar*)V;
2656:   if (!values) {
2657:     PetscCalloc1(bs*bs*nz_max,&values);
2658:   }
2659:   for (i=0; i<m; i++) {
2660:     PetscInt          row    = i + rstart;
2661:     PetscInt          ncols  = ii[i+1] - ii[i];
2662:     const PetscInt    *icols = jj + ii[i];
2663:     if (bs == 1 || !roworiented) {         /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2664:       const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
2665:       MatSetValuesBlocked_MPIBAIJ(B,1,&row,ncols,icols,svals,INSERT_VALUES);
2666:     } else {                    /* block ordering does not match so we can only insert one block at a time. */
2667:       PetscInt j;
2668:       for (j=0; j<ncols; j++) {
2669:         const PetscScalar *svals = values + (V ? (bs*bs*(ii[i]+j)) : 0);
2670:         MatSetValuesBlocked_MPIBAIJ(B,1,&row,1,&icols[j],svals,INSERT_VALUES);
2671:       }
2672:     }
2673:   }

2675:   if (!V) { PetscFree(values); }
2676:   nooffprocentries    = B->nooffprocentries;
2677:   B->nooffprocentries = PETSC_TRUE;
2678:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2679:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2680:   B->nooffprocentries = nooffprocentries;

2682:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
2683:   return(0);
2684: }

2686: /*@C
2687:    MatMPIBAIJSetPreallocationCSR - Creates a sparse parallel matrix in BAIJ format using the given nonzero structure and (optional) numerical values

2689:    Collective

2691:    Input Parameters:
2692: +  B - the matrix
2693: .  bs - the block size
2694: .  i - the indices into j for the start of each local row (starts with zero)
2695: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
2696: -  v - optional values in the matrix

2698:    Level: advanced

2700:    Notes:
2701:     The order of the entries in values is specified by the MatOption MAT_ROW_ORIENTED.  For example, C programs
2702:    may want to use the default MAT_ROW_ORIENTED=PETSC_TRUE and use an array v[nnz][bs][bs] where the second index is
2703:    over rows within a block and the last index is over columns within a block row.  Fortran programs will likely set
2704:    MAT_ROW_ORIENTED=PETSC_FALSE and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
2705:    block column and the second index is over columns within a block.

2707:    Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well

2709: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ, MatCreateMPIBAIJWithArrays(), MPIBAIJ
2710: @*/
2711: PetscErrorCode  MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2712: {

2719:   PetscTryMethod(B,"MatMPIBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
2720:   return(0);
2721: }

2723: PetscErrorCode  MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
2724: {
2725:   Mat_MPIBAIJ    *b;
2727:   PetscInt       i;
2728:   PetscMPIInt    size;

2731:   MatSetBlockSize(B,PetscAbs(bs));
2732:   PetscLayoutSetUp(B->rmap);
2733:   PetscLayoutSetUp(B->cmap);
2734:   PetscLayoutGetBlockSize(B->rmap,&bs);

2736:   if (d_nnz) {
2737:     for (i=0; i<B->rmap->n/bs; i++) {
2738:       if (d_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %D value %D",i,d_nnz[i]);
2739:     }
2740:   }
2741:   if (o_nnz) {
2742:     for (i=0; i<B->rmap->n/bs; i++) {
2743:       if (o_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %D value %D",i,o_nnz[i]);
2744:     }
2745:   }

2747:   b      = (Mat_MPIBAIJ*)B->data;
2748:   b->bs2 = bs*bs;
2749:   b->mbs = B->rmap->n/bs;
2750:   b->nbs = B->cmap->n/bs;
2751:   b->Mbs = B->rmap->N/bs;
2752:   b->Nbs = B->cmap->N/bs;

2754:   for (i=0; i<=b->size; i++) {
2755:     b->rangebs[i] = B->rmap->range[i]/bs;
2756:   }
2757:   b->rstartbs = B->rmap->rstart/bs;
2758:   b->rendbs   = B->rmap->rend/bs;
2759:   b->cstartbs = B->cmap->rstart/bs;
2760:   b->cendbs   = B->cmap->rend/bs;

2762: #if defined(PETSC_USE_CTABLE)
2763:   PetscTableDestroy(&b->colmap);
2764: #else
2765:   PetscFree(b->colmap);
2766: #endif
2767:   PetscFree(b->garray);
2768:   VecDestroy(&b->lvec);
2769:   VecScatterDestroy(&b->Mvctx);

2771:   /* Because the B will have been resized we simply destroy it and create a new one each time */
2772:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
2773:   MatDestroy(&b->B);
2774:   MatCreate(PETSC_COMM_SELF,&b->B);
2775:   MatSetSizes(b->B,B->rmap->n,size > 1 ? B->cmap->N : 0,B->rmap->n,size > 1 ? B->cmap->N : 0);
2776:   MatSetType(b->B,MATSEQBAIJ);
2777:   PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);

2779:   if (!B->preallocated) {
2780:     MatCreate(PETSC_COMM_SELF,&b->A);
2781:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
2782:     MatSetType(b->A,MATSEQBAIJ);
2783:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
2784:     MatStashCreate_Private(PetscObjectComm((PetscObject)B),bs,&B->bstash);
2785:   }

2787:   MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2788:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2789:   B->preallocated  = PETSC_TRUE;
2790:   B->was_assembled = PETSC_FALSE;
2791:   B->assembled     = PETSC_FALSE;
2792:   return(0);
2793: }

2795: extern PetscErrorCode  MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2796: extern PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);

2798: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype,MatReuse reuse,Mat *adj)
2799: {
2800:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)B->data;
2802:   Mat_SeqBAIJ    *d  = (Mat_SeqBAIJ*) b->A->data,*o = (Mat_SeqBAIJ*) b->B->data;
2803:   PetscInt       M   = B->rmap->n/B->rmap->bs,i,*ii,*jj,cnt,j,k,rstart = B->rmap->rstart/B->rmap->bs;
2804:   const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;

2807:   PetscMalloc1(M+1,&ii);
2808:   ii[0] = 0;
2809:   for (i=0; i<M; i++) {
2810:     if ((id[i+1] - id[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,id[i],id[i+1]);
2811:     if ((io[i+1] - io[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,io[i],io[i+1]);
2812:     ii[i+1] = ii[i] + id[i+1] - id[i] + io[i+1] - io[i];
2813:     /* remove one from count of matrix has diagonal */
2814:     for (j=id[i]; j<id[i+1]; j++) {
2815:       if (jd[j] == i) {ii[i+1]--;break;}
2816:     }
2817:   }
2818:   PetscMalloc1(ii[M],&jj);
2819:   cnt  = 0;
2820:   for (i=0; i<M; i++) {
2821:     for (j=io[i]; j<io[i+1]; j++) {
2822:       if (garray[jo[j]] > rstart) break;
2823:       jj[cnt++] = garray[jo[j]];
2824:     }
2825:     for (k=id[i]; k<id[i+1]; k++) {
2826:       if (jd[k] != i) {
2827:         jj[cnt++] = rstart + jd[k];
2828:       }
2829:     }
2830:     for (; j<io[i+1]; j++) {
2831:       jj[cnt++] = garray[jo[j]];
2832:     }
2833:   }
2834:   MatCreateMPIAdj(PetscObjectComm((PetscObject)B),M,B->cmap->N/B->rmap->bs,ii,jj,NULL,adj);
2835:   return(0);
2836: }

2838: #include <../src/mat/impls/aij/mpi/mpiaij.h>

2840: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat,MatType,MatReuse,Mat*);

2842: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A,MatType newtype,MatReuse reuse,Mat *newmat)
2843: {
2845:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
2846:   Mat            B;
2847:   Mat_MPIAIJ     *b;

2850:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix must be assembled");

2852:   if (reuse == MAT_REUSE_MATRIX) {
2853:     B = *newmat;
2854:   } else {
2855:     MatCreate(PetscObjectComm((PetscObject)A),&B);
2856:     MatSetType(B,MATMPIAIJ);
2857:     MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
2858:     MatSetBlockSizes(B,A->rmap->bs,A->cmap->bs);
2859:     MatSeqAIJSetPreallocation(B,0,NULL);
2860:     MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
2861:   }
2862:   b = (Mat_MPIAIJ*) B->data;

2864:   if (reuse == MAT_REUSE_MATRIX) {
2865:     MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A);
2866:     MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B);
2867:   } else {
2868:     MatDestroy(&b->A);
2869:     MatDestroy(&b->B);
2870:     MatDisAssemble_MPIBAIJ(A);
2871:     MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A);
2872:     MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B);
2873:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2874:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2875:   }
2876:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2877:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

2879:   if (reuse == MAT_INPLACE_MATRIX) {
2880:     MatHeaderReplace(A,&B);
2881:   } else {
2882:    *newmat = B;
2883:   }
2884:   return(0);
2885: }

2887: /*MC
2888:    MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.

2890:    Options Database Keys:
2891: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
2892: . -mat_block_size <bs> - set the blocksize used to store the matrix
2893: . -mat_baij_mult_version version - indicate the version of the matrix-vector product to use  (0 often indicates using BLAS)
2894: - -mat_use_hash_table <fact>

2896:    Level: beginner

2898:    Notes:
2899:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
2900:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

2902: .seealso: MatCreateBAIJ
2903: M*/

2905: PETSC_INTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat,MatType,MatReuse,Mat*);

2907: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
2908: {
2909:   Mat_MPIBAIJ    *b;
2911:   PetscBool      flg = PETSC_FALSE;

2914:   PetscNewLog(B,&b);
2915:   B->data = (void*)b;

2917:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2918:   B->assembled = PETSC_FALSE;

2920:   B->insertmode = NOT_SET_VALUES;
2921:   MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
2922:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&b->size);

2924:   /* build local table of row and column ownerships */
2925:   PetscMalloc1(b->size+1,&b->rangebs);

2927:   /* build cache for off array entries formed */
2928:   MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);

2930:   b->donotstash  = PETSC_FALSE;
2931:   b->colmap      = NULL;
2932:   b->garray      = NULL;
2933:   b->roworiented = PETSC_TRUE;

2935:   /* stuff used in block assembly */
2936:   b->barray = NULL;

2938:   /* stuff used for matrix vector multiply */
2939:   b->lvec  = NULL;
2940:   b->Mvctx = NULL;

2942:   /* stuff for MatGetRow() */
2943:   b->rowindices   = NULL;
2944:   b->rowvalues    = NULL;
2945:   b->getrowactive = PETSC_FALSE;

2947:   /* hash table stuff */
2948:   b->ht           = NULL;
2949:   b->hd           = NULL;
2950:   b->ht_size      = 0;
2951:   b->ht_flag      = PETSC_FALSE;
2952:   b->ht_fact      = 0;
2953:   b->ht_total_ct  = 0;
2954:   b->ht_insert_ct = 0;

2956:   /* stuff for MatCreateSubMatrices_MPIBAIJ_local() */
2957:   b->ijonly = PETSC_FALSE;


2960:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiadj_C",MatConvert_MPIBAIJ_MPIAdj);
2961:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiaij_C",MatConvert_MPIBAIJ_MPIAIJ);
2962:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpisbaij_C",MatConvert_MPIBAIJ_MPISBAIJ);
2963: #if defined(PETSC_HAVE_HYPRE)
2964:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_hypre_C",MatConvert_AIJ_HYPRE);
2965: #endif
2966:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIBAIJ);
2967:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIBAIJ);
2968:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJ);
2969:   PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
2970:   PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIBAIJ);
2971:   PetscObjectComposeFunction((PetscObject)B,"MatSetHashTableFactor_C",MatSetHashTableFactor_MPIBAIJ);
2972:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_is_C",MatConvert_XAIJ_IS);
2973:   PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);

2975:   PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Options for loading MPIBAIJ matrix 1","Mat");
2976:   PetscOptionsName("-mat_use_hash_table","Use hash table to save time in constructing matrix","MatSetOption",&flg);
2977:   if (flg) {
2978:     PetscReal fact = 1.39;
2979:     MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
2980:     PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,NULL);
2981:     if (fact <= 1.0) fact = 1.39;
2982:     MatMPIBAIJSetHashTableFactor(B,fact);
2983:     PetscInfo1(B,"Hash table Factor used %5.2f\n",fact);
2984:   }
2985:   PetscOptionsEnd();
2986:   return(0);
2987: }

2989: /*MC
2990:    MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.

2992:    This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
2993:    and MATMPIBAIJ otherwise.

2995:    Options Database Keys:
2996: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()

2998:   Level: beginner

3000: .seealso: MatCreateBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
3001: M*/

3003: /*@C
3004:    MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
3005:    (block compressed row).  For good matrix assembly performance
3006:    the user should preallocate the matrix storage by setting the parameters
3007:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
3008:    performance can be increased by more than a factor of 50.

3010:    Collective on Mat

3012:    Input Parameters:
3013: +  B - the matrix
3014: .  bs   - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
3015:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
3016: .  d_nz  - number of block nonzeros per block row in diagonal portion of local
3017:            submatrix  (same for all local rows)
3018: .  d_nnz - array containing the number of block nonzeros in the various block rows
3019:            of the in diagonal portion of the local (possibly different for each block
3020:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry and
3021:            set it even if it is zero.
3022: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
3023:            submatrix (same for all local rows).
3024: -  o_nnz - array containing the number of nonzeros in the various block rows of the
3025:            off-diagonal portion of the local submatrix (possibly different for
3026:            each block row) or NULL.

3028:    If the *_nnz parameter is given then the *_nz parameter is ignored

3030:    Options Database Keys:
3031: +   -mat_block_size - size of the blocks to use
3032: -   -mat_use_hash_table <fact>

3034:    Notes:
3035:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
3036:    than it must be used on all processors that share the object for that argument.

3038:    Storage Information:
3039:    For a square global matrix we define each processor's diagonal portion
3040:    to be its local rows and the corresponding columns (a square submatrix);
3041:    each processor's off-diagonal portion encompasses the remainder of the
3042:    local matrix (a rectangular submatrix).

3044:    The user can specify preallocated storage for the diagonal part of
3045:    the local submatrix with either d_nz or d_nnz (not both).  Set
3046:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
3047:    memory allocation.  Likewise, specify preallocated storage for the
3048:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

3050:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3051:    the figure below we depict these three local rows and all columns (0-11).

3053: .vb
3054:            0 1 2 3 4 5 6 7 8 9 10 11
3055:           --------------------------
3056:    row 3  |o o o d d d o o o o  o  o
3057:    row 4  |o o o d d d o o o o  o  o
3058:    row 5  |o o o d d d o o o o  o  o
3059:           --------------------------
3060: .ve

3062:    Thus, any entries in the d locations are stored in the d (diagonal)
3063:    submatrix, and any entries in the o locations are stored in the
3064:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
3065:    stored simply in the MATSEQBAIJ format for compressed row storage.

3067:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
3068:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
3069:    In general, for PDE problems in which most nonzeros are near the diagonal,
3070:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
3071:    or you will get TERRIBLE performance; see the users' manual chapter on
3072:    matrices.

3074:    You can call MatGetInfo() to get information on how effective the preallocation was;
3075:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3076:    You can also run with the option -info and look for messages with the string
3077:    malloc in them to see if additional memory allocation was needed.

3079:    Level: intermediate

3081: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocationCSR(), PetscSplitOwnership()
3082: @*/
3083: PetscErrorCode  MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3084: {

3091:   PetscTryMethod(B,"MatMPIBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,bs,d_nz,d_nnz,o_nz,o_nnz));
3092:   return(0);
3093: }

3095: /*@C
3096:    MatCreateBAIJ - Creates a sparse parallel matrix in block AIJ format
3097:    (block compressed row).  For good matrix assembly performance
3098:    the user should preallocate the matrix storage by setting the parameters
3099:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
3100:    performance can be increased by more than a factor of 50.

3102:    Collective

3104:    Input Parameters:
3105: +  comm - MPI communicator
3106: .  bs   - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
3107:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
3108: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3109:            This value should be the same as the local size used in creating the
3110:            y vector for the matrix-vector product y = Ax.
3111: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
3112:            This value should be the same as the local size used in creating the
3113:            x vector for the matrix-vector product y = Ax.
3114: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3115: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3116: .  d_nz  - number of nonzero blocks per block row in diagonal portion of local
3117:            submatrix  (same for all local rows)
3118: .  d_nnz - array containing the number of nonzero blocks in the various block rows
3119:            of the in diagonal portion of the local (possibly different for each block
3120:            row) or NULL.  If you plan to factor the matrix you must leave room for the diagonal entry
3121:            and set it even if it is zero.
3122: .  o_nz  - number of nonzero blocks per block row in the off-diagonal portion of local
3123:            submatrix (same for all local rows).
3124: -  o_nnz - array containing the number of nonzero blocks in the various block rows of the
3125:            off-diagonal portion of the local submatrix (possibly different for
3126:            each block row) or NULL.

3128:    Output Parameter:
3129: .  A - the matrix

3131:    Options Database Keys:
3132: +   -mat_block_size - size of the blocks to use
3133: -   -mat_use_hash_table <fact>

3135:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3136:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
3137:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3139:    Notes:
3140:    If the *_nnz parameter is given then the *_nz parameter is ignored

3142:    A nonzero block is any block that as 1 or more nonzeros in it

3144:    The user MUST specify either the local or global matrix dimensions
3145:    (possibly both).

3147:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
3148:    than it must be used on all processors that share the object for that argument.

3150:    Storage Information:
3151:    For a square global matrix we define each processor's diagonal portion
3152:    to be its local rows and the corresponding columns (a square submatrix);
3153:    each processor's off-diagonal portion encompasses the remainder of the
3154:    local matrix (a rectangular submatrix).

3156:    The user can specify preallocated storage for the diagonal part of
3157:    the local submatrix with either d_nz or d_nnz (not both).  Set
3158:    d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
3159:    memory allocation.  Likewise, specify preallocated storage for the
3160:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

3162:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3163:    the figure below we depict these three local rows and all columns (0-11).

3165: .vb
3166:            0 1 2 3 4 5 6 7 8 9 10 11
3167:           --------------------------
3168:    row 3  |o o o d d d o o o o  o  o
3169:    row 4  |o o o d d d o o o o  o  o
3170:    row 5  |o o o d d d o o o o  o  o
3171:           --------------------------
3172: .ve

3174:    Thus, any entries in the d locations are stored in the d (diagonal)
3175:    submatrix, and any entries in the o locations are stored in the
3176:    o (off-diagonal) submatrix.  Note that the d and the o submatrices are
3177:    stored simply in the MATSEQBAIJ format for compressed row storage.

3179:    Now d_nz should indicate the number of block nonzeros per row in the d matrix,
3180:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
3181:    In general, for PDE problems in which most nonzeros are near the diagonal,
3182:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
3183:    or you will get TERRIBLE performance; see the users' manual chapter on
3184:    matrices.

3186:    Level: intermediate

3188: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
3189: @*/
3190: PetscErrorCode  MatCreateBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
3191: {
3193:   PetscMPIInt    size;

3196:   MatCreate(comm,A);
3197:   MatSetSizes(*A,m,n,M,N);
3198:   MPI_Comm_size(comm,&size);
3199:   if (size > 1) {
3200:     MatSetType(*A,MATMPIBAIJ);
3201:     MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
3202:   } else {
3203:     MatSetType(*A,MATSEQBAIJ);
3204:     MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
3205:   }
3206:   return(0);
3207: }

3209: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
3210: {
3211:   Mat            mat;
3212:   Mat_MPIBAIJ    *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
3214:   PetscInt       len=0;

3217:   *newmat = NULL;
3218:   MatCreate(PetscObjectComm((PetscObject)matin),&mat);
3219:   MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
3220:   MatSetType(mat,((PetscObject)matin)->type_name);

3222:   mat->factortype   = matin->factortype;
3223:   mat->preallocated = PETSC_TRUE;
3224:   mat->assembled    = PETSC_TRUE;
3225:   mat->insertmode   = NOT_SET_VALUES;

3227:   a             = (Mat_MPIBAIJ*)mat->data;
3228:   mat->rmap->bs = matin->rmap->bs;
3229:   a->bs2        = oldmat->bs2;
3230:   a->mbs        = oldmat->mbs;
3231:   a->nbs        = oldmat->nbs;
3232:   a->Mbs        = oldmat->Mbs;
3233:   a->Nbs        = oldmat->Nbs;

3235:   PetscLayoutReference(matin->rmap,&mat->rmap);
3236:   PetscLayoutReference(matin->cmap,&mat->cmap);

3238:   a->size         = oldmat->size;
3239:   a->rank         = oldmat->rank;
3240:   a->donotstash   = oldmat->donotstash;
3241:   a->roworiented  = oldmat->roworiented;
3242:   a->rowindices   = NULL;
3243:   a->rowvalues    = NULL;
3244:   a->getrowactive = PETSC_FALSE;
3245:   a->barray       = NULL;
3246:   a->rstartbs     = oldmat->rstartbs;
3247:   a->rendbs       = oldmat->rendbs;
3248:   a->cstartbs     = oldmat->cstartbs;
3249:   a->cendbs       = oldmat->cendbs;

3251:   /* hash table stuff */
3252:   a->ht           = NULL;
3253:   a->hd           = NULL;
3254:   a->ht_size      = 0;
3255:   a->ht_flag      = oldmat->ht_flag;
3256:   a->ht_fact      = oldmat->ht_fact;
3257:   a->ht_total_ct  = 0;
3258:   a->ht_insert_ct = 0;

3260:   PetscArraycpy(a->rangebs,oldmat->rangebs,a->size+1);
3261:   if (oldmat->colmap) {
3262: #if defined(PETSC_USE_CTABLE)
3263:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
3264: #else
3265:     PetscMalloc1(a->Nbs,&a->colmap);
3266:     PetscLogObjectMemory((PetscObject)mat,(a->Nbs)*sizeof(PetscInt));
3267:     PetscArraycpy(a->colmap,oldmat->colmap,a->Nbs);
3268: #endif
3269:   } else a->colmap = NULL;

3271:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
3272:     PetscMalloc1(len,&a->garray);
3273:     PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
3274:     PetscArraycpy(a->garray,oldmat->garray,len);
3275:   } else a->garray = NULL;

3277:   MatStashCreate_Private(PetscObjectComm((PetscObject)matin),matin->rmap->bs,&mat->bstash);
3278:   VecDuplicate(oldmat->lvec,&a->lvec);
3279:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
3280:   VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
3281:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);

3283:   MatDuplicate(oldmat->A,cpvalues,&a->A);
3284:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
3285:   MatDuplicate(oldmat->B,cpvalues,&a->B);
3286:   PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
3287:   PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
3288:   *newmat = mat;
3289:   return(0);
3290: }

3292: /* Used for both MPIBAIJ and MPISBAIJ matrices */
3293: PetscErrorCode MatLoad_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
3294: {
3295:   PetscInt       header[4],M,N,nz,bs,m,n,mbs,nbs,rows,cols,sum,i,j,k;
3296:   PetscInt       *rowidxs,*colidxs,rs,cs,ce;
3297:   PetscScalar    *matvals;

3301:   PetscViewerSetUp(viewer);

3303:   /* read in matrix header */
3304:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
3305:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
3306:   M  = header[1]; N = header[2]; nz = header[3];
3307:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
3308:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
3309:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as MPIBAIJ");

3311:   /* set block sizes from the viewer's .info file */
3312:   MatLoad_Binary_BlockSizes(mat,viewer);
3313:   /* set local sizes if not set already */
3314:   if (mat->rmap->n < 0 && M == N) mat->rmap->n = mat->cmap->n;
3315:   if (mat->cmap->n < 0 && M == N) mat->cmap->n = mat->rmap->n;
3316:   /* set global sizes if not set already */
3317:   if (mat->rmap->N < 0) mat->rmap->N = M;
3318:   if (mat->cmap->N < 0) mat->cmap->N = N;
3319:   PetscLayoutSetUp(mat->rmap);
3320:   PetscLayoutSetUp(mat->cmap);

3322:   /* check if the matrix sizes are correct */
3323:   MatGetSize(mat,&rows,&cols);
3324:   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);
3325:   MatGetBlockSize(mat,&bs);
3326:   MatGetLocalSize(mat,&m,&n);
3327:   PetscLayoutGetRange(mat->rmap,&rs,NULL);
3328:   PetscLayoutGetRange(mat->cmap,&cs,&ce);
3329:   mbs = m/bs; nbs = n/bs;

3331:   /* read in row lengths and build row indices */
3332:   PetscMalloc1(m+1,&rowidxs);
3333:   PetscViewerBinaryReadAll(viewer,rowidxs+1,m,PETSC_DECIDE,M,PETSC_INT);
3334:   rowidxs[0] = 0; for (i=0; i<m; i++) rowidxs[i+1] += rowidxs[i];
3335:   MPIU_Allreduce(&rowidxs[m],&sum,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)viewer));
3336:   if (sum != nz) SETERRQ2(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);

3338:   /* read in column indices and matrix values */
3339:   PetscMalloc2(rowidxs[m],&colidxs,rowidxs[m],&matvals);
3340:   PetscViewerBinaryReadAll(viewer,colidxs,rowidxs[m],PETSC_DETERMINE,PETSC_DETERMINE,PETSC_INT);
3341:   PetscViewerBinaryReadAll(viewer,matvals,rowidxs[m],PETSC_DETERMINE,PETSC_DETERMINE,PETSC_SCALAR);

3343:   { /* preallocate matrix storage */
3344:     PetscBT    bt; /* helper bit set to count diagonal nonzeros */
3345:     PetscHSetI ht; /* helper hash set to count off-diagonal nonzeros */
3346:     PetscBool  sbaij,done;
3347:     PetscInt   *d_nnz,*o_nnz;

3349:     PetscBTCreate(nbs,&bt);
3350:     PetscHSetICreate(&ht);
3351:     PetscCalloc2(mbs,&d_nnz,mbs,&o_nnz);
3352:     PetscObjectTypeCompare((PetscObject)mat,MATMPISBAIJ,&sbaij);
3353:     for (i=0; i<mbs; i++) {
3354:       PetscBTMemzero(nbs,bt);
3355:       PetscHSetIClear(ht);
3356:       for (k=0; k<bs; k++) {
3357:         PetscInt row = bs*i + k;
3358:         for (j=rowidxs[row]; j<rowidxs[row+1]; j++) {
3359:           PetscInt col = colidxs[j];
3360:           if (!sbaij || col >= row) {
3361:             if (col >= cs && col < ce) {
3362:               if (!PetscBTLookupSet(bt,(col-cs)/bs)) d_nnz[i]++;
3363:             } else {
3364:               PetscHSetIQueryAdd(ht,col/bs,&done);
3365:               if (done) o_nnz[i]++;
3366:             }
3367:           }
3368:         }
3369:       }
3370:     }
3371:     PetscBTDestroy(&bt);
3372:     PetscHSetIDestroy(&ht);
3373:     MatMPIBAIJSetPreallocation(mat,bs,0,d_nnz,0,o_nnz);
3374:     MatMPISBAIJSetPreallocation(mat,bs,0,d_nnz,0,o_nnz);
3375:     PetscFree2(d_nnz,o_nnz);
3376:   }

3378:   /* store matrix values */
3379:   for (i=0; i<m; i++) {
3380:     PetscInt row = rs + i, s = rowidxs[i], e = rowidxs[i+1];
3381:     (*mat->ops->setvalues)(mat,1,&row,e-s,colidxs+s,matvals+s,INSERT_VALUES);
3382:   }

3384:   PetscFree(rowidxs);
3385:   PetscFree2(colidxs,matvals);
3386:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
3387:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
3388:   return(0);
3389: }

3391: PetscErrorCode MatLoad_MPIBAIJ(Mat mat,PetscViewer viewer)
3392: {
3394:   PetscBool      isbinary;

3397:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
3398:   if (!isbinary) SETERRQ2(PetscObjectComm((PetscObject)viewer),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)mat)->type_name);
3399:   MatLoad_MPIBAIJ_Binary(mat,viewer);
3400:   return(0);
3401: }

3403: /*@
3404:    MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

3406:    Input Parameters:
3407: +  mat  - the matrix
3408: -  fact - factor

3410:    Not Collective, each process can use a different factor

3412:    Level: advanced

3414:   Notes:
3415:    This can also be set by the command line option: -mat_use_hash_table <fact>

3417: .seealso: MatSetOption()
3418: @*/
3419: PetscErrorCode  MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
3420: {

3424:   PetscTryMethod(mat,"MatSetHashTableFactor_C",(Mat,PetscReal),(mat,fact));
3425:   return(0);
3426: }

3428: PetscErrorCode  MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
3429: {
3430:   Mat_MPIBAIJ *baij;

3433:   baij          = (Mat_MPIBAIJ*)mat->data;
3434:   baij->ht_fact = fact;
3435:   return(0);
3436: }

3438: PetscErrorCode  MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
3439: {
3440:   Mat_MPIBAIJ    *a = (Mat_MPIBAIJ*)A->data;
3441:   PetscBool      flg;

3445:   PetscObjectTypeCompare((PetscObject)A,MATMPIBAIJ,&flg);
3446:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"This function requires a MATMPIBAIJ matrix as input");
3447:   if (Ad)     *Ad     = a->A;
3448:   if (Ao)     *Ao     = a->B;
3449:   if (colmap) *colmap = a->garray;
3450:   return(0);
3451: }

3453: /*
3454:     Special version for direct calls from Fortran (to eliminate two function call overheads
3455: */
3456: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3457: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3458: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3459: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3460: #endif

3462: /*@C
3463:   MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to MatSetValuesBlocked()

3465:   Collective on Mat

3467:   Input Parameters:
3468: + mat - the matrix
3469: . min - number of input rows
3470: . im - input rows
3471: . nin - number of input columns
3472: . in - input columns
3473: . v - numerical values input
3474: - addvin - INSERT_VALUES or ADD_VALUES

3476:   Notes:
3477:     This has a complete copy of MatSetValuesBlocked_MPIBAIJ() which is terrible code un-reuse.

3479:   Level: advanced

3481: .seealso:   MatSetValuesBlocked()
3482: @*/
3483: PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin,PetscInt *min,const PetscInt im[],PetscInt *nin,const PetscInt in[],const MatScalar v[],InsertMode *addvin)
3484: {
3485:   /* convert input arguments to C version */
3486:   Mat        mat  = *matin;
3487:   PetscInt   m    = *min, n = *nin;
3488:   InsertMode addv = *addvin;

3490:   Mat_MPIBAIJ     *baij = (Mat_MPIBAIJ*)mat->data;
3491:   const MatScalar *value;
3492:   MatScalar       *barray     = baij->barray;
3493:   PetscBool       roworiented = baij->roworiented;
3494:   PetscErrorCode  ierr;
3495:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
3496:   PetscInt        rend=baij->rendbs,cstart=baij->cstartbs,stepval;
3497:   PetscInt        cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;

3500:   /* tasks normally handled by MatSetValuesBlocked() */
3501:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3502:   else if (PetscUnlikely(mat->insertmode != addv)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
3503:   if (PetscUnlikely(mat->factortype)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3504:   if (mat->assembled) {
3505:     mat->was_assembled = PETSC_TRUE;
3506:     mat->assembled     = PETSC_FALSE;
3507:   }
3508:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);


3511:   if (!barray) {
3512:     PetscMalloc1(bs2,&barray);
3513:     baij->barray = barray;
3514:   }

3516:   if (roworiented) stepval = (n-1)*bs;
3517:   else stepval = (m-1)*bs;

3519:   for (i=0; i<m; i++) {
3520:     if (im[i] < 0) continue;
3521:     if (PetscUnlikelyDebug(im[i] >= baij->Mbs)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
3522:     if (im[i] >= rstart && im[i] < rend) {
3523:       row = im[i] - rstart;
3524:       for (j=0; j<n; j++) {
3525:         /* If NumCol = 1 then a copy is not required */
3526:         if ((roworiented) && (n == 1)) {
3527:           barray = (MatScalar*)v + i*bs2;
3528:         } else if ((!roworiented) && (m == 1)) {
3529:           barray = (MatScalar*)v + j*bs2;
3530:         } else { /* Here a copy is required */
3531:           if (roworiented) {
3532:             value = v + i*(stepval+bs)*bs + j*bs;
3533:           } else {
3534:             value = v + j*(stepval+bs)*bs + i*bs;
3535:           }
3536:           for (ii=0; ii<bs; ii++,value+=stepval) {
3537:             for (jj=0; jj<bs; jj++) {
3538:               *barray++ = *value++;
3539:             }
3540:           }
3541:           barray -=bs2;
3542:         }

3544:         if (in[j] >= cstart && in[j] < cend) {
3545:           col  = in[j] - cstart;
3546:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->A,row,col,barray,addv,im[i],in[j]);
3547:         } else if (in[j] < 0) continue;
3548:         else if (PetscUnlikelyDebug(in[j] >= baij->Nbs)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);
3549:         else {
3550:           if (mat->was_assembled) {
3551:             if (!baij->colmap) {
3552:               MatCreateColmap_MPIBAIJ_Private(mat);
3553:             }

3555: #if defined(PETSC_USE_DEBUG)
3556: #if defined(PETSC_USE_CTABLE)
3557:             { PetscInt data;
3558:               PetscTableFind(baij->colmap,in[j]+1,&data);
3559:               if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
3560:             }
3561: #else
3562:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
3563: #endif
3564: #endif
3565: #if defined(PETSC_USE_CTABLE)
3566:             PetscTableFind(baij->colmap,in[j]+1,&col);
3567:             col  = (col - 1)/bs;
3568: #else
3569:             col = (baij->colmap[in[j]] - 1)/bs;
3570: #endif
3571:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
3572:               MatDisAssemble_MPIBAIJ(mat);
3573:               col  =  in[j];
3574:             }
3575:           } else col = in[j];
3576:           MatSetValuesBlocked_SeqBAIJ_Inlined(baij->B,row,col,barray,addv,im[i],in[j]);
3577:         }
3578:       }
3579:     } else {
3580:       if (!baij->donotstash) {
3581:         if (roworiented) {
3582:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
3583:         } else {
3584:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
3585:         }
3586:       }
3587:     }
3588:   }

3590:   /* task normally handled by MatSetValuesBlocked() */
3591:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
3592:   return(0);
3593: }

3595: /*@
3596:      MatCreateMPIBAIJWithArrays - creates a MPI BAIJ matrix using arrays that contain in standard block
3597:          CSR format the local rows.

3599:    Collective

3601:    Input Parameters:
3602: +  comm - MPI communicator
3603: .  bs - the block size, only a block size of 1 is supported
3604: .  m - number of local rows (Cannot be PETSC_DECIDE)
3605: .  n - This value should be the same as the local size used in creating the
3606:        x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3607:        calculated if N is given) For square matrices n is almost always m.
3608: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3609: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3610: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of block elements in that rowth block row of the matrix
3611: .   j - column indices
3612: -   a - matrix values

3614:    Output Parameter:
3615: .   mat - the matrix

3617:    Level: intermediate

3619:    Notes:
3620:        The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3621:      thus you CANNOT change the matrix entries by changing the values of a[] after you have
3622:      called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.

3624:      The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3625:      the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3626:      block, followed by the second column of the first block etc etc.  That is, the blocks are contiguous in memory
3627:      with column-major ordering within blocks.

3629:        The i and j indices are 0 based, and i indices are indices corresponding to the local j array.

3631: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3632:           MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
3633: @*/
3634: PetscErrorCode  MatCreateMPIBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3635: {

3639:   if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3640:   if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3641:   MatCreate(comm,mat);
3642:   MatSetSizes(*mat,m,n,M,N);
3643:   MatSetType(*mat,MATMPIBAIJ);
3644:   MatSetBlockSize(*mat,bs);
3645:   MatSetUp(*mat);
3646:   MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_FALSE);
3647:   MatMPIBAIJSetPreallocationCSR(*mat,bs,i,j,a);
3648:   MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_TRUE);
3649:   return(0);
3650: }

3652: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3653: {
3655:   PetscInt       m,N,i,rstart,nnz,Ii,bs,cbs;
3656:   PetscInt       *indx;
3657:   PetscScalar    *values;

3660:   MatGetSize(inmat,&m,&N);
3661:   if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3662:     Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)inmat->data;
3663:     PetscInt       *dnz,*onz,mbs,Nbs,nbs;
3664:     PetscInt       *bindx,rmax=a->rmax,j;
3665:     PetscMPIInt    rank,size;

3667:     MatGetBlockSizes(inmat,&bs,&cbs);
3668:     mbs = m/bs; Nbs = N/cbs;
3669:     if (n == PETSC_DECIDE) {
3670:       PetscSplitOwnershipBlock(comm,cbs,&n,&N);
3671:     }
3672:     nbs = n/cbs;

3674:     PetscMalloc1(rmax,&bindx);
3675:     MatPreallocateInitialize(comm,mbs,nbs,dnz,onz); /* inline function, output __end and __rstart are used below */

3677:     MPI_Comm_rank(comm,&rank);
3678:     MPI_Comm_rank(comm,&size);
3679:     if (rank == size-1) {
3680:       /* Check sum(nbs) = Nbs */
3681:       if (__end != Nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Sum of local block columns %D != global block columns %D",__end,Nbs);
3682:     }

3684:     rstart = __rstart; /* block rstart of *outmat; see inline function MatPreallocateInitialize */
3685:     for (i=0; i<mbs; i++) {
3686:       MatGetRow_SeqBAIJ(inmat,i*bs,&nnz,&indx,NULL); /* non-blocked nnz and indx */
3687:       nnz = nnz/bs;
3688:       for (j=0; j<nnz; j++) bindx[j] = indx[j*bs]/bs;
3689:       MatPreallocateSet(i+rstart,nnz,bindx,dnz,onz);
3690:       MatRestoreRow_SeqBAIJ(inmat,i*bs,&nnz,&indx,NULL);
3691:     }
3692:     PetscFree(bindx);

3694:     MatCreate(comm,outmat);
3695:     MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3696:     MatSetBlockSizes(*outmat,bs,cbs);
3697:     MatSetType(*outmat,MATBAIJ);
3698:     MatSeqBAIJSetPreallocation(*outmat,bs,0,dnz);
3699:     MatMPIBAIJSetPreallocation(*outmat,bs,0,dnz,0,onz);
3700:     MatPreallocateFinalize(dnz,onz);
3701:     MatSetOption(*outmat,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE);
3702:   }

3704:   /* numeric phase */
3705:   MatGetBlockSizes(inmat,&bs,&cbs);
3706:   MatGetOwnershipRange(*outmat,&rstart,NULL);

3708:   for (i=0; i<m; i++) {
3709:     MatGetRow_SeqBAIJ(inmat,i,&nnz,&indx,&values);
3710:     Ii   = i + rstart;
3711:     MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3712:     MatRestoreRow_SeqBAIJ(inmat,i,&nnz,&indx,&values);
3713:   }
3714:   MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3715:   MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
3716:   return(0);
3717: }