Actual source code: mpisbaij.c

  1: #define PETSCMAT_DLL

 3:  #include src/mat/impls/baij/mpi/mpibaij.h
 4:  #include mpisbaij.h
 5:  #include src/mat/impls/sbaij/seq/sbaij.h

  7: EXTERN PetscErrorCode MatSetUpMultiply_MPISBAIJ(Mat);
  8: EXTERN PetscErrorCode MatSetUpMultiply_MPISBAIJ_2comm(Mat);
  9: EXTERN PetscErrorCode DisAssemble_MPISBAIJ(Mat);
 10: EXTERN PetscErrorCode MatIncreaseOverlap_MPISBAIJ(Mat,PetscInt,IS[],PetscInt);
 11: EXTERN PetscErrorCode MatGetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
 12: EXTERN PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
 13: EXTERN PetscErrorCode MatSetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt [],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
 14: EXTERN PetscErrorCode MatSetValuesBlocked_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
 15: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
 16: EXTERN PetscErrorCode MatGetRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
 17: EXTERN PetscErrorCode MatRestoreRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
 18: EXTERN PetscErrorCode MatPrintHelp_SeqSBAIJ(Mat);
 19: EXTERN PetscErrorCode MatZeroRows_SeqSBAIJ(Mat,IS,PetscScalar*);
 20: EXTERN PetscErrorCode MatZeroRows_SeqBAIJ(Mat,IS,PetscScalar *);
 21: EXTERN PetscErrorCode MatGetRowMax_MPISBAIJ(Mat,Vec);
 22: EXTERN PetscErrorCode MatRelax_MPISBAIJ(Mat,Vec,PetscReal,MatSORType,PetscReal,PetscInt,PetscInt,Vec);

 24: /*  UGLY, ugly, ugly
 25:    When MatScalar == PetscScalar the function MatSetValuesBlocked_MPIBAIJ_MatScalar() does 
 26:    not exist. Otherwise ..._MatScalar() takes matrix elements in single precision and 
 27:    inserts them into the single precision data structure. The function MatSetValuesBlocked_MPIBAIJ()
 28:    converts the entries into single precision and then calls ..._MatScalar() to put them
 29:    into the single precision data structures.
 30: */
 31: #if defined(PETSC_USE_MAT_SINGLE)
 32: EXTERN PetscErrorCode MatSetValuesBlocked_SeqSBAIJ_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
 33: EXTERN PetscErrorCode MatSetValues_MPISBAIJ_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
 34: EXTERN PetscErrorCode MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
 35: EXTERN PetscErrorCode MatSetValues_MPISBAIJ_HT_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
 36: EXTERN PetscErrorCode MatSetValuesBlocked_MPISBAIJ_HT_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
 37: #else
 38: #define MatSetValuesBlocked_SeqSBAIJ_MatScalar      MatSetValuesBlocked_SeqSBAIJ
 39: #define MatSetValues_MPISBAIJ_MatScalar             MatSetValues_MPISBAIJ
 40: #define MatSetValuesBlocked_MPISBAIJ_MatScalar      MatSetValuesBlocked_MPISBAIJ
 41: #define MatSetValues_MPISBAIJ_HT_MatScalar          MatSetValues_MPISBAIJ_HT
 42: #define MatSetValuesBlocked_MPISBAIJ_HT_MatScalar   MatSetValuesBlocked_MPISBAIJ_HT
 43: #endif

 48: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_MPISBAIJ(Mat mat)
 49: {
 50:   Mat_MPISBAIJ   *aij = (Mat_MPISBAIJ *)mat->data;

 54:   MatStoreValues(aij->A);
 55:   MatStoreValues(aij->B);
 56:   return(0);
 57: }

 63: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_MPISBAIJ(Mat mat)
 64: {
 65:   Mat_MPISBAIJ   *aij = (Mat_MPISBAIJ *)mat->data;

 69:   MatRetrieveValues(aij->A);
 70:   MatRetrieveValues(aij->B);
 71:   return(0);
 72: }


 76: #define CHUNKSIZE  10

 78: #define  MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv) \
 79: { \
 80:  \
 81:     brow = row/bs;  \
 82:     rp   = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
 83:     rmax = aimax[brow]; nrow = ailen[brow]; \
 84:       bcol = col/bs; \
 85:       ridx = row % bs; cidx = col % bs; \
 86:       low = 0; high = nrow; \
 87:       while (high-low > 3) { \
 88:         t = (low+high)/2; \
 89:         if (rp[t] > bcol) high = t; \
 90:         else              low  = t; \
 91:       } \
 92:       for (_i=low; _i<high; _i++) { \
 93:         if (rp[_i] > bcol) break; \
 94:         if (rp[_i] == bcol) { \
 95:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
 96:           if (addv == ADD_VALUES) *bap += value;  \
 97:           else                    *bap  = value;  \
 98:           goto a_noinsert; \
 99:         } \
100:       } \
101:       if (a->nonew == 1) goto a_noinsert; \
102:       if (a->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
103:       MatSeqXAIJReallocateAIJ(a,bs2,nrow,brow,bcol,rmax,aa,ai,aj,a->mbs,rp,ap,aimax,a->nonew); \
104:       N = nrow++ - 1;  \
105:       /* shift up all the later entries in this row */ \
106:       for (ii=N; ii>=_i; ii--) { \
107:         rp[ii+1] = rp[ii]; \
108:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
109:       } \
110:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }  \
111:       rp[_i]                      = bcol;  \
112:       ap[bs2*_i + bs*cidx + ridx] = value;  \
113:       a_noinsert:; \
114:     ailen[brow] = nrow; \
115: } 
116: #ifndef MatSetValues_SeqBAIJ_B_Private
117: #define  MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv) \
118: { \
119:     brow = row/bs;  \
120:     rp   = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
121:     rmax = bimax[brow]; nrow = bilen[brow]; \
122:       bcol = col/bs; \
123:       ridx = row % bs; cidx = col % bs; \
124:       low = 0; high = nrow; \
125:       while (high-low > 3) { \
126:         t = (low+high)/2; \
127:         if (rp[t] > bcol) high = t; \
128:         else              low  = t; \
129:       } \
130:       for (_i=low; _i<high; _i++) { \
131:         if (rp[_i] > bcol) break; \
132:         if (rp[_i] == bcol) { \
133:           bap  = ap +  bs2*_i + bs*cidx + ridx; \
134:           if (addv == ADD_VALUES) *bap += value;  \
135:           else                    *bap  = value;  \
136:           goto b_noinsert; \
137:         } \
138:       } \
139:       if (b->nonew == 1) goto b_noinsert; \
140:       if (b->nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
141:       MatSeqXAIJReallocateAIJ(b,bs2,nrow,brow,bcol,rmax,ba,bi,bj,b->mbs,rp,ap,bimax,b->nonew); \
142:       N = nrow++ - 1;  \
143:       /* shift up all the later entries in this row */ \
144:       for (ii=N; ii>=_i; ii--) { \
145:         rp[ii+1] = rp[ii]; \
146:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
147:       } \
148:       if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}  \
149:       rp[_i]                      = bcol;  \
150:       ap[bs2*_i + bs*cidx + ridx] = value;  \
151:       b_noinsert:; \
152:     bilen[brow] = nrow; \
153: } 
154: #endif

156: #if defined(PETSC_USE_MAT_SINGLE)
159: PetscErrorCode MatSetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
160: {
161:   Mat_MPISBAIJ   *b = (Mat_MPISBAIJ*)mat->data;
163:   PetscInt       i,N = m*n;
164:   MatScalar      *vsingle;

167:   if (N > b->setvalueslen) {
168:     PetscFree(b->setvaluescopy);
169:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
170:     b->setvalueslen  = N;
171:   }
172:   vsingle = b->setvaluescopy;

174:   for (i=0; i<N; i++) {
175:     vsingle[i] = v[i];
176:   }
177:   MatSetValues_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
178:   return(0);
179: }

183: PetscErrorCode MatSetValuesBlocked_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
184: {
185:   Mat_MPIBAIJ    *b = (Mat_MPIBAIJ*)mat->data;
187:   PetscInt       i,N = m*n*b->bs2;
188:   MatScalar      *vsingle;

191:   if (N > b->setvalueslen) {
192:     PetscFree(b->setvaluescopy);
193:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
194:     b->setvalueslen  = N;
195:   }
196:   vsingle = b->setvaluescopy;
197:   for (i=0; i<N; i++) {
198:     vsingle[i] = v[i];
199:   }
200:   MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
201:   return(0);
202: }
203: #endif

205: /* Only add/insert a(i,j) with i<=j (blocks). 
206:    Any a(i,j) with i>j input by user is ingored. 
207: */
210: PetscErrorCode MatSetValues_MPISBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
211: {
212:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
213:   MatScalar      value;
214:   PetscTruth     roworiented = baij->roworiented;
216:   PetscInt       i,j,row,col;
217:   PetscInt       rstart_orig=mat->rmap.rstart;
218:   PetscInt       rend_orig=mat->rmap.rend,cstart_orig=mat->cmap.rstart;
219:   PetscInt       cend_orig=mat->cmap.rend,bs=mat->rmap.bs;

221:   /* Some Variables required in the macro */
222:   Mat            A = baij->A;
223:   Mat_SeqSBAIJ   *a = (Mat_SeqSBAIJ*)(A)->data;
224:   PetscInt       *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
225:   MatScalar      *aa=a->a;

227:   Mat            B = baij->B;
228:   Mat_SeqBAIJ   *b = (Mat_SeqBAIJ*)(B)->data;
229:   PetscInt      *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
230:   MatScalar     *ba=b->a;

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

236:   /* for stash */
237:   PetscInt      n_loc, *in_loc=0;
238:   MatScalar     *v_loc=0;


242:   if(!baij->donotstash){
243:     PetscMalloc(n*sizeof(PetscInt),&in_loc);
244:     PetscMalloc(n*sizeof(MatScalar),&v_loc);
245:   }

247:   for (i=0; i<m; i++) {
248:     if (im[i] < 0) continue;
249: #if defined(PETSC_USE_DEBUG)
250:     if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
251: #endif
252:     if (im[i] >= rstart_orig && im[i] < rend_orig) { /* this processor entry */
253:       row = im[i] - rstart_orig;              /* local row index */
254:       for (j=0; j<n; j++) {
255:         if (im[i]/bs > in[j]/bs){
256:           if (a->ignore_ltriangular){
257:             continue;    /* ignore lower triangular blocks */
258:           } else {
259:             SETERRQ(PETSC_ERR_USER,"Lower triangular value cannot be set for sbaij format. Ignoring these values, run with -mat_ignore_lower_triangular or call MatSetOption(mat,MAT_IGNORE_LOWER_TRIANGULAR)");
260:           }
261:         }
262:         if (in[j] >= cstart_orig && in[j] < cend_orig){  /* diag entry (A) */
263:           col = in[j] - cstart_orig;          /* local col index */
264:           brow = row/bs; bcol = col/bs;
265:           if (brow > bcol) continue;  /* ignore lower triangular blocks of A */
266:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
267:           MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv);
268:           /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
269:         } else if (in[j] < 0) continue;
270: #if defined(PETSC_USE_DEBUG)
271:         else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap.N-1);}
272: #endif
273:         else {  /* off-diag entry (B) */
274:           if (mat->was_assembled) {
275:             if (!baij->colmap) {
276:               CreateColmap_MPIBAIJ_Private(mat);
277:             }
278: #if defined (PETSC_USE_CTABLE)
279:             PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
280:             col  = col - 1;
281: #else
282:             col = baij->colmap[in[j]/bs] - 1;
283: #endif
284:             if (col < 0 && !((Mat_SeqSBAIJ*)(baij->A->data))->nonew) {
285:               DisAssemble_MPISBAIJ(mat);
286:               col =  in[j];
287:               /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
288:               B = baij->B;
289:               b = (Mat_SeqBAIJ*)(B)->data;
290:               bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
291:               ba=b->a;
292:             } else col += in[j]%bs;
293:           } else col = in[j];
294:           if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
295:           MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv);
296:           /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
297:         }
298:       }
299:     } else {  /* off processor entry */
300:       if (!baij->donotstash) {
301:         n_loc = 0;
302:         for (j=0; j<n; j++){
303:           if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
304:           in_loc[n_loc] = in[j];
305:           if (roworiented) {
306:             v_loc[n_loc] = v[i*n+j];
307:           } else {
308:             v_loc[n_loc] = v[j*m+i];
309:           }
310:           n_loc++;
311:         }
312:         MatStashValuesRow_Private(&mat->stash,im[i],n_loc,in_loc,v_loc);
313:       }
314:     }
315:   }

317:   if(!baij->donotstash){
318:     PetscFree(in_loc);
319:     PetscFree(v_loc);
320:   }
321:   return(0);
322: }

326: PetscErrorCode MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
327: {
328:   Mat_MPISBAIJ    *baij = (Mat_MPISBAIJ*)mat->data;
329:   const MatScalar *value;
330:   MatScalar       *barray=baij->barray;
331:   PetscTruth      roworiented = baij->roworiented;
332:   PetscErrorCode  ierr;
333:   PetscInt        i,j,ii,jj,row,col,rstart=baij->rstartbs;
334:   PetscInt        rend=baij->rendbs,cstart=baij->rstartbs,stepval;
335:   PetscInt        cend=baij->rendbs,bs=mat->rmap.bs,bs2=baij->bs2;

338:   if(!barray) {
339:     PetscMalloc(bs2*sizeof(MatScalar),&barray);
340:     baij->barray = barray;
341:   }

343:   if (roworiented) {
344:     stepval = (n-1)*bs;
345:   } else {
346:     stepval = (m-1)*bs;
347:   }
348:   for (i=0; i<m; i++) {
349:     if (im[i] < 0) continue;
350: #if defined(PETSC_USE_DEBUG)
351:     if (im[i] >= baij->Mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
352: #endif
353:     if (im[i] >= rstart && im[i] < rend) {
354:       row = im[i] - rstart;
355:       for (j=0; j<n; j++) {
356:         /* If NumCol = 1 then a copy is not required */
357:         if ((roworiented) && (n == 1)) {
358:           barray = (MatScalar*) v + i*bs2;
359:         } else if((!roworiented) && (m == 1)) {
360:           barray = (MatScalar*) v + j*bs2;
361:         } else { /* Here a copy is required */
362:           if (roworiented) {
363:             value = v + i*(stepval+bs)*bs + j*bs;
364:           } else {
365:             value = v + j*(stepval+bs)*bs + i*bs;
366:           }
367:           for (ii=0; ii<bs; ii++,value+=stepval) {
368:             for (jj=0; jj<bs; jj++) {
369:               *barray++  = *value++;
370:             }
371:           }
372:           barray -=bs2;
373:         }
374: 
375:         if (in[j] >= cstart && in[j] < cend){
376:           col  = in[j] - cstart;
377:           MatSetValuesBlocked_SeqSBAIJ(baij->A,1,&row,1,&col,barray,addv);
378:         }
379:         else if (in[j] < 0) continue;
380: #if defined(PETSC_USE_DEBUG)
381:         else if (in[j] >= baij->Nbs) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);}
382: #endif
383:         else {
384:           if (mat->was_assembled) {
385:             if (!baij->colmap) {
386:               CreateColmap_MPIBAIJ_Private(mat);
387:             }

389: #if defined(PETSC_USE_DEBUG)
390: #if defined (PETSC_USE_CTABLE)
391:             { PetscInt data;
392:               PetscTableFind(baij->colmap,in[j]+1,&data);
393:               if ((data - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
394:             }
395: #else
396:             if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_ERR_PLIB,"Incorrect colmap");
397: #endif
398: #endif
399: #if defined (PETSC_USE_CTABLE)
400:             PetscTableFind(baij->colmap,in[j]+1,&col);
401:             col  = (col - 1)/bs;
402: #else
403:             col = (baij->colmap[in[j]] - 1)/bs;
404: #endif
405:             if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
406:               DisAssemble_MPISBAIJ(mat);
407:               col =  in[j];
408:             }
409:           }
410:           else col = in[j];
411:           MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
412:         }
413:       }
414:     } else {
415:       if (!baij->donotstash) {
416:         if (roworiented) {
417:           MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
418:         } else {
419:           MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
420:         }
421:       }
422:     }
423:   }
424:   return(0);
425: }

429: PetscErrorCode MatGetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
430: {
431:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
433:   PetscInt       bs=mat->rmap.bs,i,j,bsrstart = mat->rmap.rstart,bsrend = mat->rmap.rend;
434:   PetscInt       bscstart = mat->cmap.rstart,bscend = mat->cmap.rend,row,col,data;

437:   for (i=0; i<m; i++) {
438:     if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
439:     if (idxm[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap.N-1);
440:     if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
441:       row = idxm[i] - bsrstart;
442:       for (j=0; j<n; j++) {
443:         if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column %D",idxn[j]);
444:         if (idxn[j] >= mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap.N-1);
445:         if (idxn[j] >= bscstart && idxn[j] < bscend){
446:           col = idxn[j] - bscstart;
447:           MatGetValues_SeqSBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
448:         } else {
449:           if (!baij->colmap) {
450:             CreateColmap_MPIBAIJ_Private(mat);
451:           }
452: #if defined (PETSC_USE_CTABLE)
453:           PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
454:           data --;
455: #else
456:           data = baij->colmap[idxn[j]/bs]-1;
457: #endif
458:           if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
459:           else {
460:             col  = data + idxn[j]%bs;
461:             MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
462:           }
463:         }
464:       }
465:     } else {
466:       SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
467:     }
468:   }
469:  return(0);
470: }

474: PetscErrorCode MatNorm_MPISBAIJ(Mat mat,NormType type,PetscReal *norm)
475: {
476:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
478:   PetscReal      sum[2],*lnorm2;

481:   if (baij->size == 1) {
482:      MatNorm(baij->A,type,norm);
483:   } else {
484:     if (type == NORM_FROBENIUS) {
485:       PetscMalloc(2*sizeof(PetscReal),&lnorm2);
486:        MatNorm(baij->A,type,lnorm2);
487:       *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2++;            /* squar power of norm(A) */
488:        MatNorm(baij->B,type,lnorm2);
489:       *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2--;             /* squar power of norm(B) */
490:       MPI_Allreduce(lnorm2,&sum,2,MPIU_REAL,MPI_SUM,mat->comm);
491:       *norm = sqrt(sum[0] + 2*sum[1]);
492:       PetscFree(lnorm2);
493:     } else if (type == NORM_INFINITY || type == NORM_1) { /* max row/column sum */
494:       Mat_SeqSBAIJ *amat=(Mat_SeqSBAIJ*)baij->A->data;
495:       Mat_SeqBAIJ  *bmat=(Mat_SeqBAIJ*)baij->B->data;
496:       PetscReal    *rsum,*rsum2,vabs;
497:       PetscInt     *jj,*garray=baij->garray,rstart=baij->rstartbs,nz;
498:       PetscInt     brow,bcol,col,bs=baij->A->rmap.bs,row,grow,gcol,mbs=amat->mbs;
499:       MatScalar    *v;

501:       PetscMalloc((2*mat->cmap.N+1)*sizeof(PetscReal),&rsum);
502:       rsum2 = rsum + mat->cmap.N;
503:       PetscMemzero(rsum,mat->cmap.N*sizeof(PetscReal));
504:       /* Amat */
505:       v = amat->a; jj = amat->j;
506:       for (brow=0; brow<mbs; brow++) {
507:         grow = bs*(rstart + brow);
508:         nz = amat->i[brow+1] - amat->i[brow];
509:         for (bcol=0; bcol<nz; bcol++){
510:           gcol = bs*(rstart + *jj); jj++;
511:           for (col=0; col<bs; col++){
512:             for (row=0; row<bs; row++){
513:               vabs = PetscAbsScalar(*v); v++;
514:               rsum[gcol+col] += vabs;
515:               /* non-diagonal block */
516:               if (bcol > 0 && vabs > 0.0) rsum[grow+row] += vabs;
517:             }
518:           }
519:         }
520:       }
521:       /* Bmat */
522:       v = bmat->a; jj = bmat->j;
523:       for (brow=0; brow<mbs; brow++) {
524:         grow = bs*(rstart + brow);
525:         nz = bmat->i[brow+1] - bmat->i[brow];
526:         for (bcol=0; bcol<nz; bcol++){
527:           gcol = bs*garray[*jj]; jj++;
528:           for (col=0; col<bs; col++){
529:             for (row=0; row<bs; row++){
530:               vabs = PetscAbsScalar(*v); v++;
531:               rsum[gcol+col] += vabs;
532:               rsum[grow+row] += vabs;
533:             }
534:           }
535:         }
536:       }
537:       MPI_Allreduce(rsum,rsum2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
538:       *norm = 0.0;
539:       for (col=0; col<mat->cmap.N; col++) {
540:         if (rsum2[col] > *norm) *norm = rsum2[col];
541:       }
542:       PetscFree(rsum);
543:     } else {
544:       SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
545:     }
546:   }
547:   return(0);
548: }

552: PetscErrorCode MatAssemblyBegin_MPISBAIJ(Mat mat,MatAssemblyType mode)
553: {
554:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
556:   PetscInt       nstash,reallocs;
557:   InsertMode     addv;

560:   if (baij->donotstash) {
561:     return(0);
562:   }

564:   /* make sure all processors are either in INSERTMODE or ADDMODE */
565:   MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
566:   if (addv == (ADD_VALUES|INSERT_VALUES)) {
567:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
568:   }
569:   mat->insertmode = addv; /* in case this processor had no cache */

571:   MatStashScatterBegin_Private(&mat->stash,mat->rmap.range);
572:   MatStashScatterBegin_Private(&mat->bstash,baij->rangebs);
573:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
574:   PetscInfo2(0,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
575:   MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
576:   PetscInfo2(0,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
577:   return(0);
578: }

582: PetscErrorCode MatAssemblyEnd_MPISBAIJ(Mat mat,MatAssemblyType mode)
583: {
584:   Mat_MPISBAIJ   *baij=(Mat_MPISBAIJ*)mat->data;
585:   Mat_SeqSBAIJ   *a=(Mat_SeqSBAIJ*)baij->A->data;
587:   PetscInt       i,j,rstart,ncols,flg,bs2=baij->bs2;
588:   PetscInt       *row,*col,other_disassembled;
589:   PetscMPIInt    n;
590:   PetscTruth     r1,r2,r3;
591:   MatScalar      *val;
592:   InsertMode     addv = mat->insertmode;

594:   /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */

597:   if (!baij->donotstash) {
598:     while (1) {
599:       MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
600:       if (!flg) break;

602:       for (i=0; i<n;) {
603:         /* Now identify the consecutive vals belonging to the same row */
604:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
605:         if (j < n) ncols = j-i;
606:         else       ncols = n-i;
607:         /* Now assemble all these values with a single function call */
608:         MatSetValues_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
609:         i = j;
610:       }
611:     }
612:     MatStashScatterEnd_Private(&mat->stash);
613:     /* Now process the block-stash. Since the values are stashed column-oriented,
614:        set the roworiented flag to column oriented, and after MatSetValues() 
615:        restore the original flags */
616:     r1 = baij->roworiented;
617:     r2 = a->roworiented;
618:     r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
619:     baij->roworiented = PETSC_FALSE;
620:     a->roworiented    = PETSC_FALSE;
621:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = PETSC_FALSE; /* b->roworinted */
622:     while (1) {
623:       MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
624:       if (!flg) break;
625: 
626:       for (i=0; i<n;) {
627:         /* Now identify the consecutive vals belonging to the same row */
628:         for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
629:         if (j < n) ncols = j-i;
630:         else       ncols = n-i;
631:         MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
632:         i = j;
633:       }
634:     }
635:     MatStashScatterEnd_Private(&mat->bstash);
636:     baij->roworiented = r1;
637:     a->roworiented    = r2;
638:     ((Mat_SeqBAIJ*)baij->B->data)->roworiented    = r3; /* b->roworinted */
639:   }

641:   MatAssemblyBegin(baij->A,mode);
642:   MatAssemblyEnd(baij->A,mode);

644:   /* determine if any processor has disassembled, if so we must 
645:      also disassemble ourselfs, in order that we may reassemble. */
646:   /*
647:      if nonzero structure of submatrix B cannot change then we know that
648:      no processor disassembled thus we can skip this stuff
649:   */
650:   if (!((Mat_SeqBAIJ*)baij->B->data)->nonew)  {
651:     MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
652:     if (mat->was_assembled && !other_disassembled) {
653:       DisAssemble_MPISBAIJ(mat);
654:     }
655:   }

657:   if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
658:     MatSetUpMultiply_MPISBAIJ(mat); /* setup Mvctx and sMvctx */
659:   }
660:   ((Mat_SeqBAIJ*)baij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
661:   MatAssemblyBegin(baij->B,mode);
662:   MatAssemblyEnd(baij->B,mode);
663: 
664:   PetscFree(baij->rowvalues);
665:   baij->rowvalues = 0;

667:   return(0);
668: }

672: static PetscErrorCode MatView_MPISBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
673: {
674:   Mat_MPISBAIJ      *baij = (Mat_MPISBAIJ*)mat->data;
675:   PetscErrorCode    ierr;
676:   PetscInt          bs = mat->rmap.bs;
677:   PetscMPIInt       size = baij->size,rank = baij->rank;
678:   PetscTruth        iascii,isdraw;
679:   PetscViewer       sviewer;
680:   PetscViewerFormat format;

683:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
684:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
685:   if (iascii) {
686:     PetscViewerGetFormat(viewer,&format);
687:     if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
688:       MatInfo info;
689:       MPI_Comm_rank(mat->comm,&rank);
690:       MatGetInfo(mat,MAT_LOCAL,&info);
691:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
692:               rank,mat->rmap.N,(PetscInt)info.nz_used*bs,(PetscInt)info.nz_allocated*bs,
693:               mat->rmap.bs,(PetscInt)info.memory);
694:       MatGetInfo(baij->A,MAT_LOCAL,&info);
695:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
696:       MatGetInfo(baij->B,MAT_LOCAL,&info);
697:       PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used*bs);
698:       PetscViewerFlush(viewer);
699:       VecScatterView(baij->Mvctx,viewer);
700:       return(0);
701:     } else if (format == PETSC_VIEWER_ASCII_INFO) {
702:       PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
703:       return(0);
704:     } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
705:       return(0);
706:     }
707:   }

709:   if (isdraw) {
710:     PetscDraw       draw;
711:     PetscTruth isnull;
712:     PetscViewerDrawGetDraw(viewer,0,&draw);
713:     PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
714:   }

716:   if (size == 1) {
717:     PetscObjectSetName((PetscObject)baij->A,mat->name);
718:     MatView(baij->A,viewer);
719:   } else {
720:     /* assemble the entire matrix onto first processor. */
721:     Mat         A;
722:     Mat_SeqSBAIJ *Aloc;
723:     Mat_SeqBAIJ *Bloc;
724:     PetscInt         M = mat->rmap.N,N = mat->cmap.N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
725:     MatScalar   *a;

727:     /* Should this be the same type as mat? */
728:     MatCreate(mat->comm,&A);
729:     if (!rank) {
730:       MatSetSizes(A,M,N,M,N);
731:     } else {
732:       MatSetSizes(A,0,0,M,N);
733:     }
734:     MatSetType(A,MATMPISBAIJ);
735:     MatMPISBAIJSetPreallocation(A,mat->rmap.bs,0,PETSC_NULL,0,PETSC_NULL);
736:     PetscLogObjectParent(mat,A);

738:     /* copy over the A part */
739:     Aloc  = (Mat_SeqSBAIJ*)baij->A->data;
740:     ai    = Aloc->i; aj = Aloc->j; a = Aloc->a;
741:     PetscMalloc(bs*sizeof(PetscInt),&rvals);

743:     for (i=0; i<mbs; i++) {
744:       rvals[0] = mat->rmap.rstart + bs*i;
745:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
746:       for (j=ai[i]; j<ai[i+1]; j++) {
747:         col = mat->cmap.rstart+aj[j]*bs;
748:         for (k=0; k<bs; k++) {
749:           MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
750:           col++; a += bs;
751:         }
752:       }
753:     }
754:     /* copy over the B part */
755:     Bloc = (Mat_SeqBAIJ*)baij->B->data;
756:     ai = Bloc->i; aj = Bloc->j; a = Bloc->a;
757:     for (i=0; i<mbs; i++) {
758:       rvals[0] = mat->rmap.rstart + bs;
759:       for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
760:       for (j=ai[i]; j<ai[i+1]; j++) {
761:         col = baij->garray[aj[j]]*bs;
762:         for (k=0; k<bs; k++) {
763:           MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
764:           col++; a += bs;
765:         }
766:       }
767:     }
768:     PetscFree(rvals);
769:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
770:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
771:     /* 
772:        Everyone has to call to draw the matrix since the graphics waits are
773:        synchronized across all processors that share the PetscDraw object
774:     */
775:     PetscViewerGetSingleton(viewer,&sviewer);
776:     if (!rank) {
777:       PetscObjectSetName((PetscObject)((Mat_MPISBAIJ*)(A->data))->A,mat->name);
778:       MatView(((Mat_MPISBAIJ*)(A->data))->A,sviewer);
779:     }
780:     PetscViewerRestoreSingleton(viewer,&sviewer);
781:     MatDestroy(A);
782:   }
783:   return(0);
784: }

788: PetscErrorCode MatView_MPISBAIJ(Mat mat,PetscViewer viewer)
789: {
791:   PetscTruth     iascii,isdraw,issocket,isbinary;

794:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
795:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
796:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
797:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
798:   if (iascii || isdraw || issocket || isbinary) {
799:     MatView_MPISBAIJ_ASCIIorDraworSocket(mat,viewer);
800:   } else {
801:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPISBAIJ matrices",((PetscObject)viewer)->type_name);
802:   }
803:   return(0);
804: }

808: PetscErrorCode MatDestroy_MPISBAIJ(Mat mat)
809: {
810:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;

814: #if defined(PETSC_USE_LOG)
815:   PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap.N,mat->cmap.N);
816: #endif
817:   MatStashDestroy_Private(&mat->stash);
818:   MatStashDestroy_Private(&mat->bstash);
819:   MatDestroy(baij->A);
820:   MatDestroy(baij->B);
821: #if defined (PETSC_USE_CTABLE)
822:   if (baij->colmap) {PetscTableDelete(baij->colmap);}
823: #else
824:   PetscFree(baij->colmap);
825: #endif
826:   PetscFree(baij->garray);
827:   if (baij->lvec)   {VecDestroy(baij->lvec);}
828:   if (baij->Mvctx)  {VecScatterDestroy(baij->Mvctx);}
829:   if (baij->slvec0) {
830:     VecDestroy(baij->slvec0);
831:     VecDestroy(baij->slvec0b);
832:   }
833:   if (baij->slvec1) {
834:     VecDestroy(baij->slvec1);
835:     VecDestroy(baij->slvec1a);
836:     VecDestroy(baij->slvec1b);
837:   }
838:   if (baij->sMvctx)  {VecScatterDestroy(baij->sMvctx);}
839:   PetscFree(baij->rowvalues);
840:   PetscFree(baij->barray);
841:   PetscFree(baij->hd);
842: #if defined(PETSC_USE_MAT_SINGLE)
843:   PetscFree(baij->setvaluescopy);
844: #endif
845:   PetscFree(baij->rangebs);
846:   PetscFree(baij);

848:   PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
849:   PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
850:   PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
851:   PetscObjectComposeFunction((PetscObject)mat,"MatMPISBAIJSetPreallocation_C","",PETSC_NULL);
852:   return(0);
853: }

857: PetscErrorCode MatMult_MPISBAIJ(Mat A,Vec xx,Vec yy)
858: {
859:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
861:   PetscInt       nt,mbs=a->mbs,bs=A->rmap.bs;
862:   PetscScalar    *x,*from,zero=0.0;
863: 
865:   VecGetLocalSize(xx,&nt);
866:   if (nt != A->cmap.n) {
867:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
868:   }

870:   /* diagonal part */
871:   (*a->A->ops->mult)(a->A,xx,a->slvec1a);
872:   VecSet(a->slvec1b,zero);

874:   /* subdiagonal part */
875:   (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);
876:   CHKMEMQ;
877:   /* copy x into the vec slvec0 */
878:   VecGetArray(a->slvec0,&from);
879:   VecGetArray(xx,&x);
880:   CHKMEMQ;
881:   PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
882:   CHKMEMQ;
883:   VecRestoreArray(a->slvec0,&from);
884: 
885:   CHKMEMQ;
886:   VecScatterBegin(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
887:   CHKMEMQ;
888:   VecRestoreArray(xx,&x);
889:   CHKMEMQ;
890:   VecScatterEnd(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
891:     CHKMEMQ;
892:   /* supperdiagonal part */
893:   (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,yy);
894:     CHKMEMQ;
895:   return(0);
896: }

900: PetscErrorCode MatMult_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy)
901: {
902:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
904:   PetscInt       nt;

907:   VecGetLocalSize(xx,&nt);
908:   if (nt != A->cmap.n) {
909:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
910:   }
911:   VecGetLocalSize(yy,&nt);
912:   if (nt != A->rmap.N) {
913:     SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
914:   }

916:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
917:   /* do diagonal part */
918:   (*a->A->ops->mult)(a->A,xx,yy);
919:   /* do supperdiagonal part */
920:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
921:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
922:   /* do subdiagonal part */
923:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
924:   VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
925:   VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);

927:   return(0);
928: }

932: PetscErrorCode MatMultAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
933: {
934:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
936:   PetscInt       mbs=a->mbs,bs=A->rmap.bs;
937:   PetscScalar    *x,*from,zero=0.0;
938: 
940:   /*
941:   PetscSynchronizedPrintf(A->comm," MatMultAdd is called ...\n");
942:   PetscSynchronizedFlush(A->comm);
943:   */
944:   /* diagonal part */
945:   (*a->A->ops->multadd)(a->A,xx,yy,a->slvec1a);
946:   VecSet(a->slvec1b,zero);

948:   /* subdiagonal part */
949:   (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);

951:   /* copy x into the vec slvec0 */
952:   VecGetArray(a->slvec0,&from);
953:   VecGetArray(xx,&x);
954:   PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
955:   VecRestoreArray(a->slvec0,&from);
956: 
957:   VecScatterBegin(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
958:   VecRestoreArray(xx,&x);
959:   VecScatterEnd(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
960: 
961:   /* supperdiagonal part */
962:   (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,zz);
963: 
964:   return(0);
965: }

969: PetscErrorCode MatMultAdd_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy,Vec zz)
970: {
971:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

975:   VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
976:   /* do diagonal part */
977:   (*a->A->ops->multadd)(a->A,xx,yy,zz);
978:   /* do supperdiagonal part */
979:   VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
980:   (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);

982:   /* do subdiagonal part */
983:   (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
984:   VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
985:   VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);

987:   return(0);
988: }

990: /*
991:   This only works correctly for square matrices where the subblock A->A is the 
992:    diagonal block
993: */
996: PetscErrorCode MatGetDiagonal_MPISBAIJ(Mat A,Vec v)
997: {
998:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1002:   /* if (a->rmap.N != a->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); */
1003:   MatGetDiagonal(a->A,v);
1004:   return(0);
1005: }

1009: PetscErrorCode MatScale_MPISBAIJ(Mat A,PetscScalar aa)
1010: {
1011:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1015:   MatScale(a->A,aa);
1016:   MatScale(a->B,aa);
1017:   return(0);
1018: }

1022: PetscErrorCode MatGetRow_MPISBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1023: {
1024:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
1025:   PetscScalar    *vworkA,*vworkB,**pvA,**pvB,*v_p;
1027:   PetscInt       bs = matin->rmap.bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1028:   PetscInt       nztot,nzA,nzB,lrow,brstart = matin->rmap.rstart,brend = matin->rmap.rend;
1029:   PetscInt       *cmap,*idx_p,cstart = mat->rstartbs;

1032:   if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1033:   mat->getrowactive = PETSC_TRUE;

1035:   if (!mat->rowvalues && (idx || v)) {
1036:     /*
1037:         allocate enough space to hold information from the longest row.
1038:     */
1039:     Mat_SeqSBAIJ *Aa = (Mat_SeqSBAIJ*)mat->A->data;
1040:     Mat_SeqBAIJ  *Ba = (Mat_SeqBAIJ*)mat->B->data;
1041:     PetscInt     max = 1,mbs = mat->mbs,tmp;
1042:     for (i=0; i<mbs; i++) {
1043:       tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; /* row length */
1044:       if (max < tmp) { max = tmp; }
1045:     }
1046:     PetscMalloc(max*bs2*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1047:     mat->rowindices = (PetscInt*)(mat->rowvalues + max*bs2);
1048:   }
1049: 
1050:   if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1051:   lrow = row - brstart;  /* local row index */

1053:   pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1054:   if (!v)   {pvA = 0; pvB = 0;}
1055:   if (!idx) {pcA = 0; if (!v) pcB = 0;}
1056:   (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1057:   (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1058:   nztot = nzA + nzB;

1060:   cmap  = mat->garray;
1061:   if (v  || idx) {
1062:     if (nztot) {
1063:       /* Sort by increasing column numbers, assuming A and B already sorted */
1064:       PetscInt imark = -1;
1065:       if (v) {
1066:         *v = v_p = mat->rowvalues;
1067:         for (i=0; i<nzB; i++) {
1068:           if (cmap[cworkB[i]/bs] < cstart)   v_p[i] = vworkB[i];
1069:           else break;
1070:         }
1071:         imark = i;
1072:         for (i=0; i<nzA; i++)     v_p[imark+i] = vworkA[i];
1073:         for (i=imark; i<nzB; i++) v_p[nzA+i]   = vworkB[i];
1074:       }
1075:       if (idx) {
1076:         *idx = idx_p = mat->rowindices;
1077:         if (imark > -1) {
1078:           for (i=0; i<imark; i++) {
1079:             idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1080:           }
1081:         } else {
1082:           for (i=0; i<nzB; i++) {
1083:             if (cmap[cworkB[i]/bs] < cstart)
1084:               idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1085:             else break;
1086:           }
1087:           imark = i;
1088:         }
1089:         for (i=0; i<nzA; i++)     idx_p[imark+i] = cstart*bs + cworkA[i];
1090:         for (i=imark; i<nzB; i++) idx_p[nzA+i]   = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1091:       }
1092:     } else {
1093:       if (idx) *idx = 0;
1094:       if (v)   *v   = 0;
1095:     }
1096:   }
1097:   *nz = nztot;
1098:   (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1099:   (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1100:   return(0);
1101: }

1105: PetscErrorCode MatRestoreRow_MPISBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1106: {
1107:   Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;

1110:   if (!baij->getrowactive) {
1111:     SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1112:   }
1113:   baij->getrowactive = PETSC_FALSE;
1114:   return(0);
1115: }

1119: PetscErrorCode MatGetRowUpperTriangular_MPISBAIJ(Mat A)
1120: {
1121:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1122:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1125:   aA->getrow_utriangular = PETSC_TRUE;
1126:   return(0);
1127: }
1130: PetscErrorCode MatRestoreRowUpperTriangular_MPISBAIJ(Mat A)
1131: {
1132:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1133:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1136:   aA->getrow_utriangular = PETSC_FALSE;
1137:   return(0);
1138: }

1142: PetscErrorCode MatRealPart_MPISBAIJ(Mat A)
1143: {
1144:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1148:   MatRealPart(a->A);
1149:   MatRealPart(a->B);
1150:   return(0);
1151: }

1155: PetscErrorCode MatImaginaryPart_MPISBAIJ(Mat A)
1156: {
1157:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1161:   MatImaginaryPart(a->A);
1162:   MatImaginaryPart(a->B);
1163:   return(0);
1164: }

1168: PetscErrorCode MatZeroEntries_MPISBAIJ(Mat A)
1169: {
1170:   Mat_MPISBAIJ   *l = (Mat_MPISBAIJ*)A->data;

1174:   MatZeroEntries(l->A);
1175:   MatZeroEntries(l->B);
1176:   return(0);
1177: }

1181: PetscErrorCode MatGetInfo_MPISBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1182: {
1183:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)matin->data;
1184:   Mat            A = a->A,B = a->B;
1186:   PetscReal      isend[5],irecv[5];

1189:   info->block_size     = (PetscReal)matin->rmap.bs;
1190:   MatGetInfo(A,MAT_LOCAL,info);
1191:   isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1192:   isend[3] = info->memory;  isend[4] = info->mallocs;
1193:   MatGetInfo(B,MAT_LOCAL,info);
1194:   isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1195:   isend[3] += info->memory;  isend[4] += info->mallocs;
1196:   if (flag == MAT_LOCAL) {
1197:     info->nz_used      = isend[0];
1198:     info->nz_allocated = isend[1];
1199:     info->nz_unneeded  = isend[2];
1200:     info->memory       = isend[3];
1201:     info->mallocs      = isend[4];
1202:   } else if (flag == MAT_GLOBAL_MAX) {
1203:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1204:     info->nz_used      = irecv[0];
1205:     info->nz_allocated = irecv[1];
1206:     info->nz_unneeded  = irecv[2];
1207:     info->memory       = irecv[3];
1208:     info->mallocs      = irecv[4];
1209:   } else if (flag == MAT_GLOBAL_SUM) {
1210:     MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1211:     info->nz_used      = irecv[0];
1212:     info->nz_allocated = irecv[1];
1213:     info->nz_unneeded  = irecv[2];
1214:     info->memory       = irecv[3];
1215:     info->mallocs      = irecv[4];
1216:   } else {
1217:     SETERRQ1(PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1218:   }
1219:   info->rows_global       = (PetscReal)A->rmap.N;
1220:   info->columns_global    = (PetscReal)A->cmap.N;
1221:   info->rows_local        = (PetscReal)A->rmap.N;
1222:   info->columns_local     = (PetscReal)A->cmap.N;
1223:   info->fill_ratio_given  = 0; /* no parallel LU/ILU/Cholesky */
1224:   info->fill_ratio_needed = 0;
1225:   info->factor_mallocs    = 0;
1226:   return(0);
1227: }

1231: PetscErrorCode MatSetOption_MPISBAIJ(Mat A,MatOption op)
1232: {
1233:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
1234:   Mat_SeqSBAIJ   *aA = (Mat_SeqSBAIJ*)a->A->data;

1238:   switch (op) {
1239:   case MAT_NO_NEW_NONZERO_LOCATIONS:
1240:   case MAT_YES_NEW_NONZERO_LOCATIONS:
1241:   case MAT_COLUMNS_UNSORTED:
1242:   case MAT_COLUMNS_SORTED:
1243:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1244:   case MAT_KEEP_ZEROED_ROWS:
1245:   case MAT_NEW_NONZERO_LOCATION_ERR:
1246:     MatSetOption(a->A,op);
1247:     MatSetOption(a->B,op);
1248:     break;
1249:   case MAT_ROW_ORIENTED:
1250:     a->roworiented = PETSC_TRUE;
1251:     MatSetOption(a->A,op);
1252:     MatSetOption(a->B,op);
1253:     break;
1254:   case MAT_ROWS_SORTED:
1255:   case MAT_ROWS_UNSORTED:
1256:   case MAT_YES_NEW_DIAGONALS:
1257:     PetscInfo(A,"Option ignored\n");
1258:     break;
1259:   case MAT_COLUMN_ORIENTED:
1260:     a->roworiented = PETSC_FALSE;
1261:     MatSetOption(a->A,op);
1262:     MatSetOption(a->B,op);
1263:     break;
1264:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1265:     a->donotstash = PETSC_TRUE;
1266:     break;
1267:   case MAT_NO_NEW_DIAGONALS:
1268:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1269:   case MAT_USE_HASH_TABLE:
1270:     a->ht_flag = PETSC_TRUE;
1271:     break;
1272:   case MAT_NOT_SYMMETRIC:
1273:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1274:   case MAT_HERMITIAN:
1275:     SETERRQ(PETSC_ERR_SUP,"Matrix must be symmetric");
1276:   case MAT_SYMMETRIC:
1277:   case MAT_STRUCTURALLY_SYMMETRIC:
1278:   case MAT_NOT_HERMITIAN:
1279:   case MAT_SYMMETRY_ETERNAL:
1280:   case MAT_NOT_SYMMETRY_ETERNAL:
1281:     break;
1282:   case MAT_IGNORE_LOWER_TRIANGULAR:
1283:     aA->ignore_ltriangular = PETSC_TRUE;
1284:     break;
1285:   case MAT_ERROR_LOWER_TRIANGULAR:
1286:     aA->ignore_ltriangular = PETSC_FALSE;
1287:     break;
1288:   case MAT_GETROW_UPPERTRIANGULAR:
1289:     aA->getrow_utriangular = PETSC_TRUE;
1290:     break;
1291:   default:
1292:     SETERRQ(PETSC_ERR_SUP,"unknown option");
1293:   }
1294:   return(0);
1295: }

1299: PetscErrorCode MatTranspose_MPISBAIJ(Mat A,Mat *B)
1300: {
1303:   MatDuplicate(A,MAT_COPY_VALUES,B);
1304:   return(0);
1305: }

1309: PetscErrorCode MatDiagonalScale_MPISBAIJ(Mat mat,Vec ll,Vec rr)
1310: {
1311:   Mat_MPISBAIJ   *baij = (Mat_MPISBAIJ*)mat->data;
1312:   Mat            a=baij->A, b=baij->B;
1314:   PetscInt       nv,m,n;
1315:   PetscTruth     flg;

1318:   if (ll != rr){
1319:     VecEqual(ll,rr,&flg);
1320:     if (!flg)
1321:       SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"For symmetric format, left and right scaling vectors must be same\n");
1322:   }
1323:   if (!ll) return(0);

1325:   MatGetLocalSize(mat,&m,&n);
1326:   if (m != n) SETERRQ2(PETSC_ERR_ARG_SIZ,"For symmetric format, local size %d %d must be same",m,n);
1327: 
1328:   VecGetLocalSize(rr,&nv);
1329:   if (nv!=n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left and right vector non-conforming local size");

1331:   VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1332: 
1333:   /* left diagonalscale the off-diagonal part */
1334:   (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1335: 
1336:   /* scale the diagonal part */
1337:   (*a->ops->diagonalscale)(a,ll,rr);

1339:   /* right diagonalscale the off-diagonal part */
1340:   VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1341:   (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1342:   return(0);
1343: }

1347: PetscErrorCode MatPrintHelp_MPISBAIJ(Mat A)
1348: {
1349:   Mat_MPISBAIJ      *a = (Mat_MPISBAIJ*)A->data;
1350:   MPI_Comm          comm = A->comm;
1351:   static PetscTruth called = PETSC_FALSE;
1352:   PetscErrorCode    ierr;

1355:   if (!a->rank) {
1356:     MatPrintHelp_SeqSBAIJ(a->A);
1357:   }
1358:   if (called) {return(0);} else called = PETSC_TRUE;
1359:   (*PetscHelpPrintf)(comm," Options for MATMPISBAIJ matrix format (the defaults):\n");
1360:   (*PetscHelpPrintf)(comm,"  -mat_use_hash_table <factor>: Use hashtable for efficient matrix assembly\n");
1361:   return(0);
1362: }

1366: PetscErrorCode MatSetUnfactored_MPISBAIJ(Mat A)
1367: {
1368:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;

1372:   MatSetUnfactored(a->A);
1373:   return(0);
1374: }

1376: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat,MatDuplicateOption,Mat *);

1380: PetscErrorCode MatEqual_MPISBAIJ(Mat A,Mat B,PetscTruth *flag)
1381: {
1382:   Mat_MPISBAIJ   *matB = (Mat_MPISBAIJ*)B->data,*matA = (Mat_MPISBAIJ*)A->data;
1383:   Mat            a,b,c,d;
1384:   PetscTruth     flg;

1388:   a = matA->A; b = matA->B;
1389:   c = matB->A; d = matB->B;

1391:   MatEqual(a,c,&flg);
1392:   if (flg) {
1393:     MatEqual(b,d,&flg);
1394:   }
1395:   MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1396:   return(0);
1397: }

1401: PetscErrorCode MatCopy_MPISBAIJ(Mat A,Mat B,MatStructure str)
1402: {
1404:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ *)A->data;
1405:   Mat_MPISBAIJ   *b = (Mat_MPISBAIJ *)B->data;

1408:   /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1409:   if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1410:     MatGetRowUpperTriangular(A);
1411:     MatCopy_Basic(A,B,str);
1412:     MatRestoreRowUpperTriangular(A);
1413:   } else {
1414:     MatCopy(a->A,b->A,str);
1415:     MatCopy(a->B,b->B,str);
1416:   }
1417:   return(0);
1418: }

1422: PetscErrorCode MatSetUpPreallocation_MPISBAIJ(Mat A)
1423: {

1427:   MatMPISBAIJSetPreallocation(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1428:   return(0);
1429: }

1431:  #include petscblaslapack.h
1434: PetscErrorCode MatAXPY_MPISBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1435: {
1437:   Mat_MPISBAIJ   *xx=(Mat_MPISBAIJ *)X->data,*yy=(Mat_MPISBAIJ *)Y->data;
1438:   PetscBLASInt   bnz,one=1;
1439:   Mat_SeqSBAIJ   *xa,*ya;
1440:   Mat_SeqBAIJ    *xb,*yb;

1443:   if (str == SAME_NONZERO_PATTERN) {
1444:     PetscScalar alpha = a;
1445:     xa = (Mat_SeqSBAIJ *)xx->A->data;
1446:     ya = (Mat_SeqSBAIJ *)yy->A->data;
1447:     bnz = (PetscBLASInt)xa->nz;
1448:     BLASaxpy_(&bnz,&alpha,xa->a,&one,ya->a,&one);
1449:     xb = (Mat_SeqBAIJ *)xx->B->data;
1450:     yb = (Mat_SeqBAIJ *)yy->B->data;
1451:     bnz = (PetscBLASInt)xb->nz;
1452:     BLASaxpy_(&bnz,&alpha,xb->a,&one,yb->a,&one);
1453:   } else {
1454:     MatGetRowUpperTriangular(X);
1455:     MatAXPY_Basic(Y,a,X,str);
1456:     MatRestoreRowUpperTriangular(X);
1457:   }
1458:   return(0);
1459: }

1463: PetscErrorCode MatGetSubMatrices_MPISBAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1464: {
1466:   PetscInt       i;
1467:   PetscTruth     flg;

1470:   for (i=0; i<n; i++) {
1471:     ISEqual(irow[i],icol[i],&flg);
1472:     if (!flg) {
1473:       SETERRQ(PETSC_ERR_SUP,"Can only get symmetric submatrix for MPISBAIJ matrices");
1474:     }
1475:   }
1476:   MatGetSubMatrices_MPIBAIJ(A,n,irow,icol,scall,B);
1477:   return(0);
1478: }
1479: 

1481: /* -------------------------------------------------------------------*/
1482: static struct _MatOps MatOps_Values = {
1483:        MatSetValues_MPISBAIJ,
1484:        MatGetRow_MPISBAIJ,
1485:        MatRestoreRow_MPISBAIJ,
1486:        MatMult_MPISBAIJ,
1487: /* 4*/ MatMultAdd_MPISBAIJ,
1488:        MatMult_MPISBAIJ,       /* transpose versions are same as non-transpose */
1489:        MatMultAdd_MPISBAIJ,
1490:        0,
1491:        0,
1492:        0,
1493: /*10*/ 0,
1494:        0,
1495:        0,
1496:        MatRelax_MPISBAIJ,
1497:        MatTranspose_MPISBAIJ,
1498: /*15*/ MatGetInfo_MPISBAIJ,
1499:        MatEqual_MPISBAIJ,
1500:        MatGetDiagonal_MPISBAIJ,
1501:        MatDiagonalScale_MPISBAIJ,
1502:        MatNorm_MPISBAIJ,
1503: /*20*/ MatAssemblyBegin_MPISBAIJ,
1504:        MatAssemblyEnd_MPISBAIJ,
1505:        0,
1506:        MatSetOption_MPISBAIJ,
1507:        MatZeroEntries_MPISBAIJ,
1508: /*25*/ 0,
1509:        0,
1510:        0,
1511:        0,
1512:        0,
1513: /*30*/ MatSetUpPreallocation_MPISBAIJ,
1514:        0,
1515:        0,
1516:        0,
1517:        0,
1518: /*35*/ MatDuplicate_MPISBAIJ,
1519:        0,
1520:        0,
1521:        0,
1522:        0,
1523: /*40*/ MatAXPY_MPISBAIJ,
1524:        MatGetSubMatrices_MPISBAIJ,
1525:        MatIncreaseOverlap_MPISBAIJ,
1526:        MatGetValues_MPISBAIJ,
1527:        MatCopy_MPISBAIJ,
1528: /*45*/ MatPrintHelp_MPISBAIJ,
1529:        MatScale_MPISBAIJ,
1530:        0,
1531:        0,
1532:        0,
1533: /*50*/ 0,
1534:        0,
1535:        0,
1536:        0,
1537:        0,
1538: /*55*/ 0,
1539:        0,
1540:        MatSetUnfactored_MPISBAIJ,
1541:        0,
1542:        MatSetValuesBlocked_MPISBAIJ,
1543: /*60*/ 0,
1544:        0,
1545:        0,
1546:        0,
1547:        0,
1548: /*65*/ 0,
1549:        0,
1550:        0,
1551:        0,
1552:        0,
1553: /*70*/ MatGetRowMax_MPISBAIJ,
1554:        0,
1555:        0,
1556:        0,
1557:        0,
1558: /*75*/ 0,
1559:        0,
1560:        0,
1561:        0,
1562:        0,
1563: /*80*/ 0,
1564:        0,
1565:        0,
1566:        0,
1567:        MatLoad_MPISBAIJ,
1568: /*85*/ 0,
1569:        0,
1570:        0,
1571:        0,
1572:        0,
1573: /*90*/ 0,
1574:        0,
1575:        0,
1576:        0,
1577:        0,
1578: /*95*/ 0,
1579:        0,
1580:        0,
1581:        0,
1582:        0,
1583: /*100*/0,
1584:        0,
1585:        0,
1586:        0,
1587:        0,
1588: /*105*/0,
1589:        MatRealPart_MPISBAIJ,
1590:        MatImaginaryPart_MPISBAIJ,
1591:        MatGetRowUpperTriangular_MPISBAIJ,
1592:        MatRestoreRowUpperTriangular_MPISBAIJ
1593: };


1599: PetscErrorCode PETSCMAT_DLLEXPORT MatGetDiagonalBlock_MPISBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1600: {
1602:   *a      = ((Mat_MPISBAIJ *)A->data)->A;
1603:   *iscopy = PETSC_FALSE;
1604:   return(0);
1605: }

1611: PetscErrorCode PETSCMAT_DLLEXPORT MatMPISBAIJSetPreallocation_MPISBAIJ(Mat B,PetscInt bs,PetscInt d_nz,PetscInt *d_nnz,PetscInt o_nz,PetscInt *o_nnz)
1612: {
1613:   Mat_MPISBAIJ   *b;
1615:   PetscInt       i,mbs,Mbs;

1618:   PetscOptionsGetInt(B->prefix,"-mat_block_size",&bs,PETSC_NULL);

1620:   if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
1621:   if (d_nz == PETSC_DECIDE || d_nz == PETSC_DEFAULT) d_nz = 3;
1622:   if (o_nz == PETSC_DECIDE || o_nz == PETSC_DEFAULT) o_nz = 1;
1623:   if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
1624:   if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

1626:   B->rmap.bs = B->cmap.bs = bs;
1627:   PetscMapInitialize(B->comm,&B->rmap);
1628:   PetscMapInitialize(B->comm,&B->cmap);

1630:   if (d_nnz) {
1631:     for (i=0; i<B->rmap.n/bs; i++) {
1632:       if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %D value %D",i,d_nnz[i]);
1633:     }
1634:   }
1635:   if (o_nnz) {
1636:     for (i=0; i<B->rmap.n/bs; i++) {
1637:       if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %D value %D",i,o_nnz[i]);
1638:     }
1639:   }
1640:   B->preallocated = PETSC_TRUE;

1642:   b   = (Mat_MPISBAIJ*)B->data;
1643:   mbs = B->rmap.n/bs;
1644:   Mbs = B->rmap.N/bs;
1645:   if (mbs*bs != B->rmap.n) {
1646:     SETERRQ2(PETSC_ERR_ARG_SIZ,"No of local rows %D must be divisible by blocksize %D",B->rmap.N,bs);
1647:   }

1649:   B->rmap.bs  = bs;
1650:   b->bs2 = bs*bs;
1651:   b->mbs = mbs;
1652:   b->nbs = mbs;
1653:   b->Mbs = Mbs;
1654:   b->Nbs = Mbs;

1656:   for (i=0; i<=b->size; i++) {
1657:     b->rangebs[i] = B->rmap.range[i]/bs;
1658:   }
1659:   b->rstartbs = B->rmap.rstart/bs;
1660:   b->rendbs   = B->rmap.rend/bs;
1661: 
1662:   b->cstartbs = B->cmap.rstart/bs;
1663:   b->cendbs   = B->cmap.rend/bs;
1664: 
1665:   MatCreate(PETSC_COMM_SELF,&b->A);
1666:   MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
1667:   MatSetType(b->A,MATSEQSBAIJ);
1668:   MatSeqSBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
1669:   PetscLogObjectParent(B,b->A);

1671:   MatCreate(PETSC_COMM_SELF,&b->B);
1672:   MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
1673:   MatSetType(b->B,MATSEQBAIJ);
1674:   MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
1675:   PetscLogObjectParent(B,b->B);

1677:   /* build cache for off array entries formed */
1678:   MatStashCreate_Private(B->comm,bs,&B->bstash);

1680:   return(0);
1681: }

1684: /*MC
1685:    MATMPISBAIJ - MATMPISBAIJ = "mpisbaij" - A matrix type to be used for distributed symmetric sparse block matrices, 
1686:    based on block compressed sparse row format.  Only the upper triangular portion of the matrix is stored.

1688:    Options Database Keys:
1689: . -mat_type mpisbaij - sets the matrix type to "mpisbaij" during a call to MatSetFromOptions()

1691:   Level: beginner

1693: .seealso: MatCreateMPISBAIJ
1694: M*/

1699: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_MPISBAIJ(Mat B)
1700: {
1701:   Mat_MPISBAIJ   *b;
1703:   PetscTruth     flg;


1707:   PetscNew(Mat_MPISBAIJ,&b);
1708:   B->data = (void*)b;
1709:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

1711:   B->ops->destroy    = MatDestroy_MPISBAIJ;
1712:   B->ops->view       = MatView_MPISBAIJ;
1713:   B->mapping    = 0;
1714:   B->factor     = 0;
1715:   B->assembled  = PETSC_FALSE;

1717:   B->insertmode = NOT_SET_VALUES;
1718:   MPI_Comm_rank(B->comm,&b->rank);
1719:   MPI_Comm_size(B->comm,&b->size);

1721:   /* build local table of row and column ownerships */
1722:   PetscMalloc((b->size+2)*sizeof(PetscInt),&b->rangebs);

1724:   /* build cache for off array entries formed */
1725:   MatStashCreate_Private(B->comm,1,&B->stash);
1726:   b->donotstash  = PETSC_FALSE;
1727:   b->colmap      = PETSC_NULL;
1728:   b->garray      = PETSC_NULL;
1729:   b->roworiented = PETSC_TRUE;

1731: #if defined(PETSC_USE_MAT_SINGLE)
1732:   /* stuff for MatSetValues_XXX in single precision */
1733:   b->setvalueslen     = 0;
1734:   b->setvaluescopy    = PETSC_NULL;
1735: #endif

1737:   /* stuff used in block assembly */
1738:   b->barray       = 0;

1740:   /* stuff used for matrix vector multiply */
1741:   b->lvec         = 0;
1742:   b->Mvctx        = 0;
1743:   b->slvec0       = 0;
1744:   b->slvec0b      = 0;
1745:   b->slvec1       = 0;
1746:   b->slvec1a      = 0;
1747:   b->slvec1b      = 0;
1748:   b->sMvctx       = 0;

1750:   /* stuff for MatGetRow() */
1751:   b->rowindices   = 0;
1752:   b->rowvalues    = 0;
1753:   b->getrowactive = PETSC_FALSE;

1755:   /* hash table stuff */
1756:   b->ht           = 0;
1757:   b->hd           = 0;
1758:   b->ht_size      = 0;
1759:   b->ht_flag      = PETSC_FALSE;
1760:   b->ht_fact      = 0;
1761:   b->ht_total_ct  = 0;
1762:   b->ht_insert_ct = 0;

1764:   PetscOptionsHasName(B->prefix,"-mat_use_hash_table",&flg);
1765:   if (flg) {
1766:     PetscReal fact = 1.39;
1767:     MatSetOption(B,MAT_USE_HASH_TABLE);
1768:     PetscOptionsGetReal(B->prefix,"-mat_use_hash_table",&fact,PETSC_NULL);
1769:     if (fact <= 1.0) fact = 1.39;
1770:     MatMPIBAIJSetHashTableFactor(B,fact);
1771:     PetscInfo1(0,"Hash table Factor used %5.2f\n",fact);
1772:   }
1773:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1774:                                      "MatStoreValues_MPISBAIJ",
1775:                                      MatStoreValues_MPISBAIJ);
1776:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1777:                                      "MatRetrieveValues_MPISBAIJ",
1778:                                      MatRetrieveValues_MPISBAIJ);
1779:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
1780:                                      "MatGetDiagonalBlock_MPISBAIJ",
1781:                                      MatGetDiagonalBlock_MPISBAIJ);
1782:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPISBAIJSetPreallocation_C",
1783:                                      "MatMPISBAIJSetPreallocation_MPISBAIJ",
1784:                                      MatMPISBAIJSetPreallocation_MPISBAIJ);
1785:   B->symmetric                  = PETSC_TRUE;
1786:   B->structurally_symmetric     = PETSC_TRUE;
1787:   B->symmetric_set              = PETSC_TRUE;
1788:   B->structurally_symmetric_set = PETSC_TRUE;
1789:   return(0);
1790: }

1793: /*MC
1794:    MATSBAIJ - MATSBAIJ = "sbaij" - A matrix type to be used for symmetric block sparse matrices.

1796:    This matrix type is identical to MATSEQSBAIJ when constructed with a single process communicator,
1797:    and MATMPISBAIJ otherwise.

1799:    Options Database Keys:
1800: . -mat_type sbaij - sets the matrix type to "sbaij" during a call to MatSetFromOptions()

1802:   Level: beginner

1804: .seealso: MatCreateMPISBAIJ,MATSEQSBAIJ,MATMPISBAIJ
1805: M*/

1810: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SBAIJ(Mat A)
1811: {
1813:   PetscMPIInt    size;

1816:   PetscObjectChangeTypeName((PetscObject)A,MATSBAIJ);
1817:   MPI_Comm_size(A->comm,&size);
1818:   if (size == 1) {
1819:     MatSetType(A,MATSEQSBAIJ);
1820:   } else {
1821:     MatSetType(A,MATMPISBAIJ);
1822:   }
1823:   return(0);
1824: }

1829: /*@C
1830:    MatMPISBAIJSetPreallocation - For good matrix assembly performance
1831:    the user should preallocate the matrix storage by setting the parameters 
1832:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
1833:    performance can be increased by more than a factor of 50.

1835:    Collective on Mat

1837:    Input Parameters:
1838: +  A - the matrix 
1839: .  bs   - size of blockk
1840: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
1841:            submatrix  (same for all local rows)
1842: .  d_nnz - array containing the number of block nonzeros in the various block rows 
1843:            in the upper triangular and diagonal part of the in diagonal portion of the local
1844:            (possibly different for each block row) or PETSC_NULL.  You must leave room 
1845:            for the diagonal entry even if it is zero.
1846: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
1847:            submatrix (same for all local rows).
1848: -  o_nnz - array containing the number of nonzeros in the various block rows of the
1849:            off-diagonal portion of the local submatrix (possibly different for
1850:            each block row) or PETSC_NULL.


1853:    Options Database Keys:
1854: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1855:                      block calculations (much slower)
1856: .   -mat_block_size - size of the blocks to use

1858:    Notes:

1860:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
1861:    than it must be used on all processors that share the object for that argument.

1863:    If the *_nnz parameter is given then the *_nz parameter is ignored

1865:    Storage Information:
1866:    For a square global matrix we define each processor's diagonal portion 
1867:    to be its local rows and the corresponding columns (a square submatrix);  
1868:    each processor's off-diagonal portion encompasses the remainder of the
1869:    local matrix (a rectangular submatrix). 

1871:    The user can specify preallocated storage for the diagonal part of
1872:    the local submatrix with either d_nz or d_nnz (not both).  Set 
1873:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1874:    memory allocation.  Likewise, specify preallocated storage for the
1875:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

1877:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1878:    the figure below we depict these three local rows and all columns (0-11).

1880: .vb
1881:            0 1 2 3 4 5 6 7 8 9 10 11
1882:           -------------------
1883:    row 3  |  o o o d d d o o o o o o
1884:    row 4  |  o o o d d d o o o o o o
1885:    row 5  |  o o o d d d o o o o o o
1886:           -------------------
1887: .ve
1888:   
1889:    Thus, any entries in the d locations are stored in the d (diagonal) 
1890:    submatrix, and any entries in the o locations are stored in the
1891:    o (off-diagonal) submatrix.  Note that the d matrix is stored in
1892:    MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.

1894:    Now d_nz should indicate the number of block nonzeros per row in the upper triangular
1895:    plus the diagonal part of the d matrix,
1896:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
1897:    In general, for PDE problems in which most nonzeros are near the diagonal,
1898:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
1899:    or you will get TERRIBLE performance; see the users' manual chapter on
1900:    matrices.

1902:    Level: intermediate

1904: .keywords: matrix, block, aij, compressed row, sparse, parallel

1906: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1907: @*/
1908: PetscErrorCode PETSCMAT_DLLEXPORT MatMPISBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
1909: {
1910:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);

1913:   PetscObjectQueryFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C",(void (**)(void))&f);
1914:   if (f) {
1915:     (*f)(B,bs,d_nz,d_nnz,o_nz,o_nnz);
1916:   }
1917:   return(0);
1918: }

1922: /*@C
1923:    MatCreateMPISBAIJ - Creates a sparse parallel matrix in symmetric block AIJ format
1924:    (block compressed row).  For good matrix assembly performance
1925:    the user should preallocate the matrix storage by setting the parameters 
1926:    d_nz (or d_nnz) and o_nz (or o_nnz).  By setting these parameters accurately,
1927:    performance can be increased by more than a factor of 50.

1929:    Collective on MPI_Comm

1931:    Input Parameters:
1932: +  comm - MPI communicator
1933: .  bs   - size of blockk
1934: .  m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1935:            This value should be the same as the local size used in creating the 
1936:            y vector for the matrix-vector product y = Ax.
1937: .  n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1938:            This value should be the same as the local size used in creating the 
1939:            x vector for the matrix-vector product y = Ax.
1940: .  M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
1941: .  N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
1942: .  d_nz  - number of block nonzeros per block row in diagonal portion of local 
1943:            submatrix  (same for all local rows)
1944: .  d_nnz - array containing the number of block nonzeros in the various block rows 
1945:            in the upper triangular portion of the in diagonal portion of the local 
1946:            (possibly different for each block block row) or PETSC_NULL.  
1947:            You must leave room for the diagonal entry even if it is zero.
1948: .  o_nz  - number of block nonzeros per block row in the off-diagonal portion of local
1949:            submatrix (same for all local rows).
1950: -  o_nnz - array containing the number of nonzeros in the various block rows of the
1951:            off-diagonal portion of the local submatrix (possibly different for
1952:            each block row) or PETSC_NULL.

1954:    Output Parameter:
1955: .  A - the matrix 

1957:    Options Database Keys:
1958: .   -mat_no_unroll - uses code that does not unroll the loops in the 
1959:                      block calculations (much slower)
1960: .   -mat_block_size - size of the blocks to use
1961: .   -mat_mpi - use the parallel matrix data structures even on one processor 
1962:                (defaults to using SeqBAIJ format on one processor)

1964:    Notes:
1965:    The number of rows and columns must be divisible by blocksize.

1967:    The user MUST specify either the local or global matrix dimensions
1968:    (possibly both).

1970:    If PETSC_DECIDE or  PETSC_DETERMINE is used for a particular argument on one processor
1971:    than it must be used on all processors that share the object for that argument.

1973:    If the *_nnz parameter is given then the *_nz parameter is ignored

1975:    Storage Information:
1976:    For a square global matrix we define each processor's diagonal portion 
1977:    to be its local rows and the corresponding columns (a square submatrix);  
1978:    each processor's off-diagonal portion encompasses the remainder of the
1979:    local matrix (a rectangular submatrix). 

1981:    The user can specify preallocated storage for the diagonal part of
1982:    the local submatrix with either d_nz or d_nnz (not both).  Set 
1983:    d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1984:    memory allocation.  Likewise, specify preallocated storage for the
1985:    off-diagonal part of the local submatrix with o_nz or o_nnz (not both).

1987:    Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1988:    the figure below we depict these three local rows and all columns (0-11).

1990: .vb
1991:            0 1 2 3 4 5 6 7 8 9 10 11
1992:           -------------------
1993:    row 3  |  o o o d d d o o o o o o
1994:    row 4  |  o o o d d d o o o o o o
1995:    row 5  |  o o o d d d o o o o o o
1996:           -------------------
1997: .ve
1998:   
1999:    Thus, any entries in the d locations are stored in the d (diagonal) 
2000:    submatrix, and any entries in the o locations are stored in the
2001:    o (off-diagonal) submatrix.  Note that the d matrix is stored in
2002:    MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.

2004:    Now d_nz should indicate the number of block nonzeros per row in the upper triangular
2005:    plus the diagonal part of the d matrix,
2006:    and o_nz should indicate the number of block nonzeros per row in the o matrix.
2007:    In general, for PDE problems in which most nonzeros are near the diagonal,
2008:    one expects d_nz >> o_nz.   For large problems you MUST preallocate memory
2009:    or you will get TERRIBLE performance; see the users' manual chapter on
2010:    matrices.

2012:    Level: intermediate

2014: .keywords: matrix, block, aij, compressed row, sparse, parallel

2016: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2017: @*/

2019: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateMPISBAIJ(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)
2020: {
2022:   PetscMPIInt    size;

2025:   MatCreate(comm,A);
2026:   MatSetSizes(*A,m,n,M,N);
2027:   MPI_Comm_size(comm,&size);
2028:   if (size > 1) {
2029:     MatSetType(*A,MATMPISBAIJ);
2030:     MatMPISBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2031:   } else {
2032:     MatSetType(*A,MATSEQSBAIJ);
2033:     MatSeqSBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2034:   }
2035:   return(0);
2036: }


2041: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2042: {
2043:   Mat            mat;
2044:   Mat_MPISBAIJ   *a,*oldmat = (Mat_MPISBAIJ*)matin->data;
2046:   PetscInt       len=0,nt,bs=matin->rmap.bs,mbs=oldmat->mbs;
2047:   PetscScalar    *array;

2050:   *newmat       = 0;
2051:   MatCreate(matin->comm,&mat);
2052:   MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
2053:   MatSetType(mat,matin->type_name);
2054:   PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2055:   PetscMapCopy(matin->comm,&matin->rmap,&mat->rmap);
2056:   PetscMapCopy(matin->comm,&matin->cmap,&mat->cmap);
2057: 
2058:   mat->factor       = matin->factor;
2059:   mat->preallocated = PETSC_TRUE;
2060:   mat->assembled    = PETSC_TRUE;
2061:   mat->insertmode   = NOT_SET_VALUES;

2063:   a = (Mat_MPISBAIJ*)mat->data;
2064:   a->bs2   = oldmat->bs2;
2065:   a->mbs   = oldmat->mbs;
2066:   a->nbs   = oldmat->nbs;
2067:   a->Mbs   = oldmat->Mbs;
2068:   a->Nbs   = oldmat->Nbs;


2071:   a->size         = oldmat->size;
2072:   a->rank         = oldmat->rank;
2073:   a->donotstash   = oldmat->donotstash;
2074:   a->roworiented  = oldmat->roworiented;
2075:   a->rowindices   = 0;
2076:   a->rowvalues    = 0;
2077:   a->getrowactive = PETSC_FALSE;
2078:   a->barray       = 0;
2079:   a->rstartbs    = oldmat->rstartbs;
2080:   a->rendbs      = oldmat->rendbs;
2081:   a->cstartbs    = oldmat->cstartbs;
2082:   a->cendbs      = oldmat->cendbs;

2084:   /* hash table stuff */
2085:   a->ht           = 0;
2086:   a->hd           = 0;
2087:   a->ht_size      = 0;
2088:   a->ht_flag      = oldmat->ht_flag;
2089:   a->ht_fact      = oldmat->ht_fact;
2090:   a->ht_total_ct  = 0;
2091:   a->ht_insert_ct = 0;
2092: 
2093:   PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+2)*sizeof(PetscInt));
2094:   MatStashCreate_Private(matin->comm,1,&mat->stash);
2095:   MatStashCreate_Private(matin->comm,matin->rmap.bs,&mat->bstash);
2096:   if (oldmat->colmap) {
2097: #if defined (PETSC_USE_CTABLE)
2098:     PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2099: #else
2100:     PetscMalloc((a->Nbs)*sizeof(PetscInt),&a->colmap);
2101:     PetscLogObjectMemory(mat,(a->Nbs)*sizeof(PetscInt));
2102:     PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2103: #endif
2104:   } else a->colmap = 0;

2106:   if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2107:     PetscMalloc(len*sizeof(PetscInt),&a->garray);
2108:     PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2109:     PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2110:   } else a->garray = 0;
2111: 
2112:    VecDuplicate(oldmat->lvec,&a->lvec);
2113:   PetscLogObjectParent(mat,a->lvec);
2114:    VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2115:   PetscLogObjectParent(mat,a->Mvctx);

2117:    VecDuplicate(oldmat->slvec0,&a->slvec0);
2118:   PetscLogObjectParent(mat,a->slvec0);
2119:    VecDuplicate(oldmat->slvec1,&a->slvec1);
2120:   PetscLogObjectParent(mat,a->slvec1);

2122:   VecGetLocalSize(a->slvec1,&nt);
2123:   VecGetArray(a->slvec1,&array);
2124:   VecCreateSeqWithArray(PETSC_COMM_SELF,bs*mbs,array,&a->slvec1a);
2125:   VecCreateSeqWithArray(PETSC_COMM_SELF,nt-bs*mbs,array+bs*mbs,&a->slvec1b);
2126:   VecRestoreArray(a->slvec1,&array);
2127:   VecGetArray(a->slvec0,&array);
2128:   VecCreateSeqWithArray(PETSC_COMM_SELF,nt-bs*mbs,array+bs*mbs,&a->slvec0b);
2129:   VecRestoreArray(a->slvec0,&array);
2130:   PetscLogObjectParent(mat,a->slvec0);
2131:   PetscLogObjectParent(mat,a->slvec1);
2132:   PetscLogObjectParent(mat,a->slvec0b);
2133:   PetscLogObjectParent(mat,a->slvec1a);
2134:   PetscLogObjectParent(mat,a->slvec1b);

2136:   /*  VecScatterCopy(oldmat->sMvctx,&a->sMvctx); - not written yet, replaced by the lazy trick: */
2137:   PetscObjectReference((PetscObject)oldmat->sMvctx);
2138:   a->sMvctx = oldmat->sMvctx;
2139:   PetscLogObjectParent(mat,a->sMvctx);

2141:    MatDuplicate(oldmat->A,cpvalues,&a->A);
2142:   PetscLogObjectParent(mat,a->A);
2143:    MatDuplicate(oldmat->B,cpvalues,&a->B);
2144:   PetscLogObjectParent(mat,a->B);
2145:   PetscFListDuplicate(mat->qlist,&matin->qlist);
2146:   *newmat = mat;
2147:   return(0);
2148: }

2150:  #include petscsys.h

2154: PetscErrorCode MatLoad_MPISBAIJ(PetscViewer viewer, MatType type,Mat *newmat)
2155: {
2156:   Mat            A;
2158:   PetscInt       i,nz,j,rstart,rend;
2159:   PetscScalar    *vals,*buf;
2160:   MPI_Comm       comm = ((PetscObject)viewer)->comm;
2161:   MPI_Status     status;
2162:   PetscMPIInt    rank,size,tag = ((PetscObject)viewer)->tag,*sndcounts = 0,*browners,maxnz,*rowners,*locrowlens;
2163:   PetscInt       header[4],*rowlengths = 0,M,N,m,*cols;
2164:   PetscInt       *procsnz = 0,jj,*mycols,*ibuf;
2165:   PetscInt       bs=1,Mbs,mbs,extra_rows;
2166:   PetscInt       *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2167:   PetscInt       dcount,kmax,k,nzcount,tmp;
2168:   int            fd;
2169: 
2171:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);

2173:   MPI_Comm_size(comm,&size);
2174:   MPI_Comm_rank(comm,&rank);
2175:   if (!rank) {
2176:     PetscViewerBinaryGetDescriptor(viewer,&fd);
2177:     PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2178:     if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2179:     if (header[3] < 0) {
2180:       SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPISBAIJ");
2181:     }
2182:   }

2184:   MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2185:   M = header[1]; N = header[2];

2187:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2189:   /* 
2190:      This code adds extra rows to make sure the number of rows is 
2191:      divisible by the blocksize
2192:   */
2193:   Mbs        = M/bs;
2194:   extra_rows = bs - M + bs*(Mbs);
2195:   if (extra_rows == bs) extra_rows = 0;
2196:   else                  Mbs++;
2197:   if (extra_rows &&!rank) {
2198:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2199:   }

2201:   /* determine ownership of all rows */
2202:   mbs        = Mbs/size + ((Mbs % size) > rank);
2203:   m          = mbs*bs;
2204:   PetscMalloc(2*(size+2)*sizeof(PetscMPIInt),&rowners);
2205:   browners   = rowners + size + 1;
2206:   MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2207:   rowners[0] = 0;
2208:   for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2209:   for (i=0; i<=size;  i++) browners[i] = rowners[i]*bs;
2210:   rstart = rowners[rank];
2211:   rend   = rowners[rank+1];
2212: 
2213:   /* distribute row lengths to all processors */
2214:   PetscMalloc((rend-rstart)*bs*sizeof(PetscMPIInt),&locrowlens);
2215:   if (!rank) {
2216:     PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2217:     PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2218:     for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2219:     PetscMalloc(size*sizeof(PetscMPIInt),&sndcounts);
2220:     for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2221:     MPI_Scatterv(rowlengths,sndcounts,browners,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2222:     PetscFree(sndcounts);
2223:   } else {
2224:     MPI_Scatterv(0,0,0,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2225:   }
2226: 
2227:   if (!rank) {   /* procs[0] */
2228:     /* calculate the number of nonzeros on each processor */
2229:     PetscMalloc(size*sizeof(PetscInt),&procsnz);
2230:     PetscMemzero(procsnz,size*sizeof(PetscInt));
2231:     for (i=0; i<size; i++) {
2232:       for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2233:         procsnz[i] += rowlengths[j];
2234:       }
2235:     }
2236:     PetscFree(rowlengths);
2237: 
2238:     /* determine max buffer needed and allocate it */
2239:     maxnz = 0;
2240:     for (i=0; i<size; i++) {
2241:       maxnz = PetscMax(maxnz,procsnz[i]);
2242:     }
2243:     PetscMalloc(maxnz*sizeof(PetscInt),&cols);

2245:     /* read in my part of the matrix column indices  */
2246:     nz     = procsnz[0];
2247:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2248:     mycols = ibuf;
2249:     if (size == 1)  nz -= extra_rows;
2250:     PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2251:     if (size == 1)  for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }

2253:     /* read in every ones (except the last) and ship off */
2254:     for (i=1; i<size-1; i++) {
2255:       nz   = procsnz[i];
2256:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2257:       MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2258:     }
2259:     /* read in the stuff for the last proc */
2260:     if (size != 1) {
2261:       nz   = procsnz[size-1] - extra_rows;  /* the extra rows are not on the disk */
2262:       PetscBinaryRead(fd,cols,nz,PETSC_INT);
2263:       for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2264:       MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2265:     }
2266:     PetscFree(cols);
2267:   } else {  /* procs[i], i>0 */
2268:     /* determine buffer space needed for message */
2269:     nz = 0;
2270:     for (i=0; i<m; i++) {
2271:       nz += locrowlens[i];
2272:     }
2273:     PetscMalloc(nz*sizeof(PetscInt),&ibuf);
2274:     mycols = ibuf;
2275:     /* receive message of column indices*/
2276:     MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2277:     MPI_Get_count(&status,MPIU_INT,&maxnz);
2278:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2279:   }

2281:   /* loop over local rows, determining number of off diagonal entries */
2282:   PetscMalloc(2*(rend-rstart+1)*sizeof(PetscInt),&dlens);
2283:   odlens   = dlens + (rend-rstart);
2284:   PetscMalloc(3*Mbs*sizeof(PetscInt),&mask);
2285:   PetscMemzero(mask,3*Mbs*sizeof(PetscInt));
2286:   masked1  = mask    + Mbs;
2287:   masked2  = masked1 + Mbs;
2288:   rowcount = 0; nzcount = 0;
2289:   for (i=0; i<mbs; i++) {
2290:     dcount  = 0;
2291:     odcount = 0;
2292:     for (j=0; j<bs; j++) {
2293:       kmax = locrowlens[rowcount];
2294:       for (k=0; k<kmax; k++) {
2295:         tmp = mycols[nzcount++]/bs; /* block col. index */
2296:         if (!mask[tmp]) {
2297:           mask[tmp] = 1;
2298:           if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp; /* entry in off-diag portion */
2299:           else masked1[dcount++] = tmp; /* entry in diag portion */
2300:         }
2301:       }
2302:       rowcount++;
2303:     }
2304: 
2305:     dlens[i]  = dcount;  /* d_nzz[i] */
2306:     odlens[i] = odcount; /* o_nzz[i] */

2308:     /* zero out the mask elements we set */
2309:     for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2310:     for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2311:   }
2312: 
2313:   /* create our matrix */
2314:   MatCreate(comm,&A);
2315:   MatSetSizes(A,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
2316:   MatSetType(A,type);
2317:   MatMPISBAIJSetPreallocation(A,bs,0,dlens,0,odlens);
2318:   MatSetOption(A,MAT_COLUMNS_SORTED);
2319: 
2320:   if (!rank) {
2321:     PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2322:     /* read in my part of the matrix numerical values  */
2323:     nz = procsnz[0];
2324:     vals = buf;
2325:     mycols = ibuf;
2326:     if (size == 1)  nz -= extra_rows;
2327:     PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2328:     if (size == 1)  for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }

2330:     /* insert into matrix */
2331:     jj      = rstart*bs;
2332:     for (i=0; i<m; i++) {
2333:       MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2334:       mycols += locrowlens[i];
2335:       vals   += locrowlens[i];
2336:       jj++;
2337:     }

2339:     /* read in other processors (except the last one) and ship out */
2340:     for (i=1; i<size-1; i++) {
2341:       nz   = procsnz[i];
2342:       vals = buf;
2343:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2344:       MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2345:     }
2346:     /* the last proc */
2347:     if (size != 1){
2348:       nz   = procsnz[i] - extra_rows;
2349:       vals = buf;
2350:       PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2351:       for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2352:       MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2353:     }
2354:     PetscFree(procsnz);

2356:   } else {
2357:     /* receive numeric values */
2358:     PetscMalloc(nz*sizeof(PetscScalar),&buf);

2360:     /* receive message of values*/
2361:     vals   = buf;
2362:     mycols = ibuf;
2363:     MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2364:     MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2365:     if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");

2367:     /* insert into matrix */
2368:     jj      = rstart*bs;
2369:     for (i=0; i<m; i++) {
2370:       MatSetValues_MPISBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2371:       mycols += locrowlens[i];
2372:       vals   += locrowlens[i];
2373:       jj++;
2374:     }
2375:   }

2377:   PetscFree(locrowlens);
2378:   PetscFree(buf);
2379:   PetscFree(ibuf);
2380:   PetscFree(rowners);
2381:   PetscFree(dlens);
2382:   PetscFree(mask);
2383:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2384:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2385:   *newmat = A;
2386:   return(0);
2387: }

2391: /*XXXXX@
2392:    MatMPISBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.

2394:    Input Parameters:
2395: .  mat  - the matrix
2396: .  fact - factor

2398:    Collective on Mat

2400:    Level: advanced

2402:   Notes:
2403:    This can also be set by the command line option: -mat_use_hash_table fact

2405: .keywords: matrix, hashtable, factor, HT

2407: .seealso: MatSetOption()
2408: @XXXXX*/


2413: PetscErrorCode MatGetRowMax_MPISBAIJ(Mat A,Vec v)
2414: {
2415:   Mat_MPISBAIJ   *a = (Mat_MPISBAIJ*)A->data;
2416:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)(a->B)->data;
2417:   PetscReal      atmp;
2418:   PetscReal      *work,*svalues,*rvalues;
2420:   PetscInt       i,bs,mbs,*bi,*bj,brow,j,ncols,krow,kcol,col,row,Mbs,bcol;
2421:   PetscMPIInt    rank,size;
2422:   PetscInt       *rowners_bs,dest,count,source;
2423:   PetscScalar    *va;
2424:   MatScalar      *ba;
2425:   MPI_Status     stat;

2428:   MatGetRowMax(a->A,v);
2429:   VecGetArray(v,&va);

2431:   MPI_Comm_size(A->comm,&size);
2432:   MPI_Comm_rank(A->comm,&rank);

2434:   bs   = A->rmap.bs;
2435:   mbs  = a->mbs;
2436:   Mbs  = a->Mbs;
2437:   ba   = b->a;
2438:   bi   = b->i;
2439:   bj   = b->j;

2441:   /* find ownerships */
2442:   rowners_bs = A->rmap.range;

2444:   /* each proc creates an array to be distributed */
2445:   PetscMalloc(bs*Mbs*sizeof(PetscReal),&work);
2446:   PetscMemzero(work,bs*Mbs*sizeof(PetscReal));

2448:   /* row_max for B */
2449:   if (rank != size-1){
2450:     for (i=0; i<mbs; i++) {
2451:       ncols = bi[1] - bi[0]; bi++;
2452:       brow  = bs*i;
2453:       for (j=0; j<ncols; j++){
2454:         bcol = bs*(*bj);
2455:         for (kcol=0; kcol<bs; kcol++){
2456:           col = bcol + kcol;                 /* local col index */
2457:           col += rowners_bs[rank+1];      /* global col index */
2458:           for (krow=0; krow<bs; krow++){
2459:             atmp = PetscAbsScalar(*ba); ba++;
2460:             row = brow + krow;    /* local row index */
2461:             if (PetscRealPart(va[row]) < atmp) va[row] = atmp;
2462:             if (work[col] < atmp) work[col] = atmp;
2463:           }
2464:         }
2465:         bj++;
2466:       }
2467:     }

2469:     /* send values to its owners */
2470:     for (dest=rank+1; dest<size; dest++){
2471:       svalues = work + rowners_bs[dest];
2472:       count   = rowners_bs[dest+1]-rowners_bs[dest];
2473:       MPI_Send(svalues,count,MPIU_REAL,dest,rank,A->comm);
2474:     }
2475:   }
2476: 
2477:   /* receive values */
2478:   if (rank){
2479:     rvalues = work;
2480:     count   = rowners_bs[rank+1]-rowners_bs[rank];
2481:     for (source=0; source<rank; source++){
2482:       MPI_Recv(rvalues,count,MPIU_REAL,MPI_ANY_SOURCE,MPI_ANY_TAG,A->comm,&stat);
2483:       /* process values */
2484:       for (i=0; i<count; i++){
2485:         if (PetscRealPart(va[i]) < rvalues[i]) va[i] = rvalues[i];
2486:       }
2487:     }
2488:   }

2490:   VecRestoreArray(v,&va);
2491:   PetscFree(work);
2492:   return(0);
2493: }

2497: PetscErrorCode MatRelax_MPISBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2498: {
2499:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
2501:   PetscInt       mbs=mat->mbs,bs=matin->rmap.bs;
2502:   PetscScalar    *x,*b,*ptr,zero=0.0;
2503:   Vec            bb1;
2504: 
2506:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2507:   if (bs > 1)
2508:     SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");

2510:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2511:     if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2512:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2513:       its--;
2514:     }

2516:     VecDuplicate(bb,&bb1);
2517:     while (its--){
2518: 
2519:       /* lower triangular part: slvec0b = - B^T*xx */
2520:       (*mat->B->ops->multtranspose)(mat->B,xx,mat->slvec0b);
2521: 
2522:       /* copy xx into slvec0a */
2523:       VecGetArray(mat->slvec0,&ptr);
2524:       VecGetArray(xx,&x);
2525:       PetscMemcpy(ptr,x,bs*mbs*sizeof(MatScalar));
2526:       VecRestoreArray(mat->slvec0,&ptr);

2528:       VecScale(mat->slvec0,-1.0);

2530:       /* copy bb into slvec1a */
2531:       VecGetArray(mat->slvec1,&ptr);
2532:       VecGetArray(bb,&b);
2533:       PetscMemcpy(ptr,b,bs*mbs*sizeof(MatScalar));
2534:       VecRestoreArray(mat->slvec1,&ptr);

2536:       /* set slvec1b = 0 */
2537:       VecSet(mat->slvec1b,zero);

2539:       VecScatterBegin(mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD,mat->sMvctx);
2540:       VecRestoreArray(xx,&x);
2541:       VecRestoreArray(bb,&b);
2542:       VecScatterEnd(mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD,mat->sMvctx);

2544:       /* upper triangular part: bb1 = bb1 - B*x */
2545:       (*mat->B->ops->multadd)(mat->B,mat->slvec1b,mat->slvec1a,bb1);
2546: 
2547:       /* local diagonal sweep */
2548:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2549:     }
2550:     VecDestroy(bb1);
2551:   } else {
2552:     SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2553:   }
2554:   return(0);
2555: }

2559: PetscErrorCode MatRelax_MPISBAIJ_2comm(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2560: {
2561:   Mat_MPISBAIJ   *mat = (Mat_MPISBAIJ*)matin->data;
2563:   Vec            lvec1,bb1;
2564: 
2566:   if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2567:   if (matin->rmap.bs > 1)
2568:     SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");

2570:   if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2571:     if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2572:       (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2573:       its--;
2574:     }

2576:     VecDuplicate(mat->lvec,&lvec1);
2577:     VecDuplicate(bb,&bb1);
2578:     while (its--){
2579:       VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2580: 
2581:       /* lower diagonal part: bb1 = bb - B^T*xx */
2582:       (*mat->B->ops->multtranspose)(mat->B,xx,lvec1);
2583:       VecScale(lvec1,-1.0);

2585:       VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2586:       VecCopy(bb,bb1);
2587:       VecScatterBegin(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);

2589:       /* upper diagonal part: bb1 = bb1 - B*x */
2590:       VecScale(mat->lvec,-1.0);
2591:       (*mat->B->ops->multadd)(mat->B,mat->lvec,bb1,bb1);

2593:       VecScatterEnd(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);
2594: 
2595:       /* diagonal sweep */
2596:       (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2597:     }
2598:     VecDestroy(lvec1);
2599:     VecDestroy(bb1);
2600:   } else {
2601:     SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2602:   }
2603:   return(0);
2604: }