Actual source code: mpispooles.c

  1: /* 
  2:    Provides an interface to the Spooles parallel sparse solver (MPI SPOOLES)
  3: */

 5:  #include src/mat/impls/aij/seq/aij.h
 6:  #include src/mat/impls/sbaij/seq/sbaij.h
 7:  #include src/mat/impls/baij/seq/baij.h
 8:  #include src/mat/impls/aij/mpi/mpiaij.h
 9:  #include src/mat/impls/sbaij/mpi/mpisbaij.h
 10:  #include src/mat/impls/aij/seq/spooles/spooles.h

 12: EXTERN int SetSpoolesOptions(Mat, Spooles_options *);

 16: PetscErrorCode MatDestroy_MPIAIJSpooles(Mat A)
 17: {
 18:   Mat_Spooles   *lu = (Mat_Spooles*)A->spptr;
 20: 
 22:   if (lu->CleanUpSpooles) {
 23:     FrontMtx_free(lu->frontmtx);
 24:     IV_free(lu->newToOldIV);
 25:     IV_free(lu->oldToNewIV);
 26:     IV_free(lu->vtxmapIV);
 27:     InpMtx_free(lu->mtxA);
 28:     ETree_free(lu->frontETree);
 29:     IVL_free(lu->symbfacIVL);
 30:     SubMtxManager_free(lu->mtxmanager);
 31:     DenseMtx_free(lu->mtxX);
 32:     DenseMtx_free(lu->mtxY);
 33:     MPI_Comm_free(&(lu->comm_spooles));
 34:     if ( lu->scat ){
 35:       VecDestroy(lu->vec_spooles);
 36:       ISDestroy(lu->iden);
 37:       ISDestroy(lu->is_petsc);
 38:       VecScatterDestroy(lu->scat);
 39:     }
 40:   }
 41:   MatConvert_Spooles_Base(A,lu->basetype,&A);
 42:   (*A->ops->destroy)(A);

 44:   return(0);
 45: }

 49: PetscErrorCode MatSolve_MPIAIJSpooles(Mat A,Vec b,Vec x)
 50: {
 51:   Mat_Spooles   *lu = (Mat_Spooles*)A->spptr;
 53:   int           size,rank,m=A->m,irow,*rowindY;
 54:   PetscScalar   *array;
 55:   DenseMtx      *newY ;
 56:   SubMtxManager *solvemanager ;
 57: #if defined(PETSC_USE_COMPLEX)
 58:   double x_real,x_imag;
 59: #endif

 62:   MPI_Comm_size(A->comm,&size);
 63:   MPI_Comm_rank(A->comm,&rank);
 64: 
 65:   /* copy b into spooles' rhs mtxY */
 66:   DenseMtx_init(lu->mtxY, lu->options.typeflag, 0, 0, m, 1, 1, m);
 67:   VecGetArray(b,&array);

 69:   DenseMtx_rowIndices(lu->mtxY, &m, &rowindY);  /* get m, rowind */
 70:   for ( irow = 0 ; irow < m ; irow++ ) {
 71:     rowindY[irow] = irow + lu->rstart;           /* global rowind */
 72: #if !defined(PETSC_USE_COMPLEX)
 73:     DenseMtx_setRealEntry(lu->mtxY, irow, 0, *array++);
 74: #else
 75:     DenseMtx_setComplexEntry(lu->mtxY,irow,0,PetscRealPart(*array),PetscImaginaryPart(*array));
 76:     array++;
 77: #endif
 78:   }
 79:   VecRestoreArray(b,&array);
 80: 
 81:   if ( lu->options.msglvl > 2 ) {
 82:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n 1 matrix in original ordering");
 83:     DenseMtx_writeForHumanEye(lu->mtxY, lu->options.msgFile);
 84:     fflush(lu->options.msgFile);
 85:   }
 86: 
 87:   /* permute and redistribute Y if necessary */
 88:   DenseMtx_permuteRows(lu->mtxY, lu->oldToNewIV);
 89:   if ( lu->options.msglvl > 2 ) {
 90:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n rhs matrix in new ordering");
 91:     DenseMtx_writeForHumanEye(lu->mtxY, lu->options.msgFile);
 92:    fflush(lu->options.msgFile);
 93:   }

 95:   MPI_Barrier(A->comm); /* for initializing firsttag, because the num. of tags used
 96:                                    by FrontMtx_MPI_split() is unknown */
 97:   lu->firsttag = 0;
 98:   newY = DenseMtx_MPI_splitByRows(lu->mtxY, lu->vtxmapIV, lu->stats, lu->options.msglvl,
 99:                                 lu->options.msgFile, lu->firsttag, lu->comm_spooles);
100:   DenseMtx_free(lu->mtxY);
101:   lu->mtxY = newY ;
102:   lu->firsttag += size ;
103:   if ( lu->options.msglvl > 2 ) {
104:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n split DenseMtx Y");
105:     DenseMtx_writeForHumanEye(lu->mtxY, lu->options.msgFile);
106:     fflush(lu->options.msgFile);
107:   }

109:   if ( FRONTMTX_IS_PIVOTING(lu->frontmtx) ) {
110:     /*   pivoting has taken place, redistribute the right hand side
111:          to match the final rows and columns in the fronts             */
112:     IV *rowmapIV ;
113:     rowmapIV = FrontMtx_MPI_rowmapIV(lu->frontmtx, lu->ownersIV, lu->options.msglvl,
114:                                     lu->options.msgFile, lu->comm_spooles);
115:     newY = DenseMtx_MPI_splitByRows(lu->mtxY, rowmapIV, lu->stats, lu->options.msglvl,
116:                                    lu->options.msgFile, lu->firsttag, lu->comm_spooles);
117:     DenseMtx_free(lu->mtxY);
118:     lu->mtxY = newY ;
119:     IV_free(rowmapIV);
120:     lu->firsttag += size;
121:   }
122:   if ( lu->options.msglvl > 2 ) {
123:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n rhs matrix after split");
124:     DenseMtx_writeForHumanEye(lu->mtxY, lu->options.msgFile);
125:     fflush(lu->options.msgFile);
126:   }

128:   if ( lu->nmycol > 0 ) IVcopy(lu->nmycol,lu->rowindX,IV_entries(lu->ownedColumnsIV)); /* must do for each solve */
129: 
130:   /* solve the linear system */
131:   solvemanager = SubMtxManager_new();
132:   SubMtxManager_init(solvemanager, NO_LOCK, 0);
133:   FrontMtx_MPI_solve(lu->frontmtx, lu->mtxX, lu->mtxY, solvemanager, lu->solvemap, lu->cpus,
134:                    lu->stats, lu->options.msglvl, lu->options.msgFile, lu->firsttag, lu->comm_spooles);
135:   SubMtxManager_free(solvemanager);
136:   if ( lu->options.msglvl > 2 ) {
137:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n solution in new ordering");
138:     DenseMtx_writeForHumanEye(lu->mtxX, lu->options.msgFile);
139:   }

141:   /* permute the solution into the original ordering */
142:   DenseMtx_permuteRows(lu->mtxX, lu->newToOldIV);
143:   if ( lu->options.msglvl > 2 ) {
144:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n solution in old ordering");
145:     DenseMtx_writeForHumanEye(lu->mtxX, lu->options.msgFile);
146:     fflush(lu->options.msgFile);
147:   }
148: 
149:   /* scatter local solution mtxX into mpi vector x */
150:   if( !lu->scat ){ /* create followings once for each numfactorization */
151:     /* vec_spooles <- mtxX */
152: #if !defined(PETSC_USE_COMPLEX) 
153:     VecCreateSeqWithArray(PETSC_COMM_SELF,lu->nmycol,lu->entX,&lu->vec_spooles);
154: #else    
155:     VecCreateSeq(PETSC_COMM_SELF,lu->nmycol,&lu->vec_spooles);
156:     VecGetArray(lu->vec_spooles,&array);
157:     for (irow = 0; irow < lu->nmycol; irow++){
158:       DenseMtx_complexEntry(lu->mtxX,irow,0,&x_real,&x_imag);
159:       array[irow] = x_real+x_imag*PETSC_i;
160:     }
161:     VecRestoreArray(lu->vec_spooles,&array);
162: #endif 
163:     ISCreateStride(PETSC_COMM_SELF,lu->nmycol,0,1,&lu->iden);
164:     ISCreateGeneral(PETSC_COMM_SELF,lu->nmycol,lu->rowindX,&lu->is_petsc);
165:     VecScatterCreate(lu->vec_spooles,lu->iden,x,lu->is_petsc,&lu->scat);
166:   }

168:   VecScatterBegin(lu->vec_spooles,x,INSERT_VALUES,SCATTER_FORWARD,lu->scat);
169:   VecScatterEnd(lu->vec_spooles,x,INSERT_VALUES,SCATTER_FORWARD,lu->scat);
170: 
171:   return(0);
172: }

176: PetscErrorCode MatFactorNumeric_MPIAIJSpooles(Mat A,Mat *F)
177: {
178:   Mat_Spooles     *lu = (Mat_Spooles*)(*F)->spptr;
179:   PetscErrorCode  ierr;
180:   int             rank,size,lookahead=0,sierr;
181:   ChvManager      *chvmanager ;
182:   Chv             *rootchv ;
183:   Graph           *graph ;
184:   IVL             *adjIVL;
185:   DV              *cumopsDV ;
186:   double          droptol=0.0,*opcounts,minops,cutoff;
187: #if !defined(PETSC_USE_COMPLEX)
188:   double          *val;
189: #endif
190:   InpMtx          *newA ;
191:   PetscScalar     *av, *bv;
192:   int             *ai, *aj, *bi,*bj, nz, *ajj, *bjj, *garray,
193:                   i,j,irow,jcol,countA,countB,jB,*row,*col,colA_start,jj;
194:   int             M=A->M,m=A->m,root,nedges,tagbound,lasttag;
195: 
197:   MPI_Comm_size(A->comm,&size);
198:   MPI_Comm_rank(A->comm,&rank);

200:   if (lu->flg == DIFFERENT_NONZERO_PATTERN) { /* first numeric factorization */
201:     /* get input parameters */
202:     SetSpoolesOptions(A, &lu->options);

204:     (*F)->ops->solve   = MatSolve_MPIAIJSpooles;
205:     (*F)->ops->destroy = MatDestroy_MPIAIJSpooles;
206:     (*F)->assembled    = PETSC_TRUE;

208:     /* to be used by MatSolve() */
209:     lu->mtxY = DenseMtx_new();
210:     lu->mtxX = DenseMtx_new();
211:     lu->scat = PETSC_NULL;

213:     IVzero(20, lu->stats);
214:     DVzero(20, lu->cpus);

216:     lu->mtxA = InpMtx_new();
217:   }
218: 
219:   /* copy A to Spooles' InpMtx object */
220:   if ( lu->options.symflag == SPOOLES_NONSYMMETRIC ) {
221:     Mat_MPIAIJ  *mat =  (Mat_MPIAIJ*)A->data;
222:     Mat_SeqAIJ  *aa=(Mat_SeqAIJ*)(mat->A)->data;
223:     Mat_SeqAIJ  *bb=(Mat_SeqAIJ*)(mat->B)->data;
224:     ai=aa->i; aj=aa->j; av=aa->a;
225:     bi=bb->i; bj=bb->j; bv=bb->a;
226:     lu->rstart = mat->rstart;
227:     nz         = aa->nz + bb->nz;
228:     garray     = mat->garray;
229:   } else {         /* SPOOLES_SYMMETRIC  */
230:     Mat_MPISBAIJ  *mat = (Mat_MPISBAIJ*)A->data;
231:     Mat_SeqSBAIJ  *aa=(Mat_SeqSBAIJ*)(mat->A)->data;
232:     Mat_SeqBAIJ    *bb=(Mat_SeqBAIJ*)(mat->B)->data;
233:     ai=aa->i; aj=aa->j; av=aa->a;
234:     bi=bb->i; bj=bb->j; bv=bb->a;
235:     lu->rstart = mat->rstart;
236:     nz         = aa->nz + bb->nz;
237:     garray     = mat->garray;
238:   }
239: 
240:   InpMtx_init(lu->mtxA, INPMTX_BY_ROWS, lu->options.typeflag, nz, 0);
241:   row   = InpMtx_ivec1(lu->mtxA);
242:   col   = InpMtx_ivec2(lu->mtxA);
243: #if !defined(PETSC_USE_COMPLEX)
244:   val   = InpMtx_dvec(lu->mtxA);
245: #endif

247:   jj = 0; irow = lu->rstart;
248:   for ( i=0; i<m; i++ ) {
249:     ajj = aj + ai[i];                 /* ptr to the beginning of this row */
250:     countA = ai[i+1] - ai[i];
251:     countB = bi[i+1] - bi[i];
252:     bjj = bj + bi[i];
253:     jB = 0;
254: 
255:     if (lu->options.symflag == SPOOLES_NONSYMMETRIC ){
256:       /* B part, smaller col index */
257:       colA_start = lu->rstart + ajj[0]; /* the smallest col index for A */
258:       for (j=0; j<countB; j++){
259:         jcol = garray[bjj[j]];
260:         if (jcol > colA_start) {
261:           jB = j;
262:           break;
263:         }
264:         row[jj] = irow; col[jj] = jcol;
265: #if !defined(PETSC_USE_COMPLEX)
266:         val[jj++] = *bv++;
267: #else
268:         InpMtx_inputComplexEntry(lu->mtxA,irow,jcol,PetscRealPart(*bv),PetscImaginaryPart(*bv));
269:         bv++; jj++;
270: #endif
271:         if (j==countB-1) jB = countB;
272:       }
273:     }
274:     /* A part */
275:     for (j=0; j<countA; j++){
276:       row[jj] = irow; col[jj] = lu->rstart + ajj[j];
277: #if !defined(PETSC_USE_COMPLEX)
278:       val[jj++] = *av++;
279: #else
280:       InpMtx_inputComplexEntry(lu->mtxA,irow,col[jj],PetscRealPart(*av),PetscImaginaryPart(*av));
281:       av++; jj++;
282: #endif
283:     }
284:     /* B part, larger col index */
285:     for (j=jB; j<countB; j++){
286:       row[jj] = irow; col[jj] = garray[bjj[j]];
287: #if !defined(PETSC_USE_COMPLEX)
288:       val[jj++] = *bv++;
289: #else
290:      InpMtx_inputComplexEntry(lu->mtxA,irow,col[jj],PetscRealPart(*bv),PetscImaginaryPart(*bv));
291:      bv++; jj++;
292: #endif
293:     }
294:     irow++;
295:   }
296: #if !defined(PETSC_USE_COMPLEX)
297:   InpMtx_inputRealTriples(lu->mtxA, nz, row, col, val);
298: #endif
299:   InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
300:   if ( lu->options.msglvl > 0 ) {
301:     printf("[%d] input matrix\n",rank);
302:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n [%d] input matrix\n",rank);
303:     InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
304:     fflush(lu->options.msgFile);
305:   }

307:   if ( lu->flg == DIFFERENT_NONZERO_PATTERN){ /* first numeric factorization */
308:     /*
309:       find a low-fill ordering
310:       (1) create the Graph object
311:       (2) order the graph using multiple minimum degree
312:       (3) find out who has the best ordering w.r.t. op count,
313:           and broadcast that front tree object
314:     */
315:     graph = Graph_new();
316:     adjIVL = InpMtx_MPI_fullAdjacency(lu->mtxA, lu->stats,
317:               lu->options.msglvl, lu->options.msgFile, lu->comm_spooles);
318:     nedges = IVL_tsize(adjIVL);
319:     Graph_init2(graph, 0, M, 0, nedges, M, nedges, adjIVL, NULL, NULL);
320:     if ( lu->options.msglvl > 2 ) {
321:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of the input matrix");
322:       Graph_writeForHumanEye(graph, lu->options.msgFile);
323:       fflush(lu->options.msgFile);
324:     }

326:     switch (lu->options.ordering) {
327:     case 0:
328:       lu->frontETree = orderViaBestOfNDandMS(graph,
329:                      lu->options.maxdomainsize, lu->options.maxzeros, lu->options.maxsize,
330:                      lu->options.seed + rank, lu->options.msglvl, lu->options.msgFile); break;
331:     case 1:
332:       lu->frontETree = orderViaMMD(graph,lu->options.seed + rank,lu->options.msglvl,lu->options.msgFile); break;
333:     case 2:
334:       lu->frontETree = orderViaMS(graph, lu->options.maxdomainsize,
335:                      lu->options.seed + rank,lu->options.msglvl,lu->options.msgFile); break;
336:     case 3:
337:       lu->frontETree = orderViaND(graph, lu->options.maxdomainsize,
338:                      lu->options.seed + rank,lu->options.msglvl,lu->options.msgFile); break;
339:     default:
340:       SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown Spooles's ordering");
341:     }

343:     Graph_free(graph);
344:     if ( lu->options.msglvl > 2 ) {
345:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree from ordering");
346:       ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
347:       fflush(lu->options.msgFile);
348:     }

350:     opcounts = DVinit(size, 0.0);
351:     opcounts[rank] = ETree_nFactorOps(lu->frontETree, lu->options.typeflag, lu->options.symflag);
352:     MPI_Allgather((void*) &opcounts[rank], 1, MPI_DOUBLE,
353:               (void*) opcounts, 1, MPI_DOUBLE, A->comm);
354:     minops = DVmin(size, opcounts, &root);
355:     DVfree(opcounts);
356: 
357:     lu->frontETree = ETree_MPI_Bcast(lu->frontETree, root,
358:                              lu->options.msglvl, lu->options.msgFile, lu->comm_spooles);
359:     if ( lu->options.msglvl > 2 ) {
360:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n best front tree");
361:       ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
362:       fflush(lu->options.msgFile);
363:     }
364: 
365:     /* get the permutations, permute the front tree, permute the matrix */
366:     lu->oldToNewIV = ETree_oldToNewVtxPerm(lu->frontETree);
367:     lu->newToOldIV = ETree_newToOldVtxPerm(lu->frontETree);

369:     ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);

371:     InpMtx_permute(lu->mtxA, IV_entries(lu->oldToNewIV), IV_entries(lu->oldToNewIV));
372: 
373:     if (  lu->options.symflag == SPOOLES_SYMMETRIC ) InpMtx_mapToUpperTriangle(lu->mtxA);

375:     InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
376:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);

378:     /* generate the owners map IV object and the map from vertices to owners */
379:     cutoff   = 1./(2*size);
380:     cumopsDV = DV_new();
381:     DV_init(cumopsDV, size, NULL);
382:     lu->ownersIV = ETree_ddMap(lu->frontETree,
383:                        lu->options.typeflag, lu->options.symflag, cumopsDV, cutoff);
384:     DV_free(cumopsDV);
385:     lu->vtxmapIV = IV_new();
386:     IV_init(lu->vtxmapIV, M, NULL);
387:     IVgather(M, IV_entries(lu->vtxmapIV),
388:              IV_entries(lu->ownersIV), ETree_vtxToFront(lu->frontETree));
389:     if ( lu->options.msglvl > 2 ) {
390:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n map from fronts to owning processes");
391:       IV_writeForHumanEye(lu->ownersIV, lu->options.msgFile);
392:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n map from vertices to owning processes");
393:       IV_writeForHumanEye(lu->vtxmapIV, lu->options.msgFile);
394:       fflush(lu->options.msgFile);
395:     }

397:     /* redistribute the matrix */
398:     lu->firsttag = 0 ;
399:     newA = InpMtx_MPI_split(lu->mtxA, lu->vtxmapIV, lu->stats,
400:                         lu->options.msglvl, lu->options.msgFile, lu->firsttag, lu->comm_spooles);
401:     lu->firsttag += size ;

403:     InpMtx_free(lu->mtxA);
404:     lu->mtxA = newA ;
405:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
406:     if ( lu->options.msglvl > 2 ) {
407:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n split InpMtx");
408:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
409:       fflush(lu->options.msgFile);
410:     }
411: 
412:     /* compute the symbolic factorization */
413:     lu->symbfacIVL = SymbFac_MPI_initFromInpMtx(lu->frontETree, lu->ownersIV, lu->mtxA,
414:                      lu->stats, lu->options.msglvl, lu->options.msgFile, lu->firsttag, lu->comm_spooles);
415:     lu->firsttag += lu->frontETree->nfront ;
416:     if ( lu->options.msglvl > 2 ) {
417:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n local symbolic factorization");
418:       IVL_writeForHumanEye(lu->symbfacIVL, lu->options.msgFile);
419:       fflush(lu->options.msgFile);
420:     }

422:     lu->mtxmanager = SubMtxManager_new();
423:     SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);
424:     lu->frontmtx = FrontMtx_new();

426:   } else { /* new num factorization using previously computed symbolic factor */
427:     if (lu->options.pivotingflag) {                  /* different FrontMtx is required */
428:       FrontMtx_free(lu->frontmtx);
429:       lu->frontmtx   = FrontMtx_new();
430:     }

432:     SubMtxManager_free(lu->mtxmanager);
433:     lu->mtxmanager = SubMtxManager_new();
434:     SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);

436:     /* permute mtxA */
437:     InpMtx_permute(lu->mtxA, IV_entries(lu->oldToNewIV), IV_entries(lu->oldToNewIV));
438:     if ( lu->options.symflag == SPOOLES_SYMMETRIC ) InpMtx_mapToUpperTriangle(lu->mtxA);
439: 
440:     InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
441:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);

443:     /* redistribute the matrix */
444:     MPI_Barrier(A->comm);
445:     lu->firsttag = 0;
446:     newA = InpMtx_MPI_split(lu->mtxA, lu->vtxmapIV, lu->stats,
447:                         lu->options.msglvl, lu->options.msgFile, lu->firsttag,lu->comm_spooles);
448:     lu->firsttag += size ;

450:     InpMtx_free(lu->mtxA);
451:     lu->mtxA = newA ;
452:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
453:     if ( lu->options.msglvl > 2 ) {
454:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n split InpMtx");
455:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
456:       fflush(lu->options.msgFile);
457:     }
458:   } /* end of if ( lu->flg == DIFFERENT_NONZERO_PATTERN) */

460:   FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag, lu->options.symflag,
461:               FRONTMTX_DENSE_FRONTS, lu->options.pivotingflag, NO_LOCK, rank,
462:               lu->ownersIV, lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);

464:     if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {
465:     if ( lu->options.patchAndGoFlag == 1 ) {
466:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
467:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 1, lu->options.toosmall, lu->options.fudge,
468:                        lu->options.storeids, lu->options.storevalues);
469:     } else if ( lu->options.patchAndGoFlag == 2 ) {
470:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
471:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 2, lu->options.toosmall, lu->options.fudge,
472:                        lu->options.storeids, lu->options.storevalues);
473:     }
474:   }

476:   /* numerical factorization */
477:   chvmanager = ChvManager_new();
478:   ChvManager_init(chvmanager, NO_LOCK, 0);

480:   tagbound = maxTagMPI(lu->comm_spooles);
481:   lasttag  = lu->firsttag + 3*lu->frontETree->nfront + 2;
482:   /* if(!rank) PetscPrintf(PETSC_COMM_SELF,"\n firsttag: %d, nfront: %d\n",lu->firsttag, lu->frontETree->nfront);*/
483:   if ( lasttag > tagbound ) {
484:       SETERRQ3(PETSC_ERR_LIB,"fatal error in FrontMtx_MPI_factorInpMtx(), tag range is [%d,%d], tag_bound = %d",\
485:                lu->firsttag, lasttag, tagbound);
486:   }
487:   rootchv = FrontMtx_MPI_factorInpMtx(lu->frontmtx, lu->mtxA, lu->options.tau, droptol,
488:                      chvmanager, lu->ownersIV, lookahead, &sierr, lu->cpus,
489:                      lu->stats, lu->options.msglvl, lu->options.msgFile, lu->firsttag,lu->comm_spooles);
490:   ChvManager_free(chvmanager);
491:   lu->firsttag = lasttag;
492:   if ( lu->options.msglvl > 2 ) {
493:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n numeric factorization");
494:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
495:     fflush(lu->options.msgFile);
496:   }

498:   if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {
499:     if ( lu->options.patchAndGoFlag == 1 ) {
500:       if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
501:         if (lu->options.msglvl > 0 ){
502:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
503:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
504:         }
505:       }
506:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
507:     } else if ( lu->options.patchAndGoFlag == 2 ) {
508:       if (lu->options.msglvl > 0 ){
509:         if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
510:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
511:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
512:         }
513:         if ( lu->frontmtx->patchinfo->fudgeDV != NULL ) {
514:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n perturbations");
515:           DV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeDV, lu->options.msgFile);
516:         }
517:       }
518:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
519:     }
520:   }
521:   if ( sierr >= 0 ) SETERRQ2(PETSC_ERR_LIB,"\n proc %d : factorization error at front %d", rank, sierr);
522: 
523:   /*  post-process the factorization and split 
524:       the factor matrices into submatrices */
525:   lasttag  = lu->firsttag + 5*size;
526:   if ( lasttag > tagbound ) {
527:       SETERRQ3(PETSC_ERR_LIB,"fatal error in FrontMtx_MPI_postProcess(), tag range is [%d,%d], tag_bound = %d",\
528:                lu->firsttag, lasttag, tagbound);
529:   }
530:   FrontMtx_MPI_postProcess(lu->frontmtx, lu->ownersIV, lu->stats, lu->options.msglvl,
531:                          lu->options.msgFile, lu->firsttag, lu->comm_spooles);
532:   lu->firsttag += 5*size ;
533:   if ( lu->options.msglvl > 2 ) {
534:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n numeric factorization after post-processing");
535:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
536:     fflush(lu->options.msgFile);
537:   }
538: 
539:   /* create the solve map object */
540:   lu->solvemap = SolveMap_new();
541:   SolveMap_ddMap(lu->solvemap, lu->frontmtx->symmetryflag,
542:                FrontMtx_upperBlockIVL(lu->frontmtx),
543:                FrontMtx_lowerBlockIVL(lu->frontmtx),
544:                size, lu->ownersIV, FrontMtx_frontTree(lu->frontmtx),
545:                lu->options.seed, lu->options.msglvl, lu->options.msgFile);
546:   if ( lu->options.msglvl > 2 ) {
547:     SolveMap_writeForHumanEye(lu->solvemap, lu->options.msgFile);
548:     fflush(lu->options.msgFile);
549:   }

551:   /* redistribute the submatrices of the factors */
552:   FrontMtx_MPI_split(lu->frontmtx, lu->solvemap,
553:                    lu->stats, lu->options.msglvl, lu->options.msgFile, lu->firsttag, lu->comm_spooles);
554:   if ( lu->options.msglvl > 2 ) {
555:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n numeric factorization after split");
556:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
557:     fflush(lu->options.msgFile);
558:   }

560:   /* create a solution DenseMtx object */
561:   lu->ownedColumnsIV = FrontMtx_ownedColumnsIV(lu->frontmtx, rank, lu->ownersIV,
562:                                          lu->options.msglvl, lu->options.msgFile);
563:   lu->nmycol = IV_size(lu->ownedColumnsIV);
564:   if ( lu->nmycol > 0) {
565:     DenseMtx_init(lu->mtxX, lu->options.typeflag, 0, 0, lu->nmycol, 1, 1, lu->nmycol);
566:     /* get pointers rowindX and entX */
567:     DenseMtx_rowIndices(lu->mtxX, &lu->nmycol, &lu->rowindX);
568:     lu->entX = DenseMtx_entries(lu->mtxX);
569:   } else { /* lu->nmycol == 0 */
570:     lu->entX    = 0;
571:     lu->rowindX = 0;
572:   }

574:   if ( lu->scat ){
575:     VecDestroy(lu->vec_spooles);
576:     ISDestroy(lu->iden);
577:     ISDestroy(lu->is_petsc);
578:     VecScatterDestroy(lu->scat);
579:   }
580:   lu->scat = PETSC_NULL;
581:   lu->flg = SAME_NONZERO_PATTERN;

583:   lu->CleanUpSpooles = PETSC_TRUE;
584:   return(0);
585: }

590: PetscErrorCode MatConvert_MPIAIJ_MPIAIJSpooles(Mat A,const MatType type,Mat *newmat)
591: {
592:   /* This routine is only called to convert a MATMPIAIJ matrix */
593:   /* to a MATMPIAIJSPOOLES matrix, so we will ignore 'MatType type'. */
595:   Mat            B=*newmat;
596:   Mat_Spooles    *lu;

599:   if (B != A) {
600:     /* This routine is inherited, so we know the type is correct. */
601:     MatDuplicate(A,MAT_COPY_VALUES,&B);
602:   }

604:   PetscNew(Mat_Spooles,&lu);
605:   B->spptr = (void*)lu;

607:   lu->basetype                  = MATMPIAIJ;
608:   lu->CleanUpSpooles            = PETSC_FALSE;
609:   lu->MatDuplicate              = A->ops->duplicate;
610:   lu->MatLUFactorSymbolic       = A->ops->lufactorsymbolic;
611:   lu->MatCholeskyFactorSymbolic = A->ops->choleskyfactorsymbolic;
612:   lu->MatView                   = A->ops->view;
613:   lu->MatAssemblyEnd            = A->ops->assemblyend;
614:   lu->MatDestroy                = A->ops->destroy;

616:   B->ops->duplicate             = MatDuplicate_Spooles;
617:   B->ops->lufactorsymbolic      = MatLUFactorSymbolic_MPIAIJSpooles;
618:   B->ops->view                  = MatView_SeqAIJSpooles;
619:   B->ops->assemblyend           = MatAssemblyEnd_MPIAIJSpooles;
620:   B->ops->destroy               = MatDestroy_MPIAIJSpooles;

622:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaijspooles_mpiaij_C",
623:                                            "MatConvert_Spooles_Base",MatConvert_Spooles_Base);
624:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpiaijspooles_C",
625:                                            "MatConvert_MPIAIJ_MPIAIJSpooles",MatConvert_MPIAIJ_MPIAIJSpooles);
626:   PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJSPOOLES);
627:   *newmat = B;
628:   return(0);
629: }

632: /*MC
633:   MATMPIAIJSPOOLES - MATMPIAIJSPOOLES = "mpiaijspooles" - A matrix type providing direct solvers (LU) for distributed matrices 
634:   via the external package Spooles.

636:   If MPIAIJSPOOLES is installed (see the manual for
637:   instructions on how to declare the existence of external packages),
638:   a matrix type can be constructed which invokes SPOOLES solvers.
639:   After calling MatCreate(...,A), simply call MatSetType(A,MATMPIAIJSPOOLES).
640:   This matrix type is only supported for double precision real.

642:   This matrix inherits from MATMPIAIJ.  As a result, MatMPIAIJSetPreallocation is 
643:   supported for this matrix type.  One can also call MatConvert for an inplace conversion to or from 
644:   the MATMPIAIJ type without data copy.

646:   Consult Spooles documentation for more information about the options database keys below.

648:   Options Database Keys:
649: + -mat_type mpiaijspooles - sets the matrix type to "mpiaijspooles" during a call to MatSetFromOptions()
650: . -mat_spooles_tau <tau> - upper bound on the magnitude of the largest element in L or U
651: . -mat_spooles_seed <seed> - random number seed used for ordering
652: . -mat_spooles_msglvl <msglvl> - message output level
653: . -mat_spooles_ordering <BestOfNDandMS,MMD,MS,ND> - ordering used
654: . -mat_spooles_maxdomainsize <n> - maximum subgraph size used by Spooles orderings
655: . -mat_spooles_maxzeros <n> - maximum number of zeros inside a supernode
656: . -mat_spooles_maxsize <n> - maximum size of a supernode
657: . -mat_spooles_FrontMtxInfo <true,fase> - print Spooles information about the computed factorization
658: . -mat_spooles_symmetryflag <0,1,2> - 0: SPOOLES_SYMMETRIC, 1: SPOOLES_HERMITIAN, 2: SPOOLES_NONSYMMETRIC
659: . -mat_spooles_patchAndGoFlag <0,1,2> - 0: no patch, 1: use PatchAndGo strategy 1, 2: use PatchAndGo strategy 2
660: . -mat_spooles_toosmall <dt> - drop tolerance for PatchAndGo strategy 1
661: . -mat_spooles_storeids <bool integer> - if nonzero, stores row and col numbers where patches were applied in an IV object
662: . -mat_spooles_fudge <delta> - fudge factor for rescaling diagonals with PatchAndGo strategy 2
663: - -mat_spooles_storevalues <bool integer> - if nonzero and PatchAndGo strategy 2 is used, store change in diagonal value in a DV object

665:    Level: beginner

667: .seealso: PCLU
668: M*/

673: PetscErrorCode MatCreate_MPIAIJSpooles(Mat A)
674: {
676:   Mat A_diag;

679:   /* Change type name before calling MatSetType to force proper construction of MPIAIJ and MPIAIJSpooles types */
680:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJSPOOLES);
681:   MatSetType(A,MATMPIAIJ);
682:   A_diag = ((Mat_MPIAIJ *)A->data)->A;
683:   MatConvert_SeqAIJ_SeqAIJSpooles(A_diag,MATSEQAIJSPOOLES,&A_diag);
684:   MatConvert_MPIAIJ_MPIAIJSpooles(A,MATMPIAIJSPOOLES,&A);
685:   return(0);
686: }