Actual source code: spooles.c

  1: #define PETCSMAT_DLL

  3: /* 
  4:    Provides an interface to the Spooles serial sparse solver
  5: */
 6:  #include src/mat/impls/aij/seq/aij.h
 7:  #include src/mat/impls/sbaij/seq/sbaij.h
 8:  #include src/mat/impls/aij/seq/spooles/spooles.h

 10: /* make sun CC happy */
 11: static void (*f)(void);

 16: PetscErrorCode  MatConvert_Spooles_Base(Mat A,MatType type,MatReuse reuse,Mat *newmat)
 17: {
 19:   Mat            B=*newmat;
 20:   Mat_Spooles    *lu=(Mat_Spooles*)A->spptr;

 23:   if (reuse == MAT_INITIAL_MATRIX) {
 24:     MatDuplicate(A,MAT_COPY_VALUES,&B);
 25:   }
 26:   /* Reset the stashed function pointers set by inherited routines */
 27:   B->ops->duplicate              = lu->MatDuplicate;
 28:   B->ops->choleskyfactorsymbolic = lu->MatCholeskyFactorSymbolic;
 29:   B->ops->lufactorsymbolic       = lu->MatLUFactorSymbolic;
 30:   B->ops->view                   = lu->MatView;
 31:   B->ops->assemblyend            = lu->MatAssemblyEnd;
 32:   B->ops->destroy                = lu->MatDestroy;

 34:   PetscObjectQueryFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C",&f);
 35:   if (f) {
 36:     PetscObjectComposeFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C","",(PetscVoidFunction)lu->MatPreallocate);
 37:   }

 39:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaijspooles_seqaij_C","",PETSC_NULL);
 40:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijspooles_C","",PETSC_NULL);
 41:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaijspooles_mpiaij_C","",PETSC_NULL);
 42:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijspooles_C","",PETSC_NULL);
 43:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsbaijspooles_seqsbaij_C","",PETSC_NULL);
 44:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsbaij_seqsbaijspooles_C","",PETSC_NULL);
 45:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpisbaijspooles_mpisbaij_C","",PETSC_NULL);
 46:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpisbaij_mpisbaijspooles_C","",PETSC_NULL);

 48:   PetscObjectChangeTypeName((PetscObject)B,type);
 49:   *newmat = B;
 50:   return(0);
 51: }

 56: PetscErrorCode MatDestroy_SeqAIJSpooles(Mat A)
 57: {
 58:   Mat_Spooles    *lu = (Mat_Spooles*)A->spptr;
 60: 
 62:   if (lu->CleanUpSpooles) {
 63:     FrontMtx_free(lu->frontmtx);
 64:     IV_free(lu->newToOldIV);
 65:     IV_free(lu->oldToNewIV);
 66:     InpMtx_free(lu->mtxA);
 67:     ETree_free(lu->frontETree);
 68:     IVL_free(lu->symbfacIVL);
 69:     SubMtxManager_free(lu->mtxmanager);
 70:     Graph_free(lu->graph);
 71:   }
 72:   MatConvert_Spooles_Base(A,lu->basetype,MAT_REUSE_MATRIX,&A);
 73:   (*A->ops->destroy)(A);
 74:   return(0);
 75: }

 79: PetscErrorCode MatSolve_SeqAIJSpooles(Mat A,Vec b,Vec x)
 80: {
 81:   Mat_Spooles      *lu = (Mat_Spooles*)A->spptr;
 82:   PetscScalar      *array;
 83:   DenseMtx         *mtxY, *mtxX ;
 84:   PetscErrorCode   ierr;
 85:   PetscInt         irow,neqns=A->cmap.n,nrow=A->rmap.n,*iv;
 86: #if defined(PETSC_USE_COMPLEX)
 87:   double           x_real,x_imag;
 88: #else
 89:   double           *entX;
 90: #endif

 93:   mtxY = DenseMtx_new();
 94:   DenseMtx_init(mtxY, lu->options.typeflag, 0, 0, nrow, 1, 1, nrow); /* column major */
 95:   VecGetArray(b,&array);

 97:   if (lu->options.useQR) {   /* copy b to mtxY */
 98:     for ( irow = 0 ; irow < nrow; irow++ )
 99: #if !defined(PETSC_USE_COMPLEX)
100:       DenseMtx_setRealEntry(mtxY, irow, 0, *array++);
101: #else
102:       DenseMtx_setComplexEntry(mtxY, irow, 0, PetscRealPart(array[irow]), PetscImaginaryPart(array[irow]));
103: #endif
104:   } else {                   /* copy permuted b to mtxY */
105:     iv = IV_entries(lu->oldToNewIV);
106:     for ( irow = 0 ; irow < nrow; irow++ )
107: #if !defined(PETSC_USE_COMPLEX)
108:       DenseMtx_setRealEntry(mtxY, *iv++, 0, *array++);
109: #else
110:       DenseMtx_setComplexEntry(mtxY,*iv++,0,PetscRealPart(array[irow]),PetscImaginaryPart(array[irow]));
111: #endif
112:   }
113:   VecRestoreArray(b,&array);

115:   mtxX = DenseMtx_new();
116:   DenseMtx_init(mtxX, lu->options.typeflag, 0, 0, neqns, 1, 1, neqns);
117:   if (lu->options.useQR) {
118:     FrontMtx_QR_solve(lu->frontmtx, lu->mtxA, mtxX, mtxY, lu->mtxmanager,
119:                   lu->cpus, lu->options.msglvl, lu->options.msgFile);
120:   } else {
121:     FrontMtx_solve(lu->frontmtx, mtxX, mtxY, lu->mtxmanager,
122:                  lu->cpus, lu->options.msglvl, lu->options.msgFile);
123:   }
124:   if ( lu->options.msglvl > 2 ) {
125:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n right hand side matrix after permutation");
126:     DenseMtx_writeForHumanEye(mtxY, lu->options.msgFile);
127:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n solution matrix in new ordering");
128:     DenseMtx_writeForHumanEye(mtxX, lu->options.msgFile);
129:     fflush(lu->options.msgFile);
130:   }

132:   /* permute solution into original ordering, then copy to x */
133:   DenseMtx_permuteRows(mtxX, lu->newToOldIV);
134:   VecGetArray(x,&array);

136: #if !defined(PETSC_USE_COMPLEX)
137:   entX = DenseMtx_entries(mtxX);
138:   DVcopy(neqns, array, entX);
139: #else
140:   for (irow=0; irow<nrow; irow++){
141:     DenseMtx_complexEntry(mtxX,irow,0,&x_real,&x_imag);
142:     array[irow] = x_real+x_imag*PETSC_i;
143:   }
144: #endif

146:   VecRestoreArray(x,&array);
147: 
148:   /* free memory */
149:   DenseMtx_free(mtxX);
150:   DenseMtx_free(mtxY);
151:   return(0);
152: }

156: PetscErrorCode MatFactorNumeric_SeqAIJSpooles(Mat A,MatFactorInfo *info,Mat *F)
157: {
158:   Mat_Spooles        *lu = (Mat_Spooles*)(*F)->spptr;
159:   ChvManager         *chvmanager ;
160:   Chv                *rootchv ;
161:   IVL                *adjIVL;
162:   PetscErrorCode     ierr;
163:   PetscInt           nz,nrow=A->rmap.n,irow,nedges,neqns=A->cmap.n,*ai,*aj,i,*diag=0,fierr;
164:   PetscScalar        *av;
165:   double             cputotal,facops;
166: #if defined(PETSC_USE_COMPLEX)
167:   PetscInt           nz_row,*aj_tmp;
168:   PetscScalar        *av_tmp;
169: #else
170:   PetscInt           *ivec1,*ivec2,j;
171:   double             *dvec;
172: #endif
173:   PetscTruth         isAIJ,isSeqAIJ;
174: 
176:   if (lu->flg == DIFFERENT_NONZERO_PATTERN) { /* first numeric factorization */
177:     (*F)->ops->solve   = MatSolve_SeqAIJSpooles;
178:     (*F)->ops->destroy = MatDestroy_SeqAIJSpooles;
179:     (*F)->assembled    = PETSC_TRUE;
180: 
181:     /* set Spooles options */
182:     SetSpoolesOptions(A, &lu->options);

184:     lu->mtxA = InpMtx_new();
185:   }

187:   /* copy A to Spooles' InpMtx object */
188:   PetscTypeCompare((PetscObject)A,MATSEQAIJSPOOLES,&isSeqAIJ);
189:   PetscTypeCompare((PetscObject)A,MATAIJSPOOLES,&isAIJ);
190:   if (isSeqAIJ || isAIJ){
191:     Mat_SeqAIJ   *mat = (Mat_SeqAIJ*)A->data;
192:     ai=mat->i; aj=mat->j; av=mat->a;
193:     if (lu->options.symflag == SPOOLES_NONSYMMETRIC) {
194:       nz=mat->nz;
195:     } else { /* SPOOLES_SYMMETRIC || SPOOLES_HERMITIAN */
196:       nz=(mat->nz + A->rmap.n)/2;
197:       diag=mat->diag;
198:     }
199:   } else { /* A is SBAIJ */
200:       Mat_SeqSBAIJ *mat = (Mat_SeqSBAIJ*)A->data;
201:       ai=mat->i; aj=mat->j; av=mat->a;
202:       nz=mat->nz;
203:   }
204:   InpMtx_init(lu->mtxA, INPMTX_BY_ROWS, lu->options.typeflag, nz, 0);
205: 
206: #if defined(PETSC_USE_COMPLEX)
207:     for (irow=0; irow<nrow; irow++) {
208:       if ( lu->options.symflag == SPOOLES_NONSYMMETRIC || !isAIJ){
209:         nz_row = ai[irow+1] - ai[irow];
210:         aj_tmp = aj + ai[irow];
211:         av_tmp = av + ai[irow];
212:       } else {
213:         nz_row = ai[irow+1] - diag[irow];
214:         aj_tmp = aj + diag[irow];
215:         av_tmp = av + diag[irow];
216:       }
217:       for (i=0; i<nz_row; i++){
218:         InpMtx_inputComplexEntry(lu->mtxA, irow, *aj_tmp++,PetscRealPart(*av_tmp),PetscImaginaryPart(*av_tmp));
219:         av_tmp++;
220:       }
221:     }
222: #else
223:     ivec1 = InpMtx_ivec1(lu->mtxA);
224:     ivec2 = InpMtx_ivec2(lu->mtxA);
225:     dvec  = InpMtx_dvec(lu->mtxA);
226:     if ( lu->options.symflag == SPOOLES_NONSYMMETRIC || !isAIJ){
227:       for (irow = 0; irow < nrow; irow++){
228:         for (i = ai[irow]; i<ai[irow+1]; i++) ivec1[i] = irow;
229:       }
230:       IVcopy(nz, ivec2, aj);
231:       DVcopy(nz, dvec, av);
232:     } else {
233:       nz = 0;
234:       for (irow = 0; irow < nrow; irow++){
235:         for (j = diag[irow]; j<ai[irow+1]; j++) {
236:           ivec1[nz] = irow;
237:           ivec2[nz] = aj[j];
238:           dvec[nz]  = av[j];
239:           nz++;
240:         }
241:       }
242:     }
243:     InpMtx_inputRealTriples(lu->mtxA, nz, ivec1, ivec2, dvec);
244: #endif

246:   InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
247:   if ( lu->options.msglvl > 0 ) {
248:     printf("\n\n input matrix");
249:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix");
250:     InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
251:     fflush(lu->options.msgFile);
252:   }

254:   if ( lu->flg == DIFFERENT_NONZERO_PATTERN){ /* first numeric factorization */
255:     /*---------------------------------------------------
256:     find a low-fill ordering
257:          (1) create the Graph object
258:          (2) order the graph 
259:     -------------------------------------------------------*/
260:     if (lu->options.useQR){
261:       adjIVL = InpMtx_adjForATA(lu->mtxA);
262:     } else {
263:       adjIVL = InpMtx_fullAdjacency(lu->mtxA);
264:     }
265:     nedges = IVL_tsize(adjIVL);

267:     lu->graph = Graph_new();
268:     Graph_init2(lu->graph, 0, neqns, 0, nedges, neqns, nedges, adjIVL, NULL, NULL);
269:     if ( lu->options.msglvl > 2 ) {
270:       if (lu->options.useQR){
271:         PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of A^T A");
272:       } else {
273:         PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of the input matrix");
274:       }
275:       Graph_writeForHumanEye(lu->graph, lu->options.msgFile);
276:       fflush(lu->options.msgFile);
277:     }

279:     switch (lu->options.ordering) {
280:     case 0:
281:       lu->frontETree = orderViaBestOfNDandMS(lu->graph,
282:                      lu->options.maxdomainsize, lu->options.maxzeros, lu->options.maxsize,
283:                      lu->options.seed, lu->options.msglvl, lu->options.msgFile); break;
284:     case 1:
285:       lu->frontETree = orderViaMMD(lu->graph,lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
286:     case 2:
287:       lu->frontETree = orderViaMS(lu->graph, lu->options.maxdomainsize,
288:                      lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
289:     case 3:
290:       lu->frontETree = orderViaND(lu->graph, lu->options.maxdomainsize,
291:                      lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
292:     default:
293:       SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown Spooles's ordering");
294:     }

296:     if ( lu->options.msglvl > 0 ) {
297:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree from ordering");
298:       ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
299:       fflush(lu->options.msgFile);
300:     }
301: 
302:     /* get the permutation, permute the front tree */
303:     lu->oldToNewIV = ETree_oldToNewVtxPerm(lu->frontETree);
304:     lu->oldToNew   = IV_entries(lu->oldToNewIV);
305:     lu->newToOldIV = ETree_newToOldVtxPerm(lu->frontETree);
306:     if (!lu->options.useQR) ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);

308:     /* permute the matrix */
309:     if (lu->options.useQR){
310:       InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
311:     } else {
312:       InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
313:       if ( lu->options.symflag == SPOOLES_SYMMETRIC) {
314:         InpMtx_mapToUpperTriangle(lu->mtxA);
315:       }
316: #if defined(PETSC_USE_COMPLEX)
317:       if ( lu->options.symflag == SPOOLES_HERMITIAN ) {
318:         InpMtx_mapToUpperTriangleH(lu->mtxA);
319:       }
320: #endif
321:       InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
322:     }
323:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);

325:     /* get symbolic factorization */
326:     if (lu->options.useQR){
327:       lu->symbfacIVL = SymbFac_initFromGraph(lu->frontETree, lu->graph);
328:       IVL_overwrite(lu->symbfacIVL, lu->oldToNewIV);
329:       IVL_sortUp(lu->symbfacIVL);
330:       ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);
331:     } else {
332:       lu->symbfacIVL = SymbFac_initFromInpMtx(lu->frontETree, lu->mtxA);
333:     }
334:     if ( lu->options.msglvl > 2 ) {
335:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n old-to-new permutation vector");
336:       IV_writeForHumanEye(lu->oldToNewIV, lu->options.msgFile);
337:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n new-to-old permutation vector");
338:       IV_writeForHumanEye(lu->newToOldIV, lu->options.msgFile);
339:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree after permutation");
340:       ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
341:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
342:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
343:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n symbolic factorization");
344:       IVL_writeForHumanEye(lu->symbfacIVL, lu->options.msgFile);
345:       fflush(lu->options.msgFile);
346:     }

348:     lu->frontmtx   = FrontMtx_new();
349:     lu->mtxmanager = SubMtxManager_new();
350:     SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);

352:   } else { /* new num factorization using previously computed symbolic factor */

354:     if (lu->options.pivotingflag) { /* different FrontMtx is required */
355:       FrontMtx_free(lu->frontmtx);
356:       lu->frontmtx   = FrontMtx_new();
357:     } else {
358:       FrontMtx_clearData (lu->frontmtx);
359:     }

361:     SubMtxManager_free(lu->mtxmanager);
362:     lu->mtxmanager = SubMtxManager_new();
363:     SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);

365:     /* permute mtxA */
366:     if (lu->options.useQR){
367:       InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
368:     } else {
369:       InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
370:       if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {
371:         InpMtx_mapToUpperTriangle(lu->mtxA);
372:       }
373:       InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
374:     }
375:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
376:     if ( lu->options.msglvl > 2 ) {
377:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
378:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
379:     }
380:   } /* end of if( lu->flg == DIFFERENT_NONZERO_PATTERN) */
381: 
382:   if (lu->options.useQR){
383:     FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag,
384:                  SPOOLES_SYMMETRIC, FRONTMTX_DENSE_FRONTS,
385:                  SPOOLES_NO_PIVOTING, NO_LOCK, 0, NULL,
386:                  lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
387:   } else {
388:     FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag, lu->options.symflag,
389:                 FRONTMTX_DENSE_FRONTS, lu->options.pivotingflag, NO_LOCK, 0, NULL,
390:                 lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
391:   }

393:   if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {  /* || SPOOLES_HERMITIAN ? */
394:     if ( lu->options.patchAndGoFlag == 1 ) {
395:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
396:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 1, lu->options.toosmall, lu->options.fudge,
397:                        lu->options.storeids, lu->options.storevalues);
398:     } else if ( lu->options.patchAndGoFlag == 2 ) {
399:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
400:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 2, lu->options.toosmall, lu->options.fudge,
401:                        lu->options.storeids, lu->options.storevalues);
402:     }
403:   }

405:   /* numerical factorization */
406:   chvmanager = ChvManager_new();
407:   ChvManager_init(chvmanager, NO_LOCK, 1);
408:   DVfill(10, lu->cpus, 0.0);
409:   if (lu->options.useQR){
410:     facops = 0.0 ;
411:     FrontMtx_QR_factor(lu->frontmtx, lu->mtxA, chvmanager,
412:                    lu->cpus, &facops, lu->options.msglvl, lu->options.msgFile);
413:     if ( lu->options.msglvl > 1 ) {
414:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
415:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n facops = %9.2f", facops);
416:     }
417:   } else {
418:     IVfill(20, lu->stats, 0);
419:     rootchv = FrontMtx_factorInpMtx(lu->frontmtx, lu->mtxA, lu->options.tau, 0.0,
420:             chvmanager, &fierr, lu->cpus,lu->stats,lu->options.msglvl,lu->options.msgFile);
421:     if (rootchv) SETERRQ(PETSC_ERR_MAT_LU_ZRPVT,"\n matrix found to be singular");
422:     if (fierr >= 0) SETERRQ1(PETSC_ERR_LIB,"\n error encountered at front %D", fierr);
423: 
424:     if(lu->options.FrontMtxInfo){
425:       PetscPrintf(PETSC_COMM_SELF,"\n %8d pivots, %8d pivot tests, %8d delayed rows and columns\n",lu->stats[0], lu->stats[1], lu->stats[2]);
426:       cputotal = lu->cpus[8] ;
427:       if ( cputotal > 0.0 ) {
428:         PetscPrintf(PETSC_COMM_SELF,
429:            "\n                               cpus   cpus/totaltime"
430:            "\n    initialize fronts       %8.3f %6.2f"
431:            "\n    load original entries   %8.3f %6.2f"
432:            "\n    update fronts           %8.3f %6.2f"
433:            "\n    assemble postponed data %8.3f %6.2f"
434:            "\n    factor fronts           %8.3f %6.2f"
435:            "\n    extract postponed data  %8.3f %6.2f"
436:            "\n    store factor entries    %8.3f %6.2f"
437:            "\n    miscellaneous           %8.3f %6.2f"
438:            "\n    total time              %8.3f \n",
439:            lu->cpus[0], 100.*lu->cpus[0]/cputotal,
440:            lu->cpus[1], 100.*lu->cpus[1]/cputotal,
441:            lu->cpus[2], 100.*lu->cpus[2]/cputotal,
442:            lu->cpus[3], 100.*lu->cpus[3]/cputotal,
443:            lu->cpus[4], 100.*lu->cpus[4]/cputotal,
444:            lu->cpus[5], 100.*lu->cpus[5]/cputotal,
445:            lu->cpus[6], 100.*lu->cpus[6]/cputotal,
446:            lu->cpus[7], 100.*lu->cpus[7]/cputotal, cputotal);
447:       }
448:     }
449:   }
450:   ChvManager_free(chvmanager);

452:   if ( lu->options.msglvl > 0 ) {
453:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
454:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
455:     fflush(lu->options.msgFile);
456:   }

458:   if ( lu->options.symflag == SPOOLES_SYMMETRIC ) { /* || SPOOLES_HERMITIAN ? */
459:     if ( lu->options.patchAndGoFlag == 1 ) {
460:       if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
461:         if (lu->options.msglvl > 0 ){
462:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
463:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
464:         }
465:       }
466:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
467:     } else if ( lu->options.patchAndGoFlag == 2 ) {
468:       if (lu->options.msglvl > 0 ){
469:         if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
470:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
471:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
472:         }
473:         if ( lu->frontmtx->patchinfo->fudgeDV != NULL ) {
474:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n perturbations");
475:           DV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeDV, lu->options.msgFile);
476:         }
477:       }
478:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
479:     }
480:   }

482:   /* post-process the factorization */
483:   FrontMtx_postProcess(lu->frontmtx, lu->options.msglvl, lu->options.msgFile);
484:   if ( lu->options.msglvl > 2 ) {
485:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix after post-processing");
486:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
487:     fflush(lu->options.msgFile);
488:   }

490:   lu->flg = SAME_NONZERO_PATTERN;
491:   lu->CleanUpSpooles = PETSC_TRUE;
492:   return(0);
493: }

498: PetscErrorCode  MatConvert_SeqAIJ_SeqAIJSpooles(Mat A,MatType type,MatReuse reuse,Mat *newmat)
499: {
501:   Mat            B=*newmat;
502:   Mat_Spooles    *lu;

505:   PetscNew(Mat_Spooles,&lu);
506:   if (reuse == MAT_INITIAL_MATRIX) {
507:     /* This routine is inherited, so we know the type is correct. */
508:     MatDuplicate(A,MAT_COPY_VALUES,&B);
509:     lu->MatDuplicate               = B->ops->duplicate;
510:     lu->MatCholeskyFactorSymbolic  = B->ops->choleskyfactorsymbolic;
511:     lu->MatLUFactorSymbolic        = B->ops->lufactorsymbolic;
512:     lu->MatView                    = B->ops->view;
513:     lu->MatAssemblyEnd             = B->ops->assemblyend;
514:     lu->MatDestroy                 = B->ops->destroy;
515:   } else {
516:     lu->MatDuplicate               = A->ops->duplicate;
517:     lu->MatCholeskyFactorSymbolic  = A->ops->choleskyfactorsymbolic;
518:     lu->MatLUFactorSymbolic        = A->ops->lufactorsymbolic;
519:     lu->MatView                    = A->ops->view;
520:     lu->MatAssemblyEnd             = A->ops->assemblyend;
521:     lu->MatDestroy                 = A->ops->destroy;
522:   }
523:   B->spptr = (void*)lu;
524:   lu->basetype                   = MATSEQAIJ;
525:   lu->useQR                      = PETSC_FALSE;
526:   lu->CleanUpSpooles             = PETSC_FALSE;

528:   B->ops->duplicate              = MatDuplicate_Spooles;
529:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJSpooles;
530:   B->ops->lufactorsymbolic       = MatLUFactorSymbolic_SeqAIJSpooles;
531:   B->ops->view                   = MatView_SeqAIJSpooles;
532:   B->ops->assemblyend            = MatAssemblyEnd_SeqAIJSpooles;
533:   B->ops->destroy                = MatDestroy_SeqAIJSpooles;

535:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaijspooles_seqaij_C",
536:                                            "MatConvert_Spooles_Base",MatConvert_Spooles_Base);
537:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijspooles_C",
538:                                            "MatConvert_SeqAIJ_SeqAIJSpooles",MatConvert_SeqAIJ_SeqAIJSpooles);
539:   /* PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJSPOOLES); */
540:   PetscObjectChangeTypeName((PetscObject)B,type);
541:   *newmat = B;
542:   return(0);
543: }

548: PetscErrorCode MatDuplicate_Spooles(Mat A, MatDuplicateOption op, Mat *M) {
550:   Mat_Spooles    *lu=(Mat_Spooles *)A->spptr;

553:   (*lu->MatDuplicate)(A,op,M);
554:   PetscMemcpy((*M)->spptr,lu,sizeof(Mat_Spooles));
555:   return(0);
556: }

558: /*MC
559:   MATSEQAIJSPOOLES - MATSEQAIJSPOOLES = "seqaijspooles" - A matrix type providing direct solvers (LU or Cholesky) for sequential matrices 
560:   via the external package SPOOLES.

562:   If SPOOLES is installed (see the manual for
563:   instructions on how to declare the existence of external packages),
564:   a matrix type can be constructed which invokes SPOOLES solvers.
565:   After calling MatCreate(...,A), simply call MatSetType(A,MATSEQAIJSPOOLES).

567:   This matrix inherits from MATSEQAIJ.  As a result, MatSeqAIJSetPreallocation is 
568:   supported for this matrix type.  One can also call MatConvert for an inplace conversion to or from 
569:   the MATSEQAIJ type without data copy.

571:   Options Database Keys:
572: + -mat_type seqaijspooles - sets the matrix type to "seqaijspooles" during a call to MatSetFromOptions()
573: . -mat_spooles_tau <tau> - upper bound on the magnitude of the largest element in L or U
574: . -mat_spooles_seed <seed> - random number seed used for ordering
575: . -mat_spooles_msglvl <msglvl> - message output level
576: . -mat_spooles_ordering <BestOfNDandMS,MMD,MS,ND> - ordering used
577: . -mat_spooles_maxdomainsize <n> - maximum subgraph size used by Spooles orderings
578: . -mat_spooles_maxzeros <n> - maximum number of zeros inside a supernode
579: . -mat_spooles_maxsize <n> - maximum size of a supernode
580: . -mat_spooles_FrontMtxInfo <true,fase> - print Spooles information about the computed factorization
581: . -mat_spooles_symmetryflag <0,1,2> - 0: SPOOLES_SYMMETRIC, 1: SPOOLES_HERMITIAN, 2: SPOOLES_NONSYMMETRIC
582: . -mat_spooles_patchAndGoFlag <0,1,2> - 0: no patch, 1: use PatchAndGo strategy 1, 2: use PatchAndGo strategy 2
583: . -mat_spooles_toosmall <dt> - drop tolerance for PatchAndGo strategy 1
584: . -mat_spooles_storeids <bool integer> - if nonzero, stores row and col numbers where patches were applied in an IV object
585: . -mat_spooles_fudge <delta> - fudge factor for rescaling diagonals with PatchAndGo strategy 2
586: - -mat_spooles_storevalues <bool integer> - if nonzero and PatchAndGo strategy 2 is used, store change in diagonal value in a DV object

588:    Level: beginner

590: .seealso: PCLU
591: M*/

596: PetscErrorCode  MatCreate_SeqAIJSpooles(Mat A)
597: {

601:   MatSetType(A,MATSEQAIJ);
602:   MatConvert_SeqAIJ_SeqAIJSpooles(A,MATSEQAIJSPOOLES,MAT_REUSE_MATRIX,&A);
603:   return(0);
604: }

607: /*MC
608:   MATAIJSPOOLES - MATAIJSPOOLES = "aijspooles" - A matrix type providing direct solvers (LU or Cholesky) for sequential and parellel matrices 
609:   via the external package SPOOLES.

611:   If SPOOLES is installed (see the manual for
612:   instructions on how to declare the existence of external packages),
613:   a matrix type can be constructed which invokes SPOOLES solvers.
614:   After calling MatCreate(...,A), simply call MatSetType(A,MATAIJSPOOLES).
615:   This matrix type is supported for double precision real and complex.

617:   This matrix inherits from MATAIJ.  As a result, MatSeqAIJSetPreallocation and MatMPIAIJSetPreallocation are
618:   supported for this matrix type.  One can also call MatConvert for an inplace conversion to or from 
619:   the MATAIJ type without data copy.

621:   Options Database Keys:
622: + -mat_type aijspooles - sets the matrix type to "aijspooles" during a call to MatSetFromOptions()
623: . -mat_spooles_tau <tau> - upper bound on the magnitude of the largest element in L or U
624: . -mat_spooles_seed <seed> - random number seed used for ordering
625: . -mat_spooles_msglvl <msglvl> - message output level
626: . -mat_spooles_ordering <BestOfNDandMS,MMD,MS,ND> - ordering used
627: . -mat_spooles_maxdomainsize <n> - maximum subgraph size used by Spooles orderings
628: . -mat_spooles_maxzeros <n> - maximum number of zeros inside a supernode
629: . -mat_spooles_maxsize <n> - maximum size of a supernode
630: . -mat_spooles_FrontMtxInfo <true,fase> - print Spooles information about the computed factorization
631: . -mat_spooles_symmetryflag <0,1,2> - 0: SPOOLES_SYMMETRIC, 1: SPOOLES_HERMITIAN, 2: SPOOLES_NONSYMMETRIC
632: . -mat_spooles_patchAndGoFlag <0,1,2> - 0: no patch, 1: use PatchAndGo strategy 1, 2: use PatchAndGo strategy 2
633: . -mat_spooles_toosmall <dt> - drop tolerance for PatchAndGo strategy 1
634: . -mat_spooles_storeids <bool integer> - if nonzero, stores row and col numbers where patches were applied in an IV object
635: . -mat_spooles_fudge <delta> - fudge factor for rescaling diagonals with PatchAndGo strategy 2
636: - -mat_spooles_storevalues <bool integer> - if nonzero and PatchAndGo strategy 2 is used, store change in diagonal value in a DV object

638:    Level: beginner

640: .seealso: PCLU
641: M*/
645: PetscErrorCode  MatCreate_AIJSpooles(Mat A)
646: {
648:   PetscMPIInt    size;

651:   MPI_Comm_size(A->comm,&size);
652:   if (size == 1) {
653:     MatSetType(A,MATSEQAIJ);
654:     MatConvert_SeqAIJ_SeqAIJSpooles(A,MATSEQAIJSPOOLES,MAT_REUSE_MATRIX,&A);
655:   } else {
656:     MatSetType(A,MATMPIAIJ);
657:     MatConvert_MPIAIJ_MPIAIJSpooles(A,MATMPIAIJSPOOLES,MAT_REUSE_MATRIX,&A);
658:   }
659:   return(0);
660: }