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:   }
 38:   PetscFree(lu);
 39:   A->spptr = PETSC_NULL;

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

 50:   PetscObjectChangeTypeName((PetscObject)B,type);
 51:   *newmat = B;
 52:   return(0);
 53: }

 58: PetscErrorCode MatDestroy_SeqAIJSpooles(Mat A)
 59: {
 60:   Mat_Spooles    *lu = (Mat_Spooles*)A->spptr;
 62: 
 64:   if (lu->CleanUpSpooles) {
 65:     FrontMtx_free(lu->frontmtx);
 66:     IV_free(lu->newToOldIV);
 67:     IV_free(lu->oldToNewIV);
 68:     InpMtx_free(lu->mtxA);
 69:     ETree_free(lu->frontETree);
 70:     IVL_free(lu->symbfacIVL);
 71:     SubMtxManager_free(lu->mtxmanager);
 72:     Graph_free(lu->graph);
 73:   }
 74:   MatConvert_Spooles_Base(A,lu->basetype,MAT_REUSE_MATRIX,&A);
 75:   (*A->ops->destroy)(A);
 76:   return(0);
 77: }

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

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

 99:   if (lu->options.useQR) {   /* copy b to mtxY */
100:     for ( irow = 0 ; irow < nrow; irow++ )
101: #if !defined(PETSC_USE_COMPLEX)
102:       DenseMtx_setRealEntry(mtxY, irow, 0, *array++);
103: #else
104:       DenseMtx_setComplexEntry(mtxY, irow, 0, PetscRealPart(array[irow]), PetscImaginaryPart(array[irow]));
105: #endif
106:   } else {                   /* copy permuted b to mtxY */
107:     iv = IV_entries(lu->oldToNewIV);
108:     for ( irow = 0 ; irow < nrow; irow++ )
109: #if !defined(PETSC_USE_COMPLEX)
110:       DenseMtx_setRealEntry(mtxY, *iv++, 0, *array++);
111: #else
112:       DenseMtx_setComplexEntry(mtxY,*iv++,0,PetscRealPart(array[irow]),PetscImaginaryPart(array[irow]));
113: #endif
114:   }
115:   VecRestoreArray(b,&array);

117:   mtxX = DenseMtx_new();
118:   DenseMtx_init(mtxX, lu->options.typeflag, 0, 0, neqns, 1, 1, neqns);
119:   if (lu->options.useQR) {
120:     FrontMtx_QR_solve(lu->frontmtx, lu->mtxA, mtxX, mtxY, lu->mtxmanager,
121:                   lu->cpus, lu->options.msglvl, lu->options.msgFile);
122:   } else {
123:     FrontMtx_solve(lu->frontmtx, mtxX, mtxY, lu->mtxmanager,
124:                  lu->cpus, lu->options.msglvl, lu->options.msgFile);
125:   }
126:   if ( lu->options.msglvl > 2 ) {
127:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n right hand side matrix after permutation");
128:     DenseMtx_writeForHumanEye(mtxY, lu->options.msgFile);
129:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n solution matrix in new ordering");
130:     DenseMtx_writeForHumanEye(mtxX, lu->options.msgFile);
131:     fflush(lu->options.msgFile);
132:   }

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

138: #if !defined(PETSC_USE_COMPLEX)
139:   entX = DenseMtx_entries(mtxX);
140:   DVcopy(neqns, array, entX);
141: #else
142:   for (irow=0; irow<nrow; irow++){
143:     DenseMtx_complexEntry(mtxX,irow,0,&x_real,&x_imag);
144:     array[irow] = x_real+x_imag*PETSC_i;
145:   }
146: #endif

148:   VecRestoreArray(x,&array);
149: 
150:   /* free memory */
151:   DenseMtx_free(mtxX);
152:   DenseMtx_free(mtxY);
153:   return(0);
154: }

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

186:     lu->mtxA = InpMtx_new();
187:   }

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

248:   InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
249:   if ( lu->options.msglvl > 0 ) {
250:     printf("\n\n input matrix");
251:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix");
252:     InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
253:     fflush(lu->options.msgFile);
254:   }

256:   if ( lu->flg == DIFFERENT_NONZERO_PATTERN){ /* first numeric factorization */
257:     /*---------------------------------------------------
258:     find a low-fill ordering
259:          (1) create the Graph object
260:          (2) order the graph 
261:     -------------------------------------------------------*/
262:     if (lu->options.useQR){
263:       adjIVL = InpMtx_adjForATA(lu->mtxA);
264:     } else {
265:       adjIVL = InpMtx_fullAdjacency(lu->mtxA);
266:     }
267:     nedges = IVL_tsize(adjIVL);

269:     lu->graph = Graph_new();
270:     Graph_init2(lu->graph, 0, neqns, 0, nedges, neqns, nedges, adjIVL, NULL, NULL);
271:     if ( lu->options.msglvl > 2 ) {
272:       if (lu->options.useQR){
273:         PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of A^T A");
274:       } else {
275:         PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of the input matrix");
276:       }
277:       Graph_writeForHumanEye(lu->graph, lu->options.msgFile);
278:       fflush(lu->options.msgFile);
279:     }

281:     switch (lu->options.ordering) {
282:     case 0:
283:       lu->frontETree = orderViaBestOfNDandMS(lu->graph,
284:                      lu->options.maxdomainsize, lu->options.maxzeros, lu->options.maxsize,
285:                      lu->options.seed, lu->options.msglvl, lu->options.msgFile); break;
286:     case 1:
287:       lu->frontETree = orderViaMMD(lu->graph,lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
288:     case 2:
289:       lu->frontETree = orderViaMS(lu->graph, lu->options.maxdomainsize,
290:                      lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
291:     case 3:
292:       lu->frontETree = orderViaND(lu->graph, lu->options.maxdomainsize,
293:                      lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
294:     default:
295:       SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown Spooles's ordering");
296:     }

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

310:     /* permute the matrix */
311:     if (lu->options.useQR){
312:       InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
313:     } else {
314:       InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
315:       if ( lu->options.symflag == SPOOLES_SYMMETRIC) {
316:         InpMtx_mapToUpperTriangle(lu->mtxA);
317:       }
318: #if defined(PETSC_USE_COMPLEX)
319:       if ( lu->options.symflag == SPOOLES_HERMITIAN ) {
320:         InpMtx_mapToUpperTriangleH(lu->mtxA);
321:       }
322: #endif
323:       InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
324:     }
325:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);

327:     /* get symbolic factorization */
328:     if (lu->options.useQR){
329:       lu->symbfacIVL = SymbFac_initFromGraph(lu->frontETree, lu->graph);
330:       IVL_overwrite(lu->symbfacIVL, lu->oldToNewIV);
331:       IVL_sortUp(lu->symbfacIVL);
332:       ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);
333:     } else {
334:       lu->symbfacIVL = SymbFac_initFromInpMtx(lu->frontETree, lu->mtxA);
335:     }
336:     if ( lu->options.msglvl > 2 ) {
337:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n old-to-new permutation vector");
338:       IV_writeForHumanEye(lu->oldToNewIV, lu->options.msgFile);
339:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n new-to-old permutation vector");
340:       IV_writeForHumanEye(lu->newToOldIV, lu->options.msgFile);
341:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree after permutation");
342:       ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
343:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
344:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
345:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n symbolic factorization");
346:       IVL_writeForHumanEye(lu->symbfacIVL, lu->options.msgFile);
347:       fflush(lu->options.msgFile);
348:     }

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

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

356:     if (lu->options.pivotingflag) { /* different FrontMtx is required */
357:       FrontMtx_free(lu->frontmtx);
358:       lu->frontmtx   = FrontMtx_new();
359:     } else {
360:       FrontMtx_clearData (lu->frontmtx);
361:     }

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

367:     /* permute mtxA */
368:     if (lu->options.useQR){
369:       InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
370:     } else {
371:       InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
372:       if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {
373:         InpMtx_mapToUpperTriangle(lu->mtxA);
374:       }
375:       InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
376:     }
377:     InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
378:     if ( lu->options.msglvl > 2 ) {
379:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
380:       InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
381:     }
382:   } /* end of if( lu->flg == DIFFERENT_NONZERO_PATTERN) */
383: 
384:   if (lu->options.useQR){
385:     FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag,
386:                  SPOOLES_SYMMETRIC, FRONTMTX_DENSE_FRONTS,
387:                  SPOOLES_NO_PIVOTING, NO_LOCK, 0, NULL,
388:                  lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
389:   } else {
390:     FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag, lu->options.symflag,
391:                 FRONTMTX_DENSE_FRONTS, lu->options.pivotingflag, NO_LOCK, 0, NULL,
392:                 lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
393:   }

395:   if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {  /* || SPOOLES_HERMITIAN ? */
396:     if ( lu->options.patchAndGoFlag == 1 ) {
397:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
398:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 1, lu->options.toosmall, lu->options.fudge,
399:                        lu->options.storeids, lu->options.storevalues);
400:     } else if ( lu->options.patchAndGoFlag == 2 ) {
401:       lu->frontmtx->patchinfo = PatchAndGoInfo_new();
402:       PatchAndGoInfo_init(lu->frontmtx->patchinfo, 2, lu->options.toosmall, lu->options.fudge,
403:                        lu->options.storeids, lu->options.storevalues);
404:     }
405:   }

407:   /* numerical factorization */
408:   chvmanager = ChvManager_new();
409:   ChvManager_init(chvmanager, NO_LOCK, 1);
410:   DVfill(10, lu->cpus, 0.0);
411:   if (lu->options.useQR){
412:     facops = 0.0 ;
413:     FrontMtx_QR_factor(lu->frontmtx, lu->mtxA, chvmanager,
414:                    lu->cpus, &facops, lu->options.msglvl, lu->options.msgFile);
415:     if ( lu->options.msglvl > 1 ) {
416:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
417:       PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n facops = %9.2f", facops);
418:     }
419:   } else {
420:     IVfill(20, lu->stats, 0);
421:     rootchv = FrontMtx_factorInpMtx(lu->frontmtx, lu->mtxA, lu->options.tau, 0.0,
422:             chvmanager, &fierr, lu->cpus,lu->stats,lu->options.msglvl,lu->options.msgFile);
423:     if (rootchv) SETERRQ(PETSC_ERR_MAT_LU_ZRPVT,"\n matrix found to be singular");
424:     if (fierr >= 0) SETERRQ1(PETSC_ERR_LIB,"\n error encountered at front %D", fierr);
425: 
426:     if(lu->options.FrontMtxInfo){
427:       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]);
428:       cputotal = lu->cpus[8] ;
429:       if ( cputotal > 0.0 ) {
430:         PetscPrintf(PETSC_COMM_SELF,
431:            "\n                               cpus   cpus/totaltime"
432:            "\n    initialize fronts       %8.3f %6.2f"
433:            "\n    load original entries   %8.3f %6.2f"
434:            "\n    update fronts           %8.3f %6.2f"
435:            "\n    assemble postponed data %8.3f %6.2f"
436:            "\n    factor fronts           %8.3f %6.2f"
437:            "\n    extract postponed data  %8.3f %6.2f"
438:            "\n    store factor entries    %8.3f %6.2f"
439:            "\n    miscellaneous           %8.3f %6.2f"
440:            "\n    total time              %8.3f \n",
441:            lu->cpus[0], 100.*lu->cpus[0]/cputotal,
442:            lu->cpus[1], 100.*lu->cpus[1]/cputotal,
443:            lu->cpus[2], 100.*lu->cpus[2]/cputotal,
444:            lu->cpus[3], 100.*lu->cpus[3]/cputotal,
445:            lu->cpus[4], 100.*lu->cpus[4]/cputotal,
446:            lu->cpus[5], 100.*lu->cpus[5]/cputotal,
447:            lu->cpus[6], 100.*lu->cpus[6]/cputotal,
448:            lu->cpus[7], 100.*lu->cpus[7]/cputotal, cputotal);
449:       }
450:     }
451:   }
452:   ChvManager_free(chvmanager);

454:   if ( lu->options.msglvl > 0 ) {
455:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
456:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
457:     fflush(lu->options.msgFile);
458:   }

460:   if ( lu->options.symflag == SPOOLES_SYMMETRIC ) { /* || SPOOLES_HERMITIAN ? */
461:     if ( lu->options.patchAndGoFlag == 1 ) {
462:       if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
463:         if (lu->options.msglvl > 0 ){
464:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
465:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
466:         }
467:       }
468:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
469:     } else if ( lu->options.patchAndGoFlag == 2 ) {
470:       if (lu->options.msglvl > 0 ){
471:         if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
472:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
473:           IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
474:         }
475:         if ( lu->frontmtx->patchinfo->fudgeDV != NULL ) {
476:           PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n perturbations");
477:           DV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeDV, lu->options.msgFile);
478:         }
479:       }
480:       PatchAndGoInfo_free(lu->frontmtx->patchinfo);
481:     }
482:   }

484:   /* post-process the factorization */
485:   FrontMtx_postProcess(lu->frontmtx, lu->options.msglvl, lu->options.msgFile);
486:   if ( lu->options.msglvl > 2 ) {
487:     PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix after post-processing");
488:     FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
489:     fflush(lu->options.msgFile);
490:   }

492:   lu->flg = SAME_NONZERO_PATTERN;
493:   lu->CleanUpSpooles = PETSC_TRUE;
494:   return(0);
495: }

500: PetscErrorCode  MatConvert_SeqAIJ_SeqAIJSpooles(Mat A,MatType type,MatReuse reuse,Mat *newmat)
501: {
503:   Mat            B=*newmat;
504:   Mat_Spooles    *lu;

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

530:   B->ops->duplicate              = MatDuplicate_Spooles;
531:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJSpooles;
532:   B->ops->lufactorsymbolic       = MatLUFactorSymbolic_SeqAIJSpooles;
533:   B->ops->view                   = MatView_Spooles;
534:   B->ops->assemblyend            = MatAssemblyEnd_SeqAIJSpooles;
535:   B->ops->destroy                = MatDestroy_SeqAIJSpooles;

537:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaijspooles_seqaij_C",
538:                                            "MatConvert_Spooles_Base",MatConvert_Spooles_Base);
539:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijspooles_C",
540:                                            "MatConvert_SeqAIJ_SeqAIJSpooles",MatConvert_SeqAIJ_SeqAIJSpooles);
541:   /* PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJSPOOLES); */
542:   PetscObjectChangeTypeName((PetscObject)B,type);
543:   *newmat = B;
544:   return(0);
545: }

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

555:   (*lu->MatDuplicate)(A,op,M);
556:   PetscMemcpy((*M)->spptr,lu,sizeof(Mat_Spooles));
557:   return(0);
558: }

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

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

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

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

590:    Level: beginner

592: .seealso: PCLU
593: M*/

598: PetscErrorCode  MatCreate_SeqAIJSpooles(Mat A)
599: {

603:   MatSetType(A,MATSEQAIJ);
604:   MatConvert_SeqAIJ_SeqAIJSpooles(A,MATSEQAIJSPOOLES,MAT_REUSE_MATRIX,&A);
605:   return(0);
606: }

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

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

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

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

640:    Level: beginner

642: .seealso: PCLU
643: M*/
647: PetscErrorCode  MatCreate_AIJSpooles(Mat A)
648: {
650:   PetscMPIInt    size;

653:   MPI_Comm_size(A->comm,&size);
654:   if (size == 1) {
655:     MatSetType(A,MATSEQAIJ);
656:     MatConvert_SeqAIJ_SeqAIJSpooles(A,MATSEQAIJSPOOLES,MAT_REUSE_MATRIX,&A);
657:   } else {
658:     MatSetType(A,MATMPIAIJ);
659:     MatConvert_MPIAIJ_MPIAIJSpooles(A,MATMPIAIJSPOOLES,MAT_REUSE_MATRIX,&A);
660:   }
661:   return(0);
662: }