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 PETSCMAT_DLLEXPORT MatConvert_Spooles_Base(Mat A,MatType type,MatReuse reuse,Mat *newmat) {
17: /* This routine is only called to convert an unfactored PETSc-Spooles matrix */
18: /* to its base PETSc type, so we will ignore 'MatType type'. */
20: Mat B=*newmat;
21: Mat_Spooles *lu=(Mat_Spooles*)A->spptr;
24: if (reuse == MAT_INITIAL_MATRIX) {
25: MatDuplicate(A,MAT_COPY_VALUES,&B);
26: }
27: /* Reset the stashed function pointers set by inherited routines */
28: B->ops->duplicate = lu->MatDuplicate;
29: B->ops->choleskyfactorsymbolic = lu->MatCholeskyFactorSymbolic;
30: B->ops->lufactorsymbolic = lu->MatLUFactorSymbolic;
31: B->ops->view = lu->MatView;
32: B->ops->assemblyend = lu->MatAssemblyEnd;
33: B->ops->destroy = lu->MatDestroy;
35: PetscObjectQueryFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C",&f);
36: if (f) {
37: PetscObjectComposeFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C","",(PetscVoidFunction)lu->MatPreallocate);
38: }
40: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaijspooles_seqaij_C","",PETSC_NULL);
41: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijspooles_C","",PETSC_NULL);
42: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaijspooles_mpiaij_C","",PETSC_NULL);
43: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijspooles_C","",PETSC_NULL);
44: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsbaijspooles_seqsbaij_C","",PETSC_NULL);
45: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsbaij_seqsbaijspooles_C","",PETSC_NULL);
46: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpisbaijspooles_mpisbaij_C","",PETSC_NULL);
47: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpisbaij_mpisbaijspooles_C","",PETSC_NULL);
49: PetscObjectChangeTypeName((PetscObject)B,type);
50: *newmat = B;
51: return(0);
52: }
57: PetscErrorCode MatDestroy_SeqAIJSpooles(Mat A)
58: {
59: Mat_Spooles *lu = (Mat_Spooles*)A->spptr;
61:
63: if (lu->CleanUpSpooles) {
64: FrontMtx_free(lu->frontmtx);
65: IV_free(lu->newToOldIV);
66: IV_free(lu->oldToNewIV);
67: InpMtx_free(lu->mtxA);
68: ETree_free(lu->frontETree);
69: IVL_free(lu->symbfacIVL);
70: SubMtxManager_free(lu->mtxmanager);
71: Graph_free(lu->graph);
72: }
73: MatConvert_Spooles_Base(A,lu->basetype,MAT_REUSE_MATRIX,&A);
74: (*A->ops->destroy)(A);
75: return(0);
76: }
80: PetscErrorCode MatSolve_SeqAIJSpooles(Mat A,Vec b,Vec x)
81: {
82: Mat_Spooles *lu = (Mat_Spooles*)A->spptr;
83: PetscScalar *array;
84: DenseMtx *mtxY, *mtxX ;
85: PetscErrorCode ierr;
86: PetscInt irow,neqns=A->cmap.n,nrow=A->rmap.n,*iv;
87: #if defined(PETSC_USE_COMPLEX)
88: double x_real,x_imag;
89: #else
90: double *entX;
91: #endif
94: mtxY = DenseMtx_new();
95: DenseMtx_init(mtxY, lu->options.typeflag, 0, 0, nrow, 1, 1, nrow); /* column major */
96: VecGetArray(b,&array);
98: if (lu->options.useQR) { /* copy b to mtxY */
99: for ( irow = 0 ; irow < nrow; irow++ )
100: #if !defined(PETSC_USE_COMPLEX)
101: DenseMtx_setRealEntry(mtxY, irow, 0, *array++);
102: #else
103: DenseMtx_setComplexEntry(mtxY, irow, 0, PetscRealPart(array[irow]), PetscImaginaryPart(array[irow]));
104: #endif
105: } else { /* copy permuted b to mtxY */
106: iv = IV_entries(lu->oldToNewIV);
107: for ( irow = 0 ; irow < nrow; irow++ )
108: #if !defined(PETSC_USE_COMPLEX)
109: DenseMtx_setRealEntry(mtxY, *iv++, 0, *array++);
110: #else
111: DenseMtx_setComplexEntry(mtxY,*iv++,0,PetscRealPart(array[irow]),PetscImaginaryPart(array[irow]));
112: #endif
113: }
114: VecRestoreArray(b,&array);
116: mtxX = DenseMtx_new();
117: DenseMtx_init(mtxX, lu->options.typeflag, 0, 0, neqns, 1, 1, neqns);
118: if (lu->options.useQR) {
119: FrontMtx_QR_solve(lu->frontmtx, lu->mtxA, mtxX, mtxY, lu->mtxmanager,
120: lu->cpus, lu->options.msglvl, lu->options.msgFile);
121: } else {
122: FrontMtx_solve(lu->frontmtx, mtxX, mtxY, lu->mtxmanager,
123: lu->cpus, lu->options.msglvl, lu->options.msgFile);
124: }
125: if ( lu->options.msglvl > 2 ) {
126: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n right hand side matrix after permutation");
127: DenseMtx_writeForHumanEye(mtxY, lu->options.msgFile);
128: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n solution matrix in new ordering");
129: DenseMtx_writeForHumanEye(mtxX, lu->options.msgFile);
130: fflush(lu->options.msgFile);
131: }
133: /* permute solution into original ordering, then copy to x */
134: DenseMtx_permuteRows(mtxX, lu->newToOldIV);
135: VecGetArray(x,&array);
137: #if !defined(PETSC_USE_COMPLEX)
138: entX = DenseMtx_entries(mtxX);
139: DVcopy(neqns, array, entX);
140: #else
141: for (irow=0; irow<nrow; irow++){
142: DenseMtx_complexEntry(mtxX,irow,0,&x_real,&x_imag);
143: array[irow] = x_real+x_imag*PETSC_i;
144: }
145: #endif
147: VecRestoreArray(x,&array);
148:
149: /* free memory */
150: DenseMtx_free(mtxX);
151: DenseMtx_free(mtxY);
152: return(0);
153: }
157: PetscErrorCode MatFactorNumeric_SeqAIJSpooles(Mat A,MatFactorInfo *info,Mat *F)
158: {
159: Mat_Spooles *lu = (Mat_Spooles*)(*F)->spptr;
160: ChvManager *chvmanager ;
161: Chv *rootchv ;
162: IVL *adjIVL;
163: PetscErrorCode ierr;
164: PetscInt nz,nrow=A->rmap.n,irow,nedges,neqns=A->cmap.n,*ai,*aj,i,*diag=0,fierr;
165: PetscScalar *av;
166: double cputotal,facops;
167: #if defined(PETSC_USE_COMPLEX)
168: PetscInt nz_row,*aj_tmp;
169: PetscScalar *av_tmp;
170: #else
171: PetscInt *ivec1,*ivec2,j;
172: double *dvec;
173: #endif
174: PetscTruth isAIJ,isSeqAIJ;
175:
177: if (lu->flg == DIFFERENT_NONZERO_PATTERN) { /* first numeric factorization */
178: (*F)->ops->solve = MatSolve_SeqAIJSpooles;
179: (*F)->ops->destroy = MatDestroy_SeqAIJSpooles;
180: (*F)->assembled = PETSC_TRUE;
181:
182: /* set Spooles options */
183: SetSpoolesOptions(A, &lu->options);
185: lu->mtxA = InpMtx_new();
186: }
188: /* copy A to Spooles' InpMtx object */
189: PetscTypeCompare((PetscObject)A,MATSEQAIJSPOOLES,&isSeqAIJ);
190: PetscTypeCompare((PetscObject)A,MATAIJSPOOLES,&isAIJ);
191: if (isSeqAIJ || isAIJ){
192: Mat_SeqAIJ *mat = (Mat_SeqAIJ*)A->data;
193: ai=mat->i; aj=mat->j; av=mat->a;
194: if (lu->options.symflag == SPOOLES_NONSYMMETRIC) {
195: nz=mat->nz;
196: } else { /* SPOOLES_SYMMETRIC || SPOOLES_HERMITIAN */
197: nz=(mat->nz + A->rmap.n)/2;
198: if (!mat->diag){
199: MatMarkDiagonal_SeqAIJ(A);
200: }
201: diag=mat->diag;
202: }
203: } else { /* A is SBAIJ */
204: Mat_SeqSBAIJ *mat = (Mat_SeqSBAIJ*)A->data;
205: ai=mat->i; aj=mat->j; av=mat->a;
206: nz=mat->nz;
207: }
208: InpMtx_init(lu->mtxA, INPMTX_BY_ROWS, lu->options.typeflag, nz, 0);
209:
210: #if defined(PETSC_USE_COMPLEX)
211: for (irow=0; irow<nrow; irow++) {
212: if ( lu->options.symflag == SPOOLES_NONSYMMETRIC || !isAIJ){
213: nz_row = ai[irow+1] - ai[irow];
214: aj_tmp = aj + ai[irow];
215: av_tmp = av + ai[irow];
216: } else {
217: nz_row = ai[irow+1] - diag[irow];
218: aj_tmp = aj + diag[irow];
219: av_tmp = av + diag[irow];
220: }
221: for (i=0; i<nz_row; i++){
222: InpMtx_inputComplexEntry(lu->mtxA, irow, *aj_tmp++,PetscRealPart(*av_tmp),PetscImaginaryPart(*av_tmp));
223: av_tmp++;
224: }
225: }
226: #else
227: ivec1 = InpMtx_ivec1(lu->mtxA);
228: ivec2 = InpMtx_ivec2(lu->mtxA);
229: dvec = InpMtx_dvec(lu->mtxA);
230: if ( lu->options.symflag == SPOOLES_NONSYMMETRIC || !isAIJ){
231: for (irow = 0; irow < nrow; irow++){
232: for (i = ai[irow]; i<ai[irow+1]; i++) ivec1[i] = irow;
233: }
234: IVcopy(nz, ivec2, aj);
235: DVcopy(nz, dvec, av);
236: } else {
237: nz = 0;
238: for (irow = 0; irow < nrow; irow++){
239: for (j = diag[irow]; j<ai[irow+1]; j++) {
240: ivec1[nz] = irow;
241: ivec2[nz] = aj[j];
242: dvec[nz] = av[j];
243: nz++;
244: }
245: }
246: }
247: InpMtx_inputRealTriples(lu->mtxA, nz, ivec1, ivec2, dvec);
248: #endif
250: InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
251: if ( lu->options.msglvl > 0 ) {
252: printf("\n\n input matrix");
253: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix");
254: InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
255: fflush(lu->options.msgFile);
256: }
258: if ( lu->flg == DIFFERENT_NONZERO_PATTERN){ /* first numeric factorization */
259: /*---------------------------------------------------
260: find a low-fill ordering
261: (1) create the Graph object
262: (2) order the graph
263: -------------------------------------------------------*/
264: if (lu->options.useQR){
265: adjIVL = InpMtx_adjForATA(lu->mtxA);
266: } else {
267: adjIVL = InpMtx_fullAdjacency(lu->mtxA);
268: }
269: nedges = IVL_tsize(adjIVL);
271: lu->graph = Graph_new();
272: Graph_init2(lu->graph, 0, neqns, 0, nedges, neqns, nedges, adjIVL, NULL, NULL);
273: if ( lu->options.msglvl > 2 ) {
274: if (lu->options.useQR){
275: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of A^T A");
276: } else {
277: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n graph of the input matrix");
278: }
279: Graph_writeForHumanEye(lu->graph, lu->options.msgFile);
280: fflush(lu->options.msgFile);
281: }
283: switch (lu->options.ordering) {
284: case 0:
285: lu->frontETree = orderViaBestOfNDandMS(lu->graph,
286: lu->options.maxdomainsize, lu->options.maxzeros, lu->options.maxsize,
287: lu->options.seed, lu->options.msglvl, lu->options.msgFile); break;
288: case 1:
289: lu->frontETree = orderViaMMD(lu->graph,lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
290: case 2:
291: lu->frontETree = orderViaMS(lu->graph, lu->options.maxdomainsize,
292: lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
293: case 3:
294: lu->frontETree = orderViaND(lu->graph, lu->options.maxdomainsize,
295: lu->options.seed,lu->options.msglvl,lu->options.msgFile); break;
296: default:
297: SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown Spooles's ordering");
298: }
300: if ( lu->options.msglvl > 0 ) {
301: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree from ordering");
302: ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
303: fflush(lu->options.msgFile);
304: }
305:
306: /* get the permutation, permute the front tree */
307: lu->oldToNewIV = ETree_oldToNewVtxPerm(lu->frontETree);
308: lu->oldToNew = IV_entries(lu->oldToNewIV);
309: lu->newToOldIV = ETree_newToOldVtxPerm(lu->frontETree);
310: if (!lu->options.useQR) ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);
312: /* permute the matrix */
313: if (lu->options.useQR){
314: InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
315: } else {
316: InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
317: if ( lu->options.symflag == SPOOLES_SYMMETRIC) {
318: InpMtx_mapToUpperTriangle(lu->mtxA);
319: }
320: #if defined(PETSC_USE_COMPLEX)
321: if ( lu->options.symflag == SPOOLES_HERMITIAN ) {
322: InpMtx_mapToUpperTriangleH(lu->mtxA);
323: }
324: #endif
325: InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
326: }
327: InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
329: /* get symbolic factorization */
330: if (lu->options.useQR){
331: lu->symbfacIVL = SymbFac_initFromGraph(lu->frontETree, lu->graph);
332: IVL_overwrite(lu->symbfacIVL, lu->oldToNewIV);
333: IVL_sortUp(lu->symbfacIVL);
334: ETree_permuteVertices(lu->frontETree, lu->oldToNewIV);
335: } else {
336: lu->symbfacIVL = SymbFac_initFromInpMtx(lu->frontETree, lu->mtxA);
337: }
338: if ( lu->options.msglvl > 2 ) {
339: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n old-to-new permutation vector");
340: IV_writeForHumanEye(lu->oldToNewIV, lu->options.msgFile);
341: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n new-to-old permutation vector");
342: IV_writeForHumanEye(lu->newToOldIV, lu->options.msgFile);
343: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n front tree after permutation");
344: ETree_writeForHumanEye(lu->frontETree, lu->options.msgFile);
345: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
346: InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
347: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n symbolic factorization");
348: IVL_writeForHumanEye(lu->symbfacIVL, lu->options.msgFile);
349: fflush(lu->options.msgFile);
350: }
352: lu->frontmtx = FrontMtx_new();
353: lu->mtxmanager = SubMtxManager_new();
354: SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);
356: } else { /* new num factorization using previously computed symbolic factor */
358: if (lu->options.pivotingflag) { /* different FrontMtx is required */
359: FrontMtx_free(lu->frontmtx);
360: lu->frontmtx = FrontMtx_new();
361: } else {
362: FrontMtx_clearData (lu->frontmtx);
363: }
365: SubMtxManager_free(lu->mtxmanager);
366: lu->mtxmanager = SubMtxManager_new();
367: SubMtxManager_init(lu->mtxmanager, NO_LOCK, 0);
369: /* permute mtxA */
370: if (lu->options.useQR){
371: InpMtx_permute(lu->mtxA, NULL, lu->oldToNew);
372: } else {
373: InpMtx_permute(lu->mtxA, lu->oldToNew, lu->oldToNew);
374: if ( lu->options.symflag == SPOOLES_SYMMETRIC ) {
375: InpMtx_mapToUpperTriangle(lu->mtxA);
376: }
377: InpMtx_changeCoordType(lu->mtxA, INPMTX_BY_CHEVRONS);
378: }
379: InpMtx_changeStorageMode(lu->mtxA, INPMTX_BY_VECTORS);
380: if ( lu->options.msglvl > 2 ) {
381: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n input matrix after permutation");
382: InpMtx_writeForHumanEye(lu->mtxA, lu->options.msgFile);
383: }
384: } /* end of if( lu->flg == DIFFERENT_NONZERO_PATTERN) */
385:
386: if (lu->options.useQR){
387: FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag,
388: SPOOLES_SYMMETRIC, FRONTMTX_DENSE_FRONTS,
389: SPOOLES_NO_PIVOTING, NO_LOCK, 0, NULL,
390: lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
391: } else {
392: FrontMtx_init(lu->frontmtx, lu->frontETree, lu->symbfacIVL, lu->options.typeflag, lu->options.symflag,
393: FRONTMTX_DENSE_FRONTS, lu->options.pivotingflag, NO_LOCK, 0, NULL,
394: lu->mtxmanager, lu->options.msglvl, lu->options.msgFile);
395: }
397: if ( lu->options.symflag == SPOOLES_SYMMETRIC ) { /* || SPOOLES_HERMITIAN ? */
398: if ( lu->options.patchAndGoFlag == 1 ) {
399: lu->frontmtx->patchinfo = PatchAndGoInfo_new();
400: PatchAndGoInfo_init(lu->frontmtx->patchinfo, 1, lu->options.toosmall, lu->options.fudge,
401: lu->options.storeids, lu->options.storevalues);
402: } else if ( lu->options.patchAndGoFlag == 2 ) {
403: lu->frontmtx->patchinfo = PatchAndGoInfo_new();
404: PatchAndGoInfo_init(lu->frontmtx->patchinfo, 2, lu->options.toosmall, lu->options.fudge,
405: lu->options.storeids, lu->options.storevalues);
406: }
407: }
409: /* numerical factorization */
410: chvmanager = ChvManager_new();
411: ChvManager_init(chvmanager, NO_LOCK, 1);
412: DVfill(10, lu->cpus, 0.0);
413: if (lu->options.useQR){
414: facops = 0.0 ;
415: FrontMtx_QR_factor(lu->frontmtx, lu->mtxA, chvmanager,
416: lu->cpus, &facops, lu->options.msglvl, lu->options.msgFile);
417: if ( lu->options.msglvl > 1 ) {
418: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
419: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n facops = %9.2f", facops);
420: }
421: } else {
422: IVfill(20, lu->stats, 0);
423: rootchv = FrontMtx_factorInpMtx(lu->frontmtx, lu->mtxA, lu->options.tau, 0.0,
424: chvmanager, &fierr, lu->cpus,lu->stats,lu->options.msglvl,lu->options.msgFile);
425: if (rootchv) SETERRQ(PETSC_ERR_MAT_LU_ZRPVT,"\n matrix found to be singular");
426: if (fierr >= 0) SETERRQ1(PETSC_ERR_LIB,"\n error encountered at front %D", fierr);
427:
428: if(lu->options.FrontMtxInfo){
429: 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]);
430: cputotal = lu->cpus[8] ;
431: if ( cputotal > 0.0 ) {
432: PetscPrintf(PETSC_COMM_SELF,
433: "\n cpus cpus/totaltime"
434: "\n initialize fronts %8.3f %6.2f"
435: "\n load original entries %8.3f %6.2f"
436: "\n update fronts %8.3f %6.2f"
437: "\n assemble postponed data %8.3f %6.2f"
438: "\n factor fronts %8.3f %6.2f"
439: "\n extract postponed data %8.3f %6.2f"
440: "\n store factor entries %8.3f %6.2f"
441: "\n miscellaneous %8.3f %6.2f"
442: "\n total time %8.3f \n",
443: lu->cpus[0], 100.*lu->cpus[0]/cputotal,
444: lu->cpus[1], 100.*lu->cpus[1]/cputotal,
445: lu->cpus[2], 100.*lu->cpus[2]/cputotal,
446: lu->cpus[3], 100.*lu->cpus[3]/cputotal,
447: lu->cpus[4], 100.*lu->cpus[4]/cputotal,
448: lu->cpus[5], 100.*lu->cpus[5]/cputotal,
449: lu->cpus[6], 100.*lu->cpus[6]/cputotal,
450: lu->cpus[7], 100.*lu->cpus[7]/cputotal, cputotal);
451: }
452: }
453: }
454: ChvManager_free(chvmanager);
456: if ( lu->options.msglvl > 0 ) {
457: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix");
458: FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
459: fflush(lu->options.msgFile);
460: }
462: if ( lu->options.symflag == SPOOLES_SYMMETRIC ) { /* || SPOOLES_HERMITIAN ? */
463: if ( lu->options.patchAndGoFlag == 1 ) {
464: if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
465: if (lu->options.msglvl > 0 ){
466: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
467: IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
468: }
469: }
470: PatchAndGoInfo_free(lu->frontmtx->patchinfo);
471: } else if ( lu->options.patchAndGoFlag == 2 ) {
472: if (lu->options.msglvl > 0 ){
473: if ( lu->frontmtx->patchinfo->fudgeIV != NULL ) {
474: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n small pivots found at these locations");
475: IV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeIV, lu->options.msgFile);
476: }
477: if ( lu->frontmtx->patchinfo->fudgeDV != NULL ) {
478: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n perturbations");
479: DV_writeForHumanEye(lu->frontmtx->patchinfo->fudgeDV, lu->options.msgFile);
480: }
481: }
482: PatchAndGoInfo_free(lu->frontmtx->patchinfo);
483: }
484: }
486: /* post-process the factorization */
487: FrontMtx_postProcess(lu->frontmtx, lu->options.msglvl, lu->options.msgFile);
488: if ( lu->options.msglvl > 2 ) {
489: PetscFPrintf(PETSC_COMM_SELF,lu->options.msgFile, "\n\n factor matrix after post-processing");
490: FrontMtx_writeForHumanEye(lu->frontmtx, lu->options.msgFile);
491: fflush(lu->options.msgFile);
492: }
494: lu->flg = SAME_NONZERO_PATTERN;
495: lu->CleanUpSpooles = PETSC_TRUE;
496: return(0);
497: }
502: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_SeqAIJ_SeqAIJSpooles(Mat A,MatType type,MatReuse reuse,Mat *newmat) {
503: /* This routine is only called to convert a MATSEQAIJ matrix */
504: /* to a MATSEQAIJSPOOLES matrix, so we will ignore 'MatType type'. */
506: Mat B=*newmat;
507: Mat_Spooles *lu;
510: if (reuse == MAT_INITIAL_MATRIX) {
511: /* This routine is inherited, so we know the type is correct. */
512: MatDuplicate(A,MAT_COPY_VALUES,&B);
513: }
514: PetscNew(Mat_Spooles,&lu);
515: B->spptr = (void*)lu;
517: lu->basetype = MATSEQAIJ;
518: lu->useQR = PETSC_FALSE;
519: lu->CleanUpSpooles = PETSC_FALSE;
520: lu->MatDuplicate = A->ops->duplicate;
521: lu->MatCholeskyFactorSymbolic = A->ops->choleskyfactorsymbolic;
522: lu->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
523: lu->MatView = A->ops->view;
524: lu->MatAssemblyEnd = A->ops->assemblyend;
525: lu->MatDestroy = A->ops->destroy;
526: B->ops->duplicate = MatDuplicate_Spooles;
527: B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJSpooles;
528: B->ops->lufactorsymbolic = MatLUFactorSymbolic_SeqAIJSpooles;
529: B->ops->view = MatView_SeqAIJSpooles;
530: B->ops->assemblyend = MatAssemblyEnd_SeqAIJSpooles;
531: B->ops->destroy = MatDestroy_SeqAIJSpooles;
533: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaijspooles_seqaij_C",
534: "MatConvert_Spooles_Base",MatConvert_Spooles_Base);
535: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijspooles_C",
536: "MatConvert_SeqAIJ_SeqAIJSpooles",MatConvert_SeqAIJ_SeqAIJSpooles);
537: /* PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJSPOOLES); */
538: PetscObjectChangeTypeName((PetscObject)B,type);
539: *newmat = B;
540: return(0);
541: }
546: PetscErrorCode MatDuplicate_Spooles(Mat A, MatDuplicateOption op, Mat *M) {
548: Mat_Spooles *lu=(Mat_Spooles *)A->spptr;
551: (*lu->MatDuplicate)(A,op,M);
552: PetscMemcpy((*M)->spptr,lu,sizeof(Mat_Spooles));
553: return(0);
554: }
556: /*MC
557: MATSEQAIJSPOOLES - MATSEQAIJSPOOLES = "seqaijspooles" - A matrix type providing direct solvers (LU or Cholesky) for sequential matrices
558: via the external package SPOOLES.
560: If SPOOLES is installed (see the manual for
561: instructions on how to declare the existence of external packages),
562: a matrix type can be constructed which invokes SPOOLES solvers.
563: After calling MatCreate(...,A), simply call MatSetType(A,MATSEQAIJSPOOLES).
565: This matrix inherits from MATSEQAIJ. As a result, MatSeqAIJSetPreallocation is
566: supported for this matrix type. One can also call MatConvert for an inplace conversion to or from
567: the MATSEQAIJ type without data copy.
569: Options Database Keys:
570: + -mat_type seqaijspooles - sets the matrix type to "seqaijspooles" during a call to MatSetFromOptions()
571: . -mat_spooles_tau <tau> - upper bound on the magnitude of the largest element in L or U
572: . -mat_spooles_seed <seed> - random number seed used for ordering
573: . -mat_spooles_msglvl <msglvl> - message output level
574: . -mat_spooles_ordering <BestOfNDandMS,MMD,MS,ND> - ordering used
575: . -mat_spooles_maxdomainsize <n> - maximum subgraph size used by Spooles orderings
576: . -mat_spooles_maxzeros <n> - maximum number of zeros inside a supernode
577: . -mat_spooles_maxsize <n> - maximum size of a supernode
578: . -mat_spooles_FrontMtxInfo <true,fase> - print Spooles information about the computed factorization
579: . -mat_spooles_symmetryflag <0,1,2> - 0: SPOOLES_SYMMETRIC, 1: SPOOLES_HERMITIAN, 2: SPOOLES_NONSYMMETRIC
580: . -mat_spooles_patchAndGoFlag <0,1,2> - 0: no patch, 1: use PatchAndGo strategy 1, 2: use PatchAndGo strategy 2
581: . -mat_spooles_toosmall <dt> - drop tolerance for PatchAndGo strategy 1
582: . -mat_spooles_storeids <bool integer> - if nonzero, stores row and col numbers where patches were applied in an IV object
583: . -mat_spooles_fudge <delta> - fudge factor for rescaling diagonals with PatchAndGo strategy 2
584: - -mat_spooles_storevalues <bool integer> - if nonzero and PatchAndGo strategy 2 is used, store change in diagonal value in a DV object
586: Level: beginner
588: .seealso: PCLU
589: M*/
594: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqAIJSpooles(Mat A)
595: {
599: /* Change type name before calling MatSetType to force proper construction of SeqAIJ and SeqAIJSpooles types */
600: PetscObjectChangeTypeName((PetscObject)A,MATSEQAIJSPOOLES);
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 PETSCMAT_DLLEXPORT MatCreate_AIJSpooles(Mat A)
646: {
648: PetscMPIInt size;
651: /* Change type name before calling MatSetType to force proper construction of SeqAIJSpooles or MPIAIJSpooles */
652: PetscObjectChangeTypeName((PetscObject)A,MATAIJSPOOLES);
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: }