Actual source code: superlu_dist.c

  1: #define PETSCMAT_DLL

  3: /* 
  4:         Provides an interface to the SuperLU_DIST_2.0 sparse solver
  5: */

  7: #include "src/mat/impls/aij/seq/aij.h"
  8: #include "src/mat/impls/aij/mpi/mpiaij.h"
  9: #if defined(PETSC_HAVE_STDLIB_H) /* This is to get arround weird problem with SuperLU on cray */
 10: #include "stdlib.h"
 11: #endif

 14: #if defined(PETSC_USE_COMPLEX)
 15: #include "superlu_zdefs.h"
 16: #else
 17: #include "superlu_ddefs.h"
 18: #endif

 21: typedef enum { GLOBAL,DISTRIBUTED
 22: } SuperLU_MatInputMode;

 24: typedef struct {
 25:   int_t                   nprow,npcol,*row,*col;
 26:   gridinfo_t              grid;
 27:   superlu_options_t       options;
 28:   SuperMatrix             A_sup;
 29:   ScalePermstruct_t       ScalePermstruct;
 30:   LUstruct_t              LUstruct;
 31:   int                     StatPrint;
 32:   int                     MatInputMode;
 33:   SOLVEstruct_t           SOLVEstruct;
 34:   MatStructure            flg;
 35:   MPI_Comm                comm_superlu;
 36: #if defined(PETSC_USE_COMPLEX)
 37:   doublecomplex           *val;
 38: #else
 39:   double                  *val;
 40: #endif

 42:   /* A few function pointers for inheritance */
 43:   PetscErrorCode (*MatDuplicate)(Mat,MatDuplicateOption,Mat*);
 44:   PetscErrorCode (*MatView)(Mat,PetscViewer);
 45:   PetscErrorCode (*MatAssemblyEnd)(Mat,MatAssemblyType);
 46:   PetscErrorCode (*MatLUFactorSymbolic)(Mat,IS,IS,MatFactorInfo*,Mat*);
 47:   PetscErrorCode (*MatDestroy)(Mat);

 49:   /* Flag to clean up (non-global) SuperLU objects during Destroy */
 50:   PetscTruth CleanUpSuperLU_Dist;
 51: } Mat_SuperLU_DIST;

 53: EXTERN PetscErrorCode MatDuplicate_SuperLU_DIST(Mat,MatDuplicateOption,Mat*);

 58: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_SuperLU_DIST_Base(Mat A,MatType type,MatReuse reuse,Mat *newmat)
 59: {
 60:   PetscErrorCode   ierr;
 61:   Mat              B=*newmat;
 62:   Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST *)A->spptr;

 65:   if (reuse == MAT_INITIAL_MATRIX) {
 66:     MatDuplicate(A,MAT_COPY_VALUES,&B);
 67:   }
 68:   /* Reset the original function pointers */
 69:   B->ops->duplicate        = lu->MatDuplicate;
 70:   B->ops->view             = lu->MatView;
 71:   B->ops->assemblyend      = lu->MatAssemblyEnd;
 72:   B->ops->lufactorsymbolic = lu->MatLUFactorSymbolic;
 73:   B->ops->destroy          = lu->MatDestroy;

 75:   PetscFree(lu);

 77:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_superlu_dist_C","",PETSC_NULL);
 78:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_superlu_dist_seqaij_C","",PETSC_NULL);
 79:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_superlu_dist_C","",PETSC_NULL);
 80:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_superlu_dist_mpiaij_C","",PETSC_NULL);

 82:   PetscObjectChangeTypeName((PetscObject)B,type);
 83:   *newmat = B;
 84:   return(0);
 85: }

 90: PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
 91: {
 92:   PetscErrorCode   ierr;
 93:   PetscMPIInt      size;
 94:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
 95: 
 97:   if (lu->CleanUpSuperLU_Dist) {
 98:     /* Deallocate SuperLU_DIST storage */
 99:     if (lu->MatInputMode == GLOBAL) {
100:       Destroy_CompCol_Matrix_dist(&lu->A_sup);
101:     } else {
102:       Destroy_CompRowLoc_Matrix_dist(&lu->A_sup);
103:       if ( lu->options.SolveInitialized ) {
104: #if defined(PETSC_USE_COMPLEX)
105:         zSolveFinalize(&lu->options, &lu->SOLVEstruct);
106: #else
107:         dSolveFinalize(&lu->options, &lu->SOLVEstruct);
108: #endif
109:       }
110:     }
111:     Destroy_LU(A->N, &lu->grid, &lu->LUstruct);
112:     ScalePermstructFree(&lu->ScalePermstruct);
113:     LUstructFree(&lu->LUstruct);

115:     /* Release the SuperLU_DIST process grid. */
116:     superlu_gridexit(&lu->grid);
117: 
118:     MPI_Comm_free(&(lu->comm_superlu));
119:   }

121:   MPI_Comm_size(A->comm,&size);
122:   if (size == 1) {
123:     MatConvert_SuperLU_DIST_Base(A,MATSEQAIJ,MAT_REUSE_MATRIX,&A);
124:   } else {
125:     MatConvert_SuperLU_DIST_Base(A,MATMPIAIJ,MAT_REUSE_MATRIX,&A);
126:   }
127:   (*A->ops->destroy)(A);
128: 
129:   return(0);
130: }

134: PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
135: {
136:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
137:   PetscErrorCode   ierr;
138:   PetscMPIInt      size;
139:   PetscInt         m=A->M, N=A->N;
140:   SuperLUStat_t    stat;
141:   double           berr[1];
142:   PetscScalar      *bptr;
143:   PetscInt         info, nrhs=1;
144:   Vec              x_seq;
145:   IS               iden;
146:   VecScatter       scat;
147: 
149:   MPI_Comm_size(A->comm,&size);
150:   if (size > 1) {
151:     if (lu->MatInputMode == GLOBAL) { /* global mat input, convert b to x_seq */
152:       VecCreateSeq(PETSC_COMM_SELF,N,&x_seq);
153:       ISCreateStride(PETSC_COMM_SELF,N,0,1,&iden);
154:       VecScatterCreate(b_mpi,iden,x_seq,iden,&scat);
155:       ISDestroy(iden);

157:       VecScatterBegin(b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD,scat);
158:       VecScatterEnd(b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD,scat);
159:       VecGetArray(x_seq,&bptr);
160:     } else { /* distributed mat input */
161:       VecCopy(b_mpi,x);
162:       VecGetArray(x,&bptr);
163:     }
164:   } else { /* size == 1 */
165:     VecCopy(b_mpi,x);
166:     VecGetArray(x,&bptr);
167:   }
168: 
169:   lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only.*/

171:   PStatInit(&stat);        /* Initialize the statistics variables. */

173:   if (lu->MatInputMode == GLOBAL) {
174: #if defined(PETSC_USE_COMPLEX)
175:     pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,(doublecomplex*)bptr, m, nrhs,
176:                    &lu->grid, &lu->LUstruct, berr, &stat, &info);
177: #else
178:     pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, m, nrhs,
179:                    &lu->grid, &lu->LUstruct, berr, &stat, &info);
180: #endif 
181:   } else { /* distributed mat input */
182: #if defined(PETSC_USE_COMPLEX)
183:     pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, (doublecomplex*)bptr, A->M, nrhs, &lu->grid,
184:             &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &info);
185:     if (info) SETERRQ1(PETSC_ERR_LIB,"pzgssvx fails, info: %d\n",info);
186: #else
187:     pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, bptr, A->M, nrhs, &lu->grid,
188:             &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &info);
189:     if (info) SETERRQ1(PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
190: #endif
191:   }
192:   if (lu->StatPrint) {
193:      PStatPrint(&lu->options, &stat, &lu->grid);     /* Print the statistics. */
194:   }
195:   PStatFree(&stat);
196: 
197:   if (size > 1) {
198:     if (lu->MatInputMode == GLOBAL){ /* convert seq x to mpi x */
199:       VecRestoreArray(x_seq,&bptr);
200:       VecScatterBegin(x_seq,x,INSERT_VALUES,SCATTER_REVERSE,scat);
201:       VecScatterEnd(x_seq,x,INSERT_VALUES,SCATTER_REVERSE,scat);
202:       VecScatterDestroy(scat);
203:       VecDestroy(x_seq);
204:     } else {
205:       VecRestoreArray(x,&bptr);
206:     }
207:   } else {
208:     VecRestoreArray(x,&bptr);
209:   }
210:   return(0);
211: }

215: PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat A,MatFactorInfo *info,Mat *F)
216: {
217:   Mat              *tseq,A_seq = PETSC_NULL;
218:   Mat_SeqAIJ       *aa,*bb;
219:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)(*F)->spptr;
220:   PetscErrorCode   ierr;
221:   PetscInt         M=A->M,N=A->N,sinfo,i,*ai,*aj,*bi,*bj,nz,rstart,*garray,
222:                    m=A->m, irow,colA_start,j,jcol,jB,countA,countB,*bjj,*ajj;
223:   PetscMPIInt      size,rank;
224:   SuperLUStat_t    stat;
225:   double           *berr=0;
226:   IS               isrow;
227:   PetscLogDouble   time0,time,time_min,time_max;
228: #if defined(PETSC_USE_COMPLEX)
229:   doublecomplex    *av, *bv;
230: #else
231:   double           *av, *bv;
232: #endif

235:   MPI_Comm_size(A->comm,&size);
236:   MPI_Comm_rank(A->comm,&rank);
237: 
238:   if (lu->StatPrint) { /* collect time for mat conversion */
239:     MPI_Barrier(A->comm);
240:     PetscGetTime(&time0);
241:   }

243:   if (lu->MatInputMode == GLOBAL) { /* global mat input */
244:     if (size > 1) { /* convert mpi A to seq mat A */
245:       ISCreateStride(PETSC_COMM_SELF,M,0,1,&isrow);
246:       MatGetSubMatrices(A,1,&isrow,&isrow,MAT_INITIAL_MATRIX,&tseq);
247:       ISDestroy(isrow);
248: 
249:       A_seq = *tseq;
250:       PetscFree(tseq);
251:       aa =  (Mat_SeqAIJ*)A_seq->data;
252:     } else {
253:       aa =  (Mat_SeqAIJ*)A->data;
254:     }

256:     /* Allocate storage, then convert Petsc NR matrix to SuperLU_DIST NC */
257:     if (lu->flg == DIFFERENT_NONZERO_PATTERN) {/* first numeric factorization */
258: #if defined(PETSC_USE_COMPLEX)
259:       zallocateA_dist(N, aa->nz, &lu->val, &lu->col, &lu->row);
260: #else
261:       dallocateA_dist(N, aa->nz, &lu->val, &lu->col, &lu->row);
262: #endif
263:     } else { /* successive numeric factorization, sparsity pattern is reused. */
264:       Destroy_CompCol_Matrix_dist(&lu->A_sup);
265:       Destroy_LU(N, &lu->grid, &lu->LUstruct);
266:       lu->options.Fact = SamePattern;
267:     }
268: #if defined(PETSC_USE_COMPLEX)
269:     zCompRow_to_CompCol_dist(M,N,aa->nz,(doublecomplex*)aa->a,aa->j,aa->i,&lu->val,&lu->col, &lu->row);
270: #else
271:     dCompRow_to_CompCol_dist(M,N,aa->nz,aa->a,aa->j,aa->i,&lu->val, &lu->col, &lu->row);
272: #endif

274:     /* Create compressed column matrix A_sup. */
275: #if defined(PETSC_USE_COMPLEX)
276:     zCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_Z, SLU_GE);
277: #else
278:     dCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_D, SLU_GE);
279: #endif
280:   } else { /* distributed mat input */
281:     Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
282:     aa=(Mat_SeqAIJ*)(mat->A)->data;
283:     bb=(Mat_SeqAIJ*)(mat->B)->data;
284:     ai=aa->i; aj=aa->j;
285:     bi=bb->i; bj=bb->j;
286: #if defined(PETSC_USE_COMPLEX)
287:     av=(doublecomplex*)aa->a;
288:     bv=(doublecomplex*)bb->a;
289: #else
290:     av=aa->a;
291:     bv=bb->a;
292: #endif
293:     rstart = mat->rstart;
294:     nz     = aa->nz + bb->nz;
295:     garray = mat->garray;
296:     rstart = mat->rstart;

298:     if (lu->flg == DIFFERENT_NONZERO_PATTERN) {/* first numeric factorization */
299: #if defined(PETSC_USE_COMPLEX)
300:       zallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row);
301: #else
302:       dallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row);
303: #endif
304:     } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
305:       /* Destroy_CompRowLoc_Matrix_dist(&lu->A_sup);  */ /* crash! */
306:       Destroy_LU(N, &lu->grid, &lu->LUstruct);
307:       lu->options.Fact = SamePattern;
308:     }
309:     nz = 0; irow = mat->rstart;
310:     for ( i=0; i<m; i++ ) {
311:       lu->row[i] = nz;
312:       countA = ai[i+1] - ai[i];
313:       countB = bi[i+1] - bi[i];
314:       ajj = aj + ai[i];  /* ptr to the beginning of this row */
315:       bjj = bj + bi[i];

317:       /* B part, smaller col index */
318:       colA_start = mat->rstart + ajj[0]; /* the smallest global col index of A */
319:       jB = 0;
320:       for (j=0; j<countB; j++){
321:         jcol = garray[bjj[j]];
322:         if (jcol > colA_start) {
323:           jB = j;
324:           break;
325:         }
326:         lu->col[nz] = jcol;
327:         lu->val[nz++] = *bv++;
328:         if (j==countB-1) jB = countB;
329:       }

331:       /* A part */
332:       for (j=0; j<countA; j++){
333:         lu->col[nz] = mat->rstart + ajj[j];
334:         lu->val[nz++] = *av++;
335:       }

337:       /* B part, larger col index */
338:       for (j=jB; j<countB; j++){
339:         lu->col[nz] = garray[bjj[j]];
340:         lu->val[nz++] = *bv++;
341:       }
342:     }
343:     lu->row[m] = nz;
344: #if defined(PETSC_USE_COMPLEX)
345:     zCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,
346:                                    lu->val, lu->col, lu->row, SLU_NR_loc, SLU_Z, SLU_GE);
347: #else
348:     dCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,
349:                                    lu->val, lu->col, lu->row, SLU_NR_loc, SLU_D, SLU_GE);
350: #endif
351:   }
352:   if (lu->StatPrint) {
353:     PetscGetTime(&time);
354:     time0 = time - time0;
355:   }

357:   /* Factor the matrix. */
358:   PStatInit(&stat);   /* Initialize the statistics variables. */

360:   if (lu->MatInputMode == GLOBAL) { /* global mat input */
361: #if defined(PETSC_USE_COMPLEX)
362:     pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,
363:                    &lu->grid, &lu->LUstruct, berr, &stat, &sinfo);
364: #else
365:     pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,
366:                    &lu->grid, &lu->LUstruct, berr, &stat, &sinfo);
367: #endif 
368:   } else { /* distributed mat input */
369: #if defined(PETSC_USE_COMPLEX)
370:     pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0, &lu->grid,
371:             &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo);
372:     if (sinfo) SETERRQ1(PETSC_ERR_LIB,"pzgssvx fails, info: %d\n",sinfo);
373: #else
374:     pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0, &lu->grid,
375:             &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo);
376:     if (sinfo) SETERRQ1(PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",sinfo);
377: #endif
378:   }

380:   if (lu->MatInputMode == GLOBAL && size > 1){
381:     MatDestroy(A_seq);
382:   }

384:   if (lu->StatPrint) {
385:     if (size > 1){
386:       MPI_Reduce(&time0,&time_max,1,MPI_DOUBLE,MPI_MAX,0,A->comm);
387:       MPI_Reduce(&time0,&time_min,1,MPI_DOUBLE,MPI_MIN,0,A->comm);
388:       MPI_Reduce(&time0,&time,1,MPI_DOUBLE,MPI_SUM,0,A->comm);
389:       time = time/size; /* average time */
390:       if (!rank)
391:         PetscPrintf(PETSC_COMM_SELF, "        Mat conversion(PETSc->SuperLU_DIST) time (max/min/avg): \n \
392:                               %g / %g / %g\n",time_max,time_min,time);
393:     } else {
394:       PetscPrintf(PETSC_COMM_SELF, "        Mat conversion(PETSc->SuperLU_DIST) time: \n \
395:                               %g\n",time0);
396:     }
397: 
398:     PStatPrint(&lu->options, &stat, &lu->grid);  /* Print the statistics. */
399:   }
400:   PStatFree(&stat);
401:   (*F)->assembled = PETSC_TRUE;
402:   lu->flg         = SAME_NONZERO_PATTERN;
403:   return(0);
404: }

406: /* Note the Petsc r and c permutations are ignored */
409: PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat A,IS r,IS c,MatFactorInfo *info,Mat *F)
410: {
411:   Mat               B;
412:   Mat_SuperLU_DIST  *lu;
413:   PetscErrorCode    ierr;
414:   PetscInt          M=A->M,N=A->N,indx;
415:   PetscMPIInt       size;
416:   superlu_options_t options;
417:   PetscTruth        flg;
418:   const char        *ptype[] = {"MMD_AT_PLUS_A","NATURAL","MMD_ATA","COLAMD"};
419:   const char        *prtype[] = {"LargeDiag","NATURAL"};

422:   /* Create the factorization matrix */
423:   MatCreate(A->comm,&B);
424:   MatSetSizes(B,A->m,A->n,M,N);
425:   MatSetType(B,A->type_name);
426:   MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
427:   MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);

429:   B->ops->lufactornumeric  = MatLUFactorNumeric_SuperLU_DIST;
430:   B->ops->solve            = MatSolve_SuperLU_DIST;
431:   B->factor                = FACTOR_LU;

433:   lu = (Mat_SuperLU_DIST*)(B->spptr);

435:   /* Set the input options */
436:   set_default_options_dist(&options);
437:   lu->MatInputMode = GLOBAL;
438:   MPI_Comm_dup(A->comm,&(lu->comm_superlu));

440:   MPI_Comm_size(A->comm,&size);
441:   lu->nprow = size/2;               /* Default process rows.      */
442:   if (!lu->nprow) lu->nprow = 1;
443:   lu->npcol = size/lu->nprow;           /* Default process columns.   */

445:   PetscOptionsBegin(A->comm,A->prefix,"SuperLU_Dist Options","Mat");
446: 
447:     PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,&lu->nprow,PETSC_NULL);
448:     PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,&lu->npcol,PETSC_NULL);
449:     if (size != lu->nprow * lu->npcol) SETERRQ(PETSC_ERR_ARG_SIZ,"Number of processes should be equal to nprow*npcol");
450: 
451:     PetscOptionsInt("-mat_superlu_dist_matinput","Matrix input mode (0: GLOBAL; 1: DISTRIBUTED)","None",lu->MatInputMode,&lu->MatInputMode,PETSC_NULL);
452:     if(lu->MatInputMode == DISTRIBUTED && size == 1) lu->MatInputMode = GLOBAL;

454:     PetscOptionsTruth("-mat_superlu_dist_equil","Equilibrate matrix","None",PETSC_TRUE,&flg,0);
455:     if (!flg) {
456:       options.Equil = NO;
457:     }

459:     PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",prtype,2,prtype[0],&indx,&flg);
460:     if (flg) {
461:       switch (indx) {
462:       case 0:
463:         options.RowPerm = LargeDiag;
464:         break;
465:       case 1:
466:         options.RowPerm = NOROWPERM;
467:         break;
468:       }
469:     }

471:     PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",ptype,4,ptype[0],&indx,&flg);
472:     if (flg) {
473:       switch (indx) {
474:       case 0:
475:         options.ColPerm = MMD_AT_PLUS_A;
476:         break;
477:       case 1:
478:         options.ColPerm = NATURAL;
479:         break;
480:       case 2:
481:         options.ColPerm = MMD_ATA;
482:         break;
483:       case 3:
484:         options.ColPerm = COLAMD;
485:         break;
486:       }
487:     }

489:     PetscOptionsTruth("-mat_superlu_dist_replacetinypivot","Replace tiny pivots","None",PETSC_TRUE,&flg,0);
490:     if (!flg) {
491:       options.ReplaceTinyPivot = NO;
492:     }

494:     options.IterRefine = NOREFINE;
495:     PetscOptionsTruth("-mat_superlu_dist_iterrefine","Use iterative refinement","None",PETSC_FALSE,&flg,0);
496:     if (flg) {
497:       options.IterRefine = DOUBLE;
498:     }

500:     if (PetscLogPrintInfo) {
501:       lu->StatPrint = (PetscInt)PETSC_TRUE;
502:     } else {
503:       lu->StatPrint = (PetscInt)PETSC_FALSE;
504:     }
505:     PetscOptionsTruth("-mat_superlu_dist_statprint","Print factorization information","None",
506:                               (PetscTruth)lu->StatPrint,(PetscTruth*)&lu->StatPrint,0);
507:   PetscOptionsEnd();

509:   /* Initialize the SuperLU process grid. */
510:   superlu_gridinit(lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid);

512:   /* Initialize ScalePermstruct and LUstruct. */
513:   ScalePermstructInit(M, N, &lu->ScalePermstruct);
514:   LUstructInit(M, N, &lu->LUstruct);

516:   lu->options            = options;
517:   lu->flg                = DIFFERENT_NONZERO_PATTERN;
518:   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
519:   *F = B;
520:   return(0);
521: }

525: PetscErrorCode MatAssemblyEnd_SuperLU_DIST(Mat A,MatAssemblyType mode) {
526:   PetscErrorCode   ierr;
527:   Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST*)(A->spptr);

530:   (*lu->MatAssemblyEnd)(A,mode);
531:   lu->MatLUFactorSymbolic  = A->ops->lufactorsymbolic;
532:   A->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
533:   return(0);
534: }

538: PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat A,PetscViewer viewer)
539: {
540:   Mat_SuperLU_DIST  *lu=(Mat_SuperLU_DIST*)A->spptr;
541:   superlu_options_t options;
542:   PetscErrorCode    ierr;

545:   /* check if matrix is superlu_dist type */
546:   if (A->ops->solve != MatSolve_SuperLU_DIST) return(0);

548:   options = lu->options;
549:   PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
550:   PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscTruths[options.Equil != NO]);
551:   PetscViewerASCIIPrintf(viewer,"  Matrix input mode %d \n",lu->MatInputMode);
552:   PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscTruths[options.ReplaceTinyPivot != NO]);
553:   PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscTruths[options.IterRefine == DOUBLE]);
554:   PetscViewerASCIIPrintf(viewer,"  Processors in row %d col partition %d \n",lu->nprow,lu->npcol);
555:   PetscViewerASCIIPrintf(viewer,"  Row permutation %s \n",(options.RowPerm == NOROWPERM) ? "NATURAL": "LargeDiag");
556:   if (options.ColPerm == NATURAL) {
557:     PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");
558:   } else if (options.ColPerm == MMD_AT_PLUS_A) {
559:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");
560:   } else if (options.ColPerm == MMD_ATA) {
561:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");
562:   } else if (options.ColPerm == COLAMD) {
563:     PetscViewerASCIIPrintf(viewer,"  Column permutation COLAMD\n");
564:   } else {
565:     SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown column permutation");
566:   }
567:   return(0);
568: }

572: PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
573: {
574:   PetscErrorCode    ierr;
575:   PetscTruth        iascii;
576:   PetscViewerFormat format;
577:   Mat_SuperLU_DIST  *lu=(Mat_SuperLU_DIST*)(A->spptr);

580:   (*lu->MatView)(A,viewer);

582:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
583:   if (iascii) {
584:     PetscViewerGetFormat(viewer,&format);
585:     if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
586:       MatFactorInfo_SuperLU_DIST(A,viewer);
587:     }
588:   }
589:   return(0);
590: }


596: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_Base_SuperLU_DIST(Mat A,MatType type,MatReuse reuse,Mat *newmat)
597: {
598:   /* This routine is only called to convert to MATSUPERLU_DIST */
599:   /* from MATSEQAIJ if A has a single process communicator */
600:   /* or MATMPIAIJ otherwise, so we will ignore 'MatType type'. */
601:   PetscErrorCode   ierr;
602:   PetscMPIInt      size;
603:   MPI_Comm         comm;
604:   Mat              B=*newmat;
605:   Mat_SuperLU_DIST *lu;

608:   if (reuse == MAT_INITIAL_MATRIX) {
609:     MatDuplicate(A,MAT_COPY_VALUES,&B);
610:   }

612:   PetscObjectGetComm((PetscObject)A,&comm);
613:   PetscNew(Mat_SuperLU_DIST,&lu);

615:   lu->MatDuplicate         = A->ops->duplicate;
616:   lu->MatView              = A->ops->view;
617:   lu->MatAssemblyEnd       = A->ops->assemblyend;
618:   lu->MatLUFactorSymbolic  = A->ops->lufactorsymbolic;
619:   lu->MatDestroy           = A->ops->destroy;
620:   lu->CleanUpSuperLU_Dist  = PETSC_FALSE;

622:   B->spptr                 = (void*)lu;
623:   B->ops->duplicate        = MatDuplicate_SuperLU_DIST;
624:   B->ops->view             = MatView_SuperLU_DIST;
625:   B->ops->assemblyend      = MatAssemblyEnd_SuperLU_DIST;
626:   B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
627:   B->ops->destroy          = MatDestroy_SuperLU_DIST;
628:   MPI_Comm_size(comm,&size);
629:   if (size == 1) {
630:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_superlu_dist_C",
631:                                              "MatConvert_Base_SuperLU_DIST",MatConvert_Base_SuperLU_DIST);
632:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_superlu_dist_seqaij_C",
633:                                              "MatConvert_SuperLU_DIST_Base",MatConvert_SuperLU_DIST_Base);
634:   } else {
635:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_superlu_dist_C",
636:                                              "MatConvert_Base_SuperLU_DIST",MatConvert_Base_SuperLU_DIST);
637:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_superlu_dist_mpiaij_C",
638:                                              "MatConvert_SuperLU_DIST_Base",MatConvert_SuperLU_DIST_Base);
639:   }
640:   PetscLogInfo((0,"MatConvert_Base_SuperLU_DIST:Using SuperLU_DIST for SeqAIJ LU factorization and solves.\n"));
641:   PetscObjectChangeTypeName((PetscObject)B,MATSUPERLU_DIST);
642:   *newmat = B;
643:   return(0);
644: }

649: PetscErrorCode MatDuplicate_SuperLU_DIST(Mat A, MatDuplicateOption op, Mat *M) {
650:   PetscErrorCode   ierr;
651:   Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST *)A->spptr;

654:   (*lu->MatDuplicate)(A,op,M);
655:   PetscMemcpy((*M)->spptr,lu,sizeof(Mat_SuperLU_DIST));
656:   return(0);
657: }

659: /*MC
660:   MATSUPERLU_DIST - MATSUPERLU_DIST = "superlu_dist" - A matrix type providing direct solvers (LU) for parallel matrices 
661:   via the external package SuperLU_DIST.

663:   If SuperLU_DIST is installed (see the manual for
664:   instructions on how to declare the existence of external packages),
665:   a matrix type can be constructed which invokes SuperLU_DIST solvers.
666:   After calling MatCreate(...,A), simply call MatSetType(A,MATSUPERLU_DIST).

668:   This matrix inherits from MATSEQAIJ when constructed with a single process communicator,
669:   and from MATMPIAIJ otherwise.  As a result, for single process communicators, 
670:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported 
671:   for communicators controlling multiple processes.  It is recommended that you call both of
672:   the above preallocation routines for simplicity.  One can also call MatConvert for an inplace
673:   conversion to or from the MATSEQAIJ or MATMPIAIJ type (depending on the communicator size)
674:   without data copy.

676:   Options Database Keys:
677: + -mat_type superlu_dist - sets the matrix type to "superlu_dist" during a call to MatSetFromOptions()
678: . -mat_superlu_dist_r <n> - number of rows in processor partition
679: . -mat_superlu_dist_c <n> - number of columns in processor partition
680: . -mat_superlu_dist_matinput <0,1> - matrix input mode; 0=global, 1=distributed
681: . -mat_superlu_dist_equil - equilibrate the matrix
682: . -mat_superlu_dist_rowperm <LargeDiag,NATURAL> - row permutation
683: . -mat_superlu_dist_colperm <MMD_AT_PLUS_A,MMD_ATA,COLAMD,NATURAL> - column permutation
684: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
685: . -mat_superlu_dist_iterrefine - use iterative refinement
686: - -mat_superlu_dist_statprint - print factorization information

688:    Level: beginner

690: .seealso: PCLU
691: M*/

696: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SuperLU_DIST(Mat A)
697: {
699:   PetscMPIInt    size;
700:   Mat            A_diag;

703:   /* Change type name before calling MatSetType to force proper construction of SeqAIJ or MPIAIJ */
704:   /*   and SuperLU_DIST types */
705:   PetscObjectChangeTypeName((PetscObject)A,MATSUPERLU_DIST);
706:   MPI_Comm_size(A->comm,&size);
707:   if (size == 1) {
708:     MatSetType(A,MATSEQAIJ);
709:   } else {
710:     MatSetType(A,MATMPIAIJ);
711:     A_diag = ((Mat_MPIAIJ *)A->data)->A;
712:     MatConvert_Base_SuperLU_DIST(A_diag,MATSUPERLU_DIST,MAT_REUSE_MATRIX,&A_diag);
713:   }
714:   MatConvert_Base_SuperLU_DIST(A,MATSUPERLU_DIST,MAT_REUSE_MATRIX,&A);
715:   return(0);
716: }