Actual source code: mumps.c

petsc-3.5.3 2015-01-31
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  2: /*
  3:     Provides an interface to the MUMPS sparse solver
  4: */

  6: #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I  "petscmat.h"  I*/
  7: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>

  9: EXTERN_C_BEGIN
 10: #if defined(PETSC_USE_COMPLEX)
 11: #if defined(PETSC_USE_REAL_SINGLE)
 12: #include <cmumps_c.h>
 13: #else
 14: #include <zmumps_c.h>
 15: #endif
 16: #else
 17: #if defined(PETSC_USE_REAL_SINGLE)
 18: #include <smumps_c.h>
 19: #else
 20: #include <dmumps_c.h>
 21: #endif
 22: #endif
 23: EXTERN_C_END
 24: #define JOB_INIT -1
 25: #define JOB_FACTSYMBOLIC 1
 26: #define JOB_FACTNUMERIC 2
 27: #define JOB_SOLVE 3
 28: #define JOB_END -2

 30: /* calls to MUMPS */
 31: #if defined(PETSC_USE_COMPLEX)
 32: #if defined(PETSC_USE_REAL_SINGLE)
 33: #define PetscMUMPS_c cmumps_c
 34: #else
 35: #define PetscMUMPS_c zmumps_c
 36: #endif
 37: #else
 38: #if defined(PETSC_USE_REAL_SINGLE)
 39: #define PetscMUMPS_c smumps_c
 40: #else
 41: #define PetscMUMPS_c dmumps_c
 42: #endif
 43: #endif


 46: /* macros s.t. indices match MUMPS documentation */
 47: #define ICNTL(I) icntl[(I)-1]
 48: #define CNTL(I) cntl[(I)-1]
 49: #define INFOG(I) infog[(I)-1]
 50: #define INFO(I) info[(I)-1]
 51: #define RINFOG(I) rinfog[(I)-1]
 52: #define RINFO(I) rinfo[(I)-1]

 54: typedef struct {
 55: #if defined(PETSC_USE_COMPLEX)
 56: #if defined(PETSC_USE_REAL_SINGLE)
 57:   CMUMPS_STRUC_C id;
 58: #else
 59:   ZMUMPS_STRUC_C id;
 60: #endif
 61: #else
 62: #if defined(PETSC_USE_REAL_SINGLE)
 63:   SMUMPS_STRUC_C id;
 64: #else
 65:   DMUMPS_STRUC_C id;
 66: #endif
 67: #endif

 69:   MatStructure matstruc;
 70:   PetscMPIInt  myid,size;
 71:   PetscInt     *irn,*jcn,nz,sym;
 72:   PetscScalar  *val;
 73:   MPI_Comm     comm_mumps;
 74:   VecScatter   scat_rhs, scat_sol;
 75:   PetscBool    isAIJ,CleanUpMUMPS;
 76:   Vec          b_seq,x_seq;
 77:   PetscInt     ICNTL9_pre;   /* check if ICNTL(9) is changed from previous MatSolve */

 79:   PetscErrorCode (*Destroy)(Mat);
 80:   PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**);
 81: } Mat_MUMPS;

 83: extern PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*);


 86: /* MatConvertToTriples_A_B */
 87: /*convert Petsc matrix to triples: row[nz], col[nz], val[nz] */
 88: /*
 89:   input:
 90:     A       - matrix in aij,baij or sbaij (bs=1) format
 91:     shift   - 0: C style output triple; 1: Fortran style output triple.
 92:     reuse   - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
 93:               MAT_REUSE_MATRIX:   only the values in v array are updated
 94:   output:
 95:     nnz     - dim of r, c, and v (number of local nonzero entries of A)
 96:     r, c, v - row and col index, matrix values (matrix triples)

 98:   The returned values r, c, and sometimes v are obtained in a single PetscMalloc(). Then in MatDestroy_MUMPS() it is
 99:   freed with PetscFree((mumps->irn);  This is not ideal code, the fact that v is ONLY sometimes part of mumps->irn means
100:   that the PetscMalloc() cannot easily be replaced with a PetscMalloc3(). 

102:  */

106: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
107: {
108:   const PetscInt *ai,*aj,*ajj,M=A->rmap->n;
109:   PetscInt       nz,rnz,i,j;
111:   PetscInt       *row,*col;
112:   Mat_SeqAIJ     *aa=(Mat_SeqAIJ*)A->data;

115:   *v=aa->a;
116:   if (reuse == MAT_INITIAL_MATRIX) {
117:     nz   = aa->nz;
118:     ai   = aa->i;
119:     aj   = aa->j;
120:     *nnz = nz;
121:     PetscMalloc1(2*nz, &row);
122:     col  = row + nz;

124:     nz = 0;
125:     for (i=0; i<M; i++) {
126:       rnz = ai[i+1] - ai[i];
127:       ajj = aj + ai[i];
128:       for (j=0; j<rnz; j++) {
129:         row[nz] = i+shift; col[nz++] = ajj[j] + shift;
130:       }
131:     }
132:     *r = row; *c = col;
133:   }
134:   return(0);
135: }

139: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
140: {
141:   Mat_SeqBAIJ    *aa=(Mat_SeqBAIJ*)A->data;
142:   const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2;
143:   PetscInt       bs,M,nz,idx=0,rnz,i,j,k,m;
145:   PetscInt       *row,*col;

148:   MatGetBlockSize(A,&bs);
149:   M = A->rmap->N/bs;
150:   *v = aa->a;
151:   if (reuse == MAT_INITIAL_MATRIX) {
152:     ai   = aa->i; aj = aa->j;
153:     nz   = bs2*aa->nz;
154:     *nnz = nz;
155:     PetscMalloc1(2*nz, &row);
156:     col  = row + nz;

158:     for (i=0; i<M; i++) {
159:       ajj = aj + ai[i];
160:       rnz = ai[i+1] - ai[i];
161:       for (k=0; k<rnz; k++) {
162:         for (j=0; j<bs; j++) {
163:           for (m=0; m<bs; m++) {
164:             row[idx]   = i*bs + m + shift;
165:             col[idx++] = bs*(ajj[k]) + j + shift;
166:           }
167:         }
168:       }
169:     }
170:     *r = row; *c = col;
171:   }
172:   return(0);
173: }

177: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
178: {
179:   const PetscInt *ai, *aj,*ajj,M=A->rmap->n;
180:   PetscInt       nz,rnz,i,j;
182:   PetscInt       *row,*col;
183:   Mat_SeqSBAIJ   *aa=(Mat_SeqSBAIJ*)A->data;

186:   *v = aa->a;
187:   if (reuse == MAT_INITIAL_MATRIX) {
188:     nz   = aa->nz;
189:     ai   = aa->i;
190:     aj   = aa->j;
191:     *v   = aa->a;
192:     *nnz = nz;
193:     PetscMalloc1(2*nz, &row);
194:     col  = row + nz;

196:     nz = 0;
197:     for (i=0; i<M; i++) {
198:       rnz = ai[i+1] - ai[i];
199:       ajj = aj + ai[i];
200:       for (j=0; j<rnz; j++) {
201:         row[nz] = i+shift; col[nz++] = ajj[j] + shift;
202:       }
203:     }
204:     *r = row; *c = col;
205:   }
206:   return(0);
207: }

211: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
212: {
213:   const PetscInt    *ai,*aj,*ajj,*adiag,M=A->rmap->n;
214:   PetscInt          nz,rnz,i,j;
215:   const PetscScalar *av,*v1;
216:   PetscScalar       *val;
217:   PetscErrorCode    ierr;
218:   PetscInt          *row,*col;
219:   Mat_SeqAIJ        *aa=(Mat_SeqAIJ*)A->data;

222:   ai   =aa->i; aj=aa->j;av=aa->a;
223:   adiag=aa->diag;
224:   if (reuse == MAT_INITIAL_MATRIX) {
225:     /* count nz in the uppper triangular part of A */
226:     nz = 0;
227:     for (i=0; i<M; i++) nz += ai[i+1] - adiag[i];
228:     *nnz = nz;

230:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
231:     col  = row + nz;
232:     val  = (PetscScalar*)(col + nz);

234:     nz = 0;
235:     for (i=0; i<M; i++) {
236:       rnz = ai[i+1] - adiag[i];
237:       ajj = aj + adiag[i];
238:       v1  = av + adiag[i];
239:       for (j=0; j<rnz; j++) {
240:         row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j];
241:       }
242:     }
243:     *r = row; *c = col; *v = val;
244:   } else {
245:     nz = 0; val = *v;
246:     for (i=0; i <M; i++) {
247:       rnz = ai[i+1] - adiag[i];
248:       ajj = aj + adiag[i];
249:       v1  = av + adiag[i];
250:       for (j=0; j<rnz; j++) {
251:         val[nz++] = v1[j];
252:       }
253:     }
254:   }
255:   return(0);
256: }

260: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
261: {
262:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
263:   PetscErrorCode    ierr;
264:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
265:   PetscInt          *row,*col;
266:   const PetscScalar *av, *bv,*v1,*v2;
267:   PetscScalar       *val;
268:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ*)A->data;
269:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ*)(mat->A)->data;
270:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ*)(mat->B)->data;

273:   ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart;
274:   av=aa->a; bv=bb->a;

276:   garray = mat->garray;

278:   if (reuse == MAT_INITIAL_MATRIX) {
279:     nz   = aa->nz + bb->nz;
280:     *nnz = nz;
281:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
282:     col  = row + nz;
283:     val  = (PetscScalar*)(col + nz);

285:     *r = row; *c = col; *v = val;
286:   } else {
287:     row = *r; col = *c; val = *v;
288:   }

290:   jj = 0; irow = rstart;
291:   for (i=0; i<m; i++) {
292:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
293:     countA = ai[i+1] - ai[i];
294:     countB = bi[i+1] - bi[i];
295:     bjj    = bj + bi[i];
296:     v1     = av + ai[i];
297:     v2     = bv + bi[i];

299:     /* A-part */
300:     for (j=0; j<countA; j++) {
301:       if (reuse == MAT_INITIAL_MATRIX) {
302:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
303:       }
304:       val[jj++] = v1[j];
305:     }

307:     /* B-part */
308:     for (j=0; j < countB; j++) {
309:       if (reuse == MAT_INITIAL_MATRIX) {
310:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
311:       }
312:       val[jj++] = v2[j];
313:     }
314:     irow++;
315:   }
316:   return(0);
317: }

321: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
322: {
323:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
324:   PetscErrorCode    ierr;
325:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
326:   PetscInt          *row,*col;
327:   const PetscScalar *av, *bv,*v1,*v2;
328:   PetscScalar       *val;
329:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ*)A->data;
330:   Mat_SeqAIJ        *aa  = (Mat_SeqAIJ*)(mat->A)->data;
331:   Mat_SeqAIJ        *bb  = (Mat_SeqAIJ*)(mat->B)->data;

334:   ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart;
335:   av=aa->a; bv=bb->a;

337:   garray = mat->garray;

339:   if (reuse == MAT_INITIAL_MATRIX) {
340:     nz   = aa->nz + bb->nz;
341:     *nnz = nz;
342:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
343:     col  = row + nz;
344:     val  = (PetscScalar*)(col + nz);

346:     *r = row; *c = col; *v = val;
347:   } else {
348:     row = *r; col = *c; val = *v;
349:   }

351:   jj = 0; irow = rstart;
352:   for (i=0; i<m; i++) {
353:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
354:     countA = ai[i+1] - ai[i];
355:     countB = bi[i+1] - bi[i];
356:     bjj    = bj + bi[i];
357:     v1     = av + ai[i];
358:     v2     = bv + bi[i];

360:     /* A-part */
361:     for (j=0; j<countA; j++) {
362:       if (reuse == MAT_INITIAL_MATRIX) {
363:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
364:       }
365:       val[jj++] = v1[j];
366:     }

368:     /* B-part */
369:     for (j=0; j < countB; j++) {
370:       if (reuse == MAT_INITIAL_MATRIX) {
371:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
372:       }
373:       val[jj++] = v2[j];
374:     }
375:     irow++;
376:   }
377:   return(0);
378: }

382: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
383: {
384:   Mat_MPIBAIJ       *mat    = (Mat_MPIBAIJ*)A->data;
385:   Mat_SeqBAIJ       *aa     = (Mat_SeqBAIJ*)(mat->A)->data;
386:   Mat_SeqBAIJ       *bb     = (Mat_SeqBAIJ*)(mat->B)->data;
387:   const PetscInt    *ai     = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj;
388:   const PetscInt    *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart;
389:   const PetscInt    bs2=mat->bs2;
390:   PetscErrorCode    ierr;
391:   PetscInt          bs,nz,i,j,k,n,jj,irow,countA,countB,idx;
392:   PetscInt          *row,*col;
393:   const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2;
394:   PetscScalar       *val;

397:   MatGetBlockSize(A,&bs);
398:   if (reuse == MAT_INITIAL_MATRIX) {
399:     nz   = bs2*(aa->nz + bb->nz);
400:     *nnz = nz;
401:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
402:     col  = row + nz;
403:     val  = (PetscScalar*)(col + nz);

405:     *r = row; *c = col; *v = val;
406:   } else {
407:     row = *r; col = *c; val = *v;
408:   }

410:   jj = 0; irow = rstart;
411:   for (i=0; i<mbs; i++) {
412:     countA = ai[i+1] - ai[i];
413:     countB = bi[i+1] - bi[i];
414:     ajj    = aj + ai[i];
415:     bjj    = bj + bi[i];
416:     v1     = av + bs2*ai[i];
417:     v2     = bv + bs2*bi[i];

419:     idx = 0;
420:     /* A-part */
421:     for (k=0; k<countA; k++) {
422:       for (j=0; j<bs; j++) {
423:         for (n=0; n<bs; n++) {
424:           if (reuse == MAT_INITIAL_MATRIX) {
425:             row[jj] = irow + n + shift;
426:             col[jj] = rstart + bs*ajj[k] + j + shift;
427:           }
428:           val[jj++] = v1[idx++];
429:         }
430:       }
431:     }

433:     idx = 0;
434:     /* B-part */
435:     for (k=0; k<countB; k++) {
436:       for (j=0; j<bs; j++) {
437:         for (n=0; n<bs; n++) {
438:           if (reuse == MAT_INITIAL_MATRIX) {
439:             row[jj] = irow + n + shift;
440:             col[jj] = bs*garray[bjj[k]] + j + shift;
441:           }
442:           val[jj++] = v2[idx++];
443:         }
444:       }
445:     }
446:     irow += bs;
447:   }
448:   return(0);
449: }

453: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
454: {
455:   const PetscInt    *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
456:   PetscErrorCode    ierr;
457:   PetscInt          rstart,nz,nza,nzb,i,j,jj,irow,countA,countB;
458:   PetscInt          *row,*col;
459:   const PetscScalar *av, *bv,*v1,*v2;
460:   PetscScalar       *val;
461:   Mat_MPIAIJ        *mat =  (Mat_MPIAIJ*)A->data;
462:   Mat_SeqAIJ        *aa  =(Mat_SeqAIJ*)(mat->A)->data;
463:   Mat_SeqAIJ        *bb  =(Mat_SeqAIJ*)(mat->B)->data;

466:   ai=aa->i; aj=aa->j; adiag=aa->diag;
467:   bi=bb->i; bj=bb->j; garray = mat->garray;
468:   av=aa->a; bv=bb->a;

470:   rstart = A->rmap->rstart;

472:   if (reuse == MAT_INITIAL_MATRIX) {
473:     nza = 0;    /* num of upper triangular entries in mat->A, including diagonals */
474:     nzb = 0;    /* num of upper triangular entries in mat->B */
475:     for (i=0; i<m; i++) {
476:       nza   += (ai[i+1] - adiag[i]);
477:       countB = bi[i+1] - bi[i];
478:       bjj    = bj + bi[i];
479:       for (j=0; j<countB; j++) {
480:         if (garray[bjj[j]] > rstart) nzb++;
481:       }
482:     }

484:     nz   = nza + nzb; /* total nz of upper triangular part of mat */
485:     *nnz = nz;
486:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
487:     col  = row + nz;
488:     val  = (PetscScalar*)(col + nz);

490:     *r = row; *c = col; *v = val;
491:   } else {
492:     row = *r; col = *c; val = *v;
493:   }

495:   jj = 0; irow = rstart;
496:   for (i=0; i<m; i++) {
497:     ajj    = aj + adiag[i];                 /* ptr to the beginning of the diagonal of this row */
498:     v1     = av + adiag[i];
499:     countA = ai[i+1] - adiag[i];
500:     countB = bi[i+1] - bi[i];
501:     bjj    = bj + bi[i];
502:     v2     = bv + bi[i];

504:     /* A-part */
505:     for (j=0; j<countA; j++) {
506:       if (reuse == MAT_INITIAL_MATRIX) {
507:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
508:       }
509:       val[jj++] = v1[j];
510:     }

512:     /* B-part */
513:     for (j=0; j < countB; j++) {
514:       if (garray[bjj[j]] > rstart) {
515:         if (reuse == MAT_INITIAL_MATRIX) {
516:           row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
517:         }
518:         val[jj++] = v2[j];
519:       }
520:     }
521:     irow++;
522:   }
523:   return(0);
524: }

528: PetscErrorCode MatDestroy_MUMPS(Mat A)
529: {
530:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

534:   if (mumps->CleanUpMUMPS) {
535:     /* Terminate instance, deallocate memories */
536:     PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
537:     VecScatterDestroy(&mumps->scat_rhs);
538:     VecDestroy(&mumps->b_seq);
539:     VecScatterDestroy(&mumps->scat_sol);
540:     VecDestroy(&mumps->x_seq);
541:     PetscFree(mumps->id.perm_in);
542:     PetscFree(mumps->irn);

544:     mumps->id.job = JOB_END;
545:     PetscMUMPS_c(&mumps->id);
546:     MPI_Comm_free(&(mumps->comm_mumps));
547:   }
548:   if (mumps->Destroy) {
549:     (mumps->Destroy)(A);
550:   }
551:   PetscFree(A->spptr);

553:   /* clear composed functions */
554:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverPackage_C",NULL);
555:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);
556:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);
557:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);
558:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);

560:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);
561:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);
562:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);
563:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);
564:   return(0);
565: }

569: PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x)
570: {
571:   Mat_MUMPS        *mumps=(Mat_MUMPS*)A->spptr;
572:   PetscScalar      *array;
573:   Vec              b_seq;
574:   IS               is_iden,is_petsc;
575:   PetscErrorCode   ierr;
576:   PetscInt         i;
577:   static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE;

580:   PetscCitationsRegister("@article{MUMPS01,\n  author = {P.~R. Amestoy and I.~S. Duff and J.-Y. L'Excellent and J. Koster},\n  title = {A fully asynchronous multifrontal solver using distributed dynamic scheduling},\n  journal = {SIAM Journal on Matrix Analysis and Applications},\n  volume = {23},\n  number = {1},\n  pages = {15--41},\n  year = {2001}\n}\n",&cite1);
581:   PetscCitationsRegister("@article{MUMPS02,\n  author = {P.~R. Amestoy and A. Guermouche and J.-Y. L'Excellent and S. Pralet},\n  title = {Hybrid scheduling for the parallel solution of linear systems},\n  journal = {Parallel Computing},\n  volume = {32},\n  number = {2},\n  pages = {136--156},\n  year = {2006}\n}\n",&cite2);
582:   mumps->id.nrhs = 1;
583:   b_seq          = mumps->b_seq;
584:   if (mumps->size > 1) {
585:     /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */
586:     VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
587:     VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
588:     if (!mumps->myid) {VecGetArray(b_seq,&array);}
589:   } else {  /* size == 1 */
590:     VecCopy(b,x);
591:     VecGetArray(x,&array);
592:   }
593:   if (!mumps->myid) { /* define rhs on the host */
594:     mumps->id.nrhs = 1;
595: #if defined(PETSC_USE_COMPLEX)
596: #if defined(PETSC_USE_REAL_SINGLE)
597:     mumps->id.rhs = (mumps_complex*)array;
598: #else
599:     mumps->id.rhs = (mumps_double_complex*)array;
600: #endif
601: #else
602:     mumps->id.rhs = array;
603: #endif
604:   }

606:   /* solve phase */
607:   /*-------------*/
608:   mumps->id.job = JOB_SOLVE;
609:   PetscMUMPS_c(&mumps->id);
610:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",mumps->id.INFOG(1));

612:   if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */
613:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
614:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
615:       VecScatterDestroy(&mumps->scat_sol);
616:     }
617:     if (!mumps->scat_sol) { /* create scatter scat_sol */
618:       ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden); /* from */
619:       for (i=0; i<mumps->id.lsol_loc; i++) {
620:         mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */
621:       }
622:       ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);  /* to */
623:       VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);
624:       ISDestroy(&is_iden);
625:       ISDestroy(&is_petsc);

627:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
628:     }

630:     VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
631:     VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
632:   }
633:   return(0);
634: }

638: PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x)
639: {
640:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

644:   mumps->id.ICNTL(9) = 0;

646:   MatSolve_MUMPS(A,b,x);

648:   mumps->id.ICNTL(9) = 1;
649:   return(0);
650: }

654: PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X)
655: {
657:   PetscBool      flg;

660:   PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
661:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
662:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
663:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
664:   SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatSolve_MUMPS() is not implemented yet");
665:   return(0);
666: }

668: #if !defined(PETSC_USE_COMPLEX)
669: /*
670:   input:
671:    F:        numeric factor
672:   output:
673:    nneg:     total number of negative pivots
674:    nzero:    0
675:    npos:     (global dimension of F) - nneg
676: */

680: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos)
681: {
682:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
684:   PetscMPIInt    size;

687:   MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);
688:   /* MUMPS 4.3.1 calls ScaLAPACK when ICNTL(13)=0 (default), which does not offer the possibility to compute the inertia of a dense matrix. Set ICNTL(13)=1 to skip ScaLAPACK */
689:   if (size > 1 && mumps->id.ICNTL(13) != 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"ICNTL(13)=%d. -mat_mumps_icntl_13 must be set as 1 for correct global matrix inertia\n",mumps->id.INFOG(13));

691:   if (nneg) *nneg = mumps->id.INFOG(12);
692:   if (nzero || npos) {
693:     if (mumps->id.ICNTL(24) != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"-mat_mumps_icntl_24 must be set as 1 for null pivot row detection");
694:     if (nzero) *nzero = mumps->id.INFOG(28);
695:     if (npos) *npos   = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
696:   }
697:   return(0);
698: }
699: #endif /* !defined(PETSC_USE_COMPLEX) */

703: PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info)
704: {
705:   Mat_MUMPS      *mumps =(Mat_MUMPS*)(F)->spptr;
707:   Mat            F_diag;
708:   PetscBool      isMPIAIJ;

711:   (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

713:   /* numerical factorization phase */
714:   /*-------------------------------*/
715:   mumps->id.job = JOB_FACTNUMERIC;
716:   if (!mumps->id.ICNTL(18)) {
717:     if (!mumps->myid) {
718: #if defined(PETSC_USE_COMPLEX)
719: #if defined(PETSC_USE_REAL_SINGLE)
720:       mumps->id.a = (mumps_complex*)mumps->val;
721: #else
722:       mumps->id.a = (mumps_double_complex*)mumps->val;
723: #endif
724: #else
725:       mumps->id.a = mumps->val;
726: #endif
727:     }
728:   } else {
729: #if defined(PETSC_USE_COMPLEX)
730: #if defined(PETSC_USE_REAL_SINGLE)
731:     mumps->id.a_loc = (mumps_complex*)mumps->val;
732: #else
733:     mumps->id.a_loc = (mumps_double_complex*)mumps->val;
734: #endif
735: #else
736:     mumps->id.a_loc = mumps->val;
737: #endif
738:   }
739:   PetscMUMPS_c(&mumps->id);
740:   if (mumps->id.INFOG(1) < 0) {
741:     if (mumps->id.INFO(1) == -13) {
742:       if (mumps->id.INFO(2) < 0) {
743:         SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: Cannot allocate required memory %d megabytes\n",-mumps->id.INFO(2));
744:       } else {
745:         SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: Cannot allocate required memory %d bytes\n",mumps->id.INFO(2));
746:       }
747:     } else SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFO(1)=%d, INFO(2)=%d\n",mumps->id.INFO(1),mumps->id.INFO(2));
748:   }
749:   if (!mumps->myid && mumps->id.ICNTL(16) > 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"  mumps->id.ICNTL(16):=%d\n",mumps->id.INFOG(16));
750: 
751:   (F)->assembled      = PETSC_TRUE;
752:   mumps->matstruc     = SAME_NONZERO_PATTERN;
753:   mumps->CleanUpMUMPS = PETSC_TRUE;

755:   if (mumps->size > 1) {
756:     PetscInt    lsol_loc;
757:     PetscScalar *sol_loc;

759:     PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);
760:     if (isMPIAIJ) F_diag = ((Mat_MPIAIJ*)(F)->data)->A;
761:     else F_diag = ((Mat_MPISBAIJ*)(F)->data)->A;
762:     F_diag->assembled = PETSC_TRUE;

764:     /* distributed solution; Create x_seq=sol_loc for repeated use */
765:     if (mumps->x_seq) {
766:       VecScatterDestroy(&mumps->scat_sol);
767:       PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
768:       VecDestroy(&mumps->x_seq);
769:     }
770:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
771:     PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);
772:     mumps->id.lsol_loc = lsol_loc;
773: #if defined(PETSC_USE_COMPLEX)
774: #if defined(PETSC_USE_REAL_SINGLE)
775:     mumps->id.sol_loc = (mumps_complex*)sol_loc;
776: #else
777:     mumps->id.sol_loc = (mumps_double_complex*)sol_loc;
778: #endif
779: #else
780:     mumps->id.sol_loc = sol_loc;
781: #endif
782:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);
783:   }
784:   return(0);
785: }

787: /* Sets MUMPS options from the options database */
790: PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A)
791: {
792:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
794:   PetscInt       icntl;
795:   PetscBool      flg;

798:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");
799:   PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);
800:   if (flg) mumps->id.ICNTL(1) = icntl;
801:   PetscOptionsInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg);
802:   if (flg) mumps->id.ICNTL(2) = icntl;
803:   PetscOptionsInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg);
804:   if (flg) mumps->id.ICNTL(3) = icntl;

806:   PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);
807:   if (flg) mumps->id.ICNTL(4) = icntl;
808:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

810:   PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): permuting and/or scaling the matrix (0 to 7)","None",mumps->id.ICNTL(6),&icntl,&flg);
811:   if (flg) mumps->id.ICNTL(6) = icntl;

813:   PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): matrix ordering (0 to 7). 3=Scotch, 4=PORD, 5=Metis","None",mumps->id.ICNTL(7),&icntl,&flg);
814:   if (flg) {
815:     if (icntl== 1 && mumps->size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"pivot order be set by the user in PERM_IN -- not supported by the PETSc/MUMPS interface\n");
816:     else mumps->id.ICNTL(7) = icntl;
817:   }

819:   PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL);
820:   PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);
821:   PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to the linear system solved (via -ksp_view)","None",mumps->id.ICNTL(11),&mumps->id.ICNTL(11),NULL);
822:   PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): efficiency control: defines the ordering strategy with scaling constraints (0 to 3)","None",mumps->id.ICNTL(12),&mumps->id.ICNTL(12),NULL);
823:   PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): efficiency control: with or without ScaLAPACK","None",mumps->id.ICNTL(13),&mumps->id.ICNTL(13),NULL);
824:   PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage of estimated workspace increase","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL);
825:   PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);

827:   PetscOptionsInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core facility (0 or 1)","None",mumps->id.ICNTL(22),&mumps->id.ICNTL(22),NULL);
828:   PetscOptionsInt("-mat_mumps_icntl_23","ICNTL(23): max size of the working memory (MB) that can allocate per processor","None",mumps->id.ICNTL(23),&mumps->id.ICNTL(23),NULL);
829:   PetscOptionsInt("-mat_mumps_icntl_24","ICNTL(24): detection of null pivot rows (0 or 1)","None",mumps->id.ICNTL(24),&mumps->id.ICNTL(24),NULL);
830:   if (mumps->id.ICNTL(24)) {
831:     mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */
832:   }

834:   PetscOptionsInt("-mat_mumps_icntl_25","ICNTL(25): computation of a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),NULL);
835:   PetscOptionsInt("-mat_mumps_icntl_26","ICNTL(26): Schur options for right-hand side or solution vector","None",mumps->id.ICNTL(26),&mumps->id.ICNTL(26),NULL);
836:   PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): experimental parameter","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),NULL);
837:   PetscOptionsInt("-mat_mumps_icntl_28","ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering","None",mumps->id.ICNTL(28),&mumps->id.ICNTL(28),NULL);
838:   PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL);
839:   PetscOptionsInt("-mat_mumps_icntl_30","ICNTL(30): compute user-specified set of entries in inv(A)","None",mumps->id.ICNTL(30),&mumps->id.ICNTL(30),NULL);
840:   PetscOptionsInt("-mat_mumps_icntl_31","ICNTL(31): factors can be discarded in the solve phase","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),NULL);
841:   PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);

843:   PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);
844:   PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);
845:   PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);
846:   PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);
847:   PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);

849:   PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);
850:   PetscOptionsEnd();
851:   return(0);
852: }

856: PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps)
857: {

861:   MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);
862:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);
863:   MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));

865:   mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps);

867:   mumps->id.job = JOB_INIT;
868:   mumps->id.par = 1;  /* host participates factorizaton and solve */
869:   mumps->id.sym = mumps->sym;
870:   PetscMUMPS_c(&mumps->id);

872:   mumps->CleanUpMUMPS = PETSC_FALSE;
873:   mumps->scat_rhs     = NULL;
874:   mumps->scat_sol     = NULL;

876:   /* set PETSc-MUMPS default options - override MUMPS default */
877:   mumps->id.ICNTL(3) = 0;
878:   mumps->id.ICNTL(4) = 0;
879:   if (mumps->size == 1) {
880:     mumps->id.ICNTL(18) = 0;   /* centralized assembled matrix input */
881:   } else {
882:     mumps->id.ICNTL(18) = 3;   /* distributed assembled matrix input */
883:     mumps->id.ICNTL(21) = 1;   /* distributed solution */
884:   }
885:   return(0);
886: }

888: /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */
891: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
892: {
893:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
895:   Vec            b;
896:   IS             is_iden;
897:   const PetscInt M = A->rmap->N;

900:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

902:   /* Set MUMPS options from the options database */
903:   PetscSetMUMPSFromOptions(F,A);

905:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

907:   /* analysis phase */
908:   /*----------------*/
909:   mumps->id.job = JOB_FACTSYMBOLIC;
910:   mumps->id.n   = M;
911:   switch (mumps->id.ICNTL(18)) {
912:   case 0:  /* centralized assembled matrix input */
913:     if (!mumps->myid) {
914:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
915:       if (mumps->id.ICNTL(6)>1) {
916: #if defined(PETSC_USE_COMPLEX)
917: #if defined(PETSC_USE_REAL_SINGLE)
918:         mumps->id.a = (mumps_complex*)mumps->val;
919: #else
920:         mumps->id.a = (mumps_double_complex*)mumps->val;
921: #endif
922: #else
923:         mumps->id.a = mumps->val;
924: #endif
925:       }
926:       if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */
927:         /*
928:         PetscBool      flag;
929:         ISEqual(r,c,&flag);
930:         if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm");
931:         ISView(r,PETSC_VIEWER_STDOUT_SELF);
932:          */
933:         if (!mumps->myid) {
934:           const PetscInt *idx;
935:           PetscInt       i,*perm_in;

937:           PetscMalloc1(M,&perm_in);
938:           ISGetIndices(r,&idx);

940:           mumps->id.perm_in = perm_in;
941:           for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */
942:           ISRestoreIndices(r,&idx);
943:         }
944:       }
945:     }
946:     break;
947:   case 3:  /* distributed assembled matrix input (size>1) */
948:     mumps->id.nz_loc = mumps->nz;
949:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
950:     if (mumps->id.ICNTL(6)>1) {
951: #if defined(PETSC_USE_COMPLEX)
952: #if defined(PETSC_USE_REAL_SINGLE)
953:       mumps->id.a_loc = (mumps_complex*)mumps->val;
954: #else
955:       mumps->id.a_loc = (mumps_double_complex*)mumps->val;
956: #endif
957: #else
958:       mumps->id.a_loc = mumps->val;
959: #endif
960:     }
961:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
962:     if (!mumps->myid) {
963:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
964:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
965:     } else {
966:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
967:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
968:     }
969:     MatGetVecs(A,NULL,&b);
970:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
971:     ISDestroy(&is_iden);
972:     VecDestroy(&b);
973:     break;
974:   }
975:   PetscMUMPS_c(&mumps->id);
976:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));

978:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
979:   F->ops->solve           = MatSolve_MUMPS;
980:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
981:   F->ops->matsolve        = 0;  /* use MatMatSolve_Basic() until mumps supports distributed rhs */
982:   return(0);
983: }

985: /* Note the Petsc r and c permutations are ignored */
988: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
989: {
990:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
992:   Vec            b;
993:   IS             is_iden;
994:   const PetscInt M = A->rmap->N;

997:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

999:   /* Set MUMPS options from the options database */
1000:   PetscSetMUMPSFromOptions(F,A);

1002:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1004:   /* analysis phase */
1005:   /*----------------*/
1006:   mumps->id.job = JOB_FACTSYMBOLIC;
1007:   mumps->id.n   = M;
1008:   switch (mumps->id.ICNTL(18)) {
1009:   case 0:  /* centralized assembled matrix input */
1010:     if (!mumps->myid) {
1011:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
1012:       if (mumps->id.ICNTL(6)>1) {
1013: #if defined(PETSC_USE_COMPLEX)
1014: #if defined(PETSC_USE_REAL_SINGLE)
1015:         mumps->id.a = (mumps_complex*)mumps->val;
1016: #else
1017:         mumps->id.a = (mumps_double_complex*)mumps->val;
1018: #endif
1019: #else
1020:         mumps->id.a = mumps->val;
1021: #endif
1022:       }
1023:     }
1024:     break;
1025:   case 3:  /* distributed assembled matrix input (size>1) */
1026:     mumps->id.nz_loc = mumps->nz;
1027:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
1028:     if (mumps->id.ICNTL(6)>1) {
1029: #if defined(PETSC_USE_COMPLEX)
1030: #if defined(PETSC_USE_REAL_SINGLE)
1031:       mumps->id.a_loc = (mumps_complex*)mumps->val;
1032: #else
1033:       mumps->id.a_loc = (mumps_double_complex*)mumps->val;
1034: #endif
1035: #else
1036:       mumps->id.a_loc = mumps->val;
1037: #endif
1038:     }
1039:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
1040:     if (!mumps->myid) {
1041:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
1042:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
1043:     } else {
1044:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
1045:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
1046:     }
1047:     MatGetVecs(A,NULL,&b);
1048:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
1049:     ISDestroy(&is_iden);
1050:     VecDestroy(&b);
1051:     break;
1052:   }
1053:   PetscMUMPS_c(&mumps->id);
1054:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));

1056:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
1057:   F->ops->solve           = MatSolve_MUMPS;
1058:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
1059:   return(0);
1060: }

1062: /* Note the Petsc r permutation and factor info are ignored */
1065: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info)
1066: {
1067:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
1069:   Vec            b;
1070:   IS             is_iden;
1071:   const PetscInt M = A->rmap->N;

1074:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

1076:   /* Set MUMPS options from the options database */
1077:   PetscSetMUMPSFromOptions(F,A);

1079:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1081:   /* analysis phase */
1082:   /*----------------*/
1083:   mumps->id.job = JOB_FACTSYMBOLIC;
1084:   mumps->id.n   = M;
1085:   switch (mumps->id.ICNTL(18)) {
1086:   case 0:  /* centralized assembled matrix input */
1087:     if (!mumps->myid) {
1088:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
1089:       if (mumps->id.ICNTL(6)>1) {
1090: #if defined(PETSC_USE_COMPLEX)
1091: #if defined(PETSC_USE_REAL_SINGLE)
1092:         mumps->id.a = (mumps_complex*)mumps->val;
1093: #else
1094:         mumps->id.a = (mumps_double_complex*)mumps->val;
1095: #endif
1096: #else
1097:         mumps->id.a = mumps->val;
1098: #endif
1099:       }
1100:     }
1101:     break;
1102:   case 3:  /* distributed assembled matrix input (size>1) */
1103:     mumps->id.nz_loc = mumps->nz;
1104:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
1105:     if (mumps->id.ICNTL(6)>1) {
1106: #if defined(PETSC_USE_COMPLEX)
1107: #if defined(PETSC_USE_REAL_SINGLE)
1108:       mumps->id.a_loc = (mumps_complex*)mumps->val;
1109: #else
1110:       mumps->id.a_loc = (mumps_double_complex*)mumps->val;
1111: #endif
1112: #else
1113:       mumps->id.a_loc = mumps->val;
1114: #endif
1115:     }
1116:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
1117:     if (!mumps->myid) {
1118:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
1119:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
1120:     } else {
1121:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
1122:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
1123:     }
1124:     MatGetVecs(A,NULL,&b);
1125:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
1126:     ISDestroy(&is_iden);
1127:     VecDestroy(&b);
1128:     break;
1129:   }
1130:   PetscMUMPS_c(&mumps->id);
1131:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));

1133:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
1134:   F->ops->solve                 = MatSolve_MUMPS;
1135:   F->ops->solvetranspose        = MatSolve_MUMPS;
1136:   F->ops->matsolve              = 0; /* use MatMatSolve_Basic() until mumps supports distributed rhs */
1137: #if !defined(PETSC_USE_COMPLEX)
1138:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
1139: #else
1140:   F->ops->getinertia = NULL;
1141: #endif
1142:   return(0);
1143: }

1147: PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer)
1148: {
1149:   PetscErrorCode    ierr;
1150:   PetscBool         iascii;
1151:   PetscViewerFormat format;
1152:   Mat_MUMPS         *mumps=(Mat_MUMPS*)A->spptr;

1155:   /* check if matrix is mumps type */
1156:   if (A->ops->solve != MatSolve_MUMPS) return(0);

1158:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1159:   if (iascii) {
1160:     PetscViewerGetFormat(viewer,&format);
1161:     if (format == PETSC_VIEWER_ASCII_INFO) {
1162:       PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");
1163:       PetscViewerASCIIPrintf(viewer,"  SYM (matrix type):                   %d \n",mumps->id.sym);
1164:       PetscViewerASCIIPrintf(viewer,"  PAR (host participation):            %d \n",mumps->id.par);
1165:       PetscViewerASCIIPrintf(viewer,"  ICNTL(1) (output for error):         %d \n",mumps->id.ICNTL(1));
1166:       PetscViewerASCIIPrintf(viewer,"  ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));
1167:       PetscViewerASCIIPrintf(viewer,"  ICNTL(3) (output for global info):   %d \n",mumps->id.ICNTL(3));
1168:       PetscViewerASCIIPrintf(viewer,"  ICNTL(4) (level of printing):        %d \n",mumps->id.ICNTL(4));
1169:       PetscViewerASCIIPrintf(viewer,"  ICNTL(5) (input mat struct):         %d \n",mumps->id.ICNTL(5));
1170:       PetscViewerASCIIPrintf(viewer,"  ICNTL(6) (matrix prescaling):        %d \n",mumps->id.ICNTL(6));
1171:       PetscViewerASCIIPrintf(viewer,"  ICNTL(7) (sequentia matrix ordering):%d \n",mumps->id.ICNTL(7));
1172:       PetscViewerASCIIPrintf(viewer,"  ICNTL(8) (scalling strategy):        %d \n",mumps->id.ICNTL(8));
1173:       PetscViewerASCIIPrintf(viewer,"  ICNTL(10) (max num of refinements):  %d \n",mumps->id.ICNTL(10));
1174:       PetscViewerASCIIPrintf(viewer,"  ICNTL(11) (error analysis):          %d \n",mumps->id.ICNTL(11));
1175:       if (mumps->id.ICNTL(11)>0) {
1176:         PetscViewerASCIIPrintf(viewer,"    RINFOG(4) (inf norm of input mat):        %g\n",mumps->id.RINFOG(4));
1177:         PetscViewerASCIIPrintf(viewer,"    RINFOG(5) (inf norm of solution):         %g\n",mumps->id.RINFOG(5));
1178:         PetscViewerASCIIPrintf(viewer,"    RINFOG(6) (inf norm of residual):         %g\n",mumps->id.RINFOG(6));
1179:         PetscViewerASCIIPrintf(viewer,"    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));
1180:         PetscViewerASCIIPrintf(viewer,"    RINFOG(9) (error estimate):               %g \n",mumps->id.RINFOG(9));
1181:         PetscViewerASCIIPrintf(viewer,"    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));
1182:       }
1183:       PetscViewerASCIIPrintf(viewer,"  ICNTL(12) (efficiency control):                         %d \n",mumps->id.ICNTL(12));
1184:       PetscViewerASCIIPrintf(viewer,"  ICNTL(13) (efficiency control):                         %d \n",mumps->id.ICNTL(13));
1185:       PetscViewerASCIIPrintf(viewer,"  ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));
1186:       /* ICNTL(15-17) not used */
1187:       PetscViewerASCIIPrintf(viewer,"  ICNTL(18) (input mat struct):                           %d \n",mumps->id.ICNTL(18));
1188:       PetscViewerASCIIPrintf(viewer,"  ICNTL(19) (Shur complement info):                       %d \n",mumps->id.ICNTL(19));
1189:       PetscViewerASCIIPrintf(viewer,"  ICNTL(20) (rhs sparse pattern):                         %d \n",mumps->id.ICNTL(20));
1190:       PetscViewerASCIIPrintf(viewer,"  ICNTL(21) (somumpstion struct):                            %d \n",mumps->id.ICNTL(21));
1191:       PetscViewerASCIIPrintf(viewer,"  ICNTL(22) (in-core/out-of-core facility):               %d \n",mumps->id.ICNTL(22));
1192:       PetscViewerASCIIPrintf(viewer,"  ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));

1194:       PetscViewerASCIIPrintf(viewer,"  ICNTL(24) (detection of null pivot rows):               %d \n",mumps->id.ICNTL(24));
1195:       PetscViewerASCIIPrintf(viewer,"  ICNTL(25) (computation of a null space basis):          %d \n",mumps->id.ICNTL(25));
1196:       PetscViewerASCIIPrintf(viewer,"  ICNTL(26) (Schur options for rhs or solution):          %d \n",mumps->id.ICNTL(26));
1197:       PetscViewerASCIIPrintf(viewer,"  ICNTL(27) (experimental parameter):                     %d \n",mumps->id.ICNTL(27));
1198:       PetscViewerASCIIPrintf(viewer,"  ICNTL(28) (use parallel or sequential ordering):        %d \n",mumps->id.ICNTL(28));
1199:       PetscViewerASCIIPrintf(viewer,"  ICNTL(29) (parallel ordering):                          %d \n",mumps->id.ICNTL(29));

1201:       PetscViewerASCIIPrintf(viewer,"  ICNTL(30) (user-specified set of entries in inv(A)):    %d \n",mumps->id.ICNTL(30));
1202:       PetscViewerASCIIPrintf(viewer,"  ICNTL(31) (factors is discarded in the solve phase):    %d \n",mumps->id.ICNTL(31));
1203:       PetscViewerASCIIPrintf(viewer,"  ICNTL(33) (compute determinant):                        %d \n",mumps->id.ICNTL(33));

1205:       PetscViewerASCIIPrintf(viewer,"  CNTL(1) (relative pivoting threshold):      %g \n",mumps->id.CNTL(1));
1206:       PetscViewerASCIIPrintf(viewer,"  CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));
1207:       PetscViewerASCIIPrintf(viewer,"  CNTL(3) (absomumpste pivoting threshold):      %g \n",mumps->id.CNTL(3));
1208:       PetscViewerASCIIPrintf(viewer,"  CNTL(4) (vamumpse of static pivoting):         %g \n",mumps->id.CNTL(4));
1209:       PetscViewerASCIIPrintf(viewer,"  CNTL(5) (fixation for null pivots):         %g \n",mumps->id.CNTL(5));

1211:       /* infomation local to each processor */
1212:       PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis): \n");
1213:       PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
1214:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %g \n",mumps->myid,mumps->id.RINFO(1));
1215:       PetscViewerFlush(viewer);
1216:       PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization): \n");
1217:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(2));
1218:       PetscViewerFlush(viewer);
1219:       PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization): \n");
1220:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(3));
1221:       PetscViewerFlush(viewer);

1223:       PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");
1224:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] %d \n",mumps->myid,mumps->id.INFO(15));
1225:       PetscViewerFlush(viewer);

1227:       PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");
1228:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(16));
1229:       PetscViewerFlush(viewer);

1231:       PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization): \n");
1232:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(23));
1233:       PetscViewerFlush(viewer);
1234:       PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);

1236:       if (!mumps->myid) { /* information from the host */
1237:         PetscViewerASCIIPrintf(viewer,"  RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));
1238:         PetscViewerASCIIPrintf(viewer,"  RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));
1239:         PetscViewerASCIIPrintf(viewer,"  RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));
1240:         PetscViewerASCIIPrintf(viewer,"  (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (%g,%g)*(2^%d)\n",mumps->id.RINFOG(12),mumps->id.RINFOG(13),mumps->id.INFOG(34));

1242:         PetscViewerASCIIPrintf(viewer,"  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));
1243:         PetscViewerASCIIPrintf(viewer,"  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));
1244:         PetscViewerASCIIPrintf(viewer,"  INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));
1245:         PetscViewerASCIIPrintf(viewer,"  INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));
1246:         PetscViewerASCIIPrintf(viewer,"  INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));
1247:         PetscViewerASCIIPrintf(viewer,"  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));
1248:         PetscViewerASCIIPrintf(viewer,"  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));
1249:         PetscViewerASCIIPrintf(viewer,"  INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));
1250:         PetscViewerASCIIPrintf(viewer,"  INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));
1251:         PetscViewerASCIIPrintf(viewer,"  INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));
1252:         PetscViewerASCIIPrintf(viewer,"  INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));
1253:         PetscViewerASCIIPrintf(viewer,"  INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));
1254:         PetscViewerASCIIPrintf(viewer,"  INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));
1255:         PetscViewerASCIIPrintf(viewer,"  INFOG(16) (estimated size (in MB) of all MUMPS internal data for factorization after analysis: value on the most memory consuming processor): %d \n",mumps->id.INFOG(16));
1256:         PetscViewerASCIIPrintf(viewer,"  INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d \n",mumps->id.INFOG(17));
1257:         PetscViewerASCIIPrintf(viewer,"  INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d \n",mumps->id.INFOG(18));
1258:         PetscViewerASCIIPrintf(viewer,"  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",mumps->id.INFOG(19));
1259:         PetscViewerASCIIPrintf(viewer,"  INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));
1260:         PetscViewerASCIIPrintf(viewer,"  INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d \n",mumps->id.INFOG(21));
1261:         PetscViewerASCIIPrintf(viewer,"  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",mumps->id.INFOG(22));
1262:         PetscViewerASCIIPrintf(viewer,"  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));
1263:         PetscViewerASCIIPrintf(viewer,"  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));
1264:         PetscViewerASCIIPrintf(viewer,"  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));
1265:         PetscViewerASCIIPrintf(viewer,"  INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));
1266:         PetscViewerASCIIPrintf(viewer,"  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29));
1267:         PetscViewerASCIIPrintf(viewer,"  INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): %d, %d\n",mumps->id.INFOG(30),mumps->id.INFOG(31));
1268:         PetscViewerASCIIPrintf(viewer,"  INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));
1269:         PetscViewerASCIIPrintf(viewer,"  INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));
1270:         PetscViewerASCIIPrintf(viewer,"  INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));
1271:       }
1272:     }
1273:   }
1274:   return(0);
1275: }

1279: PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info)
1280: {
1281:   Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr;

1284:   info->block_size        = 1.0;
1285:   info->nz_allocated      = mumps->id.INFOG(20);
1286:   info->nz_used           = mumps->id.INFOG(20);
1287:   info->nz_unneeded       = 0.0;
1288:   info->assemblies        = 0.0;
1289:   info->mallocs           = 0.0;
1290:   info->memory            = 0.0;
1291:   info->fill_ratio_given  = 0;
1292:   info->fill_ratio_needed = 0;
1293:   info->factor_mallocs    = 0;
1294:   return(0);
1295: }

1297: /* -------------------------------------------------------------------------------------------*/
1300: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival)
1301: {
1302:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1305:   mumps->id.ICNTL(icntl) = ival;
1306:   return(0);
1307: }

1311: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival)
1312: {
1313:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1316:   *ival = mumps->id.ICNTL(icntl);
1317:   return(0);
1318: }

1322: /*@
1323:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

1325:    Logically Collective on Mat

1327:    Input Parameters:
1328: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1329: .  icntl - index of MUMPS parameter array ICNTL()
1330: -  ival - value of MUMPS ICNTL(icntl)

1332:   Options Database:
1333: .   -mat_mumps_icntl_<icntl> <ival>

1335:    Level: beginner

1337:    References: MUMPS Users' Guide

1339: .seealso: MatGetFactor()
1340: @*/
1341: PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival)
1342: {

1348:   PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
1349:   return(0);
1350: }

1354: /*@
1355:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

1357:    Logically Collective on Mat

1359:    Input Parameters:
1360: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1361: -  icntl - index of MUMPS parameter array ICNTL()

1363:   Output Parameter:
1364: .  ival - value of MUMPS ICNTL(icntl)

1366:    Level: beginner

1368:    References: MUMPS Users' Guide

1370: .seealso: MatGetFactor()
1371: @*/
1372: PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival)
1373: {

1379:   PetscTryMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
1380:   return(0);
1381: }

1383: /* -------------------------------------------------------------------------------------------*/
1386: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val)
1387: {
1388:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1391:   mumps->id.CNTL(icntl) = val;
1392:   return(0);
1393: }

1397: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val)
1398: {
1399:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1402:   *val = mumps->id.CNTL(icntl);
1403:   return(0);
1404: }

1408: /*@
1409:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

1411:    Logically Collective on Mat

1413:    Input Parameters:
1414: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1415: .  icntl - index of MUMPS parameter array CNTL()
1416: -  val - value of MUMPS CNTL(icntl)

1418:   Options Database:
1419: .   -mat_mumps_cntl_<icntl> <val>

1421:    Level: beginner

1423:    References: MUMPS Users' Guide

1425: .seealso: MatGetFactor()
1426: @*/
1427: PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val)
1428: {

1434:   PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));
1435:   return(0);
1436: }

1440: /*@
1441:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

1443:    Logically Collective on Mat

1445:    Input Parameters:
1446: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1447: -  icntl - index of MUMPS parameter array CNTL()

1449:   Output Parameter:
1450: .  val - value of MUMPS CNTL(icntl)

1452:    Level: beginner

1454:    References: MUMPS Users' Guide

1456: .seealso: MatGetFactor()
1457: @*/
1458: PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val)
1459: {

1465:   PetscTryMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
1466:   return(0);
1467: }

1471: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info)
1472: {
1473:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1476:   *info = mumps->id.INFO(icntl);
1477:   return(0);
1478: }

1482: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog)
1483: {
1484:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1487:   *infog = mumps->id.INFOG(icntl);
1488:   return(0);
1489: }

1493: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo)
1494: {
1495:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1498:   *rinfo = mumps->id.RINFO(icntl);
1499:   return(0);
1500: }

1504: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog)
1505: {
1506:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

1509:   *rinfog = mumps->id.RINFOG(icntl);
1510:   return(0);
1511: }

1515: /*@
1516:   MatMumpsGetInfo - Get MUMPS parameter INFO()

1518:    Logically Collective on Mat

1520:    Input Parameters:
1521: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1522: -  icntl - index of MUMPS parameter array INFO()

1524:   Output Parameter:
1525: .  ival - value of MUMPS INFO(icntl)

1527:    Level: beginner

1529:    References: MUMPS Users' Guide

1531: .seealso: MatGetFactor()
1532: @*/
1533: PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival)
1534: {

1539:   PetscTryMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
1540:   return(0);
1541: }

1545: /*@
1546:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

1548:    Logically Collective on Mat

1550:    Input Parameters:
1551: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1552: -  icntl - index of MUMPS parameter array INFOG()

1554:   Output Parameter:
1555: .  ival - value of MUMPS INFOG(icntl)

1557:    Level: beginner

1559:    References: MUMPS Users' Guide

1561: .seealso: MatGetFactor()
1562: @*/
1563: PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival)
1564: {

1569:   PetscTryMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
1570:   return(0);
1571: }

1575: /*@
1576:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

1578:    Logically Collective on Mat

1580:    Input Parameters:
1581: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1582: -  icntl - index of MUMPS parameter array RINFO()

1584:   Output Parameter:
1585: .  val - value of MUMPS RINFO(icntl)

1587:    Level: beginner

1589:    References: MUMPS Users' Guide

1591: .seealso: MatGetFactor()
1592: @*/
1593: PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val)
1594: {

1599:   PetscTryMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
1600:   return(0);
1601: }

1605: /*@
1606:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

1608:    Logically Collective on Mat

1610:    Input Parameters:
1611: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
1612: -  icntl - index of MUMPS parameter array RINFOG()

1614:   Output Parameter:
1615: .  val - value of MUMPS RINFOG(icntl)

1617:    Level: beginner

1619:    References: MUMPS Users' Guide

1621: .seealso: MatGetFactor()
1622: @*/
1623: PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val)
1624: {

1629:   PetscTryMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
1630:   return(0);
1631: }

1633: /*MC
1634:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
1635:   distributed and sequential matrices via the external package MUMPS.

1637:   Works with MATAIJ and MATSBAIJ matrices

1639:   Options Database Keys:
1640: + -mat_mumps_icntl_4 <0,...,4> - print level
1641: . -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide)
1642: . -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guidec)
1643: . -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T
1644: . -mat_mumps_icntl_10 <n> - maximum number of iterative refinements
1645: . -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view
1646: . -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide)
1647: . -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide)
1648: . -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide)
1649: . -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide)
1650: . -mat_mumps_cntl_1 <delta> - relative pivoting threshold
1651: . -mat_mumps_cntl_2 <tol> - stopping criterion for refinement
1652: - -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold

1654:   Level: beginner

1656: .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage

1658: M*/

1662: static PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type)
1663: {
1665:   *type = MATSOLVERMUMPS;
1666:   return(0);
1667: }

1669: /* MatGetFactor for Seq and MPI AIJ matrices */
1672: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F)
1673: {
1674:   Mat            B;
1676:   Mat_MUMPS      *mumps;
1677:   PetscBool      isSeqAIJ;

1680:   /* Create the factorization matrix */
1681:   PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
1682:   MatCreate(PetscObjectComm((PetscObject)A),&B);
1683:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1684:   MatSetType(B,((PetscObject)A)->type_name);
1685:   if (isSeqAIJ) {
1686:     MatSeqAIJSetPreallocation(B,0,NULL);
1687:   } else {
1688:     MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
1689:   }

1691:   PetscNewLog(B,&mumps);

1693:   B->ops->view    = MatView_MUMPS;
1694:   B->ops->getinfo = MatGetInfo_MUMPS;

1696:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
1697:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
1698:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
1699:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
1700:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);

1702:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
1703:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
1704:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
1705:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);
1706:   if (ftype == MAT_FACTOR_LU) {
1707:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
1708:     B->factortype            = MAT_FACTOR_LU;
1709:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
1710:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
1711:     mumps->sym = 0;
1712:   } else {
1713:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
1714:     B->factortype                  = MAT_FACTOR_CHOLESKY;
1715:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
1716:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
1717:     if (A->spd_set && A->spd) mumps->sym = 1;
1718:     else                      mumps->sym = 2;
1719:   }

1721:   mumps->isAIJ    = PETSC_TRUE;
1722:   mumps->Destroy  = B->ops->destroy;
1723:   B->ops->destroy = MatDestroy_MUMPS;
1724:   B->spptr        = (void*)mumps;

1726:   PetscInitializeMUMPS(A,mumps);

1728:   *F = B;
1729:   return(0);
1730: }

1732: /* MatGetFactor for Seq and MPI SBAIJ matrices */
1735: PETSC_EXTERN PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F)
1736: {
1737:   Mat            B;
1739:   Mat_MUMPS      *mumps;
1740:   PetscBool      isSeqSBAIJ;

1743:   if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix");
1744:   if (A->rmap->bs > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with block size > 1 with MUMPS Cholesky, use AIJ matrix instead");
1745:   PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
1746:   /* Create the factorization matrix */
1747:   MatCreate(PetscObjectComm((PetscObject)A),&B);
1748:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1749:   MatSetType(B,((PetscObject)A)->type_name);
1750:   PetscNewLog(B,&mumps);
1751:   if (isSeqSBAIJ) {
1752:     MatSeqSBAIJSetPreallocation(B,1,0,NULL);

1754:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
1755:   } else {
1756:     MatMPISBAIJSetPreallocation(B,1,0,NULL,0,NULL);

1758:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
1759:   }

1761:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
1762:   B->ops->view                   = MatView_MUMPS;

1764:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
1765:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
1766:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
1767:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
1768:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);

1770:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
1771:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
1772:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
1773:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

1775:   B->factortype = MAT_FACTOR_CHOLESKY;
1776:   if (A->spd_set && A->spd) mumps->sym = 1;
1777:   else                      mumps->sym = 2;

1779:   mumps->isAIJ    = PETSC_FALSE;
1780:   mumps->Destroy  = B->ops->destroy;
1781:   B->ops->destroy = MatDestroy_MUMPS;
1782:   B->spptr        = (void*)mumps;

1784:   PetscInitializeMUMPS(A,mumps);

1786:   *F = B;
1787:   return(0);
1788: }

1792: PETSC_EXTERN PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F)
1793: {
1794:   Mat            B;
1796:   Mat_MUMPS      *mumps;
1797:   PetscBool      isSeqBAIJ;

1800:   /* Create the factorization matrix */
1801:   PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
1802:   MatCreate(PetscObjectComm((PetscObject)A),&B);
1803:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
1804:   MatSetType(B,((PetscObject)A)->type_name);
1805:   if (isSeqBAIJ) {
1806:     MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,NULL);
1807:   } else {
1808:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,NULL,0,NULL);
1809:   }

1811:   PetscNewLog(B,&mumps);
1812:   if (ftype == MAT_FACTOR_LU) {
1813:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
1814:     B->factortype            = MAT_FACTOR_LU;
1815:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
1816:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
1817:     mumps->sym = 0;
1818:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n");

1820:   B->ops->view = MatView_MUMPS;

1822:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
1823:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
1824:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
1825:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
1826:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);

1828:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
1829:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
1830:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
1831:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

1833:   mumps->isAIJ    = PETSC_TRUE;
1834:   mumps->Destroy  = B->ops->destroy;
1835:   B->ops->destroy = MatDestroy_MUMPS;
1836:   B->spptr        = (void*)mumps;

1838:   PetscInitializeMUMPS(A,mumps);

1840:   *F = B;
1841:   return(0);
1842: }