Actual source code: mumps.c

petsc-3.7.6 2017-04-24
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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>
  8: #include <petscblaslapack.h>

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

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

 46: /* declare MumpsScalar */
 47: #if defined(PETSC_USE_COMPLEX)
 48: #if defined(PETSC_USE_REAL_SINGLE)
 49: #define MumpsScalar mumps_complex
 50: #else
 51: #define MumpsScalar mumps_double_complex
 52: #endif
 53: #else
 54: #define MumpsScalar PetscScalar
 55: #endif

 57: /* macros s.t. indices match MUMPS documentation */
 58: #define ICNTL(I) icntl[(I)-1]
 59: #define CNTL(I) cntl[(I)-1]
 60: #define INFOG(I) infog[(I)-1]
 61: #define INFO(I) info[(I)-1]
 62: #define RINFOG(I) rinfog[(I)-1]
 63: #define RINFO(I) rinfo[(I)-1]

 65: typedef struct {
 66: #if defined(PETSC_USE_COMPLEX)
 67: #if defined(PETSC_USE_REAL_SINGLE)
 68:   CMUMPS_STRUC_C id;
 69: #else
 70:   ZMUMPS_STRUC_C id;
 71: #endif
 72: #else
 73: #if defined(PETSC_USE_REAL_SINGLE)
 74:   SMUMPS_STRUC_C id;
 75: #else
 76:   DMUMPS_STRUC_C id;
 77: #endif
 78: #endif

 80:   MatStructure matstruc;
 81:   PetscMPIInt  myid,size;
 82:   PetscInt     *irn,*jcn,nz,sym;
 83:   PetscScalar  *val;
 84:   MPI_Comm     comm_mumps;
 85:   PetscBool    isAIJ;
 86:   PetscInt     ICNTL9_pre;           /* check if ICNTL(9) is changed from previous MatSolve */
 87:   VecScatter   scat_rhs, scat_sol;   /* used by MatSolve() */
 88:   Vec          b_seq,x_seq;
 89:   PetscInt     ninfo,*info;          /* display INFO */
 90:   PetscInt     sizeredrhs;
 91:   PetscInt     *schur_pivots;
 92:   PetscInt     schur_B_lwork;
 93:   PetscScalar  *schur_work;
 94:   PetscScalar  *schur_sol;
 95:   PetscInt     schur_sizesol;
 96:   PetscBool    schur_factored;
 97:   PetscBool    schur_inverted;

 99:   PetscErrorCode (*Destroy)(Mat);
100:   PetscErrorCode (*ConvertToTriples)(Mat, int, MatReuse, int*, int**, int**, PetscScalar**);
101: } Mat_MUMPS;

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

107: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps)
108: {

112:   PetscFree2(mumps->id.listvar_schur,mumps->id.schur);
113:   PetscFree(mumps->id.redrhs);
114:   PetscFree(mumps->schur_sol);
115:   PetscFree(mumps->schur_pivots);
116:   PetscFree(mumps->schur_work);
117:   mumps->id.size_schur = 0;
118:   mumps->id.ICNTL(19) = 0;
119:   return(0);
120: }

124: static PetscErrorCode MatMumpsFactorSchur_Private(Mat_MUMPS* mumps)
125: {
126:   PetscBLASInt   B_N,B_ierr,B_slda;

130:   if (mumps->schur_factored) {
131:     return(0);
132:   }
133:   PetscBLASIntCast(mumps->id.size_schur,&B_N);
134:   PetscBLASIntCast(mumps->id.schur_lld,&B_slda);
135:   if (!mumps->sym) { /* MUMPS always return a full Schur matrix */
136:     if (!mumps->schur_pivots) {
137:       PetscMalloc1(B_N,&mumps->schur_pivots);
138:     }
139:     PetscFPTrapPush(PETSC_FP_TRAP_OFF);
140:     PetscStackCallBLAS("LAPACKgetrf",LAPACKgetrf_(&B_N,&B_N,(PetscScalar*)mumps->id.schur,&B_slda,mumps->schur_pivots,&B_ierr));
141:     PetscFPTrapPop();
142:     if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in GETRF Lapack routine %d",(int)B_ierr);
143:   } else { /* either full or lower-triangular (not packed) */
144:     char ord[2];
145:     if (mumps->id.ICNTL(19) == 2 || mumps->id.ICNTL(19) == 3) { /* lower triangular stored by columns or full matrix */
146:       sprintf(ord,"L");
147:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
148:       sprintf(ord,"U");
149:     }
150:     if (mumps->id.sym == 2) {
151:       if (!mumps->schur_pivots) {
152:         PetscScalar  lwork;

154:         PetscMalloc1(B_N,&mumps->schur_pivots);
155:         mumps->schur_B_lwork=-1;
156:         PetscFPTrapPush(PETSC_FP_TRAP_OFF);
157:         PetscStackCallBLAS("LAPACKsytrf",LAPACKsytrf_(ord,&B_N,(PetscScalar*)mumps->id.schur,&B_slda,mumps->schur_pivots,&lwork,&mumps->schur_B_lwork,&B_ierr));
158:         PetscFPTrapPop();
159:         if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in query to SYTRF Lapack routine %d",(int)B_ierr);
160:         PetscBLASIntCast((PetscInt)PetscRealPart(lwork),&mumps->schur_B_lwork);
161:         PetscMalloc1(mumps->schur_B_lwork,&mumps->schur_work);
162:       }
163:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
164:       PetscStackCallBLAS("LAPACKsytrf",LAPACKsytrf_(ord,&B_N,(PetscScalar*)mumps->id.schur,&B_slda,mumps->schur_pivots,mumps->schur_work,&mumps->schur_B_lwork,&B_ierr));
165:       PetscFPTrapPop();
166:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in SYTRF Lapack routine %d",(int)B_ierr);
167:     } else {
168:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
169:       PetscStackCallBLAS("LAPACKpotrf",LAPACKpotrf_(ord,&B_N,(PetscScalar*)mumps->id.schur,&B_slda,&B_ierr));
170:       PetscFPTrapPop();
171:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in POTRF Lapack routine %d",(int)B_ierr);
172:     }
173:   }
174:   mumps->schur_factored = PETSC_TRUE;
175:   return(0);
176: }

180: static PetscErrorCode MatMumpsInvertSchur_Private(Mat_MUMPS* mumps)
181: {
182:   PetscBLASInt   B_N,B_ierr,B_slda;

186:   MatMumpsFactorSchur_Private(mumps);
187:   PetscBLASIntCast(mumps->id.size_schur,&B_N);
188:   PetscBLASIntCast(mumps->id.schur_lld,&B_slda);
189:   if (!mumps->sym) { /* MUMPS always return a full Schur matrix */
190:     if (!mumps->schur_work) {
191:       PetscScalar lwork;

193:       mumps->schur_B_lwork = -1;
194:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
195:       PetscStackCallBLAS("LAPACKgetri",LAPACKgetri_(&B_N,(PetscScalar*)mumps->id.schur,&B_N,mumps->schur_pivots,&lwork,&mumps->schur_B_lwork,&B_ierr));
196:       PetscFPTrapPop();
197:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in query to GETRI Lapack routine %d",(int)B_ierr);
198:       PetscBLASIntCast((PetscInt)PetscRealPart(lwork),&mumps->schur_B_lwork);
199:       PetscMalloc1(mumps->schur_B_lwork,&mumps->schur_work);
200:     }
201:     PetscFPTrapPush(PETSC_FP_TRAP_OFF);
202:     PetscStackCallBLAS("LAPACKgetri",LAPACKgetri_(&B_N,(PetscScalar*)mumps->id.schur,&B_N,mumps->schur_pivots,mumps->schur_work,&mumps->schur_B_lwork,&B_ierr));
203:     PetscFPTrapPop();
204:     if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in GETRI Lapack routine %d",(int)B_ierr);
205:   } else { /* either full or lower-triangular (not packed) */
206:     char ord[2];
207:     if (mumps->id.ICNTL(19) == 2 || mumps->id.ICNTL(19) == 3) { /* lower triangular stored by columns or full matrix */
208:       sprintf(ord,"L");
209:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
210:       sprintf(ord,"U");
211:     }
212:     if (mumps->id.sym == 2) {
213:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
214:       PetscStackCallBLAS("LAPACKsytri",LAPACKsytri_(ord,&B_N,(PetscScalar*)mumps->id.schur,&B_N,mumps->schur_pivots,mumps->schur_work,&B_ierr));
215:       PetscFPTrapPop();
216:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in SYTRI Lapack routine %d",(int)B_ierr);
217:     } else {
218:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
219:       PetscStackCallBLAS("LAPACKpotri",LAPACKpotri_(ord,&B_N,(PetscScalar*)mumps->id.schur,&B_N,&B_ierr));
220:       PetscFPTrapPop();
221:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in POTRI Lapack routine %d",(int)B_ierr);
222:     }
223:   }
224:   mumps->schur_inverted = PETSC_TRUE;
225:   return(0);
226: }

230: static PetscErrorCode MatMumpsSolveSchur_Private(Mat_MUMPS* mumps, PetscBool sol_in_redrhs)
231: {
232:   PetscBLASInt   B_N,B_Nrhs,B_ierr,B_slda,B_rlda;
233:   PetscScalar    one=1.,zero=0.;

237:   MatMumpsFactorSchur_Private(mumps);
238:   PetscBLASIntCast(mumps->id.size_schur,&B_N);
239:   PetscBLASIntCast(mumps->id.schur_lld,&B_slda);
240:   PetscBLASIntCast(mumps->id.nrhs,&B_Nrhs);
241:   PetscBLASIntCast(mumps->id.lredrhs,&B_rlda);
242:   if (mumps->schur_inverted) {
243:     PetscInt sizesol = B_Nrhs*B_N;
244:     if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
245:       PetscFree(mumps->schur_sol);
246:       PetscMalloc1(sizesol,&mumps->schur_sol);
247:       mumps->schur_sizesol = sizesol;
248:     }
249:     if (!mumps->sym) {
250:       char type[2];
251:       if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
252:         if (!mumps->id.ICNTL(9)) { /* transpose solve */
253:           sprintf(type,"N");
254:         } else {
255:           sprintf(type,"T");
256:         }
257:       } else { /* stored by columns */
258:         if (!mumps->id.ICNTL(9)) { /* transpose solve */
259:           sprintf(type,"T");
260:         } else {
261:           sprintf(type,"N");
262:         }
263:       }
264:       PetscStackCallBLAS("BLASgemm",BLASgemm_(type,"N",&B_N,&B_Nrhs,&B_N,&one,(PetscScalar*)mumps->id.schur,&B_slda,(PetscScalar*)mumps->id.redrhs,&B_rlda,&zero,mumps->schur_sol,&B_rlda));
265:     } else {
266:       char ord[2];
267:       if (mumps->id.ICNTL(19) == 2 || mumps->id.ICNTL(19) == 3) { /* lower triangular stored by columns or full matrix */
268:         sprintf(ord,"L");
269:       } else { /* ICNTL(19) == 1 lower triangular stored by rows */
270:         sprintf(ord,"U");
271:       }
272:       PetscStackCallBLAS("BLASsymm",BLASsymm_("L",ord,&B_N,&B_Nrhs,&one,(PetscScalar*)mumps->id.schur,&B_slda,(PetscScalar*)mumps->id.redrhs,&B_rlda,&zero,mumps->schur_sol,&B_rlda));
273:     }
274:     if (sol_in_redrhs) {
275:       PetscMemcpy(mumps->id.redrhs,mumps->schur_sol,sizesol*sizeof(PetscScalar));
276:     }
277:   } else { /* Schur complement has not been inverted */
278:     MumpsScalar *orhs=NULL;

280:     if (!sol_in_redrhs) {
281:       PetscInt sizesol = B_Nrhs*B_N;
282:       if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
283:         PetscFree(mumps->schur_sol);
284:         PetscMalloc1(sizesol,&mumps->schur_sol);
285:         mumps->schur_sizesol = sizesol;
286:       }
287:       orhs = mumps->id.redrhs;
288:       PetscMemcpy(mumps->schur_sol,mumps->id.redrhs,sizesol*sizeof(PetscScalar));
289:       mumps->id.redrhs = (MumpsScalar*)mumps->schur_sol;
290:     }
291:     if (!mumps->sym) { /* MUMPS always return a full Schur matrix */
292:       char type[2];
293:       if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
294:         if (!mumps->id.ICNTL(9)) { /* transpose solve */
295:           sprintf(type,"N");
296:         } else {
297:           sprintf(type,"T");
298:         }
299:       } else { /* stored by columns */
300:         if (!mumps->id.ICNTL(9)) { /* transpose solve */
301:           sprintf(type,"T");
302:         } else {
303:           sprintf(type,"N");
304:         }
305:       }
306:       PetscFPTrapPush(PETSC_FP_TRAP_OFF);
307:       PetscStackCallBLAS("LAPACKgetrs",LAPACKgetrs_(type,&B_N,&B_Nrhs,(PetscScalar*)mumps->id.schur,&B_slda,mumps->schur_pivots,(PetscScalar*)mumps->id.redrhs,&B_rlda,&B_ierr));
308:       PetscFPTrapPop();
309:       if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in GETRS Lapack routine %d",(int)B_ierr);
310:     } else { /* either full or lower-triangular (not packed) */
311:       char ord[2];
312:       if (mumps->id.ICNTL(19) == 2 || mumps->id.ICNTL(19) == 3) { /* lower triangular stored by columns or full matrix */
313:         sprintf(ord,"L");
314:       } else { /* ICNTL(19) == 1 lower triangular stored by rows */
315:         sprintf(ord,"U");
316:       }
317:       if (mumps->id.sym == 2) {
318:         PetscFPTrapPush(PETSC_FP_TRAP_OFF);
319:         PetscStackCallBLAS("LAPACKsytrs",LAPACKsytrs_(ord,&B_N,&B_Nrhs,(PetscScalar*)mumps->id.schur,&B_slda,mumps->schur_pivots,(PetscScalar*)mumps->id.redrhs,&B_rlda,&B_ierr));
320:         PetscFPTrapPop();
321:         if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in SYTRS Lapack routine %d",(int)B_ierr);
322:       } else {
323:         PetscFPTrapPush(PETSC_FP_TRAP_OFF);
324:         PetscStackCallBLAS("LAPACKpotrs",LAPACKpotrs_(ord,&B_N,&B_Nrhs,(PetscScalar*)mumps->id.schur,&B_slda,(PetscScalar*)mumps->id.redrhs,&B_rlda,&B_ierr));
325:         PetscFPTrapPop();
326:         if (B_ierr) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in POTRS Lapack routine %d",(int)B_ierr);
327:       }
328:     }
329:     if (!sol_in_redrhs) {
330:       mumps->id.redrhs = orhs;
331:     }
332:   }
333:   return(0);
334: }

338: static PetscErrorCode MatMumpsHandleSchur_Private(Mat_MUMPS* mumps, PetscBool expansion)
339: {

343:   if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
344:     return(0);
345:   }
346:   if (!expansion) { /* prepare for the condensation step */
347:     PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur;
348:     /* allocate MUMPS internal array to store reduced right-hand sides */
349:     if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
350:       PetscFree(mumps->id.redrhs);
351:       mumps->id.lredrhs = mumps->id.size_schur;
352:       PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs);
353:       mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs;
354:     }
355:     mumps->id.ICNTL(26) = 1; /* condensation phase */
356:   } else { /* prepare for the expansion step */
357:     /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
358:     MatMumpsSolveSchur_Private(mumps,PETSC_TRUE);
359:     mumps->id.ICNTL(26) = 2; /* expansion phase */
360:     PetscMUMPS_c(&mumps->id);
361:     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));
362:     /* restore defaults */
363:     mumps->id.ICNTL(26) = -1;
364:   }
365:   return(0);
366: }

368: /*
369:   MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz] 

371:   input:
372:     A       - matrix in aij,baij or sbaij (bs=1) format
373:     shift   - 0: C style output triple; 1: Fortran style output triple.
374:     reuse   - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
375:               MAT_REUSE_MATRIX:   only the values in v array are updated
376:   output:
377:     nnz     - dim of r, c, and v (number of local nonzero entries of A)
378:     r, c, v - row and col index, matrix values (matrix triples)

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

384:  */

388: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
389: {
390:   const PetscInt *ai,*aj,*ajj,M=A->rmap->n;
391:   PetscInt       nz,rnz,i,j;
393:   PetscInt       *row,*col;
394:   Mat_SeqAIJ     *aa=(Mat_SeqAIJ*)A->data;

397:   *v=aa->a;
398:   if (reuse == MAT_INITIAL_MATRIX) {
399:     nz   = aa->nz;
400:     ai   = aa->i;
401:     aj   = aa->j;
402:     *nnz = nz;
403:     PetscMalloc1(2*nz, &row);
404:     col  = row + nz;

406:     nz = 0;
407:     for (i=0; i<M; i++) {
408:       rnz = ai[i+1] - ai[i];
409:       ajj = aj + ai[i];
410:       for (j=0; j<rnz; j++) {
411:         row[nz] = i+shift; col[nz++] = ajj[j] + shift;
412:       }
413:     }
414:     *r = row; *c = col;
415:   }
416:   return(0);
417: }

421: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
422: {
423:   Mat_SeqBAIJ    *aa=(Mat_SeqBAIJ*)A->data;
424:   const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2;
425:   PetscInt       bs,M,nz,idx=0,rnz,i,j,k,m;
427:   PetscInt       *row,*col;

430:   MatGetBlockSize(A,&bs);
431:   M = A->rmap->N/bs;
432:   *v = aa->a;
433:   if (reuse == MAT_INITIAL_MATRIX) {
434:     ai   = aa->i; aj = aa->j;
435:     nz   = bs2*aa->nz;
436:     *nnz = nz;
437:     PetscMalloc1(2*nz, &row);
438:     col  = row + nz;

440:     for (i=0; i<M; i++) {
441:       ajj = aj + ai[i];
442:       rnz = ai[i+1] - ai[i];
443:       for (k=0; k<rnz; k++) {
444:         for (j=0; j<bs; j++) {
445:           for (m=0; m<bs; m++) {
446:             row[idx]   = i*bs + m + shift;
447:             col[idx++] = bs*(ajj[k]) + j + shift;
448:           }
449:         }
450:       }
451:     }
452:     *r = row; *c = col;
453:   }
454:   return(0);
455: }

459: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
460: {
461:   const PetscInt *ai, *aj,*ajj,M=A->rmap->n;
462:   PetscInt       nz,rnz,i,j;
464:   PetscInt       *row,*col;
465:   Mat_SeqSBAIJ   *aa=(Mat_SeqSBAIJ*)A->data;

468:   *v = aa->a;
469:   if (reuse == MAT_INITIAL_MATRIX) {
470:     nz   = aa->nz;
471:     ai   = aa->i;
472:     aj   = aa->j;
473:     *v   = aa->a;
474:     *nnz = nz;
475:     PetscMalloc1(2*nz, &row);
476:     col  = row + nz;

478:     nz = 0;
479:     for (i=0; i<M; i++) {
480:       rnz = ai[i+1] - ai[i];
481:       ajj = aj + ai[i];
482:       for (j=0; j<rnz; j++) {
483:         row[nz] = i+shift; col[nz++] = ajj[j] + shift;
484:       }
485:     }
486:     *r = row; *c = col;
487:   }
488:   return(0);
489: }

493: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
494: {
495:   const PetscInt    *ai,*aj,*ajj,*adiag,M=A->rmap->n;
496:   PetscInt          nz,rnz,i,j;
497:   const PetscScalar *av,*v1;
498:   PetscScalar       *val;
499:   PetscErrorCode    ierr;
500:   PetscInt          *row,*col;
501:   Mat_SeqAIJ        *aa=(Mat_SeqAIJ*)A->data;
502:   PetscBool         missing;

505:   ai   =aa->i; aj=aa->j;av=aa->a;
506:   adiag=aa->diag;
507:   MatMissingDiagonal_SeqAIJ(A,&missing,&i);
508:   if (reuse == MAT_INITIAL_MATRIX) {
509:     /* count nz in the uppper triangular part of A */
510:     nz = 0;
511:     if (missing) {
512:       for (i=0; i<M; i++) {
513:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
514:           for (j=ai[i];j<ai[i+1];j++) {
515:             if (aj[j] < i) continue;
516:             nz++;
517:           }
518:         } else {
519:           nz += ai[i+1] - adiag[i];
520:         }
521:       }
522:     } else {
523:       for (i=0; i<M; i++) nz += ai[i+1] - adiag[i];
524:     }
525:     *nnz = nz;

527:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
528:     col  = row + nz;
529:     val  = (PetscScalar*)(col + nz);

531:     nz = 0;
532:     if (missing) {
533:       for (i=0; i<M; i++) {
534:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
535:           for (j=ai[i];j<ai[i+1];j++) {
536:             if (aj[j] < i) continue;
537:             row[nz] = i+shift;
538:             col[nz] = aj[j]+shift;
539:             val[nz] = av[j];
540:             nz++;
541:           }
542:         } else {
543:           rnz = ai[i+1] - adiag[i];
544:           ajj = aj + adiag[i];
545:           v1  = av + adiag[i];
546:           for (j=0; j<rnz; j++) {
547:             row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j];
548:           }
549:         }
550:       }
551:     } else {
552:       for (i=0; i<M; i++) {
553:         rnz = ai[i+1] - adiag[i];
554:         ajj = aj + adiag[i];
555:         v1  = av + adiag[i];
556:         for (j=0; j<rnz; j++) {
557:           row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j];
558:         }
559:       }
560:     }
561:     *r = row; *c = col; *v = val;
562:   } else {
563:     nz = 0; val = *v;
564:     if (missing) {
565:       for (i=0; i <M; i++) {
566:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
567:           for (j=ai[i];j<ai[i+1];j++) {
568:             if (aj[j] < i) continue;
569:             val[nz++] = av[j];
570:           }
571:         } else {
572:           rnz = ai[i+1] - adiag[i];
573:           v1  = av + adiag[i];
574:           for (j=0; j<rnz; j++) {
575:             val[nz++] = v1[j];
576:           }
577:         }
578:       }
579:     } else {
580:       for (i=0; i <M; i++) {
581:         rnz = ai[i+1] - adiag[i];
582:         v1  = av + adiag[i];
583:         for (j=0; j<rnz; j++) {
584:           val[nz++] = v1[j];
585:         }
586:       }
587:     }
588:   }
589:   return(0);
590: }

594: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
595: {
596:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
597:   PetscErrorCode    ierr;
598:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
599:   PetscInt          *row,*col;
600:   const PetscScalar *av, *bv,*v1,*v2;
601:   PetscScalar       *val;
602:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ*)A->data;
603:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ*)(mat->A)->data;
604:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ*)(mat->B)->data;

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

610:   garray = mat->garray;

612:   if (reuse == MAT_INITIAL_MATRIX) {
613:     nz   = aa->nz + bb->nz;
614:     *nnz = nz;
615:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
616:     col  = row + nz;
617:     val  = (PetscScalar*)(col + nz);

619:     *r = row; *c = col; *v = val;
620:   } else {
621:     row = *r; col = *c; val = *v;
622:   }

624:   jj = 0; irow = rstart;
625:   for (i=0; i<m; i++) {
626:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
627:     countA = ai[i+1] - ai[i];
628:     countB = bi[i+1] - bi[i];
629:     bjj    = bj + bi[i];
630:     v1     = av + ai[i];
631:     v2     = bv + bi[i];

633:     /* A-part */
634:     for (j=0; j<countA; j++) {
635:       if (reuse == MAT_INITIAL_MATRIX) {
636:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
637:       }
638:       val[jj++] = v1[j];
639:     }

641:     /* B-part */
642:     for (j=0; j < countB; j++) {
643:       if (reuse == MAT_INITIAL_MATRIX) {
644:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
645:       }
646:       val[jj++] = v2[j];
647:     }
648:     irow++;
649:   }
650:   return(0);
651: }

655: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
656: {
657:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
658:   PetscErrorCode    ierr;
659:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
660:   PetscInt          *row,*col;
661:   const PetscScalar *av, *bv,*v1,*v2;
662:   PetscScalar       *val;
663:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ*)A->data;
664:   Mat_SeqAIJ        *aa  = (Mat_SeqAIJ*)(mat->A)->data;
665:   Mat_SeqAIJ        *bb  = (Mat_SeqAIJ*)(mat->B)->data;

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

671:   garray = mat->garray;

673:   if (reuse == MAT_INITIAL_MATRIX) {
674:     nz   = aa->nz + bb->nz;
675:     *nnz = nz;
676:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
677:     col  = row + nz;
678:     val  = (PetscScalar*)(col + nz);

680:     *r = row; *c = col; *v = val;
681:   } else {
682:     row = *r; col = *c; val = *v;
683:   }

685:   jj = 0; irow = rstart;
686:   for (i=0; i<m; i++) {
687:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
688:     countA = ai[i+1] - ai[i];
689:     countB = bi[i+1] - bi[i];
690:     bjj    = bj + bi[i];
691:     v1     = av + ai[i];
692:     v2     = bv + bi[i];

694:     /* A-part */
695:     for (j=0; j<countA; j++) {
696:       if (reuse == MAT_INITIAL_MATRIX) {
697:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
698:       }
699:       val[jj++] = v1[j];
700:     }

702:     /* B-part */
703:     for (j=0; j < countB; j++) {
704:       if (reuse == MAT_INITIAL_MATRIX) {
705:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
706:       }
707:       val[jj++] = v2[j];
708:     }
709:     irow++;
710:   }
711:   return(0);
712: }

716: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
717: {
718:   Mat_MPIBAIJ       *mat    = (Mat_MPIBAIJ*)A->data;
719:   Mat_SeqBAIJ       *aa     = (Mat_SeqBAIJ*)(mat->A)->data;
720:   Mat_SeqBAIJ       *bb     = (Mat_SeqBAIJ*)(mat->B)->data;
721:   const PetscInt    *ai     = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj;
722:   const PetscInt    *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart;
723:   const PetscInt    bs2=mat->bs2;
724:   PetscErrorCode    ierr;
725:   PetscInt          bs,nz,i,j,k,n,jj,irow,countA,countB,idx;
726:   PetscInt          *row,*col;
727:   const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2;
728:   PetscScalar       *val;

731:   MatGetBlockSize(A,&bs);
732:   if (reuse == MAT_INITIAL_MATRIX) {
733:     nz   = bs2*(aa->nz + bb->nz);
734:     *nnz = nz;
735:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
736:     col  = row + nz;
737:     val  = (PetscScalar*)(col + nz);

739:     *r = row; *c = col; *v = val;
740:   } else {
741:     row = *r; col = *c; val = *v;
742:   }

744:   jj = 0; irow = rstart;
745:   for (i=0; i<mbs; i++) {
746:     countA = ai[i+1] - ai[i];
747:     countB = bi[i+1] - bi[i];
748:     ajj    = aj + ai[i];
749:     bjj    = bj + bi[i];
750:     v1     = av + bs2*ai[i];
751:     v2     = bv + bs2*bi[i];

753:     idx = 0;
754:     /* A-part */
755:     for (k=0; k<countA; k++) {
756:       for (j=0; j<bs; j++) {
757:         for (n=0; n<bs; n++) {
758:           if (reuse == MAT_INITIAL_MATRIX) {
759:             row[jj] = irow + n + shift;
760:             col[jj] = rstart + bs*ajj[k] + j + shift;
761:           }
762:           val[jj++] = v1[idx++];
763:         }
764:       }
765:     }

767:     idx = 0;
768:     /* B-part */
769:     for (k=0; k<countB; k++) {
770:       for (j=0; j<bs; j++) {
771:         for (n=0; n<bs; n++) {
772:           if (reuse == MAT_INITIAL_MATRIX) {
773:             row[jj] = irow + n + shift;
774:             col[jj] = bs*garray[bjj[k]] + j + shift;
775:           }
776:           val[jj++] = v2[idx++];
777:         }
778:       }
779:     }
780:     irow += bs;
781:   }
782:   return(0);
783: }

787: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
788: {
789:   const PetscInt    *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
790:   PetscErrorCode    ierr;
791:   PetscInt          rstart,nz,nza,nzb,i,j,jj,irow,countA,countB;
792:   PetscInt          *row,*col;
793:   const PetscScalar *av, *bv,*v1,*v2;
794:   PetscScalar       *val;
795:   Mat_MPIAIJ        *mat =  (Mat_MPIAIJ*)A->data;
796:   Mat_SeqAIJ        *aa  =(Mat_SeqAIJ*)(mat->A)->data;
797:   Mat_SeqAIJ        *bb  =(Mat_SeqAIJ*)(mat->B)->data;

800:   ai=aa->i; aj=aa->j; adiag=aa->diag;
801:   bi=bb->i; bj=bb->j; garray = mat->garray;
802:   av=aa->a; bv=bb->a;

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

806:   if (reuse == MAT_INITIAL_MATRIX) {
807:     nza = 0;    /* num of upper triangular entries in mat->A, including diagonals */
808:     nzb = 0;    /* num of upper triangular entries in mat->B */
809:     for (i=0; i<m; i++) {
810:       nza   += (ai[i+1] - adiag[i]);
811:       countB = bi[i+1] - bi[i];
812:       bjj    = bj + bi[i];
813:       for (j=0; j<countB; j++) {
814:         if (garray[bjj[j]] > rstart) nzb++;
815:       }
816:     }

818:     nz   = nza + nzb; /* total nz of upper triangular part of mat */
819:     *nnz = nz;
820:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
821:     col  = row + nz;
822:     val  = (PetscScalar*)(col + nz);

824:     *r = row; *c = col; *v = val;
825:   } else {
826:     row = *r; col = *c; val = *v;
827:   }

829:   jj = 0; irow = rstart;
830:   for (i=0; i<m; i++) {
831:     ajj    = aj + adiag[i];                 /* ptr to the beginning of the diagonal of this row */
832:     v1     = av + adiag[i];
833:     countA = ai[i+1] - adiag[i];
834:     countB = bi[i+1] - bi[i];
835:     bjj    = bj + bi[i];
836:     v2     = bv + bi[i];

838:     /* A-part */
839:     for (j=0; j<countA; j++) {
840:       if (reuse == MAT_INITIAL_MATRIX) {
841:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
842:       }
843:       val[jj++] = v1[j];
844:     }

846:     /* B-part */
847:     for (j=0; j < countB; j++) {
848:       if (garray[bjj[j]] > rstart) {
849:         if (reuse == MAT_INITIAL_MATRIX) {
850:           row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
851:         }
852:         val[jj++] = v2[j];
853:       }
854:     }
855:     irow++;
856:   }
857:   return(0);
858: }

862: PetscErrorCode MatGetDiagonal_MUMPS(Mat A,Vec v)
863: {
865:   SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type: MUMPS factor");
866:   return(0);
867: }

871: PetscErrorCode MatDestroy_MUMPS(Mat A)
872: {
873:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

877:   PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
878:   VecScatterDestroy(&mumps->scat_rhs);
879:   VecScatterDestroy(&mumps->scat_sol);
880:   VecDestroy(&mumps->b_seq);
881:   VecDestroy(&mumps->x_seq);
882:   PetscFree(mumps->id.perm_in);
883:   PetscFree(mumps->irn);
884:   PetscFree(mumps->info);
885:   MatMumpsResetSchur_Private(mumps);
886:   mumps->id.job = JOB_END;
887:   PetscMUMPS_c(&mumps->id);
888:   MPI_Comm_free(&mumps->comm_mumps);
889:   if (mumps->Destroy) {
890:     (mumps->Destroy)(A);
891:   }
892:   PetscFree(A->spptr);

894:   /* clear composed functions */
895:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverPackage_C",NULL);
896:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
897:   PetscObjectComposeFunction((PetscObject)A,"MatFactorInvertSchurComplement_C",NULL);
898:   PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);
899:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSchurComplement_C",NULL);
900:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSolveSchurComplement_C",NULL);
901:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSolveSchurComplementTranspose_C",NULL);
902:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);
903:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);
904:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);
905:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);
906:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);
907:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);
908:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);
909:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);
910:   return(0);
911: }

915: PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x)
916: {
917:   Mat_MUMPS        *mumps=(Mat_MUMPS*)A->spptr;
918:   PetscScalar      *array;
919:   Vec              b_seq;
920:   IS               is_iden,is_petsc;
921:   PetscErrorCode   ierr;
922:   PetscInt         i;
923:   PetscBool        second_solve = PETSC_FALSE;
924:   static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE;

927:   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);
928:   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);

930:   if (A->errortype) {
931:     PetscInfo2(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
932:     VecSetInf(x);
933:     return(0);
934:   }

936:   mumps->id.nrhs = 1;
937:   b_seq          = mumps->b_seq;
938:   if (mumps->size > 1) {
939:     /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */
940:     VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
941:     VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
942:     if (!mumps->myid) {VecGetArray(b_seq,&array);}
943:   } else {  /* size == 1 */
944:     VecCopy(b,x);
945:     VecGetArray(x,&array);
946:   }
947:   if (!mumps->myid) { /* define rhs on the host */
948:     mumps->id.nrhs = 1;
949:     mumps->id.rhs = (MumpsScalar*)array;
950:   }

952:   /*
953:      handle condensation step of Schur complement (if any)
954:      We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
955:      According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
956:      Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
957:      This requires an extra call to PetscMUMPS_c and the computation of the factors for S
958:   */
959:   if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
960:     second_solve = PETSC_TRUE;
961:     MatMumpsHandleSchur_Private(mumps,PETSC_FALSE);
962:   }
963:   /* solve phase */
964:   /*-------------*/
965:   mumps->id.job = JOB_SOLVE;
966:   PetscMUMPS_c(&mumps->id);
967:   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));

969:   /* handle expansion step of Schur complement (if any) */
970:   if (second_solve) {
971:     MatMumpsHandleSchur_Private(mumps,PETSC_TRUE);
972:   }

974:   if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */
975:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
976:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
977:       VecScatterDestroy(&mumps->scat_sol);
978:     }
979:     if (!mumps->scat_sol) { /* create scatter scat_sol */
980:       ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden); /* from */
981:       for (i=0; i<mumps->id.lsol_loc; i++) {
982:         mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */
983:       }
984:       ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);  /* to */
985:       VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);
986:       ISDestroy(&is_iden);
987:       ISDestroy(&is_petsc);

989:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
990:     }

992:     VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
993:     VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
994:   }
995:   return(0);
996: }

1000: PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x)
1001: {
1002:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

1006:   mumps->id.ICNTL(9) = 0;
1007:   MatSolve_MUMPS(A,b,x);
1008:   mumps->id.ICNTL(9) = 1;
1009:   return(0);
1010: }

1014: PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X)
1015: {
1017:   PetscBool      flg;
1018:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;
1019:   PetscInt       i,nrhs,M;
1020:   PetscScalar    *array,*bray;

1023:   PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
1024:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
1025:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
1026:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
1027:   if (B->rmap->n != X->rmap->n) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution");

1029:   MatGetSize(B,&M,&nrhs);
1030:   mumps->id.nrhs = nrhs;
1031:   mumps->id.lrhs = M;

1033:   if (mumps->size == 1) {
1034:     /* copy B to X */
1035:     MatDenseGetArray(B,&bray);
1036:     MatDenseGetArray(X,&array);
1037:     PetscMemcpy(array,bray,M*nrhs*sizeof(PetscScalar));
1038:     MatDenseRestoreArray(B,&bray);
1039:     mumps->id.rhs = (MumpsScalar*)array;
1040:     /* handle condensation step of Schur complement (if any) */
1041:     MatMumpsHandleSchur_Private(mumps,PETSC_FALSE);

1043:     /* solve phase */
1044:     /*-------------*/
1045:     mumps->id.job = JOB_SOLVE;
1046:     PetscMUMPS_c(&mumps->id);
1047:     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));

1049:     /* handle expansion step of Schur complement (if any) */
1050:     MatMumpsHandleSchur_Private(mumps,PETSC_TRUE);
1051:     MatDenseRestoreArray(X,&array);
1052:   } else {  /*--------- parallel case --------*/
1053:     PetscInt       lsol_loc,nlsol_loc,*isol_loc,*idx,*iidx,*idxx,*isol_loc_save;
1054:     MumpsScalar    *sol_loc,*sol_loc_save;
1055:     IS             is_to,is_from;
1056:     PetscInt       k,proc,j,m;
1057:     const PetscInt *rstart;
1058:     Vec            v_mpi,b_seq,x_seq;
1059:     VecScatter     scat_rhs,scat_sol;

1061:     /* create x_seq to hold local solution */
1062:     isol_loc_save = mumps->id.isol_loc; /* save it for MatSovle() */
1063:     sol_loc_save  = mumps->id.sol_loc;

1065:     lsol_loc  = mumps->id.INFO(23);
1066:     nlsol_loc = nrhs*lsol_loc;     /* length of sol_loc */
1067:     PetscMalloc2(nlsol_loc,&sol_loc,nlsol_loc,&isol_loc);
1068:     mumps->id.sol_loc = (MumpsScalar*)sol_loc;
1069:     mumps->id.isol_loc = isol_loc;

1071:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&x_seq);

1073:     /* copy rhs matrix B into vector v_mpi */
1074:     MatGetLocalSize(B,&m,NULL);
1075:     MatDenseGetArray(B,&bray);
1076:     VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);
1077:     MatDenseRestoreArray(B,&bray);

1079:     /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */
1080:     /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B;
1081:       iidx: inverse of idx, will be used by scattering xx_seq -> X       */
1082:     PetscMalloc2(nrhs*M,&idx,nrhs*M,&iidx);
1083:     MatGetOwnershipRanges(B,&rstart);
1084:     k = 0;
1085:     for (proc=0; proc<mumps->size; proc++){
1086:       for (j=0; j<nrhs; j++){
1087:         for (i=rstart[proc]; i<rstart[proc+1]; i++){
1088:           iidx[j*M + i] = k;
1089:           idx[k++]      = j*M + i;
1090:         }
1091:       }
1092:     }

1094:     if (!mumps->myid) {
1095:       VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);
1096:       ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_COPY_VALUES,&is_to);
1097:       ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);
1098:     } else {
1099:       VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);
1100:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);
1101:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);
1102:     }
1103:     VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);
1104:     VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);
1105:     ISDestroy(&is_to);
1106:     ISDestroy(&is_from);
1107:     VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);

1109:     if (!mumps->myid) { /* define rhs on the host */
1110:       VecGetArray(b_seq,&bray);
1111:       mumps->id.rhs = (MumpsScalar*)bray;
1112:       VecRestoreArray(b_seq,&bray);
1113:     }

1115:     /* solve phase */
1116:     /*-------------*/
1117:     mumps->id.job = JOB_SOLVE;
1118:     PetscMUMPS_c(&mumps->id);
1119:     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));

1121:     /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
1122:     MatDenseGetArray(X,&array);
1123:     VecPlaceArray(v_mpi,array);
1124: 
1125:     /* create scatter scat_sol */
1126:     PetscMalloc1(nlsol_loc,&idxx);
1127:     ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);
1128:     for (i=0; i<lsol_loc; i++) {
1129:       isol_loc[i] -= 1; /* change Fortran style to C style */
1130:       idxx[i] = iidx[isol_loc[i]];
1131:       for (j=1; j<nrhs; j++){
1132:         idxx[j*lsol_loc+i] = iidx[isol_loc[i]+j*M];
1133:       }
1134:     }
1135:     ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);
1136:     VecScatterCreate(x_seq,is_from,v_mpi,is_to,&scat_sol);
1137:     VecScatterBegin(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
1138:     ISDestroy(&is_from);
1139:     ISDestroy(&is_to);
1140:     VecScatterEnd(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
1141:     MatDenseRestoreArray(X,&array);

1143:     /* free spaces */
1144:     mumps->id.sol_loc = sol_loc_save;
1145:     mumps->id.isol_loc = isol_loc_save;

1147:     PetscFree2(sol_loc,isol_loc);
1148:     PetscFree2(idx,iidx);
1149:     PetscFree(idxx);
1150:     VecDestroy(&x_seq);
1151:     VecDestroy(&v_mpi);
1152:     VecDestroy(&b_seq);
1153:     VecScatterDestroy(&scat_rhs);
1154:     VecScatterDestroy(&scat_sol);
1155:   }
1156:   return(0);
1157: }

1159: #if !defined(PETSC_USE_COMPLEX)
1160: /*
1161:   input:
1162:    F:        numeric factor
1163:   output:
1164:    nneg:     total number of negative pivots
1165:    nzero:    0
1166:    npos:     (global dimension of F) - nneg
1167: */

1171: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos)
1172: {
1173:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
1175:   PetscMPIInt    size;

1178:   MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);
1179:   /* 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 */
1180:   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));

1182:   if (nneg) *nneg = mumps->id.INFOG(12);
1183:   if (nzero || npos) {
1184:     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");
1185:     if (nzero) *nzero = mumps->id.INFOG(28);
1186:     if (npos) *npos   = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
1187:   }
1188:   return(0);
1189: }
1190: #endif /* !defined(PETSC_USE_COMPLEX) */

1194: PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info)
1195: {
1196:   Mat_MUMPS      *mumps =(Mat_MUMPS*)(F)->spptr;
1198:   Mat            F_diag;
1199:   PetscBool      isMPIAIJ;

1202:   if (mumps->id.INFOG(1) < 0) {
1203:     if (mumps->id.INFOG(1) == -6) {
1204:       PetscInfo2(A,"MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1205:     }
1206:     PetscInfo2(A,"MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1207:     return(0);
1208:   }

1210:   (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1212:   /* numerical factorization phase */
1213:   /*-------------------------------*/
1214:   mumps->id.job = JOB_FACTNUMERIC;
1215:   if (!mumps->id.ICNTL(18)) { /* A is centralized */
1216:     if (!mumps->myid) {
1217:       mumps->id.a = (MumpsScalar*)mumps->val;
1218:     }
1219:   } else {
1220:     mumps->id.a_loc = (MumpsScalar*)mumps->val;
1221:   }
1222:   PetscMUMPS_c(&mumps->id);
1223:   if (mumps->id.INFOG(1) < 0) {
1224:     if (A->erroriffailure) {
1225:       SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1226:     } else {
1227:       if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
1228:         PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1229:         F->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1230:       } else if (mumps->id.INFOG(1) == -13) {
1231:         PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, cannot allocate required memory %d megabytes\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1232:         F->errortype = MAT_FACTOR_OUTMEMORY;
1233:       } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) {
1234:         PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d, problem with workarray \n",mumps->id.INFOG(1),mumps->id.INFO(2));
1235:         F->errortype = MAT_FACTOR_OUTMEMORY;
1236:       } else {
1237:         PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1238:         F->errortype = MAT_FACTOR_OTHER;
1239:       }
1240:     }
1241:   }
1242:   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));

1244:   (F)->assembled        = PETSC_TRUE;
1245:   mumps->matstruc       = SAME_NONZERO_PATTERN;
1246:   mumps->schur_factored = PETSC_FALSE;
1247:   mumps->schur_inverted = PETSC_FALSE;

1249:   /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
1250:   if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;

1252:   if (mumps->size > 1) {
1253:     PetscInt    lsol_loc;
1254:     PetscScalar *sol_loc;

1256:     PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);
1257:     if (isMPIAIJ) F_diag = ((Mat_MPIAIJ*)(F)->data)->A;
1258:     else F_diag = ((Mat_MPISBAIJ*)(F)->data)->A;
1259:     F_diag->assembled = PETSC_TRUE;

1261:     /* distributed solution; Create x_seq=sol_loc for repeated use */
1262:     if (mumps->x_seq) {
1263:       VecScatterDestroy(&mumps->scat_sol);
1264:       PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
1265:       VecDestroy(&mumps->x_seq);
1266:     }
1267:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
1268:     PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);
1269:     mumps->id.lsol_loc = lsol_loc;
1270:     mumps->id.sol_loc = (MumpsScalar*)sol_loc;
1271:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);
1272:   }
1273:   return(0);
1274: }

1276: /* Sets MUMPS options from the options database */
1279: PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A)
1280: {
1281:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
1283:   PetscInt       icntl,info[40],i,ninfo=40;
1284:   PetscBool      flg;

1287:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");
1288:   PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);
1289:   if (flg) mumps->id.ICNTL(1) = icntl;
1290:   PetscOptionsInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg);
1291:   if (flg) mumps->id.ICNTL(2) = icntl;
1292:   PetscOptionsInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg);
1293:   if (flg) mumps->id.ICNTL(3) = icntl;

1295:   PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);
1296:   if (flg) mumps->id.ICNTL(4) = icntl;
1297:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

1299:   PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)","None",mumps->id.ICNTL(6),&icntl,&flg);
1300:   if (flg) mumps->id.ICNTL(6) = icntl;

1302:   PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis","None",mumps->id.ICNTL(7),&icntl,&flg);
1303:   if (flg) {
1304:     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");
1305:     else mumps->id.ICNTL(7) = icntl;
1306:   }

1308:   PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL);
1309:   /* PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): computes the solution using A or A^T","None",mumps->id.ICNTL(9),&mumps->id.ICNTL(9),NULL); handled by MatSolveTranspose_MUMPS() */
1310:   PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);
1311:   PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): statistics related to an error analysis (via -ksp_view)","None",mumps->id.ICNTL(11),&mumps->id.ICNTL(11),NULL);
1312:   PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)","None",mumps->id.ICNTL(12),&mumps->id.ICNTL(12),NULL);
1313:   PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting","None",mumps->id.ICNTL(13),&mumps->id.ICNTL(13),NULL);
1314:   PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage increase in the estimated working space","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL);
1315:   PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);
1316:   if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
1317:     MatMumpsResetSchur_Private(mumps);
1318:   }
1319:   /* PetscOptionsInt("-mat_mumps_icntl_20","ICNTL(20): the format (dense or sparse) of the right-hand sides","None",mumps->id.ICNTL(20),&mumps->id.ICNTL(20),NULL); -- sparse rhs is not supported in PETSc API */
1320:   /* PetscOptionsInt("-mat_mumps_icntl_21","ICNTL(21): the distribution (centralized or distributed) of the solution vectors","None",mumps->id.ICNTL(21),&mumps->id.ICNTL(21),NULL); we only use distributed solution vector */

1322:   PetscOptionsInt("-mat_mumps_icntl_22","ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)","None",mumps->id.ICNTL(22),&mumps->id.ICNTL(22),NULL);
1323:   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);
1324:   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);
1325:   if (mumps->id.ICNTL(24)) {
1326:     mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */
1327:   }

1329:   PetscOptionsInt("-mat_mumps_icntl_25","ICNTL(25): compute a solution of a deficient matrix and a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),NULL);
1330:   PetscOptionsInt("-mat_mumps_icntl_26","ICNTL(26): drives the solution phase if a Schur complement matrix","None",mumps->id.ICNTL(26),&mumps->id.ICNTL(26),NULL);
1331:   PetscOptionsInt("-mat_mumps_icntl_27","ICNTL(27): the blocking size for multiple right-hand sides","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),NULL);
1332:   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);
1333:   PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL);
1334:   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);
1335:   PetscOptionsInt("-mat_mumps_icntl_31","ICNTL(31): indicates which factors may be discarded during factorization","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),NULL);
1336:   /* PetscOptionsInt("-mat_mumps_icntl_32","ICNTL(32): performs the forward elemination of the right-hand sides during factorization","None",mumps->id.ICNTL(32),&mumps->id.ICNTL(32),NULL);  -- not supported by PETSc API */
1337:   PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);

1339:   PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);
1340:   PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);
1341:   PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);
1342:   PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);
1343:   PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);

1345:   PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);

1347:   PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);
1348:   if (ninfo) {
1349:     if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo);
1350:     PetscMalloc1(ninfo,&mumps->info);
1351:     mumps->ninfo = ninfo;
1352:     for (i=0; i<ninfo; i++) {
1353:       if (info[i] < 0 || info[i]>40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"index of INFO %d must between 1 and 40\n",ninfo);
1354:       else {
1355:         mumps->info[i] = info[i];
1356:       }
1357:     }
1358:   }

1360:   PetscOptionsEnd();
1361:   return(0);
1362: }

1366: PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps)
1367: {

1371:   MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);
1372:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);
1373:   MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));

1375:   mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps);

1377:   mumps->id.job = JOB_INIT;
1378:   mumps->id.par = 1;  /* host participates factorizaton and solve */
1379:   mumps->id.sym = mumps->sym;
1380:   PetscMUMPS_c(&mumps->id);

1382:   mumps->scat_rhs     = NULL;
1383:   mumps->scat_sol     = NULL;

1385:   /* set PETSc-MUMPS default options - override MUMPS default */
1386:   mumps->id.ICNTL(3) = 0;
1387:   mumps->id.ICNTL(4) = 0;
1388:   if (mumps->size == 1) {
1389:     mumps->id.ICNTL(18) = 0;   /* centralized assembled matrix input */
1390:   } else {
1391:     mumps->id.ICNTL(18) = 3;   /* distributed assembled matrix input */
1392:     mumps->id.ICNTL(20) = 0;   /* rhs is in dense format */
1393:     mumps->id.ICNTL(21) = 1;   /* distributed solution */
1394:   }

1396:   /* schur */
1397:   mumps->id.size_schur      = 0;
1398:   mumps->id.listvar_schur   = NULL;
1399:   mumps->id.schur           = NULL;
1400:   mumps->sizeredrhs         = 0;
1401:   mumps->schur_pivots       = NULL;
1402:   mumps->schur_work         = NULL;
1403:   mumps->schur_sol          = NULL;
1404:   mumps->schur_sizesol      = 0;
1405:   mumps->schur_factored     = PETSC_FALSE;
1406:   mumps->schur_inverted     = PETSC_FALSE;
1407:   return(0);
1408: }

1412: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps)
1413: {

1417:   if (mumps->id.INFOG(1) < 0) {
1418:     if (A->erroriffailure) {
1419:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));
1420:     } else {
1421:       if (mumps->id.INFOG(1) == -6) {
1422:         PetscInfo2(F,"matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1423:         F->errortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
1424:       } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
1425:         PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1426:         F->errortype = MAT_FACTOR_OUTMEMORY;
1427:       } else {
1428:         PetscInfo2(F,"Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
1429:         F->errortype = MAT_FACTOR_OTHER;
1430:       }
1431:     }
1432:   }
1433:   return(0);
1434: }

1436: /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */
1439: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
1440: {
1441:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
1443:   Vec            b;
1444:   IS             is_iden;
1445:   const PetscInt M = A->rmap->N;

1448:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

1450:   /* Set MUMPS options from the options database */
1451:   PetscSetMUMPSFromOptions(F,A);

1453:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1455:   /* analysis phase */
1456:   /*----------------*/
1457:   mumps->id.job = JOB_FACTSYMBOLIC;
1458:   mumps->id.n   = M;
1459:   switch (mumps->id.ICNTL(18)) {
1460:   case 0:  /* centralized assembled matrix input */
1461:     if (!mumps->myid) {
1462:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
1463:       if (mumps->id.ICNTL(6)>1) {
1464:         mumps->id.a = (MumpsScalar*)mumps->val;
1465:       }
1466:       if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */
1467:         /*
1468:         PetscBool      flag;
1469:         ISEqual(r,c,&flag);
1470:         if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm");
1471:         ISView(r,PETSC_VIEWER_STDOUT_SELF);
1472:          */
1473:         if (!mumps->myid) {
1474:           const PetscInt *idx;
1475:           PetscInt       i,*perm_in;

1477:           PetscMalloc1(M,&perm_in);
1478:           ISGetIndices(r,&idx);

1480:           mumps->id.perm_in = perm_in;
1481:           for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */
1482:           ISRestoreIndices(r,&idx);
1483:         }
1484:       }
1485:     }
1486:     break;
1487:   case 3:  /* distributed assembled matrix input (size>1) */
1488:     mumps->id.nz_loc = mumps->nz;
1489:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
1490:     if (mumps->id.ICNTL(6)>1) {
1491:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
1492:     }
1493:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
1494:     if (!mumps->myid) {
1495:       VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);
1496:       ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);
1497:     } else {
1498:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
1499:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
1500:     }
1501:     MatCreateVecs(A,NULL,&b);
1502:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
1503:     ISDestroy(&is_iden);
1504:     VecDestroy(&b);
1505:     break;
1506:   }
1507:   PetscMUMPS_c(&mumps->id);
1508:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

1510:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
1511:   F->ops->solve           = MatSolve_MUMPS;
1512:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
1513:   F->ops->matsolve        = MatMatSolve_MUMPS;
1514:   return(0);
1515: }

1517: /* Note the Petsc r and c permutations are ignored */
1520: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
1521: {
1522:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
1524:   Vec            b;
1525:   IS             is_iden;
1526:   const PetscInt M = A->rmap->N;

1529:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

1531:   /* Set MUMPS options from the options database */
1532:   PetscSetMUMPSFromOptions(F,A);

1534:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1536:   /* analysis phase */
1537:   /*----------------*/
1538:   mumps->id.job = JOB_FACTSYMBOLIC;
1539:   mumps->id.n   = M;
1540:   switch (mumps->id.ICNTL(18)) {
1541:   case 0:  /* centralized assembled matrix input */
1542:     if (!mumps->myid) {
1543:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
1544:       if (mumps->id.ICNTL(6)>1) {
1545:         mumps->id.a = (MumpsScalar*)mumps->val;
1546:       }
1547:     }
1548:     break;
1549:   case 3:  /* distributed assembled matrix input (size>1) */
1550:     mumps->id.nz_loc = mumps->nz;
1551:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
1552:     if (mumps->id.ICNTL(6)>1) {
1553:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
1554:     }
1555:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
1556:     if (!mumps->myid) {
1557:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
1558:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
1559:     } else {
1560:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
1561:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
1562:     }
1563:     MatCreateVecs(A,NULL,&b);
1564:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
1565:     ISDestroy(&is_iden);
1566:     VecDestroy(&b);
1567:     break;
1568:   }
1569:   PetscMUMPS_c(&mumps->id);
1570:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

1572:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
1573:   F->ops->solve           = MatSolve_MUMPS;
1574:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
1575:   return(0);
1576: }

1578: /* Note the Petsc r permutation and factor info are ignored */
1581: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info)
1582: {
1583:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
1585:   Vec            b;
1586:   IS             is_iden;
1587:   const PetscInt M = A->rmap->N;

1590:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

1592:   /* Set MUMPS options from the options database */
1593:   PetscSetMUMPSFromOptions(F,A);

1595:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

1597:   /* analysis phase */
1598:   /*----------------*/
1599:   mumps->id.job = JOB_FACTSYMBOLIC;
1600:   mumps->id.n   = M;
1601:   switch (mumps->id.ICNTL(18)) {
1602:   case 0:  /* centralized assembled matrix input */
1603:     if (!mumps->myid) {
1604:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
1605:       if (mumps->id.ICNTL(6)>1) {
1606:         mumps->id.a = (MumpsScalar*)mumps->val;
1607:       }
1608:     }
1609:     break;
1610:   case 3:  /* distributed assembled matrix input (size>1) */
1611:     mumps->id.nz_loc = mumps->nz;
1612:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
1613:     if (mumps->id.ICNTL(6)>1) {
1614:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
1615:     }
1616:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
1617:     if (!mumps->myid) {
1618:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
1619:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
1620:     } else {
1621:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
1622:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
1623:     }
1624:     MatCreateVecs(A,NULL,&b);
1625:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
1626:     ISDestroy(&is_iden);
1627:     VecDestroy(&b);
1628:     break;
1629:   }
1630:   PetscMUMPS_c(&mumps->id);
1631:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);


1634:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
1635:   F->ops->solve                 = MatSolve_MUMPS;
1636:   F->ops->solvetranspose        = MatSolve_MUMPS;
1637:   F->ops->matsolve              = MatMatSolve_MUMPS;
1638: #if defined(PETSC_USE_COMPLEX)
1639:   F->ops->getinertia = NULL;
1640: #else
1641:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
1642: #endif
1643:   return(0);
1644: }

1648: PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer)
1649: {
1650:   PetscErrorCode    ierr;
1651:   PetscBool         iascii;
1652:   PetscViewerFormat format;
1653:   Mat_MUMPS         *mumps=(Mat_MUMPS*)A->spptr;

1656:   /* check if matrix is mumps type */
1657:   if (A->ops->solve != MatSolve_MUMPS) return(0);

1659:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1660:   if (iascii) {
1661:     PetscViewerGetFormat(viewer,&format);
1662:     if (format == PETSC_VIEWER_ASCII_INFO) {
1663:       PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");
1664:       PetscViewerASCIIPrintf(viewer,"  SYM (matrix type):                   %d \n",mumps->id.sym);
1665:       PetscViewerASCIIPrintf(viewer,"  PAR (host participation):            %d \n",mumps->id.par);
1666:       PetscViewerASCIIPrintf(viewer,"  ICNTL(1) (output for error):         %d \n",mumps->id.ICNTL(1));
1667:       PetscViewerASCIIPrintf(viewer,"  ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));
1668:       PetscViewerASCIIPrintf(viewer,"  ICNTL(3) (output for global info):   %d \n",mumps->id.ICNTL(3));
1669:       PetscViewerASCIIPrintf(viewer,"  ICNTL(4) (level of printing):        %d \n",mumps->id.ICNTL(4));
1670:       PetscViewerASCIIPrintf(viewer,"  ICNTL(5) (input mat struct):         %d \n",mumps->id.ICNTL(5));
1671:       PetscViewerASCIIPrintf(viewer,"  ICNTL(6) (matrix prescaling):        %d \n",mumps->id.ICNTL(6));
1672:       PetscViewerASCIIPrintf(viewer,"  ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));
1673:       PetscViewerASCIIPrintf(viewer,"  ICNTL(8) (scaling strategy):        %d \n",mumps->id.ICNTL(8));
1674:       PetscViewerASCIIPrintf(viewer,"  ICNTL(10) (max num of refinements):  %d \n",mumps->id.ICNTL(10));
1675:       PetscViewerASCIIPrintf(viewer,"  ICNTL(11) (error analysis):          %d \n",mumps->id.ICNTL(11));
1676:       if (mumps->id.ICNTL(11)>0) {
1677:         PetscViewerASCIIPrintf(viewer,"    RINFOG(4) (inf norm of input mat):        %g\n",mumps->id.RINFOG(4));
1678:         PetscViewerASCIIPrintf(viewer,"    RINFOG(5) (inf norm of solution):         %g\n",mumps->id.RINFOG(5));
1679:         PetscViewerASCIIPrintf(viewer,"    RINFOG(6) (inf norm of residual):         %g\n",mumps->id.RINFOG(6));
1680:         PetscViewerASCIIPrintf(viewer,"    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));
1681:         PetscViewerASCIIPrintf(viewer,"    RINFOG(9) (error estimate):               %g \n",mumps->id.RINFOG(9));
1682:         PetscViewerASCIIPrintf(viewer,"    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));
1683:       }
1684:       PetscViewerASCIIPrintf(viewer,"  ICNTL(12) (efficiency control):                         %d \n",mumps->id.ICNTL(12));
1685:       PetscViewerASCIIPrintf(viewer,"  ICNTL(13) (efficiency control):                         %d \n",mumps->id.ICNTL(13));
1686:       PetscViewerASCIIPrintf(viewer,"  ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));
1687:       /* ICNTL(15-17) not used */
1688:       PetscViewerASCIIPrintf(viewer,"  ICNTL(18) (input mat struct):                           %d \n",mumps->id.ICNTL(18));
1689:       PetscViewerASCIIPrintf(viewer,"  ICNTL(19) (Schur complement info):                       %d \n",mumps->id.ICNTL(19));
1690:       PetscViewerASCIIPrintf(viewer,"  ICNTL(20) (rhs sparse pattern):                         %d \n",mumps->id.ICNTL(20));
1691:       PetscViewerASCIIPrintf(viewer,"  ICNTL(21) (solution struct):                            %d \n",mumps->id.ICNTL(21));
1692:       PetscViewerASCIIPrintf(viewer,"  ICNTL(22) (in-core/out-of-core facility):               %d \n",mumps->id.ICNTL(22));
1693:       PetscViewerASCIIPrintf(viewer,"  ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));

1695:       PetscViewerASCIIPrintf(viewer,"  ICNTL(24) (detection of null pivot rows):               %d \n",mumps->id.ICNTL(24));
1696:       PetscViewerASCIIPrintf(viewer,"  ICNTL(25) (computation of a null space basis):          %d \n",mumps->id.ICNTL(25));
1697:       PetscViewerASCIIPrintf(viewer,"  ICNTL(26) (Schur options for rhs or solution):          %d \n",mumps->id.ICNTL(26));
1698:       PetscViewerASCIIPrintf(viewer,"  ICNTL(27) (experimental parameter):                     %d \n",mumps->id.ICNTL(27));
1699:       PetscViewerASCIIPrintf(viewer,"  ICNTL(28) (use parallel or sequential ordering):        %d \n",mumps->id.ICNTL(28));
1700:       PetscViewerASCIIPrintf(viewer,"  ICNTL(29) (parallel ordering):                          %d \n",mumps->id.ICNTL(29));

1702:       PetscViewerASCIIPrintf(viewer,"  ICNTL(30) (user-specified set of entries in inv(A)):    %d \n",mumps->id.ICNTL(30));
1703:       PetscViewerASCIIPrintf(viewer,"  ICNTL(31) (factors is discarded in the solve phase):    %d \n",mumps->id.ICNTL(31));
1704:       PetscViewerASCIIPrintf(viewer,"  ICNTL(33) (compute determinant):                        %d \n",mumps->id.ICNTL(33));

1706:       PetscViewerASCIIPrintf(viewer,"  CNTL(1) (relative pivoting threshold):      %g \n",mumps->id.CNTL(1));
1707:       PetscViewerASCIIPrintf(viewer,"  CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));
1708:       PetscViewerASCIIPrintf(viewer,"  CNTL(3) (absolute pivoting threshold):      %g \n",mumps->id.CNTL(3));
1709:       PetscViewerASCIIPrintf(viewer,"  CNTL(4) (value of static pivoting):         %g \n",mumps->id.CNTL(4));
1710:       PetscViewerASCIIPrintf(viewer,"  CNTL(5) (fixation for null pivots):         %g \n",mumps->id.CNTL(5));

1712:       /* infomation local to each processor */
1713:       PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis): \n");
1714:       PetscViewerASCIIPushSynchronized(viewer);
1715:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %g \n",mumps->myid,mumps->id.RINFO(1));
1716:       PetscViewerFlush(viewer);
1717:       PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization): \n");
1718:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(2));
1719:       PetscViewerFlush(viewer);
1720:       PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization): \n");
1721:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(3));
1722:       PetscViewerFlush(viewer);

1724:       PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");
1725:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] %d \n",mumps->myid,mumps->id.INFO(15));
1726:       PetscViewerFlush(viewer);

1728:       PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");
1729:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(16));
1730:       PetscViewerFlush(viewer);

1732:       PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization): \n");
1733:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(23));
1734:       PetscViewerFlush(viewer);

1736:       if (mumps->ninfo && mumps->ninfo <= 40){
1737:         PetscInt i;
1738:         for (i=0; i<mumps->ninfo; i++){
1739:           PetscViewerASCIIPrintf(viewer, "  INFO(%d): \n",mumps->info[i]);
1740:           PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));
1741:           PetscViewerFlush(viewer);
1742:         }
1743:       }


1746:       PetscViewerASCIIPopSynchronized(viewer);

1748:       if (!mumps->myid) { /* information from the host */
1749:         PetscViewerASCIIPrintf(viewer,"  RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));
1750:         PetscViewerASCIIPrintf(viewer,"  RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));
1751:         PetscViewerASCIIPrintf(viewer,"  RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));
1752:         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));

1754:         PetscViewerASCIIPrintf(viewer,"  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));
1755:         PetscViewerASCIIPrintf(viewer,"  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));
1756:         PetscViewerASCIIPrintf(viewer,"  INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));
1757:         PetscViewerASCIIPrintf(viewer,"  INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));
1758:         PetscViewerASCIIPrintf(viewer,"  INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));
1759:         PetscViewerASCIIPrintf(viewer,"  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));
1760:         PetscViewerASCIIPrintf(viewer,"  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));
1761:         PetscViewerASCIIPrintf(viewer,"  INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));
1762:         PetscViewerASCIIPrintf(viewer,"  INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));
1763:         PetscViewerASCIIPrintf(viewer,"  INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));
1764:         PetscViewerASCIIPrintf(viewer,"  INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));
1765:         PetscViewerASCIIPrintf(viewer,"  INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));
1766:         PetscViewerASCIIPrintf(viewer,"  INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));
1767:         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));
1768:         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));
1769:         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));
1770:         PetscViewerASCIIPrintf(viewer,"  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",mumps->id.INFOG(19));
1771:         PetscViewerASCIIPrintf(viewer,"  INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));
1772:         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));
1773:         PetscViewerASCIIPrintf(viewer,"  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",mumps->id.INFOG(22));
1774:         PetscViewerASCIIPrintf(viewer,"  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));
1775:         PetscViewerASCIIPrintf(viewer,"  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));
1776:         PetscViewerASCIIPrintf(viewer,"  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));
1777:         PetscViewerASCIIPrintf(viewer,"  INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));
1778:         PetscViewerASCIIPrintf(viewer,"  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29));
1779:         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));
1780:         PetscViewerASCIIPrintf(viewer,"  INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));
1781:         PetscViewerASCIIPrintf(viewer,"  INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));
1782:         PetscViewerASCIIPrintf(viewer,"  INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));
1783:       }
1784:     }
1785:   }
1786:   return(0);
1787: }

1791: PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info)
1792: {
1793:   Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr;

1796:   info->block_size        = 1.0;
1797:   info->nz_allocated      = mumps->id.INFOG(20);
1798:   info->nz_used           = mumps->id.INFOG(20);
1799:   info->nz_unneeded       = 0.0;
1800:   info->assemblies        = 0.0;
1801:   info->mallocs           = 0.0;
1802:   info->memory            = 0.0;
1803:   info->fill_ratio_given  = 0;
1804:   info->fill_ratio_needed = 0;
1805:   info->factor_mallocs    = 0;
1806:   return(0);
1807: }

1809: /* -------------------------------------------------------------------------------------------*/
1812: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
1813: {
1814:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
1815:   const PetscInt *idxs;
1816:   PetscInt       size,i;

1820:   if (mumps->size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS parallel Schur complements not yet supported from PETSc\n");
1821:   ISGetLocalSize(is,&size);
1822:   if (mumps->id.size_schur != size) {
1823:     PetscFree2(mumps->id.listvar_schur,mumps->id.schur);
1824:     mumps->id.size_schur = size;
1825:     mumps->id.schur_lld = size;
1826:     PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);
1827:   }
1828:   ISGetIndices(is,&idxs);
1829:   PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));
1830:   /* MUMPS expects Fortran style indices */
1831:   for (i=0;i<size;i++) mumps->id.listvar_schur[i]++;
1832:   ISRestoreIndices(is,&idxs);
1833:   if (size) { /* turn on Schur switch if we the set of indices is not empty */
1834:     if (F->factortype == MAT_FACTOR_LU) {
1835:       mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
1836:     } else {
1837:       mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
1838:     }
1839:     /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
1840:     mumps->id.ICNTL(26) = -1;
1841:   }
1842:   return(0);
1843: }

1845: /* -------------------------------------------------------------------------------------------*/
1848: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S)
1849: {
1850:   Mat            St;
1851:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
1852:   PetscScalar    *array;
1853: #if defined(PETSC_USE_COMPLEX)
1854:   PetscScalar    im = PetscSqrtScalar((PetscScalar)-1.0);
1855: #endif

1859:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
1860:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

1862:   MatCreate(PetscObjectComm((PetscObject)F),&St);
1863:   MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);
1864:   MatSetType(St,MATDENSE);
1865:   MatSetUp(St);
1866:   MatDenseGetArray(St,&array);
1867:   if (!mumps->sym) { /* MUMPS always return a full matrix */
1868:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
1869:       PetscInt i,j,N=mumps->id.size_schur;
1870:       for (i=0;i<N;i++) {
1871:         for (j=0;j<N;j++) {
1872: #if !defined(PETSC_USE_COMPLEX)
1873:           PetscScalar val = mumps->id.schur[i*N+j];
1874: #else
1875:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
1876: #endif
1877:           array[j*N+i] = val;
1878:         }
1879:       }
1880:     } else { /* stored by columns */
1881:       PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));
1882:     }
1883:   } else { /* either full or lower-triangular (not packed) */
1884:     if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
1885:       PetscInt i,j,N=mumps->id.size_schur;
1886:       for (i=0;i<N;i++) {
1887:         for (j=i;j<N;j++) {
1888: #if !defined(PETSC_USE_COMPLEX)
1889:           PetscScalar val = mumps->id.schur[i*N+j];
1890: #else
1891:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
1892: #endif
1893:           array[i*N+j] = val;
1894:           array[j*N+i] = val;
1895:         }
1896:       }
1897:     } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
1898:       PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));
1899:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
1900:       PetscInt i,j,N=mumps->id.size_schur;
1901:       for (i=0;i<N;i++) {
1902:         for (j=0;j<i+1;j++) {
1903: #if !defined(PETSC_USE_COMPLEX)
1904:           PetscScalar val = mumps->id.schur[i*N+j];
1905: #else
1906:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
1907: #endif
1908:           array[i*N+j] = val;
1909:           array[j*N+i] = val;
1910:         }
1911:       }
1912:     }
1913:   }
1914:   MatDenseRestoreArray(St,&array);
1915:   *S = St;
1916:   return(0);
1917: }

1919: /* -------------------------------------------------------------------------------------------*/
1922: PetscErrorCode MatFactorGetSchurComplement_MUMPS(Mat F,Mat* S)
1923: {
1924:   Mat            St;
1925:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;

1929:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
1930:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

1932:   /* It should be the responsibility of the user to handle different ICNTL(19) cases and factorization stages if they want to work with the raw data */
1933:   MatCreateSeqDense(PetscObjectComm((PetscObject)F),mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&St);
1934:   *S = St;
1935:   return(0);
1936: }

1938: /* -------------------------------------------------------------------------------------------*/
1941: PetscErrorCode MatFactorInvertSchurComplement_MUMPS(Mat F)
1942: {
1943:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;

1947:   if (!mumps->id.ICNTL(19)) { /* do nothing */
1948:     return(0);
1949:   }
1950:   if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");
1951:   MatMumpsInvertSchur_Private(mumps);
1952:   return(0);
1953: }

1955: /* -------------------------------------------------------------------------------------------*/
1958: PetscErrorCode MatFactorSolveSchurComplement_MUMPS(Mat F, Vec rhs, Vec sol)
1959: {
1960:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
1961:   MumpsScalar    *orhs;
1962:   PetscScalar    *osol,*nrhs,*nsol;
1963:   PetscInt       orhs_size,osol_size,olrhs_size;

1967:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
1968:   if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

1970:   /* swap pointers */
1971:   orhs = mumps->id.redrhs;
1972:   olrhs_size = mumps->id.lredrhs;
1973:   orhs_size = mumps->sizeredrhs;
1974:   osol = mumps->schur_sol;
1975:   osol_size = mumps->schur_sizesol;
1976:   VecGetArray(rhs,&nrhs);
1977:   VecGetArray(sol,&nsol);
1978:   mumps->id.redrhs = (MumpsScalar*)nrhs;
1979:   VecGetLocalSize(rhs,&mumps->sizeredrhs);
1980:   mumps->id.lredrhs = mumps->sizeredrhs;
1981:   mumps->schur_sol = nsol;
1982:   VecGetLocalSize(sol,&mumps->schur_sizesol);

1984:   /* solve Schur complement */
1985:   mumps->id.nrhs = 1;
1986:   MatMumpsSolveSchur_Private(mumps,PETSC_FALSE);
1987:   /* restore pointers */
1988:   VecRestoreArray(rhs,&nrhs);
1989:   VecRestoreArray(sol,&nsol);
1990:   mumps->id.redrhs = orhs;
1991:   mumps->id.lredrhs = olrhs_size;
1992:   mumps->sizeredrhs = orhs_size;
1993:   mumps->schur_sol = osol;
1994:   mumps->schur_sizesol = osol_size;
1995:   return(0);
1996: }

1998: /* -------------------------------------------------------------------------------------------*/
2001: PetscErrorCode MatFactorSolveSchurComplementTranspose_MUMPS(Mat F, Vec rhs, Vec sol)
2002: {
2003:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
2004:   MumpsScalar    *orhs;
2005:   PetscScalar    *osol,*nrhs,*nsol;
2006:   PetscInt       orhs_size,osol_size;

2010:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
2011:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

2013:   /* swap pointers */
2014:   orhs = mumps->id.redrhs;
2015:   orhs_size = mumps->sizeredrhs;
2016:   osol = mumps->schur_sol;
2017:   osol_size = mumps->schur_sizesol;
2018:   VecGetArray(rhs,&nrhs);
2019:   VecGetArray(sol,&nsol);
2020:   mumps->id.redrhs = (MumpsScalar*)nrhs;
2021:   VecGetLocalSize(rhs,&mumps->sizeredrhs);
2022:   mumps->schur_sol = nsol;
2023:   VecGetLocalSize(sol,&mumps->schur_sizesol);

2025:   /* solve Schur complement */
2026:   mumps->id.nrhs = 1;
2027:   mumps->id.ICNTL(9) = 0;
2028:   MatMumpsSolveSchur_Private(mumps,PETSC_FALSE);
2029:   mumps->id.ICNTL(9) = 1;
2030:   /* restore pointers */
2031:   VecRestoreArray(rhs,&nrhs);
2032:   VecRestoreArray(sol,&nsol);
2033:   mumps->id.redrhs = orhs;
2034:   mumps->sizeredrhs = orhs_size;
2035:   mumps->schur_sol = osol;
2036:   mumps->schur_sizesol = osol_size;
2037:   return(0);
2038: }

2040: /* -------------------------------------------------------------------------------------------*/
2043: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival)
2044: {
2045:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2048:   mumps->id.ICNTL(icntl) = ival;
2049:   return(0);
2050: }

2054: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival)
2055: {
2056:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2059:   *ival = mumps->id.ICNTL(icntl);
2060:   return(0);
2061: }

2065: /*@
2066:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

2068:    Logically Collective on Mat

2070:    Input Parameters:
2071: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2072: .  icntl - index of MUMPS parameter array ICNTL()
2073: -  ival - value of MUMPS ICNTL(icntl)

2075:   Options Database:
2076: .   -mat_mumps_icntl_<icntl> <ival>

2078:    Level: beginner

2080:    References:
2081: .     MUMPS Users' Guide

2083: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2084:  @*/
2085: PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival)
2086: {
2088: 
2091:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2094:   PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
2095:   return(0);
2096: }

2100: /*@
2101:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

2103:    Logically Collective on Mat

2105:    Input Parameters:
2106: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2107: -  icntl - index of MUMPS parameter array ICNTL()

2109:   Output Parameter:
2110: .  ival - value of MUMPS ICNTL(icntl)

2112:    Level: beginner

2114:    References:
2115: .     MUMPS Users' Guide

2117: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2118: @*/
2119: PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival)
2120: {

2125:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2128:   PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
2129:   return(0);
2130: }

2132: /* -------------------------------------------------------------------------------------------*/
2135: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val)
2136: {
2137:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2140:   mumps->id.CNTL(icntl) = val;
2141:   return(0);
2142: }

2146: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val)
2147: {
2148:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2151:   *val = mumps->id.CNTL(icntl);
2152:   return(0);
2153: }

2157: /*@
2158:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

2160:    Logically Collective on Mat

2162:    Input Parameters:
2163: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2164: .  icntl - index of MUMPS parameter array CNTL()
2165: -  val - value of MUMPS CNTL(icntl)

2167:   Options Database:
2168: .   -mat_mumps_cntl_<icntl> <val>

2170:    Level: beginner

2172:    References:
2173: .     MUMPS Users' Guide

2175: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2176: @*/
2177: PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val)
2178: {

2183:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2186:   PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));
2187:   return(0);
2188: }

2192: /*@
2193:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

2195:    Logically Collective on Mat

2197:    Input Parameters:
2198: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2199: -  icntl - index of MUMPS parameter array CNTL()

2201:   Output Parameter:
2202: .  val - value of MUMPS CNTL(icntl)

2204:    Level: beginner

2206:    References:
2207: .      MUMPS Users' Guide

2209: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2210: @*/
2211: PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val)
2212: {

2217:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2220:   PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
2221:   return(0);
2222: }

2226: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info)
2227: {
2228:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2231:   *info = mumps->id.INFO(icntl);
2232:   return(0);
2233: }

2237: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog)
2238: {
2239:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2242:   *infog = mumps->id.INFOG(icntl);
2243:   return(0);
2244: }

2248: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo)
2249: {
2250:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2253:   *rinfo = mumps->id.RINFO(icntl);
2254:   return(0);
2255: }

2259: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog)
2260: {
2261:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

2264:   *rinfog = mumps->id.RINFOG(icntl);
2265:   return(0);
2266: }

2270: /*@
2271:   MatMumpsGetInfo - Get MUMPS parameter INFO()

2273:    Logically Collective on Mat

2275:    Input Parameters:
2276: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2277: -  icntl - index of MUMPS parameter array INFO()

2279:   Output Parameter:
2280: .  ival - value of MUMPS INFO(icntl)

2282:    Level: beginner

2284:    References:
2285: .      MUMPS Users' Guide

2287: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2288: @*/
2289: PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival)
2290: {

2295:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2297:   PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
2298:   return(0);
2299: }

2303: /*@
2304:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

2306:    Logically Collective on Mat

2308:    Input Parameters:
2309: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2310: -  icntl - index of MUMPS parameter array INFOG()

2312:   Output Parameter:
2313: .  ival - value of MUMPS INFOG(icntl)

2315:    Level: beginner

2317:    References:
2318: .      MUMPS Users' Guide

2320: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2321: @*/
2322: PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival)
2323: {

2328:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2330:   PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
2331:   return(0);
2332: }

2336: /*@
2337:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

2339:    Logically Collective on Mat

2341:    Input Parameters:
2342: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2343: -  icntl - index of MUMPS parameter array RINFO()

2345:   Output Parameter:
2346: .  val - value of MUMPS RINFO(icntl)

2348:    Level: beginner

2350:    References:
2351: .       MUMPS Users' Guide

2353: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2354: @*/
2355: PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val)
2356: {

2361:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2363:   PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
2364:   return(0);
2365: }

2369: /*@
2370:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

2372:    Logically Collective on Mat

2374:    Input Parameters:
2375: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
2376: -  icntl - index of MUMPS parameter array RINFOG()

2378:   Output Parameter:
2379: .  val - value of MUMPS RINFOG(icntl)

2381:    Level: beginner

2383:    References:
2384: .      MUMPS Users' Guide

2386: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
2387: @*/
2388: PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val)
2389: {

2394:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
2396:   PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
2397:   return(0);
2398: }

2400: /*MC
2401:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
2402:   distributed and sequential matrices via the external package MUMPS.

2404:   Works with MATAIJ and MATSBAIJ matrices

2406:   Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch  to have PETSc installed with MUMPS

2408:   Use -pc_type cholesky or lu -pc_factor_mat_solver_package mumps to us this direct solver

2410:   Options Database Keys:
2411: +  -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages 
2412: .  -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning 
2413: .  -mat_mumps_icntl_3 -  ICNTL(3): output stream for global information, collected on the host 
2414: .  -mat_mumps_icntl_4 -  ICNTL(4): level of printing (0 to 4) 
2415: .  -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7) 
2416: .  -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis 
2417: .  -mat_mumps_icntl_8  - ICNTL(8): scaling strategy (-2 to 8 or 77) 
2418: .  -mat_mumps_icntl_10  - ICNTL(10): max num of refinements 
2419: .  -mat_mumps_icntl_11  - ICNTL(11): statistics related to an error analysis (via -ksp_view) 
2420: .  -mat_mumps_icntl_12  - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3) 
2421: .  -mat_mumps_icntl_13  - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting 
2422: .  -mat_mumps_icntl_14  - ICNTL(14): percentage increase in the estimated working space 
2423: .  -mat_mumps_icntl_19  - ICNTL(19): computes the Schur complement 
2424: .  -mat_mumps_icntl_22  - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1) 
2425: .  -mat_mumps_icntl_23  - ICNTL(23): max size of the working memory (MB) that can allocate per processor 
2426: .  -mat_mumps_icntl_24  - ICNTL(24): detection of null pivot rows (0 or 1) 
2427: .  -mat_mumps_icntl_25  - ICNTL(25): compute a solution of a deficient matrix and a null space basis 
2428: .  -mat_mumps_icntl_26  - ICNTL(26): drives the solution phase if a Schur complement matrix 
2429: .  -mat_mumps_icntl_28  - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering 
2430: .  -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis 
2431: .  -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A) 
2432: .  -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization 
2433: .  -mat_mumps_icntl_33 - ICNTL(33): compute determinant 
2434: .  -mat_mumps_cntl_1  - CNTL(1): relative pivoting threshold 
2435: .  -mat_mumps_cntl_2  -  CNTL(2): stopping criterion of refinement 
2436: .  -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold 
2437: .  -mat_mumps_cntl_4 - CNTL(4): value for static pivoting 
2438: -  -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots 

2440:   Level: beginner

2442:     Notes: When a MUMPS factorization fails inside a KSP solve, for example with a KSP_DIVERGED_PCSETUP_FAILED, one can find the MUMPS information about the failure by calling 
2443: $          KSPGetPC(ksp,&pc);
2444: $          PCFactorGetMatrix(pc,&mat);
2445: $          MatMumpsGetInfo(mat,....);
2446: $          MatMumpsGetInfog(mat,....); etc.
2447:            Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message.

2449: .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix()

2451: M*/

2455: static PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type)
2456: {
2458:   *type = MATSOLVERMUMPS;
2459:   return(0);
2460: }

2462: /* MatGetFactor for Seq and MPI AIJ matrices */
2465: static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F)
2466: {
2467:   Mat            B;
2469:   Mat_MUMPS      *mumps;
2470:   PetscBool      isSeqAIJ;

2473:   /* Create the factorization matrix */
2474:   PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
2475:   MatCreate(PetscObjectComm((PetscObject)A),&B);
2476:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
2477:   MatSetType(B,((PetscObject)A)->type_name);
2478:   if (isSeqAIJ) {
2479:     MatSeqAIJSetPreallocation(B,0,NULL);
2480:   } else {
2481:     MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
2482:   }

2484:   PetscNewLog(B,&mumps);

2486:   B->ops->view        = MatView_MUMPS;
2487:   B->ops->getinfo     = MatGetInfo_MUMPS;
2488:   B->ops->getdiagonal = MatGetDiagonal_MUMPS;

2490:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
2491:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
2492:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
2493:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
2494:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
2495:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
2496:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
2497:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
2498:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
2499:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
2500:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
2501:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
2502:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
2503:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
2504:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

2506:   if (ftype == MAT_FACTOR_LU) {
2507:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
2508:     B->factortype            = MAT_FACTOR_LU;
2509:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
2510:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
2511:     mumps->sym = 0;
2512:   } else {
2513:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
2514:     B->factortype                  = MAT_FACTOR_CHOLESKY;
2515:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
2516:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
2517: #if defined(PETSC_USE_COMPLEX)
2518:     mumps->sym = 2;
2519: #else
2520:     if (A->spd_set && A->spd) mumps->sym = 1;
2521:     else                      mumps->sym = 2;
2522: #endif
2523:   }

2525:   /* set solvertype */
2526:   PetscFree(B->solvertype);
2527:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

2529:   mumps->isAIJ    = PETSC_TRUE;
2530:   mumps->Destroy  = B->ops->destroy;
2531:   B->ops->destroy = MatDestroy_MUMPS;
2532:   B->spptr        = (void*)mumps;

2534:   PetscInitializeMUMPS(A,mumps);

2536:   *F = B;
2537:   return(0);
2538: }

2540: /* MatGetFactor for Seq and MPI SBAIJ matrices */
2543: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F)
2544: {
2545:   Mat            B;
2547:   Mat_MUMPS      *mumps;
2548:   PetscBool      isSeqSBAIJ;

2551:   if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix");
2552:   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");
2553:   PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
2554:   /* Create the factorization matrix */
2555:   MatCreate(PetscObjectComm((PetscObject)A),&B);
2556:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
2557:   MatSetType(B,((PetscObject)A)->type_name);
2558:   PetscNewLog(B,&mumps);
2559:   if (isSeqSBAIJ) {
2560:     MatSeqSBAIJSetPreallocation(B,1,0,NULL);

2562:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
2563:   } else {
2564:     MatMPISBAIJSetPreallocation(B,1,0,NULL,0,NULL);

2566:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
2567:   }

2569:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
2570:   B->ops->view                   = MatView_MUMPS;
2571:   B->ops->getdiagonal            = MatGetDiagonal_MUMPS;

2573:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
2574:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
2575:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
2576:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
2577:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
2578:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
2579:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
2580:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
2581:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
2582:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
2583:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
2584:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
2585:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
2586:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
2587:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

2589:   B->factortype = MAT_FACTOR_CHOLESKY;
2590: #if defined(PETSC_USE_COMPLEX)
2591:   mumps->sym = 2;
2592: #else
2593:   if (A->spd_set && A->spd) mumps->sym = 1;
2594:   else                      mumps->sym = 2;
2595: #endif

2597:   /* set solvertype */
2598:   PetscFree(B->solvertype);
2599:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

2601:   mumps->isAIJ    = PETSC_FALSE;
2602:   mumps->Destroy  = B->ops->destroy;
2603:   B->ops->destroy = MatDestroy_MUMPS;
2604:   B->spptr        = (void*)mumps;

2606:   PetscInitializeMUMPS(A,mumps);

2608:   *F = B;
2609:   return(0);
2610: }

2614: static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F)
2615: {
2616:   Mat            B;
2618:   Mat_MUMPS      *mumps;
2619:   PetscBool      isSeqBAIJ;

2622:   /* Create the factorization matrix */
2623:   PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
2624:   MatCreate(PetscObjectComm((PetscObject)A),&B);
2625:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
2626:   MatSetType(B,((PetscObject)A)->type_name);
2627:   if (isSeqBAIJ) {
2628:     MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,NULL);
2629:   } else {
2630:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,NULL,0,NULL);
2631:   }

2633:   PetscNewLog(B,&mumps);
2634:   if (ftype == MAT_FACTOR_LU) {
2635:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
2636:     B->factortype            = MAT_FACTOR_LU;
2637:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
2638:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
2639:     mumps->sym = 0;
2640:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n");

2642:   B->ops->view        = MatView_MUMPS;
2643:   B->ops->getdiagonal = MatGetDiagonal_MUMPS;

2645:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
2646:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
2647:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
2648:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
2649:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
2650:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
2651:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
2652:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
2653:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
2654:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
2655:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
2656:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
2657:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
2658:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
2659:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

2661:   /* set solvertype */
2662:   PetscFree(B->solvertype);
2663:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

2665:   mumps->isAIJ    = PETSC_TRUE;
2666:   mumps->Destroy  = B->ops->destroy;
2667:   B->ops->destroy = MatDestroy_MUMPS;
2668:   B->spptr        = (void*)mumps;

2670:   PetscInitializeMUMPS(A,mumps);

2672:   *F = B;
2673:   return(0);
2674: }

2678: PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_MUMPS(void)
2679: {

2683:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
2684:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
2685:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
2686:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
2687:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
2688:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
2689:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
2690:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
2691:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
2692:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
2693:   return(0);
2694: }