Date: 2016-07-24T16:54:07+00:00

Actual source code: mumps.c
petsc-3.7.3 2016-07-24
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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;

    504:   ai   =aa->i; aj=aa->j;av=aa->a;
    505:   adiag=aa->diag;
    506:   if (reuse == MAT_INITIAL_MATRIX) {
    507:     /* count nz in the uppper triangular part of A */
    508:     nz = 0;
    509:     for (i=0; i<M; i++) nz += ai[i+1] - adiag[i];
    510:     *nnz = nz;

    512:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
    513:     col  = row + nz;
    514:     val  = (PetscScalar*)(col + nz);

    516:     nz = 0;
    517:     for (i=0; i<M; i++) {
    518:       rnz = ai[i+1] - adiag[i];
    519:       ajj = aj + adiag[i];
    520:       v1  = av + adiag[i];
    521:       for (j=0; j<rnz; j++) {
    522:         row[nz] = i+shift; col[nz] = ajj[j] + shift; val[nz++] = v1[j];
    523:       }
    524:     }
    525:     *r = row; *c = col; *v = val;
    526:   } else {
    527:     nz = 0; val = *v;
    528:     for (i=0; i <M; i++) {
    529:       rnz = ai[i+1] - adiag[i];
    530:       ajj = aj + adiag[i];
    531:       v1  = av + adiag[i];
    532:       for (j=0; j<rnz; j++) {
    533:         val[nz++] = v1[j];
    534:       }
    535:     }
    536:   }
    537:   return(0);
    538: }

    542: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
    543: {
    544:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
    545:   PetscErrorCode    ierr;
    546:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
    547:   PetscInt          *row,*col;
    548:   const PetscScalar *av, *bv,*v1,*v2;
    549:   PetscScalar       *val;
    550:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ*)A->data;
    551:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ*)(mat->A)->data;
    552:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ*)(mat->B)->data;

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

    558:   garray = mat->garray;

    560:   if (reuse == MAT_INITIAL_MATRIX) {
    561:     nz   = aa->nz + bb->nz;
    562:     *nnz = nz;
    563:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
    564:     col  = row + nz;
    565:     val  = (PetscScalar*)(col + nz);

    567:     *r = row; *c = col; *v = val;
    568:   } else {
    569:     row = *r; col = *c; val = *v;
    570:   }

    572:   jj = 0; irow = rstart;
    573:   for (i=0; i<m; i++) {
    574:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
    575:     countA = ai[i+1] - ai[i];
    576:     countB = bi[i+1] - bi[i];
    577:     bjj    = bj + bi[i];
    578:     v1     = av + ai[i];
    579:     v2     = bv + bi[i];

    581:     /* A-part */
    582:     for (j=0; j<countA; j++) {
    583:       if (reuse == MAT_INITIAL_MATRIX) {
    584:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
    585:       }
    586:       val[jj++] = v1[j];
    587:     }

    589:     /* B-part */
    590:     for (j=0; j < countB; j++) {
    591:       if (reuse == MAT_INITIAL_MATRIX) {
    592:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
    593:       }
    594:       val[jj++] = v2[j];
    595:     }
    596:     irow++;
    597:   }
    598:   return(0);
    599: }

    603: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
    604: {
    605:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
    606:   PetscErrorCode    ierr;
    607:   PetscInt          rstart,nz,i,j,jj,irow,countA,countB;
    608:   PetscInt          *row,*col;
    609:   const PetscScalar *av, *bv,*v1,*v2;
    610:   PetscScalar       *val;
    611:   Mat_MPIAIJ        *mat = (Mat_MPIAIJ*)A->data;
    612:   Mat_SeqAIJ        *aa  = (Mat_SeqAIJ*)(mat->A)->data;
    613:   Mat_SeqAIJ        *bb  = (Mat_SeqAIJ*)(mat->B)->data;

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

    619:   garray = mat->garray;

    621:   if (reuse == MAT_INITIAL_MATRIX) {
    622:     nz   = aa->nz + bb->nz;
    623:     *nnz = nz;
    624:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
    625:     col  = row + nz;
    626:     val  = (PetscScalar*)(col + nz);

    628:     *r = row; *c = col; *v = val;
    629:   } else {
    630:     row = *r; col = *c; val = *v;
    631:   }

    633:   jj = 0; irow = rstart;
    634:   for (i=0; i<m; i++) {
    635:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
    636:     countA = ai[i+1] - ai[i];
    637:     countB = bi[i+1] - bi[i];
    638:     bjj    = bj + bi[i];
    639:     v1     = av + ai[i];
    640:     v2     = bv + bi[i];

    642:     /* A-part */
    643:     for (j=0; j<countA; j++) {
    644:       if (reuse == MAT_INITIAL_MATRIX) {
    645:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
    646:       }
    647:       val[jj++] = v1[j];
    648:     }

    650:     /* B-part */
    651:     for (j=0; j < countB; j++) {
    652:       if (reuse == MAT_INITIAL_MATRIX) {
    653:         row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
    654:       }
    655:       val[jj++] = v2[j];
    656:     }
    657:     irow++;
    658:   }
    659:   return(0);
    660: }

    664: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
    665: {
    666:   Mat_MPIBAIJ       *mat    = (Mat_MPIBAIJ*)A->data;
    667:   Mat_SeqBAIJ       *aa     = (Mat_SeqBAIJ*)(mat->A)->data;
    668:   Mat_SeqBAIJ       *bb     = (Mat_SeqBAIJ*)(mat->B)->data;
    669:   const PetscInt    *ai     = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj;
    670:   const PetscInt    *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart;
    671:   const PetscInt    bs2=mat->bs2;
    672:   PetscErrorCode    ierr;
    673:   PetscInt          bs,nz,i,j,k,n,jj,irow,countA,countB,idx;
    674:   PetscInt          *row,*col;
    675:   const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2;
    676:   PetscScalar       *val;

    679:   MatGetBlockSize(A,&bs);
    680:   if (reuse == MAT_INITIAL_MATRIX) {
    681:     nz   = bs2*(aa->nz + bb->nz);
    682:     *nnz = nz;
    683:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
    684:     col  = row + nz;
    685:     val  = (PetscScalar*)(col + nz);

    687:     *r = row; *c = col; *v = val;
    688:   } else {
    689:     row = *r; col = *c; val = *v;
    690:   }

    692:   jj = 0; irow = rstart;
    693:   for (i=0; i<mbs; i++) {
    694:     countA = ai[i+1] - ai[i];
    695:     countB = bi[i+1] - bi[i];
    696:     ajj    = aj + ai[i];
    697:     bjj    = bj + bi[i];
    698:     v1     = av + bs2*ai[i];
    699:     v2     = bv + bs2*bi[i];

    701:     idx = 0;
    702:     /* A-part */
    703:     for (k=0; k<countA; k++) {
    704:       for (j=0; j<bs; j++) {
    705:         for (n=0; n<bs; n++) {
    706:           if (reuse == MAT_INITIAL_MATRIX) {
    707:             row[jj] = irow + n + shift;
    708:             col[jj] = rstart + bs*ajj[k] + j + shift;
    709:           }
    710:           val[jj++] = v1[idx++];
    711:         }
    712:       }
    713:     }

    715:     idx = 0;
    716:     /* B-part */
    717:     for (k=0; k<countB; k++) {
    718:       for (j=0; j<bs; j++) {
    719:         for (n=0; n<bs; n++) {
    720:           if (reuse == MAT_INITIAL_MATRIX) {
    721:             row[jj] = irow + n + shift;
    722:             col[jj] = bs*garray[bjj[k]] + j + shift;
    723:           }
    724:           val[jj++] = v2[idx++];
    725:         }
    726:       }
    727:     }
    728:     irow += bs;
    729:   }
    730:   return(0);
    731: }

    735: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,int shift,MatReuse reuse,int *nnz,int **r, int **c, PetscScalar **v)
    736: {
    737:   const PetscInt    *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
    738:   PetscErrorCode    ierr;
    739:   PetscInt          rstart,nz,nza,nzb,i,j,jj,irow,countA,countB;
    740:   PetscInt          *row,*col;
    741:   const PetscScalar *av, *bv,*v1,*v2;
    742:   PetscScalar       *val;
    743:   Mat_MPIAIJ        *mat =  (Mat_MPIAIJ*)A->data;
    744:   Mat_SeqAIJ        *aa  =(Mat_SeqAIJ*)(mat->A)->data;
    745:   Mat_SeqAIJ        *bb  =(Mat_SeqAIJ*)(mat->B)->data;

    748:   ai=aa->i; aj=aa->j; adiag=aa->diag;
    749:   bi=bb->i; bj=bb->j; garray = mat->garray;
    750:   av=aa->a; bv=bb->a;

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

    754:   if (reuse == MAT_INITIAL_MATRIX) {
    755:     nza = 0;    /* num of upper triangular entries in mat->A, including diagonals */
    756:     nzb = 0;    /* num of upper triangular entries in mat->B */
    757:     for (i=0; i<m; i++) {
    758:       nza   += (ai[i+1] - adiag[i]);
    759:       countB = bi[i+1] - bi[i];
    760:       bjj    = bj + bi[i];
    761:       for (j=0; j<countB; j++) {
    762:         if (garray[bjj[j]] > rstart) nzb++;
    763:       }
    764:     }

    766:     nz   = nza + nzb; /* total nz of upper triangular part of mat */
    767:     *nnz = nz;
    768:     PetscMalloc((2*nz*sizeof(PetscInt)+nz*sizeof(PetscScalar)), &row);
    769:     col  = row + nz;
    770:     val  = (PetscScalar*)(col + nz);

    772:     *r = row; *c = col; *v = val;
    773:   } else {
    774:     row = *r; col = *c; val = *v;
    775:   }

    777:   jj = 0; irow = rstart;
    778:   for (i=0; i<m; i++) {
    779:     ajj    = aj + adiag[i];                 /* ptr to the beginning of the diagonal of this row */
    780:     v1     = av + adiag[i];
    781:     countA = ai[i+1] - adiag[i];
    782:     countB = bi[i+1] - bi[i];
    783:     bjj    = bj + bi[i];
    784:     v2     = bv + bi[i];

    786:     /* A-part */
    787:     for (j=0; j<countA; j++) {
    788:       if (reuse == MAT_INITIAL_MATRIX) {
    789:         row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
    790:       }
    791:       val[jj++] = v1[j];
    792:     }

    794:     /* B-part */
    795:     for (j=0; j < countB; j++) {
    796:       if (garray[bjj[j]] > rstart) {
    797:         if (reuse == MAT_INITIAL_MATRIX) {
    798:           row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
    799:         }
    800:         val[jj++] = v2[j];
    801:       }
    802:     }
    803:     irow++;
    804:   }
    805:   return(0);
    806: }

    810: PetscErrorCode MatGetDiagonal_MUMPS(Mat A,Vec v)
    811: {
    813:   SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type: MUMPS factor");
    814:   return(0);
    815: }

    819: PetscErrorCode MatDestroy_MUMPS(Mat A)
    820: {
    821:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

    825:   PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
    826:   VecScatterDestroy(&mumps->scat_rhs);
    827:   VecScatterDestroy(&mumps->scat_sol);
    828:   VecDestroy(&mumps->b_seq);
    829:   VecDestroy(&mumps->x_seq);
    830:   PetscFree(mumps->id.perm_in);
    831:   PetscFree(mumps->irn);
    832:   PetscFree(mumps->info);
    833:   MatMumpsResetSchur_Private(mumps);
    834:   mumps->id.job = JOB_END;
    835:   PetscMUMPS_c(&mumps->id);
    836:   MPI_Comm_free(&mumps->comm_mumps);
    837:   if (mumps->Destroy) {
    838:     (mumps->Destroy)(A);
    839:   }
    840:   PetscFree(A->spptr);

    842:   /* clear composed functions */
    843:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverPackage_C",NULL);
    844:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
    845:   PetscObjectComposeFunction((PetscObject)A,"MatFactorInvertSchurComplement_C",NULL);
    846:   PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);
    847:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSchurComplement_C",NULL);
    848:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSolveSchurComplement_C",NULL);
    849:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSolveSchurComplementTranspose_C",NULL);
    850:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);
    851:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);
    852:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);
    853:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);
    854:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);
    855:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);
    856:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);
    857:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);
    858:   return(0);
    859: }

    863: PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x)
    864: {
    865:   Mat_MUMPS        *mumps=(Mat_MUMPS*)A->spptr;
    866:   PetscScalar      *array;
    867:   Vec              b_seq;
    868:   IS               is_iden,is_petsc;
    869:   PetscErrorCode   ierr;
    870:   PetscInt         i;
    871:   PetscBool        second_solve = PETSC_FALSE;
    872:   static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE;

    875:   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);
    876:   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);

    878:   if (A->errortype) {
    879:     PetscInfo2(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    880:     VecSetInf(x);
    881:     return(0);
    882:   }

    884:   mumps->id.nrhs = 1;
    885:   b_seq          = mumps->b_seq;
    886:   if (mumps->size > 1) {
    887:     /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */
    888:     VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    889:     VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    890:     if (!mumps->myid) {VecGetArray(b_seq,&array);}
    891:   } else {  /* size == 1 */
    892:     VecCopy(b,x);
    893:     VecGetArray(x,&array);
    894:   }
    895:   if (!mumps->myid) { /* define rhs on the host */
    896:     mumps->id.nrhs = 1;
    897:     mumps->id.rhs = (MumpsScalar*)array;
    898:   }

    900:   /*
    901:      handle condensation step of Schur complement (if any)
    902:      We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
    903:      According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
    904:      Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
    905:      This requires an extra call to PetscMUMPS_c and the computation of the factors for S
    906:   */
    907:   if (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2) {
    908:     second_solve = PETSC_TRUE;
    909:     MatMumpsHandleSchur_Private(mumps,PETSC_FALSE);
    910:   }
    911:   /* solve phase */
    912:   /*-------------*/
    913:   mumps->id.job = JOB_SOLVE;
    914:   PetscMUMPS_c(&mumps->id);
    915:   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));

    917:   /* handle expansion step of Schur complement (if any) */
    918:   if (second_solve) {
    919:     MatMumpsHandleSchur_Private(mumps,PETSC_TRUE);
    920:   }

    922:   if (mumps->size > 1) { /* convert mumps distributed solution to petsc mpi x */
    923:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
    924:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
    925:       VecScatterDestroy(&mumps->scat_sol);
    926:     }
    927:     if (!mumps->scat_sol) { /* create scatter scat_sol */
    928:       ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden); /* from */
    929:       for (i=0; i<mumps->id.lsol_loc; i++) {
    930:         mumps->id.isol_loc[i] -= 1; /* change Fortran style to C style */
    931:       }
    932:       ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,mumps->id.isol_loc,PETSC_COPY_VALUES,&is_petsc);  /* to */
    933:       VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);
    934:       ISDestroy(&is_iden);
    935:       ISDestroy(&is_petsc);

    937:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
    938:     }

    940:     VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
    941:     VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
    942:   }
    943:   return(0);
    944: }

    948: PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x)
    949: {
    950:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

    954:   mumps->id.ICNTL(9) = 0;
    955:   MatSolve_MUMPS(A,b,x);
    956:   mumps->id.ICNTL(9) = 1;
    957:   return(0);
    958: }

    962: PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X)
    963: {
    965:   PetscBool      flg;
    966:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;
    967:   PetscInt       i,nrhs,M;
    968:   PetscScalar    *array,*bray;

    971:   PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
    972:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
    973:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
    974:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
    975:   if (B->rmap->n != X->rmap->n) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution");

    977:   MatGetSize(B,&M,&nrhs);
    978:   mumps->id.nrhs = nrhs;
    979:   mumps->id.lrhs = M;

    981:   if (mumps->size == 1) {
    982:     /* copy B to X */
    983:     MatDenseGetArray(B,&bray);
    984:     MatDenseGetArray(X,&array);
    985:     PetscMemcpy(array,bray,M*nrhs*sizeof(PetscScalar));
    986:     MatDenseRestoreArray(B,&bray);
    987:     mumps->id.rhs = (MumpsScalar*)array;
    988:     /* handle condensation step of Schur complement (if any) */
    989:     MatMumpsHandleSchur_Private(mumps,PETSC_FALSE);

    991:     /* solve phase */
    992:     /*-------------*/
    993:     mumps->id.job = JOB_SOLVE;
    994:     PetscMUMPS_c(&mumps->id);
    995:     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));

    997:     /* handle expansion step of Schur complement (if any) */
    998:     MatMumpsHandleSchur_Private(mumps,PETSC_TRUE);
    999:     MatDenseRestoreArray(X,&array);
    1000:   } else {  /*--------- parallel case --------*/
    1001:     PetscInt       lsol_loc,nlsol_loc,*isol_loc,*idx,*iidx,*idxx,*isol_loc_save;
    1002:     MumpsScalar    *sol_loc,*sol_loc_save;
    1003:     IS             is_to,is_from;
    1004:     PetscInt       k,proc,j,m;
    1005:     const PetscInt *rstart;
    1006:     Vec            v_mpi,b_seq,x_seq;
    1007:     VecScatter     scat_rhs,scat_sol;

    1009:     /* create x_seq to hold local solution */
    1010:     isol_loc_save = mumps->id.isol_loc; /* save it for MatSovle() */
    1011:     sol_loc_save  = mumps->id.sol_loc;

    1013:     lsol_loc  = mumps->id.INFO(23);
    1014:     nlsol_loc = nrhs*lsol_loc;     /* length of sol_loc */
    1015:     PetscMalloc2(nlsol_loc,&sol_loc,nlsol_loc,&isol_loc);
    1016:     mumps->id.sol_loc = (MumpsScalar*)sol_loc;
    1017:     mumps->id.isol_loc = isol_loc;

    1019:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&x_seq);

    1021:     /* copy rhs matrix B into vector v_mpi */
    1022:     MatGetLocalSize(B,&m,NULL);
    1023:     MatDenseGetArray(B,&bray);
    1024:     VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);
    1025:     MatDenseRestoreArray(B,&bray);

    1027:     /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */
    1028:     /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B;
    1029:       iidx: inverse of idx, will be used by scattering xx_seq -> X       */
    1030:     PetscMalloc2(nrhs*M,&idx,nrhs*M,&iidx);
    1031:     MatGetOwnershipRanges(B,&rstart);
    1032:     k = 0;
    1033:     for (proc=0; proc<mumps->size; proc++){
    1034:       for (j=0; j<nrhs; j++){
    1035:         for (i=rstart[proc]; i<rstart[proc+1]; i++){
    1036:           iidx[j*M + i] = k;
    1037:           idx[k++]      = j*M + i;
    1038:         }
    1039:       }
    1040:     }

    1042:     if (!mumps->myid) {
    1043:       VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);
    1044:       ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_COPY_VALUES,&is_to);
    1045:       ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);
    1046:     } else {
    1047:       VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);
    1048:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);
    1049:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);
    1050:     }
    1051:     VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);
    1052:     VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    1053:     ISDestroy(&is_to);
    1054:     ISDestroy(&is_from);
    1055:     VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);

    1057:     if (!mumps->myid) { /* define rhs on the host */
    1058:       VecGetArray(b_seq,&bray);
    1059:       mumps->id.rhs = (MumpsScalar*)bray;
    1060:       VecRestoreArray(b_seq,&bray);
    1061:     }

    1063:     /* solve phase */
    1064:     /*-------------*/
    1065:     mumps->id.job = JOB_SOLVE;
    1066:     PetscMUMPS_c(&mumps->id);
    1067:     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));

    1069:     /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
    1070:     MatDenseGetArray(X,&array);
    1071:     VecPlaceArray(v_mpi,array);
    1072:
    1073:     /* create scatter scat_sol */
    1074:     PetscMalloc1(nlsol_loc,&idxx);
    1075:     ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);
    1076:     for (i=0; i<lsol_loc; i++) {
    1077:       isol_loc[i] -= 1; /* change Fortran style to C style */
    1078:       idxx[i] = iidx[isol_loc[i]];
    1079:       for (j=1; j<nrhs; j++){
    1080:         idxx[j*lsol_loc+i] = iidx[isol_loc[i]+j*M];
    1081:       }
    1082:     }
    1083:     ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);
    1084:     VecScatterCreate(x_seq,is_from,v_mpi,is_to,&scat_sol);
    1085:     VecScatterBegin(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
    1086:     ISDestroy(&is_from);
    1087:     ISDestroy(&is_to);
    1088:     VecScatterEnd(scat_sol,x_seq,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
    1089:     MatDenseRestoreArray(X,&array);

    1091:     /* free spaces */
    1092:     mumps->id.sol_loc = sol_loc_save;
    1093:     mumps->id.isol_loc = isol_loc_save;

    1095:     PetscFree2(sol_loc,isol_loc);
    1096:     PetscFree2(idx,iidx);
    1097:     PetscFree(idxx);
    1098:     VecDestroy(&x_seq);
    1099:     VecDestroy(&v_mpi);
    1100:     VecDestroy(&b_seq);
    1101:     VecScatterDestroy(&scat_rhs);
    1102:     VecScatterDestroy(&scat_sol);
    1103:   }
    1104:   return(0);
    1105: }

    1107: #if !defined(PETSC_USE_COMPLEX)
    1108: /*
    1109:   input:
    1110:    F:        numeric factor
    1111:   output:
    1112:    nneg:     total number of negative pivots
    1113:    nzero:    0
    1114:    npos:     (global dimension of F) - nneg
    1115: */

    1119: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos)
    1120: {
    1121:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
    1123:   PetscMPIInt    size;

    1126:   MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);
    1127:   /* 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 */
    1128:   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));

    1130:   if (nneg) *nneg = mumps->id.INFOG(12);
    1131:   if (nzero || npos) {
    1132:     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");
    1133:     if (nzero) *nzero = mumps->id.INFOG(28);
    1134:     if (npos) *npos   = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
    1135:   }
    1136:   return(0);
    1137: }
    1138: #endif /* !defined(PETSC_USE_COMPLEX) */

    1142: PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info)
    1143: {
    1144:   Mat_MUMPS      *mumps =(Mat_MUMPS*)(F)->spptr;
    1146:   Mat            F_diag;
    1147:   PetscBool      isMPIAIJ;

    1150:   if (mumps->id.INFOG(1) < 0) {
    1151:     if (mumps->id.INFOG(1) == -6) {
    1152:       PetscInfo2(A,"MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1153:     }
    1154:     PetscInfo2(A,"MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1155:     return(0);
    1156:   }

    1158:   (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

    1160:   /* numerical factorization phase */
    1161:   /*-------------------------------*/
    1162:   mumps->id.job = JOB_FACTNUMERIC;
    1163:   if (!mumps->id.ICNTL(18)) { /* A is centralized */
    1164:     if (!mumps->myid) {
    1165:       mumps->id.a = (MumpsScalar*)mumps->val;
    1166:     }
    1167:   } else {
    1168:     mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1169:   }
    1170:   PetscMUMPS_c(&mumps->id);
    1171:   if (mumps->id.INFOG(1) < 0) {
    1172:     if (A->erroriffailure) {
    1173:       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));
    1174:     } else {
    1175:       if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
    1176:         PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1177:         F->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1178:       } else if (mumps->id.INFOG(1) == -13) {
    1179:         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));
    1180:         F->errortype = MAT_FACTOR_OUTMEMORY;
    1181:       } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) {
    1182:         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));
    1183:         F->errortype = MAT_FACTOR_OUTMEMORY;
    1184:       } else {
    1185:         PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1186:         F->errortype = MAT_FACTOR_OTHER;
    1187:       }
    1188:     }
    1189:   }
    1190:   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));

    1192:   (F)->assembled        = PETSC_TRUE;
    1193:   mumps->matstruc       = SAME_NONZERO_PATTERN;
    1194:   mumps->schur_factored = PETSC_FALSE;
    1195:   mumps->schur_inverted = PETSC_FALSE;

    1197:   /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
    1198:   if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;

    1200:   if (mumps->size > 1) {
    1201:     PetscInt    lsol_loc;
    1202:     PetscScalar *sol_loc;

    1204:     PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);
    1205:     if (isMPIAIJ) F_diag = ((Mat_MPIAIJ*)(F)->data)->A;
    1206:     else F_diag = ((Mat_MPISBAIJ*)(F)->data)->A;
    1207:     F_diag->assembled = PETSC_TRUE;

    1209:     /* distributed solution; Create x_seq=sol_loc for repeated use */
    1210:     if (mumps->x_seq) {
    1211:       VecScatterDestroy(&mumps->scat_sol);
    1212:       PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
    1213:       VecDestroy(&mumps->x_seq);
    1214:     }
    1215:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
    1216:     PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);
    1217:     mumps->id.lsol_loc = lsol_loc;
    1218:     mumps->id.sol_loc = (MumpsScalar*)sol_loc;
    1219:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);
    1220:   }
    1221:   return(0);
    1222: }

    1224: /* Sets MUMPS options from the options database */
    1227: PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A)
    1228: {
    1229:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
    1231:   PetscInt       icntl,info[40],i,ninfo=40;
    1232:   PetscBool      flg;

    1235:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");
    1236:   PetscOptionsInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);
    1237:   if (flg) mumps->id.ICNTL(1) = icntl;
    1238:   PetscOptionsInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg);
    1239:   if (flg) mumps->id.ICNTL(2) = icntl;
    1240:   PetscOptionsInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg);
    1241:   if (flg) mumps->id.ICNTL(3) = icntl;

    1243:   PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);
    1244:   if (flg) mumps->id.ICNTL(4) = icntl;
    1245:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

    1247:   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);
    1248:   if (flg) mumps->id.ICNTL(6) = icntl;

    1250:   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);
    1251:   if (flg) {
    1252:     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");
    1253:     else mumps->id.ICNTL(7) = icntl;
    1254:   }

    1256:   PetscOptionsInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL);
    1257:   /* 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() */
    1258:   PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);
    1259:   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);
    1260:   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);
    1261:   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);
    1262:   PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage increase in the estimated working space","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL);
    1263:   PetscOptionsInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);
    1264:   if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
    1265:     MatMumpsResetSchur_Private(mumps);
    1266:   }
    1267:   /* 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 */
    1268:   /* 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 */

    1270:   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);
    1271:   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);
    1272:   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);
    1273:   if (mumps->id.ICNTL(24)) {
    1274:     mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */
    1275:   }

    1277:   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);
    1278:   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);
    1279:   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);
    1280:   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);
    1281:   PetscOptionsInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL);
    1282:   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);
    1283:   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);
    1284:   /* 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 */
    1285:   PetscOptionsInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);

    1287:   PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);
    1288:   PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);
    1289:   PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);
    1290:   PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);
    1291:   PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);

    1293:   PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);

    1295:   PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);
    1296:   if (ninfo) {
    1297:     if (ninfo > 40) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 40\n",ninfo);
    1298:     PetscMalloc1(ninfo,&mumps->info);
    1299:     mumps->ninfo = ninfo;
    1300:     for (i=0; i<ninfo; i++) {
    1301:       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);
    1302:       else {
    1303:         mumps->info[i] = info[i];
    1304:       }
    1305:     }
    1306:   }

    1308:   PetscOptionsEnd();
    1309:   return(0);
    1310: }

    1314: PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps)
    1315: {

    1319:   MPI_Comm_rank(PetscObjectComm((PetscObject)A), &mumps->myid);
    1320:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&mumps->size);
    1321:   MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mumps->comm_mumps));

    1323:   mumps->id.comm_fortran = MPI_Comm_c2f(mumps->comm_mumps);

    1325:   mumps->id.job = JOB_INIT;
    1326:   mumps->id.par = 1;  /* host participates factorizaton and solve */
    1327:   mumps->id.sym = mumps->sym;
    1328:   PetscMUMPS_c(&mumps->id);

    1330:   mumps->scat_rhs     = NULL;
    1331:   mumps->scat_sol     = NULL;

    1333:   /* set PETSc-MUMPS default options - override MUMPS default */
    1334:   mumps->id.ICNTL(3) = 0;
    1335:   mumps->id.ICNTL(4) = 0;
    1336:   if (mumps->size == 1) {
    1337:     mumps->id.ICNTL(18) = 0;   /* centralized assembled matrix input */
    1338:   } else {
    1339:     mumps->id.ICNTL(18) = 3;   /* distributed assembled matrix input */
    1340:     mumps->id.ICNTL(20) = 0;   /* rhs is in dense format */
    1341:     mumps->id.ICNTL(21) = 1;   /* distributed solution */
    1342:   }

    1344:   /* schur */
    1345:   mumps->id.size_schur      = 0;
    1346:   mumps->id.listvar_schur   = NULL;
    1347:   mumps->id.schur           = NULL;
    1348:   mumps->sizeredrhs         = 0;
    1349:   mumps->schur_pivots       = NULL;
    1350:   mumps->schur_work         = NULL;
    1351:   mumps->schur_sol          = NULL;
    1352:   mumps->schur_sizesol      = 0;
    1353:   mumps->schur_factored     = PETSC_FALSE;
    1354:   mumps->schur_inverted     = PETSC_FALSE;
    1355:   return(0);
    1356: }

    1360: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps)
    1361: {

    1365:   if (mumps->id.INFOG(1) < 0) {
    1366:     if (A->erroriffailure) {
    1367:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));
    1368:     } else {
    1369:       if (mumps->id.INFOG(1) == -6) {
    1370:         PetscInfo2(F,"matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1371:         F->errortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
    1372:       } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
    1373:         PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1374:         F->errortype = MAT_FACTOR_OUTMEMORY;
    1375:       } else {
    1376:         PetscInfo2(F,"Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1377:         F->errortype = MAT_FACTOR_OTHER;
    1378:       }
    1379:     }
    1380:   }
    1381:   return(0);
    1382: }

    1384: /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */
    1387: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    1388: {
    1389:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
    1391:   Vec            b;
    1392:   IS             is_iden;
    1393:   const PetscInt M = A->rmap->N;

    1396:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1398:   /* Set MUMPS options from the options database */
    1399:   PetscSetMUMPSFromOptions(F,A);

    1401:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

    1403:   /* analysis phase */
    1404:   /*----------------*/
    1405:   mumps->id.job = JOB_FACTSYMBOLIC;
    1406:   mumps->id.n   = M;
    1407:   switch (mumps->id.ICNTL(18)) {
    1408:   case 0:  /* centralized assembled matrix input */
    1409:     if (!mumps->myid) {
    1410:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
    1411:       if (mumps->id.ICNTL(6)>1) {
    1412:         mumps->id.a = (MumpsScalar*)mumps->val;
    1413:       }
    1414:       if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */
    1415:         /*
    1416:         PetscBool      flag;
    1417:         ISEqual(r,c,&flag);
    1418:         if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm");
    1419:         ISView(r,PETSC_VIEWER_STDOUT_SELF);
    1420:          */
    1421:         if (!mumps->myid) {
    1422:           const PetscInt *idx;
    1423:           PetscInt       i,*perm_in;

    1425:           PetscMalloc1(M,&perm_in);
    1426:           ISGetIndices(r,&idx);

    1428:           mumps->id.perm_in = perm_in;
    1429:           for (i=0; i<M; i++) perm_in[i] = idx[i]+1; /* perm_in[]: start from 1, not 0! */
    1430:           ISRestoreIndices(r,&idx);
    1431:         }
    1432:       }
    1433:     }
    1434:     break;
    1435:   case 3:  /* distributed assembled matrix input (size>1) */
    1436:     mumps->id.nz_loc = mumps->nz;
    1437:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
    1438:     if (mumps->id.ICNTL(6)>1) {
    1439:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1440:     }
    1441:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1442:     if (!mumps->myid) {
    1443:       VecCreateSeq(PETSC_COMM_SELF,A->rmap->N,&mumps->b_seq);
    1444:       ISCreateStride(PETSC_COMM_SELF,A->rmap->N,0,1,&is_iden);
    1445:     } else {
    1446:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
    1447:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
    1448:     }
    1449:     MatCreateVecs(A,NULL,&b);
    1450:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
    1451:     ISDestroy(&is_iden);
    1452:     VecDestroy(&b);
    1453:     break;
    1454:   }
    1455:   PetscMUMPS_c(&mumps->id);
    1456:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1458:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
    1459:   F->ops->solve           = MatSolve_MUMPS;
    1460:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
    1461:   F->ops->matsolve        = MatMatSolve_MUMPS;
    1462:   return(0);
    1463: }

    1465: /* Note the Petsc r and c permutations are ignored */
    1468: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    1469: {
    1470:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
    1472:   Vec            b;
    1473:   IS             is_iden;
    1474:   const PetscInt M = A->rmap->N;

    1477:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1479:   /* Set MUMPS options from the options database */
    1480:   PetscSetMUMPSFromOptions(F,A);

    1482:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

    1484:   /* analysis phase */
    1485:   /*----------------*/
    1486:   mumps->id.job = JOB_FACTSYMBOLIC;
    1487:   mumps->id.n   = M;
    1488:   switch (mumps->id.ICNTL(18)) {
    1489:   case 0:  /* centralized assembled matrix input */
    1490:     if (!mumps->myid) {
    1491:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
    1492:       if (mumps->id.ICNTL(6)>1) {
    1493:         mumps->id.a = (MumpsScalar*)mumps->val;
    1494:       }
    1495:     }
    1496:     break;
    1497:   case 3:  /* distributed assembled matrix input (size>1) */
    1498:     mumps->id.nz_loc = mumps->nz;
    1499:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
    1500:     if (mumps->id.ICNTL(6)>1) {
    1501:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1502:     }
    1503:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1504:     if (!mumps->myid) {
    1505:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
    1506:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
    1507:     } else {
    1508:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
    1509:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
    1510:     }
    1511:     MatCreateVecs(A,NULL,&b);
    1512:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
    1513:     ISDestroy(&is_iden);
    1514:     VecDestroy(&b);
    1515:     break;
    1516:   }
    1517:   PetscMUMPS_c(&mumps->id);
    1518:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1520:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
    1521:   F->ops->solve           = MatSolve_MUMPS;
    1522:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
    1523:   return(0);
    1524: }

    1526: /* Note the Petsc r permutation and factor info are ignored */
    1529: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info)
    1530: {
    1531:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->spptr;
    1533:   Vec            b;
    1534:   IS             is_iden;
    1535:   const PetscInt M = A->rmap->N;

    1538:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1540:   /* Set MUMPS options from the options database */
    1541:   PetscSetMUMPSFromOptions(F,A);

    1543:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, &mumps->nz, &mumps->irn, &mumps->jcn, &mumps->val);

    1545:   /* analysis phase */
    1546:   /*----------------*/
    1547:   mumps->id.job = JOB_FACTSYMBOLIC;
    1548:   mumps->id.n   = M;
    1549:   switch (mumps->id.ICNTL(18)) {
    1550:   case 0:  /* centralized assembled matrix input */
    1551:     if (!mumps->myid) {
    1552:       mumps->id.nz =mumps->nz; mumps->id.irn=mumps->irn; mumps->id.jcn=mumps->jcn;
    1553:       if (mumps->id.ICNTL(6)>1) {
    1554:         mumps->id.a = (MumpsScalar*)mumps->val;
    1555:       }
    1556:     }
    1557:     break;
    1558:   case 3:  /* distributed assembled matrix input (size>1) */
    1559:     mumps->id.nz_loc = mumps->nz;
    1560:     mumps->id.irn_loc=mumps->irn; mumps->id.jcn_loc=mumps->jcn;
    1561:     if (mumps->id.ICNTL(6)>1) {
    1562:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1563:     }
    1564:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1565:     if (!mumps->myid) {
    1566:       VecCreateSeq(PETSC_COMM_SELF,A->cmap->N,&mumps->b_seq);
    1567:       ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,&is_iden);
    1568:     } else {
    1569:       VecCreateSeq(PETSC_COMM_SELF,0,&mumps->b_seq);
    1570:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);
    1571:     }
    1572:     MatCreateVecs(A,NULL,&b);
    1573:     VecScatterCreate(b,is_iden,mumps->b_seq,is_iden,&mumps->scat_rhs);
    1574:     ISDestroy(&is_iden);
    1575:     VecDestroy(&b);
    1576:     break;
    1577:   }
    1578:   PetscMUMPS_c(&mumps->id);
    1579:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1582:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
    1583:   F->ops->solve                 = MatSolve_MUMPS;
    1584:   F->ops->solvetranspose        = MatSolve_MUMPS;
    1585:   F->ops->matsolve              = MatMatSolve_MUMPS;
    1586: #if defined(PETSC_USE_COMPLEX)
    1587:   F->ops->getinertia = NULL;
    1588: #else
    1589:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
    1590: #endif
    1591:   return(0);
    1592: }

    1596: PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer)
    1597: {
    1598:   PetscErrorCode    ierr;
    1599:   PetscBool         iascii;
    1600:   PetscViewerFormat format;
    1601:   Mat_MUMPS         *mumps=(Mat_MUMPS*)A->spptr;

    1604:   /* check if matrix is mumps type */
    1605:   if (A->ops->solve != MatSolve_MUMPS) return(0);

    1607:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1608:   if (iascii) {
    1609:     PetscViewerGetFormat(viewer,&format);
    1610:     if (format == PETSC_VIEWER_ASCII_INFO) {
    1611:       PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");
    1612:       PetscViewerASCIIPrintf(viewer,"  SYM (matrix type):                   %d \n",mumps->id.sym);
    1613:       PetscViewerASCIIPrintf(viewer,"  PAR (host participation):            %d \n",mumps->id.par);
    1614:       PetscViewerASCIIPrintf(viewer,"  ICNTL(1) (output for error):         %d \n",mumps->id.ICNTL(1));
    1615:       PetscViewerASCIIPrintf(viewer,"  ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));
    1616:       PetscViewerASCIIPrintf(viewer,"  ICNTL(3) (output for global info):   %d \n",mumps->id.ICNTL(3));
    1617:       PetscViewerASCIIPrintf(viewer,"  ICNTL(4) (level of printing):        %d \n",mumps->id.ICNTL(4));
    1618:       PetscViewerASCIIPrintf(viewer,"  ICNTL(5) (input mat struct):         %d \n",mumps->id.ICNTL(5));
    1619:       PetscViewerASCIIPrintf(viewer,"  ICNTL(6) (matrix prescaling):        %d \n",mumps->id.ICNTL(6));
    1620:       PetscViewerASCIIPrintf(viewer,"  ICNTL(7) (sequentia matrix ordering):%d \n",mumps->id.ICNTL(7));
    1621:       PetscViewerASCIIPrintf(viewer,"  ICNTL(8) (scalling strategy):        %d \n",mumps->id.ICNTL(8));
    1622:       PetscViewerASCIIPrintf(viewer,"  ICNTL(10) (max num of refinements):  %d \n",mumps->id.ICNTL(10));
    1623:       PetscViewerASCIIPrintf(viewer,"  ICNTL(11) (error analysis):          %d \n",mumps->id.ICNTL(11));
    1624:       if (mumps->id.ICNTL(11)>0) {
    1625:         PetscViewerASCIIPrintf(viewer,"    RINFOG(4) (inf norm of input mat):        %g\n",mumps->id.RINFOG(4));
    1626:         PetscViewerASCIIPrintf(viewer,"    RINFOG(5) (inf norm of solution):         %g\n",mumps->id.RINFOG(5));
    1627:         PetscViewerASCIIPrintf(viewer,"    RINFOG(6) (inf norm of residual):         %g\n",mumps->id.RINFOG(6));
    1628:         PetscViewerASCIIPrintf(viewer,"    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));
    1629:         PetscViewerASCIIPrintf(viewer,"    RINFOG(9) (error estimate):               %g \n",mumps->id.RINFOG(9));
    1630:         PetscViewerASCIIPrintf(viewer,"    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));
    1631:       }
    1632:       PetscViewerASCIIPrintf(viewer,"  ICNTL(12) (efficiency control):                         %d \n",mumps->id.ICNTL(12));
    1633:       PetscViewerASCIIPrintf(viewer,"  ICNTL(13) (efficiency control):                         %d \n",mumps->id.ICNTL(13));
    1634:       PetscViewerASCIIPrintf(viewer,"  ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));
    1635:       /* ICNTL(15-17) not used */
    1636:       PetscViewerASCIIPrintf(viewer,"  ICNTL(18) (input mat struct):                           %d \n",mumps->id.ICNTL(18));
    1637:       PetscViewerASCIIPrintf(viewer,"  ICNTL(19) (Shur complement info):                       %d \n",mumps->id.ICNTL(19));
    1638:       PetscViewerASCIIPrintf(viewer,"  ICNTL(20) (rhs sparse pattern):                         %d \n",mumps->id.ICNTL(20));
    1639:       PetscViewerASCIIPrintf(viewer,"  ICNTL(21) (solution struct):                            %d \n",mumps->id.ICNTL(21));
    1640:       PetscViewerASCIIPrintf(viewer,"  ICNTL(22) (in-core/out-of-core facility):               %d \n",mumps->id.ICNTL(22));
    1641:       PetscViewerASCIIPrintf(viewer,"  ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));

    1643:       PetscViewerASCIIPrintf(viewer,"  ICNTL(24) (detection of null pivot rows):               %d \n",mumps->id.ICNTL(24));
    1644:       PetscViewerASCIIPrintf(viewer,"  ICNTL(25) (computation of a null space basis):          %d \n",mumps->id.ICNTL(25));
    1645:       PetscViewerASCIIPrintf(viewer,"  ICNTL(26) (Schur options for rhs or solution):          %d \n",mumps->id.ICNTL(26));
    1646:       PetscViewerASCIIPrintf(viewer,"  ICNTL(27) (experimental parameter):                     %d \n",mumps->id.ICNTL(27));
    1647:       PetscViewerASCIIPrintf(viewer,"  ICNTL(28) (use parallel or sequential ordering):        %d \n",mumps->id.ICNTL(28));
    1648:       PetscViewerASCIIPrintf(viewer,"  ICNTL(29) (parallel ordering):                          %d \n",mumps->id.ICNTL(29));

    1650:       PetscViewerASCIIPrintf(viewer,"  ICNTL(30) (user-specified set of entries in inv(A)):    %d \n",mumps->id.ICNTL(30));
    1651:       PetscViewerASCIIPrintf(viewer,"  ICNTL(31) (factors is discarded in the solve phase):    %d \n",mumps->id.ICNTL(31));
    1652:       PetscViewerASCIIPrintf(viewer,"  ICNTL(33) (compute determinant):                        %d \n",mumps->id.ICNTL(33));

    1654:       PetscViewerASCIIPrintf(viewer,"  CNTL(1) (relative pivoting threshold):      %g \n",mumps->id.CNTL(1));
    1655:       PetscViewerASCIIPrintf(viewer,"  CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));
    1656:       PetscViewerASCIIPrintf(viewer,"  CNTL(3) (absolute pivoting threshold):      %g \n",mumps->id.CNTL(3));
    1657:       PetscViewerASCIIPrintf(viewer,"  CNTL(4) (value of static pivoting):         %g \n",mumps->id.CNTL(4));
    1658:       PetscViewerASCIIPrintf(viewer,"  CNTL(5) (fixation for null pivots):         %g \n",mumps->id.CNTL(5));

    1660:       /* infomation local to each processor */
    1661:       PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis): \n");
    1662:       PetscViewerASCIIPushSynchronized(viewer);
    1663:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %g \n",mumps->myid,mumps->id.RINFO(1));
    1664:       PetscViewerFlush(viewer);
    1665:       PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization): \n");
    1666:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(2));
    1667:       PetscViewerFlush(viewer);
    1668:       PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization): \n");
    1669:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(3));
    1670:       PetscViewerFlush(viewer);

    1672:       PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");
    1673:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] %d \n",mumps->myid,mumps->id.INFO(15));
    1674:       PetscViewerFlush(viewer);

    1676:       PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");
    1677:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(16));
    1678:       PetscViewerFlush(viewer);

    1680:       PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization): \n");
    1681:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(23));
    1682:       PetscViewerFlush(viewer);

    1684:       if (mumps->ninfo && mumps->ninfo <= 40){
    1685:         PetscInt i;
    1686:         for (i=0; i<mumps->ninfo; i++){
    1687:           PetscViewerASCIIPrintf(viewer, "  INFO(%d): \n",mumps->info[i]);
    1688:           PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));
    1689:           PetscViewerFlush(viewer);
    1690:         }
    1691:       }

    1694:       PetscViewerASCIIPopSynchronized(viewer);

    1696:       if (!mumps->myid) { /* information from the host */
    1697:         PetscViewerASCIIPrintf(viewer,"  RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));
    1698:         PetscViewerASCIIPrintf(viewer,"  RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));
    1699:         PetscViewerASCIIPrintf(viewer,"  RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));
    1700:         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));

    1702:         PetscViewerASCIIPrintf(viewer,"  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));
    1703:         PetscViewerASCIIPrintf(viewer,"  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));
    1704:         PetscViewerASCIIPrintf(viewer,"  INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));
    1705:         PetscViewerASCIIPrintf(viewer,"  INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));
    1706:         PetscViewerASCIIPrintf(viewer,"  INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));
    1707:         PetscViewerASCIIPrintf(viewer,"  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));
    1708:         PetscViewerASCIIPrintf(viewer,"  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));
    1709:         PetscViewerASCIIPrintf(viewer,"  INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));
    1710:         PetscViewerASCIIPrintf(viewer,"  INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));
    1711:         PetscViewerASCIIPrintf(viewer,"  INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));
    1712:         PetscViewerASCIIPrintf(viewer,"  INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));
    1713:         PetscViewerASCIIPrintf(viewer,"  INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));
    1714:         PetscViewerASCIIPrintf(viewer,"  INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));
    1715:         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));
    1716:         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));
    1717:         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));
    1718:         PetscViewerASCIIPrintf(viewer,"  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",mumps->id.INFOG(19));
    1719:         PetscViewerASCIIPrintf(viewer,"  INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));
    1720:         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));
    1721:         PetscViewerASCIIPrintf(viewer,"  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",mumps->id.INFOG(22));
    1722:         PetscViewerASCIIPrintf(viewer,"  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));
    1723:         PetscViewerASCIIPrintf(viewer,"  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));
    1724:         PetscViewerASCIIPrintf(viewer,"  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));
    1725:         PetscViewerASCIIPrintf(viewer,"  INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));
    1726:         PetscViewerASCIIPrintf(viewer,"  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29));
    1727:         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));
    1728:         PetscViewerASCIIPrintf(viewer,"  INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));
    1729:         PetscViewerASCIIPrintf(viewer,"  INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));
    1730:         PetscViewerASCIIPrintf(viewer,"  INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));
    1731:       }
    1732:     }
    1733:   }
    1734:   return(0);
    1735: }

    1739: PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info)
    1740: {
    1741:   Mat_MUMPS *mumps =(Mat_MUMPS*)A->spptr;

    1744:   info->block_size        = 1.0;
    1745:   info->nz_allocated      = mumps->id.INFOG(20);
    1746:   info->nz_used           = mumps->id.INFOG(20);
    1747:   info->nz_unneeded       = 0.0;
    1748:   info->assemblies        = 0.0;
    1749:   info->mallocs           = 0.0;
    1750:   info->memory            = 0.0;
    1751:   info->fill_ratio_given  = 0;
    1752:   info->fill_ratio_needed = 0;
    1753:   info->factor_mallocs    = 0;
    1754:   return(0);
    1755: }

    1757: /* -------------------------------------------------------------------------------------------*/
    1760: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
    1761: {
    1762:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
    1763:   const PetscInt *idxs;
    1764:   PetscInt       size,i;

    1768:   if (mumps->size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS parallel Schur complements not yet supported from PETSc\n");
    1769:   ISGetLocalSize(is,&size);
    1770:   if (mumps->id.size_schur != size) {
    1771:     PetscFree2(mumps->id.listvar_schur,mumps->id.schur);
    1772:     mumps->id.size_schur = size;
    1773:     mumps->id.schur_lld = size;
    1774:     PetscMalloc2(size,&mumps->id.listvar_schur,size*size,&mumps->id.schur);
    1775:   }
    1776:   ISGetIndices(is,&idxs);
    1777:   PetscMemcpy(mumps->id.listvar_schur,idxs,size*sizeof(PetscInt));
    1778:   /* MUMPS expects Fortran style indices */
    1779:   for (i=0;i<size;i++) mumps->id.listvar_schur[i]++;
    1780:   ISRestoreIndices(is,&idxs);
    1781:   if (size) { /* turn on Schur switch if we the set of indices is not empty */
    1782:     if (F->factortype == MAT_FACTOR_LU) {
    1783:       mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
    1784:     } else {
    1785:       mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
    1786:     }
    1787:     /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
    1788:     mumps->id.ICNTL(26) = -1;
    1789:   }
    1790:   return(0);
    1791: }

    1793: /* -------------------------------------------------------------------------------------------*/
    1796: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S)
    1797: {
    1798:   Mat            St;
    1799:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
    1800:   PetscScalar    *array;
    1801: #if defined(PETSC_USE_COMPLEX)
    1802:   PetscScalar    im = PetscSqrtScalar((PetscScalar)-1.0);
    1803: #endif

    1807:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
    1808:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

    1810:   MatCreate(PetscObjectComm((PetscObject)F),&St);
    1811:   MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);
    1812:   MatSetType(St,MATDENSE);
    1813:   MatSetUp(St);
    1814:   MatDenseGetArray(St,&array);
    1815:   if (!mumps->sym) { /* MUMPS always return a full matrix */
    1816:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
    1817:       PetscInt i,j,N=mumps->id.size_schur;
    1818:       for (i=0;i<N;i++) {
    1819:         for (j=0;j<N;j++) {
    1820: #if !defined(PETSC_USE_COMPLEX)
    1821:           PetscScalar val = mumps->id.schur[i*N+j];
    1822: #else
    1823:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    1824: #endif
    1825:           array[j*N+i] = val;
    1826:         }
    1827:       }
    1828:     } else { /* stored by columns */
    1829:       PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));
    1830:     }
    1831:   } else { /* either full or lower-triangular (not packed) */
    1832:     if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
    1833:       PetscInt i,j,N=mumps->id.size_schur;
    1834:       for (i=0;i<N;i++) {
    1835:         for (j=i;j<N;j++) {
    1836: #if !defined(PETSC_USE_COMPLEX)
    1837:           PetscScalar val = mumps->id.schur[i*N+j];
    1838: #else
    1839:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    1840: #endif
    1841:           array[i*N+j] = val;
    1842:           array[j*N+i] = val;
    1843:         }
    1844:       }
    1845:     } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
    1846:       PetscMemcpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur*sizeof(PetscScalar));
    1847:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
    1848:       PetscInt i,j,N=mumps->id.size_schur;
    1849:       for (i=0;i<N;i++) {
    1850:         for (j=0;j<i+1;j++) {
    1851: #if !defined(PETSC_USE_COMPLEX)
    1852:           PetscScalar val = mumps->id.schur[i*N+j];
    1853: #else
    1854:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    1855: #endif
    1856:           array[i*N+j] = val;
    1857:           array[j*N+i] = val;
    1858:         }
    1859:       }
    1860:     }
    1861:   }
    1862:   MatDenseRestoreArray(St,&array);
    1863:   *S = St;
    1864:   return(0);
    1865: }

    1867: /* -------------------------------------------------------------------------------------------*/
    1870: PetscErrorCode MatFactorGetSchurComplement_MUMPS(Mat F,Mat* S)
    1871: {
    1872:   Mat            St;
    1873:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;

    1877:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
    1878:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

    1880:   /* 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 */
    1881:   MatCreateSeqDense(PetscObjectComm((PetscObject)F),mumps->id.size_schur,mumps->id.size_schur,(PetscScalar*)mumps->id.schur,&St);
    1882:   *S = St;
    1883:   return(0);
    1884: }

    1886: /* -------------------------------------------------------------------------------------------*/
    1889: PetscErrorCode MatFactorInvertSchurComplement_MUMPS(Mat F)
    1890: {
    1891:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;

    1895:   if (!mumps->id.ICNTL(19)) { /* do nothing */
    1896:     return(0);
    1897:   }
    1898:   if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");
    1899:   MatMumpsInvertSchur_Private(mumps);
    1900:   return(0);
    1901: }

    1903: /* -------------------------------------------------------------------------------------------*/
    1906: PetscErrorCode MatFactorSolveSchurComplement_MUMPS(Mat F, Vec rhs, Vec sol)
    1907: {
    1908:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
    1909:   MumpsScalar    *orhs;
    1910:   PetscScalar    *osol,*nrhs,*nsol;
    1911:   PetscInt       orhs_size,osol_size,olrhs_size;

    1915:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
    1916:   if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

    1918:   /* swap pointers */
    1919:   orhs = mumps->id.redrhs;
    1920:   olrhs_size = mumps->id.lredrhs;
    1921:   orhs_size = mumps->sizeredrhs;
    1922:   osol = mumps->schur_sol;
    1923:   osol_size = mumps->schur_sizesol;
    1924:   VecGetArray(rhs,&nrhs);
    1925:   VecGetArray(sol,&nsol);
    1926:   mumps->id.redrhs = (MumpsScalar*)nrhs;
    1927:   VecGetLocalSize(rhs,&mumps->sizeredrhs);
    1928:   mumps->id.lredrhs = mumps->sizeredrhs;
    1929:   mumps->schur_sol = nsol;
    1930:   VecGetLocalSize(sol,&mumps->schur_sizesol);

    1932:   /* solve Schur complement */
    1933:   mumps->id.nrhs = 1;
    1934:   MatMumpsSolveSchur_Private(mumps,PETSC_FALSE);
    1935:   /* restore pointers */
    1936:   VecRestoreArray(rhs,&nrhs);
    1937:   VecRestoreArray(sol,&nsol);
    1938:   mumps->id.redrhs = orhs;
    1939:   mumps->id.lredrhs = olrhs_size;
    1940:   mumps->sizeredrhs = orhs_size;
    1941:   mumps->schur_sol = osol;
    1942:   mumps->schur_sizesol = osol_size;
    1943:   return(0);
    1944: }

    1946: /* -------------------------------------------------------------------------------------------*/
    1949: PetscErrorCode MatFactorSolveSchurComplementTranspose_MUMPS(Mat F, Vec rhs, Vec sol)
    1950: {
    1951:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->spptr;
    1952:   MumpsScalar    *orhs;
    1953:   PetscScalar    *osol,*nrhs,*nsol;
    1954:   PetscInt       orhs_size,osol_size;

    1958:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
    1959:   else if (!mumps->id.size_schur) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur indices not set! You should call MatFactorSetSchurIS before");

    1961:   /* swap pointers */
    1962:   orhs = mumps->id.redrhs;
    1963:   orhs_size = mumps->sizeredrhs;
    1964:   osol = mumps->schur_sol;
    1965:   osol_size = mumps->schur_sizesol;
    1966:   VecGetArray(rhs,&nrhs);
    1967:   VecGetArray(sol,&nsol);
    1968:   mumps->id.redrhs = (MumpsScalar*)nrhs;
    1969:   VecGetLocalSize(rhs,&mumps->sizeredrhs);
    1970:   mumps->schur_sol = nsol;
    1971:   VecGetLocalSize(sol,&mumps->schur_sizesol);

    1973:   /* solve Schur complement */
    1974:   mumps->id.nrhs = 1;
    1975:   mumps->id.ICNTL(9) = 0;
    1976:   MatMumpsSolveSchur_Private(mumps,PETSC_FALSE);
    1977:   mumps->id.ICNTL(9) = 1;
    1978:   /* restore pointers */
    1979:   VecRestoreArray(rhs,&nrhs);
    1980:   VecRestoreArray(sol,&nsol);
    1981:   mumps->id.redrhs = orhs;
    1982:   mumps->sizeredrhs = orhs_size;
    1983:   mumps->schur_sol = osol;
    1984:   mumps->schur_sizesol = osol_size;
    1985:   return(0);
    1986: }

    1988: /* -------------------------------------------------------------------------------------------*/
    1991: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival)
    1992: {
    1993:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    1996:   mumps->id.ICNTL(icntl) = ival;
    1997:   return(0);
    1998: }

    2002: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival)
    2003: {
    2004:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2007:   *ival = mumps->id.ICNTL(icntl);
    2008:   return(0);
    2009: }

    2013: /*@
    2014:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

    2016:    Logically Collective on Mat

    2018:    Input Parameters:
    2019: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2020: .  icntl - index of MUMPS parameter array ICNTL()
    2021: -  ival - value of MUMPS ICNTL(icntl)

    2023:   Options Database:
    2024: .   -mat_mumps_icntl_<icntl> <ival>

    2026:    Level: beginner

    2028:    References:
    2029: .     MUMPS Users' Guide

    2031: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2032:  @*/
    2033: PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival)
    2034: {
    2036:
    2039:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2042:   PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
    2043:   return(0);
    2044: }

    2048: /*@
    2049:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

    2051:    Logically Collective on Mat

    2053:    Input Parameters:
    2054: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2055: -  icntl - index of MUMPS parameter array ICNTL()

    2057:   Output Parameter:
    2058: .  ival - value of MUMPS ICNTL(icntl)

    2060:    Level: beginner

    2062:    References:
    2063: .     MUMPS Users' Guide

    2065: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2066: @*/
    2067: PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival)
    2068: {

    2073:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2076:   PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2077:   return(0);
    2078: }

    2080: /* -------------------------------------------------------------------------------------------*/
    2083: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val)
    2084: {
    2085:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2088:   mumps->id.CNTL(icntl) = val;
    2089:   return(0);
    2090: }

    2094: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val)
    2095: {
    2096:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2099:   *val = mumps->id.CNTL(icntl);
    2100:   return(0);
    2101: }

    2105: /*@
    2106:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

    2108:    Logically Collective on Mat

    2110:    Input Parameters:
    2111: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2112: .  icntl - index of MUMPS parameter array CNTL()
    2113: -  val - value of MUMPS CNTL(icntl)

    2115:   Options Database:
    2116: .   -mat_mumps_cntl_<icntl> <val>

    2118:    Level: beginner

    2120:    References:
    2121: .     MUMPS Users' Guide

    2123: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2124: @*/
    2125: PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val)
    2126: {

    2131:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2134:   PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));
    2135:   return(0);
    2136: }

    2140: /*@
    2141:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

    2143:    Logically Collective on Mat

    2145:    Input Parameters:
    2146: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2147: -  icntl - index of MUMPS parameter array CNTL()

    2149:   Output Parameter:
    2150: .  val - value of MUMPS CNTL(icntl)

    2152:    Level: beginner

    2154:    References:
    2155: .      MUMPS Users' Guide

    2157: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2158: @*/
    2159: PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val)
    2160: {

    2165:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2168:   PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2169:   return(0);
    2170: }

    2174: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info)
    2175: {
    2176:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2179:   *info = mumps->id.INFO(icntl);
    2180:   return(0);
    2181: }

    2185: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog)
    2186: {
    2187:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2190:   *infog = mumps->id.INFOG(icntl);
    2191:   return(0);
    2192: }

    2196: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo)
    2197: {
    2198:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2201:   *rinfo = mumps->id.RINFO(icntl);
    2202:   return(0);
    2203: }

    2207: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog)
    2208: {
    2209:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->spptr;

    2212:   *rinfog = mumps->id.RINFOG(icntl);
    2213:   return(0);
    2214: }

    2218: /*@
    2219:   MatMumpsGetInfo - Get MUMPS parameter INFO()

    2221:    Logically Collective on Mat

    2223:    Input Parameters:
    2224: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2225: -  icntl - index of MUMPS parameter array INFO()

    2227:   Output Parameter:
    2228: .  ival - value of MUMPS INFO(icntl)

    2230:    Level: beginner

    2232:    References:
    2233: .      MUMPS Users' Guide

    2235: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2236: @*/
    2237: PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival)
    2238: {

    2243:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2245:   PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2246:   return(0);
    2247: }

    2251: /*@
    2252:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

    2254:    Logically Collective on Mat

    2256:    Input Parameters:
    2257: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2258: -  icntl - index of MUMPS parameter array INFOG()

    2260:   Output Parameter:
    2261: .  ival - value of MUMPS INFOG(icntl)

    2263:    Level: beginner

    2265:    References:
    2266: .      MUMPS Users' Guide

    2268: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2269: @*/
    2270: PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival)
    2271: {

    2276:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2278:   PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2279:   return(0);
    2280: }

    2284: /*@
    2285:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

    2287:    Logically Collective on Mat

    2289:    Input Parameters:
    2290: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2291: -  icntl - index of MUMPS parameter array RINFO()

    2293:   Output Parameter:
    2294: .  val - value of MUMPS RINFO(icntl)

    2296:    Level: beginner

    2298:    References:
    2299: .       MUMPS Users' Guide

    2301: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2302: @*/
    2303: PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val)
    2304: {

    2309:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2311:   PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2312:   return(0);
    2313: }

    2317: /*@
    2318:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

    2320:    Logically Collective on Mat

    2322:    Input Parameters:
    2323: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2324: -  icntl - index of MUMPS parameter array RINFOG()

    2326:   Output Parameter:
    2327: .  val - value of MUMPS RINFOG(icntl)

    2329:    Level: beginner

    2331:    References:
    2332: .      MUMPS Users' Guide

    2334: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2335: @*/
    2336: PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val)
    2337: {

    2342:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2344:   PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2345:   return(0);
    2346: }

    2348: /*MC
    2349:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
    2350:   distributed and sequential matrices via the external package MUMPS.

    2352:   Works with MATAIJ and MATSBAIJ matrices

    2354:   Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch  to have PETSc installed with MUMPS

    2356:   Use -pc_type cholesky or lu -pc_factor_mat_solver_package mumps to us this direct solver

    2358:   Options Database Keys:
    2359: +  -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
    2360: .  -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
    2361: .  -mat_mumps_icntl_3 -  ICNTL(3): output stream for global information, collected on the host
    2362: .  -mat_mumps_icntl_4 -  ICNTL(4): level of printing (0 to 4)
    2363: .  -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
    2364: .  -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis
    2365: .  -mat_mumps_icntl_8  - ICNTL(8): scaling strategy (-2 to 8 or 77)
    2366: .  -mat_mumps_icntl_10  - ICNTL(10): max num of refinements
    2367: .  -mat_mumps_icntl_11  - ICNTL(11): statistics related to an error analysis (via -ksp_view)
    2368: .  -mat_mumps_icntl_12  - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
    2369: .  -mat_mumps_icntl_13  - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
    2370: .  -mat_mumps_icntl_14  - ICNTL(14): percentage increase in the estimated working space
    2371: .  -mat_mumps_icntl_19  - ICNTL(19): computes the Schur complement
    2372: .  -mat_mumps_icntl_22  - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
    2373: .  -mat_mumps_icntl_23  - ICNTL(23): max size of the working memory (MB) that can allocate per processor
    2374: .  -mat_mumps_icntl_24  - ICNTL(24): detection of null pivot rows (0 or 1)
    2375: .  -mat_mumps_icntl_25  - ICNTL(25): compute a solution of a deficient matrix and a null space basis
    2376: .  -mat_mumps_icntl_26  - ICNTL(26): drives the solution phase if a Schur complement matrix
    2377: .  -mat_mumps_icntl_28  - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
    2378: .  -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
    2379: .  -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
    2380: .  -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
    2381: .  -mat_mumps_icntl_33 - ICNTL(33): compute determinant
    2382: .  -mat_mumps_cntl_1  - CNTL(1): relative pivoting threshold
    2383: .  -mat_mumps_cntl_2  -  CNTL(2): stopping criterion of refinement
    2384: .  -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
    2385: .  -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
    2386: -  -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots

    2388:   Level: beginner

    2390:     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
    2391: $          KSPGetPC(ksp,&pc);
    2392: $          PCFactorGetMatrix(pc,&mat);
    2393: $          MatMumpsGetInfo(mat,....);
    2394: $          MatMumpsGetInfog(mat,....); etc.
    2395:            Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message.

    2397: .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix()

    2399: M*/

    2403: static PetscErrorCode MatFactorGetSolverPackage_mumps(Mat A,const MatSolverPackage *type)
    2404: {
    2406:   *type = MATSOLVERMUMPS;
    2407:   return(0);
    2408: }

    2410: /* MatGetFactor for Seq and MPI AIJ matrices */
    2413: static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2414: {
    2415:   Mat            B;
    2417:   Mat_MUMPS      *mumps;
    2418:   PetscBool      isSeqAIJ;

    2421:   /* Create the factorization matrix */
    2422:   PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
    2423:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2424:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2425:   MatSetType(B,((PetscObject)A)->type_name);
    2426:   if (isSeqAIJ) {
    2427:     MatSeqAIJSetPreallocation(B,0,NULL);
    2428:   } else {
    2429:     MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
    2430:   }

    2432:   PetscNewLog(B,&mumps);

    2434:   B->ops->view        = MatView_MUMPS;
    2435:   B->ops->getinfo     = MatGetInfo_MUMPS;
    2436:   B->ops->getdiagonal = MatGetDiagonal_MUMPS;

    2438:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
    2439:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2440:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
    2441:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2442:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
    2443:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
    2444:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
    2445:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2446:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2447:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2448:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2449:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    2450:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    2451:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    2452:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

    2454:   if (ftype == MAT_FACTOR_LU) {
    2455:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
    2456:     B->factortype            = MAT_FACTOR_LU;
    2457:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
    2458:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
    2459:     mumps->sym = 0;
    2460:   } else {
    2461:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
    2462:     B->factortype                  = MAT_FACTOR_CHOLESKY;
    2463:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
    2464:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
    2465: #if defined(PETSC_USE_COMPLEX)
    2466:     mumps->sym = 2;
    2467: #else
    2468:     if (A->spd_set && A->spd) mumps->sym = 1;
    2469:     else                      mumps->sym = 2;
    2470: #endif
    2471:   }

    2473:   /* set solvertype */
    2474:   PetscFree(B->solvertype);
    2475:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    2477:   mumps->isAIJ    = PETSC_TRUE;
    2478:   mumps->Destroy  = B->ops->destroy;
    2479:   B->ops->destroy = MatDestroy_MUMPS;
    2480:   B->spptr        = (void*)mumps;

    2482:   PetscInitializeMUMPS(A,mumps);

    2484:   *F = B;
    2485:   return(0);
    2486: }

    2488: /* MatGetFactor for Seq and MPI SBAIJ matrices */
    2491: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2492: {
    2493:   Mat            B;
    2495:   Mat_MUMPS      *mumps;
    2496:   PetscBool      isSeqSBAIJ;

    2499:   if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Cannot use PETSc SBAIJ matrices with MUMPS LU, use AIJ matrix");
    2500:   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");
    2501:   PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
    2502:   /* Create the factorization matrix */
    2503:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2504:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2505:   MatSetType(B,((PetscObject)A)->type_name);
    2506:   PetscNewLog(B,&mumps);
    2507:   if (isSeqSBAIJ) {
    2508:     MatSeqSBAIJSetPreallocation(B,1,0,NULL);

    2510:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
    2511:   } else {
    2512:     MatMPISBAIJSetPreallocation(B,1,0,NULL,0,NULL);

    2514:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
    2515:   }

    2517:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
    2518:   B->ops->view                   = MatView_MUMPS;
    2519:   B->ops->getdiagonal            = MatGetDiagonal_MUMPS;

    2521:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
    2522:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2523:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
    2524:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2525:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
    2526:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
    2527:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
    2528:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2529:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2530:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2531:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2532:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    2533:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    2534:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    2535:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

    2537:   B->factortype = MAT_FACTOR_CHOLESKY;
    2538: #if defined(PETSC_USE_COMPLEX)
    2539:   mumps->sym = 2;
    2540: #else
    2541:   if (A->spd_set && A->spd) mumps->sym = 1;
    2542:   else                      mumps->sym = 2;
    2543: #endif

    2545:   /* set solvertype */
    2546:   PetscFree(B->solvertype);
    2547:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    2549:   mumps->isAIJ    = PETSC_FALSE;
    2550:   mumps->Destroy  = B->ops->destroy;
    2551:   B->ops->destroy = MatDestroy_MUMPS;
    2552:   B->spptr        = (void*)mumps;

    2554:   PetscInitializeMUMPS(A,mumps);

    2556:   *F = B;
    2557:   return(0);
    2558: }

    2562: static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2563: {
    2564:   Mat            B;
    2566:   Mat_MUMPS      *mumps;
    2567:   PetscBool      isSeqBAIJ;

    2570:   /* Create the factorization matrix */
    2571:   PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
    2572:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2573:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2574:   MatSetType(B,((PetscObject)A)->type_name);
    2575:   if (isSeqBAIJ) {
    2576:     MatSeqBAIJSetPreallocation(B,A->rmap->bs,0,NULL);
    2577:   } else {
    2578:     MatMPIBAIJSetPreallocation(B,A->rmap->bs,0,NULL,0,NULL);
    2579:   }

    2581:   PetscNewLog(B,&mumps);
    2582:   if (ftype == MAT_FACTOR_LU) {
    2583:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
    2584:     B->factortype            = MAT_FACTOR_LU;
    2585:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
    2586:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
    2587:     mumps->sym = 0;
    2588:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n");

    2590:   B->ops->view        = MatView_MUMPS;
    2591:   B->ops->getdiagonal = MatGetDiagonal_MUMPS;

    2593:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mumps);
    2594:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2595:   PetscObjectComposeFunction((PetscObject)B,"MatFactorInvertSchurComplement_C",MatFactorInvertSchurComplement_MUMPS);
    2596:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2597:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSchurComplement_C",MatFactorGetSchurComplement_MUMPS);
    2598:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplement_C",MatFactorSolveSchurComplement_MUMPS);
    2599:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSolveSchurComplementTranspose_C",MatFactorSolveSchurComplementTranspose_MUMPS);
    2600:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2601:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2602:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2603:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2604:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    2605:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    2606:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    2607:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

    2609:   /* set solvertype */
    2610:   PetscFree(B->solvertype);
    2611:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    2613:   mumps->isAIJ    = PETSC_TRUE;
    2614:   mumps->Destroy  = B->ops->destroy;
    2615:   B->ops->destroy = MatDestroy_MUMPS;
    2616:   B->spptr        = (void*)mumps;

    2618:   PetscInitializeMUMPS(A,mumps);

    2620:   *F = B;
    2621:   return(0);
    2622: }

    2626: PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_MUMPS(void)
    2627: {

    2631:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
    2632:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
    2633:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
    2634:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
    2635:   MatSolverPackageRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
    2636:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
    2637:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
    2638:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
    2639:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
    2640:   MatSolverPackageRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
    2641:   return(0);
    2642: }