Actual source code: mumps.c
petsc-3.13.6 2020-09-29
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      2: /*
      3:     Provides an interface to the MUMPS sparse solver
      4: */

      6:  #include <../src/mat/impls/aij/mpi/mpiaij.h>
      7:  #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>
      8:  #include <../src/mat/impls/sell/mpi/mpisell.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 MUMPS_c cmumps_c
     35: #else
     36: #define MUMPS_c zmumps_c
     37: #endif
     38: #else
     39: #if defined(PETSC_USE_REAL_SINGLE)
     40: #define MUMPS_c smumps_c
     41: #else
     42: #define MUMPS_c dmumps_c
     43: #endif
     44: #endif

     46: /* MUMPS uses MUMPS_INT for nonzero indices such as irn/jcn, irn_loc/jcn_loc and uses int64_t for
     47:    number of nonzeros such as nnz, nnz_loc. We typedef MUMPS_INT to PetscMUMPSInt to follow the
     48:    naming convention in PetscMPIInt, PetscBLASInt etc.
     49: */
     50: typedef MUMPS_INT PetscMUMPSInt;

     52: #if defined(INTSIZE64)            /* INTSIZE64 is a macro one used to build MUMPS in full 64-bit mode */
     53: #error "Petsc has not been tested with full 64-bit MUMPS and we choose to error out"
     54: #else
     55: #define MPIU_MUMPSINT             MPI_INT
     56: #define PETSC_MUMPS_INT_MAX       2147483647
     57: #define PETSC_MUMPS_INT_MIN       -2147483648
     58: #endif

     60: /* Cast PetscInt to PetscMUMPSInt. Usually there is no overflow since <a> is row/col indices or some small integers*/
     61: PETSC_STATIC_INLINE PetscErrorCode PetscMUMPSIntCast(PetscInt a,PetscMUMPSInt *b)
     62: {
     64: #if defined(PETSC_USE_DEBUG) && defined(PETSC_USE_64BIT_INDICES)
     65:   if (a > PETSC_MUMPS_INT_MAX || a < PETSC_MUMPS_INT_MIN) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"PetscInt too long for PetscMUMPSInt");
     66: #endif
     67:   *b = (PetscMUMPSInt)(a);
     68:   return(0);
     69: }

     71: /* Put these utility routines here since they are only used in this file */
     72: PETSC_STATIC_INLINE PetscErrorCode PetscOptionsMUMPSInt_Private(PetscOptionItems *PetscOptionsObject,const char opt[],const char text[],const char man[],PetscMUMPSInt currentvalue,PetscMUMPSInt *value,PetscBool *set,PetscMUMPSInt lb,PetscMUMPSInt ub)
     73: {
     75:   PetscInt       myval;
     76:   PetscBool      myset;
     78:   /* PetscInt's size should be always >= PetscMUMPSInt's. It is safe to call PetscOptionsInt_Private to read a PetscMUMPSInt */
     79:   PetscOptionsInt_Private(PetscOptionsObject,opt,text,man,(PetscInt)currentvalue,&myval,&myset,lb,ub);
     80:   if (myset) {PetscMUMPSIntCast(myval,value);}
     81:   if (set) *set = myset;
     82:   return(0);
     83: }
     84: #define PetscOptionsMUMPSInt(a,b,c,d,e,f) PetscOptionsMUMPSInt_Private(PetscOptionsObject,a,b,c,d,e,f,PETSC_MUMPS_INT_MIN,PETSC_MUMPS_INT_MAX)

     86: /* if using PETSc OpenMP support, we only call MUMPS on master ranks. Before/after the call, we change/restore CPUs the master ranks can run on */
     87: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
     88: #define PetscMUMPS_c(mumps) \
     89:   do { \
     90:     if (mumps->use_petsc_omp_support) { \
     91:       if (mumps->is_omp_master) { \
     92:         PetscOmpCtrlOmpRegionOnMasterBegin(mumps->omp_ctrl); \
     93:         MUMPS_c(&mumps->id); \
     94:         PetscOmpCtrlOmpRegionOnMasterEnd(mumps->omp_ctrl); \
     95:       } \
     96:       PetscOmpCtrlBarrier(mumps->omp_ctrl); \
     97:       /* Global info is same on all processes so we Bcast it within omp_comm. Local info is specific      \
     98:          to processes, so we only Bcast info[1], an error code and leave others (since they do not have   \
     99:          an easy translation between omp_comm and petsc_comm). See MUMPS-5.1.2 manual p82.                   \
    100:          omp_comm is a small shared memory communicator, hence doing multiple Bcast as shown below is OK. \
    101:       */ \
    102:       MPI_Bcast(mumps->id.infog, 40,MPIU_MUMPSINT, 0,mumps->omp_comm);  \
    103:       MPI_Bcast(mumps->id.rinfog,20,MPIU_REAL,     0,mumps->omp_comm); \
    104:       MPI_Bcast(mumps->id.info,  1, MPIU_MUMPSINT, 0,mumps->omp_comm);  \
    105:     } else { \
    106:       MUMPS_c(&mumps->id); \
    107:     } \
    108:   } while(0)
    109: #else
    110: #define PetscMUMPS_c(mumps) \
    111:   do { MUMPS_c(&mumps->id); } while (0)
    112: #endif

    114: /* declare MumpsScalar */
    115: #if defined(PETSC_USE_COMPLEX)
    116: #if defined(PETSC_USE_REAL_SINGLE)
    117: #define MumpsScalar mumps_complex
    118: #else
    119: #define MumpsScalar mumps_double_complex
    120: #endif
    121: #else
    122: #define MumpsScalar PetscScalar
    123: #endif

    125: /* macros s.t. indices match MUMPS documentation */
    126: #define ICNTL(I) icntl[(I)-1]
    127: #define CNTL(I) cntl[(I)-1]
    128: #define INFOG(I) infog[(I)-1]
    129: #define INFO(I) info[(I)-1]
    130: #define RINFOG(I) rinfog[(I)-1]
    131: #define RINFO(I) rinfo[(I)-1]

    133: typedef struct Mat_MUMPS Mat_MUMPS;
    134: struct Mat_MUMPS {
    135: #if defined(PETSC_USE_COMPLEX)
    136: #if defined(PETSC_USE_REAL_SINGLE)
    137:   CMUMPS_STRUC_C id;
    138: #else
    139:   ZMUMPS_STRUC_C id;
    140: #endif
    141: #else
    142: #if defined(PETSC_USE_REAL_SINGLE)
    143:   SMUMPS_STRUC_C id;
    144: #else
    145:   DMUMPS_STRUC_C id;
    146: #endif
    147: #endif

    149:   MatStructure   matstruc;
    150:   PetscMPIInt    myid,petsc_size;
    151:   PetscMUMPSInt  *irn,*jcn;             /* the (i,j,v) triplets passed to mumps. */
    152:   PetscScalar    *val,*val_alloc;       /* For some matrices, we can directly access their data array without a buffer. For others, we need a buffer. So comes val_alloc. */
    153:   PetscInt64     nnz;                   /* number of nonzeros. The type is called selective 64-bit in mumps */
    154:   PetscMUMPSInt  sym;
    155:   MPI_Comm       mumps_comm;
    156:   PetscMUMPSInt  ICNTL9_pre;            /* check if ICNTL(9) is changed from previous MatSolve */
    157:   VecScatter     scat_rhs, scat_sol;    /* used by MatSolve() */
    158:   Vec            b_seq,x_seq;
    159:   PetscInt       ninfo,*info;           /* which INFO to display */
    160:   PetscInt       sizeredrhs;
    161:   PetscScalar    *schur_sol;
    162:   PetscInt       schur_sizesol;
    163:   PetscMUMPSInt  *ia_alloc,*ja_alloc;   /* work arrays used for the CSR struct for sparse rhs */
    164:   PetscInt64     cur_ilen,cur_jlen;     /* current len of ia_alloc[], ja_alloc[] */
    165:   PetscErrorCode (*ConvertToTriples)(Mat,PetscInt,MatReuse,Mat_MUMPS*);

    167:   /* stuff used by petsc/mumps OpenMP support*/
    168:   PetscBool      use_petsc_omp_support;
    169:   PetscOmpCtrl   omp_ctrl;              /* an OpenMP controler that blocked processes will release their CPU (MPI_Barrier does not have this guarantee) */
    170:   MPI_Comm       petsc_comm,omp_comm;   /* petsc_comm is petsc matrix's comm */
    171:   PetscInt64     *recvcount;            /* a collection of nnz on omp_master */
    172:   PetscMPIInt    tag,omp_comm_size;
    173:   PetscBool      is_omp_master;         /* is this rank the master of omp_comm */
    174:   MPI_Request    *reqs;
    175: };

    177: /* Cast a 1-based CSR represented by (nrow, ia, ja) of type PetscInt to a CSR of type PetscMUMPSInt.
    178:    Here, nrow is number of rows, ia[] is row pointer and ja[] is column indices.
    179:  */
    180: static PetscErrorCode PetscMUMPSIntCSRCast(Mat_MUMPS *mumps,PetscInt nrow,PetscInt *ia,PetscInt *ja,PetscMUMPSInt **ia_mumps,PetscMUMPSInt **ja_mumps,PetscMUMPSInt *nnz_mumps)
    181: {
    183:   PetscInt       nnz=ia[nrow]-1; /* mumps uses 1-based indices. Uses PetscInt instead of PetscInt64 since mumps only uses PetscMUMPSInt for rhs */

    186: #if defined(PETSC_USE_64BIT_INDICES)
    187:   {
    188:     PetscInt i;
    189:     if (nrow+1 > mumps->cur_ilen) { /* realloc ia_alloc/ja_alloc to fit ia/ja */
    190:       PetscFree(mumps->ia_alloc);
    191:       PetscMalloc1(nrow+1,&mumps->ia_alloc);
    192:       mumps->cur_ilen = nrow+1;
    193:     }
    194:     if (nnz > mumps->cur_jlen) {
    195:       PetscFree(mumps->ja_alloc);
    196:       PetscMalloc1(nnz,&mumps->ja_alloc);
    197:       mumps->cur_jlen = nnz;
    198:     }
    199:     for (i=0; i<nrow+1; i++) {PetscMUMPSIntCast(ia[i],&(mumps->ia_alloc[i]));}
    200:     for (i=0; i<nnz; i++)    {PetscMUMPSIntCast(ja[i],&(mumps->ja_alloc[i]));}
    201:     *ia_mumps = mumps->ia_alloc;
    202:     *ja_mumps = mumps->ja_alloc;
    203:   }
    204: #else
    205:   *ia_mumps = ia;
    206:   *ja_mumps = ja;
    207: #endif
    208:   PetscMUMPSIntCast(nnz,nnz_mumps);
    209:   return(0);
    210: }

    212: static PetscErrorCode MatMumpsResetSchur_Private(Mat_MUMPS* mumps)
    213: {

    217:   PetscFree(mumps->id.listvar_schur);
    218:   PetscFree(mumps->id.redrhs);
    219:   PetscFree(mumps->schur_sol);
    220:   mumps->id.size_schur = 0;
    221:   mumps->id.schur_lld  = 0;
    222:   mumps->id.ICNTL(19)  = 0;
    223:   return(0);
    224: }

    226: /* solve with rhs in mumps->id.redrhs and return in the same location */
    227: static PetscErrorCode MatMumpsSolveSchur_Private(Mat F)
    228: {
    229:   Mat_MUMPS            *mumps=(Mat_MUMPS*)F->data;
    230:   Mat                  S,B,X;
    231:   MatFactorSchurStatus schurstatus;
    232:   PetscInt             sizesol;
    233:   PetscErrorCode       ierr;

    236:   MatFactorFactorizeSchurComplement(F);
    237:   MatFactorGetSchurComplement(F,&S,&schurstatus);
    238:   MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&B);
    239:   MatSetType(B,((PetscObject)S)->type_name);
    240: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
    241:   MatBindToCPU(B,S->boundtocpu);
    242: #endif
    243:   switch (schurstatus) {
    244:   case MAT_FACTOR_SCHUR_FACTORED:
    245:     MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,(PetscScalar*)mumps->id.redrhs,&X);
    246:     MatSetType(X,((PetscObject)S)->type_name);
    247: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
    248:     MatBindToCPU(X,S->boundtocpu);
    249: #endif
    250:     if (!mumps->id.ICNTL(9)) { /* transpose solve */
    251:       MatMatSolveTranspose(S,B,X);
    252:     } else {
    253:       MatMatSolve(S,B,X);
    254:     }
    255:     break;
    256:   case MAT_FACTOR_SCHUR_INVERTED:
    257:     sizesol = mumps->id.nrhs*mumps->id.size_schur;
    258:     if (!mumps->schur_sol || sizesol > mumps->schur_sizesol) {
    259:       PetscFree(mumps->schur_sol);
    260:       PetscMalloc1(sizesol,&mumps->schur_sol);
    261:       mumps->schur_sizesol = sizesol;
    262:     }
    263:     MatCreateSeqDense(PETSC_COMM_SELF,mumps->id.size_schur,mumps->id.nrhs,mumps->schur_sol,&X);
    264:     MatSetType(X,((PetscObject)S)->type_name);
    265: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
    266:     MatBindToCPU(X,S->boundtocpu);
    267: #endif
    268:     MatProductCreateWithMat(S,B,NULL,X);
    269:     if (!mumps->id.ICNTL(9)) { /* transpose solve */
    270:       MatProductSetType(X,MATPRODUCT_AtB);
    271:     } else {
    272:       MatProductSetType(X,MATPRODUCT_AB);
    273:     }
    274:     MatProductSetFromOptions(X);
    275:     MatProductSymbolic(X);
    276:     MatProductNumeric(X);

    278:     MatCopy(X,B,SAME_NONZERO_PATTERN);
    279:     break;
    280:   default:
    281:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
    282:     break;
    283:   }
    284:   MatFactorRestoreSchurComplement(F,&S,schurstatus);
    285:   MatDestroy(&B);
    286:   MatDestroy(&X);
    287:   return(0);
    288: }

    290: static PetscErrorCode MatMumpsHandleSchur_Private(Mat F, PetscBool expansion)
    291: {
    292:   Mat_MUMPS     *mumps=(Mat_MUMPS*)F->data;

    296:   if (!mumps->id.ICNTL(19)) { /* do nothing when Schur complement has not been computed */
    297:     return(0);
    298:   }
    299:   if (!expansion) { /* prepare for the condensation step */
    300:     PetscInt sizeredrhs = mumps->id.nrhs*mumps->id.size_schur;
    301:     /* allocate MUMPS internal array to store reduced right-hand sides */
    302:     if (!mumps->id.redrhs || sizeredrhs > mumps->sizeredrhs) {
    303:       PetscFree(mumps->id.redrhs);
    304:       mumps->id.lredrhs = mumps->id.size_schur;
    305:       PetscMalloc1(mumps->id.nrhs*mumps->id.lredrhs,&mumps->id.redrhs);
    306:       mumps->sizeredrhs = mumps->id.nrhs*mumps->id.lredrhs;
    307:     }
    308:     mumps->id.ICNTL(26) = 1; /* condensation phase */
    309:   } else { /* prepare for the expansion step */
    310:     /* solve Schur complement (this has to be done by the MUMPS user, so basically us) */
    311:     MatMumpsSolveSchur_Private(F);
    312:     mumps->id.ICNTL(26) = 2; /* expansion phase */
    313:     PetscMUMPS_c(mumps);
    314:     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));
    315:     /* restore defaults */
    316:     mumps->id.ICNTL(26) = -1;
    317:     /* free MUMPS internal array for redrhs if we have solved for multiple rhs in order to save memory space */
    318:     if (mumps->id.nrhs > 1) {
    319:       PetscFree(mumps->id.redrhs);
    320:       mumps->id.lredrhs = 0;
    321:       mumps->sizeredrhs = 0;
    322:     }
    323:   }
    324:   return(0);
    325: }

    327: /*
    328:   MatConvertToTriples_A_B - convert Petsc matrix to triples: row[nz], col[nz], val[nz]

    330:   input:
    331:     A       - matrix in aij,baij or sbaij format
    332:     shift   - 0: C style output triple; 1: Fortran style output triple.
    333:     reuse   - MAT_INITIAL_MATRIX: spaces are allocated and values are set for the triple
    334:               MAT_REUSE_MATRIX:   only the values in v array are updated
    335:   output:
    336:     nnz     - dim of r, c, and v (number of local nonzero entries of A)
    337:     r, c, v - row and col index, matrix values (matrix triples)

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

    343:  */

    345: PetscErrorCode MatConvertToTriples_seqaij_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    346: {
    347:   const PetscScalar *av;
    348:   const PetscInt    *ai,*aj,*ajj,M=A->rmap->n;
    349:   PetscInt64        nz,rnz,i,j,k;
    350:   PetscErrorCode    ierr;
    351:   PetscMUMPSInt     *row,*col;
    352:   Mat_SeqAIJ        *aa=(Mat_SeqAIJ*)A->data;

    355:   MatSeqAIJGetArrayRead(A,&av);
    356:   mumps->val = (PetscScalar*)av;
    357:   if (reuse == MAT_INITIAL_MATRIX) {
    358:     nz   = aa->nz;
    359:     ai   = aa->i;
    360:     aj   = aa->j;
    361:     PetscMalloc2(nz,&row,nz,&col);
    362:     for (i=k=0; i<M; i++) {
    363:       rnz = ai[i+1] - ai[i];
    364:       ajj = aj + ai[i];
    365:       for (j=0; j<rnz; j++) {
    366:         PetscMUMPSIntCast(i+shift,&row[k]);
    367:         PetscMUMPSIntCast(ajj[j] + shift,&col[k]);
    368:         k++;
    369:       }
    370:     }
    371:     mumps->irn = row;
    372:     mumps->jcn = col;
    373:     mumps->nnz = nz;
    374:   }
    375:   MatSeqAIJRestoreArrayRead(A,&av);
    376:   return(0);
    377: }

    379: PetscErrorCode MatConvertToTriples_seqsell_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    380: {
    382:   PetscInt64     nz,i,j,k,r;
    383:   Mat_SeqSELL    *a=(Mat_SeqSELL*)A->data;
    384:   PetscMUMPSInt  *row,*col;

    387:   mumps->val = a->val;
    388:   if (reuse == MAT_INITIAL_MATRIX) {
    389:     nz   = a->sliidx[a->totalslices];
    390:     PetscMalloc2(nz,&row,nz,&col);
    391:     for (i=k=0; i<a->totalslices; i++) {
    392:       for (j=a->sliidx[i],r=0; j<a->sliidx[i+1]; j++,r=((r+1)&0x07)) {
    393:         PetscMUMPSIntCast(8*i+r+shift,&row[k++]);
    394:       }
    395:     }
    396:     for (i=0;i<nz;i++) {PetscMUMPSIntCast(a->colidx[i]+shift,&col[i]);}
    397:     mumps->irn = row;
    398:     mumps->jcn = col;
    399:     mumps->nnz = nz;
    400:   }
    401:   return(0);
    402: }

    404: PetscErrorCode MatConvertToTriples_seqbaij_seqaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    405: {
    406:   Mat_SeqBAIJ    *aa=(Mat_SeqBAIJ*)A->data;
    407:   const PetscInt *ai,*aj,*ajj,bs2 = aa->bs2;
    408:   PetscInt64     M,nz,idx=0,rnz,i,j,k,m;
    409:   PetscInt       bs;
    411:   PetscMUMPSInt  *row,*col;

    414:   MatGetBlockSize(A,&bs);
    415:   M          = A->rmap->N/bs;
    416:   mumps->val = aa->a;
    417:   if (reuse == MAT_INITIAL_MATRIX) {
    418:     ai   = aa->i; aj = aa->j;
    419:     nz   = bs2*aa->nz;
    420:     PetscMalloc2(nz,&row,nz,&col);
    421:     for (i=0; i<M; i++) {
    422:       ajj = aj + ai[i];
    423:       rnz = ai[i+1] - ai[i];
    424:       for (k=0; k<rnz; k++) {
    425:         for (j=0; j<bs; j++) {
    426:           for (m=0; m<bs; m++) {
    427:             PetscMUMPSIntCast(i*bs + m + shift,&row[idx]);
    428:             PetscMUMPSIntCast(bs*ajj[k] + j + shift,&col[idx]);
    429:             idx++;
    430:           }
    431:         }
    432:       }
    433:     }
    434:     mumps->irn = row;
    435:     mumps->jcn = col;
    436:     mumps->nnz = nz;
    437:   }
    438:   return(0);
    439: }

    441: PetscErrorCode MatConvertToTriples_seqsbaij_seqsbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    442: {
    443:   const PetscInt *ai, *aj,*ajj;
    444:   PetscInt        bs;
    445:   PetscInt64      nz,rnz,i,j,k,m;
    446:   PetscErrorCode  ierr;
    447:   PetscMUMPSInt   *row,*col;
    448:   PetscScalar     *val;
    449:   Mat_SeqSBAIJ    *aa=(Mat_SeqSBAIJ*)A->data;
    450:   const PetscInt  bs2=aa->bs2,mbs=aa->mbs;
    451: #if defined(PETSC_USE_COMPLEX)
    452:   PetscBool       hermitian;
    453: #endif

    456: #if defined(PETSC_USE_COMPLEX)
    457:   MatGetOption(A,MAT_HERMITIAN,&hermitian);
    458:   if (hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy");
    459: #endif
    460:   ai   = aa->i;
    461:   aj   = aa->j;
    462:   MatGetBlockSize(A,&bs);
    463:   if (reuse == MAT_INITIAL_MATRIX) {
    464:     nz   = aa->nz;
    465:     PetscMalloc2(bs2*nz,&row,bs2*nz,&col);
    466:     if (bs>1) {
    467:       PetscMalloc1(bs2*nz,&mumps->val_alloc);
    468:       mumps->val = mumps->val_alloc;
    469:     } else {
    470:       mumps->val = aa->a;
    471:     }
    472:     mumps->irn = row;
    473:     mumps->jcn = col;
    474:   } else {
    475:     if (bs == 1) mumps->val = aa->a;
    476:     row = mumps->irn;
    477:     col = mumps->jcn;
    478:   }
    479:   val = mumps->val;

    481:   nz = 0;
    482:   if (bs>1) {
    483:     for (i=0; i<mbs; i++) {
    484:       rnz = ai[i+1] - ai[i];
    485:       ajj = aj + ai[i];
    486:       for (j=0; j<rnz; j++) {
    487:         for (k=0; k<bs; k++) {
    488:           for (m=0; m<bs; m++) {
    489:             if (ajj[j]>i || k>=m) {
    490:               if (reuse == MAT_INITIAL_MATRIX) {
    491:                 PetscMUMPSIntCast(i*bs + m + shift,&row[nz]);
    492:                 PetscMUMPSIntCast(ajj[j]*bs + k + shift,&col[nz]);
    493:               }
    494:               val[nz++] = aa->a[(ai[i]+j)*bs2 + m + k*bs];
    495:             }
    496:           }
    497:         }
    498:       }
    499:     }
    500:   } else if (reuse == MAT_INITIAL_MATRIX) {
    501:     for (i=0; i<mbs; i++) {
    502:       rnz = ai[i+1] - ai[i];
    503:       ajj = aj + ai[i];
    504:       for (j=0; j<rnz; j++) {
    505:         PetscMUMPSIntCast(i+shift,&row[nz]);
    506:         PetscMUMPSIntCast(ajj[j] + shift,&col[nz]);
    507:         nz++;
    508:       }
    509:     }
    510:     if (nz != aa->nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Different numbers of nonzeros %D != %D",nz,aa->nz);
    511:   }
    512:   if (reuse == MAT_INITIAL_MATRIX) mumps->nnz = nz;
    513:   return(0);
    514: }

    516: PetscErrorCode MatConvertToTriples_seqaij_seqsbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    517: {
    518:   const PetscInt    *ai,*aj,*ajj,*adiag,M=A->rmap->n;
    519:   PetscInt64        nz,rnz,i,j;
    520:   const PetscScalar *av,*v1;
    521:   PetscScalar       *val;
    522:   PetscErrorCode    ierr;
    523:   PetscMUMPSInt     *row,*col;
    524:   Mat_SeqAIJ        *aa=(Mat_SeqAIJ*)A->data;
    525:   PetscBool         missing;
    526: #if defined(PETSC_USE_COMPLEX)
    527:   PetscBool         hermitian;
    528: #endif

    531: #if defined(PETSC_USE_COMPLEX)
    532:   MatGetOption(A,MAT_HERMITIAN,&hermitian);
    533:   if (hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy");
    534: #endif
    535:   MatSeqAIJGetArrayRead(A,&av);
    536:   ai    = aa->i; aj = aa->j;
    537:   adiag = aa->diag;
    538:   MatMissingDiagonal_SeqAIJ(A,&missing,NULL);
    539:   if (reuse == MAT_INITIAL_MATRIX) {
    540:     /* count nz in the upper triangular part of A */
    541:     nz = 0;
    542:     if (missing) {
    543:       for (i=0; i<M; i++) {
    544:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
    545:           for (j=ai[i];j<ai[i+1];j++) {
    546:             if (aj[j] < i) continue;
    547:             nz++;
    548:           }
    549:         } else {
    550:           nz += ai[i+1] - adiag[i];
    551:         }
    552:       }
    553:     } else {
    554:       for (i=0; i<M; i++) nz += ai[i+1] - adiag[i];
    555:     }
    556:     PetscMalloc2(nz,&row,nz,&col);
    557:     PetscMalloc1(nz,&val);
    558:     mumps->nnz = nz;
    559:     mumps->irn = row;
    560:     mumps->jcn = col;
    561:     mumps->val = mumps->val_alloc = val;

    563:     nz = 0;
    564:     if (missing) {
    565:       for (i=0; i<M; i++) {
    566:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
    567:           for (j=ai[i];j<ai[i+1];j++) {
    568:             if (aj[j] < i) continue;
    569:             PetscMUMPSIntCast(i+shift,&row[nz]);
    570:             PetscMUMPSIntCast(aj[j]+shift,&col[nz]);
    571:             val[nz] = av[j];
    572:             nz++;
    573:           }
    574:         } else {
    575:           rnz = ai[i+1] - adiag[i];
    576:           ajj = aj + adiag[i];
    577:           v1  = av + adiag[i];
    578:           for (j=0; j<rnz; j++) {
    579:             PetscMUMPSIntCast(i+shift,&row[nz]);
    580:             PetscMUMPSIntCast(ajj[j] + shift,&col[nz]);
    581:             val[nz++] = v1[j];
    582:           }
    583:         }
    584:       }
    585:     } else {
    586:       for (i=0; i<M; i++) {
    587:         rnz = ai[i+1] - adiag[i];
    588:         ajj = aj + adiag[i];
    589:         v1  = av + adiag[i];
    590:         for (j=0; j<rnz; j++) {
    591:           PetscMUMPSIntCast(i+shift,&row[nz]);
    592:           PetscMUMPSIntCast(ajj[j] + shift,&col[nz]);
    593:           val[nz++] = v1[j];
    594:         }
    595:       }
    596:     }
    597:   } else {
    598:     nz = 0;
    599:     val = mumps->val;
    600:     if (missing) {
    601:       for (i=0; i <M; i++) {
    602:         if (PetscUnlikely(adiag[i] >= ai[i+1])) {
    603:           for (j=ai[i];j<ai[i+1];j++) {
    604:             if (aj[j] < i) continue;
    605:             val[nz++] = av[j];
    606:           }
    607:         } else {
    608:           rnz = ai[i+1] - adiag[i];
    609:           v1  = av + adiag[i];
    610:           for (j=0; j<rnz; j++) {
    611:             val[nz++] = v1[j];
    612:           }
    613:         }
    614:       }
    615:     } else {
    616:       for (i=0; i <M; i++) {
    617:         rnz = ai[i+1] - adiag[i];
    618:         v1  = av + adiag[i];
    619:         for (j=0; j<rnz; j++) {
    620:           val[nz++] = v1[j];
    621:         }
    622:       }
    623:     }
    624:   }
    625:   MatSeqAIJRestoreArrayRead(A,&av);
    626:   return(0);
    627: }

    629: PetscErrorCode MatConvertToTriples_mpisbaij_mpisbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    630: {
    631:   PetscErrorCode    ierr;
    632:   const PetscInt    *ai,*aj,*bi,*bj,*garray,*ajj,*bjj;
    633:   PetscInt          bs;
    634:   PetscInt64        rstart,nz,i,j,k,m,jj,irow,countA,countB;
    635:   PetscMUMPSInt     *row,*col;
    636:   const PetscScalar *av,*bv,*v1,*v2;
    637:   PetscScalar       *val;
    638:   Mat_MPISBAIJ      *mat = (Mat_MPISBAIJ*)A->data;
    639:   Mat_SeqSBAIJ      *aa  = (Mat_SeqSBAIJ*)(mat->A)->data;
    640:   Mat_SeqBAIJ       *bb  = (Mat_SeqBAIJ*)(mat->B)->data;
    641:   const PetscInt    bs2=aa->bs2,mbs=aa->mbs;
    642: #if defined(PETSC_USE_COMPLEX)
    643:   PetscBool         hermitian;
    644: #endif

    647: #if defined(PETSC_USE_COMPLEX)
    648:   MatGetOption(A,MAT_HERMITIAN,&hermitian);
    649:   if (hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy");
    650: #endif
    651:   MatGetBlockSize(A,&bs);
    652:   rstart = A->rmap->rstart;
    653:   ai = aa->i;
    654:   aj = aa->j;
    655:   bi = bb->i;
    656:   bj = bb->j;
    657:   av = aa->a;
    658:   bv = bb->a;

    660:   garray = mat->garray;

    662:   if (reuse == MAT_INITIAL_MATRIX) {
    663:     nz   = (aa->nz+bb->nz)*bs2; /* just a conservative estimate */
    664:     PetscMalloc2(nz,&row,nz,&col);
    665:     PetscMalloc1(nz,&val);
    666:     /* can not decide the exact mumps->nnz now because of the SBAIJ */
    667:     mumps->irn = row;
    668:     mumps->jcn = col;
    669:     mumps->val = mumps->val_alloc = val;
    670:   } else {
    671:     val = mumps->val;
    672:   }

    674:   jj = 0; irow = rstart;
    675:   for (i=0; i<mbs; i++) {
    676:     ajj    = aj + ai[i];                 /* ptr to the beginning of this row */
    677:     countA = ai[i+1] - ai[i];
    678:     countB = bi[i+1] - bi[i];
    679:     bjj    = bj + bi[i];
    680:     v1     = av + ai[i]*bs2;
    681:     v2     = bv + bi[i]*bs2;

    683:     if (bs>1) {
    684:       /* A-part */
    685:       for (j=0; j<countA; j++) {
    686:         for (k=0; k<bs; k++) {
    687:           for (m=0; m<bs; m++) {
    688:             if (rstart + ajj[j]*bs>irow || k>=m) {
    689:               if (reuse == MAT_INITIAL_MATRIX) {
    690:                 PetscMUMPSIntCast(irow + m + shift,&row[jj]);
    691:                 PetscMUMPSIntCast(rstart + ajj[j]*bs + k + shift,&col[jj]);
    692:               }
    693:               val[jj++] = v1[j*bs2 + m + k*bs];
    694:             }
    695:           }
    696:         }
    697:       }

    699:       /* B-part */
    700:       for (j=0; j < countB; j++) {
    701:         for (k=0; k<bs; k++) {
    702:           for (m=0; m<bs; m++) {
    703:             if (reuse == MAT_INITIAL_MATRIX) {
    704:               PetscMUMPSIntCast(irow + m + shift,&row[jj]);
    705:               PetscMUMPSIntCast(garray[bjj[j]]*bs + k + shift,&col[jj]);
    706:             }
    707:             val[jj++] = v2[j*bs2 + m + k*bs];
    708:           }
    709:         }
    710:       }
    711:     } else {
    712:       /* A-part */
    713:       for (j=0; j<countA; j++) {
    714:         if (reuse == MAT_INITIAL_MATRIX) {
    715:           PetscMUMPSIntCast(irow + shift,&row[jj]);
    716:           PetscMUMPSIntCast(rstart + ajj[j] + shift,&col[jj]);
    717:         }
    718:         val[jj++] = v1[j];
    719:       }

    721:       /* B-part */
    722:       for (j=0; j < countB; j++) {
    723:         if (reuse == MAT_INITIAL_MATRIX) {
    724:           PetscMUMPSIntCast(irow + shift,&row[jj]);
    725:           PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj]);
    726:         }
    727:         val[jj++] = v2[j];
    728:       }
    729:     }
    730:     irow+=bs;
    731:   }
    732:   mumps->nnz = jj;
    733:   return(0);
    734: }

    736: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    737: {
    738:   const PetscInt    *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
    739:   PetscErrorCode    ierr;
    740:   PetscInt64        rstart,nz,i,j,jj,irow,countA,countB;
    741:   PetscMUMPSInt     *row,*col;
    742:   const PetscScalar *av, *bv,*v1,*v2;
    743:   PetscScalar       *val;
    744:   Mat               Ad,Ao;
    745:   Mat_SeqAIJ        *aa;
    746:   Mat_SeqAIJ        *bb;

    749:   MatMPIAIJGetSeqAIJ(A,&Ad,&Ao,&garray);
    750:   MatSeqAIJGetArrayRead(Ad,&av);
    751:   MatSeqAIJGetArrayRead(Ao,&bv);

    753:   aa = (Mat_SeqAIJ*)(Ad)->data;
    754:   bb = (Mat_SeqAIJ*)(Ao)->data;
    755:   ai = aa->i;
    756:   aj = aa->j;
    757:   bi = bb->i;
    758:   bj = bb->j;

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

    762:   if (reuse == MAT_INITIAL_MATRIX) {
    763:     nz   = (PetscInt64)aa->nz + bb->nz; /* make sure the sum won't overflow PetscInt */
    764:     PetscMalloc2(nz,&row,nz,&col);
    765:     PetscMalloc1(nz,&val);
    766:     mumps->nnz = nz;
    767:     mumps->irn = row;
    768:     mumps->jcn = col;
    769:     mumps->val = mumps->val_alloc = val;
    770:   } else {
    771:     val = mumps->val;
    772:   }

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

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

    792:     /* B-part */
    793:     for (j=0; j < countB; j++) {
    794:       if (reuse == MAT_INITIAL_MATRIX) {
    795:         PetscMUMPSIntCast(irow + shift,&row[jj]);
    796:         PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj]);
    797:       }
    798:       val[jj++] = v2[j];
    799:     }
    800:     irow++;
    801:   }
    802:   MatSeqAIJRestoreArrayRead(Ad,&av);
    803:   MatSeqAIJRestoreArrayRead(Ao,&bv);
    804:   return(0);
    805: }

    807: PetscErrorCode MatConvertToTriples_mpibaij_mpiaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    808: {
    809:   Mat_MPIBAIJ       *mat    = (Mat_MPIBAIJ*)A->data;
    810:   Mat_SeqBAIJ       *aa     = (Mat_SeqBAIJ*)(mat->A)->data;
    811:   Mat_SeqBAIJ       *bb     = (Mat_SeqBAIJ*)(mat->B)->data;
    812:   const PetscInt    *ai     = aa->i, *bi = bb->i, *aj = aa->j, *bj = bb->j,*ajj, *bjj;
    813:   const PetscInt    *garray = mat->garray,mbs=mat->mbs,rstart=A->rmap->rstart;
    814:   const PetscInt    bs2=mat->bs2;
    815:   PetscErrorCode    ierr;
    816:   PetscInt          bs;
    817:   PetscInt64        nz,i,j,k,n,jj,irow,countA,countB,idx;
    818:   PetscMUMPSInt     *row,*col;
    819:   const PetscScalar *av=aa->a, *bv=bb->a,*v1,*v2;
    820:   PetscScalar       *val;

    823:   MatGetBlockSize(A,&bs);
    824:   if (reuse == MAT_INITIAL_MATRIX) {
    825:     nz   = bs2*(aa->nz + bb->nz);
    826:     PetscMalloc2(nz,&row,nz,&col);
    827:     PetscMalloc1(nz,&val);
    828:     mumps->nnz = nz;
    829:     mumps->irn = row;
    830:     mumps->jcn = col;
    831:     mumps->val = mumps->val_alloc = val;
    832:   } else {
    833:     val = mumps->val;
    834:   }

    836:   jj = 0; irow = rstart;
    837:   for (i=0; i<mbs; i++) {
    838:     countA = ai[i+1] - ai[i];
    839:     countB = bi[i+1] - bi[i];
    840:     ajj    = aj + ai[i];
    841:     bjj    = bj + bi[i];
    842:     v1     = av + bs2*ai[i];
    843:     v2     = bv + bs2*bi[i];

    845:     idx = 0;
    846:     /* A-part */
    847:     for (k=0; k<countA; k++) {
    848:       for (j=0; j<bs; j++) {
    849:         for (n=0; n<bs; n++) {
    850:           if (reuse == MAT_INITIAL_MATRIX) {
    851:             PetscMUMPSIntCast(irow + n + shift,&row[jj]);
    852:             PetscMUMPSIntCast(rstart + bs*ajj[k] + j + shift,&col[jj]);
    853:           }
    854:           val[jj++] = v1[idx++];
    855:         }
    856:       }
    857:     }

    859:     idx = 0;
    860:     /* B-part */
    861:     for (k=0; k<countB; k++) {
    862:       for (j=0; j<bs; j++) {
    863:         for (n=0; n<bs; n++) {
    864:           if (reuse == MAT_INITIAL_MATRIX) {
    865:             PetscMUMPSIntCast(irow + n + shift,&row[jj]);
    866:             PetscMUMPSIntCast(bs*garray[bjj[k]] + j + shift,&col[jj]);
    867:           }
    868:           val[jj++] = v2[idx++];
    869:         }
    870:       }
    871:     }
    872:     irow += bs;
    873:   }
    874:   return(0);
    875: }

    877: PetscErrorCode MatConvertToTriples_mpiaij_mpisbaij(Mat A,PetscInt shift,MatReuse reuse,Mat_MUMPS *mumps)
    878: {
    879:   const PetscInt    *ai, *aj,*adiag, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
    880:   PetscErrorCode    ierr;
    881:   PetscInt64        rstart,nz,nza,nzb,i,j,jj,irow,countA,countB;
    882:   PetscMUMPSInt     *row,*col;
    883:   const PetscScalar *av, *bv,*v1,*v2;
    884:   PetscScalar       *val;
    885:   Mat               Ad,Ao;
    886:   Mat_SeqAIJ        *aa;
    887:   Mat_SeqAIJ        *bb;
    888: #if defined(PETSC_USE_COMPLEX)
    889:   PetscBool         hermitian;
    890: #endif

    893: #if defined(PETSC_USE_COMPLEX)
    894:   MatGetOption(A,MAT_HERMITIAN,&hermitian);
    895:   if (hermitian) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MUMPS does not support Hermitian symmetric matrices for Choleksy");
    896: #endif
    897:   MatMPIAIJGetSeqAIJ(A,&Ad,&Ao,&garray);
    898:   MatSeqAIJGetArrayRead(Ad,&av);
    899:   MatSeqAIJGetArrayRead(Ao,&bv);

    901:   aa    = (Mat_SeqAIJ*)(Ad)->data;
    902:   bb    = (Mat_SeqAIJ*)(Ao)->data;
    903:   ai    = aa->i;
    904:   aj    = aa->j;
    905:   adiag = aa->diag;
    906:   bi    = bb->i;
    907:   bj    = bb->j;

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

    911:   if (reuse == MAT_INITIAL_MATRIX) {
    912:     nza = 0;    /* num of upper triangular entries in mat->A, including diagonals */
    913:     nzb = 0;    /* num of upper triangular entries in mat->B */
    914:     for (i=0; i<m; i++) {
    915:       nza   += (ai[i+1] - adiag[i]);
    916:       countB = bi[i+1] - bi[i];
    917:       bjj    = bj + bi[i];
    918:       for (j=0; j<countB; j++) {
    919:         if (garray[bjj[j]] > rstart) nzb++;
    920:       }
    921:     }

    923:     nz   = nza + nzb; /* total nz of upper triangular part of mat */
    924:     PetscMalloc2(nz,&row,nz,&col);
    925:     PetscMalloc1(nz,&val);
    926:     mumps->nnz = nz;
    927:     mumps->irn = row;
    928:     mumps->jcn = col;
    929:     mumps->val = mumps->val_alloc = val;
    930:   } else {
    931:     val = mumps->val;
    932:   }

    934:   jj = 0; irow = rstart;
    935:   for (i=0; i<m; i++) {
    936:     ajj    = aj + adiag[i];                 /* ptr to the beginning of the diagonal of this row */
    937:     v1     = av + adiag[i];
    938:     countA = ai[i+1] - adiag[i];
    939:     countB = bi[i+1] - bi[i];
    940:     bjj    = bj + bi[i];
    941:     v2     = bv + bi[i];

    943:     /* A-part */
    944:     for (j=0; j<countA; j++) {
    945:       if (reuse == MAT_INITIAL_MATRIX) {
    946:         PetscMUMPSIntCast(irow + shift,&row[jj]);
    947:         PetscMUMPSIntCast(rstart + ajj[j] + shift,&col[jj]);
    948:       }
    949:       val[jj++] = v1[j];
    950:     }

    952:     /* B-part */
    953:     for (j=0; j < countB; j++) {
    954:       if (garray[bjj[j]] > rstart) {
    955:         if (reuse == MAT_INITIAL_MATRIX) {
    956:           PetscMUMPSIntCast(irow + shift,&row[jj]);
    957:           PetscMUMPSIntCast(garray[bjj[j]] + shift,&col[jj]);
    958:         }
    959:         val[jj++] = v2[j];
    960:       }
    961:     }
    962:     irow++;
    963:   }
    964:   MatSeqAIJRestoreArrayRead(Ad,&av);
    965:   MatSeqAIJRestoreArrayRead(Ao,&bv);
    966:   return(0);
    967: }

    969: PetscErrorCode MatDestroy_MUMPS(Mat A)
    970: {
    972:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->data;

    975:   PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
    976:   VecScatterDestroy(&mumps->scat_rhs);
    977:   VecScatterDestroy(&mumps->scat_sol);
    978:   VecDestroy(&mumps->b_seq);
    979:   VecDestroy(&mumps->x_seq);
    980:   PetscFree(mumps->id.perm_in);
    981:   PetscFree2(mumps->irn,mumps->jcn);
    982:   PetscFree(mumps->val_alloc);
    983:   PetscFree(mumps->info);
    984:   MatMumpsResetSchur_Private(mumps);
    985:   mumps->id.job = JOB_END;
    986:   PetscMUMPS_c(mumps);
    987:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in MatDestroy_MUMPS: INFOG(1)=%d\n",mumps->id.INFOG(1));
    988: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
    989:   if (mumps->use_petsc_omp_support) { PetscOmpCtrlDestroy(&mumps->omp_ctrl); }
    990: #endif
    991:   PetscFree(mumps->ia_alloc);
    992:   PetscFree(mumps->ja_alloc);
    993:   PetscFree(mumps->recvcount);
    994:   PetscFree(mumps->reqs);
    995:   PetscFree(A->data);

    997:   /* clear composed functions */
    998:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
    999:   PetscObjectComposeFunction((PetscObject)A,"MatFactorSetSchurIS_C",NULL);
    1000:   PetscObjectComposeFunction((PetscObject)A,"MatFactorCreateSchurComplement_C",NULL);
    1001:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetIcntl_C",NULL);
    1002:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetIcntl_C",NULL);
    1003:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsSetCntl_C",NULL);
    1004:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetCntl_C",NULL);
    1005:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfo_C",NULL);
    1006:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInfog_C",NULL);
    1007:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfo_C",NULL);
    1008:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetRinfog_C",NULL);
    1009:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverse_C",NULL);
    1010:   PetscObjectComposeFunction((PetscObject)A,"MatMumpsGetInverseTranspose_C",NULL);
    1011:   return(0);
    1012: }

    1014: PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x)
    1015: {
    1016:   Mat_MUMPS        *mumps=(Mat_MUMPS*)A->data;
    1017:   PetscScalar      *array;
    1018:   Vec              b_seq;
    1019:   IS               is_iden,is_petsc;
    1020:   PetscErrorCode   ierr;
    1021:   PetscInt         i;
    1022:   PetscBool        second_solve = PETSC_FALSE;
    1023:   static PetscBool cite1 = PETSC_FALSE,cite2 = PETSC_FALSE;

    1026:   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);
    1027:   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);

    1029:   if (A->factorerrortype) {
    1030:     PetscInfo2(A,"MatSolve is called with singular matrix factor, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1031:     VecSetInf(x);
    1032:     return(0);
    1033:   }

    1035:   mumps->id.ICNTL(20) = 0; /* dense RHS */
    1036:   mumps->id.nrhs      = 1;
    1037:   b_seq               = mumps->b_seq;
    1038:   if (mumps->petsc_size > 1) {
    1039:     /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */
    1040:     VecScatterBegin(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    1041:     VecScatterEnd(mumps->scat_rhs,b,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    1042:     if (!mumps->myid) {VecGetArray(b_seq,&array);}
    1043:   } else {  /* petsc_size == 1 */
    1044:     VecCopy(b,x);
    1045:     VecGetArray(x,&array);
    1046:   }
    1047:   if (!mumps->myid) { /* define rhs on the host */
    1048:     mumps->id.nrhs = 1;
    1049:     mumps->id.rhs = (MumpsScalar*)array;
    1050:   }

    1052:   /*
    1053:      handle condensation step of Schur complement (if any)
    1054:      We set by default ICNTL(26) == -1 when Schur indices have been provided by the user.
    1055:      According to MUMPS (5.0.0) manual, any value should be harmful during the factorization phase
    1056:      Unless the user provides a valid value for ICNTL(26), MatSolve and MatMatSolve routines solve the full system.
    1057:      This requires an extra call to PetscMUMPS_c and the computation of the factors for S
    1058:   */
    1059:   if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
    1060:     if (mumps->petsc_size > 1) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n");
    1061:     second_solve = PETSC_TRUE;
    1062:     MatMumpsHandleSchur_Private(A,PETSC_FALSE);
    1063:   }
    1064:   /* solve phase */
    1065:   /*-------------*/
    1066:   mumps->id.job = JOB_SOLVE;
    1067:   PetscMUMPS_c(mumps);
    1068:   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));

    1070:   /* handle expansion step of Schur complement (if any) */
    1071:   if (second_solve) {
    1072:     MatMumpsHandleSchur_Private(A,PETSC_TRUE);
    1073:   }

    1075:   if (mumps->petsc_size > 1) { /* convert mumps distributed solution to petsc mpi x */
    1076:     if (mumps->scat_sol && mumps->ICNTL9_pre != mumps->id.ICNTL(9)) {
    1077:       /* when id.ICNTL(9) changes, the contents of lsol_loc may change (not its size, lsol_loc), recreates scat_sol */
    1078:       VecScatterDestroy(&mumps->scat_sol);
    1079:     }
    1080:     if (!mumps->scat_sol) { /* create scatter scat_sol */
    1081:       PetscInt *isol2_loc=NULL;
    1082:       ISCreateStride(PETSC_COMM_SELF,mumps->id.lsol_loc,0,1,&is_iden); /* from */
    1083:       PetscMalloc1(mumps->id.lsol_loc,&isol2_loc);
    1084:       for (i=0; i<mumps->id.lsol_loc; i++) isol2_loc[i] = mumps->id.isol_loc[i]-1; /* change Fortran style to C style */
    1085:       ISCreateGeneral(PETSC_COMM_SELF,mumps->id.lsol_loc,isol2_loc,PETSC_OWN_POINTER,&is_petsc);  /* to */
    1086:       VecScatterCreate(mumps->x_seq,is_iden,x,is_petsc,&mumps->scat_sol);
    1087:       ISDestroy(&is_iden);
    1088:       ISDestroy(&is_petsc);
    1089:       mumps->ICNTL9_pre = mumps->id.ICNTL(9); /* save current value of id.ICNTL(9) */
    1090:     }

    1092:     VecScatterBegin(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
    1093:     VecScatterEnd(mumps->scat_sol,mumps->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);
    1094:   }

    1096:   if (mumps->petsc_size > 1) {if (!mumps->myid) {VecRestoreArray(b_seq,&array);}}
    1097:   else {VecRestoreArray(x,&array);}

    1099:   PetscLogFlops(2.0*mumps->id.RINFO(3));
    1100:   return(0);
    1101: }

    1103: PetscErrorCode MatSolveTranspose_MUMPS(Mat A,Vec b,Vec x)
    1104: {
    1105:   Mat_MUMPS      *mumps=(Mat_MUMPS*)A->data;

    1109:   mumps->id.ICNTL(9) = 0;
    1110:   MatSolve_MUMPS(A,b,x);
    1111:   mumps->id.ICNTL(9) = 1;
    1112:   return(0);
    1113: }

    1115: PetscErrorCode MatMatSolve_MUMPS(Mat A,Mat B,Mat X)
    1116: {
    1117:   PetscErrorCode    ierr;
    1118:   Mat               Bt = NULL;
    1119:   PetscBool         denseX,denseB,flg,flgT;
    1120:   Mat_MUMPS         *mumps=(Mat_MUMPS*)A->data;
    1121:   PetscInt          i,nrhs,M;
    1122:   PetscScalar       *array;
    1123:   const PetscScalar *rbray;
    1124:   PetscInt          lsol_loc,nlsol_loc,*idxx,iidx = 0;
    1125:   PetscMUMPSInt     *isol_loc,*isol_loc_save;
    1126:   PetscScalar       *bray,*sol_loc,*sol_loc_save;
    1127:   IS                is_to,is_from;
    1128:   PetscInt          k,proc,j,m,myrstart;
    1129:   const PetscInt    *rstart;
    1130:   Vec               v_mpi,b_seq,msol_loc;
    1131:   VecScatter        scat_rhs,scat_sol;
    1132:   PetscScalar       *aa;
    1133:   PetscInt          spnr,*ia,*ja;
    1134:   Mat_MPIAIJ        *b = NULL;

    1137:   PetscObjectTypeCompareAny((PetscObject)X,&denseX,MATSEQDENSE,MATMPIDENSE,NULL);
    1138:   if (!denseX) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");

    1140:   PetscObjectTypeCompareAny((PetscObject)B,&denseB,MATSEQDENSE,MATMPIDENSE,NULL);
    1141:   if (denseB) {
    1142:     if (B->rmap->n != X->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix B and X must have same row distribution");
    1143:     mumps->id.ICNTL(20)= 0; /* dense RHS */
    1144:   } else { /* sparse B */
    1145:     if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
    1146:     PetscObjectTypeCompare((PetscObject)B,MATTRANSPOSEMAT,&flgT);
    1147:     if (flgT) { /* input B is transpose of actural RHS matrix,
    1148:                  because mumps requires sparse compressed COLUMN storage! See MatMatTransposeSolve_MUMPS() */
    1149:       MatTransposeGetMat(B,&Bt);
    1150:     } else SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONG,"Matrix B must be MATTRANSPOSEMAT matrix");
    1151:     mumps->id.ICNTL(20)= 1; /* sparse RHS */
    1152:   }

    1154:   MatGetSize(B,&M,&nrhs);
    1155:   mumps->id.nrhs = nrhs;
    1156:   mumps->id.lrhs = M;
    1157:   mumps->id.rhs  = NULL;

    1159:   if (mumps->petsc_size == 1) {
    1160:     PetscScalar *aa;
    1161:     PetscInt    spnr,*ia,*ja;
    1162:     PetscBool   second_solve = PETSC_FALSE;

    1164:     MatDenseGetArray(X,&array);
    1165:     mumps->id.rhs = (MumpsScalar*)array;

    1167:     if (denseB) {
    1168:       /* copy B to X */
    1169:       MatDenseGetArrayRead(B,&rbray);
    1170:       PetscArraycpy(array,rbray,M*nrhs);
    1171:       MatDenseRestoreArrayRead(B,&rbray);
    1172:     } else { /* sparse B */
    1173:       MatSeqAIJGetArray(Bt,&aa);
    1174:       MatGetRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    1175:       if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure");
    1176:       PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs);
    1177:       mumps->id.rhs_sparse  = (MumpsScalar*)aa;
    1178:     }
    1179:     /* handle condensation step of Schur complement (if any) */
    1180:     if (mumps->id.size_schur > 0 && (mumps->id.ICNTL(26) < 0 || mumps->id.ICNTL(26) > 2)) {
    1181:       second_solve = PETSC_TRUE;
    1182:       MatMumpsHandleSchur_Private(A,PETSC_FALSE);
    1183:     }
    1184:     /* solve phase */
    1185:     /*-------------*/
    1186:     mumps->id.job = JOB_SOLVE;
    1187:     PetscMUMPS_c(mumps);
    1188:     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));

    1190:     /* handle expansion step of Schur complement (if any) */
    1191:     if (second_solve) {
    1192:       MatMumpsHandleSchur_Private(A,PETSC_TRUE);
    1193:     }
    1194:     if (!denseB) { /* sparse B */
    1195:       MatSeqAIJRestoreArray(Bt,&aa);
    1196:       MatRestoreRowIJ(Bt,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    1197:       if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure");
    1198:     }
    1199:     MatDenseRestoreArray(X,&array);
    1200:     return(0);
    1201:   }

    1203:   /*--------- parallel case: MUMPS requires rhs B to be centralized on the host! --------*/
    1204:   if (mumps->petsc_size > 1 && mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Parallel Schur complements not yet supported from PETSc\n");

    1206:   /* create msol_loc to hold mumps local solution */
    1207:   isol_loc_save = mumps->id.isol_loc; /* save it for MatSolve() */
    1208:   sol_loc_save  = (PetscScalar*)mumps->id.sol_loc;

    1210:   lsol_loc  = mumps->id.lsol_loc;
    1211:   nlsol_loc = nrhs*lsol_loc;     /* length of sol_loc */
    1212:   PetscMalloc2(nlsol_loc,&sol_loc,lsol_loc,&isol_loc);
    1213:   mumps->id.sol_loc  = (MumpsScalar*)sol_loc;
    1214:   mumps->id.isol_loc = isol_loc;

    1216:   VecCreateSeqWithArray(PETSC_COMM_SELF,1,nlsol_loc,(PetscScalar*)sol_loc,&msol_loc);

    1218:   if (denseB) { /* dense B */
    1219:     /* TODO: Because of non-contiguous indices, the created vecscatter scat_rhs is not done in MPI_Gather, resulting in
    1220:        very inefficient communication. An optimization is to use VecScatterCreateToZero to gather B to rank 0. Then on rank
    1221:        0, re-arrange B into desired order, which is a local operation.
    1222:      */

    1224:     /* scatter v_mpi to b_seq because MUMPS only supports centralized rhs */
    1225:     /* wrap dense rhs matrix B into a vector v_mpi */
    1226:     MatGetLocalSize(B,&m,NULL);
    1227:     MatDenseGetArray(B,&bray);
    1228:     VecCreateMPIWithArray(PetscObjectComm((PetscObject)B),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);
    1229:     MatDenseRestoreArray(B,&bray);

    1231:     /* scatter v_mpi to b_seq in proc[0]. MUMPS requires rhs to be centralized on the host! */
    1232:     if (!mumps->myid) {
    1233:       PetscInt *idx;
    1234:       /* idx: maps from k-th index of v_mpi to (i,j)-th global entry of B */
    1235:       PetscMalloc1(nrhs*M,&idx);
    1236:       MatGetOwnershipRanges(B,&rstart);
    1237:       k = 0;
    1238:       for (proc=0; proc<mumps->petsc_size; proc++){
    1239:         for (j=0; j<nrhs; j++){
    1240:           for (i=rstart[proc]; i<rstart[proc+1]; i++) idx[k++] = j*M + i;
    1241:         }
    1242:       }

    1244:       VecCreateSeq(PETSC_COMM_SELF,nrhs*M,&b_seq);
    1245:       ISCreateGeneral(PETSC_COMM_SELF,nrhs*M,idx,PETSC_OWN_POINTER,&is_to);
    1246:       ISCreateStride(PETSC_COMM_SELF,nrhs*M,0,1,&is_from);
    1247:     } else {
    1248:       VecCreateSeq(PETSC_COMM_SELF,0,&b_seq);
    1249:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_to);
    1250:       ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_from);
    1251:     }
    1252:     VecScatterCreate(v_mpi,is_from,b_seq,is_to,&scat_rhs);
    1253:     VecScatterBegin(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);
    1254:     ISDestroy(&is_to);
    1255:     ISDestroy(&is_from);
    1256:     VecScatterEnd(scat_rhs,v_mpi,b_seq,INSERT_VALUES,SCATTER_FORWARD);

    1258:     if (!mumps->myid) { /* define rhs on the host */
    1259:       VecGetArray(b_seq,&bray);
    1260:       mumps->id.rhs = (MumpsScalar*)bray;
    1261:       VecRestoreArray(b_seq,&bray);
    1262:     }

    1264:   } else { /* sparse B */
    1265:     b = (Mat_MPIAIJ*)Bt->data;

    1267:     /* wrap dense X into a vector v_mpi */
    1268:     MatGetLocalSize(X,&m,NULL);
    1269:     MatDenseGetArray(X,&bray);
    1270:     VecCreateMPIWithArray(PetscObjectComm((PetscObject)X),1,nrhs*m,nrhs*M,(const PetscScalar*)bray,&v_mpi);
    1271:     MatDenseRestoreArray(X,&bray);

    1273:     if (!mumps->myid) {
    1274:       MatSeqAIJGetArray(b->A,&aa);
    1275:       MatGetRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    1276:       if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure");
    1277:       PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs);
    1278:       mumps->id.rhs_sparse  = (MumpsScalar*)aa;
    1279:     } else {
    1280:       mumps->id.irhs_ptr    = NULL;
    1281:       mumps->id.irhs_sparse = NULL;
    1282:       mumps->id.nz_rhs      = 0;
    1283:       mumps->id.rhs_sparse  = NULL;
    1284:     }
    1285:   }

    1287:   /* solve phase */
    1288:   /*-------------*/
    1289:   mumps->id.job = JOB_SOLVE;
    1290:   PetscMUMPS_c(mumps);
    1291:   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));

    1293:   /* scatter mumps distributed solution to petsc vector v_mpi, which shares local arrays with solution matrix X */
    1294:   MatDenseGetArray(X,&array);
    1295:   VecPlaceArray(v_mpi,array);

    1297:   /* create scatter scat_sol */
    1298:   MatGetOwnershipRanges(X,&rstart);
    1299:   /* iidx: index for scatter mumps solution to petsc X */

    1301:   ISCreateStride(PETSC_COMM_SELF,nlsol_loc,0,1,&is_from);
    1302:   PetscMalloc1(nlsol_loc,&idxx);
    1303:   for (i=0; i<lsol_loc; i++) {
    1304:     isol_loc[i] -= 1; /* change Fortran style to C style. isol_loc[i+j*lsol_loc] contains x[isol_loc[i]] in j-th vector */

    1306:     for (proc=0; proc<mumps->petsc_size; proc++){
    1307:       if (isol_loc[i] >= rstart[proc] && isol_loc[i] < rstart[proc+1]) {
    1308:         myrstart = rstart[proc];
    1309:         k        = isol_loc[i] - myrstart;        /* local index on 1st column of petsc vector X */
    1310:         iidx     = k + myrstart*nrhs;             /* maps mumps isol_loc[i] to petsc index in X */
    1311:         m        = rstart[proc+1] - rstart[proc]; /* rows of X for this proc */
    1312:         break;
    1313:       }
    1314:     }

    1316:     for (j=0; j<nrhs; j++) idxx[i+j*lsol_loc] = iidx + j*m;
    1317:   }
    1318:   ISCreateGeneral(PETSC_COMM_SELF,nlsol_loc,idxx,PETSC_COPY_VALUES,&is_to);
    1319:   VecScatterCreate(msol_loc,is_from,v_mpi,is_to,&scat_sol);
    1320:   VecScatterBegin(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
    1321:   ISDestroy(&is_from);
    1322:   ISDestroy(&is_to);
    1323:   VecScatterEnd(scat_sol,msol_loc,v_mpi,INSERT_VALUES,SCATTER_FORWARD);
    1324:   MatDenseRestoreArray(X,&array);

    1326:   /* free spaces */
    1327:   mumps->id.sol_loc  = (MumpsScalar*)sol_loc_save;
    1328:   mumps->id.isol_loc = isol_loc_save;

    1330:   PetscFree2(sol_loc,isol_loc);
    1331:   PetscFree(idxx);
    1332:   VecDestroy(&msol_loc);
    1333:   VecDestroy(&v_mpi);
    1334:   if (!denseB) {
    1335:     if (!mumps->myid) {
    1336:       b = (Mat_MPIAIJ*)Bt->data;
    1337:       MatSeqAIJRestoreArray(b->A,&aa);
    1338:       MatRestoreRowIJ(b->A,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    1339:       if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot restore IJ structure");
    1340:     }
    1341:   } else {
    1342:     VecDestroy(&b_seq);
    1343:     VecScatterDestroy(&scat_rhs);
    1344:   }
    1345:   VecScatterDestroy(&scat_sol);
    1346:   PetscLogFlops(2.0*nrhs*mumps->id.RINFO(3));
    1347:   return(0);
    1348: }

    1350: PetscErrorCode MatMatTransposeSolve_MUMPS(Mat A,Mat Bt,Mat X)
    1351: {
    1353:   PetscBool      flg;
    1354:   Mat            B;

    1357:   PetscObjectTypeCompareAny((PetscObject)Bt,&flg,MATSEQAIJ,MATMPIAIJ,NULL);
    1358:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)Bt),PETSC_ERR_ARG_WRONG,"Matrix Bt must be MATAIJ matrix");

    1360:   /* Create B=Bt^T that uses Bt's data structure */
    1361:   MatCreateTranspose(Bt,&B);

    1363:   MatMatSolve_MUMPS(A,B,X);
    1364:   MatDestroy(&B);
    1365:   return(0);
    1366: }

    1368: #if !defined(PETSC_USE_COMPLEX)
    1369: /*
    1370:   input:
    1371:    F:        numeric factor
    1372:   output:
    1373:    nneg:     total number of negative pivots
    1374:    nzero:    total number of zero pivots
    1375:    npos:     (global dimension of F) - nneg - nzero
    1376: */
    1377: PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
    1378: {
    1379:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->data;
    1381:   PetscMPIInt    size;

    1384:   MPI_Comm_size(PetscObjectComm((PetscObject)F),&size);
    1385:   /* 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 */
    1386:   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));

    1388:   if (nneg) *nneg = mumps->id.INFOG(12);
    1389:   if (nzero || npos) {
    1390:     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");
    1391:     if (nzero) *nzero = mumps->id.INFOG(28);
    1392:     if (npos) *npos   = F->rmap->N - (mumps->id.INFOG(12) + mumps->id.INFOG(28));
    1393:   }
    1394:   return(0);
    1395: }
    1396: #endif

    1398: PetscErrorCode MatMumpsGatherNonzerosOnMaster(MatReuse reuse,Mat_MUMPS *mumps)
    1399: {
    1401:   PetscInt       i,nreqs;
    1402:   PetscMUMPSInt  *irn,*jcn;
    1403:   PetscMPIInt    count;
    1404:   PetscInt64     totnnz,remain;
    1405:   const PetscInt osize=mumps->omp_comm_size;
    1406:   PetscScalar    *val;

    1409:   if (osize > 1) {
    1410:     if (reuse == MAT_INITIAL_MATRIX) {
    1411:       /* master first gathers counts of nonzeros to receive */
    1412:       if (mumps->is_omp_master) {PetscMalloc1(osize,&mumps->recvcount);}
    1413:       MPI_Gather(&mumps->nnz,1,MPIU_INT64,mumps->recvcount,1,MPIU_INT64,0/*master*/,mumps->omp_comm);

    1415:       /* Then each computes number of send/recvs */
    1416:       if (mumps->is_omp_master) {
    1417:         /* Start from 1 since self communication is not done in MPI */
    1418:         nreqs = 0;
    1419:         for (i=1; i<osize; i++) nreqs += (mumps->recvcount[i]+PETSC_MPI_INT_MAX-1)/PETSC_MPI_INT_MAX;
    1420:       } else {
    1421:         nreqs = (mumps->nnz+PETSC_MPI_INT_MAX-1)/PETSC_MPI_INT_MAX;
    1422:       }
    1423:       PetscMalloc1(nreqs*3,&mumps->reqs); /* Triple the requests since we send irn, jcn and val seperately */

    1425:       /* The following code is doing a very simple thing: omp_master rank gathers irn/jcn/val from others.
    1426:          MPI_Gatherv would be enough if it supports big counts > 2^31-1. Since it does not, and mumps->nnz
    1427:          might be a prime number > 2^31-1, we have to slice the message. Note omp_comm_size
    1428:          is very small, the current approach should have no extra overhead compared to MPI_Gatherv.
    1429:        */
    1430:       nreqs = 0; /* counter for actual send/recvs */
    1431:       if (mumps->is_omp_master) {
    1432:         for (i=0,totnnz=0; i<osize; i++) totnnz += mumps->recvcount[i]; /* totnnz = sum of nnz over omp_comm */
    1433:         PetscMalloc2(totnnz,&irn,totnnz,&jcn);
    1434:         PetscMalloc1(totnnz,&val);

    1436:         /* Self communication */
    1437:         PetscArraycpy(irn,mumps->irn,mumps->nnz);
    1438:         PetscArraycpy(jcn,mumps->jcn,mumps->nnz);
    1439:         PetscArraycpy(val,mumps->val,mumps->nnz);

    1441:         /* Replace mumps->irn/jcn etc on master with the newly allocated bigger arrays */
    1442:         PetscFree2(mumps->irn,mumps->jcn);
    1443:         PetscFree(mumps->val_alloc);
    1444:         mumps->nnz = totnnz;
    1445:         mumps->irn = irn;
    1446:         mumps->jcn = jcn;
    1447:         mumps->val = mumps->val_alloc = val;

    1449:         irn += mumps->recvcount[0]; /* recvcount[0] is old mumps->nnz on omp rank 0 */
    1450:         jcn += mumps->recvcount[0];
    1451:         val += mumps->recvcount[0];

    1453:         /* Remote communication */
    1454:         for (i=1; i<osize; i++) {
    1455:           count  = PetscMin(mumps->recvcount[i],PETSC_MPI_INT_MAX);
    1456:           remain = mumps->recvcount[i] - count;
    1457:           while (count>0) {
    1458:             MPI_Irecv(irn,count,MPIU_MUMPSINT,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1459:             MPI_Irecv(jcn,count,MPIU_MUMPSINT,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1460:             MPI_Irecv(val,count,MPIU_SCALAR,  i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1461:             irn    += count;
    1462:             jcn    += count;
    1463:             val    += count;
    1464:             count   = PetscMin(remain,PETSC_MPI_INT_MAX);
    1465:             remain -= count;
    1466:           }
    1467:         }
    1468:       } else {
    1469:         irn    = mumps->irn;
    1470:         jcn    = mumps->jcn;
    1471:         val    = mumps->val;
    1472:         count  = PetscMin(mumps->nnz,PETSC_MPI_INT_MAX);
    1473:         remain = mumps->nnz - count;
    1474:         while (count>0) {
    1475:           MPI_Isend(irn,count,MPIU_MUMPSINT,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1476:           MPI_Isend(jcn,count,MPIU_MUMPSINT,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1477:           MPI_Isend(val,count,MPIU_SCALAR,  0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1478:           irn    += count;
    1479:           jcn    += count;
    1480:           val    += count;
    1481:           count   = PetscMin(remain,PETSC_MPI_INT_MAX);
    1482:           remain -= count;
    1483:         }
    1484:       }
    1485:     } else {
    1486:       nreqs = 0;
    1487:       if (mumps->is_omp_master) {
    1488:         val = mumps->val + mumps->recvcount[0];
    1489:         for (i=1; i<osize; i++) { /* Remote communication only since self data is already in place */
    1490:           count  = PetscMin(mumps->recvcount[i],PETSC_MPI_INT_MAX);
    1491:           remain = mumps->recvcount[i] - count;
    1492:           while (count>0) {
    1493:             MPI_Irecv(val,count,MPIU_SCALAR,i,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1494:             val    += count;
    1495:             count   = PetscMin(remain,PETSC_MPI_INT_MAX);
    1496:             remain -= count;
    1497:           }
    1498:         }
    1499:       } else {
    1500:         val    = mumps->val;
    1501:         count  = PetscMin(mumps->nnz,PETSC_MPI_INT_MAX);
    1502:         remain = mumps->nnz - count;
    1503:         while (count>0) {
    1504:           MPI_Isend(val,count,MPIU_SCALAR,0,mumps->tag,mumps->omp_comm,&mumps->reqs[nreqs++]);
    1505:           val    += count;
    1506:           count   = PetscMin(remain,PETSC_MPI_INT_MAX);
    1507:           remain -= count;
    1508:         }
    1509:       }
    1510:     }
    1511:     MPI_Waitall(nreqs,mumps->reqs,MPI_STATUSES_IGNORE);
    1512:     mumps->tag++; /* It is totally fine for above send/recvs to share one mpi tag */
    1513:   }
    1514:   return(0);
    1515: }

    1517: PetscErrorCode MatFactorNumeric_MUMPS(Mat F,Mat A,const MatFactorInfo *info)
    1518: {
    1519:   Mat_MUMPS      *mumps =(Mat_MUMPS*)(F)->data;
    1521:   PetscBool      isMPIAIJ;

    1524:   if (mumps->id.INFOG(1) < 0 && !(mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0)) {
    1525:     if (mumps->id.INFOG(1) == -6) {
    1526:       PetscInfo2(A,"MatFactorNumeric is called with singular matrix structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1527:     }
    1528:     PetscInfo2(A,"MatFactorNumeric is called after analysis phase fails, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1529:     return(0);
    1530:   }

    1532:   (*mumps->ConvertToTriples)(A, 1, MAT_REUSE_MATRIX, mumps);
    1533:   MatMumpsGatherNonzerosOnMaster(MAT_REUSE_MATRIX,mumps);

    1535:   /* numerical factorization phase */
    1536:   /*-------------------------------*/
    1537:   mumps->id.job = JOB_FACTNUMERIC;
    1538:   if (!mumps->id.ICNTL(18)) { /* A is centralized */
    1539:     if (!mumps->myid) {
    1540:       mumps->id.a = (MumpsScalar*)mumps->val;
    1541:     }
    1542:   } else {
    1543:     mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1544:   }
    1545:   PetscMUMPS_c(mumps);
    1546:   if (mumps->id.INFOG(1) < 0) {
    1547:     if (A->erroriffailure) {
    1548:       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));
    1549:     } else {
    1550:       if (mumps->id.INFOG(1) == -10) { /* numerically singular matrix */
    1551:         PetscInfo2(F,"matrix is numerically singular, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1552:         F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1553:       } else if (mumps->id.INFOG(1) == -13) {
    1554:         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));
    1555:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
    1556:       } else if (mumps->id.INFOG(1) == -8 || mumps->id.INFOG(1) == -9 || (-16 < mumps->id.INFOG(1) && mumps->id.INFOG(1) < -10) ) {
    1557:         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));
    1558:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
    1559:       } else {
    1560:         PetscInfo2(F,"MUMPS in numerical factorization phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1561:         F->factorerrortype = MAT_FACTOR_OTHER;
    1562:       }
    1563:     }
    1564:   }
    1565:   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));

    1567:   F->assembled    = PETSC_TRUE;
    1568:   mumps->matstruc = SAME_NONZERO_PATTERN;
    1569:   if (F->schur) { /* reset Schur status to unfactored */
    1570: #if defined(PETSC_HAVE_CUDA)
    1571:     F->schur->offloadmask = PETSC_OFFLOAD_CPU;
    1572: #endif
    1573:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
    1574:       mumps->id.ICNTL(19) = 2;
    1575:       MatTranspose(F->schur,MAT_INPLACE_MATRIX,&F->schur);
    1576:     }
    1577:     MatFactorRestoreSchurComplement(F,NULL,MAT_FACTOR_SCHUR_UNFACTORED);
    1578:   }

    1580:   /* just to be sure that ICNTL(19) value returned by a call from MatMumpsGetIcntl is always consistent */
    1581:   if (!mumps->sym && mumps->id.ICNTL(19) && mumps->id.ICNTL(19) != 1) mumps->id.ICNTL(19) = 3;

    1583:   if (!mumps->is_omp_master) mumps->id.INFO(23) = 0;
    1584:   if (mumps->petsc_size > 1) {
    1585:     PetscInt    lsol_loc;
    1586:     PetscScalar *sol_loc;

    1588:     PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&isMPIAIJ);

    1590:     /* distributed solution; Create x_seq=sol_loc for repeated use */
    1591:     if (mumps->x_seq) {
    1592:       VecScatterDestroy(&mumps->scat_sol);
    1593:       PetscFree2(mumps->id.sol_loc,mumps->id.isol_loc);
    1594:       VecDestroy(&mumps->x_seq);
    1595:     }
    1596:     lsol_loc = mumps->id.INFO(23); /* length of sol_loc */
    1597:     PetscMalloc2(lsol_loc,&sol_loc,lsol_loc,&mumps->id.isol_loc);
    1598:     mumps->id.lsol_loc = lsol_loc;
    1599:     mumps->id.sol_loc = (MumpsScalar*)sol_loc;
    1600:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,lsol_loc,sol_loc,&mumps->x_seq);
    1601:   }
    1602:   PetscLogFlops(mumps->id.RINFO(2));
    1603:   return(0);
    1604: }

    1606: /* Sets MUMPS options from the options database */
    1607: PetscErrorCode PetscSetMUMPSFromOptions(Mat F, Mat A)
    1608: {
    1609:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->data;
    1611:   PetscMUMPSInt  icntl=0;
    1612:   PetscInt       info[80],i,ninfo=80;
    1613:   PetscBool      flg=PETSC_FALSE;

    1616:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MUMPS Options","Mat");
    1617:   PetscOptionsMUMPSInt("-mat_mumps_icntl_1","ICNTL(1): output stream for error messages","None",mumps->id.ICNTL(1),&icntl,&flg);
    1618:   if (flg) mumps->id.ICNTL(1) = icntl;
    1619:   PetscOptionsMUMPSInt("-mat_mumps_icntl_2","ICNTL(2): output stream for diagnostic printing, statistics, and warning","None",mumps->id.ICNTL(2),&icntl,&flg);
    1620:   if (flg) mumps->id.ICNTL(2) = icntl;
    1621:   PetscOptionsMUMPSInt("-mat_mumps_icntl_3","ICNTL(3): output stream for global information, collected on the host","None",mumps->id.ICNTL(3),&icntl,&flg);
    1622:   if (flg) mumps->id.ICNTL(3) = icntl;

    1624:   PetscOptionsMUMPSInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",mumps->id.ICNTL(4),&icntl,&flg);
    1625:   if (flg) mumps->id.ICNTL(4) = icntl;
    1626:   if (mumps->id.ICNTL(4) || PetscLogPrintInfo) mumps->id.ICNTL(3) = 6; /* resume MUMPS default id.ICNTL(3) = 6 */

    1628:   PetscOptionsMUMPSInt("-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);
    1629:   if (flg) mumps->id.ICNTL(6) = icntl;

    1631:   PetscOptionsMUMPSInt("-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);
    1632:   if (flg) {
    1633:     if (icntl== 1 && mumps->petsc_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");
    1634:     else mumps->id.ICNTL(7) = icntl;
    1635:   }

    1637:   PetscOptionsMUMPSInt("-mat_mumps_icntl_8","ICNTL(8): scaling strategy (-2 to 8 or 77)","None",mumps->id.ICNTL(8),&mumps->id.ICNTL(8),NULL);
    1638:   /* 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() */
    1639:   PetscOptionsMUMPSInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",mumps->id.ICNTL(10),&mumps->id.ICNTL(10),NULL);
    1640:   PetscOptionsMUMPSInt("-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);
    1641:   PetscOptionsMUMPSInt("-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);
    1642:   PetscOptionsMUMPSInt("-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);
    1643:   PetscOptionsMUMPSInt("-mat_mumps_icntl_14","ICNTL(14): percentage increase in the estimated working space","None",mumps->id.ICNTL(14),&mumps->id.ICNTL(14),NULL);
    1644:   PetscOptionsMUMPSInt("-mat_mumps_icntl_19","ICNTL(19): computes the Schur complement","None",mumps->id.ICNTL(19),&mumps->id.ICNTL(19),NULL);
    1645:   if (mumps->id.ICNTL(19) <= 0 || mumps->id.ICNTL(19) > 3) { /* reset any schur data (if any) */
    1646:     MatDestroy(&F->schur);
    1647:     MatMumpsResetSchur_Private(mumps);
    1648:   }
    1649:   /* PetscOptionsMUMPSInt("-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 */
    1650:   /* PetscOptionsMUMPSInt("-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 */

    1652:   PetscOptionsMUMPSInt("-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);
    1653:   PetscOptionsMUMPSInt("-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);
    1654:   PetscOptionsMUMPSInt("-mat_mumps_icntl_24","ICNTL(24): detection of null pivot rows (0 or 1)","None",mumps->id.ICNTL(24),&mumps->id.ICNTL(24),NULL);
    1655:   if (mumps->id.ICNTL(24)) {
    1656:     mumps->id.ICNTL(13) = 1; /* turn-off ScaLAPACK to help with the correct detection of null pivots */
    1657:   }

    1659:   PetscOptionsMUMPSInt("-mat_mumps_icntl_25","ICNTL(25): computes a solution of a deficient matrix and a null space basis","None",mumps->id.ICNTL(25),&mumps->id.ICNTL(25),NULL);
    1660:   PetscOptionsMUMPSInt("-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);
    1661:   PetscOptionsMUMPSInt("-mat_mumps_icntl_27","ICNTL(27): the blocking size for multiple right-hand sides","None",mumps->id.ICNTL(27),&mumps->id.ICNTL(27),NULL);
    1662:   PetscOptionsMUMPSInt("-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);
    1663:   PetscOptionsMUMPSInt("-mat_mumps_icntl_29","ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis","None",mumps->id.ICNTL(29),&mumps->id.ICNTL(29),NULL);
    1664:   /* PetscOptionsMUMPSInt("-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); */ /* call MatMumpsGetInverse() directly */
    1665:   PetscOptionsMUMPSInt("-mat_mumps_icntl_31","ICNTL(31): indicates which factors may be discarded during factorization","None",mumps->id.ICNTL(31),&mumps->id.ICNTL(31),NULL);
    1666:   /* PetscOptionsMUMPSInt("-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 */
    1667:   PetscOptionsMUMPSInt("-mat_mumps_icntl_33","ICNTL(33): compute determinant","None",mumps->id.ICNTL(33),&mumps->id.ICNTL(33),NULL);
    1668:   PetscOptionsMUMPSInt("-mat_mumps_icntl_35","ICNTL(35): activates Block Low Rank (BLR) based factorization","None",mumps->id.ICNTL(35),&mumps->id.ICNTL(35),NULL);
    1669:   PetscOptionsMUMPSInt("-mat_mumps_icntl_36","ICNTL(36): choice of BLR factorization variant","None",mumps->id.ICNTL(36),&mumps->id.ICNTL(36),NULL);
    1670:   PetscOptionsMUMPSInt("-mat_mumps_icntl_38","ICNTL(38): estimated compression rate of LU factors with BLR","None",mumps->id.ICNTL(38),&mumps->id.ICNTL(38),NULL);

    1672:   PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",mumps->id.CNTL(1),&mumps->id.CNTL(1),NULL);
    1673:   PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",mumps->id.CNTL(2),&mumps->id.CNTL(2),NULL);
    1674:   PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",mumps->id.CNTL(3),&mumps->id.CNTL(3),NULL);
    1675:   PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",mumps->id.CNTL(4),&mumps->id.CNTL(4),NULL);
    1676:   PetscOptionsReal("-mat_mumps_cntl_5","CNTL(5): fixation for null pivots","None",mumps->id.CNTL(5),&mumps->id.CNTL(5),NULL);
    1677:   PetscOptionsReal("-mat_mumps_cntl_7","CNTL(7): dropping parameter used during BLR","None",mumps->id.CNTL(7),&mumps->id.CNTL(7),NULL);

    1679:   PetscOptionsString("-mat_mumps_ooc_tmpdir", "out of core directory", "None", mumps->id.ooc_tmpdir, mumps->id.ooc_tmpdir, 256, NULL);

    1681:   PetscOptionsIntArray("-mat_mumps_view_info","request INFO local to each processor","",info,&ninfo,NULL);
    1682:   if (ninfo) {
    1683:     if (ninfo > 80) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"number of INFO %d must <= 80\n",ninfo);
    1684:     PetscMalloc1(ninfo,&mumps->info);
    1685:     mumps->ninfo = ninfo;
    1686:     for (i=0; i<ninfo; i++) {
    1687:       if (info[i] < 0 || info[i]>80) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_USER,"index of INFO %d must between 1 and 80\n",ninfo);
    1688:       else  mumps->info[i] = info[i];
    1689:     }
    1690:   }

    1692:   PetscOptionsEnd();
    1693:   return(0);
    1694: }

    1696: PetscErrorCode PetscInitializeMUMPS(Mat A,Mat_MUMPS *mumps)
    1697: {
    1699:   PetscInt       nthreads=0;

    1702:   mumps->petsc_comm = PetscObjectComm((PetscObject)A);
    1703:   MPI_Comm_size(mumps->petsc_comm,&mumps->petsc_size);
    1704:   MPI_Comm_rank(mumps->petsc_comm,&mumps->myid); /* so that code like "if (!myid)" still works even if mumps_comm is different */

    1706:   PetscOptionsHasName(NULL,NULL,"-mat_mumps_use_omp_threads",&mumps->use_petsc_omp_support);
    1707:   if (mumps->use_petsc_omp_support) nthreads = -1; /* -1 will let PetscOmpCtrlCreate() guess a proper value when user did not supply one */
    1708:   PetscOptionsGetInt(NULL,NULL,"-mat_mumps_use_omp_threads",&nthreads,NULL);
    1709:   if (mumps->use_petsc_omp_support) {
    1710: #if defined(PETSC_HAVE_OPENMP_SUPPORT)
    1711:     PetscOmpCtrlCreate(mumps->petsc_comm,nthreads,&mumps->omp_ctrl);
    1712:     PetscOmpCtrlGetOmpComms(mumps->omp_ctrl,&mumps->omp_comm,&mumps->mumps_comm,&mumps->is_omp_master);
    1713: #else
    1714:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP_SYS,"the system does not have PETSc OpenMP support but you added the -mat_mumps_use_omp_threads option. Configure PETSc with --with-openmp --download-hwloc (or --with-hwloc) to enable it, see more in MATSOLVERMUMPS manual\n");
    1715: #endif
    1716:   } else {
    1717:     mumps->omp_comm      = PETSC_COMM_SELF;
    1718:     mumps->mumps_comm    = mumps->petsc_comm;
    1719:     mumps->is_omp_master = PETSC_TRUE;
    1720:   }
    1721:   MPI_Comm_size(mumps->omp_comm,&mumps->omp_comm_size);
    1722:   mumps->reqs = NULL;
    1723:   mumps->tag  = 0;

    1725:   mumps->id.comm_fortran = MPI_Comm_c2f(mumps->mumps_comm);
    1726:   mumps->id.job = JOB_INIT;
    1727:   mumps->id.par = 1;  /* host participates factorizaton and solve */
    1728:   mumps->id.sym = mumps->sym;

    1730:   PetscMUMPS_c(mumps);
    1731:   if (mumps->id.INFOG(1) < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in PetscInitializeMUMPS: INFOG(1)=%d\n",mumps->id.INFOG(1));

    1733:   /* copy MUMPS default control values from master to slaves. Although slaves do not call MUMPS, they may access these values in code.
    1734:      For example, ICNTL(9) is initialized to 1 by MUMPS and slaves check ICNTL(9) in MatSolve_MUMPS.
    1735:    */
    1736:   MPI_Bcast(mumps->id.icntl,40,MPI_INT,  0,mumps->omp_comm); /* see MUMPS-5.1.2 Manual Section 9 */
    1737:   MPI_Bcast(mumps->id.cntl, 15,MPIU_REAL,0,mumps->omp_comm);

    1739:   mumps->scat_rhs = NULL;
    1740:   mumps->scat_sol = NULL;

    1742:   /* set PETSc-MUMPS default options - override MUMPS default */
    1743:   mumps->id.ICNTL(3) = 0;
    1744:   mumps->id.ICNTL(4) = 0;
    1745:   if (mumps->petsc_size == 1) {
    1746:     mumps->id.ICNTL(18) = 0;   /* centralized assembled matrix input */
    1747:   } else {
    1748:     mumps->id.ICNTL(18) = 3;   /* distributed assembled matrix input */
    1749:     mumps->id.ICNTL(20) = 0;   /* rhs is in dense format */
    1750:     mumps->id.ICNTL(21) = 1;   /* distributed solution */
    1751:   }

    1753:   /* schur */
    1754:   mumps->id.size_schur    = 0;
    1755:   mumps->id.listvar_schur = NULL;
    1756:   mumps->id.schur         = NULL;
    1757:   mumps->sizeredrhs       = 0;
    1758:   mumps->schur_sol        = NULL;
    1759:   mumps->schur_sizesol    = 0;
    1760:   return(0);
    1761: }

    1763: PetscErrorCode MatFactorSymbolic_MUMPS_ReportIfError(Mat F,Mat A,const MatFactorInfo *info,Mat_MUMPS *mumps)
    1764: {

    1768:   if (mumps->id.INFOG(1) < 0) {
    1769:     if (A->erroriffailure) {
    1770:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",mumps->id.INFOG(1));
    1771:     } else {
    1772:       if (mumps->id.INFOG(1) == -6) {
    1773:         PetscInfo2(F,"matrix is singular in structure, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1774:         F->factorerrortype = MAT_FACTOR_STRUCT_ZEROPIVOT;
    1775:       } else if (mumps->id.INFOG(1) == -5 || mumps->id.INFOG(1) == -7) {
    1776:         PetscInfo2(F,"problem of workspace, INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1777:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
    1778:       } else if (mumps->id.INFOG(1) == -16 && mumps->id.INFOG(1) == 0) {
    1779:         PetscInfo(F,"Empty matrix\n");
    1780:       } else {
    1781:         PetscInfo2(F,"Error reported by MUMPS in analysis phase: INFOG(1)=%d, INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));
    1782:         F->factorerrortype = MAT_FACTOR_OTHER;
    1783:       }
    1784:     }
    1785:   }
    1786:   return(0);
    1787: }

    1789: /* Note Petsc r(=c) permutation is used when mumps->id.ICNTL(7)==1 with centralized assembled matrix input; otherwise r and c are ignored */
    1790: PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    1791: {
    1792:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->data;
    1794:   Vec            b;
    1795:   const PetscInt M = A->rmap->N;

    1798:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1800:   /* Set MUMPS options from the options database */
    1801:   PetscSetMUMPSFromOptions(F,A);

    1803:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps);
    1804:   MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);

    1806:   /* analysis phase */
    1807:   /*----------------*/
    1808:   mumps->id.job = JOB_FACTSYMBOLIC;
    1809:   mumps->id.n   = M;
    1810:   switch (mumps->id.ICNTL(18)) {
    1811:   case 0:  /* centralized assembled matrix input */
    1812:     if (!mumps->myid) {
    1813:       mumps->id.nnz = mumps->nnz;
    1814:       mumps->id.irn = mumps->irn;
    1815:       mumps->id.jcn = mumps->jcn;
    1816:       if (mumps->id.ICNTL(6)>1) mumps->id.a = (MumpsScalar*)mumps->val;
    1817:       if (mumps->id.ICNTL(7) == 1) { /* use user-provide matrix ordering - assuming r = c ordering */
    1818:         /*
    1819:         PetscBool      flag;
    1820:         ISEqual(r,c,&flag);
    1821:         if (!flag) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"row_perm != col_perm");
    1822:         ISView(r,PETSC_VIEWER_STDOUT_SELF);
    1823:          */
    1824:         if (!mumps->myid) {
    1825:           const PetscInt *idx;
    1826:           PetscInt       i;

    1828:           PetscMalloc1(M,&mumps->id.perm_in);
    1829:           ISGetIndices(r,&idx);
    1830:           for (i=0; i<M; i++) {PetscMUMPSIntCast(idx[i]+1,&(mumps->id.perm_in[i]));} /* perm_in[]: start from 1, not 0! */
    1831:           ISRestoreIndices(r,&idx);
    1832:         }
    1833:       }
    1834:     }
    1835:     break;
    1836:   case 3:  /* distributed assembled matrix input (size>1) */
    1837:     mumps->id.nnz_loc = mumps->nnz;
    1838:     mumps->id.irn_loc = mumps->irn;
    1839:     mumps->id.jcn_loc = mumps->jcn;
    1840:     if (mumps->id.ICNTL(6)>1) mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1841:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1842:     MatCreateVecs(A,NULL,&b);
    1843:     VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq);
    1844:     VecDestroy(&b);
    1845:     break;
    1846:   }
    1847:   PetscMUMPS_c(mumps);
    1848:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1850:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
    1851:   F->ops->solve           = MatSolve_MUMPS;
    1852:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
    1853:   F->ops->matsolve        = MatMatSolve_MUMPS;
    1854:   F->ops->mattransposesolve = MatMatTransposeSolve_MUMPS;
    1855:   return(0);
    1856: }

    1858: /* Note the Petsc r and c permutations are ignored */
    1859: PetscErrorCode MatLUFactorSymbolic_BAIJMUMPS(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    1860: {
    1861:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->data;
    1863:   Vec            b;
    1864:   const PetscInt M = A->rmap->N;

    1867:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1869:   /* Set MUMPS options from the options database */
    1870:   PetscSetMUMPSFromOptions(F,A);

    1872:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps);
    1873:   MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);

    1875:   /* analysis phase */
    1876:   /*----------------*/
    1877:   mumps->id.job = JOB_FACTSYMBOLIC;
    1878:   mumps->id.n   = M;
    1879:   switch (mumps->id.ICNTL(18)) {
    1880:   case 0:  /* centralized assembled matrix input */
    1881:     if (!mumps->myid) {
    1882:       mumps->id.nnz = mumps->nnz;
    1883:       mumps->id.irn = mumps->irn;
    1884:       mumps->id.jcn = mumps->jcn;
    1885:       if (mumps->id.ICNTL(6)>1) {
    1886:         mumps->id.a = (MumpsScalar*)mumps->val;
    1887:       }
    1888:     }
    1889:     break;
    1890:   case 3:  /* distributed assembled matrix input (size>1) */
    1891:     mumps->id.nnz_loc = mumps->nnz;
    1892:     mumps->id.irn_loc = mumps->irn;
    1893:     mumps->id.jcn_loc = mumps->jcn;
    1894:     if (mumps->id.ICNTL(6)>1) {
    1895:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1896:     }
    1897:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1898:     MatCreateVecs(A,NULL,&b);
    1899:     VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq);
    1900:     VecDestroy(&b);
    1901:     break;
    1902:   }
    1903:   PetscMUMPS_c(mumps);
    1904:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1906:   F->ops->lufactornumeric = MatFactorNumeric_MUMPS;
    1907:   F->ops->solve           = MatSolve_MUMPS;
    1908:   F->ops->solvetranspose  = MatSolveTranspose_MUMPS;
    1909:   return(0);
    1910: }

    1912: /* Note the Petsc r permutation and factor info are ignored */
    1913: PetscErrorCode MatCholeskyFactorSymbolic_MUMPS(Mat F,Mat A,IS r,const MatFactorInfo *info)
    1914: {
    1915:   Mat_MUMPS      *mumps = (Mat_MUMPS*)F->data;
    1917:   Vec            b;
    1918:   const PetscInt M = A->rmap->N;

    1921:   mumps->matstruc = DIFFERENT_NONZERO_PATTERN;

    1923:   /* Set MUMPS options from the options database */
    1924:   PetscSetMUMPSFromOptions(F,A);

    1926:   (*mumps->ConvertToTriples)(A, 1, MAT_INITIAL_MATRIX, mumps);
    1927:   MatMumpsGatherNonzerosOnMaster(MAT_INITIAL_MATRIX,mumps);

    1929:   /* analysis phase */
    1930:   /*----------------*/
    1931:   mumps->id.job = JOB_FACTSYMBOLIC;
    1932:   mumps->id.n   = M;
    1933:   switch (mumps->id.ICNTL(18)) {
    1934:   case 0:  /* centralized assembled matrix input */
    1935:     if (!mumps->myid) {
    1936:       mumps->id.nnz = mumps->nnz;
    1937:       mumps->id.irn = mumps->irn;
    1938:       mumps->id.jcn = mumps->jcn;
    1939:       if (mumps->id.ICNTL(6)>1) {
    1940:         mumps->id.a = (MumpsScalar*)mumps->val;
    1941:       }
    1942:     }
    1943:     break;
    1944:   case 3:  /* distributed assembled matrix input (size>1) */
    1945:     mumps->id.nnz_loc = mumps->nnz;
    1946:     mumps->id.irn_loc = mumps->irn;
    1947:     mumps->id.jcn_loc = mumps->jcn;
    1948:     if (mumps->id.ICNTL(6)>1) {
    1949:       mumps->id.a_loc = (MumpsScalar*)mumps->val;
    1950:     }
    1951:     /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
    1952:     MatCreateVecs(A,NULL,&b);
    1953:     VecScatterCreateToZero(b,&mumps->scat_rhs,&mumps->b_seq);
    1954:     VecDestroy(&b);
    1955:     break;
    1956:   }
    1957:   PetscMUMPS_c(mumps);
    1958:   MatFactorSymbolic_MUMPS_ReportIfError(F,A,info,mumps);

    1960:   F->ops->choleskyfactornumeric = MatFactorNumeric_MUMPS;
    1961:   F->ops->solve                 = MatSolve_MUMPS;
    1962:   F->ops->solvetranspose        = MatSolve_MUMPS;
    1963:   F->ops->matsolve              = MatMatSolve_MUMPS;
    1964:   F->ops->mattransposesolve     = MatMatTransposeSolve_MUMPS;
    1965: #if defined(PETSC_USE_COMPLEX)
    1966:   F->ops->getinertia = NULL;
    1967: #else
    1968:   F->ops->getinertia = MatGetInertia_SBAIJMUMPS;
    1969: #endif
    1970:   return(0);
    1971: }

    1973: PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer)
    1974: {
    1975:   PetscErrorCode    ierr;
    1976:   PetscBool         iascii;
    1977:   PetscViewerFormat format;
    1978:   Mat_MUMPS         *mumps=(Mat_MUMPS*)A->data;

    1981:   /* check if matrix is mumps type */
    1982:   if (A->ops->solve != MatSolve_MUMPS) return(0);

    1984:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1985:   if (iascii) {
    1986:     PetscViewerGetFormat(viewer,&format);
    1987:     if (format == PETSC_VIEWER_ASCII_INFO) {
    1988:       PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");
    1989:       PetscViewerASCIIPrintf(viewer,"  SYM (matrix type):                   %d \n",mumps->id.sym);
    1990:       PetscViewerASCIIPrintf(viewer,"  PAR (host participation):            %d \n",mumps->id.par);
    1991:       PetscViewerASCIIPrintf(viewer,"  ICNTL(1) (output for error):         %d \n",mumps->id.ICNTL(1));
    1992:       PetscViewerASCIIPrintf(viewer,"  ICNTL(2) (output of diagnostic msg): %d \n",mumps->id.ICNTL(2));
    1993:       PetscViewerASCIIPrintf(viewer,"  ICNTL(3) (output for global info):   %d \n",mumps->id.ICNTL(3));
    1994:       PetscViewerASCIIPrintf(viewer,"  ICNTL(4) (level of printing):        %d \n",mumps->id.ICNTL(4));
    1995:       PetscViewerASCIIPrintf(viewer,"  ICNTL(5) (input mat struct):         %d \n",mumps->id.ICNTL(5));
    1996:       PetscViewerASCIIPrintf(viewer,"  ICNTL(6) (matrix prescaling):        %d \n",mumps->id.ICNTL(6));
    1997:       PetscViewerASCIIPrintf(viewer,"  ICNTL(7) (sequential matrix ordering):%d \n",mumps->id.ICNTL(7));
    1998:       PetscViewerASCIIPrintf(viewer,"  ICNTL(8) (scaling strategy):        %d \n",mumps->id.ICNTL(8));
    1999:       PetscViewerASCIIPrintf(viewer,"  ICNTL(10) (max num of refinements):  %d \n",mumps->id.ICNTL(10));
    2000:       PetscViewerASCIIPrintf(viewer,"  ICNTL(11) (error analysis):          %d \n",mumps->id.ICNTL(11));
    2001:       if (mumps->id.ICNTL(11)>0) {
    2002:         PetscViewerASCIIPrintf(viewer,"    RINFOG(4) (inf norm of input mat):        %g\n",mumps->id.RINFOG(4));
    2003:         PetscViewerASCIIPrintf(viewer,"    RINFOG(5) (inf norm of solution):         %g\n",mumps->id.RINFOG(5));
    2004:         PetscViewerASCIIPrintf(viewer,"    RINFOG(6) (inf norm of residual):         %g\n",mumps->id.RINFOG(6));
    2005:         PetscViewerASCIIPrintf(viewer,"    RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",mumps->id.RINFOG(7),mumps->id.RINFOG(8));
    2006:         PetscViewerASCIIPrintf(viewer,"    RINFOG(9) (error estimate):               %g \n",mumps->id.RINFOG(9));
    2007:         PetscViewerASCIIPrintf(viewer,"    RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",mumps->id.RINFOG(10),mumps->id.RINFOG(11));
    2008:       }
    2009:       PetscViewerASCIIPrintf(viewer,"  ICNTL(12) (efficiency control):                         %d \n",mumps->id.ICNTL(12));
    2010:       PetscViewerASCIIPrintf(viewer,"  ICNTL(13) (sequential factorization of the root node):  %d \n",mumps->id.ICNTL(13));
    2011:       PetscViewerASCIIPrintf(viewer,"  ICNTL(14) (percentage of estimated workspace increase): %d \n",mumps->id.ICNTL(14));
    2012:       /* ICNTL(15-17) not used */
    2013:       PetscViewerASCIIPrintf(viewer,"  ICNTL(18) (input mat struct):                           %d \n",mumps->id.ICNTL(18));
    2014:       PetscViewerASCIIPrintf(viewer,"  ICNTL(19) (Schur complement info):                      %d \n",mumps->id.ICNTL(19));
    2015:       PetscViewerASCIIPrintf(viewer,"  ICNTL(20) (rhs sparse pattern):                         %d \n",mumps->id.ICNTL(20));
    2016:       PetscViewerASCIIPrintf(viewer,"  ICNTL(21) (solution struct):                            %d \n",mumps->id.ICNTL(21));
    2017:       PetscViewerASCIIPrintf(viewer,"  ICNTL(22) (in-core/out-of-core facility):               %d \n",mumps->id.ICNTL(22));
    2018:       PetscViewerASCIIPrintf(viewer,"  ICNTL(23) (max size of memory can be allocated locally):%d \n",mumps->id.ICNTL(23));

    2020:       PetscViewerASCIIPrintf(viewer,"  ICNTL(24) (detection of null pivot rows):               %d \n",mumps->id.ICNTL(24));
    2021:       PetscViewerASCIIPrintf(viewer,"  ICNTL(25) (computation of a null space basis):          %d \n",mumps->id.ICNTL(25));
    2022:       PetscViewerASCIIPrintf(viewer,"  ICNTL(26) (Schur options for rhs or solution):          %d \n",mumps->id.ICNTL(26));
    2023:       PetscViewerASCIIPrintf(viewer,"  ICNTL(27) (experimental parameter):                     %d \n",mumps->id.ICNTL(27));
    2024:       PetscViewerASCIIPrintf(viewer,"  ICNTL(28) (use parallel or sequential ordering):        %d \n",mumps->id.ICNTL(28));
    2025:       PetscViewerASCIIPrintf(viewer,"  ICNTL(29) (parallel ordering):                          %d \n",mumps->id.ICNTL(29));

    2027:       PetscViewerASCIIPrintf(viewer,"  ICNTL(30) (user-specified set of entries in inv(A)):    %d \n",mumps->id.ICNTL(30));
    2028:       PetscViewerASCIIPrintf(viewer,"  ICNTL(31) (factors is discarded in the solve phase):    %d \n",mumps->id.ICNTL(31));
    2029:       PetscViewerASCIIPrintf(viewer,"  ICNTL(33) (compute determinant):                        %d \n",mumps->id.ICNTL(33));
    2030:       PetscViewerASCIIPrintf(viewer,"  ICNTL(35) (activate BLR based factorization):           %d \n",mumps->id.ICNTL(35));
    2031:       PetscViewerASCIIPrintf(viewer,"  ICNTL(36) (choice of BLR factorization variant):        %d \n",mumps->id.ICNTL(36));
    2032:       PetscViewerASCIIPrintf(viewer,"  ICNTL(38) (estimated compression rate of LU factors):   %d \n",mumps->id.ICNTL(38));

    2034:       PetscViewerASCIIPrintf(viewer,"  CNTL(1) (relative pivoting threshold):      %g \n",mumps->id.CNTL(1));
    2035:       PetscViewerASCIIPrintf(viewer,"  CNTL(2) (stopping criterion of refinement): %g \n",mumps->id.CNTL(2));
    2036:       PetscViewerASCIIPrintf(viewer,"  CNTL(3) (absolute pivoting threshold):      %g \n",mumps->id.CNTL(3));
    2037:       PetscViewerASCIIPrintf(viewer,"  CNTL(4) (value of static pivoting):         %g \n",mumps->id.CNTL(4));
    2038:       PetscViewerASCIIPrintf(viewer,"  CNTL(5) (fixation for null pivots):         %g \n",mumps->id.CNTL(5));
    2039:       PetscViewerASCIIPrintf(viewer,"  CNTL(7) (dropping parameter for BLR):       %g \n",mumps->id.CNTL(7));

    2041:       /* infomation local to each processor */
    2042:       PetscViewerASCIIPrintf(viewer, "  RINFO(1) (local estimated flops for the elimination after analysis): \n");
    2043:       PetscViewerASCIIPushSynchronized(viewer);
    2044:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %g \n",mumps->myid,mumps->id.RINFO(1));
    2045:       PetscViewerFlush(viewer);
    2046:       PetscViewerASCIIPrintf(viewer, "  RINFO(2) (local estimated flops for the assembly after factorization): \n");
    2047:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(2));
    2048:       PetscViewerFlush(viewer);
    2049:       PetscViewerASCIIPrintf(viewer, "  RINFO(3) (local estimated flops for the elimination after factorization): \n");
    2050:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d]  %g \n",mumps->myid,mumps->id.RINFO(3));
    2051:       PetscViewerFlush(viewer);

    2053:       PetscViewerASCIIPrintf(viewer, "  INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");
    2054:       PetscViewerASCIISynchronizedPrintf(viewer,"  [%d] %d \n",mumps->myid,mumps->id.INFO(15));
    2055:       PetscViewerFlush(viewer);

    2057:       PetscViewerASCIIPrintf(viewer, "  INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");
    2058:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(16));
    2059:       PetscViewerFlush(viewer);

    2061:       PetscViewerASCIIPrintf(viewer, "  INFO(23) (num of pivots eliminated on this processor after factorization): \n");
    2062:       PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(23));
    2063:       PetscViewerFlush(viewer);

    2065:       if (mumps->ninfo && mumps->ninfo <= 80){
    2066:         PetscInt i;
    2067:         for (i=0; i<mumps->ninfo; i++){
    2068:           PetscViewerASCIIPrintf(viewer, "  INFO(%d): \n",mumps->info[i]);
    2069:           PetscViewerASCIISynchronizedPrintf(viewer,"    [%d] %d \n",mumps->myid,mumps->id.INFO(mumps->info[i]));
    2070:           PetscViewerFlush(viewer);
    2071:         }
    2072:       }
    2073:       PetscViewerASCIIPopSynchronized(viewer);

    2075:       if (!mumps->myid) { /* information from the host */
    2076:         PetscViewerASCIIPrintf(viewer,"  RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",mumps->id.RINFOG(1));
    2077:         PetscViewerASCIIPrintf(viewer,"  RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",mumps->id.RINFOG(2));
    2078:         PetscViewerASCIIPrintf(viewer,"  RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",mumps->id.RINFOG(3));
    2079:         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));

    2081:         PetscViewerASCIIPrintf(viewer,"  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(3));
    2082:         PetscViewerASCIIPrintf(viewer,"  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",mumps->id.INFOG(4));
    2083:         PetscViewerASCIIPrintf(viewer,"  INFOG(5) (estimated maximum front size in the complete tree): %d \n",mumps->id.INFOG(5));
    2084:         PetscViewerASCIIPrintf(viewer,"  INFOG(6) (number of nodes in the complete tree): %d \n",mumps->id.INFOG(6));
    2085:         PetscViewerASCIIPrintf(viewer,"  INFOG(7) (ordering option effectively use after analysis): %d \n",mumps->id.INFOG(7));
    2086:         PetscViewerASCIIPrintf(viewer,"  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",mumps->id.INFOG(8));
    2087:         PetscViewerASCIIPrintf(viewer,"  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",mumps->id.INFOG(9));
    2088:         PetscViewerASCIIPrintf(viewer,"  INFOG(10) (total integer space store the matrix factors after factorization): %d \n",mumps->id.INFOG(10));
    2089:         PetscViewerASCIIPrintf(viewer,"  INFOG(11) (order of largest frontal matrix after factorization): %d \n",mumps->id.INFOG(11));
    2090:         PetscViewerASCIIPrintf(viewer,"  INFOG(12) (number of off-diagonal pivots): %d \n",mumps->id.INFOG(12));
    2091:         PetscViewerASCIIPrintf(viewer,"  INFOG(13) (number of delayed pivots after factorization): %d \n",mumps->id.INFOG(13));
    2092:         PetscViewerASCIIPrintf(viewer,"  INFOG(14) (number of memory compress after factorization): %d \n",mumps->id.INFOG(14));
    2093:         PetscViewerASCIIPrintf(viewer,"  INFOG(15) (number of steps of iterative refinement after solution): %d \n",mumps->id.INFOG(15));
    2094:         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));
    2095:         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));
    2096:         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));
    2097:         PetscViewerASCIIPrintf(viewer,"  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",mumps->id.INFOG(19));
    2098:         PetscViewerASCIIPrintf(viewer,"  INFOG(20) (estimated number of entries in the factors): %d \n",mumps->id.INFOG(20));
    2099:         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));
    2100:         PetscViewerASCIIPrintf(viewer,"  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",mumps->id.INFOG(22));
    2101:         PetscViewerASCIIPrintf(viewer,"  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",mumps->id.INFOG(23));
    2102:         PetscViewerASCIIPrintf(viewer,"  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",mumps->id.INFOG(24));
    2103:         PetscViewerASCIIPrintf(viewer,"  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",mumps->id.INFOG(25));
    2104:         PetscViewerASCIIPrintf(viewer,"  INFOG(28) (after factorization: number of null pivots encountered): %d\n",mumps->id.INFOG(28));
    2105:         PetscViewerASCIIPrintf(viewer,"  INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): %d\n",mumps->id.INFOG(29));
    2106:         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));
    2107:         PetscViewerASCIIPrintf(viewer,"  INFOG(32) (after analysis: type of analysis done): %d\n",mumps->id.INFOG(32));
    2108:         PetscViewerASCIIPrintf(viewer,"  INFOG(33) (value used for ICNTL(8)): %d\n",mumps->id.INFOG(33));
    2109:         PetscViewerASCIIPrintf(viewer,"  INFOG(34) (exponent of the determinant if determinant is requested): %d\n",mumps->id.INFOG(34));
    2110:         PetscViewerASCIIPrintf(viewer,"  INFOG(35) (after factorization: number of entries taking into account BLR factor compression - sum over all processors): %d\n",mumps->id.INFOG(35));
    2111:         PetscViewerASCIIPrintf(viewer,"  INFOG(36) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - value on the most memory consuming processor): %d \n",mumps->id.INFOG(36));
    2112:         PetscViewerASCIIPrintf(viewer,"  INFOG(37) (after analysis: estimated size of all MUMPS internal data for running BLR in-core - sum over all processors): %d \n",mumps->id.INFOG(37));
    2113:         PetscViewerASCIIPrintf(viewer,"  INFOG(38) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - value on the most memory consuming processor): %d \n",mumps->id.INFOG(38));
    2114:         PetscViewerASCIIPrintf(viewer,"  INFOG(39) (after analysis: estimated size of all MUMPS internal data for running BLR out-of-core - sum over all processors): %d \n",mumps->id.INFOG(39));
    2115:       }
    2116:     }
    2117:   }
    2118:   return(0);
    2119: }

    2121: PetscErrorCode MatGetInfo_MUMPS(Mat A,MatInfoType flag,MatInfo *info)
    2122: {
    2123:   Mat_MUMPS *mumps =(Mat_MUMPS*)A->data;

    2126:   info->block_size        = 1.0;
    2127:   info->nz_allocated      = mumps->id.INFOG(20);
    2128:   info->nz_used           = mumps->id.INFOG(20);
    2129:   info->nz_unneeded       = 0.0;
    2130:   info->assemblies        = 0.0;
    2131:   info->mallocs           = 0.0;
    2132:   info->memory            = 0.0;
    2133:   info->fill_ratio_given  = 0;
    2134:   info->fill_ratio_needed = 0;
    2135:   info->factor_mallocs    = 0;
    2136:   return(0);
    2137: }

    2139: /* -------------------------------------------------------------------------------------------*/
    2140: PetscErrorCode MatFactorSetSchurIS_MUMPS(Mat F, IS is)
    2141: {
    2142:   Mat_MUMPS         *mumps =(Mat_MUMPS*)F->data;
    2143:   const PetscScalar *arr;
    2144:   const PetscInt    *idxs;
    2145:   PetscInt          size,i;
    2146:   PetscErrorCode    ierr;

    2149:   ISGetLocalSize(is,&size);
    2150:   if (mumps->petsc_size > 1) {
    2151:     PetscBool ls,gs; /* gs is false if any rank other than root has non-empty IS */

    2153:     ls   = mumps->myid ? (size ? PETSC_FALSE : PETSC_TRUE) : PETSC_TRUE; /* always true on root; false on others if their size != 0 */
    2154:     MPI_Allreduce(&ls,&gs,1,MPIU_BOOL,MPI_LAND,mumps->petsc_comm);
    2155:     if (!gs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MUMPS distributed parallel Schur complements not yet supported from PETSc\n");
    2156:   }

    2158:   /* Schur complement matrix */
    2159:   MatDestroy(&F->schur);
    2160:   MatCreateSeqDense(PETSC_COMM_SELF,size,size,NULL,&F->schur);
    2161:   MatDenseGetArrayRead(F->schur,&arr);
    2162:   mumps->id.schur      = (MumpsScalar*)arr;
    2163:   mumps->id.size_schur = size;
    2164:   mumps->id.schur_lld  = size;
    2165:   MatDenseRestoreArrayRead(F->schur,&arr);
    2166:   if (mumps->sym == 1) {
    2167:     MatSetOption(F->schur,MAT_SPD,PETSC_TRUE);
    2168:   }

    2170:   /* MUMPS expects Fortran style indices */
    2171:   PetscFree(mumps->id.listvar_schur);
    2172:   PetscMalloc1(size,&mumps->id.listvar_schur);
    2173:   ISGetIndices(is,&idxs);
    2174:   for (i=0; i<size; i++) {PetscMUMPSIntCast(idxs[i]+1,&(mumps->id.listvar_schur[i]));}
    2175:   ISRestoreIndices(is,&idxs);
    2176:   if (mumps->petsc_size > 1) {
    2177:     mumps->id.ICNTL(19) = 1; /* MUMPS returns Schur centralized on the host */
    2178:   } else {
    2179:     if (F->factortype == MAT_FACTOR_LU) {
    2180:       mumps->id.ICNTL(19) = 3; /* MUMPS returns full matrix */
    2181:     } else {
    2182:       mumps->id.ICNTL(19) = 2; /* MUMPS returns lower triangular part */
    2183:     }
    2184:   }
    2185:   /* set a special value of ICNTL (not handled my MUMPS) to be used in the solve phase by PETSc */
    2186:   mumps->id.ICNTL(26) = -1;
    2187:   return(0);
    2188: }

    2190: /* -------------------------------------------------------------------------------------------*/
    2191: PetscErrorCode MatFactorCreateSchurComplement_MUMPS(Mat F,Mat* S)
    2192: {
    2193:   Mat            St;
    2194:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->data;
    2195:   PetscScalar    *array;
    2196: #if defined(PETSC_USE_COMPLEX)
    2197:   PetscScalar    im = PetscSqrtScalar((PetscScalar)-1.0);
    2198: #endif

    2202:   if (!mumps->id.ICNTL(19)) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ORDER,"Schur complement mode not selected! You should call MatFactorSetSchurIS to enable it");
    2203:   MatCreate(PETSC_COMM_SELF,&St);
    2204:   MatSetSizes(St,PETSC_DECIDE,PETSC_DECIDE,mumps->id.size_schur,mumps->id.size_schur);
    2205:   MatSetType(St,MATDENSE);
    2206:   MatSetUp(St);
    2207:   MatDenseGetArray(St,&array);
    2208:   if (!mumps->sym) { /* MUMPS always return a full matrix */
    2209:     if (mumps->id.ICNTL(19) == 1) { /* stored by rows */
    2210:       PetscInt i,j,N=mumps->id.size_schur;
    2211:       for (i=0;i<N;i++) {
    2212:         for (j=0;j<N;j++) {
    2213: #if !defined(PETSC_USE_COMPLEX)
    2214:           PetscScalar val = mumps->id.schur[i*N+j];
    2215: #else
    2216:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    2217: #endif
    2218:           array[j*N+i] = val;
    2219:         }
    2220:       }
    2221:     } else { /* stored by columns */
    2222:       PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur);
    2223:     }
    2224:   } else { /* either full or lower-triangular (not packed) */
    2225:     if (mumps->id.ICNTL(19) == 2) { /* lower triangular stored by columns */
    2226:       PetscInt i,j,N=mumps->id.size_schur;
    2227:       for (i=0;i<N;i++) {
    2228:         for (j=i;j<N;j++) {
    2229: #if !defined(PETSC_USE_COMPLEX)
    2230:           PetscScalar val = mumps->id.schur[i*N+j];
    2231: #else
    2232:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    2233: #endif
    2234:           array[i*N+j] = val;
    2235:           array[j*N+i] = val;
    2236:         }
    2237:       }
    2238:     } else if (mumps->id.ICNTL(19) == 3) { /* full matrix */
    2239:       PetscArraycpy(array,mumps->id.schur,mumps->id.size_schur*mumps->id.size_schur);
    2240:     } else { /* ICNTL(19) == 1 lower triangular stored by rows */
    2241:       PetscInt i,j,N=mumps->id.size_schur;
    2242:       for (i=0;i<N;i++) {
    2243:         for (j=0;j<i+1;j++) {
    2244: #if !defined(PETSC_USE_COMPLEX)
    2245:           PetscScalar val = mumps->id.schur[i*N+j];
    2246: #else
    2247:           PetscScalar val = mumps->id.schur[i*N+j].r + im*mumps->id.schur[i*N+j].i;
    2248: #endif
    2249:           array[i*N+j] = val;
    2250:           array[j*N+i] = val;
    2251:         }
    2252:       }
    2253:     }
    2254:   }
    2255:   MatDenseRestoreArray(St,&array);
    2256:   *S   = St;
    2257:   return(0);
    2258: }

    2260: /* -------------------------------------------------------------------------------------------*/
    2261: PetscErrorCode MatMumpsSetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt ival)
    2262: {
    2264:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2267:   PetscMUMPSIntCast(ival,&mumps->id.ICNTL(icntl));
    2268:   return(0);
    2269: }

    2271: PetscErrorCode MatMumpsGetIcntl_MUMPS(Mat F,PetscInt icntl,PetscInt *ival)
    2272: {
    2273:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2276:   *ival = mumps->id.ICNTL(icntl);
    2277:   return(0);
    2278: }

    2280: /*@
    2281:   MatMumpsSetIcntl - Set MUMPS parameter ICNTL()

    2283:    Logically Collective on Mat

    2285:    Input Parameters:
    2286: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2287: .  icntl - index of MUMPS parameter array ICNTL()
    2288: -  ival - value of MUMPS ICNTL(icntl)

    2290:   Options Database:
    2291: .   -mat_mumps_icntl_<icntl> <ival>

    2293:    Level: beginner

    2295:    References:
    2296: .     MUMPS Users' Guide

    2298: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2299:  @*/
    2300: PetscErrorCode MatMumpsSetIcntl(Mat F,PetscInt icntl,PetscInt ival)
    2301: {

    2306:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2309:   PetscTryMethod(F,"MatMumpsSetIcntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
    2310:   return(0);
    2311: }

    2313: /*@
    2314:   MatMumpsGetIcntl - Get MUMPS parameter ICNTL()

    2316:    Logically Collective on Mat

    2318:    Input Parameters:
    2319: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2320: -  icntl - index of MUMPS parameter array ICNTL()

    2322:   Output Parameter:
    2323: .  ival - value of MUMPS ICNTL(icntl)

    2325:    Level: beginner

    2327:    References:
    2328: .     MUMPS Users' Guide

    2330: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2331: @*/
    2332: PetscErrorCode MatMumpsGetIcntl(Mat F,PetscInt icntl,PetscInt *ival)
    2333: {

    2338:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2341:   PetscUseMethod(F,"MatMumpsGetIcntl_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2342:   return(0);
    2343: }

    2345: /* -------------------------------------------------------------------------------------------*/
    2346: PetscErrorCode MatMumpsSetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal val)
    2347: {
    2348:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2351:   mumps->id.CNTL(icntl) = val;
    2352:   return(0);
    2353: }

    2355: PetscErrorCode MatMumpsGetCntl_MUMPS(Mat F,PetscInt icntl,PetscReal *val)
    2356: {
    2357:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2360:   *val = mumps->id.CNTL(icntl);
    2361:   return(0);
    2362: }

    2364: /*@
    2365:   MatMumpsSetCntl - Set MUMPS parameter CNTL()

    2367:    Logically Collective on Mat

    2369:    Input Parameters:
    2370: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2371: .  icntl - index of MUMPS parameter array CNTL()
    2372: -  val - value of MUMPS CNTL(icntl)

    2374:   Options Database:
    2375: .   -mat_mumps_cntl_<icntl> <val>

    2377:    Level: beginner

    2379:    References:
    2380: .     MUMPS Users' Guide

    2382: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2383: @*/
    2384: PetscErrorCode MatMumpsSetCntl(Mat F,PetscInt icntl,PetscReal val)
    2385: {

    2390:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2393:   PetscTryMethod(F,"MatMumpsSetCntl_C",(Mat,PetscInt,PetscReal),(F,icntl,val));
    2394:   return(0);
    2395: }

    2397: /*@
    2398:   MatMumpsGetCntl - Get MUMPS parameter CNTL()

    2400:    Logically Collective on Mat

    2402:    Input Parameters:
    2403: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2404: -  icntl - index of MUMPS parameter array CNTL()

    2406:   Output Parameter:
    2407: .  val - value of MUMPS CNTL(icntl)

    2409:    Level: beginner

    2411:    References:
    2412: .      MUMPS Users' Guide

    2414: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2415: @*/
    2416: PetscErrorCode MatMumpsGetCntl(Mat F,PetscInt icntl,PetscReal *val)
    2417: {

    2422:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2425:   PetscUseMethod(F,"MatMumpsGetCntl_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2426:   return(0);
    2427: }

    2429: PetscErrorCode MatMumpsGetInfo_MUMPS(Mat F,PetscInt icntl,PetscInt *info)
    2430: {
    2431:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2434:   *info = mumps->id.INFO(icntl);
    2435:   return(0);
    2436: }

    2438: PetscErrorCode MatMumpsGetInfog_MUMPS(Mat F,PetscInt icntl,PetscInt *infog)
    2439: {
    2440:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2443:   *infog = mumps->id.INFOG(icntl);
    2444:   return(0);
    2445: }

    2447: PetscErrorCode MatMumpsGetRinfo_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfo)
    2448: {
    2449:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2452:   *rinfo = mumps->id.RINFO(icntl);
    2453:   return(0);
    2454: }

    2456: PetscErrorCode MatMumpsGetRinfog_MUMPS(Mat F,PetscInt icntl,PetscReal *rinfog)
    2457: {
    2458:   Mat_MUMPS *mumps =(Mat_MUMPS*)F->data;

    2461:   *rinfog = mumps->id.RINFOG(icntl);
    2462:   return(0);
    2463: }

    2465: PetscErrorCode MatMumpsGetInverse_MUMPS(Mat F,Mat spRHS)
    2466: {
    2468:   Mat            Bt = NULL,Btseq = NULL;
    2469:   PetscBool      flg;
    2470:   Mat_MUMPS      *mumps =(Mat_MUMPS*)F->data;
    2471:   PetscScalar    *aa;
    2472:   PetscInt       spnr,*ia,*ja,M,nrhs;

    2476:   PetscObjectTypeCompare((PetscObject)spRHS,MATTRANSPOSEMAT,&flg);
    2477:   if (flg) {
    2478:     MatTransposeGetMat(spRHS,&Bt);
    2479:   } else SETERRQ(PetscObjectComm((PetscObject)spRHS),PETSC_ERR_ARG_WRONG,"Matrix spRHS must be type MATTRANSPOSEMAT matrix");

    2481:   MatMumpsSetIcntl(F,30,1);

    2483:   if (mumps->petsc_size > 1) {
    2484:     Mat_MPIAIJ *b = (Mat_MPIAIJ*)Bt->data;
    2485:     Btseq = b->A;
    2486:   } else {
    2487:     Btseq = Bt;
    2488:   }

    2490:   MatGetSize(spRHS,&M,&nrhs);
    2491:   mumps->id.nrhs = nrhs;
    2492:   mumps->id.lrhs = M;
    2493:   mumps->id.rhs  = NULL;

    2495:   if (!mumps->myid) {
    2496:     MatSeqAIJGetArray(Btseq,&aa);
    2497:     MatGetRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    2498:     if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure");
    2499:     PetscMUMPSIntCSRCast(mumps,spnr,ia,ja,&mumps->id.irhs_ptr,&mumps->id.irhs_sparse,&mumps->id.nz_rhs);
    2500:     mumps->id.rhs_sparse  = (MumpsScalar*)aa;
    2501:   } else {
    2502:     mumps->id.irhs_ptr    = NULL;
    2503:     mumps->id.irhs_sparse = NULL;
    2504:     mumps->id.nz_rhs      = 0;
    2505:     mumps->id.rhs_sparse  = NULL;
    2506:   }
    2507:   mumps->id.ICNTL(20)   = 1; /* rhs is sparse */
    2508:   mumps->id.ICNTL(21)   = 0; /* solution is in assembled centralized format */

    2510:   /* solve phase */
    2511:   /*-------------*/
    2512:   mumps->id.job = JOB_SOLVE;
    2513:   PetscMUMPS_c(mumps);
    2514:   if (mumps->id.INFOG(1) < 0)
    2515:     SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d INFO(2)=%d\n",mumps->id.INFOG(1),mumps->id.INFO(2));

    2517:   if (!mumps->myid) {
    2518:     MatSeqAIJRestoreArray(Btseq,&aa);
    2519:     MatRestoreRowIJ(Btseq,1,PETSC_FALSE,PETSC_FALSE,&spnr,(const PetscInt**)&ia,(const PetscInt**)&ja,&flg);
    2520:     if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot get IJ structure");
    2521:   }
    2522:   return(0);
    2523: }

    2525: /*@
    2526:   MatMumpsGetInverse - Get user-specified set of entries in inverse of A

    2528:    Logically Collective on Mat

    2530:    Input Parameters:
    2531: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2532: -  spRHS - sequential sparse matrix in MATTRANSPOSEMAT format holding specified indices in processor[0]

    2534:   Output Parameter:
    2535: . spRHS - requested entries of inverse of A

    2537:    Level: beginner

    2539:    References:
    2540: .      MUMPS Users' Guide

    2542: .seealso: MatGetFactor(), MatCreateTranspose()
    2543: @*/
    2544: PetscErrorCode MatMumpsGetInverse(Mat F,Mat spRHS)
    2545: {

    2550:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2551:   PetscUseMethod(F,"MatMumpsGetInverse_C",(Mat,Mat),(F,spRHS));
    2552:   return(0);
    2553: }

    2555: PetscErrorCode MatMumpsGetInverseTranspose_MUMPS(Mat F,Mat spRHST)
    2556: {
    2558:   Mat            spRHS;

    2561:   MatCreateTranspose(spRHST,&spRHS);
    2562:   MatMumpsGetInverse_MUMPS(F,spRHS);
    2563:   MatDestroy(&spRHS);
    2564:   return(0);
    2565: }

    2567: /*@
    2568:   MatMumpsGetInverseTranspose - Get user-specified set of entries in inverse of matrix A^T

    2570:    Logically Collective on Mat

    2572:    Input Parameters:
    2573: +  F - the factored matrix of A obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2574: -  spRHST - sequential sparse matrix in MATAIJ format holding specified indices of A^T in processor[0]

    2576:   Output Parameter:
    2577: . spRHST - requested entries of inverse of A^T

    2579:    Level: beginner

    2581:    References:
    2582: .      MUMPS Users' Guide

    2584: .seealso: MatGetFactor(), MatCreateTranspose(), MatMumpsGetInverse()
    2585: @*/
    2586: PetscErrorCode MatMumpsGetInverseTranspose(Mat F,Mat spRHST)
    2587: {
    2589:   PetscBool      flg;

    2593:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2594:   PetscObjectTypeCompareAny((PetscObject)spRHST,&flg,MATSEQAIJ,MATMPIAIJ,NULL);
    2595:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)spRHST),PETSC_ERR_ARG_WRONG,"Matrix spRHST must be MATAIJ matrix");

    2597:   PetscUseMethod(F,"MatMumpsGetInverseTranspose_C",(Mat,Mat),(F,spRHST));
    2598:   return(0);
    2599: }

    2601: /*@
    2602:   MatMumpsGetInfo - Get MUMPS parameter INFO()

    2604:    Logically Collective on Mat

    2606:    Input Parameters:
    2607: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2608: -  icntl - index of MUMPS parameter array INFO()

    2610:   Output Parameter:
    2611: .  ival - value of MUMPS INFO(icntl)

    2613:    Level: beginner

    2615:    References:
    2616: .      MUMPS Users' Guide

    2618: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2619: @*/
    2620: PetscErrorCode MatMumpsGetInfo(Mat F,PetscInt icntl,PetscInt *ival)
    2621: {

    2626:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2628:   PetscUseMethod(F,"MatMumpsGetInfo_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2629:   return(0);
    2630: }

    2632: /*@
    2633:   MatMumpsGetInfog - Get MUMPS parameter INFOG()

    2635:    Logically Collective on Mat

    2637:    Input Parameters:
    2638: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2639: -  icntl - index of MUMPS parameter array INFOG()

    2641:   Output Parameter:
    2642: .  ival - value of MUMPS INFOG(icntl)

    2644:    Level: beginner

    2646:    References:
    2647: .      MUMPS Users' Guide

    2649: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2650: @*/
    2651: PetscErrorCode MatMumpsGetInfog(Mat F,PetscInt icntl,PetscInt *ival)
    2652: {

    2657:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2659:   PetscUseMethod(F,"MatMumpsGetInfog_C",(Mat,PetscInt,PetscInt*),(F,icntl,ival));
    2660:   return(0);
    2661: }

    2663: /*@
    2664:   MatMumpsGetRinfo - Get MUMPS parameter RINFO()

    2666:    Logically Collective on Mat

    2668:    Input Parameters:
    2669: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2670: -  icntl - index of MUMPS parameter array RINFO()

    2672:   Output Parameter:
    2673: .  val - value of MUMPS RINFO(icntl)

    2675:    Level: beginner

    2677:    References:
    2678: .       MUMPS Users' Guide

    2680: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2681: @*/
    2682: PetscErrorCode MatMumpsGetRinfo(Mat F,PetscInt icntl,PetscReal *val)
    2683: {

    2688:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2690:   PetscUseMethod(F,"MatMumpsGetRinfo_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2691:   return(0);
    2692: }

    2694: /*@
    2695:   MatMumpsGetRinfog - Get MUMPS parameter RINFOG()

    2697:    Logically Collective on Mat

    2699:    Input Parameters:
    2700: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
    2701: -  icntl - index of MUMPS parameter array RINFOG()

    2703:   Output Parameter:
    2704: .  val - value of MUMPS RINFOG(icntl)

    2706:    Level: beginner

    2708:    References:
    2709: .      MUMPS Users' Guide

    2711: .seealso: MatGetFactor(), MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog()
    2712: @*/
    2713: PetscErrorCode MatMumpsGetRinfog(Mat F,PetscInt icntl,PetscReal *val)
    2714: {

    2719:   if (!F->factortype) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
    2721:   PetscUseMethod(F,"MatMumpsGetRinfog_C",(Mat,PetscInt,PetscReal*),(F,icntl,val));
    2722:   return(0);
    2723: }

    2725: /*MC
    2726:   MATSOLVERMUMPS -  A matrix type providing direct solvers (LU and Cholesky) for
    2727:   distributed and sequential matrices via the external package MUMPS.

    2729:   Works with MATAIJ and MATSBAIJ matrices

    2731:   Use ./configure --download-mumps --download-scalapack --download-parmetis --download-metis --download-ptscotch to have PETSc installed with MUMPS

    2733:   Use ./configure --with-openmp --download-hwloc (or --with-hwloc) to enable running MUMPS in MPI+OpenMP hybrid mode and non-MUMPS in flat-MPI mode. See details below.

    2735:   Use -pc_type cholesky or lu -pc_factor_mat_solver_type mumps to use this direct solver

    2737:   Options Database Keys:
    2738: +  -mat_mumps_icntl_1 - ICNTL(1): output stream for error messages
    2739: .  -mat_mumps_icntl_2 - ICNTL(2): output stream for diagnostic printing, statistics, and warning
    2740: .  -mat_mumps_icntl_3 -  ICNTL(3): output stream for global information, collected on the host
    2741: .  -mat_mumps_icntl_4 -  ICNTL(4): level of printing (0 to 4)
    2742: .  -mat_mumps_icntl_6 - ICNTL(6): permutes to a zero-free diagonal and/or scale the matrix (0 to 7)
    2743: .  -mat_mumps_icntl_7 - ICNTL(7): computes a symmetric permutation in sequential analysis (0 to 7). 3=Scotch, 4=PORD, 5=Metis
    2744: .  -mat_mumps_icntl_8  - ICNTL(8): scaling strategy (-2 to 8 or 77)
    2745: .  -mat_mumps_icntl_10  - ICNTL(10): max num of refinements
    2746: .  -mat_mumps_icntl_11  - ICNTL(11): statistics related to an error analysis (via -ksp_view)
    2747: .  -mat_mumps_icntl_12  - ICNTL(12): an ordering strategy for symmetric matrices (0 to 3)
    2748: .  -mat_mumps_icntl_13  - ICNTL(13): parallelism of the root node (enable ScaLAPACK) and its splitting
    2749: .  -mat_mumps_icntl_14  - ICNTL(14): percentage increase in the estimated working space
    2750: .  -mat_mumps_icntl_19  - ICNTL(19): computes the Schur complement
    2751: .  -mat_mumps_icntl_22  - ICNTL(22): in-core/out-of-core factorization and solve (0 or 1)
    2752: .  -mat_mumps_icntl_23  - ICNTL(23): max size of the working memory (MB) that can allocate per processor
    2753: .  -mat_mumps_icntl_24  - ICNTL(24): detection of null pivot rows (0 or 1)
    2754: .  -mat_mumps_icntl_25  - ICNTL(25): compute a solution of a deficient matrix and a null space basis
    2755: .  -mat_mumps_icntl_26  - ICNTL(26): drives the solution phase if a Schur complement matrix
    2756: .  -mat_mumps_icntl_28  - ICNTL(28): use 1 for sequential analysis and ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
    2757: .  -mat_mumps_icntl_29 - ICNTL(29): parallel ordering 1 = ptscotch, 2 = parmetis
    2758: .  -mat_mumps_icntl_30 - ICNTL(30): compute user-specified set of entries in inv(A)
    2759: .  -mat_mumps_icntl_31 - ICNTL(31): indicates which factors may be discarded during factorization
    2760: .  -mat_mumps_icntl_33 - ICNTL(33): compute determinant
    2761: .  -mat_mumps_icntl_35 - ICNTL(35): level of activation of BLR (Block Low-Rank) feature
    2762: .  -mat_mumps_icntl_36 - ICNTL(36): controls the choice of BLR factorization variant
    2763: .  -mat_mumps_icntl_38 - ICNTL(38): sets the estimated compression rate of LU factors with BLR
    2764: .  -mat_mumps_cntl_1  - CNTL(1): relative pivoting threshold
    2765: .  -mat_mumps_cntl_2  -  CNTL(2): stopping criterion of refinement
    2766: .  -mat_mumps_cntl_3 - CNTL(3): absolute pivoting threshold
    2767: .  -mat_mumps_cntl_4 - CNTL(4): value for static pivoting
    2768: .  -mat_mumps_cntl_5 - CNTL(5): fixation for null pivots
    2769: .  -mat_mumps_cntl_7 - CNTL(7): precision of the dropping parameter used during BLR factorization
    2770: -  -mat_mumps_use_omp_threads [m] - run MUMPS in MPI+OpenMP hybrid mode as if omp_set_num_threads(m) is called before calling MUMPS.
    2771:                                    Default might be the number of cores per CPU package (socket) as reported by hwloc and suggested by the MUMPS manual.

    2773:   Level: beginner

    2775:     Notes:
    2776:     MUMPS Cholesky does not handle (complex) Hermitian matrices http://mumps.enseeiht.fr/doc/userguide_5.2.1.pdf so using it will error if the matrix is Hermitian.

    2778:     When a MUMPS factorization fails inside a KSP solve, for example with a KSP_DIVERGED_PC_FAILED, one can find the MUMPS information about the failure by calling
    2779: $          KSPGetPC(ksp,&pc);
    2780: $          PCFactorGetMatrix(pc,&mat);
    2781: $          MatMumpsGetInfo(mat,....);
    2782: $          MatMumpsGetInfog(mat,....); etc.
    2783:            Or you can run with -ksp_error_if_not_converged and the program will be stopped and the information printed in the error message.

    2785:    Two modes to run MUMPS/PETSc with OpenMP

    2787: $     Set OMP_NUM_THREADS and run with fewer MPI ranks than cores. For example, if you want to have 16 OpenMP
    2788: $     threads per rank, then you may use "export OMP_NUM_THREADS=16 && mpirun -n 4 ./test".

    2790: $     -mat_mumps_use_omp_threads [m] and run your code with as many MPI ranks as the number of cores. For example,
    2791: $     if a compute node has 32 cores and you run on two nodes, you may use "mpirun -n 64 ./test -mat_mumps_use_omp_threads 16"

    2793:    To run MUMPS in MPI+OpenMP hybrid mode (i.e., enable multithreading in MUMPS), but still run the non-MUMPS part
    2794:    (i.e., PETSc part) of your code in the so-called flat-MPI (aka pure-MPI) mode, you need to configure PETSc with --with-openmp --download-hwloc
    2795:    (or --with-hwloc), and have an MPI that supports MPI-3.0's process shared memory (which is usually available). Since MUMPS calls BLAS
    2796:    libraries, to really get performance, you should have multithreaded BLAS libraries such as Intel MKL, AMD ACML, Cray libSci or OpenBLAS
    2797:    (PETSc will automatically try to utilized a threaded BLAS if --with-openmp is provided).

    2799:    If you run your code through a job submission system, there are caveats in MPI rank mapping. We use MPI_Comm_split_type() to obtain MPI
    2800:    processes on each compute node. Listing the processes in rank ascending order, we split processes on a node into consecutive groups of
    2801:    size m and create a communicator called omp_comm for each group. Rank 0 in an omp_comm is called the master rank, and others in the omp_comm
    2802:    are called slave ranks (or slaves). Only master ranks are seen to MUMPS and slaves are not. We will free CPUs assigned to slaves (might be set
    2803:    by CPU binding policies in job scripts) and make the CPUs available to the master so that OMP threads spawned by MUMPS can run on the CPUs.
    2804:    In a multi-socket compute node, MPI rank mapping is an issue. Still use the above example and suppose your compute node has two sockets,
    2805:    if you interleave MPI ranks on the two sockets, in other words, even ranks are placed on socket 0, and odd ranks are on socket 1, and bind
    2806:    MPI ranks to cores, then with -mat_mumps_use_omp_threads 16, a master rank (and threads it spawns) will use half cores in socket 0, and half
    2807:    cores in socket 1, that definitely hurts locality. On the other hand, if you map MPI ranks consecutively on the two sockets, then the
    2808:    problem will not happen. Therefore, when you use -mat_mumps_use_omp_threads, you need to keep an eye on your MPI rank mapping and CPU binding.
    2809:    For example, with the Slurm job scheduler, one can use srun --cpu-bind=verbose -m block:block to map consecutive MPI ranks to sockets and
    2810:    examine the mapping result.

    2812:    PETSc does not control thread binding in MUMPS. So to get best performance, one still has to set OMP_PROC_BIND and OMP_PLACES in job scripts,
    2813:    for example, export OMP_PLACES=threads and export OMP_PROC_BIND=spread. One does not need to export OMP_NUM_THREADS=m in job scripts as PETSc
    2814:    calls omp_set_num_threads(m) internally before calling MUMPS.

    2816:    References:
    2817: +   1. - Heroux, Michael A., R. Brightwell, and Michael M. Wolf. "Bi-modal MPI and MPI+ threads computing on scalable multicore systems." IJHPCA (Submitted) (2011).
    2818: -   2. - Gutierrez, Samuel K., et al. "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications." Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International. IEEE, 2017.

    2820: .seealso: PCFactorSetMatSolverType(), MatSolverType, MatMumpsSetICntl(), MatMumpsGetIcntl(), MatMumpsSetCntl(), MatMumpsGetCntl(), MatMumpsGetInfo(), MatMumpsGetInfog(), MatMumpsGetRinfo(), MatMumpsGetRinfog(), KSPGetPC(), PCGetFactor(), PCFactorGetMatrix()

    2822: M*/

    2824: static PetscErrorCode MatFactorGetSolverType_mumps(Mat A,MatSolverType *type)
    2825: {
    2827:   *type = MATSOLVERMUMPS;
    2828:   return(0);
    2829: }

    2831: /* MatGetFactor for Seq and MPI AIJ matrices */
    2832: static PetscErrorCode MatGetFactor_aij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2833: {
    2834:   Mat            B;
    2836:   Mat_MUMPS      *mumps;
    2837:   PetscBool      isSeqAIJ;

    2840:  #if defined(PETSC_USE_COMPLEX)
    2841:   if (A->hermitian && !A->symmetric && ftype == MAT_FACTOR_CHOLESKY) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian CHOLESKY Factor is not supported");
    2842:  #endif
    2843:   /* Create the factorization matrix */
    2844:   PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
    2845:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2846:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2847:   PetscStrallocpy("mumps",&((PetscObject)B)->type_name);
    2848:   MatSetUp(B);

    2850:   PetscNewLog(B,&mumps);

    2852:   B->ops->view    = MatView_MUMPS;
    2853:   B->ops->getinfo = MatGetInfo_MUMPS;

    2855:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);
    2856:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2857:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2858:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2859:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2860:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2861:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2862:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    2863:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    2864:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    2865:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);
    2866:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);
    2867:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);

    2869:   if (ftype == MAT_FACTOR_LU) {
    2870:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
    2871:     B->factortype            = MAT_FACTOR_LU;
    2872:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqaij;
    2873:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij;
    2874:     mumps->sym = 0;
    2875:   } else {
    2876:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
    2877:     B->factortype                  = MAT_FACTOR_CHOLESKY;
    2878:     if (isSeqAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqaij_seqsbaij;
    2879:     else mumps->ConvertToTriples = MatConvertToTriples_mpiaij_mpisbaij;
    2880: #if defined(PETSC_USE_COMPLEX)
    2881:     mumps->sym = 2;
    2882: #else
    2883:     if (A->spd_set && A->spd) mumps->sym = 1;
    2884:     else                      mumps->sym = 2;
    2885: #endif
    2886:   }

    2888:   /* set solvertype */
    2889:   PetscFree(B->solvertype);
    2890:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    2892:   B->ops->destroy = MatDestroy_MUMPS;
    2893:   B->data         = (void*)mumps;

    2895:   PetscInitializeMUMPS(A,mumps);

    2897:   *F = B;
    2898:   return(0);
    2899: }

    2901: /* MatGetFactor for Seq and MPI SBAIJ matrices */
    2902: static PetscErrorCode MatGetFactor_sbaij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2903: {
    2904:   Mat            B;
    2906:   Mat_MUMPS      *mumps;
    2907:   PetscBool      isSeqSBAIJ;

    2910:  #if defined(PETSC_USE_COMPLEX)
    2911:   if (A->hermitian && !A->symmetric) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian CHOLESKY Factor is not supported");
    2912:  #endif
    2913:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2914:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2915:   PetscStrallocpy("mumps",&((PetscObject)B)->type_name);
    2916:   MatSetUp(B);

    2918:   PetscNewLog(B,&mumps);
    2919:   PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&isSeqSBAIJ);
    2920:   if (isSeqSBAIJ) {
    2921:     mumps->ConvertToTriples = MatConvertToTriples_seqsbaij_seqsbaij;
    2922:   } else {
    2923:     mumps->ConvertToTriples = MatConvertToTriples_mpisbaij_mpisbaij;
    2924:   }

    2926:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_MUMPS;
    2927:   B->ops->view                   = MatView_MUMPS;
    2928:   B->ops->getinfo                = MatGetInfo_MUMPS;

    2930:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);
    2931:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2932:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2933:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2934:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2935:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2936:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2937:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    2938:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    2939:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    2940:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);
    2941:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);
    2942:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);

    2944:   B->factortype = MAT_FACTOR_CHOLESKY;
    2945: #if defined(PETSC_USE_COMPLEX)
    2946:   mumps->sym = 2;
    2947: #else
    2948:   if (A->spd_set && A->spd) mumps->sym = 1;
    2949:   else                      mumps->sym = 2;
    2950: #endif

    2952:   /* set solvertype */
    2953:   PetscFree(B->solvertype);
    2954:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    2956:   B->ops->destroy = MatDestroy_MUMPS;
    2957:   B->data         = (void*)mumps;

    2959:   PetscInitializeMUMPS(A,mumps);

    2961:   *F = B;
    2962:   return(0);
    2963: }

    2965: static PetscErrorCode MatGetFactor_baij_mumps(Mat A,MatFactorType ftype,Mat *F)
    2966: {
    2967:   Mat            B;
    2969:   Mat_MUMPS      *mumps;
    2970:   PetscBool      isSeqBAIJ;

    2973:   /* Create the factorization matrix */
    2974:   PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&isSeqBAIJ);
    2975:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    2976:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    2977:   PetscStrallocpy("mumps",&((PetscObject)B)->type_name);
    2978:   MatSetUp(B);

    2980:   PetscNewLog(B,&mumps);
    2981:   if (ftype == MAT_FACTOR_LU) {
    2982:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_BAIJMUMPS;
    2983:     B->factortype            = MAT_FACTOR_LU;
    2984:     if (isSeqBAIJ) mumps->ConvertToTriples = MatConvertToTriples_seqbaij_seqaij;
    2985:     else mumps->ConvertToTriples = MatConvertToTriples_mpibaij_mpiaij;
    2986:     mumps->sym = 0;
    2987:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot use PETSc BAIJ matrices with MUMPS Cholesky, use SBAIJ or AIJ matrix instead\n");

    2989:   B->ops->view        = MatView_MUMPS;
    2990:   B->ops->getinfo     = MatGetInfo_MUMPS;

    2992:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);
    2993:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    2994:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    2995:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    2996:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    2997:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    2998:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    2999:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    3000:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    3001:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    3002:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);
    3003:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverse_C",MatMumpsGetInverse_MUMPS);
    3004:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInverseTranspose_C",MatMumpsGetInverseTranspose_MUMPS);

    3006:   /* set solvertype */
    3007:   PetscFree(B->solvertype);
    3008:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    3010:   B->ops->destroy = MatDestroy_MUMPS;
    3011:   B->data         = (void*)mumps;

    3013:   PetscInitializeMUMPS(A,mumps);

    3015:   *F = B;
    3016:   return(0);
    3017: }

    3019: /* MatGetFactor for Seq and MPI SELL matrices */
    3020: static PetscErrorCode MatGetFactor_sell_mumps(Mat A,MatFactorType ftype,Mat *F)
    3021: {
    3022:   Mat            B;
    3024:   Mat_MUMPS      *mumps;
    3025:   PetscBool      isSeqSELL;

    3028:   /* Create the factorization matrix */
    3029:   PetscObjectTypeCompare((PetscObject)A,MATSEQSELL,&isSeqSELL);
    3030:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    3031:   MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
    3032:   PetscStrallocpy("mumps",&((PetscObject)B)->type_name);
    3033:   MatSetUp(B);

    3035:   PetscNewLog(B,&mumps);

    3037:   B->ops->view        = MatView_MUMPS;
    3038:   B->ops->getinfo     = MatGetInfo_MUMPS;

    3040:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mumps);
    3041:   PetscObjectComposeFunction((PetscObject)B,"MatFactorSetSchurIS_C",MatFactorSetSchurIS_MUMPS);
    3042:   PetscObjectComposeFunction((PetscObject)B,"MatFactorCreateSchurComplement_C",MatFactorCreateSchurComplement_MUMPS);
    3043:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetIcntl_C",MatMumpsSetIcntl_MUMPS);
    3044:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetIcntl_C",MatMumpsGetIcntl_MUMPS);
    3045:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsSetCntl_C",MatMumpsSetCntl_MUMPS);
    3046:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetCntl_C",MatMumpsGetCntl_MUMPS);
    3047:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfo_C",MatMumpsGetInfo_MUMPS);
    3048:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetInfog_C",MatMumpsGetInfog_MUMPS);
    3049:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfo_C",MatMumpsGetRinfo_MUMPS);
    3050:   PetscObjectComposeFunction((PetscObject)B,"MatMumpsGetRinfog_C",MatMumpsGetRinfog_MUMPS);

    3052:   if (ftype == MAT_FACTOR_LU) {
    3053:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
    3054:     B->factortype            = MAT_FACTOR_LU;
    3055:     if (isSeqSELL) mumps->ConvertToTriples = MatConvertToTriples_seqsell_seqaij;
    3056:     else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented");
    3057:     mumps->sym = 0;
    3058:   } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"To be implemented");

    3060:   /* set solvertype */
    3061:   PetscFree(B->solvertype);
    3062:   PetscStrallocpy(MATSOLVERMUMPS,&B->solvertype);

    3064:   B->ops->destroy = MatDestroy_MUMPS;
    3065:   B->data         = (void*)mumps;

    3067:   PetscInitializeMUMPS(A,mumps);

    3069:   *F = B;
    3070:   return(0);
    3071: }

    3073: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MUMPS(void)
    3074: {

    3078:   MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
    3079:   MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
    3080:   MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
    3081:   MatSolverTypeRegister(MATSOLVERMUMPS,MATMPIBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
    3082:   MatSolverTypeRegister(MATSOLVERMUMPS,MATMPISBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
    3083:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_mumps);
    3084:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_mumps);
    3085:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_LU,MatGetFactor_baij_mumps);
    3086:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_baij_mumps);
    3087:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSBAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_sbaij_mumps);
    3088:   MatSolverTypeRegister(MATSOLVERMUMPS,MATSEQSELL,MAT_FACTOR_LU,MatGetFactor_sell_mumps);
    3089:   return(0);
    3090: }