xref: /petsc/src/mat/impls/aij/mpi/mumps/mumps.c (revision b24902e06ab141841ce6546a773244be2474cd18)

#define PETSCMAT_DLL

/*
    Provides an interface to the MUMPS sparse solver
*/
#include "src/mat/impls/aij/seq/aij.h"
#include "src/mat/impls/aij/mpi/mpiaij.h"
#include "src/mat/impls/sbaij/seq/sbaij.h"
#include "src/mat/impls/sbaij/mpi/mpisbaij.h"

EXTERN_C_BEGIN
#if defined(PETSC_USE_COMPLEX)
#include "zmumps_c.h"
#else
#include "dmumps_c.h"
#endif
EXTERN_C_END
#define JOB_INIT -1
#define JOB_END -2
/* macros s.t. indices match MUMPS documentation */
#define ICNTL(I) icntl[(I)-1]
#define CNTL(I) cntl[(I)-1]
#define INFOG(I) infog[(I)-1]
#define INFO(I) info[(I)-1]
#define RINFOG(I) rinfog[(I)-1]
#define RINFO(I) rinfo[(I)-1]

typedef struct {
#if defined(PETSC_USE_COMPLEX)
  ZMUMPS_STRUC_C id;
#else
  DMUMPS_STRUC_C id;
#endif
  MatStructure   matstruc;
  PetscMPIInt    myid,size;
  PetscInt       *irn,*jcn,sym,nSolve;
  PetscScalar    *val;
  MPI_Comm       comm_mumps;
  VecScatter     scat_rhs, scat_sol;
  PetscTruth     isAIJ,CleanUpMUMPS;
  Vec            b_seq,x_seq;
} Mat_MUMPS;

EXTERN PetscErrorCode MatDuplicate_MUMPS(Mat,MatDuplicateOption,Mat*);

/* convert Petsc mpiaij matrix to triples: row[nz], col[nz], val[nz] */
/*
  input:
    A       - matrix in mpiaij or mpisbaij (bs=1) format
    shift   - 0: C style output triple; 1: Fortran style output triple.
    valOnly - FALSE: spaces are allocated and values are set for the triple
              TRUE:  only the values in v array are updated
  output:
    nnz     - dim of r, c, and v (number of local nonzero entries of A)
    r, c, v - row and col index, matrix values (matrix triples)
 */
PetscErrorCode MatConvertToTriples(Mat A,int shift,PetscTruth valOnly,int *nnz,int **r, int **c, PetscScalar **v)
{
  PetscInt       *ai, *aj, *bi, *bj, rstart,nz, *garray;
  PetscErrorCode ierr;
  PetscInt       i,j,jj,jB,irow,m=A->rmap.n,*ajj,*bjj,countA,countB,colA_start,jcol;
  PetscInt       *row,*col;
  PetscScalar    *av, *bv,*val;
  Mat_MUMPS      *mumps=(Mat_MUMPS*)A->spptr;

  PetscFunctionBegin;
  if (mumps->isAIJ){
    Mat_MPIAIJ    *mat =  (Mat_MPIAIJ*)A->data;
    Mat_SeqAIJ    *aa=(Mat_SeqAIJ*)(mat->A)->data;
    Mat_SeqAIJ    *bb=(Mat_SeqAIJ*)(mat->B)->data;
    nz = aa->nz + bb->nz;
    ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap.rstart;
    garray = mat->garray;
    av=aa->a; bv=bb->a;

  } else {
    Mat_MPISBAIJ  *mat =  (Mat_MPISBAIJ*)A->data;
    Mat_SeqSBAIJ  *aa=(Mat_SeqSBAIJ*)(mat->A)->data;
    Mat_SeqBAIJ    *bb=(Mat_SeqBAIJ*)(mat->B)->data;
    if (A->rmap.bs > 1) SETERRQ1(PETSC_ERR_SUP," bs=%d is not supported yet\n", A->rmap.bs);
    nz = aa->nz + bb->nz;
    ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap.rstart;
    garray = mat->garray;
    av=aa->a; bv=bb->a;
  }

  if (!valOnly){
    ierr = PetscMalloc(nz*sizeof(PetscInt) ,&row);CHKERRQ(ierr);
    ierr = PetscMalloc(nz*sizeof(PetscInt),&col);CHKERRQ(ierr);
    ierr = PetscMalloc(nz*sizeof(PetscScalar),&val);CHKERRQ(ierr);
    *r = row; *c = col; *v = val;
  } else {
    row = *r; col = *c; val = *v;
  }
  *nnz = nz;

  jj = 0; irow = rstart;
  for ( i=0; i<m; i++ ) {
    ajj = aj + ai[i];                 /* ptr to the beginning of this row */
    countA = ai[i+1] - ai[i];
    countB = bi[i+1] - bi[i];
    bjj = bj + bi[i];

    /* get jB, the starting local col index for the 2nd B-part */
    colA_start = rstart + ajj[0]; /* the smallest col index for A */
    j=-1;
    do {
      j++;
      if (j == countB) break;
      jcol = garray[bjj[j]];
    } while (jcol < colA_start);
    jB = j;

    /* B-part, smaller col index */
    colA_start = rstart + ajj[0]; /* the smallest col index for A */
    for (j=0; j<jB; j++){
      jcol = garray[bjj[j]];
      if (!valOnly){
        row[jj] = irow + shift; col[jj] = jcol + shift;

      }
      val[jj++] = *bv++;
    }
    /* A-part */
    for (j=0; j<countA; j++){
      if (!valOnly){
        row[jj] = irow + shift; col[jj] = rstart + ajj[j] + shift;
      }
      val[jj++] = *av++;
    }
    /* B-part, larger col index */
    for (j=jB; j<countB; j++){
      if (!valOnly){
        row[jj] = irow + shift; col[jj] = garray[bjj[j]] + shift;
      }
      val[jj++] = *bv++;
    }
    irow++;
  }

  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatDestroy_MUMPS"
PetscErrorCode MatDestroy_MUMPS(Mat A)
{
  Mat_MUMPS      *lu=(Mat_MUMPS*)A->spptr;
  PetscErrorCode ierr;
  PetscMPIInt    size=lu->size;
  PetscErrorCode (*specialdestroy)(Mat);

  PetscFunctionBegin;
  if (lu->CleanUpMUMPS) {
    /* Terminate instance, deallocate memories */
    if (size > 1){
      ierr = PetscFree(lu->id.sol_loc);CHKERRQ(ierr);
      ierr = VecScatterDestroy(lu->scat_rhs);CHKERRQ(ierr);
      ierr = VecDestroy(lu->b_seq);CHKERRQ(ierr);
      if (lu->nSolve && lu->scat_sol){ierr = VecScatterDestroy(lu->scat_sol);CHKERRQ(ierr);}
      if (lu->nSolve && lu->x_seq){ierr = VecDestroy(lu->x_seq);CHKERRQ(ierr);}
      ierr = PetscFree(lu->val);CHKERRQ(ierr);
    }
    lu->id.job=JOB_END;
#if defined(PETSC_USE_COMPLEX)
    zmumps_c(&lu->id);
#else
    dmumps_c(&lu->id);
#endif
    ierr = PetscFree(lu->irn);CHKERRQ(ierr);
    ierr = PetscFree(lu->jcn);CHKERRQ(ierr);
    ierr = MPI_Comm_free(&(lu->comm_mumps));CHKERRQ(ierr);
  }
  ierr = (*A->ops->destroy)(A);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatSolve_MUMPS"
PetscErrorCode MatSolve_MUMPS(Mat A,Vec b,Vec x)
{
  Mat_MUMPS      *lu=(Mat_MUMPS*)A->spptr;
  PetscScalar    *array;
  Vec            x_seq;
  IS             is_iden,is_petsc;
  PetscErrorCode ierr;
  PetscInt       i;

  PetscFunctionBegin;
  lu->id.nrhs = 1;
  x_seq = lu->b_seq;
  if (lu->size > 1){
    /* MUMPS only supports centralized rhs. Scatter b into a seqential rhs vector */
    ierr = VecScatterBegin(lu->scat_rhs,b,x_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(lu->scat_rhs,b,x_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
    if (!lu->myid) {ierr = VecGetArray(x_seq,&array);CHKERRQ(ierr);}
  } else {  /* size == 1 */
    ierr = VecCopy(b,x);CHKERRQ(ierr);
    ierr = VecGetArray(x,&array);CHKERRQ(ierr);
  }
  if (!lu->myid) { /* define rhs on the host */
#if defined(PETSC_USE_COMPLEX)
    lu->id.rhs = (mumps_double_complex*)array;
#else
    lu->id.rhs = array;
#endif
  }
  if (lu->size == 1){
    ierr = VecRestoreArray(x,&array);CHKERRQ(ierr);
  } else if (!lu->myid){
    ierr = VecRestoreArray(x_seq,&array);CHKERRQ(ierr);
  }

  if (lu->size > 1){
    /* distributed solution */
    lu->id.ICNTL(21) = 1;
    if (!lu->nSolve){
      /* Create x_seq=sol_loc for repeated use */
      PetscInt    lsol_loc;
      PetscScalar *sol_loc;
      lsol_loc = lu->id.INFO(23); /* length of sol_loc */
      ierr = PetscMalloc((1+lsol_loc)*(sizeof(PetscScalar)+sizeof(PetscInt)),&sol_loc);CHKERRQ(ierr);
      lu->id.isol_loc = (PetscInt *)(sol_loc + lsol_loc);
      lu->id.lsol_loc = lsol_loc;
#if defined(PETSC_USE_COMPLEX)
      lu->id.sol_loc  = (ZMUMPS_DOUBLE *)sol_loc;
#else
      lu->id.sol_loc  = (DMUMPS_DOUBLE *)sol_loc;
#endif
      ierr = VecCreateSeqWithArray(PETSC_COMM_SELF,lsol_loc,sol_loc,&lu->x_seq);CHKERRQ(ierr);
    }
  }

  /* solve phase */
  /*-------------*/
  lu->id.job = 3;
#if defined(PETSC_USE_COMPLEX)
  zmumps_c(&lu->id);
#else
  dmumps_c(&lu->id);
#endif
  if (lu->id.INFOG(1) < 0) {
    SETERRQ1(PETSC_ERR_LIB,"Error reported by MUMPS in solve phase: INFOG(1)=%d\n",lu->id.INFOG(1));
  }

  if (lu->size > 1) { /* convert mumps distributed solution to petsc mpi x */
    if (!lu->nSolve){ /* create scatter scat_sol */
      ierr = ISCreateStride(PETSC_COMM_SELF,lu->id.lsol_loc,0,1,&is_iden);CHKERRQ(ierr); /* from */
      for (i=0; i<lu->id.lsol_loc; i++){
        lu->id.isol_loc[i] -= 1; /* change Fortran style to C style */
      }
      ierr = ISCreateGeneral(PETSC_COMM_SELF,lu->id.lsol_loc,lu->id.isol_loc,&is_petsc);CHKERRQ(ierr);  /* to */
      ierr = VecScatterCreate(lu->x_seq,is_iden,x,is_petsc,&lu->scat_sol);CHKERRQ(ierr);
      ierr = ISDestroy(is_iden);CHKERRQ(ierr);
      ierr = ISDestroy(is_petsc);CHKERRQ(ierr);
    }
    ierr = VecScatterBegin(lu->scat_sol,lu->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
    ierr = VecScatterEnd(lu->scat_sol,lu->x_seq,x,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
  }
  lu->nSolve++;
  PetscFunctionReturn(0);
}

/*
  input:
   F:        numeric factor
  output:
   nneg:     total number of negative pivots
   nzero:    0
   npos:     (global dimension of F) - nneg
*/

#undef __FUNCT__
#define __FUNCT__ "MatGetInertia_SBAIJMUMPS"
PetscErrorCode MatGetInertia_SBAIJMUMPS(Mat F,int *nneg,int *nzero,int *npos)
{
  Mat_MUMPS      *lu =(Mat_MUMPS*)F->spptr;
  PetscErrorCode ierr;
  PetscMPIInt    size;

  PetscFunctionBegin;
  ierr = MPI_Comm_size(((PetscObject)F)->comm,&size);CHKERRQ(ierr);
  /* 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 */
  if (size > 1 && lu->id.ICNTL(13) != 1){
    SETERRQ1(PETSC_ERR_ARG_WRONG,"ICNTL(13)=%d. -mat_mumps_icntl_13 must be set as 1 for correct global matrix inertia\n",lu->id.INFOG(13));
  }
  if (nneg){
    if (!lu->myid){
      *nneg = lu->id.INFOG(12);
    }
    ierr = MPI_Bcast(nneg,1,MPI_INT,0,lu->comm_mumps);CHKERRQ(ierr);
  }
  if (nzero) *nzero = 0;
  if (npos)  *npos  = F->rmap.N - (*nneg);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatFactorNumeric_MUMPS"
PetscErrorCode MatFactorNumeric_MUMPS(Mat A,MatFactorInfo *info,Mat *F)
{
  Mat_MUMPS      *lu =(Mat_MUMPS*)(*F)->spptr;
  Mat_MUMPS      *lua=(Mat_MUMPS*)(A)->spptr;
  PetscErrorCode ierr;
  PetscInt       rnz,nnz,nz=0,i,M=A->rmap.N,*ai,*aj,icntl;
  PetscTruth     valOnly,flg;
  Mat            F_diag;
  IS             is_iden;
  Vec            b;

  PetscFunctionBegin;
  if (lu->matstruc == DIFFERENT_NONZERO_PATTERN){
    (*F)->ops->solve    = MatSolve_MUMPS;

    /* Initialize a MUMPS instance */
    ierr = MPI_Comm_rank(((PetscObject)A)->comm, &lu->myid);
    ierr = MPI_Comm_size(((PetscObject)A)->comm,&lu->size);CHKERRQ(ierr);
    lua->myid = lu->myid; lua->size = lu->size;
    lu->id.job = JOB_INIT;
    ierr = MPI_Comm_dup(((PetscObject)A)->comm,&(lu->comm_mumps));CHKERRQ(ierr);
    lu->id.comm_fortran = MPI_Comm_c2f(lu->comm_mumps);

    /* Set mumps options */
    ierr = PetscOptionsBegin(((PetscObject)A)->comm,((PetscObject)A)->prefix,"MUMPS Options","Mat");CHKERRQ(ierr);
    lu->id.par=1;  /* host participates factorizaton and solve */
    lu->id.sym=lu->sym;
    if (lu->sym == 2){
      ierr = PetscOptionsInt("-mat_mumps_sym","SYM: (1,2)","None",lu->id.sym,&icntl,&flg);CHKERRQ(ierr);
      if (flg && icntl == 1) lu->id.sym=icntl;  /* matrix is spd */
    }
#if defined(PETSC_USE_COMPLEX)
    zmumps_c(&lu->id);
#else
    dmumps_c(&lu->id);
#endif

    if (lu->size == 1){
      lu->id.ICNTL(18) = 0;   /* centralized assembled matrix input */
    } else {
      lu->id.ICNTL(18) = 3;   /* distributed assembled matrix input */
    }

    icntl=-1;
    lu->id.ICNTL(4) = 0;  /* level of printing; overwrite mumps default ICNTL(4)=2 */
    ierr = PetscOptionsInt("-mat_mumps_icntl_4","ICNTL(4): level of printing (0 to 4)","None",lu->id.ICNTL(4),&icntl,&flg);CHKERRQ(ierr);
    if ((flg && icntl > 0) || PetscLogPrintInfo) {
      lu->id.ICNTL(4)=icntl; /* and use mumps default icntl(i), i=1,2,3 */
    } else { /* no output */
      lu->id.ICNTL(1) = 0;  /* error message, default= 6 */
      lu->id.ICNTL(2) = -1; /* output stream for diagnostic printing, statistics, and warning. default=0 */
      lu->id.ICNTL(3) = -1; /* output stream for global information, default=6 */
    }
    ierr = PetscOptionsInt("-mat_mumps_icntl_6","ICNTL(6): matrix prescaling (0 to 7)","None",lu->id.ICNTL(6),&lu->id.ICNTL(6),PETSC_NULL);CHKERRQ(ierr);
    icntl=-1;
    ierr = PetscOptionsInt("-mat_mumps_icntl_7","ICNTL(7): matrix ordering (0 to 7)","None",lu->id.ICNTL(7),&icntl,&flg);CHKERRQ(ierr);
    if (flg) {
      if (icntl== 1){
        SETERRQ(PETSC_ERR_SUP,"pivot order be set by the user in PERM_IN -- not supported by the PETSc/MUMPS interface\n");
      } else {
        lu->id.ICNTL(7) = icntl;
      }
    }
    ierr = PetscOptionsInt("-mat_mumps_icntl_9","ICNTL(9): A or A^T x=b to be solved. 1: A; otherwise: A^T","None",lu->id.ICNTL(9),&lu->id.ICNTL(9),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_10","ICNTL(10): max num of refinements","None",lu->id.ICNTL(10),&lu->id.ICNTL(10),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_11","ICNTL(11): error analysis, a positive value returns statistics (by -ksp_view)","None",lu->id.ICNTL(11),&lu->id.ICNTL(11),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_12","ICNTL(12): efficiency control","None",lu->id.ICNTL(12),&lu->id.ICNTL(12),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_13","ICNTL(13): efficiency control","None",lu->id.ICNTL(13),&lu->id.ICNTL(13),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_14","ICNTL(14): percentage of estimated workspace increase","None",lu->id.ICNTL(14),&lu->id.ICNTL(14),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsInt("-mat_mumps_icntl_15","ICNTL(15): efficiency control","None",lu->id.ICNTL(15),&lu->id.ICNTL(15),PETSC_NULL);CHKERRQ(ierr);

    ierr = PetscOptionsReal("-mat_mumps_cntl_1","CNTL(1): relative pivoting threshold","None",lu->id.CNTL(1),&lu->id.CNTL(1),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsReal("-mat_mumps_cntl_2","CNTL(2): stopping criterion of refinement","None",lu->id.CNTL(2),&lu->id.CNTL(2),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsReal("-mat_mumps_cntl_3","CNTL(3): absolute pivoting threshold","None",lu->id.CNTL(3),&lu->id.CNTL(3),PETSC_NULL);CHKERRQ(ierr);
    ierr = PetscOptionsReal("-mat_mumps_cntl_4","CNTL(4): value for static pivoting","None",lu->id.CNTL(4),&lu->id.CNTL(4),PETSC_NULL);CHKERRQ(ierr);
    PetscOptionsEnd();
  }

  /* define matrix A */
  switch (lu->id.ICNTL(18)){
  case 0:  /* centralized assembled matrix input (size=1) */
    if (!lu->myid) {
      if (lua->isAIJ){
        Mat_SeqAIJ   *aa = (Mat_SeqAIJ*)A->data;
        nz               = aa->nz;
        ai = aa->i; aj = aa->j; lu->val = aa->a;
      } else {
        Mat_SeqSBAIJ *aa = (Mat_SeqSBAIJ*)A->data;
        nz                  =  aa->nz;
        ai = aa->i; aj = aa->j; lu->val = aa->a;
      }
      if (lu->matstruc == DIFFERENT_NONZERO_PATTERN){ /* first numeric factorization, get irn and jcn */
        ierr = PetscMalloc(nz*sizeof(PetscInt),&lu->irn);CHKERRQ(ierr);
        ierr = PetscMalloc(nz*sizeof(PetscInt),&lu->jcn);CHKERRQ(ierr);
        nz = 0;
        for (i=0; i<M; i++){
          rnz = ai[i+1] - ai[i];
          while (rnz--) {  /* Fortran row/col index! */
            lu->irn[nz] = i+1; lu->jcn[nz] = (*aj)+1; aj++; nz++;
          }
        }
      }
    }
    break;
  case 3:  /* distributed assembled matrix input (size>1) */
    if (lu->matstruc == DIFFERENT_NONZERO_PATTERN){
      valOnly = PETSC_FALSE;
    } else {
      valOnly = PETSC_TRUE; /* only update mat values, not row and col index */
    }
    ierr = MatConvertToTriples(A,1,valOnly, &nnz, &lu->irn, &lu->jcn, &lu->val);CHKERRQ(ierr);
    break;
  default: SETERRQ(PETSC_ERR_SUP,"Matrix input format is not supported by MUMPS.");
  }

  /* analysis phase */
  /*----------------*/
  if (lu->matstruc == DIFFERENT_NONZERO_PATTERN){
    lu->id.job = 1;

    lu->id.n = M;
    switch (lu->id.ICNTL(18)){
    case 0:  /* centralized assembled matrix input */
      if (!lu->myid) {
        lu->id.nz =nz; lu->id.irn=lu->irn; lu->id.jcn=lu->jcn;
        if (lu->id.ICNTL(6)>1){
#if defined(PETSC_USE_COMPLEX)
          lu->id.a = (mumps_double_complex*)lu->val;
#else
          lu->id.a = lu->val;
#endif
        }
      }
      break;
    case 3:  /* distributed assembled matrix input (size>1) */
      lu->id.nz_loc = nnz;
      lu->id.irn_loc=lu->irn; lu->id.jcn_loc=lu->jcn;
      if (lu->id.ICNTL(6)>1) {
#if defined(PETSC_USE_COMPLEX)
        lu->id.a_loc = (mumps_double_complex*)lu->val;
#else
        lu->id.a_loc = lu->val;
#endif
      }
      /* MUMPS only supports centralized rhs. Create scatter scat_rhs for repeated use in MatSolve() */
      if (!lu->myid){
        ierr = VecCreateSeq(PETSC_COMM_SELF,A->cmap.N,&lu->b_seq);CHKERRQ(ierr);
        ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap.N,0,1,&is_iden);CHKERRQ(ierr);
      } else {
        ierr = VecCreateSeq(PETSC_COMM_SELF,0,&lu->b_seq);CHKERRQ(ierr);
        ierr = ISCreateStride(PETSC_COMM_SELF,0,0,1,&is_iden);CHKERRQ(ierr);
      }
      ierr = VecCreate(((PetscObject)A)->comm,&b);CHKERRQ(ierr);
      ierr = VecSetSizes(b,A->rmap.n,PETSC_DECIDE);CHKERRQ(ierr);
      ierr = VecSetFromOptions(b);CHKERRQ(ierr);

      ierr = VecScatterCreate(b,is_iden,lu->b_seq,is_iden,&lu->scat_rhs);CHKERRQ(ierr);
      ierr = ISDestroy(is_iden);CHKERRQ(ierr);
      ierr = VecDestroy(b);CHKERRQ(ierr);
      break;
    }
#if defined(PETSC_USE_COMPLEX)
    zmumps_c(&lu->id);
#else
    dmumps_c(&lu->id);
#endif
    if (lu->id.INFOG(1) < 0) {
      SETERRQ1(PETSC_ERR_LIB,"Error reported by MUMPS in analysis phase: INFOG(1)=%d\n",lu->id.INFOG(1));
    }
  }

  /* numerical factorization phase */
  /*-------------------------------*/
  lu->id.job = 2;
  if(!lu->id.ICNTL(18)) {
    if (!lu->myid) {
#if defined(PETSC_USE_COMPLEX)
      lu->id.a = (mumps_double_complex*)lu->val;
#else
      lu->id.a = lu->val;
#endif
    }
  } else {
#if defined(PETSC_USE_COMPLEX)
    lu->id.a_loc = (mumps_double_complex*)lu->val;
#else
    lu->id.a_loc = lu->val;
#endif
  }
#if defined(PETSC_USE_COMPLEX)
  zmumps_c(&lu->id);
#else
  dmumps_c(&lu->id);
#endif
  if (lu->id.INFOG(1) < 0) {
    if (lu->id.INFO(1) == -13) {
      SETERRQ1(PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: Cannot allocate required memory %d megabytes\n",lu->id.INFO(2));
    } else {
      SETERRQ2(PETSC_ERR_LIB,"Error reported by MUMPS in numerical factorization phase: INFO(1)=%d, INFO(2)=%d\n",lu->id.INFO(1),lu->id.INFO(2));
    }
  }

  if (!lu->myid && lu->id.ICNTL(16) > 0){
    SETERRQ1(PETSC_ERR_LIB,"  lu->id.ICNTL(16):=%d\n",lu->id.INFOG(16));
  }

  if (lu->size > 1){
    if ((*F)->factor == FACTOR_LU){
      F_diag = ((Mat_MPIAIJ *)(*F)->data)->A;
    } else {
      F_diag = ((Mat_MPISBAIJ *)(*F)->data)->A;
    }
    F_diag->assembled = PETSC_TRUE;
    if (lu->nSolve){
      ierr = VecScatterDestroy(lu->scat_sol);CHKERRQ(ierr);
      ierr = PetscFree(lu->id.sol_loc);CHKERRQ(ierr);
      ierr = VecDestroy(lu->x_seq);CHKERRQ(ierr);
    }
  }
  (*F)->assembled   = PETSC_TRUE;
  lu->matstruc      = SAME_NONZERO_PATTERN;
  lu->CleanUpMUMPS  = PETSC_TRUE;
  lu->nSolve        = 0;
  PetscFunctionReturn(0);
}


/* Note the Petsc r and c permutations are ignored */
#undef __FUNCT__
#define __FUNCT__ "MatLUFactorSymbolic_AIJMUMPS"
PetscErrorCode MatLUFactorSymbolic_AIJMUMPS(Mat A,IS r,IS c,MatFactorInfo *info,Mat *F)
{
  Mat_MUMPS      *lu;
  PetscErrorCode ierr;

  PetscFunctionBegin;

  /* Create the factorization matrix */
  lu                      = (Mat_MUMPS*)(*F)->spptr;
  lu->sym                 = 0;
  lu->matstruc            = DIFFERENT_NONZERO_PATTERN;
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatGetFactor_mpiaij_mumps"
PetscErrorCode MatGetFactor_mpiaij_mumps(Mat A,Mat *F)
{
  Mat            B;
  PetscErrorCode ierr;
  Mat_MUMPS      *mumps;

  PetscFunctionBegin;
  /* Create the factorization matrix */
  ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr);
  ierr = MatSetSizes(B,A->rmap.n,A->cmap.n,A->rmap.N,A->cmap.N);CHKERRQ(ierr);
  ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr);
  ierr = MatSeqAIJSetPreallocation(B,0,PETSC_NULL);CHKERRQ(ierr);
  ierr = MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr);

  B->ops->lufactornumeric  = MatFactorNumeric_MUMPS;
  B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMUMPS;
  B->factor                = FACTOR_LU;

  ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr);
  mumps->CleanUpMUMPS              = PETSC_FALSE;
  mumps->isAIJ                     = PETSC_TRUE;
  mumps->scat_rhs                  = PETSC_NULL;
  mumps->scat_sol                  = PETSC_NULL;
  mumps->nSolve                    = 0;

  B->spptr                         = (void*)mumps;

  *F = B;
  PetscFunctionReturn(0);
}


/* Note the Petsc r permutation is ignored */
#undef __FUNCT__
#define __FUNCT__ "MatCholeskyFactorSymbolic_SBAIJMUMPS"
PetscErrorCode MatCholeskyFactorSymbolic_SBAIJMUMPS(Mat A,IS r,MatFactorInfo *info,Mat *F)
{
  Mat_MUMPS      *lu;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  lu                            = (Mat_MUMPS*)(*F)->spptr;
  lu->sym                       = 2;
  lu->matstruc                  = DIFFERENT_NONZERO_PATTERN;
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatGetFactor_mpisbaij_mumps"
PetscErrorCode MatGetFactor_mpisbaij_mumps(Mat A,Mat *F)
{
  Mat            B;
  PetscErrorCode ierr;
  Mat_MUMPS      *mumps;

  PetscFunctionBegin;
  /* Create the factorization matrix */
  ierr = MatCreate(((PetscObject)A)->comm,&B);CHKERRQ(ierr);
  ierr = MatSetSizes(B,A->rmap.n,A->cmap.n,A->rmap.N,A->cmap.N);CHKERRQ(ierr);
  ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr);
  ierr = MatSeqSBAIJSetPreallocation(B,1,0,PETSC_NULL);CHKERRQ(ierr);
  ierr = MatMPISBAIJSetPreallocation(B,1,0,PETSC_NULL,0,PETSC_NULL);CHKERRQ(ierr);

  B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SBAIJMUMPS;
  B->ops->choleskyfactornumeric  = MatFactorNumeric_MUMPS;
  B->ops->getinertia             = MatGetInertia_SBAIJMUMPS;
  B->factor                      = FACTOR_CHOLESKY;

  ierr = PetscNewLog(B,Mat_MUMPS,&mumps);CHKERRQ(ierr);
  mumps->CleanUpMUMPS              = PETSC_FALSE;
  mumps->isAIJ                     = PETSC_TRUE;
  mumps->scat_rhs                  = PETSC_NULL;
  mumps->scat_sol                  = PETSC_NULL;
  mumps->nSolve                    = 0;
  B->spptr                         = (void*)mumps;
  *F = B;
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatFactorInfo_MUMPS"
PetscErrorCode MatFactorInfo_MUMPS(Mat A,PetscViewer viewer) {
  Mat_MUMPS      *lu=(Mat_MUMPS*)A->spptr;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  /* check if matrix is mumps type */
  if (A->ops->solve != MatSolve_MUMPS) PetscFunctionReturn(0);

  ierr = PetscViewerASCIIPrintf(viewer,"MUMPS run parameters:\n");CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  SYM (matrix type):                  %d \n",lu->id.sym);CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  PAR (host participation):           %d \n",lu->id.par);CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(1) (output for error):        %d \n",lu->id.ICNTL(1));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(2) (output of diagnostic msg):%d \n",lu->id.ICNTL(2));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(3) (output for global info):  %d \n",lu->id.ICNTL(3));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(4) (level of printing):       %d \n",lu->id.ICNTL(4));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(5) (input mat struct):        %d \n",lu->id.ICNTL(5));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(6) (matrix prescaling):       %d \n",lu->id.ICNTL(6));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(7) (matrix ordering):         %d \n",lu->id.ICNTL(7));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(8) (scalling strategy):       %d \n",lu->id.ICNTL(8));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(9) (A/A^T x=b is solved):     %d \n",lu->id.ICNTL(9));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(10) (max num of refinements): %d \n",lu->id.ICNTL(10));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(11) (error analysis):         %d \n",lu->id.ICNTL(11));CHKERRQ(ierr);
  if (!lu->myid && lu->id.ICNTL(11)>0) {
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(4) (inf norm of input mat):        %g\n",lu->id.RINFOG(4));CHKERRQ(ierr);
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(5) (inf norm of solution):         %g\n",lu->id.RINFOG(5));CHKERRQ(ierr);
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(6) (inf norm of residual):         %g\n",lu->id.RINFOG(6));CHKERRQ(ierr);
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(7),RINFOG(8) (backward error est): %g, %g\n",lu->id.RINFOG(7),lu->id.RINFOG(8));CHKERRQ(ierr);
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(9) (error estimate):               %g \n",lu->id.RINFOG(9));CHKERRQ(ierr);
    ierr = PetscPrintf(PETSC_COMM_SELF,"        RINFOG(10),RINFOG(11)(condition numbers): %g, %g\n",lu->id.RINFOG(10),lu->id.RINFOG(11));CHKERRQ(ierr);

  }
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(12) (efficiency control):                         %d \n",lu->id.ICNTL(12));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(13) (efficiency control):                         %d \n",lu->id.ICNTL(13));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(14) (percentage of estimated workspace increase): %d \n",lu->id.ICNTL(14));CHKERRQ(ierr);
  /* ICNTL(15-17) not used */
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(18) (input mat struct):                           %d \n",lu->id.ICNTL(18));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(19) (Shur complement info):                       %d \n",lu->id.ICNTL(19));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(20) (rhs sparse pattern):                         %d \n",lu->id.ICNTL(20));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  ICNTL(21) (solution struct):                            %d \n",lu->id.ICNTL(21));CHKERRQ(ierr);

  ierr = PetscViewerASCIIPrintf(viewer,"  CNTL(1) (relative pivoting threshold):      %g \n",lu->id.CNTL(1));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  CNTL(2) (stopping criterion of refinement): %g \n",lu->id.CNTL(2));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  CNTL(3) (absolute pivoting threshold):      %g \n",lu->id.CNTL(3));CHKERRQ(ierr);
  ierr = PetscViewerASCIIPrintf(viewer,"  CNTL(4) (value of static pivoting):         %g \n",lu->id.CNTL(4));CHKERRQ(ierr);

  /* infomation local to each processor */
  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      RINFO(1) (local estimated flops for the elimination after analysis): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d] %g \n",lu->myid,lu->id.RINFO(1));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);
  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      RINFO(2) (local estimated flops for the assembly after factorization): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d]  %g \n",lu->myid,lu->id.RINFO(2));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);
  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      RINFO(3) (local estimated flops for the elimination after factorization): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d]  %g \n",lu->myid,lu->id.RINFO(3));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);
  /*
  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      INFO(2) (info about error or warning ): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d] %d \n",lu->myid,lu->id.INFO(2));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);
  */

  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d] %d \n",lu->myid,lu->id.INFO(15));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);

  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d] %d \n",lu->myid,lu->id.INFO(16));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);

  if (!lu->myid) {ierr = PetscPrintf(PETSC_COMM_SELF, "      INFO(23) (num of pivots eliminated on this processor after factorization): \n");CHKERRQ(ierr);}
  ierr = PetscSynchronizedPrintf(((PetscObject)A)->comm,"             [%d] %d \n",lu->myid,lu->id.INFO(23));CHKERRQ(ierr);
  ierr = PetscSynchronizedFlush(((PetscObject)A)->comm);

  if (!lu->myid){ /* information from the host */
    ierr = PetscViewerASCIIPrintf(viewer,"  RINFOG(1) (global estimated flops for the elimination after analysis): %g \n",lu->id.RINFOG(1));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  RINFOG(2) (global estimated flops for the assembly after factorization): %g \n",lu->id.RINFOG(2));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  RINFOG(3) (global estimated flops for the elimination after factorization): %g \n",lu->id.RINFOG(3));CHKERRQ(ierr);

    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(3) (estimated real workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(3));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(4) (estimated integer workspace for factors on all processors after analysis): %d \n",lu->id.INFOG(4));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(5) (estimated maximum front size in the complete tree): %d \n",lu->id.INFOG(5));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(6) (number of nodes in the complete tree): %d \n",lu->id.INFOG(6));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(7) (ordering option effectively uese after analysis): %d \n",lu->id.INFOG(7));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): %d \n",lu->id.INFOG(8));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(9) (total real/complex workspace to store the matrix factors after factorization): %d \n",lu->id.INFOG(9));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(10) (total integer space store the matrix factors after factorization): %d \n",lu->id.INFOG(10));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(11) (order of largest frontal matrix after factorization): %d \n",lu->id.INFOG(11));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(12) (number of off-diagonal pivots): %d \n",lu->id.INFOG(12));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(13) (number of delayed pivots after factorization): %d \n",lu->id.INFOG(13));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(14) (number of memory compress after factorization): %d \n",lu->id.INFOG(14));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(15) (number of steps of iterative refinement after solution): %d \n",lu->id.INFOG(15));CHKERRQ(ierr);
    ierr = 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",lu->id.INFOG(16));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): %d \n",lu->id.INFOG(17));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): %d \n",lu->id.INFOG(18));CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): %d \n",lu->id.INFOG(19));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(20) (estimated number of entries in the factors): %d \n",lu->id.INFOG(20));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): %d \n",lu->id.INFOG(21));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): %d \n",lu->id.INFOG(22));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(23) (after analysis: value of ICNTL(6) effectively used): %d \n",lu->id.INFOG(23));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(24) (after analysis: value of ICNTL(12) effectively used): %d \n",lu->id.INFOG(24));CHKERRQ(ierr);
     ierr = PetscViewerASCIIPrintf(viewer,"  INFOG(25) (after factorization: number of pivots modified by static pivoting): %d \n",lu->id.INFOG(25));CHKERRQ(ierr);
  }

  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "MatView_MUMPS"
PetscErrorCode MatView_MUMPS(Mat A,PetscViewer viewer)
{
  PetscErrorCode    ierr;
  PetscTruth        iascii;
  PetscViewerFormat format;
  Mat_MUMPS         *mumps=(Mat_MUMPS*)(A->spptr);

  PetscFunctionBegin;
    ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr);
  if (iascii) {
    ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr);
    if (format == PETSC_VIEWER_ASCII_INFO){
      ierr = MatFactorInfo_MUMPS(A,viewer);CHKERRQ(ierr);
    }
  }
  PetscFunctionReturn(0);
}


/*MC
  MATAIJMUMPS - MATAIJMUMPS = "aijmumps" - A matrix type providing direct solvers (LU) for distributed
  and sequential matrices via the external package MUMPS.

  If MUMPS is installed (see the manual for instructions
  on how to declare the existence of external packages),
  a matrix type can be constructed which invokes MUMPS solvers.
  After calling MatCreate(...,A), simply call MatSetType(A,MATAIJMUMPS), then
  optionally call MatSeqAIJSetPreallocation() or MatMPIAIJSetPreallocation() etc DO NOT
  call MatCreateSeqAIJ/MPIAIJ() directly or the preallocation information will be LOST!

  If created with a single process communicator, this matrix type inherits from MATSEQAIJ.
  Otherwise, this matrix type inherits from MATMPIAIJ.  Hence for single process communicators,
  MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
  for communicators controlling multiple processes.  It is recommended that you call both of
  the above preallocation routines for simplicity.  One can also call MatConvert() for an inplace
  conversion to or from the MATSEQAIJ or MATMPIAIJ type (depending on the communicator size)
  without data copy AFTER the matrix values are set.

  Options Database Keys:
+ -mat_type aijmumps - sets the matrix type to "aijmumps" during a call to MatSetFromOptions()
. -mat_mumps_sym <0,1,2> - 0 the matrix is unsymmetric, 1 symmetric positive definite, 2 symmetric
. -mat_mumps_icntl_4 <0,1,2,3,4> - print level
. -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide)
. -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guide)
. -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T
. -mat_mumps_icntl_10 <n> - maximum number of iterative refinements
. -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view
. -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_cntl_1 <delta> - relative pivoting threshold
. -mat_mumps_cntl_2 <tol> - stopping criterion for refinement
- -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold

  Level: beginner

.seealso: MATSBAIJMUMPS
M*/


/*MC
  MATSBAIJMUMPS - MATSBAIJMUMPS = "sbaijmumps" - A symmetric matrix type providing direct solvers (Cholesky) for
  distributed and sequential matrices via the external package MUMPS.

  If MUMPS is installed (see the manual for instructions
  on how to declare the existence of external packages),
  a matrix type can be constructed which invokes MUMPS solvers.
  After calling MatCreate(...,A), simply call MatSetType(A,MATSBAIJMUMPS), then
  optionally call MatSeqSBAIJSetPreallocation() or MatMPISBAIJSetPreallocation() DO NOT
  call MatCreateSeqSBAIJ/MPISBAIJ() directly or the preallocation information will be LOST!

  If created with a single process communicator, this matrix type inherits from MATSEQSBAIJ.
  Otherwise, this matrix type inherits from MATMPISBAIJ.  Hence for single process communicators,
  MatSeqSBAIJSetPreallocation() is supported, and similarly MatMPISBAIJSetPreallocation() is supported
  for communicators controlling multiple processes.  It is recommended that you call both of
  the above preallocation routines for simplicity.  One can also call MatConvert() for an inplace
  conversion to or from the MATSEQSBAIJ or MATMPISBAIJ type (depending on the communicator size)
  without data copy AFTER the matrix values have been set.

  Options Database Keys:
+ -mat_type sbaijmumps - sets the matrix type to "sbaijmumps" during a call to MatSetFromOptions()
. -mat_mumps_sym <0,1,2> - 0 the matrix is unsymmetric, 1 symmetric positive definite, 2 symmetric
. -mat_mumps_icntl_4 <0,...,4> - print level
. -mat_mumps_icntl_6 <0,...,7> - matrix prescaling options (see MUMPS User's Guide)
. -mat_mumps_icntl_7 <0,...,7> - matrix orderings (see MUMPS User's Guide)
. -mat_mumps_icntl_9 <1,2> - A or A^T x=b to be solved: 1 denotes A, 2 denotes A^T
. -mat_mumps_icntl_10 <n> - maximum number of iterative refinements
. -mat_mumps_icntl_11 <n> - error analysis, a positive value returns statistics during -ksp_view
. -mat_mumps_icntl_12 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_13 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_14 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_icntl_15 <n> - efficiency control (see MUMPS User's Guide)
. -mat_mumps_cntl_1 <delta> - relative pivoting threshold
. -mat_mumps_cntl_2 <tol> - stopping criterion for refinement
- -mat_mumps_cntl_3 <adelta> - absolute pivoting threshold

  Level: beginner

.seealso: MATAIJMUMPS
M*/