impls/spai/ispai.c

/*
   3/99 Modified by Stephen Barnard to support SPAI version 3.0
*/

/*
      Provides an interface to the SPAI Sparse Approximate Inverse Preconditioner
   Code written by Stephen Barnard.

      Note: there is some BAD memory bleeding below!

      This code needs work

   1) get rid of all memory bleeding
   2) fix PETSc/interface so that it gets if the matrix is symmetric from the matrix
      rather than having the sp flag for PC_SPAI
   3) fix to set the block size based on the matrix block size

*/

#include <petsc-private/pcimpl.h>        /*I "petscpc.h" I*/
#include "petscspai.h"

/*
    These are the SPAI include files
*/
EXTERN_C_BEGIN
#define MPI /* required for setting SPAI_Comm correctly in basics.h */
#include <spai.h>
#include <matrix.h>
EXTERN_C_END

extern PetscErrorCode ConvertMatToMatrix(MPI_Comm,Mat,Mat,matrix**);
extern PetscErrorCode ConvertMatrixToMat(MPI_Comm,matrix *,Mat *);
extern PetscErrorCode ConvertVectorToVec(MPI_Comm,vector *v,Vec *Pv);
extern PetscErrorCode MM_to_PETSC(char *,char *,char *);

typedef struct {

  matrix   *B;              /* matrix in SPAI format */
  matrix   *BT;             /* transpose of matrix in SPAI format */
  matrix   *M;              /* the approximate inverse in SPAI format */

  Mat      PM;              /* the approximate inverse PETSc format */

  double   epsilon;         /* tolerance */
  int      nbsteps;         /* max number of "improvement" steps per line */
  int      max;             /* max dimensions of is_I, q, etc. */
  int      maxnew;          /* max number of new entries per step */
  int      block_size;      /* constant block size */
  int      cache_size;      /* one of (1,2,3,4,5,6) indicting size of cache */
  int      verbose;         /* SPAI prints timing and statistics */

  int      sp;              /* symmetric nonzero pattern */
  MPI_Comm comm_spai;     /* communicator to be used with spai */
} PC_SPAI;

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSetUp_SPAI"
static PetscErrorCode PCSetUp_SPAI(PC pc)
{
  PC_SPAI        *ispai = (PC_SPAI*)pc->data;
  PetscErrorCode ierr;
  Mat            AT;

  PetscFunctionBegin;
  init_SPAI();

  if (ispai->sp) {
    ierr = ConvertMatToMatrix(ispai->comm_spai,pc->pmat,pc->pmat,&ispai->B);CHKERRQ(ierr);
  } else {
    /* Use the transpose to get the column nonzero structure. */
    ierr = MatTranspose(pc->pmat,MAT_INITIAL_MATRIX,&AT);CHKERRQ(ierr);
    ierr = ConvertMatToMatrix(ispai->comm_spai,pc->pmat,AT,&ispai->B);CHKERRQ(ierr);
    ierr = MatDestroy(&AT);CHKERRQ(ierr);
  }

  /* Destroy the transpose */
  /* Don't know how to do it. PETSc developers? */

  /* construct SPAI preconditioner */
  /* FILE *messages */     /* file for warning messages */
  /* double epsilon */     /* tolerance */
  /* int nbsteps */        /* max number of "improvement" steps per line */
  /* int max */            /* max dimensions of is_I, q, etc. */
  /* int maxnew */         /* max number of new entries per step */
  /* int block_size */     /* block_size == 1 specifies scalar elments
                              block_size == n specifies nxn constant-block elements
                              block_size == 0 specifies variable-block elements */
  /* int cache_size */     /* one of (1,2,3,4,5,6) indicting size of cache */
                           /* cache_size == 0 indicates no caching */
  /* int    verbose    */  /* verbose == 0 specifies that SPAI is silent
                              verbose == 1 prints timing and matrix statistics */

  ierr = bspai(ispai->B,&ispai->M,
               stdout,
               ispai->epsilon,
               ispai->nbsteps,
               ispai->max,
               ispai->maxnew,
               ispai->block_size,
               ispai->cache_size,
               ispai->verbose);CHKERRQ(ierr);

  ierr = ConvertMatrixToMat(((PetscObject)pc)->comm,ispai->M,&ispai->PM);CHKERRQ(ierr);

  /* free the SPAI matrices */
  sp_free_matrix(ispai->B);
  sp_free_matrix(ispai->M);

  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCApply_SPAI"
static PetscErrorCode PCApply_SPAI(PC pc,Vec xx,Vec y)
{
  PC_SPAI        *ispai = (PC_SPAI*)pc->data;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  /* Now using PETSc's multiply */
  ierr = MatMult(ispai->PM,xx,y);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCDestroy_SPAI"
static PetscErrorCode PCDestroy_SPAI(PC pc)
{
  PetscErrorCode ierr;
  PC_SPAI        *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ierr = MatDestroy(&ispai->PM);CHKERRQ(ierr);
  ierr = MPI_Comm_free(&(ispai->comm_spai));CHKERRQ(ierr);
  ierr = PetscFree(pc->data);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCView_SPAI"
static PetscErrorCode PCView_SPAI(PC pc,PetscViewer viewer)
{
  PC_SPAI        *ispai = (PC_SPAI*)pc->data;
  PetscErrorCode ierr;
  PetscBool      iascii;

  PetscFunctionBegin;
  ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr);
  if (iascii) {
    ierr = PetscViewerASCIIPrintf(viewer,"    SPAI preconditioner\n");CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    epsilon %G\n",   ispai->epsilon);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    nbsteps %d\n",   ispai->nbsteps);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    max %d\n",       ispai->max);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    maxnew %d\n",    ispai->maxnew);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    block_size %d\n",ispai->block_size);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    cache_size %d\n",ispai->cache_size);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    verbose %d\n",   ispai->verbose);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer,"    sp %d\n",        ispai->sp);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetEpsilon_SPAI"
PetscErrorCode  PCSPAISetEpsilon_SPAI(PC pc,double epsilon1)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->epsilon = epsilon1;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetNBSteps_SPAI"
PetscErrorCode  PCSPAISetNBSteps_SPAI(PC pc,int nbsteps1)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->nbsteps = nbsteps1;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

/* added 1/7/99 g.h. */
EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetMax_SPAI"
PetscErrorCode  PCSPAISetMax_SPAI(PC pc,int max1)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->max = max1;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetMaxNew_SPAI"
PetscErrorCode  PCSPAISetMaxNew_SPAI(PC pc,int maxnew1)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->maxnew = maxnew1;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetBlockSize_SPAI"
PetscErrorCode  PCSPAISetBlockSize_SPAI(PC pc,int block_size1)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->block_size = block_size1;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetCacheSize_SPAI"
PetscErrorCode  PCSPAISetCacheSize_SPAI(PC pc,int cache_size)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->cache_size = cache_size;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetVerbose_SPAI"
PetscErrorCode  PCSPAISetVerbose_SPAI(PC pc,int verbose)
{
  PC_SPAI    *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->verbose = verbose;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetSp_SPAI"
PetscErrorCode  PCSPAISetSp_SPAI(PC pc,int sp)
{
  PC_SPAI *ispai = (PC_SPAI*)pc->data;

  PetscFunctionBegin;
  ispai->sp = sp;
  PetscFunctionReturn(0);
}
EXTERN_C_END

/* -------------------------------------------------------------------*/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetEpsilon"
/*@
  PCSPAISetEpsilon -- Set the tolerance for the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- eps - epsilon (default .4)

  Notes:  Espilon must be between 0 and 1. It controls the
                 quality of the approximation of M to the inverse of
                 A. Higher values of epsilon lead to more work, more
                 fill, and usually better preconditioners. In many
                 cases the best choice of epsilon is the one that
                 divides the total solution time equally between the
                 preconditioner and the solver.

  Level: intermediate

.seealso: PCSPAI, PCSetType()
  @*/
PetscErrorCode  PCSPAISetEpsilon(PC pc,double epsilon1)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetEpsilon_C",(PC,double),(pc,epsilon1));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetNBSteps"
/*@
  PCSPAISetNBSteps - set maximum number of improvement steps per row in
        the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - number of steps (default 5)

  Notes:  SPAI constructs to approximation to every column of
                 the exact inverse of A in a series of improvement
                 steps. The quality of the approximation is determined
                 by epsilon. If an approximation achieving an accuracy
                 of epsilon is not obtained after ns steps, SPAI simply
                 uses the best approximation constructed so far.

  Level: intermediate

.seealso: PCSPAI, PCSetType(), PCSPAISetMaxNew()
@*/
PetscErrorCode  PCSPAISetNBSteps(PC pc,int nbsteps1)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetNBSteps_C",(PC,int),(pc,nbsteps1));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

/* added 1/7/99 g.h. */
#undef __FUNCT__
#define __FUNCT__ "PCSPAISetMax"
/*@
  PCSPAISetMax - set the size of various working buffers in
        the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - size (default is 5000)

  Level: intermediate

.seealso: PCSPAI, PCSetType()
@*/
PetscErrorCode  PCSPAISetMax(PC pc,int max1)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetMax_C",(PC,int),(pc,max1));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetMaxNew"
/*@
  PCSPAISetMaxNew - set maximum number of new nonzero candidates per step
   in SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - maximum number (default 5)

  Level: intermediate

.seealso: PCSPAI, PCSetType(), PCSPAISetNBSteps()
@*/
PetscErrorCode  PCSPAISetMaxNew(PC pc,int maxnew1)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetMaxNew_C",(PC,int),(pc,maxnew1));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetBlockSize"
/*@
  PCSPAISetBlockSize - set the block size for the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - block size (default 1)

  Notes: A block
                 size of 1 treats A as a matrix of scalar elements. A
                 block size of s > 1 treats A as a matrix of sxs
                 blocks. A block size of 0 treats A as a matrix with
                 variable sized blocks, which are determined by
                 searching for dense square diagonal blocks in A.
                 This can be very effective for finite-element
                 matrices.

                 SPAI will convert A to block form, use a block
                 version of the preconditioner algorithm, and then
                 convert the result back to scalar form.

                 In many cases the a block-size parameter other than 1
                 can lead to very significant improvement in
                 performance.


  Level: intermediate

.seealso: PCSPAI, PCSetType()
@*/
PetscErrorCode  PCSPAISetBlockSize(PC pc,int block_size1)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetBlockSize_C",(PC,int),(pc,block_size1));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetCacheSize"
/*@
  PCSPAISetCacheSize - specify cache size in the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n -  cache size {0,1,2,3,4,5} (default 5)

  Notes:    SPAI uses a hash table to cache messages and avoid
                 redundant communication. If suggest always using
                 5. This parameter is irrelevant in the serial
                 version.

  Level: intermediate

.seealso: PCSPAI, PCSetType()
@*/
PetscErrorCode  PCSPAISetCacheSize(PC pc,int cache_size)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetCacheSize_C",(PC,int),(pc,cache_size));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetVerbose"
/*@
  PCSPAISetVerbose - verbosity level for the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - level (default 1)

  Notes: print parameters, timings and matrix statistics

  Level: intermediate

.seealso: PCSPAI, PCSetType()
@*/
PetscErrorCode  PCSPAISetVerbose(PC pc,int verbose)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetVerbose_C",(PC,int),(pc,verbose));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSPAISetSp"
/*@
  PCSPAISetSp - specify a symmetric matrix sparsity pattern in the SPAI preconditioner

  Input Parameters:
+ pc - the preconditioner
- n - 0 or 1

  Notes: If A has a symmetric nonzero pattern use -sp 1 to
                 improve performance by eliminating some communication
                 in the parallel version. Even if A does not have a
                 symmetric nonzero pattern -sp 1 may well lead to good
                 results, but the code will not follow the published
                 SPAI algorithm exactly.


  Level: intermediate

.seealso: PCSPAI, PCSetType()
@*/
PetscErrorCode  PCSPAISetSp(PC pc,int sp)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscTryMethod(pc,"PCSPAISetSp_C",(PC,int),(pc,sp));CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

/**********************************************************************/

#undef __FUNCT__
#define __FUNCT__ "PCSetFromOptions_SPAI"
static PetscErrorCode PCSetFromOptions_SPAI(PC pc)
{
  PC_SPAI        *ispai = (PC_SPAI*)pc->data;
  PetscErrorCode ierr;
  int            nbsteps1,max1,maxnew1,block_size1,cache_size,verbose,sp;
  double         epsilon1;
  PetscBool      flg;

  PetscFunctionBegin;
  ierr = PetscOptionsHead("SPAI options");CHKERRQ(ierr);
    ierr = PetscOptionsReal("-pc_spai_epsilon","","PCSPAISetEpsilon",ispai->epsilon,&epsilon1,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetEpsilon(pc,epsilon1);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_nbsteps","","PCSPAISetNBSteps",ispai->nbsteps,&nbsteps1,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetNBSteps(pc,nbsteps1);CHKERRQ(ierr);
    }
    /* added 1/7/99 g.h. */
    ierr = PetscOptionsInt("-pc_spai_max","","PCSPAISetMax",ispai->max,&max1,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetMax(pc,max1);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_maxnew","","PCSPAISetMaxNew",ispai->maxnew,&maxnew1,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetMaxNew(pc,maxnew1);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_block_size","","PCSPAISetBlockSize",ispai->block_size,&block_size1,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetBlockSize(pc,block_size1);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_cache_size","","PCSPAISetCacheSize",ispai->cache_size,&cache_size,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetCacheSize(pc,cache_size);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_verbose","","PCSPAISetVerbose",ispai->verbose,&verbose,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetVerbose(pc,verbose);CHKERRQ(ierr);
    }
    ierr = PetscOptionsInt("-pc_spai_sp","","PCSPAISetSp",ispai->sp,&sp,&flg);CHKERRQ(ierr);
    if (flg) {
      ierr = PCSPAISetSp(pc,sp);CHKERRQ(ierr);
    }
  ierr = PetscOptionsTail();CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

/**********************************************************************/

/*MC
   PCSPAI - Use the Sparse Approximate Inverse method of Grote and Barnard
     as a preconditioner (SIAM J. Sci. Comput.; vol 18, nr 3)

   Options Database Keys:
+  -pc_spai_epsilon <eps> - set tolerance
.  -pc_spai_nbstep <n> - set nbsteps
.  -pc_spai_max <m> - set max
.  -pc_spai_max_new <m> - set maxnew
.  -pc_spai_block_size <n> - set block size
.  -pc_spai_cache_size <n> - set cache size
.  -pc_spai_sp <m> - set sp
-  -pc_spai_set_verbose <true,false> - verbose output

   Notes: This only works with AIJ matrices.

   Level: beginner

   Concepts: approximate inverse

.seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC,
    PCSPAISetEpsilon(), PCSPAISetMax(), PCSPAISetMaxNew(), PCSPAISetBlockSize(),
    PCSPAISetVerbose(), PCSPAISetSp()
M*/

EXTERN_C_BEGIN
#undef __FUNCT__
#define __FUNCT__ "PCCreate_SPAI"
PetscErrorCode  PCCreate_SPAI(PC pc)
{
  PC_SPAI        *ispai;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr               = PetscNewLog(pc,PC_SPAI,&ispai);CHKERRQ(ierr);
  pc->data           = ispai;

  pc->ops->destroy         = PCDestroy_SPAI;
  pc->ops->apply           = PCApply_SPAI;
  pc->ops->applyrichardson = 0;
  pc->ops->setup           = PCSetUp_SPAI;
  pc->ops->view            = PCView_SPAI;
  pc->ops->setfromoptions  = PCSetFromOptions_SPAI;

  ispai->epsilon    = .4;
  ispai->nbsteps    = 5;
  ispai->max        = 5000;
  ispai->maxnew     = 5;
  ispai->block_size = 1;
  ispai->cache_size = 5;
  ispai->verbose    = 0;

  ispai->sp         = 1;
  ierr = MPI_Comm_dup(((PetscObject)pc)->comm,&(ispai->comm_spai));CHKERRQ(ierr);

  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetEpsilon_C",
                    "PCSPAISetEpsilon_SPAI",
                     PCSPAISetEpsilon_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetNBSteps_C",
                    "PCSPAISetNBSteps_SPAI",
                     PCSPAISetNBSteps_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetMax_C",
                    "PCSPAISetMax_SPAI",
                     PCSPAISetMax_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetMaxNew_CC",
                    "PCSPAISetMaxNew_SPAI",
                     PCSPAISetMaxNew_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetBlockSize_C",
                    "PCSPAISetBlockSize_SPAI",
                     PCSPAISetBlockSize_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetCacheSize_C",
                    "PCSPAISetCacheSize_SPAI",
                     PCSPAISetCacheSize_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetVerbose_C",
                    "PCSPAISetVerbose_SPAI",
                     PCSPAISetVerbose_SPAI);CHKERRQ(ierr);
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)pc,"PCSPAISetSp_C",
                    "PCSPAISetSp_SPAI",
                     PCSPAISetSp_SPAI);CHKERRQ(ierr);

  PetscFunctionReturn(0);
}
EXTERN_C_END

/**********************************************************************/

/*
   Converts from a PETSc matrix to an SPAI matrix
*/
#undef __FUNCT__
#define __FUNCT__ "ConvertMatToMatrix"
PetscErrorCode ConvertMatToMatrix(MPI_Comm comm, Mat A,Mat AT,matrix **B)
{
  matrix                  *M;
  int                     i,j,col;
  int                     row_indx;
  int                     len,pe,local_indx,start_indx;
  int                     *mapping;
  PetscErrorCode          ierr;
  const int               *cols;
  const double            *vals;
  int                     *num_ptr,n,mnl,nnl,nz,rstart,rend;
  PetscMPIInt             size,rank;
  struct compressed_lines *rows;

  PetscFunctionBegin;
  ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr);
  ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr);
  ierr = MatGetSize(A,&n,&n);CHKERRQ(ierr);
  ierr = MatGetLocalSize(A,&mnl,&nnl);CHKERRQ(ierr);

  /*
    not sure why a barrier is required. commenting out
  ierr = MPI_Barrier(comm);CHKERRQ(ierr);
  */

  M = new_matrix((SPAI_Comm)comm);

  M->n = n;
  M->bs = 1;
  M->max_block_size = 1;

  M->mnls          = (int*)malloc(sizeof(int)*size);
  M->start_indices = (int*)malloc(sizeof(int)*size);
  M->pe            = (int*)malloc(sizeof(int)*n);
  M->block_sizes   = (int*)malloc(sizeof(int)*n);
  for (i=0; i<n; i++) M->block_sizes[i] = 1;

  ierr = MPI_Allgather(&mnl,1,MPI_INT,M->mnls,1,MPI_INT,comm);CHKERRQ(ierr);

  M->start_indices[0] = 0;
  for (i=1; i<size; i++) {
    M->start_indices[i] = M->start_indices[i-1] + M->mnls[i-1];
  }

  M->mnl = M->mnls[M->myid];
  M->my_start_index = M->start_indices[M->myid];

  for (i=0; i<size; i++) {
    start_indx = M->start_indices[i];
    for (j=0; j<M->mnls[i]; j++)
      M->pe[start_indx+j] = i;
  }

  if (AT) {
    M->lines = new_compressed_lines(M->mnls[rank],1);CHKERRQ(ierr);
  } else {
    M->lines = new_compressed_lines(M->mnls[rank],0);CHKERRQ(ierr);
  }

  rows = M->lines;

  /* Determine the mapping from global indices to pointers */
  ierr       = PetscMalloc(M->n*sizeof(int),&mapping);CHKERRQ(ierr);
  pe         = 0;
  local_indx = 0;
  for (i=0; i<M->n; i++) {
    if (local_indx >= M->mnls[pe]) {
      pe++;
      local_indx = 0;
    }
    mapping[i] = local_indx + M->start_indices[pe];
    local_indx++;
  }


  ierr = PetscMalloc(mnl*sizeof(int),&num_ptr);CHKERRQ(ierr);

  /*********************************************************/
  /************** Set up the row structure *****************/
  /*********************************************************/

  /* count number of nonzeros in every row */
  ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr);
  for (i=rstart; i<rend; i++) {
    ierr = MatGetRow(A,i,&num_ptr[i-rstart],PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
    ierr = MatRestoreRow(A,i,&num_ptr[i-rstart],PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
  }

  /* allocate buffers */
  len = 0;
  for (i=0; i<mnl; i++) {
    if (len < num_ptr[i]) len = num_ptr[i];
  }

  for (i=rstart; i<rend; i++) {
    row_indx             = i-rstart;
    len                  = num_ptr[row_indx];
    rows->ptrs[row_indx] = (int*)malloc(len*sizeof(int));
    rows->A[row_indx]    = (double*)malloc(len*sizeof(double));
  }

  /* copy the matrix */
  for (i=rstart; i<rend; i++) {
    row_indx = i - rstart;
    ierr     = MatGetRow(A,i,&nz,&cols,&vals);CHKERRQ(ierr);
    for (j=0; j<nz; j++) {
      col = cols[j];
      len = rows->len[row_indx]++;
      rows->ptrs[row_indx][len] = mapping[col];
      rows->A[row_indx][len]    = vals[j];
    }
    rows->slen[row_indx] = rows->len[row_indx];
    ierr = MatRestoreRow(A,i,&nz,&cols,&vals);CHKERRQ(ierr);
  }


  /************************************************************/
  /************** Set up the column structure *****************/
  /*********************************************************/

  if (AT) {

    /* count number of nonzeros in every column */
    for (i=rstart; i<rend; i++) {
      ierr = MatGetRow(AT,i,&num_ptr[i-rstart],PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
      ierr = MatRestoreRow(AT,i,&num_ptr[i-rstart],PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
    }

    /* allocate buffers */
    len = 0;
    for (i=0; i<mnl; i++) {
      if (len < num_ptr[i]) len = num_ptr[i];
    }

    for (i=rstart; i<rend; i++) {
      row_indx = i-rstart;
      len      = num_ptr[row_indx];
      rows->rptrs[row_indx] = (int*)malloc(len*sizeof(int));
    }

    /* copy the matrix (i.e., the structure) */
    for (i=rstart; i<rend; i++) {
      row_indx = i - rstart;
      ierr     = MatGetRow(AT,i,&nz,&cols,&vals);CHKERRQ(ierr);
      for (j=0; j<nz; j++) {
        col = cols[j];
        len = rows->rlen[row_indx]++;
        rows->rptrs[row_indx][len] = mapping[col];
      }
      ierr = MatRestoreRow(AT,i,&nz,&cols,&vals);CHKERRQ(ierr);
    }
  }

  ierr = PetscFree(num_ptr);CHKERRQ(ierr);
  ierr = PetscFree(mapping);CHKERRQ(ierr);

  order_pointers(M);
  M->maxnz = calc_maxnz(M);

  *B = M;

  PetscFunctionReturn(0);
}

/**********************************************************************/

/*
   Converts from an SPAI matrix B  to a PETSc matrix PB.
   This assumes that the the SPAI matrix B is stored in
   COMPRESSED-ROW format.
*/
#undef __FUNCT__
#define __FUNCT__ "ConvertMatrixToMat"
PetscErrorCode ConvertMatrixToMat(MPI_Comm comm,matrix *B,Mat *PB)
{
  PetscMPIInt    size,rank;
  PetscErrorCode ierr;
  int            m,n,M,N;
  int            d_nz,o_nz;
  int            *d_nnz,*o_nnz;
  int            i,k,global_row,global_col,first_diag_col,last_diag_col;
  PetscScalar    val;

  PetscFunctionBegin;
  ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr);
  ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr);

  m = n = B->mnls[rank];
  d_nz = o_nz = 0;

  /* Determine preallocation for MatCreateMPIAIJ */
  ierr = PetscMalloc(m*sizeof(PetscInt),&d_nnz);CHKERRQ(ierr);
  ierr = PetscMalloc(m*sizeof(PetscInt),&o_nnz);CHKERRQ(ierr);
  for (i=0; i<m; i++) d_nnz[i] = o_nnz[i] = 0;
  first_diag_col = B->start_indices[rank];
  last_diag_col = first_diag_col + B->mnls[rank];
  for (i=0; i<B->mnls[rank]; i++) {
    for (k=0; k<B->lines->len[i]; k++) {
      global_col = B->lines->ptrs[i][k];
      if ((global_col >= first_diag_col) && (global_col < last_diag_col)) d_nnz[i]++;
      else o_nnz[i]++;
    }
  }

  M = N = B->n;
  /* Here we only know how to create AIJ format */
  ierr = MatCreate(comm,PB);CHKERRQ(ierr);
  ierr = MatSetSizes(*PB,m,n,M,N);CHKERRQ(ierr);
  ierr = MatSetType(*PB,MATAIJ);CHKERRQ(ierr);
  ierr = MatSeqAIJSetPreallocation(*PB,d_nz,d_nnz);CHKERRQ(ierr);
  ierr = MatMPIAIJSetPreallocation(*PB,d_nz,d_nnz,o_nz,o_nnz);CHKERRQ(ierr);

  for (i=0; i<B->mnls[rank]; i++) {
    global_row = B->start_indices[rank]+i;
    for (k=0; k<B->lines->len[i]; k++) {
      global_col = B->lines->ptrs[i][k];
      val = B->lines->A[i][k];
      ierr = MatSetValues(*PB,1,&global_row,1,&global_col,&val,ADD_VALUES);CHKERRQ(ierr);
    }
  }

  ierr = PetscFree(d_nnz);CHKERRQ(ierr);
  ierr = PetscFree(o_nnz);CHKERRQ(ierr);

  ierr = MatAssemblyBegin(*PB,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
  ierr = MatAssemblyEnd(*PB,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);

  PetscFunctionReturn(0);
}

/**********************************************************************/

/*
   Converts from an SPAI vector v  to a PETSc vec Pv.
*/
#undef __FUNCT__
#define __FUNCT__ "ConvertVectorToVec"
PetscErrorCode ConvertVectorToVec(MPI_Comm comm,vector *v,Vec *Pv)
{
  PetscErrorCode ierr;
  PetscMPIInt    size,rank;
  int            m,M,i,*mnls,*start_indices,*global_indices;

  PetscFunctionBegin;
  ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr);
  ierr = MPI_Comm_rank(comm,&rank);CHKERRQ(ierr);

  m = v->mnl;
  M = v->n;


  ierr = VecCreateMPI(comm,m,M,Pv);CHKERRQ(ierr);

  ierr = PetscMalloc(size*sizeof(int),&mnls);CHKERRQ(ierr);
  ierr = MPI_Allgather(&v->mnl,1,MPI_INT,mnls,1,MPI_INT,comm);CHKERRQ(ierr);

  ierr = PetscMalloc(size*sizeof(int),&start_indices);CHKERRQ(ierr);
  start_indices[0] = 0;
  for (i=1; i<size; i++)
    start_indices[i] = start_indices[i-1] +mnls[i-1];

  ierr = PetscMalloc(v->mnl*sizeof(int),&global_indices);CHKERRQ(ierr);
  for (i=0; i<v->mnl; i++)
    global_indices[i] = start_indices[rank] + i;

  ierr = PetscFree(mnls);CHKERRQ(ierr);
  ierr = PetscFree(start_indices);CHKERRQ(ierr);

  ierr = VecSetValues(*Pv,v->mnl,global_indices,v->v,INSERT_VALUES);CHKERRQ(ierr);
  ierr = VecAssemblyBegin(*Pv);CHKERRQ(ierr);
  ierr = VecAssemblyEnd(*Pv);CHKERRQ(ierr);

  ierr = PetscFree(global_indices);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}