EXTERN_C_BEGIN
/* ----------------------------------------------------------------------------*/
/*
MatGetColoring_SL_Minpack - Uses the smallest-last (SL) coloring of minpack
*/
#undef __FUNCT__
#define __FUNCT__ "MatGetColoring_SL_Minpack"
PetscErrorCode MatGetColoring_SL_Minpack(Mat mat,MatColoringType name,ISColoring *iscoloring)
{
PetscErrorCode ierr;
PetscInt *list,*work,clique,*seq,*coloring,n;
const PetscInt *ria,*rja,*cia,*cja;
PetscInt ncolors,i;
PetscBool done;
Mat mat_seq = mat;
PetscMPIInt size;
MPI_Comm comm;
ISColoring iscoloring_seq;
PetscInt bs = 1,rstart,rend,N_loc,nc;
ISColoringValue *colors_loc;
PetscBool flg1,flg2;
PetscFunctionBegin;
/* this is ugly way to get blocksize but cannot call MatGetBlockSize() because AIJ can have bs > 1 */
ierr = PetscObjectTypeCompare((PetscObject)mat,MATSEQBAIJ,&flg1);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject)mat,MATMPIBAIJ,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
ierr = MatGetBlockSize(mat,&bs);CHKERRQ(ierr);
}
ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr);
ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr);
if (size > 1){
/* create a sequential iscoloring on all processors */
ierr = MatGetSeqNonzeroStructure(mat,&mat_seq);CHKERRQ(ierr);
}
ierr = MatGetRowIJ(mat_seq,1,PETSC_FALSE,PETSC_TRUE,&n,&ria,&rja,&done);CHKERRQ(ierr);
ierr = MatGetColumnIJ(mat_seq,1,PETSC_FALSE,PETSC_TRUE,&n,&cia,&cja,&done);CHKERRQ(ierr);
if (!done) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Ordering requires IJ");
ierr = MatFDColoringDegreeSequence_Minpack(n,cja,cia,rja,ria,&seq);CHKERRQ(ierr);
ierr = PetscMalloc2(n,PetscInt,&list,4*n,PetscInt,&work);CHKERRQ(ierr);
MINPACKslo(&n,cja,cia,rja,ria,seq,list,&clique,work,work+n,work+2*n,work+3*n);
ierr = PetscMalloc(n*sizeof(PetscInt),&coloring);CHKERRQ(ierr);
MINPACKseq(&n,cja,cia,rja,ria,list,coloring,&ncolors,work);
ierr = PetscFree2(list,work);CHKERRQ(ierr);
ierr = PetscFree(seq);CHKERRQ(ierr);
ierr = MatRestoreRowIJ(mat_seq,1,PETSC_FALSE,PETSC_TRUE,&n,&ria,&rja,&done);CHKERRQ(ierr);
ierr = MatRestoreColumnIJ(mat_seq,1,PETSC_FALSE,PETSC_TRUE,&n,&cia,&cja,&done);CHKERRQ(ierr);
/* shift coloring numbers to start at zero and shorten */
if (ncolors > IS_COLORING_MAX-1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Maximum color size exceeded");
{
ISColoringValue *s = (ISColoringValue*) coloring;
for (i=0; i<n; i++) {
s[i] = (ISColoringValue) (coloring[i]-1);
}
ierr = MatColoringPatch(mat_seq,ncolors,n,s,iscoloring);CHKERRQ(ierr);
}
if (size > 1) {
ierr = MatDestroySeqNonzeroStructure(&mat_seq);CHKERRQ(ierr);
/* convert iscoloring_seq to a parallel iscoloring */
iscoloring_seq = *iscoloring;
rstart = mat->rmap->rstart/bs;
rend = mat->rmap->rend/bs;
N_loc = rend - rstart; /* number of local nodes */
/* get local colors for each local node */
ierr = PetscMalloc((N_loc+1)*sizeof(ISColoringValue),&colors_loc);CHKERRQ(ierr);
for (i=rstart; i<rend; i++){
colors_loc[i-rstart] = iscoloring_seq->colors[i];
}
/* create a parallel iscoloring */
nc=iscoloring_seq->n;
ierr = ISColoringCreate(comm,nc,N_loc,colors_loc,iscoloring);CHKERRQ(ierr);
ierr = ISColoringDestroy(&iscoloring_seq);CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
PetscErrorCode MINPACKdsm(PetscInt *m,PetscInt *n,PetscInt *npairs,PetscInt *indrow,PetscInt *indcol,PetscInt *ngrp,PetscInt *maxgrp,
PetscInt *mingrp,PetscInt *info,PetscInt *ipntr,PetscInt *jpntr,PetscInt *iwa,PetscInt *liwa)
{
/* System generated locals */
PetscInt i__1,i__2,i__3;
/* Local variables */
PetscInt i,j,maxclq,numgrp;
/* Given the sparsity pattern of an m by n matrix A, this */
/* subroutine determines a partition of the columns of A */
/* consistent with the direct determination of A. */
/* The sparsity pattern of the matrix A is specified by */
/* the arrays indrow and indcol. On input the indices */
/* for the non-zero elements of A are */
/* indrow(k),indcol(k), k = 1,2,...,npairs. */
/* The (indrow,indcol) pairs may be specified in any order. */
/* Duplicate input pairs are permitted, but the subroutine */
/* eliminates them. */
/* The subroutine partitions the columns of A into groups */
/* such that columns in the same group do not have a */
/* non-zero in the same row position. A partition of the */
/* columns of A with this property is consistent with the */
/* direct determination of A. */
/* The subroutine statement is */
/* subroutine dsm(m,n,npairs,indrow,indcol,ngrp,maxgrp,mingrp, */
/* info,ipntr,jpntr,iwa,liwa) */
/* where */
/* m is a positive integer input variable set to the number */
/* of rows of A. */
/* n is a positive integer input variable set to the number */
/* of columns of A. */
/* npairs is a positive integer input variable set to the */
/* number of (indrow,indcol) pairs used to describe the */
/* sparsity pattern of A. */
/* indrow is an integer array of length npairs. On input indrow */
/* must contain the row indices of the non-zero elements of A. */
/* On output indrow is permuted so that the corresponding */
/* column indices are in non-decreasing order. The column */
/* indices can be recovered from the array jpntr. */
/* indcol is an integer array of length npairs. On input indcol */
/* must contain the column indices of the non-zero elements of */
/* A. On output indcol is permuted so that the corresponding */
/* row indices are in non-decreasing order. The row indices */
/* can be recovered from the array ipntr. */
/* ngrp is an integer output array of length n which specifies */
/* the partition of the columns of A. Column jcol belongs */
/* to group ngrp(jcol). */
/* maxgrp is an integer output variable which specifies the */
/* number of groups in the partition of the columns of A. */
/* mingrp is an integer output variable which specifies a lower */
/* bound for the number of groups in any consistent partition */
/* of the columns of A. */
/* info is an integer output variable set as follows. For */
/* normal termination info = 1. If m, n, or npairs is not */
/* positive or liwa is less than max(m,6*n), then info = 0. */
/* If the k-th element of indrow is not an integer between */
/* 1 and m or the k-th element of indcol is not an integer */
/* between 1 and n, then info = -k. */
/* ipntr is an integer output array of length m + 1 which */
/* specifies the locations of the column indices in indcol. */
/* The column indices for row i are */
/* indcol(k), k = ipntr(i),...,ipntr(i+1)-1. */
/* Note that ipntr(m+1)-1 is then the number of non-zero */
/* elements of the matrix A. */
/* jpntr is an integer output array of length n + 1 which */
/* specifies the locations of the row indices in indrow. */
/* The row indices for column j are */
/* indrow(k), k = jpntr(j),...,jpntr(j+1)-1. */
/* Note that jpntr(n+1)-1 is then the number of non-zero */
/* elements of the matrix A. */
/* iwa is an integer work array of length liwa. */
/* liwa is a positive integer input variable not less than */
/* max(m,6*n). */
/* Subprograms called */
/* MINPACK-supplied ... degr,ido,numsrt,seq,setr,slo,srtdat */
/* FORTRAN-supplied ... max */
/* Argonne National Laboratory. MINPACK Project. December 1984. */
/* Thomas F. Coleman, Burton S. Garbow, Jorge J. More' */
PetscFunctionBegin;
/* Parameter adjustments */
--iwa;
--jpntr;
--ipntr;
--ngrp;
--indcol;
--indrow;
*info = 0;
/* Determine a lower bound for the number of groups. */
*mingrp = 0;
i__1 = *m;
for (i = 1; i <= i__1; ++i) {
/* Computing MAX */
i__2 = *mingrp,i__3 = ipntr[i + 1] - ipntr[i];
*mingrp = PetscMax(i__2,i__3);
}
/* Determine the degree sequence for the intersection */
/* graph of the columns of A. */
MINPACKdegr(n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[*n * 5 + 1],&
iwa[*n + 1]);
/* Color the intersection graph of the columns of A */
/* with the smallest-last (SL) ordering. */
MINPACKslo(n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[*n * 5 + 1],&
iwa[(*n << 2) + 1],&maxclq,&iwa[1],&iwa[*n + 1],&iwa[(*n << 1)
+ 1],&iwa[*n * 3 + 1]);
MINPACKseq(n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[(*n << 2) + 1],
&ngrp[1],maxgrp,&iwa[*n + 1]);
*mingrp = PetscMax(*mingrp,maxclq);
/* Exit if the smallest-last ordering is optimal. */
if (*maxgrp == *mingrp) {
PetscFunctionReturn(0);
}
/* Color the intersection graph of the columns of A */
/* with the incidence-degree (ID) ordering. */
MINPACKido(m,n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[*n * 5 + 1],
&iwa[(*n << 2) + 1],&maxclq,&iwa[1],&iwa[*n + 1],&iwa[(*n <<
1) + 1],&iwa[*n * 3 + 1]);
MINPACKseq(n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[(*n << 2) + 1],
&iwa[1],&numgrp,&iwa[*n + 1]);
*mingrp = PetscMax(*mingrp,maxclq);
/* Retain the better of the two orderings so far. */
if (numgrp < *maxgrp) {
*maxgrp = numgrp;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
ngrp[j] = iwa[j];
}
/* Exit if the incidence-degree ordering is optimal. */
if (*maxgrp == *mingrp) {
PetscFunctionReturn(0);
}
}
/* Color the intersection graph of the columns of A */
/* with the largest-first (LF) ordering. */
i__1 = *n - 1;
MINPACKnumsrt(n,&i__1,&iwa[*n * 5 + 1],&c_n1,&iwa[(*n << 2) + 1],&iwa[(*n
<< 1) + 1],&iwa[*n + 1]);
MINPACKseq(n,&indrow[1],&jpntr[1],&indcol[1],&ipntr[1],&iwa[(*n << 2) + 1],
&iwa[1],&numgrp,&iwa[*n + 1]);
/* Retain the best of the three orderings and exit. */
if (numgrp < *maxgrp) {
*maxgrp = numgrp;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
ngrp[j] = iwa[j];
}
}
PetscFunctionReturn(0);
}
