static PetscErrorCode KSPSetUp_PIPEFCG(KSP ksp)
{
PetscErrorCode ierr;
KSP_PIPEFCG *pipefcg;
const PetscInt nworkstd = 5;
PetscFunctionBegin;
pipefcg = (KSP_PIPEFCG*)ksp->data;
/* Allocate "standard" work vectors (not including the basis and transformed basis vectors) */
ierr = KSPSetWorkVecs(ksp,nworkstd);CHKERRQ(ierr);
/* Allocated space for pointers to additional work vectors
note that mmax is the number of previous directions, so we add 1 for the current direction,
and an extra 1 for the prealloc (which might be empty) */
ierr = PetscMalloc4(pipefcg->mmax+1,&(pipefcg->Pvecs),pipefcg->mmax+1,&(pipefcg->pPvecs),pipefcg->mmax+1,&(pipefcg->Svecs),pipefcg->mmax+1,&(pipefcg->pSvecs));CHKERRQ(ierr);
ierr = PetscMalloc4(pipefcg->mmax+1,&(pipefcg->Qvecs),pipefcg->mmax+1,&(pipefcg->pQvecs),pipefcg->mmax+1,&(pipefcg->ZETAvecs),pipefcg->mmax+1,&(pipefcg->pZETAvecs));CHKERRQ(ierr);
ierr = PetscMalloc4(pipefcg->mmax+1,&(pipefcg->Pold),pipefcg->mmax+1,&(pipefcg->Sold),pipefcg->mmax+1,&(pipefcg->Qold),pipefcg->mmax+1,&(pipefcg->ZETAold));CHKERRQ(ierr);
ierr = PetscMalloc1(pipefcg->mmax+1,&(pipefcg->chunksizes));CHKERRQ(ierr);
ierr = PetscMalloc3(pipefcg->mmax+2,&(pipefcg->dots),pipefcg->mmax+1,&(pipefcg->etas),pipefcg->mmax+2,&(pipefcg->redux));CHKERRQ(ierr);
/* If the requested number of preallocated vectors is greater than mmax reduce nprealloc */
if(pipefcg->nprealloc > pipefcg->mmax+1){
ierr = PetscInfo2(NULL,"Requested nprealloc=%d is greater than m_max+1=%d. Resetting nprealloc = m_max+1.\n",pipefcg->nprealloc, pipefcg->mmax+1);CHKERRQ(ierr);
}
/* Preallocate additional work vectors */
ierr = KSPAllocateVectors_PIPEFCG(ksp,pipefcg->nprealloc,pipefcg->nprealloc);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ksp,(pipefcg->mmax+1)*4*sizeof(Vec*)+(pipefcg->mmax+1)*4*sizeof(Vec**)+(pipefcg->mmax+1)*4*sizeof(Vec*)+
(pipefcg->mmax+1)*sizeof(PetscInt)+(pipefcg->mmax+2)*sizeof(Vec*)+(pipefcg->mmax+2)*sizeof(PetscScalar)+(pipefcg->mmax+1)*sizeof(PetscReal));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PetscDTReconstructPoly - create matrix representing polynomial reconstruction using cell intervals and evaluation at target intervals
Not Collective
Input Arguments:
+ degree - degree of reconstruction polynomial
. nsource - number of source intervals
. sourcex - sorted coordinates of source cell boundaries (length nsource+1)
. ntarget - number of target intervals
- targetx - sorted coordinates of target cell boundaries (length ntarget+1)
Output Arguments:
. R - reconstruction matrix, utarget = sum_s R[t*nsource+s] * usource[s]
Level: advanced
.seealso: PetscDTLegendreEval()
@*/
PetscErrorCode PetscDTReconstructPoly(PetscInt degree,PetscInt nsource,const PetscReal *sourcex,PetscInt ntarget,const PetscReal *targetx,PetscReal *R)
{
PetscErrorCode ierr;
PetscInt i,j,k,*bdegrees,worksize;
PetscReal xmin,xmax,center,hscale,*sourcey,*targety,*Bsource,*Bsinv,*Btarget;
PetscScalar *tau,*work;
PetscFunctionBegin;
PetscValidRealPointer(sourcex,3);
PetscValidRealPointer(targetx,5);
PetscValidRealPointer(R,6);
if (degree >= nsource) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Reconstruction degree %D must be less than number of source intervals %D",degree,nsource);
#if defined(PETSC_USE_DEBUG)
for (i=0; i<nsource; i++) {
if (sourcex[i] >= sourcex[i+1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_CORRUPT,"Source interval %D has negative orientation (%G,%G)",i,sourcex[i],sourcex[i+1]);
}
for (i=0; i<ntarget; i++) {
if (targetx[i] >= targetx[i+1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_CORRUPT,"Target interval %D has negative orientation (%G,%G)",i,targetx[i],targetx[i+1]);
}
#endif
xmin = PetscMin(sourcex[0],targetx[0]);
xmax = PetscMax(sourcex[nsource],targetx[ntarget]);
center = (xmin + xmax)/2;
hscale = (xmax - xmin)/2;
worksize = nsource;
ierr = PetscMalloc4(degree+1,PetscInt,&bdegrees,nsource+1,PetscReal,&sourcey,nsource*(degree+1),PetscReal,&Bsource,worksize,PetscScalar,&work);CHKERRQ(ierr);
ierr = PetscMalloc4(nsource,PetscScalar,&tau,nsource*(degree+1),PetscReal,&Bsinv,ntarget+1,PetscReal,&targety,ntarget*(degree+1),PetscReal,&Btarget);CHKERRQ(ierr);
for (i=0; i<=nsource; i++) sourcey[i] = (sourcex[i]-center)/hscale;
for (i=0; i<=degree; i++) bdegrees[i] = i+1;
ierr = PetscDTLegendreIntegrate(nsource,sourcey,degree+1,bdegrees,PETSC_TRUE,Bsource);CHKERRQ(ierr);
ierr = PetscDTPseudoInverseQR(nsource,nsource,degree+1,Bsource,Bsinv,tau,nsource,work);CHKERRQ(ierr);
for (i=0; i<=ntarget; i++) targety[i] = (targetx[i]-center)/hscale;
ierr = PetscDTLegendreIntegrate(ntarget,targety,degree+1,bdegrees,PETSC_FALSE,Btarget);CHKERRQ(ierr);
for (i=0; i<ntarget; i++) {
PetscReal rowsum = 0;
for (j=0; j<nsource; j++) {
PetscReal sum = 0;
for (k=0; k<degree+1; k++) {
sum += Btarget[i*(degree+1)+k] * Bsinv[k*nsource+j];
}
R[i*nsource+j] = sum;
rowsum += sum;
}
for (j=0; j<nsource; j++) R[i*nsource+j] /= rowsum; /* normalize each row */
}
ierr = PetscFree4(bdegrees,sourcey,Bsource,work);CHKERRQ(ierr);
ierr = PetscFree4(tau,Bsinv,targety,Btarget);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
EXTERN_C_BEGIN
/*
MatOrdering_ND - Find the nested dissection ordering of a given matrix.
*/
#undef __FUNCT__
#define __FUNCT__ "MatOrdering_ND"
PetscErrorCode PETSCMAT_DLLEXPORT MatOrdering_ND(Mat mat,const MatOrderingType type,IS *row,IS *col)
{
PetscErrorCode ierr;
PetscInt i, *mask,*xls,*ls,nrow,*ia,*ja,*perm;
PetscTruth done;
PetscFunctionBegin;
ierr = MatGetRowIJ(mat,1,PETSC_TRUE,PETSC_TRUE,&nrow,&ia,&ja,&done);CHKERRQ(ierr);
if (!done) SETERRQ1(PETSC_ERR_SUP,"Cannot get rows for matrix type %s",((PetscObject)mat)->type_name);
ierr = PetscMalloc4(nrow,PetscInt,&mask,nrow,PetscInt,&perm,nrow+1,PetscInt,&xls,nrow,PetscInt,&ls);CHKERRQ(ierr);
SPARSEPACKgennd(&nrow,ia,ja,mask,perm,xls,ls);
ierr = MatRestoreRowIJ(mat,1,PETSC_TRUE,PETSC_TRUE,&nrow,&ia,&ja,&done);CHKERRQ(ierr);
/* shift because Sparsepack indices start at one */
for (i=0; i<nrow; i++) perm[i]--;
ierr = ISCreateGeneral(PETSC_COMM_SELF,nrow,perm,row);CHKERRQ(ierr);
ierr = ISCreateGeneral(PETSC_COMM_SELF,nrow,perm,col);CHKERRQ(ierr);
ierr = PetscFree4(mask,perm,xls,ls);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode MatLUFactorSymbolic_Essl(Mat B,Mat A,IS r,IS c,const MatFactorInfo *info)
{
Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
PetscErrorCode ierr;
Mat_Essl *essl;
PetscReal f = 1.0;
PetscFunctionBegin;
essl = (Mat_Essl*)(B->spptr);
/* allocate the work arrays required by ESSL */
f = info->fill;
ierr = PetscBLASIntCast(a->nz,&essl->nz);CHKERRQ(ierr);
ierr = PetscBLASIntCast((PetscInt)(a->nz*f),&essl->lna);CHKERRQ(ierr);
ierr = PetscBLASIntCast(100 + 10*A->rmap->n,&essl->naux);CHKERRQ(ierr);
/* since malloc is slow on IBM we try a single malloc */
ierr = PetscMalloc4(essl->lna,&essl->a,essl->naux,&essl->aux,essl->lna,&essl->ia,essl->lna,&essl->ja);CHKERRQ(ierr);
essl->CleanUpESSL = PETSC_TRUE;
ierr = PetscLogObjectMemory((PetscObject)B,essl->lna*(2*sizeof(int)+sizeof(PetscScalar)) + essl->naux*sizeof(PetscScalar));CHKERRQ(ierr);
B->ops->lufactornumeric = MatLUFactorNumeric_Essl;
PetscFunctionReturn(0);
}
PetscErrorCode KSPSetUp_CG(KSP ksp)
{
KSP_CG *cgP = (KSP_CG*)ksp->data;
PetscErrorCode ierr;
PetscInt maxit = ksp->max_it,nwork = 3;
PetscFunctionBegin;
/*
This implementation of CG only handles left preconditioning
so generate an error otherwise.
*/
if (ksp->pc_side == PC_RIGHT) {
SETERRQ(PETSC_ERR_SUP,"No right preconditioning for KSPCG");
} else if (ksp->pc_side == PC_SYMMETRIC) {
SETERRQ(PETSC_ERR_SUP,"No symmetric preconditioning for KSPCG");
}
/* get work vectors needed by CG */
if (cgP->singlereduction) nwork += 2;
ierr = KSPDefaultGetWork(ksp,nwork);CHKERRQ(ierr);
/*
If user requested computations of eigenvalues then allocate work
work space needed
*/
if (ksp->calc_sings) {
/* get space to store tridiagonal matrix for Lanczos */
ierr = PetscMalloc4(maxit+1,PetscScalar,&cgP->e,maxit+1,PetscScalar,&cgP->d,maxit+1,PetscReal,&cgP->ee,maxit+1,PetscReal,&cgP->dd);CHKERRQ(ierr);
ierr = PetscLogObjectMemory(ksp,2*(maxit+1)*(sizeof(PetscScalar)+sizeof(PetscReal)));CHKERRQ(ierr);
ksp->ops->computeextremesingularvalues = KSPComputeExtremeSingularValues_CG;
ksp->ops->computeeigenvalues = KSPComputeEigenvalues_CG;
}
PetscFunctionReturn(0);
}
PetscErrorCode KSPSetUp_FCG(KSP ksp)
{
PetscErrorCode ierr;
KSP_FCG *fcg = (KSP_FCG*)ksp->data;
PetscInt maxit = ksp->max_it;
const PetscInt nworkstd = 2;
PetscFunctionBegin;
/* Allocate "standard" work vectors (not including the basis and transformed basis vectors) */
ierr = KSPSetWorkVecs(ksp,nworkstd);CHKERRQ(ierr);
/* Allocated space for pointers to additional work vectors
note that mmax is the number of previous directions, so we add 1 for the current direction,
and an extra 1 for the prealloc (which might be empty) */
ierr = PetscMalloc5(fcg->mmax+1,&fcg->Pvecs,fcg->mmax+1,&fcg->Cvecs,fcg->mmax+1,&fcg->pPvecs,fcg->mmax+1,&fcg->pCvecs,fcg->mmax+2,&fcg->chunksizes);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ksp,2*(fcg->mmax+1)*sizeof(Vec*) + 2*(fcg->mmax + 1)*sizeof(Vec**) + (fcg->mmax + 2)*sizeof(PetscInt));CHKERRQ(ierr);
/* Preallocate additional work vectors */
ierr = KSPAllocateVectors_FCG(ksp,fcg->nprealloc,fcg->nprealloc);CHKERRQ(ierr);
/*
If user requested computations of eigenvalues then allocate work
work space needed
*/
if (ksp->calc_sings) {
/* get space to store tridiagonal matrix for Lanczos */
ierr = PetscMalloc4(maxit,&fcg->e,maxit,&fcg->d,maxit,&fcg->ee,maxit,&fcg->dd);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ksp,2*(maxit+1)*(sizeof(PetscScalar)+sizeof(PetscReal)));CHKERRQ(ierr);
ksp->ops->computeextremesingularvalues = KSPComputeExtremeSingularValues_CG;
ksp->ops->computeeigenvalues = KSPComputeEigenvalues_CG;
}
PetscFunctionReturn(0);
}
PetscErrorCode KSPSetUp_CG(KSP ksp)
{
KSP_CG *cgP = (KSP_CG*)ksp->data;
PetscErrorCode ierr;
PetscInt maxit = ksp->max_it,nwork = 3;
PetscFunctionBegin;
/* get work vectors needed by CG */
if (cgP->singlereduction) nwork += 2;
ierr = KSPSetWorkVecs(ksp,nwork);CHKERRQ(ierr);
/*
If user requested computations of eigenvalues then allocate work
work space needed
*/
if (ksp->calc_sings) {
/* get space to store tridiagonal matrix for Lanczos */
ierr = PetscMalloc4(maxit+1,&cgP->e,maxit+1,&cgP->d,maxit+1,&cgP->ee,maxit+1,&cgP->dd);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ksp,2*(maxit+1)*(sizeof(PetscScalar)+sizeof(PetscReal)));CHKERRQ(ierr);
ksp->ops->computeextremesingularvalues = KSPComputeExtremeSingularValues_CG;
ksp->ops->computeeigenvalues = KSPComputeEigenvalues_CG;
}
PetscFunctionReturn(0);
}
/*@C
PetscDrawViewPortsCreate - Splits a window into smaller view ports. Each processor shares all the viewports.
Collective on PetscDraw
Input Parameters:
+ draw - the drawing context
- nports - the number of ports
Output Parameter:
. ports - a PetscDrawViewPorts context (C structure)
Options Database:
. -draw_ports - display multiple fields in the same window with PetscDrawPorts instead of in seperate windows
Level: advanced
Concepts: drawing^in subset of window
.seealso: PetscDrawSplitViewPort(), PetscDrawSetViewPort(), PetscDrawViewPortsSet(), PetscDrawViewPortsDestroy()
@*/
PetscErrorCode PetscDrawViewPortsCreate(PetscDraw draw,PetscInt nports,PetscDrawViewPorts **newports)
{
PetscDrawViewPorts *ports;
PetscInt i,n;
PetscBool isnull;
PetscMPIInt rank;
PetscReal *xl,*xr,*yl,*yr,h;
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(draw,PETSC_DRAW_CLASSID,1);
if (nports < 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Number of divisions must be positive: %d", nports);
PetscValidPointer(newports,3);
ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr);
if (isnull) {*newports = NULL; PetscFunctionReturn(0);}
ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)draw),&rank);CHKERRQ(ierr);
ierr = PetscNew(&ports);CHKERRQ(ierr); *newports = ports;
ports->draw = draw;
ports->nports = nports;
ierr = PetscObjectReference((PetscObject)draw);CHKERRQ(ierr);
/* save previous drawport of window */
ierr = PetscDrawGetViewPort(draw,&ports->port_xl,&ports->port_yl,&ports->port_xr,&ports->port_yr);CHKERRQ(ierr);
n = (PetscInt)(.1 + PetscSqrtReal((PetscReal)nports));
while (n*n < nports) n++;
h = 1.0/n;
ierr = PetscMalloc4(n*n,&xl,n*n,&xr,n*n,&yl,n*n,&yr);CHKERRQ(ierr);
ports->xl = xl;
ports->xr = xr;
ports->yl = yl;
ports->yr = yr;
ierr = PetscDrawSetCoordinates(draw,0.0,0.0,1.0,1.0);CHKERRQ(ierr);
ierr = PetscDrawCollectiveBegin(draw);CHKERRQ(ierr);
for (i=0; i<n*n; i++) {
xl[i] = (i % n)*h;
xr[i] = xl[i] + h;
yl[i] = (i / n)*h;
yr[i] = yl[i] + h;
if (!rank) {
ierr = PetscDrawLine(draw,xl[i],yl[i],xl[i],yr[i],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xl[i],yr[i],xr[i],yr[i],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xr[i],yr[i],xr[i],yl[i],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xr[i],yl[i],xl[i],yl[i],PETSC_DRAW_BLACK);CHKERRQ(ierr);
}
xl[i] += .05*h;
xr[i] -= .05*h;
yl[i] += .05*h;
yr[i] -= .05*h;
}
ierr = PetscDrawCollectiveEnd(draw);CHKERRQ(ierr);
ierr = PetscDrawFlush(draw);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode NEPSolve_Interpol(NEP nep)
{
PetscErrorCode ierr;
NEP_INTERPOL *ctx = (NEP_INTERPOL*)nep->data;
Mat *A; /*T=nep->function,Tp=nep->jacobian;*/
PetscScalar *x,*fx,t;
PetscReal *cs,a,b,s;
PetscInt i,j,k,deg=ctx->deg;
PetscFunctionBegin;
ierr = PetscMalloc4(deg+1,&A,(deg+1)*(deg+1),&cs,deg+1,&x,(deg+1)*nep->nt,&fx);CHKERRQ(ierr);
ierr = RGIntervalGetEndpoints(nep->rg,&a,&b,NULL,NULL);CHKERRQ(ierr);
ierr = ChebyshevNodes(deg,a,b,x,cs);CHKERRQ(ierr);
for (j=0;j<nep->nt;j++) {
for (i=0;i<=deg;i++) {
ierr = FNEvaluateFunction(nep->f[j],x[i],&fx[i+j*(deg+1)]);CHKERRQ(ierr);
}
}
/* Polynomial coefficients */
for (k=0;k<=deg;k++) {
ierr = MatDuplicate(nep->A[0],MAT_COPY_VALUES,&A[k]);CHKERRQ(ierr);
t = 0.0;
for (i=0;i<deg+1;i++) t += fx[i]*cs[i*(deg+1)+k];
t *= 2.0/(deg+1);
if (k==0) t /= 2.0;
ierr = MatScale(A[k],t);CHKERRQ(ierr);
for (j=1;j<nep->nt;j++) {
t = 0.0;
for (i=0;i<deg+1;i++) t += fx[i+j*(deg+1)]*cs[i*(deg+1)+k];
t *= 2.0/(deg+1);
if (k==0) t /= 2.0;
ierr = MatAXPY(A[k],t,nep->A[j],SUBSET_NONZERO_PATTERN);CHKERRQ(ierr);
}
}
ierr = PEPSetOperators(ctx->pep,deg+1,A);CHKERRQ(ierr);
for (k=0;k<=deg;k++) {
ierr = MatDestroy(&A[k]);CHKERRQ(ierr);
}
ierr = PetscFree4(A,cs,x,fx);CHKERRQ(ierr);
/* Solve polynomial eigenproblem */
ierr = PEPSolve(ctx->pep);CHKERRQ(ierr);
ierr = PEPGetConverged(ctx->pep,&nep->nconv);CHKERRQ(ierr);
ierr = PEPGetIterationNumber(ctx->pep,&nep->its);CHKERRQ(ierr);
ierr = PEPGetConvergedReason(ctx->pep,(PEPConvergedReason*)&nep->reason);CHKERRQ(ierr);
s = 2.0/(b-a);
for (i=0;i<nep->nconv;i++) {
ierr = PEPGetEigenpair(ctx->pep,i,&nep->eigr[i],&nep->eigi[i],NULL,NULL);CHKERRQ(ierr);
nep->eigr[i] /= s;
nep->eigr[i] += (a+b)/2.0;
nep->eigi[i] /= s;
}
nep->state = NEP_STATE_EIGENVECTORS;
PetscFunctionReturn(0);
}
/*@C
PetscDTGaussJacobiQuadrature - create Gauss-Jacobi quadrature for a simplex
Not Collective
Input Arguments:
+ dim - The simplex dimension
. order - The number of points in one dimension
. a - left end of interval (often-1)
- b - right end of interval (often +1)
Output Argument:
. q - A PetscQuadrature object
Level: intermediate
References:
Karniadakis and Sherwin.
FIAT
.seealso: PetscDTGaussTensorQuadrature(), PetscDTGaussQuadrature()
@*/
PetscErrorCode PetscDTGaussJacobiQuadrature(PetscInt dim, PetscInt order, PetscReal a, PetscReal b, PetscQuadrature *q)
{
PetscInt npoints = dim > 1 ? dim > 2 ? order*PetscSqr(order) : PetscSqr(order) : order;
PetscReal *px, *wx, *py, *wy, *pz, *wz, *x, *w;
PetscInt i, j, k;
PetscErrorCode ierr;
PetscFunctionBegin;
if ((a != -1.0) || (b != 1.0)) SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must use default internal right now");
ierr = PetscMalloc1(npoints*dim, &x);CHKERRQ(ierr);
ierr = PetscMalloc1(npoints, &w);CHKERRQ(ierr);
switch (dim) {
case 0:
ierr = PetscFree(x);CHKERRQ(ierr);
ierr = PetscFree(w);CHKERRQ(ierr);
ierr = PetscMalloc1(1, &x);CHKERRQ(ierr);
ierr = PetscMalloc1(1, &w);CHKERRQ(ierr);
x[0] = 0.0;
w[0] = 1.0;
break;
case 1:
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 0.0, 0.0, x, w);CHKERRQ(ierr);
break;
case 2:
ierr = PetscMalloc4(order,&px,order,&wx,order,&py,order,&wy);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 0.0, 0.0, px, wx);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 1.0, 0.0, py, wy);CHKERRQ(ierr);
for (i = 0; i < order; ++i) {
for (j = 0; j < order; ++j) {
ierr = PetscDTMapSquareToTriangle_Internal(px[i], py[j], &x[(i*order+j)*2+0], &x[(i*order+j)*2+1]);CHKERRQ(ierr);
w[i*order+j] = 0.5 * wx[i] * wy[j];
}
}
ierr = PetscFree4(px,wx,py,wy);CHKERRQ(ierr);
break;
case 3:
ierr = PetscMalloc6(order,&px,order,&wx,order,&py,order,&wy,order,&pz,order,&wz);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 0.0, 0.0, px, wx);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 1.0, 0.0, py, wy);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(order, 2.0, 0.0, pz, wz);CHKERRQ(ierr);
for (i = 0; i < order; ++i) {
for (j = 0; j < order; ++j) {
for (k = 0; k < order; ++k) {
ierr = PetscDTMapCubeToTetrahedron_Internal(px[i], py[j], pz[k], &x[((i*order+j)*order+k)*3+0], &x[((i*order+j)*order+k)*3+1], &x[((i*order+j)*order+k)*3+2]);CHKERRQ(ierr);
w[(i*order+j)*order+k] = 0.125 * wx[i] * wy[j] * wz[k];
}
}
}
ierr = PetscFree6(px,wx,py,wy,pz,wz);CHKERRQ(ierr);
break;
default:
SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Cannot construct quadrature rule for dimension %d", dim);
}
ierr = PetscQuadratureCreate(PETSC_COMM_SELF, q);CHKERRQ(ierr);
ierr = PetscQuadratureSetData(*q, dim, npoints, x, w);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode KSPSetUp_AGMRES(KSP ksp)
{
PetscErrorCode ierr;
PetscInt hes;
PetscInt nloc;
KSP_AGMRES *agmres = (KSP_AGMRES*)ksp->data;
PetscInt neig = agmres->neig;
PetscInt max_k = agmres->max_k;
PetscInt N = MAXKSPSIZE;
PetscInt lwork = PetscMax(8 * N + 16, 4 * neig * (N - neig));
PetscFunctionBegin;
if (ksp->pc_side == PC_SYMMETRIC) SETERRQ(PetscObjectComm((PetscObject)ksp),PETSC_ERR_SUP,"no symmetric preconditioning for KSPAGMRES");
max_k = agmres->max_k;
N = MAXKSPSIZE;
/* Preallocate space during the call to KSPSetup_GMRES for the Krylov basis */
agmres->q_preallocate = PETSC_TRUE; /* No allocation on the fly */
/* Preallocate space to compute later the eigenvalues in GMRES */
ksp->calc_sings = PETSC_TRUE;
agmres->max_k = N; /* Set the augmented size to be allocated in KSPSetup_GMRES */
ierr = KSPSetUp_DGMRES(ksp);CHKERRQ(ierr);
agmres->max_k = max_k;
hes = (N + 1) * (N + 1);
/* Data for the Newton basis GMRES */
ierr = PetscMalloc4(max_k,PetscScalar,&agmres->Rshift,max_k,PetscScalar,&agmres->Ishift,hes,PetscScalar,&agmres->Rloc,((N+1)*4),PetscScalar,&agmres->wbufptr);CHKERRQ(ierr);
ierr = PetscMalloc7((N+1),PetscScalar,&agmres->Scale,(N+1),PetscScalar,&agmres->sgn,(N+1),PetscScalar,&agmres->tloc,(N+1),PetscScalar,&agmres->temp,(N+1),PetscScalar,&agmres->tau,lwork,PetscScalar,&agmres->work,(N+1),PetscScalar,&agmres->nrs);CHKERRQ(ierr);
ierr = PetscMemzero(agmres->Rshift, max_k*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->Ishift, max_k*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->Scale, (N+1)*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->Rloc, (N+1)*(N+1)*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->sgn, (N+1)*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->tloc, (N+1)*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->temp, (N+1)*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = PetscMemzero(agmres->wbufptr, (N+1)*4*sizeof(PetscScalar));CHKERRQ(ierr);
/* Allocate space for the vectors in the orthogonalized basis*/
ierr = VecGetLocalSize(agmres->vecs[0], &nloc);CHKERRQ(ierr);
ierr = PetscMalloc(nloc*(N+1)*sizeof(PetscScalar), &agmres->Qloc);CHKERRQ(ierr);
/* Init the ring of processors for the roddec orthogonalization */
ierr = KSPAGMRESRoddecInitNeighboor(ksp);CHKERRQ(ierr);
if (agmres->neig < 1) PetscFunctionReturn(0);
/* Allocate space for the deflation */
ierr = PetscMalloc(N*sizeof(PetscScalar), &agmres->select);CHKERRQ(ierr);
ierr = VecDuplicateVecs(VEC_V(0), N, &agmres->TmpU);CHKERRQ(ierr);
ierr = PetscMalloc2(N*N, PetscScalar, &agmres->MatEigL, N*N, PetscScalar, &agmres->MatEigR);CHKERRQ(ierr);
/* ierr = PetscMalloc6(N*N, PetscScalar, &agmres->Q, N*N, PetscScalar, &agmres->Z, N, PetscScalar, &agmres->wr, N, PetscScalar, &agmres->wi, N, PetscScalar, &agmres->beta, N, PetscScalar, &agmres->modul);CHKERRQ(ierr); */
ierr = PetscMalloc3(N*N, PetscScalar, &agmres->Q, N*N, PetscScalar, &agmres->Z, N, PetscScalar, &agmres->beta);CHKERRQ(ierr);
ierr = PetscMalloc2((N+1),PetscInt,&agmres->perm,(2*neig*N),PetscInt,&agmres->iwork);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscDTGaussJacobiQuadrature - create Gauss-Jacobi quadrature for a simplex
Not Collective
Input Arguments:
+ dim - The simplex dimension
. npoints - number of points
. a - left end of interval (often-1)
- b - right end of interval (often +1)
Output Arguments:
+ points - quadrature points
- weights - quadrature weights
Level: intermediate
References:
Karniadakis and Sherwin.
FIAT
.seealso: PetscDTGaussQuadrature()
@*/
PetscErrorCode PetscDTGaussJacobiQuadrature(PetscInt dim, PetscInt npoints, PetscReal a, PetscReal b, PetscReal *points[], PetscReal *weights[])
{
PetscReal *px, *wx, *py, *wy, *pz, *wz, *x, *w;
PetscInt i, j, k;
PetscErrorCode ierr;
PetscFunctionBegin;
if ((a != -1.0) || (b != 1.0)) SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must use default internal right now");
switch (dim) {
case 1:
ierr = PetscMalloc(npoints * sizeof(PetscReal), &x);CHKERRQ(ierr);
ierr = PetscMalloc(npoints * sizeof(PetscReal), &w);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 0.0, 0.0, x, w);CHKERRQ(ierr);
break;
case 2:
ierr = PetscMalloc(npoints*npoints*2 * sizeof(PetscReal), &x);CHKERRQ(ierr);
ierr = PetscMalloc(npoints*npoints * sizeof(PetscReal), &w);CHKERRQ(ierr);
ierr = PetscMalloc4(npoints,PetscReal,&px,npoints,PetscReal,&wx,npoints,PetscReal,&py,npoints,PetscReal,&wy);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 0.0, 0.0, px, wx);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 1.0, 0.0, py, wy);CHKERRQ(ierr);
for (i = 0; i < npoints; ++i) {
for (j = 0; j < npoints; ++j) {
ierr = PetscDTMapSquareToTriangle_Internal(px[i], py[j], &x[(i*npoints+j)*2+0], &x[(i*npoints+j)*2+1]);CHKERRQ(ierr);
w[i*npoints+j] = 0.5 * wx[i] * wy[j];
}
}
ierr = PetscFree4(px,wx,py,wy);CHKERRQ(ierr);
break;
case 3:
ierr = PetscMalloc(npoints*npoints*3 * sizeof(PetscReal), &x);CHKERRQ(ierr);
ierr = PetscMalloc(npoints*npoints * sizeof(PetscReal), &w);CHKERRQ(ierr);
ierr = PetscMalloc6(npoints,PetscReal,&px,npoints,PetscReal,&wx,npoints,PetscReal,&py,npoints,PetscReal,&wy,npoints,PetscReal,&pz,npoints,PetscReal,&wz);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 0.0, 0.0, px, wx);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 1.0, 0.0, py, wy);CHKERRQ(ierr);
ierr = PetscDTGaussJacobiQuadrature1D_Internal(npoints, 2.0, 0.0, pz, wz);CHKERRQ(ierr);
for (i = 0; i < npoints; ++i) {
for (j = 0; j < npoints; ++j) {
for (k = 0; k < npoints; ++k) {
ierr = PetscDTMapCubeToTetrahedron_Internal(px[i], py[j], pz[k], &x[((i*npoints+j)*npoints+k)*3+0], &x[((i*npoints+j)*npoints+k)*3+1], &x[((i*npoints+j)*npoints+k)*3+2]);CHKERRQ(ierr);
w[(i*npoints+j)*npoints+k] = 0.125 * wx[i] * wy[j] * wz[k];
}
}
}
ierr = PetscFree6(px,wx,py,wy,pz,wz);CHKERRQ(ierr);
break;
default:
SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Cannot construct quadrature rule for dimension %d", dim);
}
if (points) *points = x;
if (weights) *weights = w;
PetscFunctionReturn(0);
}
/*@C
PetscGatherMessageLengths2 - Computes info about messages that a MPI-node will receive,
including (from-id,length) pairs for each message. Same functionality as PetscGatherMessageLengths()
except it takes TWO ilenths and output TWO olengths.
Collective on MPI_Comm
Input Parameters:
+ comm - Communicator
. nsends - number of messages that are to be sent.
. nrecvs - number of messages being received
- ilengths1, ilengths2 - array of integers of length sizeof(comm)
a non zero ilengths[i] represent a message to i of length ilengths[i]
Output Parameters:
+ onodes - list of node-ids from which messages are expected
- olengths1, olengths2 - corresponding message lengths
Level: developer
Concepts: mpi utility
Notes:
With this info, the correct MPI_Irecv() can be posted with the correct
from-id, with a buffer with the right amount of memory required.
The calling function deallocates the memory in onodes and olengths
To determine nrecevs, one can use PetscGatherNumberOfMessages()
.seealso: PetscGatherMessageLengths() and PetscGatherNumberOfMessages()
@*/
PetscErrorCode PetscGatherMessageLengths2(MPI_Comm comm,PetscMPIInt nsends,PetscMPIInt nrecvs,const PetscMPIInt ilengths1[],const PetscMPIInt ilengths2[],PetscMPIInt **onodes,PetscMPIInt **olengths1,PetscMPIInt **olengths2)
{
PetscErrorCode ierr;
PetscMPIInt size,tag,i,j,*buf_s = NULL,*buf_r = NULL,*buf_j = NULL;
MPI_Request *s_waits = NULL,*r_waits = NULL;
MPI_Status *w_status = NULL;
PetscFunctionBegin;
ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr);
ierr = PetscCommGetNewTag(comm,&tag);CHKERRQ(ierr);
/* cannot use PetscMalloc5() because r_waits and s_waits must be contiguous for the call to MPI_Waitall() */
ierr = PetscMalloc4(nrecvs+nsends,&r_waits,2*nrecvs,&buf_r,2*nsends,&buf_s,nrecvs+nsends,&w_status);CHKERRQ(ierr);
s_waits = r_waits + nrecvs;
/* Post the Irecv to get the message length-info */
ierr = PetscMalloc1(nrecvs+1,olengths1);CHKERRQ(ierr);
ierr = PetscMalloc1(nrecvs+1,olengths2);CHKERRQ(ierr);
for (i=0; i<nrecvs; i++) {
buf_j = buf_r + (2*i);
ierr = MPI_Irecv(buf_j,2,MPI_INT,MPI_ANY_SOURCE,tag,comm,r_waits+i);CHKERRQ(ierr);
}
/* Post the Isends with the message length-info */
for (i=0,j=0; i<size; ++i) {
if (ilengths1[i]) {
buf_j = buf_s + (2*j);
buf_j[0] = *(ilengths1+i);
buf_j[1] = *(ilengths2+i);
ierr = MPI_Isend(buf_j,2,MPI_INT,i,tag,comm,s_waits+j);CHKERRQ(ierr);
j++;
}
}
if (j != nsends) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"j %d not equal to expected number of sends %d\n",j,nsends);
/* Post waits on sends and receivs */
if (nrecvs+nsends) {ierr = MPI_Waitall(nrecvs+nsends,r_waits,w_status);CHKERRQ(ierr);}
/* Pack up the received data */
ierr = PetscMalloc1(nrecvs+1,onodes);CHKERRQ(ierr);
for (i=0; i<nrecvs; ++i) {
(*onodes)[i] = w_status[i].MPI_SOURCE;
buf_j = buf_r + (2*i);
(*olengths1)[i] = buf_j[0];
(*olengths2)[i] = buf_j[1];
}
ierr = PetscFree4(r_waits,buf_r,buf_s,w_status);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode MatStashSortCompress_Private(MatStash *stash,InsertMode insertmode)
{
PetscErrorCode ierr;
PetscMatStashSpace space;
PetscInt n = stash->n,bs = stash->bs,bs2 = bs*bs,cnt,*row,*col,*perm,rowstart,i;
PetscScalar **valptr;
PetscFunctionBegin;
ierr = PetscMalloc4(n,&row,n,&col,n,&valptr,n,&perm);CHKERRQ(ierr);
for (space=stash->space_head,cnt=0; space; space=space->next) {
for (i=0; i<space->local_used; i++) {
row[cnt] = space->idx[i];
col[cnt] = space->idy[i];
valptr[cnt] = &space->val[i*bs2];
perm[cnt] = cnt; /* Will tell us where to find valptr after sorting row[] and col[] */
cnt++;
}
}
if (cnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MatStash n %D, but counted %D entries",n,cnt);
ierr = PetscSortIntWithArrayPair(n,row,col,perm);CHKERRQ(ierr);
/* Scan through the rows, sorting each one, combining duplicates, and packing send buffers */
for (rowstart=0,cnt=0,i=1; i<=n; i++) {
if (i == n || row[i] != row[rowstart]) { /* Sort the last row. */
PetscInt colstart;
ierr = PetscSortIntWithArray(i-rowstart,&col[rowstart],&perm[rowstart]);CHKERRQ(ierr);
for (colstart=rowstart; colstart<i; ) { /* Compress multiple insertions to the same location */
PetscInt j,l;
MatStashBlock *block;
ierr = PetscSegBufferGet(stash->segsendblocks,1,&block);CHKERRQ(ierr);
block->row = row[rowstart];
block->col = col[colstart];
ierr = PetscMemcpy(block->vals,valptr[perm[colstart]],bs2*sizeof(block->vals[0]));CHKERRQ(ierr);
for (j=colstart+1; j<i && col[j] == col[colstart]; j++) { /* Add any extra stashed blocks at the same (row,col) */
if (insertmode == ADD_VALUES) {
for (l=0; l<bs2; l++) block->vals[l] += valptr[perm[j]][l];
} else {
ierr = PetscMemcpy(block->vals,valptr[perm[j]],bs2*sizeof(block->vals[0]));CHKERRQ(ierr);
}
}
colstart = j;
}
rowstart = i;
}
}
ierr = PetscFree4(row,col,valptr,perm);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode SNESSetUp_QN(SNES snes)
{
SNES_QN *qn = (SNES_QN*)snes->data;
PetscErrorCode ierr;
DM dm;
PetscFunctionBegin;
if (!snes->vec_sol) {
ierr = SNESGetDM(snes,&dm);CHKERRQ(ierr);
ierr = DMCreateGlobalVector(dm,&snes->vec_sol);CHKERRQ(ierr);
}
ierr = VecDuplicateVecs(snes->vec_sol, qn->m, &qn->U);CHKERRQ(ierr);
if (qn->type != SNES_QN_BROYDEN) ierr = VecDuplicateVecs(snes->vec_sol, qn->m, &qn->V);CHKERRQ(ierr);
ierr = PetscMalloc4(qn->m,&qn->alpha,qn->m,&qn->beta,qn->m,&qn->dXtdF,qn->m,&qn->lambda);CHKERRQ(ierr);
if (qn->singlereduction) {
ierr = PetscMalloc3(qn->m*qn->m,&qn->dXdFmat,qn->m,&qn->dFtdX,qn->m,&qn->YtdX);CHKERRQ(ierr);
}
ierr = SNESSetWorkVecs(snes,4);CHKERRQ(ierr);
/* set method defaults */
if (qn->scale_type == SNES_QN_SCALE_DEFAULT) {
if (qn->type == SNES_QN_BADBROYDEN) {
qn->scale_type = SNES_QN_SCALE_NONE;
} else {
qn->scale_type = SNES_QN_SCALE_SHANNO;
}
}
if (qn->restart_type == SNES_QN_RESTART_DEFAULT) {
if (qn->type == SNES_QN_LBFGS) {
qn->restart_type = SNES_QN_RESTART_POWELL;
} else {
qn->restart_type = SNES_QN_RESTART_PERIODIC;
}
}
if (qn->scale_type == SNES_QN_SCALE_JACOBIAN) {
ierr = SNESSetUpMatrices(snes);CHKERRQ(ierr);
}
if (snes->pcside == PC_LEFT && snes->functype == SNES_FUNCTION_DEFAULT) {snes->functype = SNES_FUNCTION_UNPRECONDITIONED;}
PetscFunctionReturn(0);
}
/*@
NEPAllocateSolution - Allocate memory storage for common variables such
as eigenvalues and eigenvectors.
Collective on NEP
Input Parameters:
+ nep - eigensolver context
- extra - number of additional positions, used for methods that require a
working basis slightly larger than ncv
Developers Note:
This is PETSC_EXTERN because it may be required by user plugin NEP
implementations.
Level: developer
@*/
PetscErrorCode NEPAllocateSolution(NEP nep,PetscInt extra)
{
PetscErrorCode ierr;
PetscInt oldsize,newc,requested;
PetscLogDouble cnt;
Mat T;
Vec t;
PetscFunctionBegin;
requested = nep->ncv + extra;
/* oldsize is zero if this is the first time setup is called */
ierr = BVGetSizes(nep->V,NULL,NULL,&oldsize);CHKERRQ(ierr);
newc = PetscMax(0,requested-oldsize);
/* allocate space for eigenvalues and friends */
if (requested != oldsize) {
if (oldsize) {
ierr = PetscFree4(nep->eigr,nep->eigi,nep->errest,nep->perm);CHKERRQ(ierr);
}
ierr = PetscMalloc4(requested,&nep->eigr,requested,&nep->eigi,requested,&nep->errest,requested,&nep->perm);CHKERRQ(ierr);
cnt = newc*sizeof(PetscScalar) + newc*sizeof(PetscReal) + newc*sizeof(PetscInt);
ierr = PetscLogObjectMemory((PetscObject)nep,cnt);CHKERRQ(ierr);
}
/* allocate V */
if (!nep->V) { ierr = NEPGetBV(nep,&nep->V);CHKERRQ(ierr); }
if (!oldsize) {
if (!((PetscObject)(nep->V))->type_name) {
ierr = BVSetType(nep->V,BVSVEC);CHKERRQ(ierr);
}
if (nep->split) T = nep->A[0];
else {
ierr = NEPGetFunction(nep,&T,NULL,NULL,NULL);CHKERRQ(ierr);
}
ierr = MatGetVecs(T,&t,NULL);CHKERRQ(ierr);
ierr = BVSetSizesFromVec(nep->V,t,requested);CHKERRQ(ierr);
ierr = VecDestroy(&t);CHKERRQ(ierr);
} else {
ierr = BVResize(nep->V,requested,PETSC_FALSE);CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
PetscErrorCode KSPSetUp_CGNE(KSP ksp)
{
KSP_CG *cgP = (KSP_CG*)ksp->data;
PetscErrorCode ierr;
PetscInt maxit = ksp->max_it;
PetscFunctionBegin;
/* get work vectors needed by CGNE */
ierr = KSPSetWorkVecs(ksp,4);CHKERRQ(ierr);
/*
If user requested computations of eigenvalues then allocate work
work space needed
*/
if (ksp->calc_sings) {
/* get space to store tridiagonal matrix for Lanczos */
ierr = PetscMalloc4(maxit+1,&cgP->e,maxit+1,&cgP->d,maxit+1,&cgP->ee,maxit+1,&cgP->dd);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ksp,2*(maxit+1)*(sizeof(PetscScalar)+sizeof(PetscReal)));CHKERRQ(ierr);
}
PetscFunctionReturn(0);
}
/*
MatGetOrdering_ND - Find the nested dissection ordering of a given matrix.
*/
PETSC_INTERN PetscErrorCode MatGetOrdering_ND(Mat mat,MatOrderingType type,IS *row,IS *col)
{
PetscErrorCode ierr;
PetscInt i, *mask,*xls,*ls,nrow,*perm;
const PetscInt *ia,*ja;
PetscBool done;
PetscFunctionBegin;
ierr = MatGetRowIJ(mat,1,PETSC_TRUE,PETSC_TRUE,&nrow,&ia,&ja,&done);CHKERRQ(ierr);
if (!done) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Cannot get rows for matrix type %s",((PetscObject)mat)->type_name);
ierr = PetscMalloc4(nrow,&mask,nrow,&perm,nrow+1,&xls,nrow,&ls);CHKERRQ(ierr);
SPARSEPACKgennd(&nrow,ia,ja,mask,perm,xls,ls);
ierr = MatRestoreRowIJ(mat,1,PETSC_TRUE,PETSC_TRUE,NULL,&ia,&ja,&done);CHKERRQ(ierr);
/* shift because Sparsepack indices start at one */
for (i=0; i<nrow; i++) perm[i]--;
ierr = ISCreateGeneral(PETSC_COMM_SELF,nrow,perm,PETSC_COPY_VALUES,row);CHKERRQ(ierr);
ierr = ISCreateGeneral(PETSC_COMM_SELF,nrow,perm,PETSC_COPY_VALUES,col);CHKERRQ(ierr);
ierr = PetscFree4(mask,perm,xls,ls);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIDense(Mat A,Mat B,PetscReal fill,Mat *C)
{
PetscErrorCode ierr;
Mat_MPIAIJ *aij = (Mat_MPIAIJ*) A->data;
PetscInt nz = aij->B->cmap->n;
PetscContainer cont;
MPIAIJ_MPIDense *contents;
VecScatter ctx = aij->Mvctx;
VecScatter_MPI_General *from = (VecScatter_MPI_General*) ctx->fromdata;
VecScatter_MPI_General *to = ( VecScatter_MPI_General*) ctx->todata;
PetscInt m=A->rmap->n,n=B->cmap->n;
PetscFunctionBegin;
ierr = MatCreate(((PetscObject)B)->comm,C);CHKERRQ(ierr);
ierr = MatSetSizes(*C,m,n,A->rmap->N,B->cmap->N);CHKERRQ(ierr);
ierr = MatSetType(*C,MATMPIDENSE);CHKERRQ(ierr);
ierr = MatAssemblyBegin(*C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
ierr = MatAssemblyEnd(*C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
ierr = PetscContainerCreate(((PetscObject)A)->comm,&cont);CHKERRQ(ierr);
ierr = PetscNew(MPIAIJ_MPIDense,&contents);CHKERRQ(ierr);
ierr = PetscContainerSetPointer(cont,contents);CHKERRQ(ierr);
ierr = PetscContainerSetUserDestroy(cont,MPIAIJ_MPIDenseDestroy);CHKERRQ(ierr);
/* Create work matrix used to store off processor rows of B needed for local product */
ierr = MatCreateSeqDense(PETSC_COMM_SELF,nz,B->cmap->N,PETSC_NULL,&contents->workB);CHKERRQ(ierr);
/* Create work arrays needed */
ierr = PetscMalloc4(B->cmap->N*from->starts[from->n],PetscScalar,&contents->rvalues,
B->cmap->N*to->starts[to->n],PetscScalar,&contents->svalues,
from->n,MPI_Request,&contents->rwaits,
to->n,MPI_Request,&contents->swaits);CHKERRQ(ierr);
ierr = PetscObjectCompose((PetscObject)(*C),"workB",(PetscObject)cont);CHKERRQ(ierr);
ierr = PetscContainerDestroy(cont);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode MatIncreaseOverlap_MPISBAIJ_Once(Mat C,PetscInt is_max,IS is[])
{
Mat_MPISBAIJ *c = (Mat_MPISBAIJ*)C->data;
PetscErrorCode ierr;
PetscMPIInt size,rank,tag1,tag2,*len_s,nrqr,nrqs,*id_r1,*len_r1,flag,len;
const PetscInt *idx_i;
PetscInt idx,isz,col,*n,*data1,**data1_start,*data2,*data2_i,*data,*data_i,
Mbs,i,j,k,*odata1,*odata2,
proc_id,**odata2_ptr,*ctable=0,*btable,len_max,len_est;
PetscInt proc_end=0,*iwork,len_unused,nodata2;
PetscInt ois_max; /* max no of is[] in each of processor */
char *t_p;
MPI_Comm comm;
MPI_Request *s_waits1,*s_waits2,r_req;
MPI_Status *s_status,r_status;
PetscBT *table; /* mark indices of this processor's is[] */
PetscBT table_i;
PetscBT otable; /* mark indices of other processors' is[] */
PetscInt bs=C->rmap->bs,Bn = c->B->cmap->n,Bnbs = Bn/bs,*Bowners;
IS garray_local,garray_gl;
PetscFunctionBegin;
comm = ((PetscObject)C)->comm;
size = c->size;
rank = c->rank;
Mbs = c->Mbs;
ierr = PetscObjectGetNewTag((PetscObject)C,&tag1);CHKERRQ(ierr);
ierr = PetscObjectGetNewTag((PetscObject)C,&tag2);CHKERRQ(ierr);
/* create tables used in
step 1: table[i] - mark c->garray of proc [i]
step 3: table[i] - mark indices of is[i] when whose=MINE
table[0] - mark incideces of is[] when whose=OTHER */
len = PetscMax(is_max, size);CHKERRQ(ierr);
ierr = PetscMalloc2(len,PetscBT,&table,(Mbs/PETSC_BITS_PER_BYTE+1)*len,char,&t_p);CHKERRQ(ierr);
for (i=0; i<len; i++) {
table[i] = t_p + (Mbs/PETSC_BITS_PER_BYTE+1)*i;
}
ierr = MPI_Allreduce(&is_max,&ois_max,1,MPIU_INT,MPI_MAX,comm);CHKERRQ(ierr);
/* 1. Send this processor's is[] to other processors */
/*---------------------------------------------------*/
/* allocate spaces */
ierr = PetscMalloc(is_max*sizeof(PetscInt),&n);CHKERRQ(ierr);
len = 0;
for (i=0; i<is_max; i++) {
ierr = ISGetLocalSize(is[i],&n[i]);CHKERRQ(ierr);
len += n[i];
}
if (!len) {
is_max = 0;
} else {
len += 1 + is_max; /* max length of data1 for one processor */
}
ierr = PetscMalloc((size*len+1)*sizeof(PetscInt),&data1);CHKERRQ(ierr);
ierr = PetscMalloc(size*sizeof(PetscInt*),&data1_start);CHKERRQ(ierr);
for (i=0; i<size; i++) data1_start[i] = data1 + i*len;
ierr = PetscMalloc4(size,PetscInt,&len_s,size,PetscInt,&btable,size,PetscInt,&iwork,size+1,PetscInt,&Bowners);CHKERRQ(ierr);
/* gather c->garray from all processors */
ierr = ISCreateGeneral(comm,Bnbs,c->garray,&garray_local);CHKERRQ(ierr);
ierr = ISAllGather(garray_local, &garray_gl);CHKERRQ(ierr);
ierr = ISDestroy(garray_local);CHKERRQ(ierr);
ierr = MPI_Allgather(&Bnbs,1,MPIU_INT,Bowners+1,1,MPIU_INT,comm);CHKERRQ(ierr);
Bowners[0] = 0;
for (i=0; i<size; i++) Bowners[i+1] += Bowners[i];
if (is_max){
/* hash table ctable which maps c->row to proc_id) */
ierr = PetscMalloc(Mbs*sizeof(PetscInt),&ctable);CHKERRQ(ierr);
for (proc_id=0,j=0; proc_id<size; proc_id++) {
for (; j<C->rmap->range[proc_id+1]/bs; j++) {
ctable[j] = proc_id;
}
}
/* hash tables marking c->garray */
ierr = ISGetIndices(garray_gl,&idx_i);
for (i=0; i<size; i++){
table_i = table[i];
ierr = PetscBTMemzero(Mbs,table_i);CHKERRQ(ierr);
for (j = Bowners[i]; j<Bowners[i+1]; j++){ /* go through B cols of proc[i]*/
ierr = PetscBTSet(table_i,idx_i[j]);CHKERRQ(ierr);
}
}
ierr = ISRestoreIndices(garray_gl,&idx_i);CHKERRQ(ierr);
} /* if (is_max) */
ierr = ISDestroy(garray_gl);CHKERRQ(ierr);
/* evaluate communication - mesg to who, length, and buffer space */
for (i=0; i<size; i++) len_s[i] = 0;
/* header of data1 */
for (proc_id=0; proc_id<size; proc_id++){
iwork[proc_id] = 0;
*data1_start[proc_id] = is_max;
data1_start[proc_id]++;
for (j=0; j<is_max; j++) {
if (proc_id == rank){
*data1_start[proc_id] = n[j];
} else {
*data1_start[proc_id] = 0;
}
data1_start[proc_id]++;
}
}
for (i=0; i<is_max; i++) {
ierr = ISGetIndices(is[i],&idx_i);CHKERRQ(ierr);
for (j=0; j<n[i]; j++){
idx = idx_i[j];
*data1_start[rank] = idx; data1_start[rank]++; /* for local proccessing */
proc_end = ctable[idx];
for (proc_id=0; proc_id<=proc_end; proc_id++){ /* for others to process */
if (proc_id == rank ) continue; /* done before this loop */
if (proc_id < proc_end && !PetscBTLookup(table[proc_id],idx))
continue; /* no need for sending idx to [proc_id] */
*data1_start[proc_id] = idx; data1_start[proc_id]++;
len_s[proc_id]++;
}
}
/* update header data */
for (proc_id=0; proc_id<size; proc_id++){
if (proc_id== rank) continue;
*(data1 + proc_id*len + 1 + i) = len_s[proc_id] - iwork[proc_id];
iwork[proc_id] = len_s[proc_id] ;
}
ierr = ISRestoreIndices(is[i],&idx_i);CHKERRQ(ierr);
}
nrqs = 0; nrqr = 0;
for (i=0; i<size; i++){
data1_start[i] = data1 + i*len;
if (len_s[i]){
nrqs++;
len_s[i] += 1 + is_max; /* add no. of header msg */
}
}
for (i=0; i<is_max; i++) {
ierr = ISDestroy(is[i]);CHKERRQ(ierr);
}
ierr = PetscFree(n);CHKERRQ(ierr);
ierr = PetscFree(ctable);CHKERRQ(ierr);
/* Determine the number of messages to expect, their lengths, from from-ids */
ierr = PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&nrqr);CHKERRQ(ierr);
ierr = PetscGatherMessageLengths(comm,nrqs,nrqr,len_s,&id_r1,&len_r1);CHKERRQ(ierr);
/* Now post the sends */
ierr = PetscMalloc2(size,MPI_Request,&s_waits1,size,MPI_Request,&s_waits2);CHKERRQ(ierr);
k = 0;
for (proc_id=0; proc_id<size; proc_id++){ /* send data1 to processor [proc_id] */
if (len_s[proc_id]){
ierr = MPI_Isend(data1_start[proc_id],len_s[proc_id],MPIU_INT,proc_id,tag1,comm,s_waits1+k);CHKERRQ(ierr);
k++;
}
}
/* 2. Receive other's is[] and process. Then send back */
/*-----------------------------------------------------*/
len = 0;
for (i=0; i<nrqr; i++){
if (len_r1[i] > len)len = len_r1[i];
}
ierr = PetscFree(len_r1);CHKERRQ(ierr);
ierr = PetscFree(id_r1);CHKERRQ(ierr);
for (proc_id=0; proc_id<size; proc_id++)
len_s[proc_id] = iwork[proc_id] = 0;
ierr = PetscMalloc((len+1)*sizeof(PetscInt),&odata1);CHKERRQ(ierr);
ierr = PetscMalloc(size*sizeof(PetscInt**),&odata2_ptr);CHKERRQ(ierr);
ierr = PetscBTCreate(Mbs,otable);CHKERRQ(ierr);
len_max = ois_max*(Mbs+1); /* max space storing all is[] for each receive */
len_est = 2*len_max; /* estimated space of storing is[] for all receiving messages */
ierr = PetscMalloc((len_est+1)*sizeof(PetscInt),&odata2);CHKERRQ(ierr);
nodata2 = 0; /* nodata2+1: num of PetscMalloc(,&odata2_ptr[]) called */
odata2_ptr[nodata2] = odata2;
len_unused = len_est; /* unused space in the array odata2_ptr[nodata2]-- needs to be >= len_max */
k = 0;
while (k < nrqr){
/* Receive messages */
ierr = MPI_Iprobe(MPI_ANY_SOURCE,tag1,comm,&flag,&r_status);CHKERRQ(ierr);
if (flag){
ierr = MPI_Get_count(&r_status,MPIU_INT,&len);CHKERRQ(ierr);
proc_id = r_status.MPI_SOURCE;
ierr = MPI_Irecv(odata1,len,MPIU_INT,proc_id,r_status.MPI_TAG,comm,&r_req);CHKERRQ(ierr);
ierr = MPI_Wait(&r_req,&r_status);CHKERRQ(ierr);
/* Process messages */
/* make sure there is enough unused space in odata2 array */
if (len_unused < len_max){ /* allocate more space for odata2 */
ierr = PetscMalloc((len_est+1)*sizeof(PetscInt),&odata2);CHKERRQ(ierr);
odata2_ptr[++nodata2] = odata2;
len_unused = len_est;
}
ierr = MatIncreaseOverlap_MPISBAIJ_Local(C,odata1,OTHER,odata2,&otable);CHKERRQ(ierr);
len = 1 + odata2[0];
for (i=0; i<odata2[0]; i++){
len += odata2[1 + i];
}
/* Send messages back */
ierr = MPI_Isend(odata2,len,MPIU_INT,proc_id,tag2,comm,s_waits2+k);CHKERRQ(ierr);
k++;
odata2 += len;
len_unused -= len;
len_s[proc_id] = len; /* num of messages sending back to [proc_id] by this proc */
}
}
ierr = PetscFree(odata1);CHKERRQ(ierr);
ierr = PetscBTDestroy(otable);CHKERRQ(ierr);
/* 3. Do local work on this processor's is[] */
/*-------------------------------------------*/
/* make sure there is enough unused space in odata2(=data) array */
len_max = is_max*(Mbs+1); /* max space storing all is[] for this processor */
if (len_unused < len_max){ /* allocate more space for odata2 */
ierr = PetscMalloc((len_est+1)*sizeof(PetscInt),&odata2);CHKERRQ(ierr);
odata2_ptr[++nodata2] = odata2;
len_unused = len_est;
}
data = odata2;
ierr = MatIncreaseOverlap_MPISBAIJ_Local(C,data1_start[rank],MINE,data,table);CHKERRQ(ierr);
ierr = PetscFree(data1_start);CHKERRQ(ierr);
/* 4. Receive work done on other processors, then merge */
/*------------------------------------------------------*/
/* get max number of messages that this processor expects to recv */
ierr = MPI_Allreduce(len_s,iwork,size,MPIU_INT,MPI_MAX,comm);CHKERRQ(ierr);
ierr = PetscMalloc((iwork[rank]+1)*sizeof(PetscInt),&data2);CHKERRQ(ierr);
ierr = PetscFree4(len_s,btable,iwork,Bowners);CHKERRQ(ierr);
k = 0;
while (k < nrqs){
/* Receive messages */
ierr = MPI_Iprobe(MPI_ANY_SOURCE,tag2,comm,&flag,&r_status);
if (flag){
ierr = MPI_Get_count(&r_status,MPIU_INT,&len);CHKERRQ(ierr);
proc_id = r_status.MPI_SOURCE;
ierr = MPI_Irecv(data2,len,MPIU_INT,proc_id,r_status.MPI_TAG,comm,&r_req);CHKERRQ(ierr);
ierr = MPI_Wait(&r_req,&r_status);CHKERRQ(ierr);
if (len > 1+is_max){ /* Add data2 into data */
data2_i = data2 + 1 + is_max;
for (i=0; i<is_max; i++){
table_i = table[i];
data_i = data + 1 + is_max + Mbs*i;
isz = data[1+i];
for (j=0; j<data2[1+i]; j++){
col = data2_i[j];
if (!PetscBTLookupSet(table_i,col)) {data_i[isz++] = col;}
}
data[1+i] = isz;
if (i < is_max - 1) data2_i += data2[1+i];
}
}
k++;
}
}
ierr = PetscFree(data2);CHKERRQ(ierr);
ierr = PetscFree2(table,t_p);CHKERRQ(ierr);
/* phase 1 sends are complete */
ierr = PetscMalloc(size*sizeof(MPI_Status),&s_status);CHKERRQ(ierr);
if (nrqs) {ierr = MPI_Waitall(nrqs,s_waits1,s_status);CHKERRQ(ierr);}
ierr = PetscFree(data1);CHKERRQ(ierr);
/* phase 2 sends are complete */
if (nrqr){ierr = MPI_Waitall(nrqr,s_waits2,s_status);CHKERRQ(ierr);}
ierr = PetscFree2(s_waits1,s_waits2);CHKERRQ(ierr);
ierr = PetscFree(s_status);CHKERRQ(ierr);
/* 5. Create new is[] */
/*--------------------*/
for (i=0; i<is_max; i++) {
data_i = data + 1 + is_max + Mbs*i;
ierr = ISCreateGeneral(PETSC_COMM_SELF,data[1+i],data_i,is+i);CHKERRQ(ierr);
}
for (k=0; k<=nodata2; k++){
ierr = PetscFree(odata2_ptr[k]);CHKERRQ(ierr);
}
ierr = PetscFree(odata2_ptr);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscDrawViewPortsCreateRect - Splits a window into smaller
view ports. Each processor shares all the viewports. The number
of views in the x- and y-directions is specified.
Collective on PetscDraw
Input Parameters:
+ draw - the drawing context
. nx - the number of x divisions
- ny - the number of y divisions
Output Parameter:
. ports - a PetscDrawViewPorts context (C structure)
Level: advanced
Concepts: drawing^in subset of window
.seealso: PetscDrawSplitViewPort(), PetscDrawSetViewPort(), PetscDrawViewPortsSet(), PetscDrawViewPortsDestroy()
@*/
PetscErrorCode PetscDrawViewPortsCreateRect(PetscDraw draw,PetscInt nx,PetscInt ny,PetscDrawViewPorts **newports)
{
PetscDrawViewPorts *ports;
PetscReal *xl,*xr,*yl,*yr,hx,hy;
PetscInt i,j,k,n;
PetscBool isnull;
PetscMPIInt rank;
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(draw,PETSC_DRAW_CLASSID,1);
if ((nx < 1) || (ny < 1)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Number of divisions must be positive: %d x %d", nx, ny);
PetscValidPointer(newports,3);
ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr);
if (isnull) {*newports = NULL; PetscFunctionReturn(0);}
ierr = MPI_Comm_rank(PetscObjectComm((PetscObject)draw),&rank);CHKERRQ(ierr);
n = nx*ny;
hx = 1.0/nx;
hy = 1.0/ny;
ierr = PetscNew(&ports);CHKERRQ(ierr); *newports = ports;
ports->draw = draw;
ports->nports = n;
ierr = PetscObjectReference((PetscObject) draw);CHKERRQ(ierr);
/* save previous drawport of window */
ierr = PetscDrawGetViewPort(draw,&ports->port_xl,&ports->port_yl,&ports->port_xr,&ports->port_yr);CHKERRQ(ierr);
ierr = PetscMalloc4(n,&xl,n,&xr,n,&yl,n,&yr);CHKERRQ(ierr);
ports->xr = xr;
ports->xl = xl;
ports->yl = yl;
ports->yr = yr;
ierr = PetscDrawSetCoordinates(draw,0.0,0.0,1.0,1.0);CHKERRQ(ierr);
ierr = PetscDrawCollectiveBegin(draw);CHKERRQ(ierr);
for (i = 0; i < nx; i++) {
for (j = 0; j < ny; j++) {
k = j*nx+i;
xl[k] = i*hx;
xr[k] = xl[k] + hx;
yl[k] = j*hy;
yr[k] = yl[k] + hy;
if (!rank) {
ierr = PetscDrawLine(draw,xl[k],yl[k],xl[k],yr[k],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xl[k],yr[k],xr[k],yr[k],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xr[k],yr[k],xr[k],yl[k],PETSC_DRAW_BLACK);CHKERRQ(ierr);
ierr = PetscDrawLine(draw,xr[k],yl[k],xl[k],yl[k],PETSC_DRAW_BLACK);CHKERRQ(ierr);
}
xl[k] += .05*hx;
xr[k] -= .05*hx;
yl[k] += .05*hy;
yr[k] -= .05*hy;
}
}
ierr = PetscDrawCollectiveEnd(draw);CHKERRQ(ierr);
ierr = PetscDrawFlush(draw);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*
FormFunctionLocal - Form the local residual F from the local input X
Input Parameters:
+ dm - The mesh
. X - Local input vector
- user - The user context
Output Parameter:
. F - Local output vector
Note:
We form the residual one batch of elements at a time. This allows us to offload work onto an accelerator,
like a GPU, or vectorize on a multicore machine.
.seealso: FormJacobianLocal()
*/
PetscErrorCode FormFunctionLocal(DM dm, Vec X, Vec F, AppCtx *user)
{
const PetscInt debug = user->debug;
const PetscInt dim = user->dim;
PetscReal *coords, *v0, *J, *invJ, *detJ;
PetscScalar *elemVec, *u;
const PetscInt numCells = cEnd - cStart;
PetscInt cellDof = 0;
PetscInt maxQuad = 0;
PetscInt jacSize = 1;
PetscInt cStart, cEnd, c, field;
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = PetscLogEventBegin(user->residualEvent,0,0,0,0);CHKERRQ(ierr);
ierr = VecSet(F, 0.0);CHKERRQ(ierr);
ierr = DMDAGetHeightStratum(dm, 0, &cStart, &cEnd);CHKERRQ(ierr);
for(field = 0; field < numFields; ++field) {
PetscInt dof = 1;
for(d = 0; d < dim; ++d) {dof *= user->q[field].numBasisFuncs*user->q[field].numComponents;}
cellDof += dof;
maxQuad = PetscMax(maxQuad, user->q[field].numQuadPoints);
}
for(d = 0; d < dim; ++d) {jacSize *= maxQuad;}
ierr = PetscMalloc3(dim,PetscReal,&coords,dim,PetscReal,&v0,jacSize,PetscReal,&J);CHKERRQ(ierr);
ierr = PetscMalloc4(numCells*cellDof,PetscScalar,&u,numCells*jacSize,PetscReal,&invJ,numCells,PetscReal,&detJ,numCells*cellDof,PetscScalar,&elemVec);CHKERRQ(ierr);
for(c = cStart; c < cEnd; ++c) {
const PetscScalar *x;
PetscInt i;
ierr = DMDAComputeCellGeometry(dm, c, v0, J, &invJ[c*jacSize], &detJ[c]);CHKERRQ(ierr);
if (detJ[c] <= 0.0) SETERRQ2(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid determinant %g for element %d", detJ[c], c);
ierr = DMDAVecGetClosure(dm, PETSC_NULL, X, c, &x);CHKERRQ(ierr);
for(i = 0; i < cellDof; ++i) {
u[c*cellDof+i] = x[i];
}
}
for(field = 0; field < numFields; ++field) {
const PetscInt numQuadPoints = user->q[field].numQuadPoints;
const PetscInt numBasisFuncs = user->q[field].numBasisFuncs;
void (*f0)(PetscScalar u[], const PetscScalar gradU[], PetscScalar f0[]) = user->f0Funcs[field];
void (*f1)(PetscScalar u[], const PetscScalar gradU[], PetscScalar f1[]) = user->f1Funcs[field];
/* Conforming batches */
PetscInt blockSize = numBasisFuncs*numQuadPoints;
PetscInt numBlocks = 1;
PetscInt batchSize = numBlocks * blockSize;
PetscInt numBatches = user->numBatches;
PetscInt numChunks = numCells / (numBatches*batchSize);
ierr = IntegrateResidualBatchCPU(numChunks*numBatches*batchSize, numFields, field, u, invJ, detJ, user->q, f0, f1, elemVec, user);CHKERRQ(ierr);
/* Remainder */
PetscInt numRemainder = numCells % (numBatches * batchSize);
PetscInt offset = numCells - numRemainder;
ierr = IntegrateResidualBatchCPU(numRemainder, numFields, field, &u[offset*cellDof], &invJ[offset*dim*dim], &detJ[offset],
user->q, f0, f1, &elemVec[offset*cellDof], user);CHKERRQ(ierr);
}
for(c = cStart; c < cEnd; ++c) {
if (debug) {ierr = DMPrintCellVector(c, "Residual", cellDof, &elemVec[c*cellDof]);CHKERRQ(ierr);}
ierr = DMComplexVecSetClosure(dm, PETSC_NULL, F, c, &elemVec[c*cellDof], ADD_VALUES);CHKERRQ(ierr);
}
ierr = PetscFree4(u,invJ,detJ,elemVec);CHKERRQ(ierr);
ierr = PetscFree3(coords,v0,J);CHKERRQ(ierr);
if (user->showResidual) {
PetscInt p;
ierr = PetscPrintf(PETSC_COMM_WORLD, "Residual:\n");CHKERRQ(ierr);
for(p = 0; p < user->numProcs; ++p) {
if (p == user->rank) {
Vec f;
ierr = VecDuplicate(F, &f);CHKERRQ(ierr);
ierr = VecCopy(F, f);CHKERRQ(ierr);
ierr = VecChop(f, 1.0e-10);CHKERRQ(ierr);
ierr = VecView(f, PETSC_VIEWER_STDOUT_SELF);CHKERRQ(ierr);
ierr = VecDestroy(&f);CHKERRQ(ierr);
}
ierr = PetscBarrier((PetscObject) dm);CHKERRQ(ierr);
}
}
ierr = PetscLogEventEnd(user->residualEvent,0,0,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
int main(int argc, char **args)
{
Mat A, L;
AppCtx ctx;
PetscViewer viewer;
PetscErrorCode ierr;
ierr = PetscInitialize(&argc, &args, (char *) 0, help);CHKERRQ(ierr);
ierr = ProcessOptions(&ctx);CHKERRQ(ierr);
/* Load matrix */
ierr = PetscViewerBinaryOpen(PETSC_COMM_WORLD, ctx.matFilename, FILE_MODE_READ, &viewer);CHKERRQ(ierr);
ierr = MatCreate(PETSC_COMM_WORLD, &A);CHKERRQ(ierr);
ierr = MatLoad(A, viewer);CHKERRQ(ierr);
ierr = PetscViewerDestroy(&viewer);CHKERRQ(ierr);
/* Make graph Laplacian from matrix */
ierr = MatLaplacian(A, 1.0e-12, &L);CHKERRQ(ierr);
/* Check Laplacian */
PetscReal norm;
Vec x, y;
ierr = MatGetVecs(L, &x, NULL);CHKERRQ(ierr);
ierr = VecDuplicate(x, &y);CHKERRQ(ierr);
ierr = VecSet(x, 1.0);CHKERRQ(ierr);
ierr = MatMult(L, x, y);CHKERRQ(ierr);
ierr = VecNorm(y, NORM_INFINITY, &norm);CHKERRQ(ierr);
if (norm > 1.0e-10) SETERRQ(PetscObjectComm((PetscObject) y), PETSC_ERR_PLIB, "Invalid graph Laplacian");
ierr = VecDestroy(&x);CHKERRQ(ierr);
ierr = VecDestroy(&y);CHKERRQ(ierr);
/* Compute Fiedler vector, and perhaps more vectors */
Mat LD;
PetscScalar *a, *realpart, *imagpart, *eigvec, *work, sdummy;
PetscBLASInt bn, bN, lwork, lierr, idummy;
PetscInt n, i;
ierr = MatConvert(L, MATDENSE, MAT_INITIAL_MATRIX, &LD);CHKERRQ(ierr);
ierr = MatGetLocalSize(LD, &n, NULL);CHKERRQ(ierr);
ierr = MatDenseGetArray(LD, &a);CHKERRQ(ierr);
ierr = PetscBLASIntCast(n, &bn);CHKERRQ(ierr);
ierr = PetscBLASIntCast(n, &bN);CHKERRQ(ierr);
ierr = PetscBLASIntCast(5*n,&lwork);CHKERRQ(ierr);
ierr = PetscBLASIntCast(1,&idummy);CHKERRQ(ierr);
ierr = PetscMalloc4(n,PetscScalar,&realpart,n,PetscScalar,&imagpart,n*n,PetscScalar,&eigvec,lwork,PetscScalar,&work);CHKERRQ(ierr);
ierr = PetscFPTrapPush(PETSC_FP_TRAP_OFF);CHKERRQ(ierr);
PetscStackCall("LAPACKgeev", LAPACKgeev_("N","V",&bn,a,&bN,realpart,imagpart,&sdummy,&idummy,eigvec,&bN,work,&lwork,&lierr));
if (lierr) SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in LAPACK routine %d", (int) lierr);
ierr = PetscFPTrapPop();CHKERRQ(ierr);
PetscReal *r, *c;
PetscInt *perm;
ierr = PetscMalloc3(n,PetscInt,&perm,n,PetscReal,&r,n,PetscReal,&c);CHKERRQ(ierr);
for (i = 0; i < n; ++i) perm[i] = i;
ierr = PetscSortRealWithPermutation(n,realpart,perm);CHKERRQ(ierr);
for (i = 0; i < n; ++i) {
r[i] = realpart[perm[i]];
c[i] = imagpart[perm[i]];
}
for (i = 0; i < n; ++i) {
realpart[i] = r[i];
imagpart[i] = c[i];
}
/* Output spectrum */
if (ctx.showSpectrum) {
ierr = PetscPrintf(PETSC_COMM_SELF, "Spectrum\n");CHKERRQ(ierr);
for (i = 0; i < n; ++i) {ierr = PetscPrintf(PETSC_COMM_SELF, "%d: Real %g Imag %g\n", i, realpart[i], imagpart[i]);CHKERRQ(ierr);}
}
/* Check lowest eigenvalue and eigenvector */
PetscInt evInd = perm[0];
if ((realpart[0] > 1.0e-12) || (imagpart[0] > 1.0e-12)) SETERRQ(PetscObjectComm((PetscObject) L), PETSC_ERR_PLIB, "Graph Laplacian must have lowest eigenvalue 0");
for (i = 0; i < n; ++i) {
if (fabs(eigvec[evInd*n+i] - eigvec[evInd*n+0]) > 1.0e-10) SETERRQ3(PetscObjectComm((PetscObject) L), PETSC_ERR_PLIB, "Graph Laplacian must have constant lowest eigenvector ev_%d %g != ev_0 %g", i, eigvec[evInd*n+i], eigvec[evInd*n+0]);
}
/* Output Fiedler vector */
evInd = perm[1];
if (ctx.showFiedler) {
ierr = PetscPrintf(PETSC_COMM_SELF, "Fiedler vector, Re{ev} %g\n", realpart[1]);CHKERRQ(ierr);
for (i = 0; i < n; ++i) {ierr = PetscPrintf(PETSC_COMM_SELF, "%d: %g\n", i, eigvec[evInd*n+i]);CHKERRQ(ierr);}
}
/* Construct Fiedler partition */
IS fIS, fIS2;
PetscInt *fperm, *fperm2, pos, neg, posSize = 0;
ierr = PetscMalloc(n * sizeof(PetscInt), &fperm);CHKERRQ(ierr);
for (i = 0; i < n; ++i) {
if (eigvec[evInd*n+i] > 0.0) ++posSize;
}
ierr = PetscMalloc(n * sizeof(PetscInt), &fperm2);CHKERRQ(ierr);
for (i = 0; i < n; ++i) fperm[i] = i;
ierr = PetscSortRealWithPermutation(n, &eigvec[evInd*n], fperm);CHKERRQ(ierr);
for (i = 0; i < n; ++i) fperm2[n-1-i] = fperm[i];
for (i = 0, pos = 0, neg = posSize; i < n; ++i) {
if (eigvec[evInd*n+i] > 0.0) fperm[pos++] = i;
else fperm[neg++] = i;
}
ierr = ISCreateGeneral(PetscObjectComm((PetscObject) L), n, fperm, PETSC_OWN_POINTER, &fIS);CHKERRQ(ierr);
ierr = ISSetPermutation(fIS);CHKERRQ(ierr);
ierr = ISCreateGeneral(PetscObjectComm((PetscObject) L), n, fperm2, PETSC_OWN_POINTER, &fIS2);CHKERRQ(ierr);
ierr = ISSetPermutation(fIS2);CHKERRQ(ierr);
ierr = PetscFree3(perm,r,c);CHKERRQ(ierr);
ierr = PetscFree4(realpart,imagpart,eigvec,work);CHKERRQ(ierr);
ierr = MatDenseRestoreArray(LD, &a);CHKERRQ(ierr);
ierr = MatDestroy(&LD);CHKERRQ(ierr);
ierr = MatDestroy(&L);CHKERRQ(ierr);
/* Permute matrix */
Mat AR, AR2;
ierr = MatPermute(A, fIS, fIS, &AR);CHKERRQ(ierr);
ierr = MatView(A, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatView(AR, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = ISDestroy(&fIS);CHKERRQ(ierr);
ierr = MatPermute(A, fIS2, fIS2, &AR2);CHKERRQ(ierr);
ierr = MatView(AR2, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = ISDestroy(&fIS2);CHKERRQ(ierr);
ierr = MatDestroy(&AR);CHKERRQ(ierr);
AR = AR2;
/* Extract blocks and reorder */
Mat AP, AN, APR, ANR;
IS ispos, isneg, rpermpos, cpermpos, rpermneg, cpermneg;
PetscInt bw, bwr;
ierr = ISCreateStride(PETSC_COMM_SELF, posSize, 0, 1, &ispos);CHKERRQ(ierr);
ierr = ISCreateStride(PETSC_COMM_SELF, n - posSize, posSize, 1, &isneg);CHKERRQ(ierr);
ierr = MatGetSubMatrix(AR, ispos, ispos, MAT_INITIAL_MATRIX, &AP);CHKERRQ(ierr);
ierr = MatGetSubMatrix(AR, isneg, isneg, MAT_INITIAL_MATRIX, &AN);CHKERRQ(ierr);
ierr = ISDestroy(&ispos);CHKERRQ(ierr);
ierr = ISDestroy(&isneg);CHKERRQ(ierr);
ierr = MatGetOrdering(AP, ctx.matOrdtype, &rpermpos, &cpermpos);CHKERRQ(ierr);
ierr = MatGetOrdering(AN, ctx.matOrdtype, &rpermneg, &cpermneg);CHKERRQ(ierr);
ierr = MatPermute(AP, rpermpos, cpermpos, &APR);CHKERRQ(ierr);
ierr = MatComputeBandwidth(AP, 0.0, &bw);CHKERRQ(ierr);
ierr = MatComputeBandwidth(APR, 0.0, &bwr);CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD, "Reduced positive bandwidth from %d to %d\n", bw, bwr);CHKERRQ(ierr);
ierr = MatPermute(AN, rpermneg, cpermneg, &ANR);CHKERRQ(ierr);
ierr = MatComputeBandwidth(AN, 0.0, &bw);CHKERRQ(ierr);
ierr = MatComputeBandwidth(ANR, 0.0, &bwr);CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_WORLD, "Reduced negative bandwidth from %d to %d\n", bw, bwr);CHKERRQ(ierr);
ierr = MatView(AP, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatView(APR, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatView(AN, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatView(ANR, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
/* Reorder original matrix */
Mat ARR;
IS rperm, cperm;
PetscInt *idx;
const PetscInt *cidx;
ierr = PetscMalloc(n * sizeof(PetscInt), &idx);CHKERRQ(ierr);
ierr = ISGetIndices(rpermpos, &cidx);CHKERRQ(ierr);
for (i = 0; i < posSize; ++i) idx[i] = cidx[i];
ierr = ISRestoreIndices(rpermpos, &cidx);CHKERRQ(ierr);
ierr = ISGetIndices(rpermneg, &cidx);CHKERRQ(ierr);
for (i = posSize; i < n; ++i) idx[i] = cidx[i-posSize] + posSize;
ierr = ISRestoreIndices(rpermneg, &cidx);CHKERRQ(ierr);
ierr = ISCreateGeneral(PETSC_COMM_SELF, n, idx, PETSC_OWN_POINTER, &rperm);CHKERRQ(ierr);
ierr = ISSetPermutation(rperm);CHKERRQ(ierr);
ierr = PetscMalloc(n * sizeof(PetscInt), &idx);CHKERRQ(ierr);
ierr = ISGetIndices(cpermpos, &cidx);CHKERRQ(ierr);
for (i = 0; i < posSize; ++i) idx[i] = cidx[i];
ierr = ISRestoreIndices(cpermpos, &cidx);CHKERRQ(ierr);
ierr = ISGetIndices(cpermneg, &cidx);CHKERRQ(ierr);
for (i = posSize; i < n; ++i) idx[i] = cidx[i-posSize] + posSize;
ierr = ISRestoreIndices(cpermneg, &cidx);CHKERRQ(ierr);
ierr = ISCreateGeneral(PETSC_COMM_SELF, n, idx, PETSC_OWN_POINTER, &cperm);CHKERRQ(ierr);
ierr = ISSetPermutation(cperm);CHKERRQ(ierr);
ierr = MatPermute(AR, rperm, cperm, &ARR);CHKERRQ(ierr);
ierr = MatView(ARR, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = ISDestroy(&rperm);CHKERRQ(ierr);
ierr = ISDestroy(&cperm);CHKERRQ(ierr);
ierr = ISDestroy(&rpermpos);CHKERRQ(ierr);
ierr = ISDestroy(&cpermpos);CHKERRQ(ierr);
ierr = ISDestroy(&rpermneg);CHKERRQ(ierr);
ierr = ISDestroy(&cpermneg);CHKERRQ(ierr);
ierr = MatDestroy(&AP);CHKERRQ(ierr);
ierr = MatDestroy(&AN);CHKERRQ(ierr);
ierr = MatDestroy(&APR);CHKERRQ(ierr);
ierr = MatDestroy(&ANR);CHKERRQ(ierr);
/* Compare bands */
Mat B, BR;
ierr = MatCreateSubMatrixBanded(A, 50, 0.95, &B);CHKERRQ(ierr);
ierr = MatCreateSubMatrixBanded(ARR, 50, 0.95, &BR);CHKERRQ(ierr);
ierr = MatView(B, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatView(BR, PETSC_VIEWER_DRAW_WORLD);CHKERRQ(ierr);
ierr = MatDestroy(&B);CHKERRQ(ierr);
ierr = MatDestroy(&BR);CHKERRQ(ierr);
/* Cleanup */
ierr = MatDestroy(&ARR);CHKERRQ(ierr);
ierr = MatDestroy(&AR);CHKERRQ(ierr);
ierr = MatDestroy(&A);CHKERRQ(ierr);
ierr = PetscFinalize();
return 0;
}
PetscErrorCode KSPComputeShifts_DGMRES(KSP ksp)
{
PetscErrorCode ierr;
KSP_AGMRES *agmres = (KSP_AGMRES*)(ksp->data);
PetscInt max_k = agmres->max_k; /* size of the (non augmented) Krylov subspace */
PetscInt Neig = 0;
PetscInt max_it = ksp->max_it;
/* Perform one cycle of dgmres to find the eigenvalues and compute the first approximations of the eigenvectors */
PetscFunctionBegin;
ierr = PetscLogEventBegin(KSP_AGMRESComputeShifts, ksp, 0,0,0);CHKERRQ(ierr);
/* Send the size of the augmented basis to DGMRES */
ksp->max_it = max_k; /* set this to have DGMRES performing only one cycle */
ksp->ops->buildsolution = KSPBuildSolution_DGMRES;
ierr = KSPSolve_DGMRES(ksp);
ksp->guess_zero = PETSC_FALSE;
if (ksp->reason == KSP_CONVERGED_RTOL) {
ierr = PetscLogEventEnd(KSP_AGMRESComputeShifts, ksp, 0,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
} else ksp->reason = KSP_CONVERGED_ITERATING;
if ((agmres->r == 0) && (agmres->neig > 0)) { /* Compute the eigenvalues for the shifts and the eigenvectors (to augment the Newton basis) */
agmres->HasSchur = PETSC_FALSE;
ierr = KSPDGMRESComputeDeflationData_DGMRES (ksp, &Neig);CHKERRQ (ierr);
Neig = max_k;
} else { /* From DGMRES, compute only the eigenvalues needed as Shifts for the Newton Basis */
ierr = KSPDGMRESComputeSchurForm_DGMRES(ksp, &Neig);CHKERRQ(ierr);
}
/* It may happen that the Ritz values from one cycle of GMRES are not accurate enough to provide a good stability. In this case, another cycle of GMRES is performed. The two sets of values thus generated are sorted and the most accurate are kept as shifts */
PetscBool flg;
ierr = PetscOptionsHasName(NULL, "-ksp_agmres_ImproveShifts", &flg);CHKERRQ(ierr);
if (!flg) {
ierr = KSPAGMRESLejaOrdering(agmres->wr, agmres->wi, agmres->Rshift, agmres->Ishift, max_k);CHKERRQ(ierr);
} else { /* Perform another cycle of DGMRES to find another set of eigenvalues */
PetscInt i;
PetscScalar *wr, *wi,*Rshift, *Ishift;
ierr = PetscMalloc4(2*max_k, &wr, 2*max_k, &wi, 2*max_k, &Rshift, 2*max_k, &Ishift);CHKERRQ(ierr);
for (i = 0; i < max_k; i++) {
wr[i] = agmres->wr[i];
wi[i] = agmres->wi[i];
}
ierr = KSPSolve_DGMRES(ksp);
ksp->guess_zero = PETSC_FALSE;
if (ksp->reason == KSP_CONVERGED_RTOL) PetscFunctionReturn(0);
else ksp->reason = KSP_CONVERGED_ITERATING;
if (agmres->neig > 0) { /* Compute the eigenvalues for the shifts) and the eigenvectors (to augment the Newton basis */
agmres->HasSchur = PETSC_FALSE;
ierr = KSPDGMRESComputeDeflationData_DGMRES(ksp, &Neig);CHKERRQ(ierr);
Neig = max_k;
} else { /* From DGMRES, compute only the eigenvalues needed as Shifts for the Newton Basis */
ierr = KSPDGMRESComputeSchurForm_DGMRES(ksp, &Neig);CHKERRQ(ierr);
}
for (i = 0; i < max_k; i++) {
wr[max_k+i] = agmres->wr[i];
wi[max_k+i] = agmres->wi[i];
}
ierr = KSPAGMRESLejaOrdering(wr, wi, Rshift, Ishift, 2*max_k);CHKERRQ(ierr);
for (i = 0; i< max_k; i++) {
agmres->Rshift[i] = Rshift[i];
agmres->Ishift[i] = Ishift[i];
}
ierr = PetscFree(Rshift);CHKERRQ(ierr);
ierr = PetscFree(wr);CHKERRQ(ierr);
ierr = PetscFree(Ishift);CHKERRQ(ierr);
ierr = PetscFree(wi);CHKERRQ(ierr);
}
agmres->HasShifts = PETSC_TRUE;
ksp->max_it = max_it;
ierr = PetscLogEventEnd(KSP_AGMRESComputeShifts, ksp, 0,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
AOCreateMapping - Creates a basic application mapping using two integer arrays.
Input Parameters:
+ comm - MPI communicator that is to share AO
. napp - size of integer arrays
. myapp - integer array that defines an ordering
- mypetsc - integer array that defines another ordering (may be NULL to indicate the identity ordering)
Output Parameter:
. aoout - the new application mapping
Options Database Key:
. -ao_view : call AOView() at the conclusion of AOCreateMapping()
Level: beginner
Notes: the arrays myapp and mypetsc need NOT contain the all the integers 0 to napp-1, that is there CAN be "holes" in the indices.
Use AOCreateBasic() or AOCreateBasicIS() if they do not have holes for better performance.
.keywords: AO, create
.seealso: AOCreateBasic(), AOCreateBasic(), AOCreateMappingIS(), AODestroy()
@*/
PetscErrorCode AOCreateMapping(MPI_Comm comm,PetscInt napp,const PetscInt myapp[],const PetscInt mypetsc[],AO *aoout)
{
AO ao;
AO_Mapping *aomap;
PetscInt *allpetsc, *allapp;
PetscInt *petscPerm, *appPerm;
PetscInt *petsc;
PetscMPIInt size, rank,*lens, *disp,nnapp;
PetscInt N, start;
PetscInt i;
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidPointer(aoout,5);
*aoout = 0;
ierr = AOInitializePackage();CHKERRQ(ierr);
ierr = PetscHeaderCreate(ao, AO_CLASSID, "AO", "Application Ordering", "AO", comm, AODestroy, AOView);CHKERRQ(ierr);
ierr = PetscNewLog(ao,&aomap);CHKERRQ(ierr);
ierr = PetscMemcpy(ao->ops, &AOps, sizeof(AOps));CHKERRQ(ierr);
ao->data = (void*) aomap;
/* transmit all lengths to all processors */
ierr = MPI_Comm_size(comm, &size);CHKERRQ(ierr);
ierr = MPI_Comm_rank(comm, &rank);CHKERRQ(ierr);
ierr = PetscMalloc2(size, &lens,size,&disp);CHKERRQ(ierr);
nnapp = napp;
ierr = MPI_Allgather(&nnapp, 1, MPI_INT, lens, 1, MPI_INT, comm);CHKERRQ(ierr);
N = 0;
for (i = 0; i < size; i++) {
disp[i] = N;
N += lens[i];
}
aomap->N = N;
ao->N = N;
ao->n = N;
/* If mypetsc is 0 then use "natural" numbering */
if (!mypetsc) {
start = disp[rank];
ierr = PetscMalloc1(napp+1, &petsc);CHKERRQ(ierr);
for (i = 0; i < napp; i++) petsc[i] = start + i;
} else {
petsc = (PetscInt*)mypetsc;
}
/* get all indices on all processors */
ierr = PetscMalloc4(N, &allapp,N,&appPerm,N,&allpetsc,N,&petscPerm);CHKERRQ(ierr);
ierr = MPI_Allgatherv((void*)myapp, napp, MPIU_INT, allapp, lens, disp, MPIU_INT, comm);CHKERRQ(ierr);
ierr = MPI_Allgatherv((void*)petsc, napp, MPIU_INT, allpetsc, lens, disp, MPIU_INT, comm);CHKERRQ(ierr);
ierr = PetscFree2(lens,disp);CHKERRQ(ierr);
/* generate a list of application and PETSc node numbers */
ierr = PetscMalloc4(N, &aomap->app,N,&aomap->appPerm,N,&aomap->petsc,N,&aomap->petscPerm);CHKERRQ(ierr);
ierr = PetscLogObjectMemory((PetscObject)ao, 4*N * sizeof(PetscInt));CHKERRQ(ierr);
for (i = 0; i < N; i++) {
appPerm[i] = i;
petscPerm[i] = i;
}
ierr = PetscSortIntWithPermutation(N, allpetsc, petscPerm);CHKERRQ(ierr);
ierr = PetscSortIntWithPermutation(N, allapp, appPerm);CHKERRQ(ierr);
/* Form sorted arrays of indices */
for (i = 0; i < N; i++) {
aomap->app[i] = allapp[appPerm[i]];
aomap->petsc[i] = allpetsc[petscPerm[i]];
}
/* Invert petscPerm[] into aomap->petscPerm[] */
for (i = 0; i < N; i++) aomap->petscPerm[petscPerm[i]] = i;
/* Form map between aomap->app[] and aomap->petsc[] */
for (i = 0; i < N; i++) aomap->appPerm[i] = aomap->petscPerm[appPerm[i]];
/* Invert appPerm[] into allapp[] */
for (i = 0; i < N; i++) allapp[appPerm[i]] = i;
/* Form map between aomap->petsc[] and aomap->app[] */
for (i = 0; i < N; i++) aomap->petscPerm[i] = allapp[petscPerm[i]];
#if defined(PETSC_USE_DEBUG)
/* Check that the permutations are complementary */
for (i = 0; i < N; i++) {
if (i != aomap->appPerm[aomap->petscPerm[i]]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Invalid ordering");
}
#endif
/* Cleanup */
if (!mypetsc) {
ierr = PetscFree(petsc);CHKERRQ(ierr);
}
ierr = PetscFree4(allapp,appPerm,allpetsc,petscPerm);CHKERRQ(ierr);
ierr = AOViewFromOptions(ao,NULL,"-ao_view");CHKERRQ(ierr);
*aoout = ao;
PetscFunctionReturn(0);
}
PetscErrorCode MatApplyPAPt_Symbolic_SeqAIJ_SeqAIJ(Mat A,Mat P,Mat *C)
{
/* Note: This code is virtually identical to that of MatApplyPtAP_SeqAIJ_Symbolic */
/* and MatMatMult_SeqAIJ_SeqAIJ_Symbolic. Perhaps they could be merged nicely. */
PetscErrorCode ierr;
PetscFreeSpaceList free_space=PETSC_NULL,current_space=PETSC_NULL;
Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*p=(Mat_SeqAIJ*)P->data,*c;
PetscInt *ai=a->i,*aj=a->j,*ajj,*pi=p->i,*pj=p->j,*pti,*ptj,*ptjj;
PetscInt *ci,*cj,*paj,*padenserow,*pasparserow,*denserow,*sparserow;
PetscInt an=A->cmap->N,am=A->rmap->N,pn=P->cmap->N,pm=P->rmap->N;
PetscInt i,j,k,pnzi,arow,anzj,panzi,ptrow,ptnzj,cnzi;
MatScalar *ca;
PetscFunctionBegin;
/* some error checking which could be moved into interface layer */
if (pn!=am) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",pn,am);
if (am!=an) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",am, an);
/* Set up timers */
ierr = PetscLogEventBegin(MAT_Applypapt_symbolic,A,P,0,0);CHKERRQ(ierr);
/* Create ij structure of P^T */
ierr = MatGetSymbolicTranspose_SeqAIJ(P,&pti,&ptj);CHKERRQ(ierr);
/* Allocate ci array, arrays for fill computation and */
/* free space for accumulating nonzero column info */
ierr = PetscMalloc(((pm+1)*1)*sizeof(PetscInt),&ci);CHKERRQ(ierr);
ci[0] = 0;
ierr = PetscMalloc4(an,PetscInt,&padenserow,an,PetscInt,&pasparserow,pm,PetscInt,&denserow,pm,PetscInt,&sparserow);CHKERRQ(ierr);
ierr = PetscMemzero(padenserow,an*sizeof(PetscInt));CHKERRQ(ierr);
ierr = PetscMemzero(pasparserow,an*sizeof(PetscInt));CHKERRQ(ierr);
ierr = PetscMemzero(denserow,pm*sizeof(PetscInt));CHKERRQ(ierr);
ierr = PetscMemzero(sparserow,pm*sizeof(PetscInt));CHKERRQ(ierr);
/* Set initial free space to be nnz(A) scaled by aspect ratio of Pt. */
/* This should be reasonable if sparsity of PAPt is similar to that of A. */
ierr = PetscFreeSpaceGet((ai[am]/pn)*pm,&free_space);CHKERRQ(ierr);
current_space = free_space;
/* Determine fill for each row of C: */
for (i=0;i<pm;i++) {
pnzi = pi[i+1] - pi[i];
panzi = 0;
/* Get symbolic sparse row of PA: */
for (j=0;j<pnzi;j++) {
arow = *pj++;
anzj = ai[arow+1] - ai[arow];
ajj = aj + ai[arow];
for (k=0;k<anzj;k++) {
if (!padenserow[ajj[k]]) {
padenserow[ajj[k]] = -1;
pasparserow[panzi++] = ajj[k];
}
}
}
/* Using symbolic row of PA, determine symbolic row of C: */
paj = pasparserow;
cnzi = 0;
for (j=0;j<panzi;j++) {
ptrow = *paj++;
ptnzj = pti[ptrow+1] - pti[ptrow];
ptjj = ptj + pti[ptrow];
for (k=0;k<ptnzj;k++) {
if (!denserow[ptjj[k]]) {
denserow[ptjj[k]] = -1;
sparserow[cnzi++] = ptjj[k];
}
}
}
/* sort sparse representation */
ierr = PetscSortInt(cnzi,sparserow);CHKERRQ(ierr);
/* If free space is not available, make more free space */
/* Double the amount of total space in the list */
if (current_space->local_remaining<cnzi) {
ierr = PetscFreeSpaceGet(cnzi+current_space->total_array_size,¤t_space);CHKERRQ(ierr);
}
/* Copy data into free space, and zero out dense row */
ierr = PetscMemcpy(current_space->array,sparserow,cnzi*sizeof(PetscInt));CHKERRQ(ierr);
current_space->array += cnzi;
current_space->local_used += cnzi;
current_space->local_remaining -= cnzi;
for (j=0;j<panzi;j++) {
padenserow[pasparserow[j]] = 0;
}
for (j=0;j<cnzi;j++) {
denserow[sparserow[j]] = 0;
}
ci[i+1] = ci[i] + cnzi;
}
/* column indices are in the list of free space */
/* Allocate space for cj, initialize cj, and */
/* destroy list of free space and other temporary array(s) */
ierr = PetscMalloc((ci[pm]+1)*sizeof(PetscInt),&cj);CHKERRQ(ierr);
ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
ierr = PetscFree4(padenserow,pasparserow,denserow,sparserow);CHKERRQ(ierr);
/* Allocate space for ca */
ierr = PetscMalloc((ci[pm]+1)*sizeof(MatScalar),&ca);CHKERRQ(ierr);
ierr = PetscMemzero(ca,(ci[pm]+1)*sizeof(MatScalar));CHKERRQ(ierr);
/* put together the new matrix */
ierr = MatCreateSeqAIJWithArrays(((PetscObject)A)->comm,pm,pm,ci,cj,ca,C);CHKERRQ(ierr);
(*C)->rmap->bs = P->cmap->bs;
(*C)->cmap->bs = P->cmap->bs;
/* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
/* Since these are PETSc arrays, change flags to free them as necessary. */
c = (Mat_SeqAIJ *)((*C)->data);
c->free_a = PETSC_TRUE;
c->free_ij = PETSC_TRUE;
c->nonew = 0;
/* Clean up. */
ierr = MatRestoreSymbolicTranspose_SeqAIJ(P,&pti,&ptj);CHKERRQ(ierr);
ierr = PetscLogEventEnd(MAT_Applypapt_symbolic,A,P,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode GreedyColoringLocalDistanceTwo_Private(MatColoring mc,PetscReal *wts,PetscInt *lperm,ISColoringValue *colors)
{
MC_Greedy *gr = (MC_Greedy *) mc->data;
PetscInt i,j,k,l,s,e,n,nd,nd_global,n_global,idx,ncols,maxcolors,mcol,mcol_global,nd1cols,*mask,masksize,*d1cols,*bad,*badnext,nbad,badsize,ccol,no,cbad;
Mat m = mc->mat, mt;
Mat_MPIAIJ *aij = (Mat_MPIAIJ*)m->data;
Mat md=NULL,mo=NULL;
const PetscInt *md_i,*mo_i,*md_j,*mo_j;
const PetscInt *rmd_i,*rmo_i,*rmd_j,*rmo_j;
PetscBool isMPIAIJ,isSEQAIJ;
PetscInt pcol,*dcolors,*ocolors;
ISColoringValue *badidx;
const PetscInt *cidx;
PetscReal *owts,*colorweights;
PetscInt *oconf,*conf;
PetscSF sf;
PetscLayout layout;
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = MatGetSize(m,&n_global,NULL);CHKERRQ(ierr);
ierr = MatGetOwnershipRange(m,&s,&e);CHKERRQ(ierr);
n=e-s;
nd_global = 0;
/* get the matrix communication structures */
ierr = PetscObjectTypeCompare((PetscObject)m, MATMPIAIJ, &isMPIAIJ); CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject)m, MATSEQAIJ, &isSEQAIJ); CHKERRQ(ierr);
if (isMPIAIJ) {
Mat_SeqAIJ *dseq;
Mat_SeqAIJ *oseq;
md=aij->A;
dseq = (Mat_SeqAIJ*)md->data;
mo=aij->B;
oseq = (Mat_SeqAIJ*)mo->data;
md_i = dseq->i;
md_j = dseq->j;
mo_i = oseq->i;
mo_j = oseq->j;
rmd_i = dseq->i;
rmd_j = dseq->j;
rmo_i = oseq->i;
rmo_j = oseq->j;
} else if (isSEQAIJ) {
Mat_SeqAIJ *dseq;
/* no off-processor nodes */
md=m;
dseq = (Mat_SeqAIJ*)md->data;
md_i = dseq->i;
md_j = dseq->j;
mo_i = NULL;
mo_j = NULL;
rmd_i = dseq->i;
rmd_j = dseq->j;
rmo_i = NULL;
rmo_j = NULL;
} else SETERRQ(PetscObjectComm((PetscObject)mc),PETSC_ERR_ARG_WRONG,"Matrix must be AIJ for greedy coloring");
if (!gr->symmetric) {
ierr = MatTranspose(m, MAT_INITIAL_MATRIX, &mt);CHKERRQ(ierr);
if (isSEQAIJ) {
Mat_SeqAIJ *dseq = (Mat_SeqAIJ*) mt->data;
rmd_i = dseq->i;
rmd_j = dseq->j;
rmo_i = NULL;
rmo_j = NULL;
} else SETERRQ(PetscObjectComm((PetscObject) mc), PETSC_ERR_SUP, "Nonsymmetric greedy coloring only works in serial");
}
/* create the vectors and communication structures if necessary */
no=0;
if (mo) {
ierr = VecGetLocalSize(aij->lvec,&no);CHKERRQ(ierr);
ierr = PetscSFCreate(PetscObjectComm((PetscObject)m),&sf);CHKERRQ(ierr);
ierr = MatGetLayouts(m,&layout,NULL);CHKERRQ(ierr);
ierr = PetscSFSetGraphLayout(sf,layout,no,NULL,PETSC_COPY_VALUES,aij->garray);CHKERRQ(ierr);
}
ierr = MatColoringGetMaxColors(mc,&maxcolors);CHKERRQ(ierr);
masksize=n;
nbad=0;
badsize=n;
ierr = PetscMalloc1(masksize,&mask);CHKERRQ(ierr);
ierr = PetscMalloc4(n,&d1cols,n,&dcolors,n,&conf,n,&bad);CHKERRQ(ierr);
ierr = PetscMalloc2(badsize,&badidx,badsize,&badnext);CHKERRQ(ierr);
for(i=0;i<masksize;i++) {
mask[i]=-1;
}
for (i=0;i<n;i++) {
dcolors[i]=maxcolors;
bad[i]=-1;
}
for (i=0;i<badsize;i++) {
badnext[i]=-1;
}
if (mo) {
ierr = PetscMalloc3(no,&owts,no,&oconf,no,&ocolors);CHKERRQ(ierr);
ierr = PetscSFBcastBegin(sf,MPIU_REAL,wts,owts);CHKERRQ(ierr);
ierr = PetscSFBcastEnd(sf,MPIU_REAL,wts,owts);CHKERRQ(ierr);
for (i=0;i<no;i++) {
ocolors[i]=maxcolors;
}
} else { /* Appease overzealous -Wmaybe-initialized */
owts = NULL;
oconf = NULL;
ocolors = NULL;
}
mcol=0;
while (nd_global < n_global) {
nd=n;
/* assign lowest possible color to each local vertex */
mcol_global=0;
ierr = PetscLogEventBegin(MATCOLORING_Local,mc,0,0,0);CHKERRQ(ierr);
for (i=0;i<n;i++) {
idx=lperm[i];
if (dcolors[idx] == maxcolors) {
/* entries in bad */
cbad=bad[idx];
while (cbad>=0) {
ccol=badidx[cbad];
if (ccol>=masksize) {
PetscInt *newmask;
ierr = PetscMalloc1(masksize*2,&newmask);CHKERRQ(ierr);
for(k=0;k<2*masksize;k++) {
newmask[k]=-1;
}
for(k=0;k<masksize;k++) {
newmask[k]=mask[k];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
mask=newmask;
masksize*=2;
}
mask[ccol]=idx;
cbad=badnext[cbad];
}
/* diagonal distance-one rows */
nd1cols=0;
ncols = rmd_i[idx+1]-rmd_i[idx];
cidx = &(rmd_j[rmd_i[idx]]);
for (j=0;j<ncols;j++) {
d1cols[nd1cols] = cidx[j];
nd1cols++;
ccol=dcolors[cidx[j]];
if (ccol != maxcolors) {
if (ccol>=masksize) {
PetscInt *newmask;
ierr = PetscMalloc1(masksize*2,&newmask);CHKERRQ(ierr);
for(k=0;k<2*masksize;k++) {
newmask[k]=-1;
}
for(k=0;k<masksize;k++) {
newmask[k]=mask[k];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
mask=newmask;
masksize*=2;
}
mask[ccol]=idx;
}
}
/* off-diagonal distance-one rows */
if (mo) {
ncols = rmo_i[idx+1]-rmo_i[idx];
cidx = &(rmo_j[rmo_i[idx]]);
for (j=0;j<ncols;j++) {
ccol=ocolors[cidx[j]];
if (ccol != maxcolors) {
if (ccol>=masksize) {
PetscInt *newmask;
ierr = PetscMalloc1(masksize*2,&newmask);CHKERRQ(ierr);
for(k=0;k<2*masksize;k++) {
newmask[k]=-1;
}
for(k=0;k<masksize;k++) {
newmask[k]=mask[k];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
mask=newmask;
masksize*=2;
}
mask[ccol]=idx;
}
}
}
/* diagonal distance-two rows */
for (j=0;j<nd1cols;j++) {
ncols = md_i[d1cols[j]+1]-md_i[d1cols[j]];
cidx = &(md_j[md_i[d1cols[j]]]);
for (l=0;l<ncols;l++) {
ccol=dcolors[cidx[l]];
if (ccol != maxcolors) {
if (ccol>=masksize) {
PetscInt *newmask;
ierr = PetscMalloc1(masksize*2,&newmask);CHKERRQ(ierr);
for(k=0;k<2*masksize;k++) {
newmask[k]=-1;
}
for(k=0;k<masksize;k++) {
newmask[k]=mask[k];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
mask=newmask;
masksize*=2;
}
mask[ccol]=idx;
}
}
}
/* off-diagonal distance-two rows */
if (mo) {
for (j=0;j<nd1cols;j++) {
ncols = mo_i[d1cols[j]+1]-mo_i[d1cols[j]];
cidx = &(mo_j[mo_i[d1cols[j]]]);
for (l=0;l<ncols;l++) {
ccol=ocolors[cidx[l]];
if (ccol != maxcolors) {
if (ccol>=masksize) {
PetscInt *newmask;
ierr = PetscMalloc1(masksize*2,&newmask);CHKERRQ(ierr);
for(k=0;k<2*masksize;k++) {
newmask[k]=-1;
}
for(k=0;k<masksize;k++) {
newmask[k]=mask[k];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
mask=newmask;
masksize*=2;
}
mask[ccol]=idx;
}
}
}
}
/* assign this one the lowest color possible by seeing if there's a gap in the sequence of sorted neighbor colors */
for (j=0;j<masksize;j++) {
if (mask[j]!=idx) {
break;
}
}
pcol=j;
if (pcol>maxcolors) pcol=maxcolors;
dcolors[idx]=pcol;
if (pcol>mcol) mcol=pcol;
}
}
ierr = PetscLogEventEnd(MATCOLORING_Local,mc,0,0,0);CHKERRQ(ierr);
if (mo) {
/* transfer neighbor colors */
ierr = PetscSFBcastBegin(sf,MPIU_INT,dcolors,ocolors);CHKERRQ(ierr);
ierr = PetscSFBcastEnd(sf,MPIU_INT,dcolors,ocolors);CHKERRQ(ierr);
/* find the maximum color assigned locally and allocate a mask */
ierr = MPIU_Allreduce(&mcol,&mcol_global,1,MPIU_INT,MPI_MAX,PetscObjectComm((PetscObject)mc));CHKERRQ(ierr);
ierr = PetscMalloc1(mcol_global+1,&colorweights);CHKERRQ(ierr);
/* check for conflicts */
for (i=0;i<n;i++) {
conf[i]=PETSC_FALSE;
}
for (i=0;i<no;i++) {
oconf[i]=PETSC_FALSE;
}
for (i=0;i<n;i++) {
ncols = mo_i[i+1]-mo_i[i];
cidx = &(mo_j[mo_i[i]]);
if (ncols > 0) {
/* fill in the mask */
for (j=0;j<mcol_global+1;j++) {
colorweights[j]=0;
}
colorweights[dcolors[i]]=wts[i];
/* fill in the off-diagonal part of the mask */
for (j=0;j<ncols;j++) {
ccol=ocolors[cidx[j]];
if (ccol < maxcolors) {
if (colorweights[ccol] < owts[cidx[j]]) {
colorweights[ccol] = owts[cidx[j]];
}
}
}
/* fill in the on-diagonal part of the mask */
ncols = md_i[i+1]-md_i[i];
cidx = &(md_j[md_i[i]]);
for (j=0;j<ncols;j++) {
ccol=dcolors[cidx[j]];
if (ccol < maxcolors) {
if (colorweights[ccol] < wts[cidx[j]]) {
colorweights[ccol] = wts[cidx[j]];
}
}
}
/* go back through and set up on and off-diagonal conflict vectors */
ncols = md_i[i+1]-md_i[i];
cidx = &(md_j[md_i[i]]);
for (j=0;j<ncols;j++) {
ccol=dcolors[cidx[j]];
if (ccol < maxcolors) {
if (colorweights[ccol] > wts[cidx[j]]) {
conf[cidx[j]]=PETSC_TRUE;
}
}
}
ncols = mo_i[i+1]-mo_i[i];
cidx = &(mo_j[mo_i[i]]);
for (j=0;j<ncols;j++) {
ccol=ocolors[cidx[j]];
if (ccol < maxcolors) {
if (colorweights[ccol] > owts[cidx[j]]) {
oconf[cidx[j]]=PETSC_TRUE;
}
}
}
}
}
nd_global=0;
ierr = PetscFree(colorweights);CHKERRQ(ierr);
ierr = PetscLogEventBegin(MATCOLORING_Comm,mc,0,0,0);CHKERRQ(ierr);
ierr = PetscSFReduceBegin(sf,MPIU_INT,oconf,conf,MPIU_SUM);CHKERRQ(ierr);
ierr = PetscSFReduceEnd(sf,MPIU_INT,oconf,conf,MPIU_SUM);CHKERRQ(ierr);
ierr = PetscLogEventEnd(MATCOLORING_Comm,mc,0,0,0);CHKERRQ(ierr);
/* go through and unset local colors that have conflicts */
for (i=0;i<n;i++) {
if (conf[i]>0) {
/* push this color onto the bad stack */
badidx[nbad]=dcolors[i];
badnext[nbad]=bad[i];
bad[i]=nbad;
nbad++;
if (nbad>=badsize) {
PetscInt *newbadnext;
ISColoringValue *newbadidx;
ierr = PetscMalloc2(badsize*2,&newbadidx,badsize*2,&newbadnext);CHKERRQ(ierr);
for(k=0;k<2*badsize;k++) {
newbadnext[k]=-1;
}
for(k=0;k<badsize;k++) {
newbadidx[k]=badidx[k];
newbadnext[k]=badnext[k];
}
ierr = PetscFree2(badidx,badnext);CHKERRQ(ierr);
badidx=newbadidx;
badnext=newbadnext;
badsize*=2;
}
dcolors[i] = maxcolors;
nd--;
}
}
}
ierr = MPIU_Allreduce(&nd,&nd_global,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)mc));CHKERRQ(ierr);
}
if (mo) {
ierr = PetscSFDestroy(&sf);CHKERRQ(ierr);
ierr = PetscFree3(owts,oconf,ocolors);CHKERRQ(ierr);
}
for (i=0;i<n;i++) {
colors[i]=dcolors[i];
}
ierr = PetscFree(mask);CHKERRQ(ierr);
ierr = PetscFree4(d1cols,dcolors,conf,bad);CHKERRQ(ierr);
ierr = PetscFree2(badidx,badnext);CHKERRQ(ierr);
if (!gr->symmetric) {ierr = MatDestroy(&mt);CHKERRQ(ierr);}
PetscFunctionReturn(0);
}
/*
PEPBuildDiagonalScaling - compute two diagonal matrices to be applied for balancing
in polynomial eigenproblems.
*/
PetscErrorCode PEPBuildDiagonalScaling(PEP pep)
{
PetscErrorCode ierr;
PetscInt it,i,j,k,nmat,nr,e,nz,lst,lend,nc=0,*cols,emax,emin,emaxl,eminl;
const PetscInt *cidx,*ridx;
Mat M,*T,A;
PetscMPIInt n;
PetscBool cont=PETSC_TRUE,flg=PETSC_FALSE;
PetscScalar *array,*Dr,*Dl,t;
PetscReal l2,d,*rsum,*aux,*csum,w=1.0;
MatStructure str;
MatInfo info;
PetscFunctionBegin;
l2 = 2*PetscLogReal(2.0);
nmat = pep->nmat;
ierr = PetscMPIIntCast(pep->n,&n);
ierr = STGetMatStructure(pep->st,&str);CHKERRQ(ierr);
ierr = PetscMalloc1(nmat,&T);CHKERRQ(ierr);
for (k=0;k<nmat;k++) {
ierr = STGetTOperators(pep->st,k,&T[k]);CHKERRQ(ierr);
}
/* Form local auxiliar matrix M */
ierr = PetscObjectTypeCompareAny((PetscObject)T[0],&cont,MATMPIAIJ,MATSEQAIJ);CHKERRQ(ierr);
if (!cont) SETERRQ(PetscObjectComm((PetscObject)T[0]),PETSC_ERR_SUP,"Only for MPIAIJ or SEQAIJ matrix types");
ierr = PetscObjectTypeCompare((PetscObject)T[0],MATMPIAIJ,&cont);CHKERRQ(ierr);
if (cont) {
ierr = MatMPIAIJGetLocalMat(T[0],MAT_INITIAL_MATRIX,&M);CHKERRQ(ierr);
flg = PETSC_TRUE;
} else {
ierr = MatDuplicate(T[0],MAT_COPY_VALUES,&M);CHKERRQ(ierr);
}
ierr = MatGetInfo(M,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (i=0;i<nz;i++) {
t = PetscAbsScalar(array[i]);
array[i] = t*t;
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
for (k=1;k<nmat;k++) {
if (flg) {
ierr = MatMPIAIJGetLocalMat(T[k],MAT_INITIAL_MATRIX,&A);CHKERRQ(ierr);
} else {
if (str==SAME_NONZERO_PATTERN) {
ierr = MatCopy(T[k],A,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
} else {
ierr = MatDuplicate(T[k],MAT_COPY_VALUES,&A);CHKERRQ(ierr);
}
}
ierr = MatGetInfo(A,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = MatSeqAIJGetArray(A,&array);CHKERRQ(ierr);
for (i=0;i<nz;i++) {
t = PetscAbsScalar(array[i]);
array[i] = t*t;
}
ierr = MatSeqAIJRestoreArray(A,&array);CHKERRQ(ierr);
w *= pep->slambda*pep->slambda*pep->sfactor;
ierr = MatAXPY(M,w,A,str);CHKERRQ(ierr);
if (flg || str!=SAME_NONZERO_PATTERN || k==nmat-2) {
ierr = MatDestroy(&A);CHKERRQ(ierr);
}
}
ierr = MatGetRowIJ(M,0,PETSC_FALSE,PETSC_FALSE,&nr,&ridx,&cidx,&cont);CHKERRQ(ierr);
if (!cont) SETERRQ(PetscObjectComm((PetscObject)T[0]), PETSC_ERR_SUP,"It is not possible to compute scaling diagonals for these PEP matrices");
ierr = MatGetInfo(M,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = VecGetOwnershipRange(pep->Dl,&lst,&lend);CHKERRQ(ierr);
ierr = PetscMalloc4(nr,&rsum,pep->n,&csum,pep->n,&aux,PetscMin(pep->n-lend+lst,nz),&cols);CHKERRQ(ierr);
ierr = VecSet(pep->Dr,1.0);CHKERRQ(ierr);
ierr = VecSet(pep->Dl,1.0);CHKERRQ(ierr);
ierr = VecGetArray(pep->Dl,&Dl);CHKERRQ(ierr);
ierr = VecGetArray(pep->Dr,&Dr);CHKERRQ(ierr);
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
ierr = PetscMemzero(aux,pep->n*sizeof(PetscReal));CHKERRQ(ierr);
for (j=0;j<nz;j++) {
/* Search non-zero columns outsize lst-lend */
if (aux[cidx[j]]==0 && (cidx[j]<lst || lend<=cidx[j])) cols[nc++] = cidx[j];
/* Local column sums */
aux[cidx[j]] += PetscAbsScalar(array[j]);
}
for (it=0;it<pep->sits && cont;it++) {
emaxl = 0; eminl = 0;
/* Column sum */
if (it>0) { /* it=0 has been already done*/
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
ierr = PetscMemzero(aux,pep->n*sizeof(PetscReal));CHKERRQ(ierr);
for (j=0;j<nz;j++) aux[cidx[j]] += PetscAbsScalar(array[j]);
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
}
ierr = MPI_Allreduce(aux,csum,n,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)pep->Dr));
/* Update Dr */
for (j=lst;j<lend;j++) {
d = PetscLogReal(csum[j])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
Dr[j-lst] *= d;
aux[j] = d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
for (j=0;j<nc;j++) {
d = PetscLogReal(csum[cols[j]])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
aux[cols[j]] = d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
/* Scale M */
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (j=0;j<nz;j++) {
array[j] *= aux[cidx[j]];
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
/* Row sum */
ierr = PetscMemzero(rsum,nr*sizeof(PetscReal));CHKERRQ(ierr);
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (i=0;i<nr;i++) {
for (j=ridx[i];j<ridx[i+1];j++) rsum[i] += PetscAbsScalar(array[j]);
/* Update Dl */
d = PetscLogReal(rsum[i])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
Dl[i] *= d;
/* Scale M */
for (j=ridx[i];j<ridx[i+1];j++) array[j] *= d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
/* Compute global max and min */
ierr = MPI_Allreduce(&emaxl,&emax,1,MPIU_INT,MPIU_MAX,PetscObjectComm((PetscObject)pep->Dl));
ierr = MPI_Allreduce(&eminl,&emin,1,MPIU_INT,MPIU_MIN,PetscObjectComm((PetscObject)pep->Dl));
if (emax<=emin+2) cont = PETSC_FALSE;
}
ierr = VecRestoreArray(pep->Dr,&Dr);CHKERRQ(ierr);
ierr = VecRestoreArray(pep->Dl,&Dl);CHKERRQ(ierr);
/* Free memory*/
ierr = MatDestroy(&M);CHKERRQ(ierr);
ierr = PetscFree4(rsum,csum,aux,cols);CHKERRQ(ierr);
ierr = PetscFree(T);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode EPSSolve_Lanczos(EPS eps)
{
EPS_LANCZOS *lanczos = (EPS_LANCZOS*)eps->data;
PetscErrorCode ierr;
PetscInt nconv,i,j,k,l,x,n,*perm,restart,ncv=eps->ncv,r,ld;
Vec vi,vj,w;
Mat U;
PetscScalar *Y,*ritz,stmp;
PetscReal *d,*e,*bnd,anorm,beta,norm,rtmp,resnorm;
PetscBool breakdown;
char *conv,ctmp;
PetscFunctionBegin;
ierr = DSGetLeadingDimension(eps->ds,&ld);CHKERRQ(ierr);
ierr = PetscMalloc4(ncv,&ritz,ncv,&bnd,ncv,&perm,ncv,&conv);CHKERRQ(ierr);
/* The first Lanczos vector is the normalized initial vector */
ierr = EPSGetStartVector(eps,0,NULL);CHKERRQ(ierr);
anorm = -1.0;
nconv = 0;
/* Restart loop */
while (eps->reason == EPS_CONVERGED_ITERATING) {
eps->its++;
/* Compute an ncv-step Lanczos factorization */
n = PetscMin(nconv+eps->mpd,ncv);
ierr = DSGetArrayReal(eps->ds,DS_MAT_T,&d);CHKERRQ(ierr);
e = d + ld;
ierr = EPSBasicLanczos(eps,d,e,nconv,&n,&breakdown,anorm);CHKERRQ(ierr);
beta = e[n-1];
ierr = DSRestoreArrayReal(eps->ds,DS_MAT_T,&d);CHKERRQ(ierr);
ierr = DSSetDimensions(eps->ds,n,0,nconv,0);CHKERRQ(ierr);
ierr = DSSetState(eps->ds,DS_STATE_INTERMEDIATE);CHKERRQ(ierr);
ierr = BVSetActiveColumns(eps->V,nconv,n);CHKERRQ(ierr);
/* Solve projected problem */
ierr = DSSolve(eps->ds,ritz,NULL);CHKERRQ(ierr);
ierr = DSSort(eps->ds,ritz,NULL,NULL,NULL,NULL);CHKERRQ(ierr);
/* Estimate ||A|| */
for (i=nconv;i<n;i++)
anorm = PetscMax(anorm,PetscAbsReal(PetscRealPart(ritz[i])));
/* Compute residual norm estimates as beta*abs(Y(m,:)) + eps*||A|| */
ierr = DSGetArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
for (i=nconv;i<n;i++) {
resnorm = beta*PetscAbsScalar(Y[n-1+i*ld]) + PETSC_MACHINE_EPSILON*anorm;
ierr = (*eps->converged)(eps,ritz[i],eps->eigi[i],resnorm,&bnd[i],eps->convergedctx);CHKERRQ(ierr);
if (bnd[i]<eps->tol) conv[i] = 'C';
else conv[i] = 'N';
}
ierr = DSRestoreArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
/* purge repeated ritz values */
if (lanczos->reorthog == EPS_LANCZOS_REORTHOG_LOCAL) {
for (i=nconv+1;i<n;i++) {
if (conv[i] == 'C' && PetscAbsScalar((ritz[i]-ritz[i-1])/ritz[i]) < eps->tol) conv[i] = 'R';
}
}
/* Compute restart vector */
if (breakdown) {
ierr = PetscInfo2(eps,"Breakdown in Lanczos method (it=%D norm=%g)\n",eps->its,(double)beta);CHKERRQ(ierr);
} else {
restart = nconv;
while (restart<n && conv[restart] != 'N') restart++;
if (restart >= n) {
breakdown = PETSC_TRUE;
} else {
for (i=restart+1;i<n;i++) {
if (conv[i] == 'N') {
ierr = SlepcSCCompare(eps->sc,ritz[restart],0.0,ritz[i],0.0,&r);CHKERRQ(ierr);
if (r>0) restart = i;
}
}
ierr = DSGetArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
ierr = BVMultColumn(eps->V,1.0,0.0,n,Y+restart*ld+nconv);CHKERRQ(ierr);
ierr = DSRestoreArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
}
}
/* Count and put converged eigenvalues first */
for (i=nconv;i<n;i++) perm[i] = i;
for (k=nconv;k<n;k++) {
if (conv[perm[k]] != 'C') {
j = k + 1;
while (j<n && conv[perm[j]] != 'C') j++;
if (j>=n) break;
l = perm[k]; perm[k] = perm[j]; perm[j] = l;
}
}
/* Sort eigenvectors according to permutation */
ierr = DSGetArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
for (i=nconv;i<k;i++) {
x = perm[i];
if (x != i) {
j = i + 1;
while (perm[j] != i) j++;
/* swap eigenvalues i and j */
stmp = ritz[x]; ritz[x] = ritz[i]; ritz[i] = stmp;
rtmp = bnd[x]; bnd[x] = bnd[i]; bnd[i] = rtmp;
ctmp = conv[x]; conv[x] = conv[i]; conv[i] = ctmp;
perm[j] = x; perm[i] = i;
/* swap eigenvectors i and j */
for (l=0;l<n;l++) {
stmp = Y[l+x*ld]; Y[l+x*ld] = Y[l+i*ld]; Y[l+i*ld] = stmp;
}
}
}
ierr = DSRestoreArray(eps->ds,DS_MAT_Q,&Y);CHKERRQ(ierr);
/* compute converged eigenvectors */
ierr = DSGetMat(eps->ds,DS_MAT_Q,&U);CHKERRQ(ierr);
ierr = BVMultInPlace(eps->V,U,nconv,k);CHKERRQ(ierr);
ierr = MatDestroy(&U);CHKERRQ(ierr);
/* purge spurious ritz values */
if (lanczos->reorthog == EPS_LANCZOS_REORTHOG_LOCAL) {
for (i=nconv;i<k;i++) {
ierr = BVGetColumn(eps->V,i,&vi);CHKERRQ(ierr);
ierr = VecNorm(vi,NORM_2,&norm);CHKERRQ(ierr);
ierr = VecScale(vi,1.0/norm);CHKERRQ(ierr);
w = eps->work[0];
ierr = STApply(eps->st,vi,w);CHKERRQ(ierr);
ierr = VecAXPY(w,-ritz[i],vi);CHKERRQ(ierr);
ierr = BVRestoreColumn(eps->V,i,&vi);CHKERRQ(ierr);
ierr = VecNorm(w,NORM_2,&norm);CHKERRQ(ierr);
ierr = (*eps->converged)(eps,ritz[i],eps->eigi[i],norm,&bnd[i],eps->convergedctx);CHKERRQ(ierr);
if (bnd[i]>=eps->tol) conv[i] = 'S';
}
for (i=nconv;i<k;i++) {
if (conv[i] != 'C') {
j = i + 1;
while (j<k && conv[j] != 'C') j++;
if (j>=k) break;
/* swap eigenvalues i and j */
stmp = ritz[j]; ritz[j] = ritz[i]; ritz[i] = stmp;
rtmp = bnd[j]; bnd[j] = bnd[i]; bnd[i] = rtmp;
ctmp = conv[j]; conv[j] = conv[i]; conv[i] = ctmp;
/* swap eigenvectors i and j */
ierr = BVGetColumn(eps->V,i,&vi);CHKERRQ(ierr);
ierr = BVGetColumn(eps->V,j,&vj);CHKERRQ(ierr);
ierr = VecSwap(vi,vj);CHKERRQ(ierr);
ierr = BVRestoreColumn(eps->V,i,&vi);CHKERRQ(ierr);
ierr = BVRestoreColumn(eps->V,j,&vj);CHKERRQ(ierr);
}
}
k = i;
}
/* store ritz values and estimated errors */
for (i=nconv;i<n;i++) {
eps->eigr[i] = ritz[i];
eps->errest[i] = bnd[i];
}
ierr = EPSMonitor(eps,eps->its,nconv,eps->eigr,eps->eigi,eps->errest,n);CHKERRQ(ierr);
nconv = k;
if (eps->its >= eps->max_it) eps->reason = EPS_DIVERGED_ITS;
if (nconv >= eps->nev) eps->reason = EPS_CONVERGED_TOL;
if (eps->reason == EPS_CONVERGED_ITERATING) { /* copy restart vector */
ierr = BVCopyColumn(eps->V,n,nconv);CHKERRQ(ierr);
if (lanczos->reorthog == EPS_LANCZOS_REORTHOG_LOCAL && !breakdown) {
/* Reorthonormalize restart vector */
ierr = BVOrthogonalizeColumn(eps->V,nconv,NULL,&norm,&breakdown);CHKERRQ(ierr);
ierr = BVScaleColumn(eps->V,nconv,1.0/norm);CHKERRQ(ierr);
}
if (breakdown) {
/* Use random vector for restarting */
ierr = PetscInfo(eps,"Using random vector for restart\n");CHKERRQ(ierr);
ierr = EPSGetStartVector(eps,nconv,&breakdown);CHKERRQ(ierr);
}
if (breakdown) { /* give up */
eps->reason = EPS_DIVERGED_BREAKDOWN;
ierr = PetscInfo(eps,"Unable to generate more start vectors\n");CHKERRQ(ierr);
}
}
}
eps->nconv = nconv;
ierr = PetscFree4(ritz,bnd,perm,conv);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscSFSetGraph - Set a parallel star forest
Collective
Input Arguments:
+ sf - star forest
. nroots - number of root vertices on the current process (these are possible targets for other process to attach leaves)
. nleaves - number of leaf vertices on the current process, each of these references a root on any process
. ilocal - locations of leaves in leafdata buffers, pass NULL for contiguous storage
. localmode - copy mode for ilocal
. iremote - remote locations of root vertices for each leaf on the current process
- remotemode - copy mode for iremote
Level: intermediate
.seealso: PetscSFCreate(), PetscSFView(), PetscSFGetGraph()
@*/
PetscErrorCode PetscSFSetGraph(PetscSF sf,PetscInt nroots,PetscInt nleaves,const PetscInt *ilocal,PetscCopyMode localmode,const PetscSFNode *iremote,PetscCopyMode remotemode)
{
PetscErrorCode ierr;
PetscTable table;
PetscTablePosition pos;
PetscMPIInt size;
PetscInt i,*rcount,*ranks;
PetscFunctionBegin;
PetscValidHeaderSpecific(sf,PETSCSF_CLASSID,1);
ierr = PetscLogEventBegin(PETSCSF_SetGraph,sf,0,0,0);CHKERRQ(ierr);
if (nleaves && ilocal) PetscValidIntPointer(ilocal,4);
if (nleaves) PetscValidPointer(iremote,6);
if (nroots < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"roots %D, cannot be negative",nroots);
if (nleaves < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nleaves %D, cannot be negative",nleaves);
ierr = PetscSFReset(sf);CHKERRQ(ierr);
sf->nroots = nroots;
sf->nleaves = nleaves;
if (ilocal) {
switch (localmode) {
case PETSC_COPY_VALUES:
ierr = PetscMalloc1(nleaves,&sf->mine_alloc);CHKERRQ(ierr);
sf->mine = sf->mine_alloc;
ierr = PetscMemcpy(sf->mine,ilocal,nleaves*sizeof(*sf->mine));CHKERRQ(ierr);
sf->minleaf = PETSC_MAX_INT;
sf->maxleaf = PETSC_MIN_INT;
for (i=0; i<nleaves; i++) {
sf->minleaf = PetscMin(sf->minleaf,ilocal[i]);
sf->maxleaf = PetscMax(sf->maxleaf,ilocal[i]);
}
break;
case PETSC_OWN_POINTER:
sf->mine_alloc = (PetscInt*)ilocal;
sf->mine = sf->mine_alloc;
break;
case PETSC_USE_POINTER:
sf->mine = (PetscInt*)ilocal;
break;
default: SETERRQ(PetscObjectComm((PetscObject)sf),PETSC_ERR_ARG_OUTOFRANGE,"Unknown localmode");
}
}
if (!ilocal || nleaves > 0) {
sf->minleaf = 0;
sf->maxleaf = nleaves - 1;
}
switch (remotemode) {
case PETSC_COPY_VALUES:
ierr = PetscMalloc1(nleaves,&sf->remote_alloc);CHKERRQ(ierr);
sf->remote = sf->remote_alloc;
ierr = PetscMemcpy(sf->remote,iremote,nleaves*sizeof(*sf->remote));CHKERRQ(ierr);
break;
case PETSC_OWN_POINTER:
sf->remote_alloc = (PetscSFNode*)iremote;
sf->remote = sf->remote_alloc;
break;
case PETSC_USE_POINTER:
sf->remote = (PetscSFNode*)iremote;
break;
default: SETERRQ(PetscObjectComm((PetscObject)sf),PETSC_ERR_ARG_OUTOFRANGE,"Unknown remotemode");
}
ierr = MPI_Comm_size(PetscObjectComm((PetscObject)sf),&size);CHKERRQ(ierr);
ierr = PetscTableCreate(10,size,&table);CHKERRQ(ierr);
for (i=0; i<nleaves; i++) {
/* Log 1-based rank */
ierr = PetscTableAdd(table,iremote[i].rank+1,1,ADD_VALUES);CHKERRQ(ierr);
}
ierr = PetscTableGetCount(table,&sf->nranks);CHKERRQ(ierr);
ierr = PetscMalloc4(sf->nranks,&sf->ranks,sf->nranks+1,&sf->roffset,nleaves,&sf->rmine,nleaves,&sf->rremote);CHKERRQ(ierr);
ierr = PetscMalloc2(sf->nranks,&rcount,sf->nranks,&ranks);CHKERRQ(ierr);
ierr = PetscTableGetHeadPosition(table,&pos);CHKERRQ(ierr);
for (i=0; i<sf->nranks; i++) {
ierr = PetscTableGetNext(table,&pos,&ranks[i],&rcount[i]);CHKERRQ(ierr);
ranks[i]--; /* Convert back to 0-based */
}
ierr = PetscTableDestroy(&table);CHKERRQ(ierr);
ierr = PetscSortIntWithArray(sf->nranks,ranks,rcount);CHKERRQ(ierr);
sf->roffset[0] = 0;
for (i=0; i<sf->nranks; i++) {
ierr = PetscMPIIntCast(ranks[i],sf->ranks+i);CHKERRQ(ierr);
sf->roffset[i+1] = sf->roffset[i] + rcount[i];
rcount[i] = 0;
}
for (i=0; i<nleaves; i++) {
PetscInt lo,hi,irank;
/* Search for index of iremote[i].rank in sf->ranks */
lo = 0; hi = sf->nranks;
while (hi - lo > 1) {
PetscInt mid = lo + (hi - lo)/2;
if (iremote[i].rank < sf->ranks[mid]) hi = mid;
else lo = mid;
}
if (hi - lo == 1 && iremote[i].rank == sf->ranks[lo]) irank = lo;
else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Could not find rank %D in array",iremote[i].rank);
sf->rmine[sf->roffset[irank] + rcount[irank]] = ilocal ? ilocal[i] : i;
sf->rremote[sf->roffset[irank] + rcount[irank]] = iremote[i].index;
rcount[irank]++;
}
ierr = PetscFree2(rcount,ranks);CHKERRQ(ierr);
#if !defined(PETSC_USE_64BIT_INDICES)
if (nroots == PETSC_DETERMINE) {
/* Jed, if you have a better way to do this, put it in */
PetscInt *numRankLeaves, *leafOff, *leafIndices, *numRankRoots, *rootOff, *rootIndices, maxRoots = 0;
/* All to all to determine number of leaf indices from each (you can do this using Scan and asynch messages) */
ierr = PetscMalloc4(size,&numRankLeaves,size+1,&leafOff,size,&numRankRoots,size+1,&rootOff);CHKERRQ(ierr);
ierr = PetscMemzero(numRankLeaves, size * sizeof(PetscInt));CHKERRQ(ierr);
for (i = 0; i < nleaves; ++i) ++numRankLeaves[iremote[i].rank];
ierr = MPI_Alltoall(numRankLeaves, 1, MPIU_INT, numRankRoots, 1, MPIU_INT, PetscObjectComm((PetscObject)sf));CHKERRQ(ierr);
/* Could set nroots to this maximum */
for (i = 0; i < size; ++i) maxRoots += numRankRoots[i];
/* Gather all indices */
ierr = PetscMalloc2(nleaves,&leafIndices,maxRoots,&rootIndices);CHKERRQ(ierr);
leafOff[0] = 0;
for (i = 0; i < size; ++i) leafOff[i+1] = leafOff[i] + numRankLeaves[i];
for (i = 0; i < nleaves; ++i) leafIndices[leafOff[iremote[i].rank]++] = iremote[i].index;
leafOff[0] = 0;
for (i = 0; i < size; ++i) leafOff[i+1] = leafOff[i] + numRankLeaves[i];
rootOff[0] = 0;
for (i = 0; i < size; ++i) rootOff[i+1] = rootOff[i] + numRankRoots[i];
ierr = MPI_Alltoallv(leafIndices, numRankLeaves, leafOff, MPIU_INT, rootIndices, numRankRoots, rootOff, MPIU_INT, PetscObjectComm((PetscObject)sf));CHKERRQ(ierr);
/* Sort and reduce */
ierr = PetscSortRemoveDupsInt(&maxRoots, rootIndices);CHKERRQ(ierr);
ierr = PetscFree2(leafIndices,rootIndices);CHKERRQ(ierr);
ierr = PetscFree4(numRankLeaves,leafOff,numRankRoots,rootOff);CHKERRQ(ierr);
sf->nroots = maxRoots;
}
#endif
sf->graphset = PETSC_TRUE;
ierr = PetscLogEventEnd(PETSCSF_SetGraph,sf,0,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
