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Block solver complete for staggered. Now stable on mass 0.003 and

Browse Source 
gives 8x (!) speed up on Haswell laptop vs. standard CG for 8 RHS solves.

166 iterations vs. 537 iterations so algorithmic gain + 2x in flop rate gain.

Better than a slap in the face with a wet kipper.
This commit is contained in:
Azusa Yamaguchi 2017-06-20 12:37:41 +01:00
parent 0a8faac271
commit e9cc21900f
3 changed files with 321 additions and 222 deletions
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295
lib/algorithms/iterative/BlockConjugateGradient.h
View File

@ -33,6 +33,8 @@ directory
namespace Grid {
enum BlockCGtype { BlockCG, BlockCGrQ, CGmultiRHS };
//////////////////////////////////////////////////////////////////////////
// Block conjugate gradient. Dimension zero should be the block direction
//////////////////////////////////////////////////////////////////////////
@ -40,24 +42,274 @@ template <class Field>
class BlockConjugateGradient : public OperatorFunction<Field> {
public:
typedef typename Field::scalar_type scomplex;
int blockDim ;
int Nblock;
BlockCGtype CGtype;
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
// Defaults true.
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
BlockConjugateGradient(int _Orthog,RealD tol, Integer maxit, bool err_on_no_conv = true)
BlockConjugateGradient(BlockCGtype cgtype,int _Orthog,RealD tol, Integer maxit, bool err_on_no_conv = true)
: Tolerance(tol),
CGtype(cgtype),
blockDim(_Orthog),
MaxIterations(maxit),
ErrorOnNoConverge(err_on_no_conv){};
////////////////////////////////////////////////////////////////////////////////////////////////////
// Thin QR factorisation (google it)
////////////////////////////////////////////////////////////////////////////////////////////////////
void ThinQRfact (Eigen::MatrixXcd &m_rr,
Eigen::MatrixXcd &C,
Eigen::MatrixXcd &Cinv,
Field & Q,
const Field & R)
{
int Orthog = blockDim; // First dimension is block dim; this is an assumption
////////////////////////////////////////////////////////////////////////////////////////////////////
//Dimensions
// R_{ferm x Nblock} = Q_{ferm x Nblock} x C_{Nblock x Nblock} -> ferm x Nblock
//
// Rdag R = m_rr = Herm = L L^dag <-- Cholesky decomposition (LLT routine in Eigen)
//
// Q C = R => Q = R C^{-1}
//
// Want Ident = Q^dag Q = C^{-dag} R^dag R C^{-1} = C^{-dag} L L^dag C^{-1} = 1_{Nblock x Nblock}
//
// Set C = L^{dag}, and then Q^dag Q = ident
//
// Checks:
// Cdag C = Rdag R ; passes.
// QdagQ = 1 ; passes
////////////////////////////////////////////////////////////////////////////////////////////////////
sliceInnerProductMatrix(m_rr,R,R,Orthog);
////////////////////////////////////////////////////////////////////////////////////////////////////
// Cholesky from Eigen
// There exists a ldlt that is documented as more stable
////////////////////////////////////////////////////////////////////////////////////////////////////
Eigen::MatrixXcd L = m_rr.llt().matrixL();
C = L.adjoint();
Cinv = C.inverse();
////////////////////////////////////////////////////////////////////////////////////////////////////
// Q = R C^{-1}
//
// Q_j = R_i Cinv(i,j)
//
// NB maddMatrix conventions are Right multiplication X[j] a[j,i] already
////////////////////////////////////////////////////////////////////////////////////////////////////
// FIXME:: make a sliceMulMatrix to avoid zero vector
sliceMulMatrix(Q,Cinv,R,Orthog);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Call one of several implementations
////////////////////////////////////////////////////////////////////////////////////////////////////
void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
{
if ( CGtype == BlockCGrQ ) {
BlockCGrQsolve(Linop,Src,Psi);
} else if (CGtype == BlockCG ) {
BlockCGsolve(Linop,Src,Psi);
} else if (CGtype == CGmultiRHS ) {
CGmultiRHSsolve(Linop,Src,Psi);
} else {
assert(0);
}
}
////////////////////////////////////////////////////////////////////////////
// BlockCGrQ implementation:
//--------------------------
// X is guess/Solution
// B is RHS
// Solve A X_i = B_i ; i refers to Nblock index
////////////////////////////////////////////////////////////////////////////
void BlockCGrQsolve(LinearOperatorBase<Field> &Linop, const Field &B, Field &X)
{
int Orthog = blockDim; // First dimension is block dim; this is an assumption
Nblock = B._grid->_fdimensions[Orthog];
std::cout<<GridLogMessage<<" Block Conjugate Gradient : Orthog "<<Orthog<<" Nblock "<<Nblock<<std::endl;
X.checkerboard = B.checkerboard;
conformable(X, B);
Field tmp(B);
Field Q(B);
Field D(B);
Field Z(B);
Field AD(B);
Eigen::MatrixXcd m_DZ = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_M = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_rr = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_C = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Cinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_S = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Sinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_tmp = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_tmp1 = Eigen::MatrixXcd::Identity(Nblock,Nblock);
// Initial residual computation & set up
std::vector<RealD> residuals(Nblock);
std::vector<RealD> ssq(Nblock);
sliceNorm(ssq,B,Orthog);
RealD sssum=0;
for(int b=0;b<Nblock;b++) sssum+=ssq[b];
sliceNorm(residuals,B,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
sliceNorm(residuals,X,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
/************************************************************************
* Block conjugate gradient rQ (Sebastien Birk Thesis, after Dubrulle 2001)
************************************************************************
* Dimensions:
*
* X,B==(Nferm x Nblock)
* A==(Nferm x Nferm)
*
* Nferm = Nspin x Ncolour x Ncomplex x Nlattice_site
*
* QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
* for k:
* Z = AD
* M = [D^dag Z]^{-1}
* X = X + D MC
* QS = Q - ZM
* D = Q + D S^dag
* C = S C
*/
///////////////////////////////////////
// Initial block: initial search dir is guess
///////////////////////////////////////
std::cout << GridLogMessage<<"BlockCGrQ algorithm initialisation " <<std::endl;
//1. QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
Linop.HermOp(X, AD);
tmp = B - AD;
ThinQRfact (m_rr, m_C, m_Cinv, Q, tmp);
D=Q;
std::cout << GridLogMessage<<"BlockCGrQ computed initial residual and QR fact " <<std::endl;
///////////////////////////////////////
// Timers
///////////////////////////////////////
GridStopWatch sliceInnerTimer;
GridStopWatch sliceMaddTimer;
GridStopWatch QRTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k;
for (k = 1; k <= MaxIterations; k++) {
//3. Z = AD
MatrixTimer.Start();
Linop.HermOp(D, Z);
MatrixTimer.Stop();
//4. M = [D^dag Z]^{-1}
sliceInnerTimer.Start();
sliceInnerProductMatrix(m_DZ,D,Z,Orthog);
sliceInnerTimer.Stop();
m_M = m_DZ.inverse();
//5. X = X + D MC
m_tmp = m_M * m_C;
sliceMaddTimer.Start();
sliceMaddMatrix(X,m_tmp, D,X,Orthog);
sliceMaddTimer.Stop();
//6. QS = Q - ZM
sliceMaddTimer.Start();
sliceMaddMatrix(tmp,m_M,Z,Q,Orthog,-1.0);
sliceMaddTimer.Stop();
QRTimer.Start();
ThinQRfact (m_rr, m_S, m_Sinv, Q, tmp);
QRTimer.Stop();
//7. D = Q + D S^dag
m_tmp = m_S.adjoint();
sliceMaddTimer.Start();
sliceMaddMatrix(D,m_tmp,D,Q,Orthog);
sliceMaddTimer.Stop();
//8. C = S C
m_C = m_S*m_C;
/*********************
* convergence monitor
*********************
*/
m_rr = m_C.adjoint() * m_C;
RealD max_resid=0;
RealD rrsum=0;
RealD rr;
for(int b=0;b<Nblock;b++) {
rrsum+=real(m_rr(b,b));
rr = real(m_rr(b,b))/ssq[b];
if ( rr > max_resid ) max_resid = rr;
}
std::cout << GridLogIterative << "\titeration "<<k<<" rr_sum "<<rrsum<<" ssq_sum "<< sssum
<<" ave "<<std::sqrt(rrsum/sssum) << " max "<< max_resid <<std::endl;
if ( max_resid < Tolerance*Tolerance ) {
SolverTimer.Stop();
std::cout << GridLogMessage<<"BlockCGrQ converged in "<<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tblock "<<b<<" computed resid "
<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
Linop.HermOp(X, AD);
AD = AD-B;
std::cout << GridLogMessage <<"\t True residual is " << std::sqrt(norm2(AD)/norm2(B)) <<std::endl;
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tInnerProd " << sliceInnerTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMaddMatrix " << sliceMaddTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tThinQRfact " << QRTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
return;
}
}
std::cout << GridLogMessage << "BlockConjugateGradient(rQ) did NOT converge" << std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
//////////////////////////////////////////////////////////////////////////
// Block conjugate gradient; Original O'Leary Dimension zero should be the block direction
//////////////////////////////////////////////////////////////////////////
void BlockCGsolve(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
{
int Orthog = blockDim; // First dimension is block dim; this is an assumption
Nblock = Src._grid->_fdimensions[Orthog];
@ -163,8 +415,9 @@ void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
*********************
*/
RealD max_resid=0;
RealD rr;
for(int b=0;b<Nblock;b++){
RealD rr = real(m_rr(b,b))/ssq[b];
rr = real(m_rr(b,b))/ssq[b];
if ( rr > max_resid ) max_resid = rr;
}
@ -174,13 +427,14 @@ void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
std::cout << GridLogMessage<<"BlockCG converged in "<<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tblock "<<b<<" resid "<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
std::cout << GridLogMessage<< "\t\tblock "<<b<<" computed resid "
<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
Linop.HermOp(Psi, AP);
AP = AP-Src;
std::cout << GridLogMessage <<"\t A__ True residual is " << std::sqrt(norm2(AP)/norm2(Src)) <<std::endl;
std::cout << GridLogMessage <<"\t True residual is " << std::sqrt(norm2(AP)/norm2(Src)) <<std::endl;
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
@ -198,33 +452,11 @@ void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
};
//////////////////////////////////////////////////////////////////////////
// multiRHS conjugate gradient. Dimension zero should be the block direction
// Use this for spread out across nodes
//////////////////////////////////////////////////////////////////////////
template <class Field>
class MultiRHSConjugateGradient : public OperatorFunction<Field> {
public:
typedef typename Field::scalar_type scomplex;
int blockDim;
int Nblock;
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
// Defaults true.
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
MultiRHSConjugateGradient(int Orthog,RealD tol, Integer maxit, bool err_on_no_conv = true)
: Tolerance(tol),
blockDim(Orthog),
MaxIterations(maxit),
ErrorOnNoConverge(err_on_no_conv){};
void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
void CGmultiRHSsolve(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
{
int Orthog = blockDim; // First dimension is block dim
Nblock = Src._grid->_fdimensions[Orthog];
@ -331,7 +563,7 @@ void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
std::cout << GridLogMessage<<"MultiRHS solver converged in " <<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tBlock "<<b<<" resid "<< std::sqrt(v_rr[b]/ssq[b])<<std::endl;
std::cout << GridLogMessage<< "\t\tBlock "<<b<<" computed resid "<< std::sqrt(v_rr[b]/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
@ -357,9 +589,8 @@ void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
};
}
#endif

235
lib/lattice/Lattice_reduction.h
View File

@ -369,71 +369,6 @@ static void sliceMaddVector(Lattice<vobj> &R,std::vector<RealD> &a,const Lattice
}
};
/*
template<class vobj>
static void sliceMaddVectorSlow (Lattice<vobj> &R,std::vector<RealD> &a,const Lattice<vobj> &X,const Lattice<vobj> &Y,
int Orthog,RealD scale=1.0)
{
// FIXME: Implementation is slow
// Best base the linear combination by constructing a
// set of vectors of size grid->_rdimensions[Orthog].
typedef typename vobj::scalar_object sobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_type vector_type;
int Nblock = X._grid->GlobalDimensions()[Orthog];
GridBase *FullGrid = X._grid;
GridBase *SliceGrid = makeSubSliceGrid(FullGrid,Orthog);
Lattice<vobj> Xslice(SliceGrid);
Lattice<vobj> Rslice(SliceGrid);
// If we based this on Cshift it would work for spread out
// but it would be even slower
for(int i=0;i<Nblock;i++){
ExtractSlice(Rslice,Y,i,Orthog);
ExtractSlice(Xslice,X,i,Orthog);
Rslice = Rslice + Xslice*(scale*a[i]);
InsertSlice(Rslice,R,i,Orthog);
}
};
template<class vobj>
static void sliceInnerProductVectorSlow( std::vector<ComplexD> & vec, const Lattice<vobj> &lhs,const Lattice<vobj> &rhs,int Orthog)
{
// FIXME: Implementation is slow
// Look at localInnerProduct implementation,
// and do inside a site loop with block strided iterators
typedef typename vobj::scalar_object sobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_type vector_type;
typedef typename vobj::tensor_reduced scalar;
typedef typename scalar::scalar_object scomplex;
int Nblock = lhs._grid->GlobalDimensions()[Orthog];
vec.resize(Nblock);
std::vector<scomplex> sip(Nblock);
Lattice<scalar> IP(lhs._grid);
IP=localInnerProduct(lhs,rhs);
sliceSum(IP,sip,Orthog);
for(int ss=0;ss<Nblock;ss++){
vec[ss] = TensorRemove(sip[ss]);
}
}
*/
//////////////////////////////////////////////////////////////////////////////////////////
// FIXME: Implementation is slow
// If we based this on Cshift it would work for spread out
// but it would be even slower
//
// Repeated extract slice is inefficient
//
// Best base the linear combination by constructing a
// set of vectors of size grid->_rdimensions[Orthog].
//////////////////////////////////////////////////////////////////////////////////////////
inline GridBase *makeSubSliceGrid(const GridBase *BlockSolverGrid,int Orthog)
{
int NN = BlockSolverGrid->_ndimension;
@ -453,7 +388,6 @@ inline GridBase *makeSubSliceGrid(const GridBase *BlockSolverGrid,int Or
return (GridBase *)new GridCartesian(latt_phys,simd_phys,mpi_phys);
}
template<class vobj>
static void sliceMaddMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice<vobj> &X,const Lattice<vobj> &Y,int Orthog,RealD scale=1.0)
{
@ -469,64 +403,10 @@ static void sliceMaddMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice
Lattice<vobj> Xslice(SliceGrid);
Lattice<vobj> Rslice(SliceGrid);
#if 0
// R[i] = Y[i] + X[j] a(j,i)
for(int i=0;i<Nblock;i++){
ExtractSlice(Rslice,Y,i,Orthog);
for(int j=0;j<Nblock;j++){
ExtractSlice(Xslice,X,j,Orthog);
Rslice = Rslice + Xslice*(scale*aa(j,i));
}
InsertSlice(Rslice,R,i,Orthog);
}
#endif
#if 0
int nh = FullGrid->_ndimension;
int nl = SliceGrid->_ndimension;
#pragma omp parallel
{
std::vector<int> lcoor(nl); // sliced coor
std::vector<int> hcoor(nh); // unsliced coor
std::vector<sobj> s_x(Nblock);
#pragma omp for
for(int idx=0;idx<SliceGrid->lSites();idx++){
SliceGrid->LocalIndexToLocalCoor(idx,lcoor);
int ddl=0;
for(int d=0;d<nh;d++){
if ( d!=Orthog ) {
hcoor[d]=lcoor[ddl++];
}
}
sobj dot;
for(int i=0;i<Nblock;i++){
hcoor[Orthog] = i;
peekLocalSite(s_x[i],X,hcoor);
}
for(int i=0;i<Nblock;i++){
hcoor[Orthog] = i;
peekLocalSite(dot,Y,hcoor);
for(int j=0;j<Nblock;j++){
dot = dot + s_x[j]*(scale*aa(j,i));
}
pokeLocalSite(dot,R,hcoor);
}
}
}
#endif
#if 1
assert( FullGrid->_simd_layout[Orthog]==1);
int nh = FullGrid->_ndimension;
int nl = SliceGrid->_ndimension;
//FIXME package in a convenient iterator
//Should loop over a plane orthogonal to direction "Orthog"
int stride=FullGrid->_slice_stride[Orthog];
@ -535,7 +415,6 @@ static void sliceMaddMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice
int ostride=FullGrid->_ostride[Orthog];
#pragma omp parallel
{
std::vector<vobj> s_x(Nblock);
#pragma omp for collapse(2)
@ -543,13 +422,11 @@ static void sliceMaddMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice
for(int b=0;b<block;b++){
int o = n*stride + b;
for(int i=0;i<Nblock;i++){
s_x[i] = X[o+i*ostride];
}
vobj dot;
for(int i=0;i<Nblock;i++){
dot = Y[o+i*ostride];
for(int j=0;j<Nblock;j++){
@ -559,15 +436,63 @@ static void sliceMaddMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice
}
}}
}
#endif
};
template<class vobj>
static void sliceMulMatrix (Lattice<vobj> &R,Eigen::MatrixXcd &aa,const Lattice<vobj> &X,int Orthog,RealD scale=1.0)
{
typedef typename vobj::scalar_object sobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_type vector_type;
int Nblock = X._grid->GlobalDimensions()[Orthog];
GridBase *FullGrid = X._grid;
GridBase *SliceGrid = makeSubSliceGrid(FullGrid,Orthog);
Lattice<vobj> Xslice(SliceGrid);
Lattice<vobj> Rslice(SliceGrid);
assert( FullGrid->_simd_layout[Orthog]==1);
int nh = FullGrid->_ndimension;
int nl = SliceGrid->_ndimension;
//FIXME package in a convenient iterator
//Should loop over a plane orthogonal to direction "Orthog"
int stride=FullGrid->_slice_stride[Orthog];
int block =FullGrid->_slice_block [Orthog];
int nblock=FullGrid->_slice_nblock[Orthog];
int ostride=FullGrid->_ostride[Orthog];
#pragma omp parallel
{
std::vector<vobj> s_x(Nblock);
#pragma omp for collapse(2)
for(int n=0;n<nblock;n++){
for(int b=0;b<block;b++){
int o = n*stride + b;
for(int i=0;i<Nblock;i++){
s_x[i] = X[o+i*ostride];
}
vobj dot;
for(int i=0;i<Nblock;i++){
dot = s_x[0]*(scale*aa(0,i));
for(int j=1;j<Nblock;j++){
dot = dot + s_x[j]*(scale*aa(j,i));
}
R[o+i*ostride]=dot;
}
}}
}
};
template<class vobj>
static void sliceInnerProductMatrix( Eigen::MatrixXcd &mat, const Lattice<vobj> &lhs,const Lattice<vobj> &rhs,int Orthog)
{
// FIXME: Implementation is slow
// Not sure of best solution.. think about it
typedef typename vobj::scalar_object sobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_type vector_type;
@ -582,63 +507,6 @@ static void sliceInnerProductMatrix( Eigen::MatrixXcd &mat, const Lattice<vobj>
mat = Eigen::MatrixXcd::Zero(Nblock,Nblock);
#if 0
for(int i=0;i<Nblock;i++){
ExtractSlice(Lslice,lhs,i,Orthog);
for(int j=0;j<Nblock;j++){
ExtractSlice(Rslice,rhs,j,Orthog);
mat(i,j) = innerProduct(Lslice,Rslice);
}
}
#endif
#if 0
int nh = FullGrid->_ndimension;
int nl = SliceGrid->_ndimension;
#pragma omp parallel
{
std::vector<int> lcoor(nl); // sliced coor
std::vector<int> hcoor(nh); // unsliced coor
std::vector<sobj> Left(Nblock);
std::vector<sobj> Right(Nblock);
Eigen::MatrixXcd mat_thread = Eigen::MatrixXcd::Zero(Nblock,Nblock);
#pragma omp for
for(int idx=0;idx<SliceGrid->lSites();idx++){
SliceGrid->LocalIndexToLocalCoor(idx,lcoor);
int ddl=0;
for(int d=0;d<nh;d++){
if ( d!=Orthog ) {
hcoor[d]=lcoor[ddl++];
}
}
// Get the scalar objects
for(int i=0;i<Nblock;i++){
hcoor[Orthog] = i;
peekLocalSite(Left[i] ,lhs,hcoor);
peekLocalSite(Right[i],rhs,hcoor);
}
for(int i=0;i<Nblock;i++){
for(int j=0;j<Nblock;j++){
std::complex<double> ip = innerProduct(Left[i],Right[j]);
mat_thread(i,j) += ip;
}}
}
#pragma omp critical
{
mat += mat_thread;
}
}
#endif
#if 1
assert( FullGrid->_simd_layout[Orthog]==1);
int nh = FullGrid->_ndimension;
int nl = SliceGrid->_ndimension;
@ -681,7 +549,6 @@ static void sliceInnerProductMatrix( Eigen::MatrixXcd &mat, const Lattice<vobj>
mat += mat_thread;
}
}
#endif
return;
}

13
tests/solver/Test_staggered_block_cg_unprec.cc
View File

@ -51,7 +51,7 @@ int main (int argc, char ** argv)
typedef typename ImprovedStaggeredFermion5DR::ComplexField ComplexField;
typename ImprovedStaggeredFermion5DR::ImplParams params;
const int Ls=4;
const int Ls=8;
Grid_init(&argc,&argv);
@ -80,12 +80,13 @@ int main (int argc, char ** argv)
ConjugateGradient<FermionField> CG(1.0e-8,10000);
int blockDim = 0;
BlockConjugateGradient<FermionField> BCG(blockDim,1.0e-8,10000);
MultiRHSConjugateGradient<FermionField> mCG(blockDim,1.0e-8,10000);
BlockConjugateGradient<FermionField> BCGrQ(BlockCGrQ,blockDim,1.0e-8,10000);
BlockConjugateGradient<FermionField> BCG (BlockCG,blockDim,1.0e-8,10000);
BlockConjugateGradient<FermionField> mCG (CGmultiRHS,blockDim,1.0e-8,10000);
std::cout << GridLogMessage << "************************************************************************ "<<std::endl;
std::cout << GridLogMessage << "****************************************************************** "<<std::endl;
std::cout << GridLogMessage << " Calling 4d CG "<<std::endl;
std::cout << GridLogMessage << "************************************************************************ "<<std::endl;
std::cout << GridLogMessage << "****************************************************************** "<<std::endl;
ImprovedStaggeredFermionR Ds4d(Umu,Umu,*UGrid,*UrbGrid,mass);
MdagMLinearOperator<ImprovedStaggeredFermionR,FermionField> HermOp4d(Ds4d);
FermionField src4d(UGrid); random(pRNG,src4d);
@ -112,7 +113,7 @@ int main (int argc, char ** argv)
std::cout << GridLogMessage << " Calling Block CG for "<<Ls <<" right hand sides" <<std::endl;
std::cout << GridLogMessage << "************************************************************************ "<<std::endl;
result=zero;
BCG(HermOp,src,result);
BCGrQ(HermOp,src,result);
std::cout << GridLogMessage << "************************************************************************ "<<std::endl;