portelli/Grid
1
0
Fork 0
mirror of https://github.com/paboyle/Grid.git synced 2025-06-18 15:57:05 +01:00
Code Releases Activity

WilsonMG: Move tests for Wilson & WilsonClover into the same file

Browse Source
This commit is contained in:
Daniel Richtmann
2018-03-23 20:46:10 +01:00
parent bbe1d5b49e
commit 3c3ec4e267
2 changed files with 373 additions and 1089 deletions
Download Patch File Download Diff File Expand all files Collapse all files

614
tests/solver/Test_wilson_mg.cc
View File

@ -1,614 +0,0 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./tests/solver/Test_wilson_mg.cc
Copyright (C) 2017
Author: Daniel Richtmann <daniel.richtmann@ur.de>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#include <Grid/Grid.h>
#include <Grid/algorithms/iterative/PrecGeneralisedConjugateResidual.h>
using namespace std;
using namespace Grid;
using namespace Grid::QCD;
template<class Field, int nbasis> class TestVectorAnalyzer {
public:
void operator()(LinearOperatorBase<Field> &Linop, std::vector<Field> const &vectors, int nn = nbasis) {
auto positiveOnes = 0;
std::vector<Field> tmp(4, vectors[0]._grid);
Gamma g5(Gamma::Algebra::Gamma5);
std::cout << GridLogMessage << "Test vector analysis:" << std::endl;
for(auto i = 0; i < nn; ++i) {
Linop.Op(vectors[i], tmp[3]);
tmp[0] = g5 * tmp[3];
auto lambda = innerProduct(vectors[i], tmp[0]) / innerProduct(vectors[i], vectors[i]);
tmp[1] = tmp[0] - lambda * vectors[i];
auto mu = ::sqrt(norm2(tmp[1]) / norm2(vectors[i]));
auto nrm = ::sqrt(norm2(vectors[i]));
if(real(lambda) > 0)
positiveOnes++;
std::cout << GridLogMessage << std::scientific << std::setprecision(2) << std::setw(2) << std::showpos << "vector " << i << ": "
<< "singular value: " << lambda << ", singular vector precision: " << mu << ", norm: " << nrm << std::endl;
}
std::cout << GridLogMessage << std::scientific << std::setprecision(2) << std::setw(2) << std::showpos << positiveOnes << " out of "
<< nn << " vectors were positive" << std::endl;
}
};
// clang-format off
struct MultigridParams : Serializable {
public:
GRID_SERIALIZABLE_CLASS_MEMBERS(MultigridParams,
int, nLevels,
std::vector<std::vector<int>>, blockSizes);
MultigridParams(){};
};
MultigridParams mgParams;
// clang-format on
struct LevelInfo {
public:
std::vector<std::vector<int>> Seeds;
std::vector<GridCartesian *> Grids;
std::vector<GridParallelRNG> PRNGs;
LevelInfo(GridCartesian *FineGrid, MultigridParams const &Params) {
auto nCoarseLevels = Params.blockSizes.size();
assert(nCoarseLevels == Params.nLevels - 1);
// set up values for finest grid
Grids.push_back(FineGrid);
Seeds.push_back({1, 2, 3, 4});
PRNGs.push_back(GridParallelRNG(Grids.back()));
PRNGs.back().SeedFixedIntegers(Seeds.back());
// set up values for coarser grids
for(int level = 1; level < Params.nLevels; ++level) {
auto Nd = Grids[level - 1]->_ndimension;
auto tmp = Grids[level - 1]->_fdimensions;
assert(tmp.size() == Nd);
Seeds.push_back(std::vector<int>(Nd));
for(int d = 0; d < Nd; ++d) {
tmp[d] /= Params.blockSizes[level - 1][d];
Seeds[level][d] = (level)*Nd + d + 1;
}
Grids.push_back(SpaceTimeGrid::makeFourDimGrid(tmp, GridDefaultSimd(Nd, vComplex::Nsimd()), GridDefaultMpi()));
PRNGs.push_back(GridParallelRNG(Grids[level]));
PRNGs[level].SeedFixedIntegers(Seeds[level]);
}
std::cout << GridLogMessage << "Constructed " << Params.nLevels << " levels" << std::endl;
// The construction above corresponds to the finest level having level == 0
// (simply because it's not as ugly to implement), but we need it the
// other way round (i.e., the coarsest level to have level == 0) for the MG
// Preconditioner -> reverse the vectors
std::reverse(Seeds.begin(), Seeds.end());
std::reverse(Grids.begin(), Grids.end());
std::reverse(PRNGs.begin(), PRNGs.end());
for(int level = 0; level < Params.nLevels; ++level) {
std::cout << GridLogMessage << "level = " << level << ":" << std::endl;
Grids[level]->show_decomposition();
}
}
};
template<class Field> void testLinearOperator(LinearOperatorBase<Field> &LinOp, GridBase *Grid, std::string const &name = "") {
std::vector<int> seeds({1, 2, 3, 4});
GridParallelRNG RNG(Grid);
RNG.SeedFixedIntegers(seeds);
{
std::cout << GridLogMessage << "Testing that Mdiag + Σ_μ Mdir_μ == M for operator " << name << ":" << std::endl;
// clang-format off
Field src(Grid); random(RNG, src);
Field ref(Grid); ref = zero;
Field result(Grid); result = zero;
Field diag(Grid); diag = zero;
Field sumDir(Grid); sumDir = zero;
Field tmp(Grid);
Field err(Grid);
// clang-format on
std::cout << setprecision(9);
std::cout << GridLogMessage << " norm2(src)\t\t\t\t= " << norm2(src) << std::endl;
LinOp.OpDiag(src, diag);
std::cout << GridLogMessage << " norm2(Mdiag * src)\t\t\t= " << norm2(diag) << std::endl;
for(int dir = 0; dir < 4; dir++) {
for(auto disp : {+1, -1}) {
LinOp.OpDir(src, tmp, dir, disp);
std::cout << GridLogMessage << " norm2(Mdir_{" << dir << "," << disp << "} * src)\t\t= " << norm2(tmp) << std::endl;
sumDir = sumDir + tmp;
}
}
std::cout << GridLogMessage << " norm2(Σ_μ Mdir_μ * src)\t\t= " << norm2(sumDir) << std::endl;
result = diag + sumDir;
std::cout << GridLogMessage << " norm2((Mdiag + Σ_μ Mdir_μ) * src)\t= " << norm2(result) << std::endl;
LinOp.Op(src, ref);
std::cout << GridLogMessage << " norm2(M * src)\t\t\t= " << norm2(ref) << std::endl;
err = ref - result;
std::cout << GridLogMessage << " Absolute deviation\t\t\t= " << norm2(err) << std::endl;
std::cout << GridLogMessage << " Relative deviation\t\t\t= " << norm2(err) / norm2(ref) << std::endl;
}
{
std::cout << GridLogMessage << "Testing hermiticity stochastically for operator " << name << ":" << std::endl;
// clang-format off
Field phi(Grid); random(RNG, phi);
Field chi(Grid); random(RNG, chi);
Field MPhi(Grid);
Field MdagChi(Grid);
// clang-format on
LinOp.Op(phi, MPhi);
LinOp.AdjOp(chi, MdagChi);
ComplexD chiMPhi = innerProduct(chi, MPhi);
ComplexD phiMdagChi = innerProduct(phi, MdagChi);
ComplexD phiMPhi = innerProduct(phi, MPhi);
ComplexD chiMdagChi = innerProduct(chi, MdagChi);
std::cout << GridLogMessage << " chiMPhi = " << chiMPhi << " phiMdagChi = " << phiMdagChi
<< " difference = " << chiMPhi - conjugate(phiMdagChi) << std::endl;
std::cout << GridLogMessage << " phiMPhi = " << phiMPhi << " chiMdagChi = " << chiMdagChi << " <- should be real if hermitian"
<< std::endl;
}
{
std::cout << GridLogMessage << "Testing linearity for operator " << name << ":" << std::endl;
// clang-format off
Field phi(Grid); random(RNG, phi);
Field chi(Grid); random(RNG, chi);
Field phiPlusChi(Grid);
Field MPhi(Grid);
Field MChi(Grid);
Field MPhiPlusChi(Grid);
Field linearityError(Grid);
// clang-format on
LinOp.Op(phi, MPhi);
LinOp.Op(chi, MChi);
phiPlusChi = phi + chi;
LinOp.Op(phiPlusChi, MPhiPlusChi);
linearityError = MPhiPlusChi - MPhi;
linearityError = linearityError - MChi;
std::cout << GridLogMessage << " norm2(linearityError) = " << norm2(linearityError) << std::endl;
}
}
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nBasis, int level, class Matrix>
class MultiGridPreconditioner : public LinearFunction<Lattice<Fobj>> {
public:
/////////////////////////////////////////////
// Type Definitions
/////////////////////////////////////////////
typedef Aggregation<Fobj, CoarseScalar, nBasis> Aggregates;
typedef CoarsenedMatrix<Fobj, CoarseScalar, nBasis> CoarseMatrix;
typedef typename Aggregates::CoarseVector CoarseVector;
typedef typename Aggregates::siteVector CoarseSiteVector;
typedef Matrix FineMatrix;
typedef typename Aggregates::FineField FineVector;
typedef MultiGridPreconditioner<CoarseSiteVector, CoarseScalar, nCoarseSpins, nBasis, level - 1, CoarseMatrix> NextPreconditionerLevel;
/////////////////////////////////////////////
// Member Data
/////////////////////////////////////////////
LevelInfo & _LevelInfo;
FineMatrix & _FineMatrix;
FineMatrix & _SmootherMatrix;
Aggregates _Aggregates;
CoarseMatrix _CoarseMatrix;
std::unique_ptr<NextPreconditionerLevel> _NextPreconditionerLevel;
/////////////////////////////////////////////
// Member Functions
/////////////////////////////////////////////
MultiGridPreconditioner(LevelInfo &LvlInfo, FineMatrix &FineMat, FineMatrix &SmootherMat)
: _LevelInfo(LvlInfo)
, _FineMatrix(FineMat)
, _SmootherMatrix(SmootherMat)
, _Aggregates(_LevelInfo.Grids[level - 1], _LevelInfo.Grids[level], 0)
, _CoarseMatrix(*_LevelInfo.Grids[level - 1]) {
_NextPreconditionerLevel = std::unique_ptr<NextPreconditionerLevel>(
new NextPreconditionerLevel(_LevelInfo, _CoarseMatrix, _CoarseMatrix));
}
void setup() {
Gamma g5(Gamma::Algebra::Gamma5);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
_Aggregates.CreateSubspace(_LevelInfo.PRNGs[level], fineMdagMOp /*, nb */); // NOTE: Don't specify nb to see the orthogonalization check
// TestVectorAnalyzer<FineVector, nbasis> fineTVA;
// fineTVA(fineMdagMOp, _Aggregates.subspace);
static_assert((nBasis & 0x1) == 0, "MG Preconditioner only supports an even number of basis vectors");
int nb = nBasis / 2;
for(
int n = 0; n < nb;
n++) { // TODO: to get this to work for more than two levels, I would need to either implement coarse spins or have a template specialization of this class also for the finest level
_Aggregates.subspace[n + nb] = g5 * _Aggregates.subspace[n];
}
_CoarseMatrix.CoarsenOperator(_LevelInfo.Grids[level], fineMdagMOp, _Aggregates);
_NextPreconditionerLevel->setup();
}
virtual void operator()(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
// TODO: implement a W-cycle and a toggle to switch between the cycling strategies
vCycle(in, out);
// kCycle(in, out);
}
void vCycle(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
RealD inputNorm = norm2(in);
CoarseVector coarseSrc(_LevelInfo.Grids[level - 1]);
CoarseVector coarseSol(_LevelInfo.Grids[level - 1]);
coarseSol = zero;
FineVector fineTmp(in._grid);
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(1.0e-14, 1, fineTrivialPreconditioner, 1, false);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
MdagMLinearOperator<FineMatrix, FineVector> fineSmootherMdagMOp(_SmootherMatrix);
_Aggregates.ProjectToSubspace(coarseSrc, in);
(*_NextPreconditionerLevel)(coarseSrc, coarseSol);
_Aggregates.PromoteFromSubspace(coarseSol, out);
fineMdagMOp.Op(out, fineTmp);
fineTmp = in - fineTmp;
auto r = norm2(fineTmp);
auto residualAfterCoarseGridCorrection = std::sqrt(r / inputNorm);
fineFGMRES(fineSmootherMdagMOp, in, out);
fineMdagMOp.Op(out, fineTmp);
fineTmp = in - fineTmp;
r = norm2(fineTmp);
auto residualAfterPostSmoother = std::sqrt(r / inputNorm);
std::cout << GridLogMG << " Level " << level << ": Input norm = " << std::sqrt(inputNorm)
<< " Coarse residual = " << residualAfterCoarseGridCorrection << " Post-Smoother residual = " << residualAfterPostSmoother
<< std::endl;
}
void kCycle(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
RealD inputNorm = norm2(in);
CoarseVector coarseSrc(_LevelInfo.Grids[level - 1]);
CoarseVector coarseSol(_LevelInfo.Grids[level - 1]);
coarseSol = zero;
FineVector fineTmp(in._grid);
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(1.0e-14, 1, fineTrivialPreconditioner, 1, false);
FlexibleGeneralisedMinimalResidual<CoarseVector> coarseFGMRES(1.0e-14, 1, *_NextPreconditionerLevel, 1, false);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
MdagMLinearOperator<FineMatrix, FineVector> fineSmootherMdagMOp(_SmootherMatrix);
MdagMLinearOperator<CoarseMatrix, CoarseVector> coarseMdagMOp(_CoarseMatrix);
_Aggregates.ProjectToSubspace(coarseSrc, in);
coarseFGMRES(coarseMdagMOp, coarseSrc, coarseSol);
_Aggregates.PromoteFromSubspace(coarseSol, out);
fineMdagMOp.Op(out, fineTmp);
fineTmp = in - fineTmp;
auto r = norm2(fineTmp);
auto residualAfterCoarseGridCorrection = std::sqrt(r / inputNorm);
fineFGMRES(fineSmootherMdagMOp, in, out);
fineMdagMOp.Op(out, fineTmp);
fineTmp = in - fineTmp;
r = norm2(fineTmp);
auto residualAfterPostSmoother = std::sqrt(r / inputNorm);
std::cout << GridLogMG << " Level " << level << ": Input norm = " << std::sqrt(inputNorm)
<< " Coarse residual = " << residualAfterCoarseGridCorrection << " Post-Smoother residual = " << residualAfterPostSmoother
<< std::endl;
}
void runChecks() {
auto tolerance = 1e-13; // TODO: this obviously depends on the precision we use, current value is for double
auto coarseLevel = level - 1;
std::vector<FineVector> fineTmps(2, _LevelInfo.Grids[level]);
std::vector<CoarseVector> coarseTmps(4, _LevelInfo.Grids[level - 1]);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
MdagMLinearOperator<CoarseMatrix, CoarseVector> coarseMdagMOp(_CoarseMatrix);
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (1 - P R) v" << std::endl;
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
for(auto i = 0; i < _Aggregates.subspace.size(); ++i) {
_Aggregates.ProjectToSubspace(coarseTmps[0], _Aggregates.subspace[i]); // R v_i
_Aggregates.PromoteFromSubspace(coarseTmps[0], fineTmps[0]); // P R v_i
fineTmps[1] = _Aggregates.subspace[i] - fineTmps[0]; // v_i - P R v_i
auto deviation = std::sqrt(norm2(fineTmps[1]) / norm2(_Aggregates.subspace[i]));
std::cout << GridLogMG << " Level " << level << ": Vector " << i << ": norm2(v_i) = " << norm2(_Aggregates.subspace[i])
<< " | norm2(R v_i) = " << norm2(coarseTmps[0]) << " | norm2(P R v_i) = " << norm2(fineTmps[0])
<< " | relative deviation = " << deviation;
if(deviation > tolerance) {
std::cout << " > " << tolerance << " -> check failed" << std::endl;
// abort();
} else {
std::cout << " < " << tolerance << " -> check passed" << std::endl;
}
}
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (1 - R P) v_c" << std::endl;
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
_Aggregates.PromoteFromSubspace(coarseTmps[0], fineTmps[0]); // P v_c
_Aggregates.ProjectToSubspace(coarseTmps[1], fineTmps[0]); // R P v_c
coarseTmps[2] = coarseTmps[0] - coarseTmps[1]; // v_c - R P v_c
auto deviation = std::sqrt(norm2(coarseTmps[2]) / norm2(coarseTmps[0]));
std::cout << GridLogMG << " Level " << level << ": norm2(v_c) = " << norm2(coarseTmps[0])
<< " | norm2(R P v_c) = " << norm2(coarseTmps[1]) << " | norm2(P v_c) = " << norm2(fineTmps[0])
<< " | relative deviation = " << deviation;
if(deviation > tolerance) {
std::cout << " > " << tolerance << " -> check failed" << std::endl;
// abort();
} else {
std::cout << " < " << tolerance << " -> check passed" << std::endl;
}
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (R D P - D_c) v_c" << std::endl;
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
_Aggregates.PromoteFromSubspace(coarseTmps[0], fineTmps[0]); // P v_c
fineMdagMOp.Op(fineTmps[0], fineTmps[1]); // D P v_c
_Aggregates.ProjectToSubspace(coarseTmps[1], fineTmps[1]); // R D P v_c
coarseMdagMOp.Op(coarseTmps[0], coarseTmps[2]); // D_c v_c
coarseTmps[3] = coarseTmps[1] - coarseTmps[2]; // R D P v_c - D_c v_c
deviation = std::sqrt(norm2(coarseTmps[3]) / norm2(coarseTmps[1]));
std::cout << GridLogMG << " Level " << level << ": norm2(R D P v_c) = " << norm2(coarseTmps[1])
<< " | norm2(D_c v_c) = " << norm2(coarseTmps[2]) << " | relative deviation = " << deviation;
if(deviation > tolerance) {
std::cout << " > " << tolerance << " -> check failed" << std::endl;
// abort();
} else {
std::cout << " < " << tolerance << " -> check passed" << std::endl;
}
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == |(Im(v_c^dag D_c^dag D_c v_c)|" << std::endl;
std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
coarseMdagMOp.Op(coarseTmps[0], coarseTmps[1]); // D_c v_c
coarseMdagMOp.AdjOp(coarseTmps[1], coarseTmps[2]); // D_c^dag D_c v_c
auto dot = innerProduct(coarseTmps[0], coarseTmps[2]); //v_c^dag D_c^dag D_c v_c
deviation = abs(imag(dot)) / abs(real(dot));
std::cout << GridLogMG << " Level " << level << ": Re(v_c^dag D_c^dag D_c v_c) = " << real(dot)
<< " | Im(v_c^dag D_c^dag D_c v_c) = " << imag(dot) << " | relative deviation = " << deviation;
if(deviation > tolerance) {
std::cout << " > " << tolerance << " -> check failed" << std::endl;
// abort();
} else {
std::cout << " < " << tolerance << " -> check passed"
<< std::endl; // TODO: this check will work only when I got Mdag in CoarsenedMatrix to work
}
_NextPreconditionerLevel->runChecks();
}
};
// Specialize the coarsest level, this corresponds to counting downwards with level: coarsest = 0, finest = N
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, class Matrix>
class MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, 0, Matrix> : public LinearFunction<Lattice<Fobj>> {
public:
/////////////////////////////////////////////
// Type Definitions
/////////////////////////////////////////////
typedef Matrix FineMatrix;
typedef Lattice<Fobj> FineVector;
/////////////////////////////////////////////
// Member Data
/////////////////////////////////////////////
LevelInfo & _LevelInfo;
FineMatrix &_FineMatrix;
FineMatrix &_SmootherMatrix;
/////////////////////////////////////////////
// Member Functions
/////////////////////////////////////////////
MultiGridPreconditioner(LevelInfo &LvlInfo, FineMatrix &FineMat, FineMatrix &SmootherMat)
: _LevelInfo(LvlInfo), _FineMatrix(FineMat), _SmootherMatrix(SmootherMat) {}
void setup() {}
virtual void operator()(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(1.0e-14, 1, fineTrivialPreconditioner, 1, false);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
fineFGMRES(fineMdagMOp, in, out);
}
void runChecks() {}
};
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, class Matrix>
using FourLevelMGPreconditioner = MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, 4 - 1, Matrix>;
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, class Matrix>
using ThreeLevelMGPreconditioner = MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, 3 - 1, Matrix>;
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, class Matrix>
using TwoLevelMGPreconditioner = MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, 2 - 1, Matrix>;
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, int nlevel, class Matrix>
using NLevelMGPreconditioner = MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, nlevel - 1, Matrix>;
int main(int argc, char **argv) {
Grid_init(&argc, &argv);
GridCartesian *FGrid = SpaceTimeGrid::makeFourDimGrid(GridDefaultLatt(), GridDefaultSimd(Nd, vComplex::Nsimd()), GridDefaultMpi());
GridRedBlackCartesian *FrbGrid = SpaceTimeGrid::makeFourDimRedBlackGrid(FGrid);
std::vector<int> fSeeds({1, 2, 3, 4});
GridParallelRNG fPRNG(FGrid);
fPRNG.SeedFixedIntegers(fSeeds);
Gamma g5(Gamma::Algebra::Gamma5);
// clang-format off
LatticeFermion src(FGrid); gaussian(fPRNG, src);
LatticeFermion result(FGrid); result = zero;
LatticeGaugeField Umu(FGrid); SU3::HotConfiguration(fPRNG, Umu);
// clang-format on
RealD mass = 0.5;
const int nbasis = 20;
WilsonFermionR Dw(Umu, *FGrid, *FrbGrid, mass);
// mgParams.blockSizes = {{2, 2, 2, 2}, {2, 2, 1, 1}, {1, 1, 2, 1}};
// mgParams.blockSizes = {{2, 2, 2, 2}, {2, 2, 1, 1}};
mgParams.blockSizes = {{2, 2, 2, 2}};
mgParams.nLevels = mgParams.blockSizes.size() + 1;
std::cout << mgParams << std::endl;
LevelInfo levelInfo(FGrid, mgParams);
MdagMLinearOperator<WilsonFermionR, LatticeFermion> FineMdagMOp(Dw);
TrivialPrecon<LatticeFermion> TrivialPrecon;
TwoLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, WilsonFermionR> TwoLevelMGPrecon(levelInfo, Dw, Dw);
// ThreeLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, WilsonFermionR> ThreeLevelMGPrecon(levelInfo, Dw, Dw);
// FourLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, WilsonFermionR> FourLevelMGPrecon(levelInfo, Dw, Dw);
// NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 4, WilsonFermionR> FourLevelMGPrecon(levelInfo, Dw, Dw);
TwoLevelMGPrecon.setup();
TwoLevelMGPrecon.runChecks();
// ThreeLevelMGPrecon.setup();
// ThreeLevelMGPrecon.runChecks();
// FourLevelMGPrecon.setup();
// FourLevelMGPrecon.runChecks();
// NLevelMGPrecon.setup();
// NLevelMGPrecon.runChecks();
std::vector<std::unique_ptr<OperatorFunction<LatticeFermion>>> solvers;
solvers.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TrivialPrecon, 1000, false));
solvers.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TwoLevelMGPrecon, 1000, false));
// solvers.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, ThreeLevelMGPrecon, 1000, false));
// solvers.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, FourLevelMGPrecon, 1000, false));
// solvers.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, NLevelMGPrecon, 1000, false));
for(auto const &solver : solvers) {
std::cout << "Starting with a new solver" << std::endl;
result = zero;
(*solver)(FineMdagMOp, src, result);
std::cout << std::endl;
}
Grid_finalize();
}