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917a92118a
Grid/tests/solver/Test_wilsonclover_mg.cc
Daniel Richtmann 917a92118a
WilsonMG: Move operator test to MG testing routine
2018-03-28 12:19:25 +02:00

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./tests/solver/Test_wilsonclover_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>
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;
}
};
// TODO: Can think about having one parameter struct per level and then a
// vector of these structs. How well would that work together with the
// serialization strategy of Grid?
// clang-format off
struct MultiGridParams : Serializable {
public:
GRID_SERIALIZABLE_CLASS_MEMBERS(MultiGridParams,
int, nLevels,
std::vector<std::vector<int>>, blockSizes, // size == nLevels - 1
std::vector<double>, smootherTol, // size == nLevels - 1
std::vector<int>, smootherMaxOuterIter, // size == nLevels - 1
std::vector<int>, smootherMaxInnerIter, // size == nLevels - 1
bool, kCycle,
std::vector<double>, kCycleTol, // size == nLevels - 1
std::vector<int>, kCycleMaxOuterIter, // size == nLevels - 1
std::vector<int>, kCycleMaxInnerIter, // size == nLevels - 1
double, coarseSolverTol,
int, coarseSolverMaxOuterIter,
int, coarseSolverMaxInnerIter);
MultiGridParams(){};
};
MultiGridParams mgParams;
// clang-format on
void checkParameterValidity(MultiGridParams const &params) {
auto correctSize = mgParams.nLevels - 1;
assert(correctSize == params.blockSizes.size());
assert(correctSize == params.smootherTol.size());
assert(correctSize == params.smootherMaxOuterIter.size());
assert(correctSize == params.smootherMaxInnerIter.size());
assert(correctSize == params.kCycleTol.size());
assert(correctSize == params.kCycleMaxOuterIter.size());
assert(correctSize == params.kCycleMaxInnerIter.size());
}
struct LevelInfo {
public:
std::vector<std::vector<int>> Seeds;
std::vector<GridCartesian *> Grids;
std::vector<GridParallelRNG> PRNGs;
LevelInfo(GridCartesian *FineGrid, MultiGridParams const &mgParams) {
auto nCoarseLevels = mgParams.blockSizes.size();
assert(nCoarseLevels == mgParams.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 < mgParams.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] /= mgParams.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 " << mgParams.nLevels << " levels" << std::endl;
for(int level = 0; level < mgParams.nLevels; ++level) {
std::cout << GridLogMessage << "level = " << level << ":" << std::endl;
Grids[level]->show_decomposition();
}
}
};
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nBasis, int nCoarserLevels, class Matrix>
class MultiGridPreconditioner : public LinearFunction<Lattice<Fobj>> {
public:
/////////////////////////////////////////////
// Type Definitions
/////////////////////////////////////////////
// clang-format off
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, nCoarserLevels - 1, CoarseMatrix> NextPreconditionerLevel;
// clang-format on
/////////////////////////////////////////////
// Member Data
/////////////////////////////////////////////
int _CurrentLevel;
int _NextCoarserLevel;
MultiGridParams & _MultiGridParams;
LevelInfo & _LevelInfo;
FineMatrix & _FineMatrix;
FineMatrix & _SmootherMatrix;
Aggregates _Aggregates;
CoarseMatrix _CoarseMatrix;
std::unique_ptr<NextPreconditionerLevel> _NextPreconditionerLevel;
/////////////////////////////////////////////
// Member Functions
/////////////////////////////////////////////
MultiGridPreconditioner(MultiGridParams &mgParams, LevelInfo &LvlInfo, FineMatrix &FineMat, FineMatrix &SmootherMat)
: _CurrentLevel(mgParams.nLevels - (nCoarserLevels + 1)) // _Level = 0 corresponds to finest
, _NextCoarserLevel(_CurrentLevel + 1) // incremented for instances on coarser levels
, _MultiGridParams(mgParams)
, _LevelInfo(LvlInfo)
, _FineMatrix(FineMat)
, _SmootherMatrix(SmootherMat)
, _Aggregates(_LevelInfo.Grids[_NextCoarserLevel], _LevelInfo.Grids[_CurrentLevel], 0)
, _CoarseMatrix(*_LevelInfo.Grids[_NextCoarserLevel]) {
_NextPreconditionerLevel
= std::unique_ptr<NextPreconditionerLevel>(new NextPreconditionerLevel(_MultiGridParams, _LevelInfo, _CoarseMatrix, _CoarseMatrix));
}
void setup() {
Gamma g5(Gamma::Algebra::Gamma5);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
// NOTE: Don't specify nb here to see the orthogonalization check
_Aggregates.CreateSubspace(_LevelInfo.PRNGs[_CurrentLevel], fineMdagMOp /*, nb */);
// 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;
// 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
for(int n = 0; n < nb; n++) {
_Aggregates.subspace[n + nb] = g5 * _Aggregates.subspace[n];
}
_CoarseMatrix.CoarsenOperator(_LevelInfo.Grids[_CurrentLevel], fineMdagMOp, _Aggregates);
_NextPreconditionerLevel->setup();
}
virtual void operator()(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
// TODO: implement a W-cycle
if(_MultiGridParams.kCycle)
kCycle(in, out);
else
vCycle(in, out);
}
void vCycle(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
RealD inputNorm = norm2(in);
CoarseVector coarseSrc(_LevelInfo.Grids[_NextCoarserLevel]);
CoarseVector coarseSol(_LevelInfo.Grids[_NextCoarserLevel]);
coarseSol = zero;
FineVector fineTmp(in._grid);
auto maxSmootherIter = _MultiGridParams.smootherMaxOuterIter[_CurrentLevel] * _MultiGridParams.smootherMaxInnerIter[_CurrentLevel];
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(_MultiGridParams.smootherTol[_CurrentLevel],
maxSmootherIter,
fineTrivialPreconditioner,
_MultiGridParams.smootherMaxInnerIter[_CurrentLevel],
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 " << _CurrentLevel << ": V-cycle: 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[_NextCoarserLevel]);
CoarseVector coarseSol(_LevelInfo.Grids[_NextCoarserLevel]);
coarseSol = zero;
FineVector fineTmp(in._grid);
auto smootherMaxIter = _MultiGridParams.smootherMaxOuterIter[_CurrentLevel] * _MultiGridParams.smootherMaxInnerIter[_CurrentLevel];
auto kCycleMaxIter = _MultiGridParams.kCycleMaxOuterIter[_CurrentLevel] * _MultiGridParams.kCycleMaxInnerIter[_CurrentLevel];
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(_MultiGridParams.smootherTol[_CurrentLevel],
smootherMaxIter,
fineTrivialPreconditioner,
_MultiGridParams.smootherMaxInnerIter[_CurrentLevel],
false);
FlexibleGeneralisedMinimalResidual<CoarseVector> coarseFGMRES(_MultiGridParams.kCycleTol[_CurrentLevel],
kCycleMaxIter,
*_NextPreconditionerLevel,
_MultiGridParams.kCycleMaxInnerIter[_CurrentLevel],
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 " << _CurrentLevel << ": K-cycle: 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
std::vector<FineVector> fineTmps(7, _LevelInfo.Grids[_CurrentLevel]);
std::vector<CoarseVector> coarseTmps(4, _LevelInfo.Grids[_NextCoarserLevel]);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
MdagMLinearOperator<CoarseMatrix, CoarseVector> coarseMdagMOp(_CoarseMatrix);
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (M - (Mdiag + Σ_μ Mdir_μ)) * v" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[_CurrentLevel], fineTmps[0]);
fineMdagMOp.Op(fineTmps[0], fineTmps[1]); // M * v
fineMdagMOp.OpDiag(fineTmps[0], fineTmps[2]); // Mdiag * v
fineTmps[4] = zero;
for(int dir = 0; dir < 4; dir++) { // Σ_μ Mdir_μ * v
for(auto disp : {+1, -1}) {
fineMdagMOp.OpDir(fineTmps[0], fineTmps[3], dir, disp);
fineTmps[4] = fineTmps[4] + fineTmps[3];
}
}
fineTmps[5] = fineTmps[2] + fineTmps[4]; // (Mdiag + Σ_μ Mdir_μ) * v
fineTmps[6] = fineTmps[1] - fineTmps[5];
auto deviation = std::sqrt(norm2(fineTmps[6]) / norm2(fineTmps[1]));
std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2(M * v) = " << norm2(fineTmps[1]) << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2(Mdiag * v) = " << norm2(fineTmps[2]) << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2(Σ_μ Mdir_μ * v) = " << norm2(fineTmps[4]) << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2((Mdiag + Σ_μ Mdir_μ) * v) = " << norm2(fineTmps[5]) << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": 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 " << _CurrentLevel << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (1 - P R) v" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << 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
deviation = std::sqrt(norm2(fineTmps[1]) / norm2(_Aggregates.subspace[i]));
std::cout << GridLogMG << " Level " << _CurrentLevel << ": 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 " << _CurrentLevel << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (1 - R P) v_c" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[_NextCoarserLevel], 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
deviation = std::sqrt(norm2(coarseTmps[2]) / norm2(coarseTmps[0]));
std::cout << GridLogMG << " Level " << _CurrentLevel << ": 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 " << _CurrentLevel << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (R D P - D_c) v_c" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[_NextCoarserLevel], 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 " << _CurrentLevel << ": 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 " << _CurrentLevel << ": **************************************************" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == |(Im(v_c^dag D_c^dag D_c v_c)|" << std::endl;
std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
random(_LevelInfo.PRNGs[_NextCoarserLevel], 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 " << _CurrentLevel << ": 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();
}
};
// Specialization for the coarsest level
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
/////////////////////////////////////////////
MultiGridParams &_MultiGridParams;
LevelInfo & _LevelInfo;
FineMatrix & _FineMatrix;
FineMatrix & _SmootherMatrix;
/////////////////////////////////////////////
// Member Functions
/////////////////////////////////////////////
MultiGridPreconditioner(MultiGridParams &mgParams, LevelInfo &LvlInfo, FineMatrix &FineMat, FineMatrix &SmootherMat)
: _MultiGridParams(mgParams), _LevelInfo(LvlInfo), _FineMatrix(FineMat), _SmootherMatrix(SmootherMat) {}
void setup() {}
virtual void operator()(Lattice<Fobj> const &in, Lattice<Fobj> &out) {
auto coarseSolverMaxIter = _MultiGridParams.coarseSolverMaxOuterIter * _MultiGridParams.coarseSolverMaxInnerIter;
// On the coarsest level we only have a fine what I above call the fine level, no coarse one
TrivialPrecon<FineVector> fineTrivialPreconditioner;
FlexibleGeneralisedMinimalResidual<FineVector> fineFGMRES(
_MultiGridParams.coarseSolverTol, coarseSolverMaxIter, fineTrivialPreconditioner, _MultiGridParams.coarseSolverMaxInnerIter, false);
MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
fineFGMRES(fineMdagMOp, in, out);
}
void runChecks() {}
};
template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, int nLevels, class Matrix>
using NLevelMGPreconditioner = MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, nLevels - 1, Matrix>;
int main(int argc, char **argv) {
Grid_init(&argc, &argv);
typename WilsonCloverFermionR::ImplParams wcImplparams;
WilsonAnisotropyCoefficients wilsonAnisCoeff;
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;
RealD csw_r = 1.0;
RealD csw_t = 1.0;
const int nbasis = 20;
WilsonFermionR Dw(Umu, *FGrid, *FrbGrid, mass);
WilsonCloverFermionR Dwc(Umu, *FGrid, *FrbGrid, mass, csw_r, csw_t, wilsonAnisCoeff, wcImplparams);
// Params for two-level MG preconditioner
mgParams.nLevels = 2;
mgParams.blockSizes = {{2, 2, 2, 2}};
mgParams.smootherTol = {1e-14};
mgParams.smootherMaxOuterIter = {1};
mgParams.smootherMaxInnerIter = {1};
mgParams.kCycle = true;
mgParams.kCycleTol = {1e-14};
mgParams.kCycleMaxOuterIter = {1};
mgParams.kCycleMaxInnerIter = {1};
mgParams.coarseSolverTol = 1e-14;
mgParams.coarseSolverMaxOuterIter = 1;
mgParams.coarseSolverMaxInnerIter = 1;
// // Params for three-level MG preconditioner
// mgParams.nLevels = 3;
// mgParams.blockSizes = {{2, 2, 2, 2}, {2, 2, 1, 1}};
// mgParams.smootherTol = {1e-14, 1e-14};
// mgParams.smootherMaxOuterIter = {1, 1};
// mgParams.smootherMaxInnerIter = {1, 1};
// mgParams.kCycle = true;
// mgParams.kCycleTol = {1e-14, 1e-14};
// mgParams.kCycleMaxOuterIter = {1, 1};
// mgParams.kCycleMaxInnerIter = {1, 1};
// mgParams.coarseSolverTol = 1e-14;
// mgParams.coarseSolverMaxOuterIter = 1;
// mgParams.coarseSolverMaxInnerIter = 1;
// // // Params for four-level MG preconditioner
// mgParams.nLevels = 4;
// mgParams.blockSizes = {{2, 2, 2, 2}, {2, 2, 1, 1}, {1, 1, 2, 1}};
// mgParams.smootherTol = {1e-14, 1e-14, 1e-14};
// mgParams.smootherMaxOuterIter = {1, 1, 1};
// mgParams.smootherMaxInnerIter = {1, 1, 1};
// mgParams.kCycle = true;
// mgParams.kCycleTol = {1e-14, 1e-14, 1e-14};
// mgParams.kCycleMaxOuterIter = {1, 1, 1};
// mgParams.kCycleMaxInnerIter = {1, 1, 1};
// mgParams.coarseSolverTol = 1e-14;
// mgParams.coarseSolverMaxOuterIter = 1;
// mgParams.coarseSolverMaxInnerIter = 1;
checkParameterValidity(mgParams);
std::cout << mgParams << std::endl;
LevelInfo levelInfo(FGrid, mgParams);
static_assert(std::is_same<LatticeFermion, typename WilsonFermionR::FermionField>::value, "");
static_assert(std::is_same<LatticeFermion, typename WilsonCloverFermionR::FermionField>::value, "");
MdagMLinearOperator<WilsonFermionR, LatticeFermion> MdagMOpDw(Dw);
MdagMLinearOperator<WilsonCloverFermionR, LatticeFermion> MdagMOpDwc(Dwc);
std::cout << GridLogMessage << "**************************************************" << std::endl;
std::cout << GridLogMessage << "Testing Multigrid for Wilson" << std::endl;
std::cout << GridLogMessage << "**************************************************" << std::endl;
TrivialPrecon<LatticeFermion> TrivialPrecon;
NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 2, WilsonFermionR> TwoLevelMGPreconDw(mgParams, levelInfo, Dw, Dw);
// NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 3, WilsonFermionR> ThreeLevelMGPreconDw(mgParams, levelInfo, Dw, Dw);
// NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 4, WilsonFermionR> FourLevelMGPreconDw(mgParams, levelInfo, Dw, Dw);
TwoLevelMGPreconDw.setup();
TwoLevelMGPreconDw.runChecks();
// ThreeLevelMGPreconDw.setup();
// ThreeLevelMGPreconDw.runChecks();
// FourLevelMGPreconDw.setup();
// FourLevelMGPreconDw.runChecks();
// NLevelMGPreconDw.setup();
// NLevelMGPreconDw.runChecks();
std::vector<std::unique_ptr<OperatorFunction<LatticeFermion>>> solversDw;
solversDw.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TrivialPrecon, 100, false));
solversDw.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TwoLevelMGPreconDw, 100, false));
// solversDw.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, ThreeLevelMGPreconDw, 100, false));
// solversDw.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, FourLevelMGPreconDw, 100, false));
for(auto const &solver : solversDw) {
std::cout << "Starting with a new solver" << std::endl;
result = zero;
(*solver)(MdagMOpDw, src, result);
std::cout << std::endl;
}
std::cout << GridLogMessage << "**************************************************" << std::endl;
std::cout << GridLogMessage << "Testing Multigrid for Wilson Clover" << std::endl;
std::cout << GridLogMessage << "**************************************************" << std::endl;
NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 2, WilsonCloverFermionR> TwoLevelMGPreconDwc(
mgParams, levelInfo, Dwc, Dwc);
// NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 3, WilsonCloverFermionR> ThreeLevelMGPreconDwc(mgParams, velInfo, Dwc, Dwc);
// NLevelMGPreconditioner<vSpinColourVector, vTComplex, 1, nbasis, 4, WilsonCloverFermionR> FourLevelMGPreconDwc(lelevelInfo, Dwc, Dwc);
TwoLevelMGPreconDwc.setup();
TwoLevelMGPreconDwc.runChecks();
// ThreeLevelMGPreconDwc.setup();
// ThreeLevelMGPreconDwc.runChecks();
// FourLevelMGPreconDwc.setup();
// FourLevelMGPreconDwc.runChecks();
// NLevelMGPreconDwc.setup();
// NLevelMGPreconDwc.runChecks();
std::vector<std::unique_ptr<OperatorFunction<LatticeFermion>>> solversDwc;
solversDwc.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TrivialPrecon, 100, false));
solversDwc.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, TwoLevelMGPreconDwc, 100, false));
// solversDwc.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, ThreeLevelMGPreconDwc, 100, false));
// solversDwc.emplace_back(new FlexibleGeneralisedMinimalResidual<LatticeFermion>(1.0e-12, 50000, FourLevelMGPreconDwc, 100, false));
for(auto const &solver : solversDwc) {
std::cout << "Starting with a new solver" << std::endl;
result = zero;
(*solver)(MdagMOpDwc, src, result);
std::cout << std::endl;
}
Grid_finalize();
}
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