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WilsonMG: Rationalize the level counting strategy
This commit is contained in:
Daniel Richtmann
parent
b78456bdf4
commit
99107038f9
@ -119,15 +119,6 @@ public:
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std::cout << GridLogMessage << "Constructed " << mgParams.nLevels << " levels" << std::endl;
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// The construction above corresponds to the finest level having level == 0
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// (simply because it's not as ugly to implement), but we need it the
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// other way round (i.e., the coarsest level to have level == 0) for the MG
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// Preconditioner -> reverse the vectors
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std::reverse(Seeds.begin(), Seeds.end());
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std::reverse(Grids.begin(), Grids.end());
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std::reverse(PRNGs.begin(), PRNGs.end());
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for(int level = 0; level < mgParams.nLevels; ++level) {
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std::cout << GridLogMessage << "level = " << level << ":" << std::endl;
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Grids[level]->show_decomposition();
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@ -234,25 +225,29 @@ template<class Field> void testLinearOperator(LinearOperatorBase<Field> &LinOp,
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}
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}
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template<class Fobj, class CoarseScalar, int nCoarseSpins, int nBasis, int level, class Matrix>
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template<class Fobj, class CoarseScalar, int nCoarseSpins, int nBasis, int nCoarserLevels, class Matrix>
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class MultiGridPreconditioner : public LinearFunction<Lattice<Fobj>> {
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public:
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/////////////////////////////////////////////
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// Type Definitions
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/////////////////////////////////////////////
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typedef Aggregation<Fobj, CoarseScalar, nBasis> Aggregates;
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typedef CoarsenedMatrix<Fobj, CoarseScalar, nBasis> CoarseMatrix;
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typedef typename Aggregates::CoarseVector CoarseVector;
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typedef typename Aggregates::siteVector CoarseSiteVector;
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typedef Matrix FineMatrix;
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typedef typename Aggregates::FineField FineVector;
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typedef MultiGridPreconditioner<CoarseSiteVector, CoarseScalar, nCoarseSpins, nBasis, level - 1, CoarseMatrix> NextPreconditionerLevel;
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// clang-format off
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typedef Aggregation<Fobj, CoarseScalar, nBasis> Aggregates;
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typedef CoarsenedMatrix<Fobj, CoarseScalar, nBasis> CoarseMatrix;
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typedef typename Aggregates::CoarseVector CoarseVector;
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typedef typename Aggregates::siteVector CoarseSiteVector;
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typedef Matrix FineMatrix;
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typedef typename Aggregates::FineField FineVector;
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typedef MultiGridPreconditioner<CoarseSiteVector, CoarseScalar, nCoarseSpins, nBasis, nCoarserLevels - 1, CoarseMatrix> NextPreconditionerLevel;
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// clang-format on
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/////////////////////////////////////////////
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// Member Data
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/////////////////////////////////////////////
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int _CurrentLevel;
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int _NextCoarserLevel;
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MultiGridParams & _MultiGridParams;
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LevelInfo & _LevelInfo;
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FineMatrix & _FineMatrix;
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@ -266,12 +261,14 @@ public:
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/////////////////////////////////////////////
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MultiGridPreconditioner(MultiGridParams &mgParams, LevelInfo &LvlInfo, FineMatrix &FineMat, FineMatrix &SmootherMat)
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: _MultiGridParams(mgParams)
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: _CurrentLevel(mgParams.nLevels - (nCoarserLevels + 1)) // _Level = 0 corresponds to finest
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, _NextCoarserLevel(_CurrentLevel + 1) // incremented for instances on coarser levels
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, _MultiGridParams(mgParams)
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, _LevelInfo(LvlInfo)
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, _FineMatrix(FineMat)
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, _SmootherMatrix(SmootherMat)
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, _Aggregates(_LevelInfo.Grids[level - 1], _LevelInfo.Grids[level], 0)
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, _CoarseMatrix(*_LevelInfo.Grids[level - 1]) {
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, _Aggregates(_LevelInfo.Grids[_NextCoarserLevel], _LevelInfo.Grids[_CurrentLevel], 0)
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, _CoarseMatrix(*_LevelInfo.Grids[_NextCoarserLevel]) {
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_NextPreconditionerLevel
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= std::unique_ptr<NextPreconditionerLevel>(new NextPreconditionerLevel(_MultiGridParams, _LevelInfo, _CoarseMatrix, _CoarseMatrix));
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}
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@ -281,7 +278,8 @@ public:
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Gamma g5(Gamma::Algebra::Gamma5);
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MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
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_Aggregates.CreateSubspace(_LevelInfo.PRNGs[level], fineMdagMOp /*, nb */); // NOTE: Don't specify nb to see the orthogonalization check
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// NOTE: Don't specify nb here to see the orthogonalization check
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_Aggregates.CreateSubspace(_LevelInfo.PRNGs[_CurrentLevel], fineMdagMOp /*, nb */);
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// TestVectorAnalyzer<FineVector, nbasis> fineTVA;
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// fineTVA(fineMdagMOp, _Aggregates.subspace);
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@ -294,7 +292,7 @@ public:
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_Aggregates.subspace[n + nb] = g5 * _Aggregates.subspace[n];
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}
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_CoarseMatrix.CoarsenOperator(_LevelInfo.Grids[level], fineMdagMOp, _Aggregates);
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_CoarseMatrix.CoarsenOperator(_LevelInfo.Grids[_CurrentLevel], fineMdagMOp, _Aggregates);
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_NextPreconditionerLevel->setup();
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}
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@ -312,8 +310,8 @@ public:
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RealD inputNorm = norm2(in);
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CoarseVector coarseSrc(_LevelInfo.Grids[level - 1]);
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CoarseVector coarseSol(_LevelInfo.Grids[level - 1]);
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CoarseVector coarseSrc(_LevelInfo.Grids[_NextCoarserLevel]);
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CoarseVector coarseSol(_LevelInfo.Grids[_NextCoarserLevel]);
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coarseSol = zero;
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FineVector fineTmp(in._grid);
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@ -340,7 +338,7 @@ public:
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r = norm2(fineTmp);
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auto residualAfterPostSmoother = std::sqrt(r / inputNorm);
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std::cout << GridLogMG << " Level " << level << ": V-cycle: Input norm = " << std::sqrt(inputNorm)
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": V-cycle: Input norm = " << std::sqrt(inputNorm)
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<< " Coarse residual = " << residualAfterCoarseGridCorrection << " Post-Smoother residual = " << residualAfterPostSmoother
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<< std::endl;
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}
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@ -349,8 +347,8 @@ public:
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RealD inputNorm = norm2(in);
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CoarseVector coarseSrc(_LevelInfo.Grids[level - 1]);
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CoarseVector coarseSol(_LevelInfo.Grids[level - 1]);
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CoarseVector coarseSrc(_LevelInfo.Grids[_NextCoarserLevel]);
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CoarseVector coarseSol(_LevelInfo.Grids[_NextCoarserLevel]);
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coarseSol = zero;
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FineVector fineTmp(in._grid);
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@ -379,25 +377,24 @@ public:
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r = norm2(fineTmp);
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auto residualAfterPostSmoother = std::sqrt(r / inputNorm);
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std::cout << GridLogMG << " Level " << level << ": K-cycle: Input norm = " << std::sqrt(inputNorm)
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": K-cycle: Input norm = " << std::sqrt(inputNorm)
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<< " Coarse residual = " << residualAfterCoarseGridCorrection << " Post-Smoother residual = " << residualAfterPostSmoother
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<< std::endl;
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}
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void runChecks() {
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auto tolerance = 1e-13; // TODO: this obviously depends on the precision we use, current value is for double
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auto coarseLevel = level - 1;
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auto tolerance = 1e-13; // TODO: this obviously depends on the precision we use, current value is for double
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std::vector<FineVector> fineTmps(2, _LevelInfo.Grids[level]);
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std::vector<CoarseVector> coarseTmps(4, _LevelInfo.Grids[level - 1]);
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std::vector<FineVector> fineTmps(2, _LevelInfo.Grids[_CurrentLevel]);
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std::vector<CoarseVector> coarseTmps(4, _LevelInfo.Grids[_NextCoarserLevel]);
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MdagMLinearOperator<FineMatrix, FineVector> fineMdagMOp(_FineMatrix);
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MdagMLinearOperator<CoarseMatrix, CoarseVector> coarseMdagMOp(_CoarseMatrix);
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (1 - P R) v" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (1 - P R) v" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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for(auto i = 0; i < _Aggregates.subspace.size(); ++i) {
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_Aggregates.ProjectToSubspace(coarseTmps[0], _Aggregates.subspace[i]); // R v_i
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@ -406,7 +403,7 @@ public:
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fineTmps[1] = _Aggregates.subspace[i] - fineTmps[0]; // v_i - P R v_i
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auto deviation = std::sqrt(norm2(fineTmps[1]) / norm2(_Aggregates.subspace[i]));
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std::cout << GridLogMG << " Level " << level << ": Vector " << i << ": norm2(v_i) = " << norm2(_Aggregates.subspace[i])
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": Vector " << i << ": norm2(v_i) = " << norm2(_Aggregates.subspace[i])
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<< " | norm2(R v_i) = " << norm2(coarseTmps[0]) << " | norm2(P R v_i) = " << norm2(fineTmps[0])
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<< " | relative deviation = " << deviation;
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@ -418,11 +415,11 @@ public:
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}
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}
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (1 - R P) v_c" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (1 - R P) v_c" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
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random(_LevelInfo.PRNGs[_NextCoarserLevel], coarseTmps[0]);
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_Aggregates.PromoteFromSubspace(coarseTmps[0], fineTmps[0]); // P v_c
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_Aggregates.ProjectToSubspace(coarseTmps[1], fineTmps[0]); // R P v_c
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@ -430,7 +427,7 @@ public:
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coarseTmps[2] = coarseTmps[0] - coarseTmps[1]; // v_c - R P v_c
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auto deviation = std::sqrt(norm2(coarseTmps[2]) / norm2(coarseTmps[0]));
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std::cout << GridLogMG << " Level " << level << ": norm2(v_c) = " << norm2(coarseTmps[0])
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2(v_c) = " << norm2(coarseTmps[0])
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<< " | norm2(R P v_c) = " << norm2(coarseTmps[1]) << " | norm2(P v_c) = " << norm2(fineTmps[0])
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<< " | relative deviation = " << deviation;
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@ -441,11 +438,11 @@ public:
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std::cout << " < " << tolerance << " -> check passed" << std::endl;
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}
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == (R D P - D_c) v_c" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == (R D P - D_c) v_c" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
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random(_LevelInfo.PRNGs[_NextCoarserLevel], coarseTmps[0]);
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_Aggregates.PromoteFromSubspace(coarseTmps[0], fineTmps[0]); // P v_c
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fineMdagMOp.Op(fineTmps[0], fineTmps[1]); // D P v_c
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@ -456,7 +453,7 @@ public:
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coarseTmps[3] = coarseTmps[1] - coarseTmps[2]; // R D P v_c - D_c v_c
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deviation = std::sqrt(norm2(coarseTmps[3]) / norm2(coarseTmps[1]));
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std::cout << GridLogMG << " Level " << level << ": norm2(R D P v_c) = " << norm2(coarseTmps[1])
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": norm2(R D P v_c) = " << norm2(coarseTmps[1])
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<< " | norm2(D_c v_c) = " << norm2(coarseTmps[2]) << " | relative deviation = " << deviation;
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if(deviation > tolerance) {
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@ -466,11 +463,11 @@ public:
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std::cout << " < " << tolerance << " -> check passed" << std::endl;
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}
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": MG correctness check: 0 == |(Im(v_c^dag D_c^dag D_c v_c)|" << std::endl;
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std::cout << GridLogMG << " Level " << level << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": MG correctness check: 0 == |(Im(v_c^dag D_c^dag D_c v_c)|" << std::endl;
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": **************************************************" << std::endl;
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random(_LevelInfo.PRNGs[coarseLevel], coarseTmps[0]);
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random(_LevelInfo.PRNGs[_NextCoarserLevel], coarseTmps[0]);
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coarseMdagMOp.Op(coarseTmps[0], coarseTmps[1]); // D_c v_c
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coarseMdagMOp.AdjOp(coarseTmps[1], coarseTmps[2]); // D_c^dag D_c v_c
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@ -478,7 +475,7 @@ public:
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auto dot = innerProduct(coarseTmps[0], coarseTmps[2]); //v_c^dag D_c^dag D_c v_c
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deviation = abs(imag(dot)) / abs(real(dot));
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std::cout << GridLogMG << " Level " << level << ": Re(v_c^dag D_c^dag D_c v_c) = " << real(dot)
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std::cout << GridLogMG << " Level " << _CurrentLevel << ": Re(v_c^dag D_c^dag D_c v_c) = " << real(dot)
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<< " | Im(v_c^dag D_c^dag D_c v_c) = " << imag(dot) << " | relative deviation = " << deviation;
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if(deviation > tolerance) {
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@ -493,7 +490,7 @@ public:
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}
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};
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// Specialize the coarsest level, this corresponds to counting downwards with level: coarsest = 0, finest = N
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// Specialization for the coarsest level
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template<class Fobj, class CoarseScalar, int nCoarseSpins, int nbasis, class Matrix>
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class MultiGridPreconditioner<Fobj, CoarseScalar, nCoarseSpins, nbasis, 0, Matrix> : public LinearFunction<Lattice<Fobj>> {
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public:
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