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66d001ec9e
Modified the Wilson flow adaptive smearing step size update to implement the original Ramos definition of the distance, where previously it used the norm of a difference which scales with the volume and so would choose too coarse or too fine steps depending on the volume. This is based on Chulwoo's code. Added a test comparing adaptive (with tuneable tolerance) to iterative Wilson flow smearing on a random gauge configuration.
154 lines
5.0 KiB
C++
154 lines
5.0 KiB
C++
/*************************************************************************************
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Grid physics library, www.github.com/paboyle/Grid
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Source file: ./tests/hmc/Test_WilsonFlow_adaptive.cc
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Copyright (C) 2017
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Author: Christopher Kelly <ckelly@bnl.gov>
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This program is free software; you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation; either version 2 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License along
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with this program; if not, write to the Free Software Foundation, Inc.,
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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See the full license in the file "LICENSE" in the top level distribution
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directory
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*************************************************************************************/
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/* END LEGAL */
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#include <Grid/Grid.h>
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using namespace Grid;
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//Linearly interpolate between two nearest times
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RealD interpolate(const RealD t_int, const std::vector<std::pair<RealD,RealD> > &data){
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RealD tdiff1=1e32; int t1_idx=-1;
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RealD tdiff2=1e32; int t2_idx=-1;
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for(int i=0;i<data.size();i++){
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RealD diff = fabs(data[i].first-t_int);
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//std::cout << "targ " << t_int << " cur " << data[i].first << " diff " << diff << " best diff1 " << tdiff1 << " diff2 " << tdiff2 << std::endl;
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if(diff < tdiff1){
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if(tdiff1 < tdiff2){ //swap out tdiff2
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tdiff2 = tdiff1; t2_idx = t1_idx;
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}
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tdiff1 = diff; t1_idx = i;
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}
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else if(diff < tdiff2){ tdiff2 = diff; t2_idx = i; }
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}
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assert(t1_idx != -1 && t2_idx != -1);
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RealD t2 = data[t2_idx].first, v2 = data[t2_idx].second;
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RealD t1 = data[t1_idx].first, v1 = data[t1_idx].second;
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//v = a + bt
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//v2-v1 = b(t2-t1)
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RealD b = (v2-v1)/(t2-t1);
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RealD a = v1 - b*t1;
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RealD vout = a + b*t_int;
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//std::cout << "Interpolate to " << t_int << " two closest points " << t1 << " " << t2
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//<< " with values " << v1 << " "<< v2 << " : got " << vout << std::endl;
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return vout;
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}
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int main(int argc, char **argv) {
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Grid_init(&argc, &argv);
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GridLogLayout();
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auto latt_size = GridDefaultLatt();
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auto simd_layout = GridDefaultSimd(Nd, vComplex::Nsimd());
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auto mpi_layout = GridDefaultMpi();
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GridCartesian Grid(latt_size, simd_layout, mpi_layout);
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GridRedBlackCartesian RBGrid(&Grid);
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std::vector<int> seeds({1, 2, 3, 4, 5});
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GridSerialRNG sRNG;
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GridParallelRNG pRNG(&Grid);
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pRNG.SeedFixedIntegers(seeds);
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LatticeGaugeField U(&Grid);
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SU<Nc>::HotConfiguration(pRNG, U);
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int Nstep = 300;
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RealD epsilon = 0.01;
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RealD maxTau = Nstep*epsilon;
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RealD tolerance = 1e-4;
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for(int i=1;i<argc;i++){
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std::string sarg(argv[i]);
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if(sarg == "--tolerance"){
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std::stringstream ss; ss << argv[i+1]; ss >> tolerance;
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}
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}
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std::cout << "Adaptive smear tolerance " << tolerance << std::endl;
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//Setup iterative Wilson flow
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WilsonFlow<PeriodicGimplD> wflow(epsilon,Nstep);
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wflow.resetActions();
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std::vector<std::pair<RealD, RealD> > meas_orig;
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wflow.addMeasurement(1, [&wflow,&meas_orig](int step, RealD t, const LatticeGaugeField &U){
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std::cout << GridLogMessage << "[WilsonFlow] Computing Cloverleaf energy density for step " << step << std::endl;
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meas_orig.push_back( {t, wflow.energyDensityCloverleaf(t,U)} );
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});
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//Setup adaptive Wilson flow
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WilsonFlowAdaptive<PeriodicGimplD> wflow_ad(epsilon,maxTau,tolerance);
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wflow_ad.resetActions();
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std::vector<std::pair<RealD, RealD> > meas_adaptive;
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wflow_ad.addMeasurement(1, [&wflow_ad,&meas_adaptive](int step, RealD t, const LatticeGaugeField &U){
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std::cout << GridLogMessage << "[WilsonFlow] Computing Cloverleaf energy density for step " << step << std::endl;
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meas_adaptive.push_back( {t, wflow_ad.energyDensityCloverleaf(t,U)} );
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});
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//Run
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LatticeGaugeFieldD Vtmp(U.Grid());
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wflow.smear(Vtmp, U); //basic smear
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Vtmp = Zero();
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wflow_ad.smear(Vtmp, U);
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//Output values for plotting
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{
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std::ofstream out("wflow_t2E_orig.dat");
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out.precision(16);
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for(auto const &e: meas_orig){
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out << e.first << " " << e.second << std::endl;
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}
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}
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{
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std::ofstream out("wflow_t2E_adaptive.dat");
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out.precision(16);
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for(auto const &e: meas_adaptive){
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out << e.first << " " << e.second << std::endl;
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}
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}
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//Compare at times available with adaptive smearing
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for(int i=0;i<meas_adaptive.size();i++){
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RealD t = meas_adaptive[i].first;
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RealD v_adaptive = meas_adaptive[i].second;
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RealD v_orig = interpolate(t, meas_orig); //should be very precise due to fine timestep
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std::cout << t << " orig: " << v_orig << " adaptive: " << v_adaptive << " reldiff: " << (v_adaptive-v_orig)/v_orig << std::endl;
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}
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std::cout << GridLogMessage << "Done" << std::endl;
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Grid_finalize();
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}
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