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	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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| 
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| Grid physics library, www.github.com/paboyle/Grid
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| 
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| Source file: ./tests/hmc/Test_WilsonFlow_adaptive.cc
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| 
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| Copyright (C) 2017
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| 
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| Author: Christopher Kelly <ckelly@bnl.gov>
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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| using namespace Grid;
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| 
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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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| 
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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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| 
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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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|   
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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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|   
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|   LatticeGaugeField U(&Grid);
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|   SU<Nc>::HotConfiguration(pRNG, U);
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| 
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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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| 
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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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| 
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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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| 
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|   std::vector<std::pair<RealD, RealD> > meas_orig;
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| 
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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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| 
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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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| 
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|   std::vector<std::pair<RealD, RealD> > meas_adaptive;
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| 
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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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| 
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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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| 
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|   Vtmp = Zero();
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|   wflow_ad.smear(Vtmp, U);
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| 
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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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|   
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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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|   
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|   std::cout << GridLogMessage << "Done" << std::endl;
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|   Grid_finalize();
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| }
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