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https://github.com/paboyle/Grid.git
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Got unpreconditioned conjugate gradient to run and converge on a random (uniform random,
not even SU(3) for now) gauge field. Convergence history is correctly indepdendent of decomposition on 1,2,4,8,16 mpi tasks. Found a couple of simd bugs which required fixed and enhanced the Grid_simd.cc test suite. Implemented the Mdag, M, MdagM, Meooe Mooee schur type stuff in the wilson dop.
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@ -18,6 +18,7 @@ public:
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std::cout << Tolerance<<std::endl;
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};
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void operator() (LinearOperatorBase<Field> &Linop,const Field &src, Field &psi) {assert(0);};
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void operator() (HermitianOperatorBase<Field> &Linop,const Field &src, Field &psi){
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RealD cp,c,a,d,b,ssq,qq,b_pred;
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@ -61,21 +62,27 @@ public:
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Linop.OpAndNorm(p,mmp,d,qq);
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// std::cout <<std::setprecision(4)<< "ConjugateGradient: d,qq "<<d<< " "<<qq <<std::endl;
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a = c/d;
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b_pred = a*(a*qq-d)/c;
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cp = axpy_norm(r,mmp,r,-a);
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// std::cout <<std::setprecision(4)<< "ConjugateGradient: a,bp "<<a<< " "<<b_pred <<std::endl;
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cp = axpy_norm(r,-a,mmp,r);
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b = cp/c;
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// std::cout <<std::setprecision(4)<< "ConjugateGradient: cp,b "<<cp<< " "<<b <<std::endl;
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// Fuse these loops ; should be really easy
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psi= a*p+psi;
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p = p*b+r;
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std::cout << "Iteration " <<k<<" residual "<<cp<< " target"<< rsq<<std::endl;
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std::cout<<"ConjugateGradient: Iteration " <<k<<" residual "<<cp<< " target"<< rsq<<std::endl;
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// Stopping condition
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if ( cp <= rsq ) {
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Linop.Op(p,mmp);
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Linop.Op(psi,mmp);
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p=mmp-src;
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RealD mmpnorm = sqrt(norm2(mmp));
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@ -83,8 +90,11 @@ public:
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RealD srcnorm = sqrt(norm2(src));
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RealD resnorm = sqrt(norm2(p));
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RealD true_residual = resnorm/srcnorm;
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std::cout<<"ConjugateGradient: Converged on iteration " <<k<<" residual "<<cp<< " target"<< rsq<<std::endl;
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std::cout<<"ConjugateGradient: true residual is "<<true_residual<<" sol "<<psinorm<<" src "<<srcnorm<<std::endl;
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std::cout<<"ConjugateGradient: target residual was "<<Tolerance<<std::endl;
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return;
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}
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}
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std::cout<<"ConjugateGradient did NOT converge"<<std::endl;
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