mirror of
https://gitlab.com/libeigen/eigen.git
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144 lines
3.2 KiB
C++
144 lines
3.2 KiB
C++
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#include <iostream>
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#include <Eigen/Core>
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#include <bench/BenchTimer.h>
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using namespace Eigen;
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using namespace std;
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#define END 9
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template<int S> struct map_size { enum { ret = S }; };
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template<> struct map_size<10> { enum { ret = 20 }; };
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template<> struct map_size<11> { enum { ret = 50 }; };
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template<> struct map_size<12> { enum { ret = 100 }; };
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template<> struct map_size<13> { enum { ret = 300 }; };
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template<int M, int N,int K> struct alt_prod
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{
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enum {
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ret = M==1 && N==1 ? InnerProduct
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: K==1 ? OuterProduct
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: M==1 ? GemvProduct
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: N==1 ? GemvProduct
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: GemmProduct
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};
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};
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void print_mode(int mode)
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{
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if(mode==InnerProduct) std::cout << "i";
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if(mode==OuterProduct) std::cout << "o";
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if(mode==CoeffBasedProductMode) std::cout << "c";
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if(mode==LazyCoeffBasedProductMode) std::cout << "l";
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if(mode==GemvProduct) std::cout << "v";
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if(mode==GemmProduct) std::cout << "m";
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}
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template<int Mode, typename Lhs, typename Rhs, typename Res>
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EIGEN_DONT_INLINE void prod(const Lhs& a, const Rhs& b, Res& c)
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{
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c.noalias() += typename ProductReturnType<Lhs,Rhs,Mode>::Type(a,b);
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}
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template<int M, int N, int K, typename Scalar, int Mode>
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EIGEN_DONT_INLINE void bench_prod()
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{
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typedef Matrix<Scalar,M,K> Lhs; Lhs a; a.setRandom();
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typedef Matrix<Scalar,K,N> Rhs; Rhs b; b.setRandom();
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typedef Matrix<Scalar,M,N> Res; Res c; c.setRandom();
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BenchTimer t;
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double n = 2.*double(M)*double(N)*double(K);
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int rep = 100000./n;
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rep /= 2;
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if(rep<1) rep = 1;
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do {
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rep *= 2;
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t.reset();
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BENCH(t,1,rep,prod<CoeffBasedProductMode>(a,b,c));
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} while(t.best()<0.1);
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t.reset();
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BENCH(t,5,rep,prod<Mode>(a,b,c));
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print_mode(Mode);
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std::cout << int(1e-6*n*rep/t.best()) << "\t";
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}
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template<int N> struct print_n;
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template<int M, int N, int K> struct loop_on_m;
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template<int M, int N, int K, typename Scalar, int Mode> struct loop_on_n;
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template<int M, int N, int K>
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struct loop_on_k
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{
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static void run()
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{
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std::cout << "K=" << K << "\t";
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print_n<N>::run();
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std::cout << "\n";
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loop_on_m<M,N,K>::run();
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std::cout << "\n\n";
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loop_on_k<M,N,K+1>::run();
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}
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};
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template<int M, int N>
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struct loop_on_k<M,N,END> { static void run(){} };
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template<int M, int N, int K>
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struct loop_on_m
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{
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static void run()
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{
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std::cout << M << "f\t";
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loop_on_n<M,N,K,float,CoeffBasedProductMode>::run();
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std::cout << "\n";
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std::cout << M << "f\t";
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loop_on_n<M,N,K,float,-1>::run();
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std::cout << "\n";
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loop_on_m<M+1,N,K>::run();
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}
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};
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template<int N, int K>
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struct loop_on_m<END,N,K> { static void run(){} };
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template<int M, int N, int K, typename Scalar, int Mode>
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struct loop_on_n
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{
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static void run()
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{
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bench_prod<M,N,K,Scalar,Mode==-1? alt_prod<M,N,K>::ret : Mode>();
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loop_on_n<M,N+1,K,Scalar,Mode>::run();
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}
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};
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template<int M, int K, typename Scalar, int Mode>
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struct loop_on_n<M,END,K,Scalar,Mode> { static void run(){} };
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template<int N> struct print_n
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{
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static void run()
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{
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std::cout << map_size<N>::ret << "\t";
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print_n<N+1>::run();
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}
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};
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template<> struct print_n<END> { static void run(){} };
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int main()
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{
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loop_on_k<1,1,1>::run();
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return 0;
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}
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