mirror of
https://gitlab.com/libeigen/eigen.git
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272 lines
9.0 KiB
C++
272 lines
9.0 KiB
C++
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// g++-4.4 bench_gemm.cpp -I .. -O2 -DNDEBUG -lrt -fopenmp && OMP_NUM_THREADS=2 ./a.out
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// icpc bench_gemm.cpp -I .. -O3 -DNDEBUG -lrt -openmp && OMP_NUM_THREADS=2 ./a.out
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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 std;
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using namespace Eigen;
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#ifndef SCALAR
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// #define SCALAR std::complex<float>
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#define SCALAR float
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#endif
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typedef SCALAR Scalar;
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typedef NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<RealScalar,Dynamic,Dynamic> A;
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typedef Matrix</*Real*/Scalar,Dynamic,Dynamic> B;
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typedef Matrix<Scalar,Dynamic,Dynamic> C;
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typedef Matrix<RealScalar,Dynamic,Dynamic> M;
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#ifdef HAVE_BLAS
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extern "C" {
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#include <Eigen/src/misc/blas.h>
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}
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static float fone = 1;
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static float fzero = 0;
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static double done = 1;
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static double szero = 0;
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static std::complex<float> cfone = 1;
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static std::complex<float> cfzero = 0;
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static std::complex<double> cdone = 1;
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static std::complex<double> cdzero = 0;
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static char notrans = 'N';
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static char trans = 'T';
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static char nonunit = 'N';
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static char lower = 'L';
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static char right = 'R';
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static int intone = 1;
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void blas_gemm(const MatrixXf& a, const MatrixXf& b, MatrixXf& c)
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{
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int M = c.rows(); int N = c.cols(); int K = a.cols();
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int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
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sgemm_(¬rans,¬rans,&M,&N,&K,&fone,
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const_cast<float*>(a.data()),&lda,
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const_cast<float*>(b.data()),&ldb,&fone,
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c.data(),&ldc);
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}
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EIGEN_DONT_INLINE void blas_gemm(const MatrixXd& a, const MatrixXd& b, MatrixXd& c)
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{
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int M = c.rows(); int N = c.cols(); int K = a.cols();
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int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
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dgemm_(¬rans,¬rans,&M,&N,&K,&done,
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const_cast<double*>(a.data()),&lda,
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const_cast<double*>(b.data()),&ldb,&done,
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c.data(),&ldc);
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}
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void blas_gemm(const MatrixXcf& a, const MatrixXcf& b, MatrixXcf& c)
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{
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int M = c.rows(); int N = c.cols(); int K = a.cols();
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int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
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cgemm_(¬rans,¬rans,&M,&N,&K,(float*)&cfone,
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const_cast<float*>((const float*)a.data()),&lda,
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const_cast<float*>((const float*)b.data()),&ldb,(float*)&cfone,
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(float*)c.data(),&ldc);
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}
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void blas_gemm(const MatrixXcd& a, const MatrixXcd& b, MatrixXcd& c)
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{
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int M = c.rows(); int N = c.cols(); int K = a.cols();
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int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
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zgemm_(¬rans,¬rans,&M,&N,&K,(double*)&cdone,
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const_cast<double*>((const double*)a.data()),&lda,
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const_cast<double*>((const double*)b.data()),&ldb,(double*)&cdone,
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(double*)c.data(),&ldc);
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}
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#endif
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void matlab_cplx_cplx(const M& ar, const M& ai, const M& br, const M& bi, M& cr, M& ci)
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{
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cr.noalias() += ar * br;
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cr.noalias() -= ai * bi;
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ci.noalias() += ar * bi;
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ci.noalias() += ai * br;
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}
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void matlab_real_cplx(const M& a, const M& br, const M& bi, M& cr, M& ci)
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{
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cr.noalias() += a * br;
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ci.noalias() += a * bi;
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}
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void matlab_cplx_real(const M& ar, const M& ai, const M& b, M& cr, M& ci)
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{
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cr.noalias() += ar * b;
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ci.noalias() += ai * b;
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}
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template<typename A, typename B, typename C>
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EIGEN_DONT_INLINE void gemm(const A& a, const B& b, C& c)
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{
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c.noalias() += a * b;
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}
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int main(int argc, char ** argv)
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{
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std::ptrdiff_t l1 = internal::queryL1CacheSize();
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std::ptrdiff_t l2 = internal::queryTopLevelCacheSize();
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std::cout << "L1 cache size = " << (l1>0 ? l1/1024 : -1) << " KB\n";
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std::cout << "L2/L3 cache size = " << (l2>0 ? l2/1024 : -1) << " KB\n";
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typedef internal::gebp_traits<Scalar,Scalar> Traits;
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std::cout << "Register blocking = " << Traits::mr << " x " << Traits::nr << "\n";
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int rep = 1; // number of repetitions per try
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int tries = 2; // number of tries, we keep the best
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int s = 2048;
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int cache_size = -1;
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bool need_help = false;
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for (int i=1; i<argc; ++i)
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{
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if(argv[i][0]=='s')
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s = atoi(argv[i]+1);
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else if(argv[i][0]=='c')
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cache_size = atoi(argv[i]+1);
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else if(argv[i][0]=='t')
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tries = atoi(argv[i]+1);
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else if(argv[i][0]=='p')
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rep = atoi(argv[i]+1);
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else
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need_help = true;
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}
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if(need_help)
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{
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std::cout << argv[0] << " s<matrix size> c<cache size> t<nb tries> p<nb repeats>\n";
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return 1;
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}
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if(cache_size>0)
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setCpuCacheSizes(cache_size,96*cache_size);
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int m = s;
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int n = s;
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int p = s;
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A a(m,p); a.setRandom();
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B b(p,n); b.setRandom();
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C c(m,n); c.setOnes();
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C rc = c;
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std::cout << "Matrix sizes = " << m << "x" << p << " * " << p << "x" << n << "\n";
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std::ptrdiff_t mc(m), nc(n), kc(p);
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internal::computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc);
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std::cout << "blocking size (mc x kc) = " << mc << " x " << kc << "\n";
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C r = c;
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// check the parallel product is correct
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#if defined EIGEN_HAS_OPENMP
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int procs = omp_get_max_threads();
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if(procs>1)
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{
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#ifdef HAVE_BLAS
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blas_gemm(a,b,r);
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#else
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omp_set_num_threads(1);
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r.noalias() += a * b;
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omp_set_num_threads(procs);
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#endif
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c.noalias() += a * b;
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if(!r.isApprox(c)) std::cerr << "Warning, your parallel product is crap!\n\n";
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}
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#elif defined HAVE_BLAS
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blas_gemm(a,b,r);
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c.noalias() += a * b;
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if(!r.isApprox(c)) std::cerr << "Warning, your product is crap!\n\n";
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#else
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gemm(a,b,c);
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r.noalias() += a.cast<Scalar>() * b.cast<Scalar>();
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if(!r.isApprox(c)) std::cerr << "Warning, your product is crap!\n\n";
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#endif
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#ifdef HAVE_BLAS
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BenchTimer tblas;
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c = rc;
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BENCH(tblas, tries, rep, blas_gemm(a,b,c));
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std::cout << "blas cpu " << tblas.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tblas.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << tblas.total(CPU_TIMER) << "s)\n";
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std::cout << "blas real " << tblas.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tblas.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << tblas.total(REAL_TIMER) << "s)\n";
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#endif
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BenchTimer tmt;
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c = rc;
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BENCH(tmt, tries, rep, gemm(a,b,c));
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std::cout << "eigen cpu " << tmt.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmt.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << tmt.total(CPU_TIMER) << "s)\n";
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std::cout << "eigen real " << tmt.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmt.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";
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#ifdef EIGEN_HAS_OPENMP
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if(procs>1)
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{
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BenchTimer tmono;
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omp_set_num_threads(1);
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Eigen::internal::setNbThreads(1);
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c = rc;
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BENCH(tmono, tries, rep, gemm(a,b,c));
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std::cout << "eigen mono cpu " << tmono.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmono.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << tmono.total(CPU_TIMER) << "s)\n";
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std::cout << "eigen mono real " << tmono.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmono.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << tmono.total(REAL_TIMER) << "s)\n";
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std::cout << "mt speed up x" << tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER) << " => " << (100.0*tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER))/procs << "%\n";
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}
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#endif
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#ifdef DECOUPLED
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if((NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex))
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{
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M ar(m,p); ar.setRandom();
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M ai(m,p); ai.setRandom();
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M br(p,n); br.setRandom();
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M bi(p,n); bi.setRandom();
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M cr(m,n); cr.setRandom();
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M ci(m,n); ci.setRandom();
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BenchTimer t;
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BENCH(t, tries, rep, matlab_cplx_cplx(ar,ai,br,bi,cr,ci));
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std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
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std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
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}
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if((!NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex))
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{
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M a(m,p); a.setRandom();
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M br(p,n); br.setRandom();
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M bi(p,n); bi.setRandom();
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M cr(m,n); cr.setRandom();
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M ci(m,n); ci.setRandom();
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BenchTimer t;
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BENCH(t, tries, rep, matlab_real_cplx(a,br,bi,cr,ci));
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std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
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std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
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}
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if((NumTraits<A::Scalar>::IsComplex) && (!NumTraits<B::Scalar>::IsComplex))
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{
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M ar(m,p); ar.setRandom();
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M ai(m,p); ai.setRandom();
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M b(p,n); b.setRandom();
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M cr(m,n); cr.setRandom();
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M ci(m,n); ci.setRandom();
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BenchTimer t;
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BENCH(t, tries, rep, matlab_cplx_real(ar,ai,b,cr,ci));
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std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
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std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
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}
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#endif
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return 0;
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}
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