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129 lines
4.0 KiB
C++
129 lines
4.0 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 <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 float
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#endif
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typedef SCALAR Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> M;
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#ifdef HAVE_BLAS
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extern "C" {
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#include <bench/btl/libs/C_BLAS/blas.h>
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void sgemm_kernel(int actual_mc, int cols, int actual_kc, float alpha,
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float* blockA, float* blockB, float* res, int resStride);
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void sgemm_oncopy(int actual_kc, int cols, const float* rhs, int rhsStride, float* blockB);
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void sgemm_itcopy(int actual_kc, int cols, const float* rhs, int rhsStride, float* blockB);
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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 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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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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#endif
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void gemm(const M& a, const M& b, M& 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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int rep = 1; // number of repetitions per try
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int tries = 5; // number of tries, we keep the best
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int s = 2048;
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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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M a(m,n); a.setRandom();
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M b(n,p); b.setRandom();
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M c(m,p); c.setOnes();
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M r = c;
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// check the parallel product is correct
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#ifdef 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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#endif
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#ifdef HAVE_BLAS
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BenchTimer tblas;
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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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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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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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return 0;
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}
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