How to speed up Eigen library's matrix product?

Question

I\'m studying simple multiplication of two big matrices using the Eigen library. This multiplication appears to be noticeably slower than both Matlab and Python for the same

Answer 1

First of all, when doing performance comparison, makes sure you disabled turbo-boost (TB). On my system, using gcc 4.5 from macport and without turbo-boost, I get 3.5s, that corresponds to 8.4 GFLOPS while the theoretical peak of my 2.3 core i7 is 9.2GFLOPS, so not too bad.

MatLab is based on Intel MKL, and seeing the reported performance, it clearly uses a multithreaded version. It is unlikely that an small library as Eigen can beat Intel on its own CPU!

Numpy can uses any BLAS library, Atlas, MKL, OpenBLAS, eigen-blas, etc. I guess that in your case it was using Atlas which is fast too.

Finally, here is how you can get better performance: enable multi-threading in Eigen by compiling with -fopenmp. By default Eigen uses for the number of the thread the default number of thread defined by OpenMP. Unfortunately this number corresponds to the number of logic cores, and not physical cores, so make sure hyper-threading is disabled or define the OMP_NUM_THREADS environment variable to the physical number of cores. Here I get 1.25s (without TB), and 0.95s with TB.