I have found that I get better performance from some openblas routines by
setting the number of blas threads to the number of physical CPU cores
(half the number returned by CPU_CORES when hyperthreading is enabled):
Base.blas_set_num_threads(div(CPU_CORES,2))
--Peter
On Thursday, September 18, 2014 3:09:17 PM UTC-7, Stephan Buchert wrote:
>
> Thanks for the tips. I have now compiled julia on my laptop, and the
> results are:
>
> julia> versioninfo()
> Julia Version 0.3.0+6
> Commit 7681878* (2014-08-20 20:43 UTC)
> Platform Info:
> System: Linux (x86_64-redhat-linux)
> CPU: Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
> WORD_SIZE: 64
> BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
> LAPACK: libopenblas
> LIBM: libopenlibm
> LLVM: libLLVM-3.3
>
> julia> include("code/julia/bench.jl")
> LU decomposition, elapsed time: 0.123349203 seconds
> FFT , elapsed time: 0.20440579 seconds
>
> Matlab r2104a, with [L,U,P] = lu(A); instead of just lu(A);
> LU decomposition, elapsed time: 0.0586 seconds
> FFT elapsed time: 0.0809 seconds
>
> So a great improvement, but julia seems still 2-3 times slower than
> matlab, the underlying linear algebra libraries, respectively, and for
> these two very limited bench marks. Perhaps Matlab found a way to speed
> their lin.alg. up recently?
>
> The Fedora precompiled openblas was installed already at the first test
> (and presumably used by julia), but, as Andreas has also pointed out, it
> seems to be significantly slower than an openblas library compiled now with
> the julia installation.
>
>
