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.
