OpenBLAS uses threads by default, but Milan reported that Fedora's
maintainer had them disabled. Hence, unless you are using Fedora, you
should have threaded OpenBLAS.
What is the best setup for fast linear algebra operations ?
That question doesn't have a single answer. Often when people want to show
performance of linear algebra libraries they run a single routine on a big
matrix. In that case you'll often benefit from many threads. However, in
many applications you solve smaller problems many times. In this case, many
threads can actually be a problem and you could be better off with turning
off OpenBLAS threading. So it depends on your problem.
Med venlig hilsen
Andreas Noack
2014-09-25 5:52 GMT-04:00 Ján Dolinský <jan.dolin...@2bridgz.com>:
> Hello,
>
> How do I make Julia to use threaded version of OpenBLAS ? Do I have to
> compile using some special option or there is a config file ?
> What is the best setup for fast linear algebra operations ?
>
> Best Regards,
> Jan
>
> Dňa nedeľa, 21. septembra 2014 9:50:52 UTC+2 Stephan Buchert napísal(-a):
>
>> Wow, I have now LU a little bit faster on the latest julia Fedora package
>> than on my locally compiled julia:
>>
>> julia> versioninfo()
>> Julia Version 0.3.0
>> Platform Info:
>> System: Linux (x86_64-redhat-linux)
>> CPU: Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz
>> WORD_SIZE: 64
>> BLAS: libopenblas (DYNAMIC_ARCH NO_AFFINITY Haswell)
>> LAPACK: libopenblasp.so.0
>> LIBM: libopenlibm
>> LLVM: libLLVM-3.3
>>
>> julia> include("code/julia/bench.jl")
>> LU decomposition, elapsed time: 0.07222901 seconds, was 0.123 seconds
>> with my julia
>> FFT , elapsed time: 0.248571629 seconds
>>
>> Thanks for making and improving the Fedora package
>>
>
