Thanks for the information. I will install 0.5.0-rc4 binary and try a small 
benchmark among Julia, Matlab (forgot which version I have probably 2013), 
also Anaconda Python 2.7 with and without MKL. I will avoid loops in doing 
so.

Will drop an email regarding commercial solution over at Julia Computing.



On Saturday, September 17, 2016 at 1:00:26 AM UTC-5, Tony Kelman wrote:
>
> That benchmark doesn't say what they were using for "normal backends" - 
> was it openblas or atlas or the reference blas, which set of kernels, were 
> they using multithreading, etc. The open source Julia build will almost 
> certainly have faster FFT's than SciPy without MKL, since Julia uses FFTW 
> but SciPy uses a slower non-GPL-licensed implementation. Note that we had a 
> bug on our buildbots regarding optimization flags in some dependencies so 
> you may want to wait until 0.4.7 binaries or test with 0.5.0-rc4 or newer 
> for comparing FFT performance.
>
> Julia Computing offers custom solutions that aren't necessarily listed on 
> the products page of our website yet, you can inquire at the contact 
> address there.
>

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