Now, mkl is available in conda freely for everybody. You don't need the
accelerate packages, just a recent enough conda. On older conda, you can
update it, it don't need a new installation.

Fred

On Wed, Apr 19, 2017 at 2:12 PM Jesse Livezey <[email protected]>
wrote:

> From tests I've done, the MKL fft library is comparable and sometimes
> faster than the FFTW package. Both are much faster than the numpy fft. It's
> available in the accelerate package from Continuum (mkl conda package only
> has blas).
>
> It also looks like intel has a free conda channel, which builds scipy and
> numpy against MKL
>
> https://software.intel.com/en-us/forums/intel-distribution-for-python/topic/713736
>
> On Wednesday, April 19, 2017 at 10:54:33 AM UTC-7, nouiz wrote:
>
>> Blas won't help for fft. So you would need a faster fft library then what
>> we use and modify the perform() method of those ops to use that new lib.
>>
>> FFTW is one possible faster implementation. There is others. I can't
>> comment on which one would be better. Search the web, I recall seeing some
>> people comparing fft lib that is available in python.
>>
>> If you modify those ops for that, pushing upstream those change would be
>> great.
>>
>> Fred
>>
>>
>>
>> On Tue, Apr 18, 2017 at 12:16 PM <[email protected]> wrote:
>>
> Hi,
>>>
>>> I have implemented a layer which uses functions theano.tensor.fft.rfft
>>> and theano.tensor.fft.irfft. What might be the best way to improve the
>>> speed of that layer on cpu? Installing FFTW, an optimized BLAS library?
>>>
>>> Thanks
>>> Cha.
>>>
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