btw, as @Jesse mentioned, the Intel distribution Python already includes 
Theano w/ performance improvements out of the box :)

Intel-optimized Deep Learning
.. Update 2 incorporates two Intel-optimized Deep Learning frameworks, 
Caffe* and Theano*, into the distribution so Python users can take 
advantage of these optimizations out of the box.

On Thursday, April 20, 2017 at 2:12:40 AM UTC+8, Jesse Livezey 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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>>

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