[Numpy-discussion] Intel random number package

Todd toddrjen at gmail.com
Thu Oct 27 10:30:36 EDT 2016


On Thu, Oct 27, 2016 at 4:25 AM, Ralf Gommers <ralf.gommers at gmail.com>
wrote:
>
>
> On Thu, Oct 27, 2016 at 10:25 AM, Pavlyk, Oleksandr <
> oleksandr.pavlyk at intel.com> wrote:
>
>> Please see responses inline.
>>
>>
>>
>> *From:* NumPy-Discussion [mailto:numpy-discussion-bounces at scipy.org] *On
>> Behalf Of *Todd
>> *Sent:* Wednesday, October 26, 2016 4:04 PM
>> *To:* Discussion of Numerical Python <numpy-discussion at scipy.org>
>> *Subject:* Re: [Numpy-discussion] Intel random number package
>>
>>
>>
>> On Wed, Oct 26, 2016 at 4:30 PM, Pavlyk, Oleksandr <
>> oleksandr.pavlyk at intel.com> wrote:
>>
>> Another point already raised by Nathaniel is that for numpy's randomness
>> ideally should provide a way to override default algorithm for sampling
>> from a particular distribution.  For example RandomState object that
>> implements PCG may rely on default acceptance-rejection algorithm for
>> sampling from Gamma, while the RandomState object that provides interface
>> to MKL might want to call into MKL directly.
>>
>>
>>
>> The approach that pyfftw uses at least for scipy, which may also work
>> here, is that you can monkey-patch the scipy.fftpack module at runtime,
>> replacing it with pyfftw's drop-in replacement.  scipy then proceeds to use
>> pyfftw instead of its built-in fftpack implementation.  Might such an
>> approach work here?  Users can either use this alternative randomstate
>> replacement directly, or they can replace numpy's with it at runtime and
>> numpy will then proceed to use the alternative.
>>
>
> The only reason that pyfftw uses monkeypatching is that the better
> approach is not possible due to license constraints with FFTW (it's GPL).
>

Yes, that is exactly why I brought it up.  Better approaches are also not
possible with MKL due to license constraints.  It is a very similar
situation overall.
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