On Thu, Oct 27, 2016 at 10:25 AM, Pavlyk, Oleksandr <oleksandr.pavlyk@intel.com> wrote:

Please see responses inline.

From: NumPy-Discussion [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Todd
Sent: Wednesday, October 26, 2016 4:04 PM
To: Discussion of Numerical Python <numpy-discussion@scipy.org>
Subject: Re: [Numpy-discussion] Intel random number package

On Wed, Oct 26, 2016 at 4:30 PM, Pavlyk, Oleksandr <oleksandr.pavlyk@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).

I think the monkey-patching approach will work.

It will work, for a while at least, but it's bad design.

We're all on the same page I think that a separate submodule for random_intel is a no go, but as an explicitly switchable backend for functions with the same signature it would be fine imho. Of course we don't have that backend infrastructure today, but it's something we want and have been discussing anyway.

Ralf