[Numpy-discussion] Intel random number package
Todd
toddrjen at gmail.com
Wed Oct 26 17:03:37 EDT 2016
On Wed, Oct 26, 2016 at 4:30 PM, Pavlyk, Oleksandr <
oleksandr.pavlyk at intel.com> wrote:
>
> The module under review, similarly to randomstate package, provides
> alternative basic pseudo-random number generators (BRNGs), like MT2203,
> MCG31, MRG32K3A, Wichmann-Hill. The scope of support differ, with
> randomstate implementing some generators absent in MKL and vice-versa.
>
>
Is there a reason that randomstate shouldn't implement those generators?
> Thinking about the possibility of providing the functionality of this
> module within the framework of randomstate, I find that randomstate
> implements samplers from statistical distributions as functions that take
> the state of the underlying BRNG, and produce a single variate, e.g.:
>
> https://github.com/bashtage/ng-numpy-randomstate/blob/master/randomstate/
> distributions.c#L23-L26
>
> This design stands in a way of efficient use of MKL, which generates a
> whole vector of variates at a time. This can be done faster than sampling a
> variate at a time by using vectorized instructions. So I wrote
> mkl_distributions.cpp to provide functions that return a given size vector
> of sampled variates from each supported distribution.
>
I don't know a huge amount about pseudo-random number generators, but this
seems superficially to be something that would benefit random number
generation as a whole independently of whether MKL is used. Might it be
possible to modify the numpy implementation to support this sort of
vectorized approach?
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.
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