[Numpy-discussion] Suggestion: Port Theano RNG implementation to NumPy
nouiz at nouiz.org
Tue Feb 18 11:00:56 EST 2014
I won't go in the discussion of which RNG is better for some problems.
I'll just tell why we pick this one.
We needed a parallel RNG and we wanted to use the same RNG on CPU and
on GPU. We discussed with a professor in our department that is well
know in that field(Pierre L'Ecuyer) and he recommanded this one for
our problem. For the GPU, we don't want an rng that have too much
Robert K. commented that this would need refactoring of numpy.random
and then it would be easy to have many rng.
On Tue, Feb 18, 2014 at 10:56 AM, Matthieu Brucher
<matthieu.brucher at gmail.com> wrote:
> The main issue with PRNG and MT is that you don't know how to
> initialize all MT generators properly. A hash-based PRNG is much more
> efficient in that regard (see Random123 for a more detailed
> >From what I heard, if MT is indeed chosen for RNG in numerical world,
> in parallel world, it is not as obvious because of this pitfall.
> 2014-02-18 15:50 GMT+00:00 Sturla Molden <sturla.molden at gmail.com>:
>> AFAIK, CMRG (MRG31k3p) is more equidistributed than Mersenne Twister, but
>> the period is much shorter. However, MT is getting acceptance as the PRNG
>> of choice for numerical work. And when we are doing stochastic simulations
>> in Python, the speed of the PRNG is unlikely to be the bottleneck.
>> Frédéric Bastien <nouiz at nouiz.org> wrote:
>>> In a ticket I did a coment and Charles suggested that I post it here:
>>> In Theano we have an C implementation of a faster RNG: MRG31k3p. It is
>>> faster on CPU, and we have a GPU implementation. It would be
>>> relatively easy to parallize on the CPU with OpenMP.
>>> If someone is interested to port this to numpy, their wouldn't be any
>>> dependency problem. No license problem as Theano license have the same
>>> license as NumPy.
>>> The speed difference is significant, but I don't recall numbers.
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