[Numpy-discussion] Suggestion: Port Theano RNG implementation to NumPy
matthieu.brucher at gmail.com
Tue Feb 18 10:56:40 EST 2014
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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