numpy performance and random numbers
sturlamolden at yahoo.no
Sat Dec 19 16:47:48 CET 2009
On 19 Des, 16:20, Carl Johan Rehn <car... at gmail.com> wrote:
> How about mulit-core or (perhaps more exciting) GPU and CUDA? I must
> admit that I am extremely interested in trying the CUDA-alternative.
> Obviously, cuBLAS is not an option here, so what is the safest route
> for a novice parallel-programmer?
The problem with PRNG is that they are iterative in nature, and
maintain global states. They are therefore very hard to vectorize. A
GPU will not help. The GPU has hundreds of computational cores that
can run kernels, but you only get to utilize one.
Parallel PRNGs are an unsolved problem in computer science.
More information about the Python-list