numpy performance and random numbers

[r0g]

sturlamolden wrote:
> 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.
> 
> 
> 
> 
> 



Surely you could have as many totally independent cores as you like
independently spitting out random bits whenever they feel like it to
make an equally random bitstream?  would have thought the only issue
would be ensuring high quality bitstream was used to seed each thread no?

Roger.