Thank you very much. I'm using the Colt random number generator directly. 
I've managed to reduce computing time by orders of magnitude using type 
hints and java arrays in some critical parts. I haven't had the time to 
write a report on this for the list, since have been busy with other 
projects, but this will come as well as the release of the source code.

Thanks again,

Jose.

On Tuesday, June 3, 2014 1:46:55 PM UTC-4, Mars0i wrote:
>
> Jose,
>
> This is an old thread, and whatever problems you might be dealing with 
> now, they're probably not the same ones as when the thread was active.  
> However, I think that if parallel code uses the built-in Clojure random 
> number functions, there is probably a bottleneck in access to the RNG.  
> With Criterium's bench function on an 8-core machine:
>
> (def ones (doall (repeat 1000 1)))
> (bench (def _ (doall (map rand ones))))   ; 189 microseconds average time
> (bench (def _ (doall (pmap rand ones))))  ; 948 microseconds average time
>
> One solution that doesn't involve generating the numbers in advance is to 
> create separate RNGs, as discussed in this thread 
> <https://groups.google.com/forum/#!searchin/clojure/random/clojure/cRVS19PB06E/8FsmtsYx6SkJ>.
>   
> This is a strategy that I am starting to explore.
>
> Related notes for anyone interested:
>
> As of Incanter 1.5.5 at least some functions such as sample are based on 
> Clojure's bult-in rand, so they would have this problem as well.
>
> clojure.data.generators allows rebinding the RNG, and provides 
> reservoir-sample and a replacement for the default rand-nth.
>
> The bigml/sampling 
> <https://github.com/bigmlcom/sampling/tree/master/src/bigml/sampling> 
> library provides sampling and random number functions with optional 
> generation of a new RNG.
>

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