Will take a look at the bigml/sampling library... On Tuesday, June 3, 2014 7:52:06 PM UTC-4, Jose M. Perez Sanchez wrote: > > > 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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