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Gilles commented on RNG-50:
---------------------------
bq. I still think that the simple solution of a one time cache construction inside PoissonSampler is the best option
That's certainly the least intrusive.
Would it be good enough for your use-case?
> PoissonSampler single use speed improvements
> --------------------------------------------
>
> Key: RNG-50
> URL: https://issues.apache.org/jira/browse/RNG-50
> Project: Commons RNG
> Issue Type: Improvement
> Affects Versions: 1.0
> Reporter: Alex D Herbert
> Priority: Minor
> Attachments: PoissonSamplerTest.java
>
>
> The Sampler architecture of {{org.apache.commons.rng.sampling.distribution}} is nicely written for fast sampling of small dataset sizes. The constructors for the samplers do not check the input parameters are valid for the respective distributions (in contrast to the old {{org.apache.commons.math3.random.distribution}} classes). I assume this is a design choice for speed. Thus most of the samplers can be used within a loop to sample just one value with very little overhead.
> The {{PoissonSampler}} precomputes log factorial numbers upon construction if the mean is above 40. This is done using the {{InternalUtils.FactorialLog}} class. As of version 1.0 this internal class is currently only used in the {{PoissonSampler}}.
> The cache size is limited to 2*PIVOT (where PIVOT=40). But it creates and precomputes the cache every time a PoissonSampler is constructed if the mean is above the PIVOT value.
> Why not create this once in a static block for the PoissonSampler?
> {code:java}
> /** {@code log(n!)}. */
> private static final FactorialLog factorialLog;
>
> static
> {
> factorialLog = FactorialLog.create().withCache((int) (2 * PoissonSampler.PIVOT));
> }
> {code}
> This will make the construction cost of a new {{PoissonSampler}} negligible. If the table is computed dynamically as a static construction method then the overhead will be in the first use. Thus the following call will be much faster:
> {code:java}
> UniformRandomProvider rng = ...;
> int value = new PoissonSampler(rng, 50).sample();
> {code}
> I have tested this modification (see attached file) and the results are:
> {noformat}
> Mean 40 Single construction ( 7330792) vs Loop construction (24334724) (3.319522.2x faster)
> Mean 40 Single construction ( 7330792) vs Loop construction with static FactorialLog ( 7990656) (1.090013.2x faster)
> Mean 50 Single construction ( 6390303) vs Loop construction (19389026) (3.034132.2x faster)
> Mean 50 Single construction ( 6390303) vs Loop construction with static FactorialLog ( 6146556) (0.961857.2x faster)
> Mean 60 Single construction ( 6041165) vs Loop construction (21337678) (3.532047.2x faster)
> Mean 60 Single construction ( 6041165) vs Loop construction with static FactorialLog ( 5329129) (0.882136.2x faster)
> Mean 70 Single construction ( 6064003) vs Loop construction (23963516) (3.951765.2x faster)
> Mean 70 Single construction ( 6064003) vs Loop construction with static FactorialLog ( 5306081) (0.875013.2x faster)
> Mean 80 Single construction ( 6064772) vs Loop construction (26381365) (4.349935.2x faster)
> Mean 80 Single construction ( 6064772) vs Loop construction with static FactorialLog ( 6341274) (1.045591.2x faster)
> {noformat}
> Thus the speed improvements would be approximately 3-4 fold for single use Poisson sampling.
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