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Alex D Herbert commented on RNG50:

PS.
I've just noticed that the
{{AhrensDieterMarsagliaTsangGammaSampler}}
can be split into two samplers as per the PoissonSampler. One would not require a GaussianSampler
to be created, and the one that does can precompute some values outside of the sample algorithm,
including a {{Math.sqrt()}}.
I'm going to do a similar speed test for that to see how it performs.
> PoissonSampler single use speed improvements
> 
>
> Key: RNG50
> URL: https://issues.apache.org/jira/browse/RNG50
> Project: Commons RNG
> Issue Type: Improvement
> Affects Versions: 1.0
> Reporter: Alex D Herbert
> Priority: Minor
> Attachments: PoissonSamplerTest.java, jmhresult.csv
>
>
> 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 34 fold for single use Poisson sampling.

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