Alex D Herbert created RNG50:

Summary: 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
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 34 fold for single use Poisson sampling.

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