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https://issues.apache.org/jira/browse/RNG-50?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16558843#comment-16558843
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Alex D Herbert commented on RNG-50:
-----------------------------------
OK. The PoissonSampler does check the input mean parameter. It doesn't check
the RNG is not null. I was looking for example at the
{{BoxMullerGaussianSampler}} and the {{AhrensDieterMarsagliaTsangGammaSampler}}
where the constructors params are not checked. It is these three samplers that
I am interested in for my use case which is modelling the signal from point
sources of light on a microscope camera, as in super-resolution microscopy.
Ignoring the Gaussian and Gamma sampling the use case of the Poisson sampling
is to model photon shot noise on a camera image, e.g 512x512 is 262,144 pixels.
Each pixel value is likely to be unique during a simulation that creates
floating-point images. This will be repeated for potentially thousands of
frames. So this is a lot of single Poisson samples.
I understand that you only want to create a cache if it is to be used. However
it is being recreated each time the PoissonSampler is used for mean above 40.
This is a sub-optimal cache.
My code fix to set the {{FactorialLog}} as static would mean that any use of
the {{PoissonSampler}} with a mean under 40 would suffer the overhead of
computing the cache. However the cache will be computed only once. Subsequent
uses would not suffer.
A better solution would be improved reuse of previously computed values in the
{{FactorialLog}} class. This can be done using a single static master table of
{{log(n!)}} values that can be copied. Here is an example that I created for my
own functions that require {{log(n!)}}:
[LogFactorial|https://github.com/aherbert/GDSC-SMLM/blob/master/src/main/java/uk/ac/sussex/gdsc/smlm/function/LogFactorial.java]
The master table can be increased in size and reduced. An instance can be
created that reuses values from the master table and computes the rest it needs.
If the {{FactorialLog}} class is modified then when it is used by other
samplers then they can take advantage of any already computed values.
> 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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