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From "Alex D Herbert (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (RNG-50) PoissonSampler single use speed improvements
Date Wed, 01 Aug 2018 19:36:00 GMT

    [ https://issues.apache.org/jira/browse/RNG-50?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16565872#comment-16565872
] 

Alex D Herbert commented on RNG-50:
-----------------------------------

I don't see why you have to precompute everything. The cache helps in this simple example.
I've taken the liberty to change the name and have it be constructed by the PoissonSampler
as a suggest API:

{code:java}
PoissonSamplerCache cache = PoissonSampler.createCache(0, 256);
UniformRandomProvider rng = RandomSource.create(RandomSource.SPLIT_MIX_64);
for (int i=0; i<N; i++) {
    doSomethingWith(new PoissonSampler(rng, rng.nextDouble() * 256, cache).sample());
}
{code}

If N=10000000 then pre-computation is fine.

However if N=10 then pre-computation of the cache would be a waste.

With a lazy initialised cache you don't have to know the loop size and make a decision on
whether it should be used. With a cache that has non-synchronised lazy initialisation then
you can even use it across threads with the caveat that some initialisation may be repeated
until the JVM writes local CPU memory back to main memory. Or just use the {{AtomicReferenceArray}}
as above.

So for my use case I would have an idea of the range of values I'd expect for the Poisson
mean. I could then use the cache system to improve performance.
  
{quote}What I had in mind is a global cache
{quote}
 
 Q. How global is global? Across the simulation and then destroyed (as above)? In this case
any user of the library could keep hold of the cache for use again if they want so I think
this is fine.

> 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, jmh-result.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 3-4 fold for single use Poisson sampling.



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