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From "Alex D Herbert (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (RNG-50) PoissonSampler single use speed improvements
Date Fri, 03 Aug 2018 21:59:00 GMT

    [ https://issues.apache.org/jira/browse/RNG-50?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16568839#comment-16568839
] 

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

{quote}I do not follow: Do you mean that the rest of the previous comment should be ignored?
{quote}
I wrote that at the top because in the original post I said the unsync and sync version were
the same speed. So if you read the original it had changed.

Here is a more extensive benchmark:
||Source||Range||Name||Relative Score||
|KISS|4|NoCache|1|
|KISS|4|Cache|0.637002514107754|
|KISS|4|SyncCache|0.606805802031795|
|KISS|16|NoCache|1|
|KISS|16|Cache|0.663954180665926|
|KISS|16|SyncCache|0.631319224692749|
|KISS|64|NoCache|1|
|KISS|64|Cache|0.636384669613506|
|KISS|64|SyncCache|0.609715929448392|
|KISS|256|NoCache|1|
|KISS|256|Cache|0.648985318438977|
|KISS|256|SyncCache|0.626782442178428|
|KISS|1024|NoCache|1|
|KISS|1024|Cache|0.659225625944472|
|KISS|1024|SyncCache|0.641191069564397|
|KISS|4096|NoCache|1|
|KISS|4096|Cache|0.683340577614337|
|KISS|4096|SyncCache|0.663516180493439|
|SPLIT_MIX_64|4|NoCache|1|
|SPLIT_MIX_64|4|Cache|0.628425684341808|
|SPLIT_MIX_64|4|SyncCache|0.593939891208052|
|SPLIT_MIX_64|16|NoCache|1|
|SPLIT_MIX_64|16|Cache|0.644051788271398|
|SPLIT_MIX_64|16|SyncCache|0.611315259381778|
|SPLIT_MIX_64|64|NoCache|1|
|SPLIT_MIX_64|64|Cache|0.640776657597466|
|SPLIT_MIX_64|64|SyncCache|0.614925898338969|
|SPLIT_MIX_64|256|NoCache|1|
|SPLIT_MIX_64|256|Cache|0.647514729530435|
|SPLIT_MIX_64|256|SyncCache|0.623509940647398|
|SPLIT_MIX_64|1024|NoCache|1|
|SPLIT_MIX_64|1024|Cache|0.627242797856745|
|SPLIT_MIX_64|1024|SyncCache|0.613652464877662|
|SPLIT_MIX_64|4096|NoCache|1|
|SPLIT_MIX_64|4096|SyncCache|0.668044213459518|
|SPLIT_MIX_64|4096|Cache|0.64825539875968|
|WELL_1024_A|4|NoCache|1|
|WELL_1024_A|4|Cache|0.674680507463973|
|WELL_1024_A|4|SyncCache|0.64990985639085|
|WELL_1024_A|16|NoCache|1|
|WELL_1024_A|16|Cache|0.674488306896242|
|WELL_1024_A|16|SyncCache|0.647697233808698|
|WELL_1024_A|64|NoCache|1|
|WELL_1024_A|64|Cache|0.67452109541707|
|WELL_1024_A|64|SyncCache|0.673070926611252|
|WELL_1024_A|256|NoCache|1|
|WELL_1024_A|256|SyncCache|0.699218305156117|
|WELL_1024_A|256|Cache|0.693893305588347|
|WELL_1024_A|1024|NoCache|1|
|WELL_1024_A|1024|SyncCache|0.662001634421658|
|WELL_1024_A|1024|Cache|0.650959461660946|
|WELL_1024_A|4096|NoCache|1|
|WELL_1024_A|4096|SyncCache|0.724667718309707|
|WELL_1024_A|4096|Cache|0.713244863157027|
|WELL_44497_B|4|NoCache|1|
|WELL_44497_B|4|SyncCache|0.693214912971548|
|WELL_44497_B|4|Cache|0.676796240713726|
|WELL_44497_B|16|NoCache|1|
|WELL_44497_B|16|SyncCache|0.691494549936262|
|WELL_44497_B|16|Cache|0.682040619982067|
|WELL_44497_B|64|NoCache|1|
|WELL_44497_B|64|SyncCache|0.707794461549788|
|WELL_44497_B|64|Cache|0.696961073242649|
|WELL_44497_B|256|NoCache|1|
|WELL_44497_B|256|SyncCache|0.700939276921851|
|WELL_44497_B|256|Cache|0.694691018842925|
|WELL_44497_B|1024|NoCache|1|
|WELL_44497_B|1024|SyncCache|0.711826139604225|
|WELL_44497_B|1024|Cache|0.675221230709037|
|WELL_44497_B|4096|NoCache|1|
|WELL_44497_B|4096|SyncCache|0.778102849989416|
|WELL_44497_B|4096|Cache|0.74339413761269|

This time done with 20 repeats on a Mac laptop with a core i7 chip. Last timings were on a
Linux Xeon setup. It's definitely faster but less so on this machine.

Q. So, worth adding to the library?

 

 

> 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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