Most of the samplers in the library have very small states that are easy
to compute. Some have computations that are more expensive, such as the
LargeMeanPoissonSampler or the DiscreteProbabilityCollectionSampler.
However once the state is computed the only part of the state that
changes is the RNG. I would like to suggest a way to copy samplers as
something like:
DiscreteSampler newInstance(UniformRandomProvider)
The new instance would share all the private state of the first sampler
except the RNG. This can be used for multi-threaded applications which
require a new sampler per thread but sample from the same distribution.
A particular case in point is the as yet not integrated
MarsagliaTsangWangSmallMeanPoissonSampler (see RNG-91 [1]) which has a
"large" state [2] that takes a "long" time [3] to compute but is
effectively immutable. This could be shared across instances saving
memory for parallel application.
A copy instance would be almost zero set-up time and provide opportunity
for caching of commonly used samplers.
Alex
[1] https://issues.apache.org/jira/browse/RNG-91
[2] kB, or possibly MB, of tabulated data
[3] Set-up cost for a Poisson sampler is in the order of 30 to 165 times
as long as a SmallMeanPoissonSampler for a mean of 2 and 32. Note
however that construction still takes only 1.1 and 4.5 microseconds for
the "long" time.
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