PoissonRandom.jl: Fast Poisson Random Numbers

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Usage

Pkg.add("PoissonRandom")

# Simple Poisson random
pois_rand(λ)

# Using another RNG
using RandomNumbers
rng = Xorshifts.Xoroshiro128Plus()
pois_rand(rng, λ)

Implementation

It mixes two methods. A simple count method and a method from a normal approximation. See this blog post for details.

Benchmark

using RandomNumbers,
      Distributions, BenchmarkTools, StaticArrays,
      RecursiveArrayTools, Plots, PoissonRandom
labels = ["count_rand", "ad_rand", "pois_rand", "Distributions.jl"]
rng = Xorshifts.Xoroshiro128Plus()

function n_count(rng, λ, n)
    tmp = 0
    for i in 1:n
        tmp += PoissonRandom.count_rand(rng, λ)
    end
end

function n_pois(rng, λ, n)
    tmp = 0
    for i in 1:n
        tmp += pois_rand(rng, λ)
    end
end

function n_ad(rng, λ, n)
    tmp = 0
    for i in 1:n
        tmp += PoissonRandom.ad_rand(rng, λ)
    end
end

function n_dist(λ, n)
    tmp = 0
    for i in 1:n
        tmp += rand(Poisson(λ))
    end
end

function time_λ(rng, λ, n)
    t1 = @elapsed n_count(rng, λ, n)
    t2 = @elapsed n_ad(rng, λ, n)
    t3 = @elapsed n_pois(rng, λ, n)
    t4 = @elapsed n_dist(λ, n)
    @SArray [t1, t2, t3, t4]
end

# Compile
time_λ(rng, 5, 5000000)
# Run with a bunch of λ
times = VectorOfArray([time_λ(rng, n, 5000000) for n in 1:20])'
plot(times, labels = labels, lw = 3)

So this package ends up about 30% or so faster than Distributions.jl (the method at the far edge is λ-independent so that goes on forever).