Julia vs. R Speed for sampling the Wishart distribution

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I updated Julia, and implemented it in parallel. It is fast now, and a bit faster than R. I do not think implementing it in parallel in R would have much gain, because of the initial overhead.

Julia

using Distributions; using LinearAlgebra;

A = zeros(20,20,50000)

function parallel_test(nu)
           inv_wish = rand(Wishart(nu, Matrix{Float64}(I,nu,nu)))
           end
 
@time Threads.@threads  for i = 1:50000
    A[:,:,i] = parallel_test(20);
end;

1.043198 seconds (3.16 M allocations: 968.912 MiB, 8.96% gc time)

Now R:

system.time(replicate(50000, rWishart(1, 20, diag(20)) ))

 user  system elapsed 
   1.13    0.16    1.2

I also checked with out storing the matrices in Julia, and here it was often less than 1 second.

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