Consider the basic iteration to generate N random numbers and save them in an array (assume either that we are not interested in array comprehensions and also that we don't know the calling rand(N))
function random_numbers(N::Int)
array = zeros(N)
for i in 1:N
array[i] = rand()
end
array
end
I am interested in a similar function that takes advantage of the cores of my laptop to generate the same array. I have checked this nice blog where the macros @everywhere, @spawn and @parallel are introduced but there the calculation is carried out "on-the-fly" and an array is not needed to save the data.
I have the impression that this is very basic and can be done easily using perhaps the function pmap but I am unfamiliar with parallel computing.
My aim is to apply this method to a function that I have built to generate random numbers drawn from an unusual distribution.
As suggested in the comment more clarification in the question is always welcome. However, it seems pmap will do what is required. The relevant documentation is here.
The following is a an example. Note, the time spent in the pmap method is half of the regular map. With 16 cores, the situation might be substantially better:
julia> addprocs(2)
2-element Array{Int64,1}:
2
3
julia> @everywhere long_rand() = foldl(+,0,(randn() for i=1:10_000_000))
julia> long_rand()
-1165.9596619177153
julia> @time map(x->long_rand(), zeros(10,10))
8.455930 seconds (204.89 k allocations: 11.069 MiB)
10×10 Array{Float64,2}:
⋮
⋮
julia> @time pmap(x->long_rand(), zeros(10,10));
6.125479 seconds (773.08 k allocations: 42.242 MiB, 0.25% gc time)
julia> @time pmap(x->long_rand(), zeros(10,10))
4.609745 seconds (20.99 k allocations: 954.991 KiB)
10×10 Array{Float64,2}:
⋮
⋮