[julia-users] memory allocation in nested loops

[Niccolo' Antonello]

Hi all,

I'm trying to make this code to go faster:

n,m = 100,25
f = 100
d = randn(3,n)
r = randn(3,m)

Y = zeros(Complex{Float64},f,m)
X = ones(f,n)+im*ones(f,n)

function nested_loop!(Y::Array{Complex{Float64}},X::Array{Complex{Float64}},
                      d::Array{Float64,2},n::Int64,m::Int64,f::Int64)
        for i_1 = 1:f
                for i_3 = 1:n
                        k = 2*π*i_1*d[:,i_3]
                        for i_2 = 1:m
                                A = exp(im*dot(k,r[:,i_2]))
                                Y[i_1,i_2] += X[i_1,i_3] * A
                        end
                end
        end
end

@time nested_loop!(Y,X,d,n,m,f)
Profile.clear_malloc_data()
Y = zeros(Complex{Float64},f,m)
@time nested_loop!(Y,X,d,n,m,f)

I see that it is allocating a lot of memory. 
So I also tried to put k and A on Arrays and preallocate that, but this 
didn't remove the allocations. 
Could you please tell me what could be possibly done to make this code 
faster?
I thank you!