Julia Vectorisation

# vectorised function from Julia blog
function vectorised(x,y)
    r = exp(-abs(x-y))
    return r
end
 
# recommended devectorised function
function devectorised(x,y)
    r = similar(x)
    for i = 1:length(x)
        r[i] = exp(-abs(x[i]-y[i]))
    end
    return r
end
 
# timer func for vectorised (adapted from john Myles White original code)
function timevec(N, x,y)
    timings = Array(Float64, N)
 
    # Force compilation
    vectorised(x,y)
 
    for itr in 1:N
        timings[itr] = @elapsed vectorised(x,y)
    end
 
    return timings
end
 
# timer func for devectorised (adapted from john Myles White original code)
function timedevec(N, x,y)
    timings = Array(Float64, N)
 
    # Force compilation
    devectorised(x,y)
 
    for itr in 1:N
        timings[itr] = @elapsed devectorised(x,y)
    end
 
    return timings
end
 
 
# data (thx HarlanH for pointing out the correct way to do this)
x= [1: 2e6]
y= x * 2
 
# tldr RESULTS !!!!!!!!!!!!!!!!!!
 
median(timevec(50,x,y))
 
median(timedevec(50,x,y))