A slightly different comparison of R and Julia vectorisation closer to what most R people mean when they say "vectorised". The code example is plucked from the Julia blog discussion on code optimisation. Surprisingly for this use case Julia devectorised (~=0.18 secs) is just a little better than Julia vectorised (~= 0.25 secs), which is a little…

R and Julia Vectorisation

This is sort of a reply to this post: 

http://www.johnmyleswhite.com/notebook/2013/12/22/the-relationship-between-vectorized-and-devectorized-code/

which arises from a a discussion of this

http://julialang.org/blog/2013/09/fast-numeric/


So....

## This is Julia code
# executable code here: https://gist.github.com/Malarkey73/8097545

# 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
julia> x= [1: 2e6]
julia> y= x * 2

# tldr RESULTS !!!!!!!!!!!!!!!!!!

julia> median(timevec(50,x,y))
0.24664389599999997

julia> median(timedevec(50,x,y))
0.176243553

------------------------------------------------------
#This is now R
# executable code with added Rcpp here: https://gist.github.com/hadley/8097300 

# vectorised function
vectorised <- function(x, y) 
  {
    r = exp(-abs(x-y))
    return(r)
  }

#devectorised function
devectorised <- function(x,y) 
{
  r=rep(NA, length(x))
  for(i in seq_along(x))
  {
    r[i]= exp(-abs(x[i]-y[i]))
  }
  return(r)

}

# data
> x= 1:2e6
> y= x * 2

# tldr RESULTS !!!!!!!!!!!!!!!!!!

> microbenchmark(
  vectorised(x,y),
  devectorised(x,y), 
  unit = "s", times=5)


Unit: seconds
expr                    min        lq    median        uq        max      neval
vectorised(x, y)    0.2058464 0.2165744 0.2610062 0.2612965  0.2805144     5
devectorised(x, y)  9.7923054 9.8095265 9.8097871 9.8606076 10.0144012     5

@HarlanH Sorry there was actually another typo above, devectorised Julia is fractionally faster both for the wrong way I did it and the right way you have pointed out (for this use case).

NB both ways give me the same answer.

Interesting example. This is a very close analogue to the second set of snippets in my post: almost all of the time is spent in memory allocation for r, so there's not much to be gained from devectorization.

I chose this function because the original was a loop 1e6 * 2-fold vector addition. So this is just a 2e6 iteration. It's the sort of code you see in every day data munging, or matrix algebra. I want to learn more Julia so i may try benchmarking Data.frames logical indexing and and apply type operations because these are the true data analysis workhorses.