Julia, I Love You

Julia is a new language for scientific computing that is winning praise from a slew of very smart people, including Harlan Harris, Chris Fonnesbeck, Douglas Bates, Vince Buffalo and Shane Conway. As a language, it has lofty design goals, which, if attained, will make it noticeably superior to Matlab, R and Python for scientific programming. In the core development team’s own words:

Remarkably, Julia seems to be on its way to meeting those goals. Last night, I decided to see for myself whether Julia would live up to the hype. So I taught myself just enough of the language to write an implementation of the slowest R code I’ve ever written: the Metropolis algorithm-style sampler Drew and I use in Chapter 7 of Machine Learning for Hackers to show off randomized, iterative optimization algorithms. You can find both the original R code and my new Julia code on GitHub in two files name cipher.R and cipher.jl, respectively.

In my opinion, the new code in Julia is easier to read than the R code because Julia has fewer syntactic quirks than R. More importantly, the Julia code runs much faster than the R code without any real effort put into speed optimization. For the sample text I tried to decipher, the Julia code completes 50,000 iterations of the sampler in 51 seconds, while the R code completes the same 50,000 iterations in 67 minutes — making the R code more than 75 slower than the Julia code.

Having seen that example alone, I would be convinced Julia is a real contender for the future of scientific computing. But this iterative sampling algorithm is not close to being the harshest comparison between Julia and R on my machine. For a more powerful example (lifted straight from the Julia docs), we can compare Julia and R code for computing the 25th Fibonacci number recursively.

First, the Julia code:

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fib(n)= n <2? n : fib(n -1)+ fib(n -2)
@elapsed fib(25)

Second, the R code:

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fib <-function(n){
 ifelse(n <2, n, fib(n -1)+ fib(n -2))}
 
start <- Sys.time()
fib(25)
end <- Sys.time()
end - start

The Julia code takes around 8 milliseconds to complete, whereas the R code takes around 4000 milliseconds. In this case, R is 500 times slower than Julia. To me, that’s sufficient reason to want to start focusing my time on implementing the algorithms I care about in Julia. I hope others will consider doing the same.