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by aaronjg
5194 days ago
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I find the example benchmark to be rather ridiculous. Recursion is slow in R because R is an interpreted language, and does not implement tail-call optimization. If I wanted to implement the Fibonacci numbers in R I would do it like this: x<-numeric(25); x[1:2] <- 1; for(i in 3:25) x[i] <- x[i-1] + x[i-2]; x[25] Which takes about 1/10,000 the time of the recursive version (about .4 milliseconds on my machine). So the conclusion I draw from the article is not that Julia is faster than R, but that you should know what your languages strengths are, and write code accordingly. I'd love to see some examples of real statistical work were Julia's syntax is as easy as R, but the performance is superior. |
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So one of the claims of Julia is that you write code in the most natural manner, and depend on the compiler to make it fast. So, for example, the claim is that adding the optional type parameters do not necessarily make your code faster... type inference is good enough in most cases (there are obvious counterexamples, eg with global variables, but its true in the majority of cases). Also, unlike for eg. Matlab or R, vectorising your code does not necessarily produce performance gains, arrays are good enough. One of the side effects is that the standard library is implemented in the language itself, and thus extending built-in types is a breeze.
Therefore, I think its a very interesting effort in itself. Whether it is good enough to displace any other language is a completely separate issue. As said below, the base of useful libraries in languages such as R is phenomenal. But that should not, I think, preclude admiring a very interesting new language, for the possibilities that it hints at, if only as a highly engaging mental activity.
Personally, and subjectively, I think the combination of the above, and multiple dispatch, causes the language to have a highly pleasing sense of elegance.