Re:What's wrong with statistics in Julia?
by vegabook on Saturday November 29, 2014 @07:37PM
(#8675560)
For my own use case, and I use 5-10-giga-plus financial tick datasets, I find Numpy to outperform Julia 3:1. R users who need perf have already found other ways (like me). I think this Julia performance argument is hugely overrated. When I don't need Numpy, R gives me more than everything I will eve need and I don't see where these developers are going to come from to reinvent the wheel for a language which does not target their user base. I just don't see what Julia brings to the table. Now if it had gone the whole hog and give us full infrastructure for ingesting massive streaming data, then it would have skated to where the puck is going to be (see Clojure/Storm). As it stands, the only main benefit I see for Julia, is as an open source (and better) replacement for Matlab.
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Re:What's wrong with statistics in Julia?
by complicated on Saturday November 29, 2014 @08:13PM
(#8675639)
Actually, I think your point rather showcases that Julia could be useful for the statistics community! Due to performance reasons you occasionally have to abandon R to use Numpy, but for everything else you use R. Thus, in a sense you still have the problem that Julia tries to solve: You constantly have to jump between two different languages depending on the problem size.
Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.
Re:What's wrong with statistics in Julia?
by vegabook on Saturday November 29, 2014 @08:44PM
(#8675681)
Yes and yes. Julia is better because, having seen the mistakes of 3 x 20 year old languages, it is creating an (incrementally) better PythonRMatlab base language (not libraries). Had Julia dropped into my lap 5-10 years ago I would have been a fool to use anything else. The fact is though, it is very late to a party where everyone has already danced the night away, and just as the music is about to change (massively parallel multi node functional).
I think (this is gut - not an authority!) that Julia is very well placed for the massively parallel multi-node functional future of which you speak. lazy.jl and the cluster implementations such as spark.jl are evolving.
Re:What's wrong with statistics in Julia?
by infinite8s on Saturday November 29, 2014 @08:19PM
(#8675649)
With true coroutine support it is only a matter of time before someone writes a pytoolz like package for Julia.