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Julia Programming Language Receives $600k Donation
jones_supa writes: The Julia programming language has received a $600k donation from Moore Foundation. The foundation wants to get the language into a production version. This has a goal to create more efficient and powerful scientific computing tools to assist in data-driven research. The money will be granted over the next two years so the Julia Language team can move their core open computing language and libraries into the first production version. The Julia Language project aims to create a dynamic programming language that is general purpose but designed to excel at numerical computing and data science. It is especially good at running MATLAB and R style programs.
...are we doomed to forever playing with our own Python?
So they're re-inventing Python?
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Julia is aimed at people who do math-heavy problems (like computational physics), so that might be why you haven't heard of it. I think it's been on /. before.
I've never used Julia (the computing resources I have access to don't support Julia), but I've been following it, and the language looks pretty impressive: the ease of python/matlab with the speed of fortran/c. It's pretty impressive for a language you can use interactively.
as a jaded sysadmin im in the wrong business. Ive learned perl and python and bash and even picked up a case of php along the way but if all it takes for hipsters to belch a half a million dollars at me is a language? then this is where it begins, slashdot.
My language is called twerk-cankle. It was named after a dance that comes from a small island in the osowhatwhocares islands and is ritually performed with great efficiency. my classes are called palpatisms and you instantiate them by using the pelvic_thrust operator. all code is terminated with yoskrilldropit(hard) which calls a completely gender neutral subroutine to issue enlightenments to my interpreter. The interpreter which you can download under the BSD, MIT, PCP, GPL, and my own personal DERP license uses spare CPU cycles to search the ram heap for Kony. Ive also released a debugger called shitlord which runs as an elevated user once after checking its privilege and enrolling a small orphinage of women and nuns into the girls who code program. Processes when threaded are done so in a way that recognizes france's tragedy and assert a macklemore call to find 99 cent urine scented clothing on the memory bus.
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Take a look at the benchmarks on the Julia frontpage and reevaluate your statement.
Look... you can either play with Julia or your Python.
Are asking for 10 years of experience with Julia, right?
Considering that you have now heard of it, apparently that $600k "advertisement" did indeed change that.
It doesn't sound like something that most of us will use based on the problem domain and the other languages mentioned, I'm sure that there are some people who will find it highly useful and create some products that enhance all our lives.
Yet another slow bloated language.
You need speed? You need accuracy? Go with the power of Java.
Never mind lack of IEEE-754 compliance.
Java is the "good enough" language to handle all tasks.
MATLAB and R style programs?
So what you're saying is Julia's a steaming sack of shit?
It appears the ignorance level here on Slashdot has risen too an all time high. With the exception of one or two, none of the have any idea what they are talking about. People should be more careful about bashing things they know absolutely nothing about. Julia is a fairly kickass language and if you are a coder it should be on your radar -- up there with Go, Rust, Elixir, Clojure, etc. Having dabbled in most languages, I'd say it just might be the best of the lot, although I do dislike its module system.
I doubt anyone paid $600K to DICE to post Julia on Slashdot. There are cheaper ways to advertise.
We have a ton of languages out there. Why not narrow it down to just a few and focus on those? For security-minded work, Ada (since it can be proven to be secure mathematically.) For basic scripting, bash or powershell. For running across platforms, Java [1]. For Web stuff , HTML5 or JavaScript.
We really don't need more languages, because they are more about the brags (knowing the latest language for Web pages to beat the H-1Bs to the trendy jobs) as opposed to actual work. At best, we might need C and C++ for low level work, a compiled language for cross-platform stuff, then a JIT interpreted language just because they are in vogue.
If we -have- to have a language, we need a scripting language for secure programming, just to make it more idiot-resistant.
[1]: Well, something Java-like that actually allows a JVM on one platform to run code written in another JVM without dying. Basically what Java promised, but was never delivered.
why are there so many clueless people who love talking about stuff they have no idea about?
It should have been given to the Python Software Foundation or the PyPy project.
Fast, efficient, designed with the sciences in mind? Sounds just like Fortran (later versions that is: design is probably too strong a word for F77 and co). Fortran also has the advantage of a large base of fast, well tested (and primarily science/maths) code to go along with it. Also many people are already fluent in it.
But, still, good luck to them. Reinventing wheels is fun, particularly if you get paid to do it.
python has already won the scientific race lol...
Who's got time to learn stuff when you spend all your time in social media?
There are some really fascinating - and potentially revitalizing - new languages out there, and these are among the most interesting. Julia is comparable to R running like greased lightening. It deserves attention and $600K. It has C level speed, which is remarkable given its domain. Elm is comparable to Haskell but much more approachable -- especially for web development. Elixir is like Ruby..maybe Ruby goes on a date with Erlang and they fall in love. Kinda functional; kinda something wild. There are some other FASCINATING languages coming out, too. For instance, you might check out red ... keeping in mind how the unknown REBOL did in in the redmonks comparison of conciseness + other compelling strengths.
What i would say is that we are in urgent need of better languages. The hardware, network, and critical deployment goes asymptotic while we mostly get is out-of-proportion bloat and bugs as our languages progress linearly, at best. We desperately need software upgrade in a real sense -- not the sense we generally keep getting with more of the shopworn procedural/object-oriented paradigm.
A good functional language that reminds me of the ML language. Looks like a decent replacement for fortran. Mandelbrot numbers appear suspect to me, as by definition, LuaJIT is written in C, and could not have magically performed better than C, unless their C implementation of mandelbrot was really crappy to begin with.
Non sequitur: Your facts are uncoordinated.
Get the language into a production version
I was not aware it was not production ready, and I now understand the fact is was not is a good explanation why it was such a pain to build. The thing embeds numerous third party libraries, and even the full distribution of LLVM.
Any language that purports to be a good for technical computing needs to get away from a forced base for indexing arrays. No, this is not a 0 or 1 problem. Arrays should be numbered from whatever the programmer specifies. The Pascal-type languages including Ada have this feature and it prevents many many errors. Maybe the $600K can buy this, but somehow I doubt it as this fixed-index-base is usually in the mindsets of the language's designers.
I guess it really is true that you can write Fortran in any language.
Organization? You must be joking..
As a physicist, I do a lot of numerical programming. I like julia, but it isn't quite useful to me yet. The problem is that it takes too long to run something the first time you run it in a session, and there's no way to save a compiled binary so I have to take the hit each time I run it. In particular, loading libraries takes too long. Maybe this boost will help it get there.
I believe I read above that with PyCall.jl you can put your Python inside of Julia.
I heard Julia does JSON.
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I suspect that most people doing scientific computation have heard of Julia. It's a pretty neat language (and has a lot shallower a learning curve than Fortran, which is the big player in the space. If you're not in a field that uses Fortran a lot, Julia probably isn't too relevant to you). I had a student last year work on hoisting bounds checking out of loops to expose better optimisation opportunities for autovectorisation. In combination with Polly, this got a factor of 4-8 speedup for a lot of workloads and also paves the way for things like GPU offloading for critical loops. There are definitely a lot of places where some of that $600K could be spent on small improvements that would give huge improvements to end users.
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When I read about Julia I thought that. HIgh level features, beautiful syntax, macros and metaprogramming and has better perfomance than Python and it is better at multiprocessing.
Whatever Julia is, it can't be worse than R's syntax.
Many people are talking about stuff they don't understand, but I thought my sarcasm was obvious enough.
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This may read like I'm a Julia fan-boy ... I guess I am.
I found out about Julia from the Machine Learning course from Coursera. Not directly, for at that time it was Octave; the advice given there was "trust me, for machine learning, this syntax is better." Indeed for many machine learning algorithms, the basis of understanding it, is vector and matrix operations. The innovation of Matlab which both Octave, which is essentially a gnu, open-source implementation of Matlab, and Julia is making vector valued variables first class (e.g. M*X, M^-1 where M is a matrix and X is a vector) makes things succinct and clear -- btw M^-1 is a representation of the inverse of M, an O^3 order algorithm in 4 characters?
Now yes, Python has numpy, which is close syntactically, but there are yet other comparisons were is not quite so easy, and Julia has an advantage here in that it's so new that devs are still tolerant of syntax changes -- for instance the behavior of {} was changed between Julia 0.3 and 0.4. And so if there's something new on the horizon that needs a re-org, Julia is better able to handle it.
The other thing of course which Julia and Python and R communities are attempting to do is to figure out the best way to extract the optimizations available from LLVM, and owing to it's close ties to and ability to modify to conform to changes of LLVM, Julia also has an advantage. As I've posted before, expect Julia to be able to scale almost linearly on the Xenon Phi (Knight's Landing+) for HPC linear algebra oriented applications -- expect this by Julia 0.5.