The Julia Programming Language

http://julialang.org/blog/2012/02/why-we-created-julia/

by jemeshsu • 14 years ago

499 203 14 years ago

julialang.org

yarapavan• 14 years ago

Congrats! Great job!!

almost• 14 years ago

Awesome stuff: http://julialang.org/manual/metaprogramming/

samth3• 14 years ago

It's great the metaprogramming support seems to be a requirement for new languages these days.

Unfortunately, this one combines the inconvenience of Template Haskell (explicit invocation of macros with @) and the bugs of Common Lisp (the section on hygiene says, basically, "we have gensym"). Fortunately, the language is young, and I hope they can improve this story.

tomp3• 14 years ago

What else would you suggest?

I find that the @ syntax is amazing, since the reader (programmer) knows exactly which form is a macro (and can look it up), as opposed to introducing arbitrary, often very confusing syntax.

mjwalshe2• 14 years ago

And the moon on a stick :-) Not sure many "technical" programmers would take c as the desert island programing language.

Sound interesting though if it can make hadoop easier to use id take that as win I dont think its going to replace fortran as a HPC language.

coffeeaddicted• 14 years ago

C as desert island programming language but making all indexing 1-based: "the first element of any integer-indexed object is found at index 1, not index 0, and the last element is found at index n rather than n-1, when the string has a length of n." Also begin end blocks - maybe there was a pascal ninja hidden in the syntax committee room?

julien_p• 14 years ago

More likely Matlab than Pascal.

  Julia's syntax is intended to be familiar to users of MATLAB®.

jfager5• 14 years ago

Matlab, Octave, Mathematica, and other mathematical programming environments use 1-based indexing, it's a well-established norm for the domain.

jfager5• 14 years ago

Just spent the last hour reading through the docs and playing around with it. This is some damn sexy stuff. It's been a long time since I've been this excited by a language.

disgruntledphd22• 14 years ago

I'm a little worried about some of those benchmarks. They appear to have benchmarked R from a few years back against 2011 Matlab, for instance. In addition, they provide no code used for the benchmarks. That being said, this looks really interesting, and I'm gonna bookmark it for when I have time to examine it properly (after the damn phd is finally submitted).

Jabbles• 14 years ago

"The source code for the various implementations can be found here:" (And then follow the links for each language...)

EricBurnett• 14 years ago

They do link to the code: https://github.com/JuliaLang/julia/tree/master/test/perf . It doesn't look like a very reliable microbenchmark - run test x 5 times - but it should provide a useful starting point if you want to run your own.

disgruntledphd22• 14 years ago

Thanks guys, I'm not sure how I missed that. The benchmarks look interesting, but it is strange how old an R version they used. That being said, its a really interesting language, and I look forward to playing with it.

plessthanpt05• 14 years ago

Pretty tied into Python/Numpy ecosystem & 3rd party libs; however, after reading that (somewhat over the top, albeit very persuasive) posting, I'm definitely going to dig a bit deeper. Really nice first impression, especially like the clean syntax and calling of c -- looks promising, thanks!

karpathy• 14 years ago

Nice! I do a fair amount of scientific computing in MATLAB. This looks to have a lot of the powerful array syntax / functions of MATLAB, but with the neat feel of Python, and all kinds of extras.

vilya• 14 years ago

This looks like it would be a great language to tackle the Project Euler problems with - which is usually a good sign, in my book. A big-int type would be nice though.

kib22• 14 years ago

No Windows support, really ?!

simonw• 14 years ago

That's pretty common for new language implementations - Node.js was around for a couple of years before the Windows port was ready.

To be honest, if you're targeting early adopters of a programming language Linux and Mac support is probably a lot more important than Windows. Smart Windows users can always run Linux in a VM.

mjwalshe2• 14 years ago

and most users of this sort of languages aren't going to be running it on windows but on Unix systems.

ViralBShah12• 14 years ago

It is true that a number of people do their scientific computing on Windows+Matlab or Windows+R, but that most parallel stuff is typically on linux clusters. It would be nice if julia worked on both.

It is not by design that we do not have Windows support. It's just that none of us uses windows. We do believe that code is largely portable, and with a little effort, it can be built on cygwin or mingw. Nothing like a native port though. Maybe someone who is familiar with windows will come along and contribute. This is a common question our friends and colleagues ask us.

bo1024• 14 years ago

Look at the goal applications: scientific computing, automatic multithreading/parallelism, distributed tasks across multiple machines.

That doesn't sound trivial to port to Windows, and I'm sure their own application was their first priority.

drats3• 14 years ago

Looks very much like a python without the legacy and a touch of ruby, ipython as the default shell, better multiprocessing, better os/system access, decorators and mathematics. Nice clear documentation too.

Given how similar to python they are their main competitor is pypy, which isn't too far behind them in performance I'd suspect (absent from the benchmarks though). I was fully expecting someone to make a non-compatible fork of pypy to create a faster python-like lang/something like julia. They should quickly get some elegant small libraries like sinatra/bottle and requests (access to dbs with http interfaces as well). A robust requests+lxml library built-in would be simply amazing.

DasIch• 14 years ago

PyPy provides an entire framework for people to write interpreters in. If you were to write one you wouldn't fork PyPy, you would use it.

drats3• 14 years ago

Well you are being a bit nitpicky as pypy is both the python implementation and the JIT/Rpython/framework bit. What I mean was someone holding very close to the python implementation in almost everything (syntax etc.) but streamlining it/breaking compatibility/giving it a new name ("python3-as-it-should-have-been" perhaps?).

Further, if we are going to be nitpicky, when you use something like that for its intended purpose it's still a fork - I forked Twitter bootstrap for my new project/I used Twitter bootstrap for my new project - so "you wouldn't fork PyPy" is a false statement.

fijal5• 14 years ago

PyPy is missing complex support in numpy (on trunk anyway) and numpy.random to run those benchmarks. Those that run:

  fib - roughly the same time as CPython
  parse_int - 10x over CPython which is extrapolating to their python vs julia about 20% slower
  quicksort - 4x faster than cpython much slower than julia.
  pi_sum - 20x faster than cpython, a bit faster than julia?

Those are very unscientific measurments. Both fib and qsort are recursive benchmarks (why would you write qsort recursively??), so I guess the JIT does not have time to kick in (and PyPys support kinda sucks for recursion at least in terms of warmup times).

On a slightly unrelated note - a modified lxml library runs on pypy albeit a lot slower right now.

Cheers, fijal

EDIT: updated formatting

mjw2• 14 years ago

I wonder what they think about or have learned from http://en.wikipedia.org/wiki/Fortress_(programming_language) , another recent-ish attempt to deliver a modern and powerful scientific programming language.

Personally I'm a little wary of being ghettoised into something overly domain-specific for scientific/numerical computing. Really good interop may mitigate that -- something which can navigate the unholy mix of C, C++, fortran, matlab, octave, R and python routines one comes across trying to reproduce others research work, would indeed be awesome.

I do wonder if some of the noble demands of this project might be better delegated to library developers though, after adding a bare minimum of syntax and feature support to a powerful general-purpose language. For now Python+numpy+scipy seems a great 90% solution here.

wbhart10• 14 years ago

And what should the library writers use? The great advantage of this is that much of it can be done in one language.

From the article: "The library, mostly written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, FFTs, and string processing."

fijal5• 14 years ago

Python+numpy+scipy does work only if you actually don't write much python or at least not run much python. Otherwise it sucks

joelthelion2• 14 years ago

Just wait for Numpy on PyPy to be available :)

ViralBShah12• 14 years ago

I wouldn't say that it sucks - that's way too strong. I am one of the authors of Circuitscape (http://www.circuitscape.org), which uses python+numpy+scipy. The community loves it, the fact that its python, open source, embeddable in other tools, etc.

However, much of the code is written in a vectorized style for performance reasons, as is the case with many high level scientific computing languages. This leads to unnatural code in some cases, and also uses too much memory. First I thought IronPython was the way, but have been looking forward to pypy+numpy+scipy eagerly.

If I were to use julia, the code would be a lot more natural, because type inference and all the compiler goodies make it possible to simply write loops over arrays when I need them. This was one of the reasons we started working on julia, because everything else just seemed to fall just a little bit short.

mjw2• 14 years ago

OK, perhaps I should have qualified that. It's a good 90% solution for a lot of use cases, although obviously more useful for implementing algorithms in terms of numerical building blocks, than ultra-performance-critical code where you want to write a lot of your own tight inner loops. Is that what you meant?

The parallelisation story isn't great at the moment either, although seems like it has the potential to improve. Still 'sucks' is pretty harsh. For me, looking at how far it's come since I first played with it, I'm impressed. For machine learning, the majority of the building blocks one needs are there, and you get to sit back and put them together using a nice, clean, widely-adopted general purpose programming language. And unlike MATLAB still maintain a decent amount of control over things like memory usage and which BLAS routines it's calling.

Adding bindings for new libraries is more of a pain than it should be though on the occasions where you do really need some fortran or C++ library that doesn't have bindings yet. A language which bridges the gap between high and low levels (not C++!) and has great interop would be very interesting. I guess I'm just hopeful that this kind of thing can be achieved in a general-purpose language (new or existing) which like Python is adopted across the wider software engineering community. Perhaps that's my unreasonable demand to add to their list :)

pwang2• 14 years ago

> Otherwise it sucks

Seriously? I mean, I know you are very excited about your work on PyPy, but why do you have to go around saying baseless stuff like this? There are many, many thousands of people who write lots of Python code every day that rely on Numpy, Scipy, and Matplotlib to get their work done, and who seem to be quite pleased with it. There is a lot of work left to do on all parts of that stack, but that's a far cry from "it sucks".

teoliphant• 14 years ago

Nice, Fijal. It's good that I can count on you for un-biased opinions.

pron7• 14 years ago

I've been waiting for Fortress for a LONG time, but either progress has been real slow or the Fortress team has trouble communicating their progress to the community. Probably both.

From a quick look I can tell that while Fortress is more similar to Scala, with classes, mixins and static typing, Julia is closer to Clojure, with no encapsulation, dynamic typing, homoiconicity, and separation of behavior (methods) from concrete types (akin to Clojure's protocols).

I really like the choices Julia's designers have made, particularly multiple-dispatch methods, final concrete types and lispy macros. The language seems very elegant. Not too crazy about "begin" and "end" syntax, though :)

StefanKarpinski31• 14 years ago

Yeah, we've been waiting for a while for Fortress too. It seems like a lot of time and effort has gone into a WYSIWYG IDE and not as much into the actual language implementation :-(

Chapel has made a lot of progress in the past 2.5 years (while we've been working on Julia) and is certainly a contender.

Julia certainly aims to be more dynamic like Clojure, but specially designed to be good for numerical and technical stuff, and yes, the begin/end is due to Matlab — scientists who have a lot of code in Matlab already are a major target demographic. As it is, a lot of Matlab code ports over with relatively trivial changes (see: http://julialang.org/manual/getting-started/#Major+Differenc...), which was the point of syntactic similarity to Matlab.

gtani2• 14 years ago

I think i've been waiting for Chapel more than X10 or Fortress, but scala and clojure are assuming the mantles as general purpose languages.

http://bartoszmilewski.wordpress.com/2011/11/07/supercomputi...

http://lambda-the-ultimate.org/node/4405

StefanKarpinski31• 14 years ago

I huge part of the goal here is to reduce the need for the "unholy mix of C, C++, fortran, matlab, octave, R and python routines" in both academic research work and machine learning / data science code in industrial settings. The whole project kicked off with me ranting about how I was sick of cobbling things together in six or seven different languages.

So interop is a very, very high priority. We have pretty good C ABI interop now. You can just call a C function like this:

  ccall(:getpid, Uint32, ())

and it works. It works in the repl too and you can dynamically load new libraries in the repl. See this manual page for more details: http://julialang.org/manual/calling-c-and-fortran-code/.

We still want better C/C++ interop though. I've already looked into using libclang so that if you have the headers you don't even have to declare what the interface to a function is. That was very preliminary work, but I got some stuff working. Making Julia versions of C struct types transparently would be another goal here.

Another major interop issue is support for arrays of inline structs (as compared to arrays pointers to heap-allocated structs). C can of course do this, but in any language where objects are boxed, it becomes very tricky. We're working on it, however, anyone who wants to discuss, hop on julia-dev@googlegroups.com :-)

pron7• 14 years ago

> Another major interop issue is support for arrays of inline structs

Oh, so arrays of complex numbers are implemented as arrays of pointers to objects? That would give really bad CPU cache performance.

dkarl4• 14 years ago

in any language where objects are boxed, it becomes very tricky. We're working on it, however

Naive question (and I'll hold my tongue on my naive guesses): I understand why it is necessary to box user-defined types on the JVM, but why build this restriction into a new language that doesn't run on a restricted platform? Especially when performance and C interop are high priorities?

Or perhaps I misunderstood, and your statement should be read as sparsevector suggested.

wbhart10• 14 years ago

Is the documentation still accurate on that there is no way to pass structs from Julia to C?

One other slight difficulty seems to be the lack of finilisers, e.g. to clean up a GMP bignum when there are no remaining references to it.

wan111• 14 years ago

要让社会有大进步，这是必须地

ludwigvan3• 14 years ago

Very exciting. The real power of MATLAB likes in its toolboxes, though, I wonder if there will be an easy (and possibly automatic) way to convert toolboxes.

One more thing: it would be awesome if the `manipulate` from Mathematica could be incorporated somehow in that web interface. See:

http://www.wolfram.com/broadcast/videos/manipulate/

ViralBShah12• 14 years ago

MATLAB toolboxes are simply awesome for users, even though they are expensive. We hope that if enough people find julia useful, many such libraries will be built. Having written a ton of mex and matlab stuff, and a lot of julia libraries, I find that julia is nicer to use personally - but of course I am biased. :-)

I do believe that open source + good compiler + simple C/Fortran calling interface will lead to others being able to write toolboxes in julia itself and plugin libraries when needed.

wbhart10• 14 years ago

Much praise!! These guys have incredibly good taste. Almost every single thing I can think of that I want in a programming language, they have it. All in the one language!

The fact that it has parametric types, parametric polymorphism, macros, performance almost as good as C, good C/Fortran interop, 64 bit integers and an interactive REPL all in the one language just blows my mind.

I wasn't able to tell if it is possible to overload operators, which is another thing essential to mathematical code.

I was also unsure why the keyword end was needed at the end of code blocks. It seems that indentation could take care of that.

I also didn't see bignums as a default type (though you can use an external library to get them).

However, all in all, I think this is the first 21st Century language and find it very exciting!

ludwigvan3• 14 years ago

I think they are aiming for smooth transition for MATLAB users. They also have 1-index based arrays as opposed to 0-index .

wbhart10• 14 years ago

It would certainly be nice if there was an option to use 0 based indices in blocks of code. It's understandable in that they are pitching at the technical community, and many mathematical papers and books are written with 1 based indices. But I am a mathematician who prefers 0 based indices.

maximilian• 14 years ago

Fortran is 1-index as well. I think MATLAB gets its notational style from Fortran.

TheoLib• 14 years ago

A number of programming languages allow an arbitrary range of indices for an array, including Ada, Fortran 77, and Pascal. See the "Specifiable Base Index" column in this table at Wikipedia: http://en.wikipedia.org/wiki/Comparison_of_programming_langu...

Bromskloss• 14 years ago

Actually, the main reason I want to do away with Matlab and Octave is that I can't stand the 1-indexing! When voicing that opinion among collegues, I have heard no-one disagree with me. If you are actually stuck with this in Julia as well, I don't think I will have anything more to do with it.

cheald2• 14 years ago

When reading through the manual, I was struck at how similar the language seems to be to Lua, at least superficially.

Lua also has 1-based arrays. Heh.

marshray• 14 years ago

I noticed that too.

The language syntax seemed uninteresting, which is not really a bad thing but what's the case for the need for a whole 'nother language in that category?

moonchrome• 14 years ago

>64 bit integers

I don't think lua has integers ie. like Javascript everything is a double, that can be changed by redefining a macro and recompiling AFAIK but it's still one global numeric type (and you can't change it for luajit).

StefanKarpinski31• 14 years ago

Thanks for the incredibly high praise. It definitely is possible to overload operators — operators are just functions with special syntax (see http://julialang.org/manual/functions/#Operators+Are+Functio...). We don't have bignum support, but adding it via GMP would be fairly easy. Just too many things to do!

wbhart10• 14 years ago

Great news about the operators. I didn't see an example of overloading there, but I probably rushed through it a bit quickly.

I have been working on and off on a BSD licensed bignum library. Maybe in a few years....

andrewcooke3• 14 years ago

this is surprisingly complete for a relatively new(?) project.

one notable restriction is that inheritance is only for interface, not implementation.

also, can anyone find a sequence abstraction (like lists)? arrays seem to be fixed size and i don't see anything else apart from coroutines. am i missing something?!

[perhaps not, if it's intended for numerical work. on reflection i am moving more and more towards generators (in python) and iterables (in java, using the guava iterables library to construct maps, filters etc) rather than variable length collections, so maybe this is not such a big deal. it's effectively how clojure operates, too...]

pron7• 14 years ago

> one notable restriction is that inheritance is only for interface, not implementation.

Not quite. You do inherit methods defined on abstract types.

StefanKarpinski31• 14 years ago

pron is right: you can inherit behavior from abstract types and abstract types, unlike interfaces, can have code written to them. (In single-dispatch OO languages, you are in the strange situation that you can write code to interfaces if they are arguments but not if they are the receiver of a method; thus, you're in a situation where you can either dispatch on an interface or you can write code for it, but never both at the same time.)

Arrays are not fixed size: there are push, pop, shift and unshift operations on them just like Perl, Ruby, etc. This uses the usual allocation-doubling approach so that the entire array doesn't need to be copied every time, but it's still usually much better to pre-allocate the correct size. Of course for small arrays that are typical in Perl, Ruby, etc., it hardly matters. If you're building an vector of 1 billion floats, however, you don't want to grow it incrementally.

The sequence/iterable abstraction is duck-typed: an object has to implement methods for three generic functions:

  i = start(x)
  done(x,i)
  next(x,i)

The state i can be anything.

Lack of implementation inheritance is one of those things that intro to OO books make a big deal of, but when you don't have it, you don't miss it at all — or at least I don't. I've never found tacking a few fields onto the end of an object to be very useful. I don't want to inherit memory layout — I want to inherit behavior. Julia's type system lets you write behavior to abstract types and inherit that for various potentially completely different underlying implementations.

andrewcooke3• 14 years ago

thanks guys.

pron7• 14 years ago

I don't know if I'd call it duck-typing when methods are not grouped into interfaces. I think you could say that in Julia, each method is an interface. I find it much cleaner than actual "duck-typing" (the way it's done in , say, Scala with structural types) because then you have both an interface, which specifies a contract, as well as the method name, which also specifies a contract when duck-typing is used, even when it's found in different interfaces.

Must compound types in Julia be concrete? I don't think I saw it in the documentation.

nnnnni2• 14 years ago

Obligatory xkcd: http://xkcd.com/927/

It's about standards, but it still applies.

StefanKarpinski31• 14 years ago

I realize this is just a comic, but it's one of the reasons that we try to be as compatible with C and Fortran libraries as possible — that's where the vast bulk of high-quality scientific computing work has been done, and we want to be as compatible with it as possible. The goal here is not to duplicate all the work that's already been done, but to allow existing high-quality libraries to be smoothly and easily used together in one environment. That's very much the same philosophy that NumPy and SciPy have, and indeed, it much the same goal. The biggest issue with NumPy, IMO, is that Python arrays aren't designed with linear algebra and interoperability with libraries like BLAS and LAPACK in mind. This leads to the somewhat maddening distinction between Python arrays and NumPy vectors and matrices — and lots of conversion back and forth between them.

nnnnni2• 14 years ago

I also posted that comment without reading the article -- that was just my first response to "yet another programming language".

I'm sure that it's filling a real need. I just wish that groups could cooperate to make a handful of languages/libraries better rather than having 100 competing ones.

rwmj• 14 years ago

They want something like OCaml ...

kghose• 14 years ago

So, the usual caveats about writing code to match the languages strengths apply here. For example the fib function plays to a python weakness - function overhead. Rewriting the code to be a simple loop removes the inefficiency:

  import time
  
  def fib(n):
    if n < 1:
      return None
    if n == 1:
      return 0
    if n == 2:
      return 1
    x0 = 0
    x1 = 1
    for m in range(2,n):
      x = x0 + x1
      x0 = x1
      x1 = x
    return x
  
  if __name__=="__main__":
    assert fib(21) == 6765
    tmin = float('inf')
    for i in xrange(5):
      t = time.time()
      f = fib(20)
      t = time.time()-t
      if t < tmin: tmin = t
    print str(tmin*1000)

StefanKarpinski31• 14 years ago

That's quite true, but this micro-benchmark wasn't chosen to make Python look bad — it was chosen to test how good each language was at function calls. So using a loop defeats the point of the benchmark. Using double recursion to compute Fibonacci numbers is also stupid and could obviously be avoided in all languages.

The story the micro-benchmark tells is that Julia's pretty good at function calls, but JavaScript is even better (and, of course, C/C++ is the gold standard). In general the V8 engine is really amazing. We had the advantage of being able to design the language to make the execution fast (with the constraints of all that we wanted to be able to do with it), but V8 makes a language that was in no way designed for performance and makes it blazingly fast.

After I wrote the benchmark code for JavaScript and saw just how fast it was I had a moment of "should we be doing scientific computing in JavaScript?" Now wouldn't that be nuts?

ViralBShah12• 14 years ago

Yes, I pretty much had the same thought, but then I started thinking about multiple dispatch, calling C/Fortran libraries, polyhedral optimizations, and I realized that V8 developers may not have the same design targets in mind.

mkl• 14 years ago

The language looks interesting, but I am a bit concerned about the license situation, as in my understanding they have misinterpreted the GPL with regard to shared libraries. Quoting:

Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL). Core functionality is included in a shared library, so users can easily and legally combine Julia with their own C/Fortran code or proprietary third-party libraries.

The FSF disagrees (http://www.gnu.org/licenses/gpl-faq.html#NFUseGPLPlugins):

If the program dynamically links plug-ins, and they make function calls to each other and share data structures, we believe they form a single program, which must be treated as an extension of both the main program and the plug-ins. In order to use the GPL-covered plug-ins, the main program must be released under the GPL or a GPL-compatible free software license, and that the terms of the GPL must be followed when the main program is distributed for use with these plug-ins.

_delirium• 14 years ago

I believe what they're saying is that there are two things:

1. The Julia core, which consists of the language runtime and core functionality, is MIT licensed, and builds into an MIT-licensed shared library.

2. The Julia "environment", which includes a user interface, third-party libraries, etc., some of which are GPL, and which is therefore GPL as a whole.

I believe they're saying that you can link #1 with proprietary code, not meaning to imply that you can link #2 with proprietary code (because as you point out that wouldn't work). How useful that is probably depends on how many of the libraries the average application needs are in bucket #1.

StefanKarpinski31• 14 years ago

That is exactly right. At the moment I think the only GPL library Julia uses is readline — but obviously that's pretty important for the repl, so we chose to do it this way.

tomp3• 14 years ago

The FSF interpretation is undermined by the existence of non-GPL Linux programs (that link, call and use data structures from the (GPL-licensed) Linux API).

remmers• 14 years ago

Is Julia homoiconic? Could someone give an example of a Julia macro?

absconditus• 14 years ago

http://julialang.org/manual/metaprogramming/

bandarman4• 14 years ago

This is very exciting - congratulations on the launch of Julia. Now good luck as you move towards building a strong community!

meemo• 14 years ago

I hope this really takes off. Though, for it to really take off it needs a large ecosystem. The fact that the source is hosted on Github is a good start.

bandarman4• 14 years ago

Anyone else having compile issues? I'm on Ubuntu 11.10 (Server x64) and make fails consistently.

ViralBShah12• 14 years ago

Is it due to openblas not building? This may help:

https://github.com/JuliaLang/julia/issues/370

bandarman4• 14 years ago

Thanks for your suggestion. I tried that, but it wasn't it. I'm still getting lots of build errors, culminating with this:

Saving to: `lapack-3.4.0.tgz'

2012-02-19 01:03:26 (55.3 KB/s) - `lapack-3.4.0.tgz' saved [6127787/6127787]

make[3]: gfortran: Command not found make[3]: * [lsame.o] Error 127 /bin/sh: ./testlsame: not found /bin/sh: ./testslamch: not found /bin/sh: ./testdlamch: not found /bin/sh: ./testsecond: not found /bin/sh: ./testdsecnd: not found /bin/sh: ./testieee: not found /bin/sh: ./testversion: not found make[2]: * [lapack_install] Error 127 make[1]: * [lapack-3.4.0/INSTALL/dlamch.o] Error 2 make: * [julia-release] Error 2

llimllib• 14 years ago

make worked very smoothly here on my Mac, downloaded and installed its own deps.

Great work guys.

olaf2• 14 years ago

Yes, I get some 32-bit issues, gfortran and some packages (-dev) were not installed, still getting an error 139:

JULIA sys0.ji Segmentation fault make: [sys0.ji] Fehler 139

ya3r• 14 years ago

If you look at the Github repo, you will notice the second most used programming language in the repo (C being the first) is Objective-J ! I wonder why?

jfager5• 14 years ago

Because the julia source file names end in .j, probably.

StefanKarpinski31• 14 years ago

bingo.

mjcohenw• 14 years ago

I am currently building it - OS X Lion. First, I had to install wget:) Then, I got a certificate error on the https, so I pasted the command into Firefox and got the tarball. After copying it to the julia dir, I edited the tar command and ran it. Finally, doing make right now.

Here is the certificate error: Connecting to github.com (github.com)|207.97.227.239|:443... connected. ERROR: The certificate of `github.com' is not trusted. ERROR: The certificate of `github.com' hasn't got a known issuer.

ViralBShah12• 14 years ago

We used to use curl until recently so that it would build just fine on OS X. We changed it to wget, because it just seems so much easier to use than curl, and we had to do some nasty stuff at one point to download a few libraries that I couldn't figure out how to do with curl.

BTW, do try out the mac binaries if the build is an issue. We are still trying to make it all build seamlessly!

StefanKarpinski31• 14 years ago

For the record "ease of use" was not why we switched to wget — there was (for a while) a dependency that could only be downloaded using wget's recursive downloading. That's not the case anymore, however, so we should really switch back to curl.

jfager5• 14 years ago

For anyone else who hits this, this should get you going:

http://loopkid.net/articles/2011/09/20/ssl-certificate-error...

Basically, you need to get the right certs and then tell wget to use them.

fuzzythinker• 14 years ago

Slightly off topic, but V8's benchmarks really surprised me

warmfuzzykitten5• 14 years ago

When you try to install julia on MacOS X 10.7.3, you may see the make fail because wget is not installed. Easy to fix with:

brew install wget

[Edit] git page says gfortran (and wget) are downloaded and compiled, but if they're not already installed make fails. So...

brew install gfortran

The need to do this separately may have to do with licensing?

[Edit] And if you're not root, install to /usr/share/julia will fail. So you'll need to do:

sudo make install

I'm sure all this is perfectly obvious to Unix-heads who are inured to this sort of abuse, but I'm a Mac-head, used to things that Just Work, and I hate this shit.

StefanKarpinski31• 14 years ago

Sorry! The only thing I can say in our defense is that this is pretty trivial compared to installing a lot of scientific computing packages. But seriously, we'd like a drag-and-drop Mac installer. Anyone want to do that? (Only half kidding.)

Stepping back a bit, this is one of the reasons why having an entirely web-based experience is appealing — then you can let people use a known-good setup without needing to mess around with installing a fairly extensive amount of software just to get basic things to work. Then there's also the general appeal of doing big data work a la Google docs or Gmail. The trick is getting the user experience to be good enough on the web.

warmfuzzykitten5• 14 years ago

Please don't let perfection tomorrow keep you from meaningful improvements today. Just better instructions for Mac installs would be helpful!

ViralBShah12• 14 years ago

Someone just submitted a patch that uses curl on OS X. Also, no need for root, because julia will run out of the directory it is built in. Hopefully, someone who knows more about mac packaging will build a drag-and-drop installer.

jberryman• 14 years ago

Their site seems to be taking a hammering. Any mirrors?

StefanKarpinski31• 14 years ago

We're GitHub hosted so hopefully this doesn't take GitHub down! (j/k)

spitfire• 14 years ago

It's not yet another language that we need. It's more high level functions.

C or fortran can be as fast as they want, but in mathematica I can do MorphologicalComponents[] and get the components. having these functions available to me speeds up my time to discovery by 1000x or more.

kylebrown• 14 years ago

I'm more concerned with visualizing the data than with compiler optimizations or types/operators.

I see that d3.js is included in the package, but a cursory glance at the docs and I didn't see any examples of how to generate a chart.

sparky2• 14 years ago

Would be interesting to see how Julia perf compares to C++ compiled or JITed with LLVM via G++ 4.6+Dragonegg or Clang 3.0/svn. Would be more apples-to-apples, since both would use the same middle- and back-end. G++ 4.2.1 is a bit obsolete at this point, but as it probably came by default on the MBP they tested on, it's understandable.

StefanKarpinski31• 14 years ago

That would certainly be doable. If the performance is better, we can certainly switch to using that for our benchmarks. The idea for the benchmarks is to compare to a "gold standard" — hence the fact that the best results are taken across all optimization levels. We could even take the best results across multiple C compilers to give ourselves the absolute hardest comparison :-)

andrewcooke3• 14 years ago

a bit late to the party here, but i see no-one has mentioned gpus. since julia supports calling c libraries i guess this will work, but i wondered if anyone knew of any work on closer integration?

(also, julia is not a great name for googling....)

blacksqr2• 14 years ago

Pardon the possible naïveté, but I'm so old I remember Ada. Seems that language was designed to address a very similar problem space. What does Julia offer that Ada doesn't? Or perhaps: why is ada so deficient that spending 2+ years inventing a new language was a better proposition than taking the time to improve Ada?

SomeCallMeTim• 14 years ago

While there's a resurgence of interest in ADA recently, the general sense is that it's WAY overdesigned and heavy.

I came across this bit of humor a while back that communicates the general feeling well:

http://bit.csc.lsu.edu/~gb/csc4101/Reading/gigo-1997-04.html

If the problem IS that it's huge and inelegant, then the solution isn't to try to improve it, but to start from a clean slate. Julia looks like a reasonable attempt.

blacksqr2• 14 years ago

Funny, I remember reading similar conspiracy claims about C.

k-a-r• 14 years ago

awesome language but the manual need some works in user-friendliness

cad• 14 years ago

Do you also want ice cream on top?

jtchang• 14 years ago

How does one open up a socket in Julia?

prtamil• 14 years ago

For analogy there are few original liquor (vodka,rum,Whiskey,Brandy,Wine etc..) but god damn so many cocktails, so many cocktails.

Same in Programming Language design. Few original concepts, Lisp,C,Smalltalk, but so many cocktails even my grandma is creating one. All we want is more libraries.

Flam• 14 years ago

Is V8 really faster than NumPy and R? Interesting..

joelthelion2• 14 years ago

Wait for numpy to be ported on PyPy :)

bitcracker• 14 years ago

Thoughts on porting NumPy to PyPy:

http://technicaldiscovery.blogspot.com/2011/10/thoughts-on-p...

ulvund• 14 years ago

If you have worked a lot with R you won't be surprised that anything is faster than R

nichol4s• 14 years ago

No, not really.

Not only do some of the example's seem to be created specially to show the benefit of the JIT compiler (see pisum fe). Most of the example's do not really use the features of LaPack/BLAS, the one where it does (a matrix multiplication in rand_mat_mult) shows that all languages which use these optimized libraries beat Javascript and handwritten C++ with a magnitude of 2.

Thus, be careful with making such a generalisation from this benchmark. Also, it is much simpler to simply work with a language with proper support for multi-dimensional matrix slicing than having to do this all by hand.

ViralBShah12• 14 years ago

That is exactly the point of julia. It provides you access to BLAS/LAPACK when you need it, and it still gives you something fast when vectorization is not natural. It basically does not require vectorization as a prerequisite for performance. Julia wouldn't be the first language to attempt this - Matlab does this reasonably well, but R, Octave, Scilab, etc. do not.

Julia does have multi-dimensional arrays and slicing also. If you had to do all this by hand, it would just be simpler to use C. http://julialang.org/manual/arrays/

StefanKarpinski31• 14 years ago

As you note, the last benchmark does precisely what you suggest, comparing completely vectorized code that just calls BLAS. This is a really uninteresting benchmark, however, since everyone is close to C++ (although NumPy, Octave and R still introduce 20%, 69% and 165% overhead, respectively) — because you're really just comparing C++ against C++.

One of the key concepts behind Julia is that not only the end-user, but also the numerical library writer, should benefit from using a high-level language. In Julia, almost all of the library code is in Julia, and it's as fast as the library code written for R, Matlab or NumPy in C. There are also a lot of situations where vectorized code is either awkward or inefficient — especially in terms of creating a lot of unnecessary temporary arrays. Languages where the high-level language is slow force you to do everything vectorized — in Julia, you're not forced to do that. If you want to write a C-style scalar loop, you can and it will be fast (and you don't even need type annotations to make it fast, as shown by the benchmarks).

V8 is really impressively fast, but JavaScript as a language is not very well suited to scientific or technical computing.

mbq• 14 years ago

R bench is glaringly unfair -- it consists of dumb ports of scalar algorithms while R is an array language.

MrMan• 14 years ago

I have made the same rants about numeric computing language issues as the Julia creators. I would avidly adopt Julia based on the documentation on the website. It seems to me that this is a beautifully conceived language given the concerns it attempts to address. Amazing even if the performance never improves.

The R thread on the dev list gives an accurate representation of the obstacles to Julia being widely adopted, however. One might become very unhappy while using R, but sometimes you have to use it, because something you want is only in R, due to the ubiquity of that tool for stats.

If Python, which does not have that many disadvantages besides not having been explicitly designed to appeal to users like me, cannot unseat Matlab and R, Julia will have a difficult time.

But I will give it a try, and will implement some simple but core algorithms that I use a lot.

Another idea to test Julia's usefulness would be to port a tool like Waffles to Julia. In my opinion implementing such a sensible tool like Waffles in C++ is a heartbreak.

anamax• 14 years ago

Julia is the subject of today's http://www.stanford.edu/class/ee380/

The webcast is delayed and there's always something interesting that happens after the camera goes off.

SeanLuke2• 14 years ago

Is there some reason they neglect to compare against Mathematica? Seems like a glaring omission to me.

StefanKarpinski31• 14 years ago

Running Mathematica scripts automatically from the command-line is major pain. That and just writing the benchmark code. A contribution would be most welcomed.

SeanLuke2• 14 years ago

Mathematica has always had a command line.

    MathKernel -script "foo.m" -noprompt

As to Mathematica being a pain to write code for: I've found it to be a pretty nice language, certainly a far cry better designed than MATLAB.

jhrobert• 14 years ago

"All indexing in Julia is 1-based"

I once designed/implemented a language where the biggest mistake I made was using 1-based indexing. At first it looked like it was going to be easier to understand/use but 0-based indexing is actually much more convenient when dealing with indexes arithmetic.

PS: the "metaprogramming" stuff is fabulous.

The Julia Programming Language

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