Static, Ahead of Time Compiled Julia

http://juliacomputing.com/blog/2016/02/09/static-julia.html

by Lofkin • 10 years ago

112 84 10 years ago

juliacomputing.com

pjmlp4• 10 years ago

Julia is really following Lisp and Dylan's footsteps!

Congratulations to all involved in pushing the actual state of dynamic languages.

jakub_h2• 10 years ago

"following Lisp and Dylan's footsteps"

I hope not, especially regarding popularity.

pjmlp4• 10 years ago

I was thinking in terms of language design and tooling. :)

Agree with you.

jhoechtl• 10 years ago

I personally find the syntax of the language and quality of the current implementation (speed!) excellent. However, it doesn't experience the marketing languages like Rust or Golang receive.

What I personally also find worrisome is the perception (at least for me) that Julia is confined to scientific computing whereas I find it should really be a general purpose language.

vmorgulis2• 10 years ago

... that Julia is confined to scientific computing whereas I find it should really be a general purpose language.

It is intentional. Julia could be the new Fortran like Rust could be the new C++.

z92• 10 years ago

I am all for replacing Python with Julia.

pjmlp4• 10 years ago

Me too, maybe this will pressure more adoption of PyPy.

renox• 10 years ago

What I personally also find worrisome is the perception (at least for me) that Julia is confined to scientific computing whereas I find it should really be a general purpose language.

With 1-based array indexing? Very unlikely.

plinkplonk• 10 years ago

There is no technical reason that Julia can't be used for general purpose computing. (Well personally, I'd like nested namespaces, but) it is an amazing language. Just a matter of someone putting in the time to build the required libraries and glue code. Which will happen in time.

elcritch• 10 years ago

The lack of AOT has been the main thing holding me back from adopting it for more general purpose computing. Now with AOT I can "compile" the code and get some reasonable expectation of performance and ease of deployment. I'm probably going to start using Julia and (hopefully) extending the web server packages. Basically it feels similar enough to JavaScript for web development but with more expressiveness, so I'm pretty excited by the prospect.

Now this turns up a terrible delima, do I try to use Julia or Rust for writing embedded controllers? Rust has macros and direct memory control, but the article mentions. :)

I also think the more of us that recommend Julia for general compute, the more likely it'll get used that way.

vmorgulis2• 10 years ago

Julia Computing carried out this work under contract from the Johns Hopkins Applied Physics Laboratory (JHU APL) for the Federal Aviation Administration (FAA) to support its TCAS (Traffic Collision Avoidance System) program.

It's great! Looks like a very interesting contract.

johnsea• 10 years ago

In case you are interested, here are slides (http://juliacon.org/2015/images/juliacon2015_moss_v3.pdf) and a presentation (https://www.youtube.com/watch?v=19zm1Fn0S9M) about TCAS (and its successor, ACAS X)

blt3• 10 years ago

Julia seems like an almost perfect MATLAB replacement. (for my personal preference, I would like a slightly more static/rigid language, but I understand why that's not the right choice for Julia's target users.) There is just one problem... I really, really wish they had dropped the 1-based indexing and <= upper bound on index ranges. It is so annoying.

jfaucett• 10 years ago

"I really, really wish they had dropped the 1-based indexing and <= upper bound on index ranges."

really? There are quite a few things I don't like about julia, but using 1-indexing and "end" makes implementing algorithms much clearer IMHO.

The main pain points for me in julia are the module/pkg system and that the runtime is not just batteries but more like 10 generators included i.e. it could be way more minimal. But I get that the goal is to have a powerful scientific computing language and not to build a multipurpose language that emphasizes modular construction of code units and production ready package and build management utilities.

All in all, when judged by how well julia achieves its self-stated goals, I think it is excellent.

halflings• 10 years ago

This is kind of bike-shedding (since that's such a small part of the language), but I also think there are arguments for using 0-indexing with open upper bounds.

Guido explains his choice best:

https://plus.google.com/115212051037621986145/posts/YTUxbXYZ...

blt3• 10 years ago

the 10 generators are critical if it's going to supplant Matlab. one of the biggest selling points for Matlab is that you don't have to spend a bunch of time searching for libraries, installing, configuring, matching versions, etc. You just spend a bunch of money on Toolboxes instead.

kgabis2• 10 years ago

I think this [0] is worth reading before starting a project in julia (it's quite shocking). Does anyone know if anything has changed in julia's development process over the last year?

[0] http://danluu.com/julialang/

sveme• 10 years ago

Though it would have been fair to point out that this rant was written about his experiences the last time he used Julia, in Oct. 2014, nearly 18 months ago.

conjectures4• 10 years ago

The language works well for what is effectively still a beta. Sure I'd like more documentation and tests, but I'm happy to get features first. The alternative for me is trying to do some non trivial cluster computing in C or in python, either of which would suck.

lmm3• 10 years ago

What kind of cluster computing? I've had good experiences with Scala and Spark, though that might be for a different use case.

Tarrosion2• 10 years ago

My areas of focus are pretty different from that author's - e.g. I haven't tried contributing to the core language. But I've been using Julia for my research code for ~4 months now, and it's been an absolute joy.

The language allows super-readable code while remaining quite fast. The sort of research I'm doing involves interacting a bit with data then doing a bunch of simulations, and Julia excels at this. The expressiveness in how Julia handles anonymous functions, optional arguments, tuple construction/deconstruction, etc. allows for really concise configurations of how my little simulations run.

My only pain points:

- DataFrames are not as fast as the rest of the language.

- I'd like an infix function composition operator. (Possibly this exists, but I can't find it in the documentation).

9.5/10, absolutely recommend.

Xcelerate2• 10 years ago

I'd like an infix function composition operator.

Do you mean like the pipe symbol?

    rand() |> println

Or are you referring to something else?

fundamental• 10 years ago

I thoroughly enjoy julia and there's no other FLOSS to replace it, but a fair bit of that rant does still seem to stand (from my own experience).

Bugs are quite easy to come across and updating software feels more like a dice roll than a normal upgrade. Parallel computing in particular has been in a pre-alpha state for ages (which may be more of a documentation issue than an implementation one). Packages were previously very slow to load, though with pre-compilation this was partially fixed (~1 order of magnitude difference). I don't write robust software in julia, so I don't know how the error handling side has been evolving. The API inconsistencies have been getting fixed, but this typically results in broken packages until the compatibility package (Compat.jl) includes some workaround. The core code is difficult to get through and architectural-level documentation was absent last I checked.

Even with these development flaws it's still by and large an enjoyable experience to use. It solves some hard problems and it makes my own work (mainly DSP/ML) move a lot faster. I would recommend it, but as their versioning scheme indicates, there's no 1.0 release yet.

ChrisFoster3• 10 years ago

Given that syntax and core API changes still occur, it's quite amazing that Compat.jl can achieve a very high level of source code compatibility between versions. This and the solid deprecation system are some of the things which make me confident enough to start using julia in production.

ChrisFoster3• 10 years ago

I'm an enthusiastic julia user, observer and very minor contributor. IMO a lot of the issues in this constructive rant have been addressed to some extent. For context, here's the previous HN discussion https://news.ycombinator.com/item?id=8809422

Going through the post in order:

The stable releases still have some bugs as you would expect in a young language, but 0.4 is now well below my tolerance level. For a rough idea, I now use julia daily and encounter a bug perhaps once every one or two weeks. In 0.4 I haven't encountered any bug which was a real show stopper and couldn't easily be worked around.

Testing has gotten a whole heap nicer with a decent test framework in Base (accessible in 0.4 via BaseTestNext.jl). Testing and package manager integrate in a simple but effective way which really makes the friction for writing a suite of tests for new packages very low, much lower than other languages I've used. I can't speak for actual coverage in Base, but I know it's now actually being measured and work has gone into the coverage tools.

I'm going to skip over the complaints about error handling, because others have already responded to this, for example StefanKarpinski's post to the julia-users list https://groups.google.com/d/msg/julia-users/GyH8nhExY9I/0Bzn...

Consistent benchmarking is currently being addressed, with great work going on at BenchmarkTrackers.jl, and a proper setup with dedicated benchmarking hardware for the language itself. I don't have the depth of knowledge to comment on Dan's other complaints regarding skewed benchmarking.

Regarding contributing, my experience is that contributions to Base and the runtime by unknowns (myself, say) are generally met with the fair skepticism and good taste that all good maintainers should display. Sometimes I feel the core devs could do more to encourage new contributors, and the environment can feel slightly hostile when suggesting new features. I'm not sure how to entirely avoid this, when a core job of a good maintainer is to say "no" to a lot of poorly considered requests! Much of the code in Base is still commented in a minimalistic fashion, if at all. In contrast my experience in contributing to packages has been almost entirely positive, with a lot of excitement and energy leading to some really great code and interactions.

With precompliation, slow package load times have really been improved to the extent that they're no longer a major hassle, but there's still room for improvement here.

The real sting in the tail of this blog post is the paragraph about nastiness in the community. There was a couple of unfortunately worded (though not unambiguously malicious) mails on the julia-users list following Dan's post, but the discussion was largely constructive and helpful. I've no idea about the "private and semi-private communications" and I can only hope things were patched up there.

Overall I've found the julia experience almost entirely positive. It's a joy to work with for numerical and statistical problems, and we're moving forward at work to get our first major pieces of julia code into production.

StefanKarpinski7• 10 years ago

I've no idea about the "private and semi-private communications" and I can only hope things were patched up there.

I'm the co-creator that Dan was talking about. He wrote a bunch of less-than-charitable comments on the aforementioned semi-private forum – not specifically to me, but where he surely knew I would read them – to which I responded with:

https://gist.github.com/StefanKarpinski/c72219ff8ce261172b11

You can judge for yourself whether I was nasty or dishonest. Things were, unfortunately, not patched up. Dan posted a number of responses, deleted all of them before I could read them, then left the conversation permanently.

kgabis2• 10 years ago

Thanks for the lengthy response!

StefanKarpinski7• 10 years ago

The main valid complaints in Dan's post were:

1. Insufficient testing & coverage. Code coverage is now at 84% of base Julia, from somewhere around 50% at the time he wrote this post. While you can always have more tests (and that is happening), I certainly don't think that this is a major complaint at this point.

2. Package issues. Julia now has package precompilation so package loading is pretty fast. The package manager itself was rewritten to use libgit2, which has made it much faster, especially on Windows where shelling out is painfully slow.

3. Travis uptime. This is much better. There was a specific mystery issue going on when Dan wrote that post. That issue has been fixed. We also do Windows CI on AppVeyor these days.

4. Documentation of Julia internals. Given the quite comprehensive developer docs that now exist, it's hard to consider this unaddressed:

http://julia.readthedocs.org/en/latest/devdocs/julia/

So the legitimate issues raised in that blog post are fixed.

jacobolus• 10 years ago

The main valid complaints [...] the legitimate issues raised [...]

This is a really passive-aggressive weaselly phrasing. I’d recommend reconsidering this type of tone in public discussion responses.

Instead of suggesting that the other complaints were invalid or illegitimate, you could just not mention them at all, or at least use nicer language in brushing them aside. E.g. “... the main actionable complaints...” or “the main technical complaints ...”

* * *

[...] I certainly don't think that this is a major complaint at this point. [...] it's hard to consider this unaddressed [...] fixed

After reading the original post and your responses, I think the responses come across as pretty smug and dismissive.

Third-party readers would probably be more optimistic if you just left it at “we’ve made a lot of improvement since then and we’re still working on it” or similar.

Xcelerate2• 10 years ago

I, for one, much prefer tons of bugs if that's what's necessary to keep the language evolving. I like the fact that powerful features are rapidly being added to the language, even if it breaks backward compatibility. Most of my programming is scientific in nature, so I write lots of one-off scripts. "Fixing" these scripts for the newest version of Julia rarely takes more than a few minutes.

dognotdog• 10 years ago

Considering that Matlab, which is supposed to be a stable platform, seems about as buggy as Julia with a lot of regressions, API changes, and new bugs every release, I, too, prefer Julia, as it's not only a nicer language with better programmer and execution efficiency, but it's also open source.

I'd rather try to fix a bug in some undocumented codebase than wait 6 months for a new black box.

wbhart2• 10 years ago

This article was pointed out to me, more than once now, as a user of Julia. However, I don't see that it has too much relevance to me as a user of the language, even if its quite outdated points were still valid today.

I am part of a team that now has over 60,000 lines of Julia code in our computer algebra package(s), and the intersection between our experience as users and the points danluu made is almost nil.

If one looks at the ranking of the language on Tiobe, it's quite obviously being used a lot for a language that hasn't even reached 1.0 yet.

I think the issues one is likely to have with Julia as an evolving language have completely moved on from those made in danluu's article. In fact, it might be useful for someone to write a "constructive criticism" blog article about the state of affairs today.

My personal opinion is that I would wait until 1.0 (which will arrive in less than 2 years time) if I were a fortune 500 company, unless you really need to be ahead of the game and are prepared to contribute to the development of the actual language itself. But for just about anything else, if you need the features Julia provides, it's probably vastly superior to the alternatives today.

Our experience is you will occasionally have to adjust some of your code to handle changes to the language prior to 1.0, and we've had to do that a few times so far (at most a few hours work each 0.x point release, even with our large, complex code base). And this mainly applies if you are really pushing Julia hard and exploring interesting corners of the language. Other than this, its more than stable enough for serious work.

illumen• 10 years ago

In the literature this is called Gradual Typing. Or optional typing.

Python uses it in the last couple of versions.

vanderZwan3• 10 years ago

For example, the Julia community seems to have coined the term “type-stability” to describe a concept that static / compiled languages have historically enforced and dynamic / scripting languages have historically disregarded.

I was not aware that this was a Julia neologism. It seems like such an appropriate term for discussing how to make code make the most out of JIT-compilation.

jakub_h2• 10 years ago

I thought that type stability was actually a crucial notion in real-world dynamic language implementations? Witness inline caches and polymorphic inline caches, for example.

StefanKarpinski7• 10 years ago

The concept is certainly not new, but googling "type stability" seems to only turn up Julia-related pages or other, unrelated uses of the term, so it seems like this usage may have been coined in the Julia community. If you find prior uses with this meaning, please do let me know.

openasocket4• 10 years ago

The closest concept I can think of is the concept of "strong typing," that a value does not change type based on its context, which is a fairly widely used term. It seems like Julia has some form of weak typing, at least between the different numeric primitives, and "type stability" refers to avoiding any usage of weak typing. I don't know a lot about Julia, though, so I could be way off here.

StefanKarpinski7• 10 years ago

"Strong typing" and "weak typing" don't really mean anything:

http://blogs.perl.org/users/ovid/2010/08/what-to-know-before...

You probably mean "static typing" versus "dynamic typing" which I wrote a bit about in the context of Julia here:

http://stackoverflow.com/questions/28078089/is-julia-dynamic...

Basically, I think "type stability" hasn't really been a thing in the past because in dynamic languages, people have traditionally not cared about ensuring that return types are predictable based on argument types, and in static languages, a program is incorrect if that's not the case. As people care more and more about being able to statically predict the behavior of programs in dynamic languages, the concept of type-stability in dynamic languages becomes increasingly important.

igouy2• 10 years ago

So what is “strong typing”? This appears to be a meaningless phrase, and people often use it in a non-sensical fashion. To some it seems to mean “The language has a type checker”. To others it means “The language is sound” (that is, the type checker and run-time system are related). To most, it seems to just mean, “A language like Pascal, C or Java, related in a way I can’t quite make precise”.

"Programming Languages: Application and Interpretation" Shriram Krishnamurthi, 2003.

tempodox• 10 years ago

I come from AOT languages and have lost my patience by now. Julia is overrated.

Static, Ahead of Time Compiled Julia

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