StochasticAD.jl VS julia

This is disregarding the development of said ecosystems though. The point is that Python has been quite inhibitory to the development of this ecosystem. There are many corpses of automatic differentiation libraries (starting from autograd and tangent and then to things like theano to finally tensorflow and pytorch) and many corpses of JIT compilers and accelerators (Cython, Numba, pypy, and TensorFlow XLA, now PyTorch v2's JIT, etc.).

What has been found over the last decade is that a large part of that is due to the design of the languages. Jan Vitek for example has a great talk which describes how difficult it is to write a JIT compiler for R due to certain design choices in the language (https://www.youtube.com/watch?v=VdD0nHbcyk4, or the more detailed version https://www.youtube.com/watch?v=HStF1RJOyxI). There are certain language constructs that void lots of optimizations which have to then be worked around, which is why Python JITs choose subsets of the language to avoid specific parts that are not easy to optimize or not possible to optimize. This is why each take a domain-specific subset, a different subset of the language for numba vs jax vs etc., to choose something that is nice for ML vs for more generic codes.

With all of that, it's perfectly reasonable to point out that there have been languages which have been designed to not have the compilation difficulties, which have resulted having a single (JIT) compiler for the language. And by extension, it has made building machine learning and autodiff libraries not something that's a Google or Meta scale project (for example, PyTorch involves building GPU code bindings and a specialized JIT, not something very accessible). Julia is a language to point to here, but I think well-designed static languages like Rust also deserve a mention. How much further would we have gone if every new ML project didn't build a new compiler and a new automatic differentiation engine? What if the development was more modular and people could easy just work on the one thing they cared about?

As a nice example, for last NeurIPS we put out a paper on automatic differentiation of discrete stochastic models, i.e. extending AD to automatically handle cases like agent-based models. The code is open source (https://github.com/gaurav-arya/StochasticAD.jl), and you can see it's almost all written by a (talented) undergraduate over a span of about 6 months. It requires the JIT compilation because it works on a lot of things that are not solely in big matrix multiplication GPU kernels, but Julia provides that. And multiple dispatch gives GPU support. Done. The closest thing in PyTorch, storchastic, gets exponential scaling instead of StochasticAD's linear, and isn't quite compatible with a lot of what's required for ML, so it benchmarks as thousands of times slower than the simple Julia code. Of course, when Meta needs it they can and will put the minds of 5-10 top PhDs on it to build it out into a feature of PyTorch over 2 years and have a nice release. But at the end of the day we really need to ask, is that how it should be?

Found relevant code at https://github.com/gaurav-arya/StochasticAD.jl + all code implementations here

34. Julia - $74,963

I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.

3. dispatch on all the arguments

the first solution is clean, but people really like dispatch.

the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.

the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.

the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.

nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html

lisps of course lack UFCS.

see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779

so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!

https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md

Visit official site: https://julialang.org/

No. It runs natively on ARM.

julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release

https://github.com/JuliaLang/julia/issues/51086#issuecomment...

So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.

But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.

Still, the musl author has some reservations against making `jemalloc` the default:

https://www.openwall.com/lists/musl/2018/04/23/2

> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.

With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".

I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.

https://github.com/JuliaLang/julia/issues/34753

I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.

However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.

The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.

Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.

Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...

I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...

but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...