nogil
julia
nogil | julia | |
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31 | 350 | |
2,854 | 44,534 | |
- | 0.5% | |
5.7 | 10.0 | |
2 months ago | 5 days ago | |
Python | Julia | |
GNU General Public License v3.0 or later | MIT License |
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nogil
- Proof-of-Concept Multithreaded Python Without the GIL
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Our Plan for Python 3.13
This might be a dumb question, but why would removing the GIL break FFI? Is it just that existing no-GIL implementations/proposals have discarded/ignored it, or is there a fundamental requirement, e.g. C programs unavoidably interact directly with the GIL? I know that the C-API is only stable between minor releases [0] compiled in the same manner [1], so it's not like the ecosystem is dependent upon it never changing.
I cannot seem to find much discussion about this. I have found a no-GIL interpreter that works with numpy, scikit, etc. [2][3] so it doesn't seem to be a hard limit. (That said, it was not stated if that particular no-GIL implementation requires specially built versions of C-API libs or if it's a drop-in replacement.)
[0]: https://docs.python.org/3/c-api/stable.html#c-api-stability
[1]: https://docs.python.org/3/c-api/stable.html#platform-conside...
[2]: https://github.com/colesbury/nogil
[3]: https://discuss.python.org/t/pep-703-making-the-global-inter...
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Real Multithreading Is Coming to Python
https://github.com/colesbury/nogil does manage to get rid of the GIL, but it's not certain to make it into Python core. The main problem is the amount of existing libraries that depend on the existence of the GIL without realizing it - breaking those would be extremely disruptive.
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[D] The hype around Mojo lang
CPython is also investigating the removal of the GIL (PEP703, nogil). I think requiring the GIL is a wider thing that libraries will need to address anyway. But also, for the same reason as above I'd be surprised if the Modular team thought that saying "you can run all your python code unchanged" was a good idea if there was a secret "except for code that uses numpy" muttered under the breath.
- PEP 684 was accepted – Per-interpreter GIL in Python 3.12
- PEP 703 – Making the Global Interpreter Lock Optional in CPython
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Python 3.11.0 final is now available
I'm worried about the speedup
My understanding is that it's based on the most recent attempt to remove the GIL by Sam Gross
https://github.com/colesbury/nogil
In addition to some ways to try to not have nogil have as much overhead he added a lot of unrelated speed improvements so that python without the gil would still be faster not slower in single thread mode. They seem to have merged those performance patches first that means if they add his Gil removal patches in say python 3.12 it will still be substantially slower then 3.11 although faster then 3.10. I hope that doesn't stop them from removing the gil (at least by default)
- Removed the GIL back in 1996 from Python 1.4, primarily to create a re-entrant Python interpreter.
- I Tried Removing Python's GIL Back in 1996
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Faster CPython 3.12 Plan
Looks like it's still active to me:
https://github.com/colesbury/nogil/
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
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.
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Dart 3.3
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!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
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
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Rust std:fs slower than Python
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".
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Eleven strategies for making reproducible research the norm
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
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
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.
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Getaddrinfo() on glibc calls getenv(), oh boy
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...
What are some alternatives?
hpy - HPy: a better API for Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
mypyc - Compile type annotated Python to fast C extensions
NetworkX - Network Analysis in Python
numpy - The fundamental package for scientific computing with Python.
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
python-feedstock - A conda-smithy repository for python.
Numba - NumPy aware dynamic Python compiler using LLVM
sbcl - Mirror of Steel Bank Common Lisp (SBCL)'s official repository
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp