py2many
julia
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py2many | julia | |
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29 | 350 | |
590 | 44,510 | |
2.2% | 0.9% | |
8.1 | 10.0 | |
25 days ago | 2 days ago | |
Python | Julia | |
MIT License | MIT License |
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py2many
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Transpiler, a Meaningless Word
> Another problem is that there are hundreds of built-in library functions that need to be compiled from Python from C
An approach I've advocated as one of the main authors of py2many is that all of the python builtin functions be written in a subset of python[1] and then compiled into native code. This has the benefit of avoiding GIL, problems with C-API among other things.
Do checkout the examples here[2] which work out of the box for many of the 8-9 supported backends.
[1] https://github.com/py2many/py2many/blob/main/doc/langspec.md
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py2many VS kithon - a user suggested alternative
2 projects | 17 Jun 2023
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Why I'm still using Python
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Reimplement a large enough, commonly used subset of python stdlib using this dialect and we may be in the business of writing cross platform apps (perhaps start with android and Ubuntu/Gnome)
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Codon: A high-performance Python compiler
For py2many, there is an informal specification here:
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Would be great if all the authors of "python-like" languages get together and come up with a couple of specs.
I say a couple, because there are ones that support the python runtime (such as cython) and the ones which don't (like py2many).
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A Python-compatible statically typed language erg-lang/erg
It'd not fully solve your issue, but have you ever seen https://github.com/py2many/py2many ?
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Omyyyy/pycom: A Python compiler, down to native code, using C++
Cython doesn't consume python3 type hints and needs special type hints of its own. But it's certainly more mature than other players in the field.
What we need is a rpython suitable for app programming and a stdlib written in that dialect.
https://github.com/py2many/py2many/blob/main/doc/langspec.md
- I made a Python compiler, that can compile Python source down to fast, standalone executables.
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
No intermediate AST. To understand the various stages of transpilation and separation of language specific and independent rewriters, this file is a good starting point:
https://github.com/adsharma/py2many/blob/main/py2many/cli.py...
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Implicit Overflow Considered Harmful (and how to fix it)
Link to the test that's relevant for this discussion:
https://github.com/adsharma/py2many/blob/main/tests/cases/in...
This is an explicit deviation from python's bigint, which doesn't map very well to systemsey languages. The next logical step is to build on this to have dependent and refinement types.
Work in progress here:
https://github.com/adsharma/Typpete
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?
pybind11 - Seamless operability between C++11 and Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
PyO3 - Rust bindings for the Python interpreter
NetworkX - Network Analysis in Python
PythonNet - Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
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.
PyCall.jl - Package to call Python functions from the Julia language
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Numba - NumPy aware dynamic Python compiler using LLVM
sqlglot - Python SQL Parser and Transpiler
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp