JuliaInterpreter.jl
rust
JuliaInterpreter.jl | rust | |
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5 | 2,683 | |
157 | 93,041 | |
0.6% | 1.2% | |
7.6 | 10.0 | |
25 days ago | 5 days ago | |
Julia | Rust | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
JuliaInterpreter.jl
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Do you use Julia for general purpose tasks?
The projects page is a list of suggestions of projects that someone has already said they want to run. If you can find a mentor, you can submit a project for anything. For potential performance improvements, I'd look at https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/206, https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/312, and https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/314. I'm not sure if Tim Holy or Kristoffer have time to mentor a project, but if you're interested in doing a gsoc, ask around in the Julia slack/zulip, and you might be able to find a mentor.
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Julia 1.7 has been released
I would not go as far as calling it very naive, there has certainly been some work put into optimizing performance within the current design.
There are probably some gains to be had by using a different storage format for the IR though as proposed in [1], but it is difficult to say how much of a difference that will make in practice.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl/pull/309
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What's Bad about Julia?
You're right, done some more research and there seems to be an interpreter in the compiler: https://github.com/JuliaDebug/JuliaInterpreter.jl. It's only enabled by putting an annotation, and is mainly used for the debugger, but it's still there.
Still, it still seems to try executing the internal SSA IR in its raw form (which is more geared towards compiling rather than dynamic execution in a VM). I was talking more towards a conventional bytecode interpreter (which you can optimize the hell out of it like LuaJIT did). A bytecode format that is carefully designed for fast execution (in either a stack-based or register-based VM) would be much better for interpreters, but I'm not sure if Julia's language semantics / object model can allow it. Maybe some intelligent people out there can make the whole thing work, is what I was trying to say.
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Julia: faster than Fortran, cleaner than Numpy
It could, but that is a lot more work than it sounds. It might be easier to make it possible to swap out the compiler for one that is much faster (LLVM is slow but does good optimisations, other compilers like cranelift are faster but produce slower code). There is a Julia interpreter but it was written in Julia itself (it was written to support debuggers), so it doesn't really solve the latency issues.
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Julia: Faster than Fortran, cleaner than Numpy
If you need to run small scripts and can't switch to a persistent-REPL-based workflow, you might consider starting Julia with the `--compile=min` option. You can also reduce startup times dramatically by building a sysimg with PackageCompiler.jl
There is also technically an interpreter if you want to go that way [1], so in principle it might be possible to do the same trick javascript does, but someone would have to implement that.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl
rust
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Create a Custom GitHub Action in Rust
If you haven't dipped your touch-typing fingers into Rust yet, you really owe it to yourself. Rust is a modern programming language with features that make it suitable not only for systems programming -- its original purpose, but just about any other environment, too; there are frameworks that let your build web services, web applications including user interfaces, software for embedded devices, machine learning solutions, and of course, command-line tools. Since a custom GitHub Action is essentially a command-line tool that interacts with the system through files and environment variables, Rust is perfectly suited for that as well.
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Why Does Windows Use Backslash as Path Separator?
Here's an example of someone citing a disagreement between CRT and shell32:
https://github.com/rust-lang/rust/issues/44650
This in addition to the Rust CVE mentioned elsewhere in the thread which was rooted in this issue:
https://blog.rust-lang.org/2024/04/09/cve-2024-24576.html
Here are some quick programs to test contrasting approaches. I don't have examples of inputs where they parse differently on hand right now, but I know they exist. This was also a problem that was frequently discussed internally when I worked at MSFT.
#include
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I hate Rust (programming language)
> instead of choosing a certain numbered version of the random library (if I remember correctly) I let cargo download the latest version which had a completely different API.
Yeah, they didn't follow the instructions and got burned. I still think that multiple things went wrong simultaneously for that experience. I wonder if more prevalent uses of `#[doc(alias = "name")]` being leveraged by https://github.com/rust-lang/rust/pull/120730 (which now that I check only accounts for methods and not functions, I should get on that!) so that when changing APIs around people at least get a slightly better experience.
- Rust Weird Exprs
- Critical safety flaw found in Rust on Windows (CVE-2024-24576)
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Unformat Rust code into perfect rectangles
Almost fixed the compiler: https://github.com/rust-lang/rust/pull/123325
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Implement React v18 from Scratch Using WASM and Rust - [1] Build the Project
Rust: A secure, efficient, and modern programming language (omitting ten thousand words). You can simply follow the installation instructions provided on the official website.
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Show HN: Fancy-ANSI – Small JavaScript library for converting ANSI to HTML
Recently did something similar in Rust but for generating SVGs. We've adopted it for snapshot testing of cargo and rustc's output. Don't have a good PR handy for showing Github's rendering of changes in the SVG (text, side-by-side, swiping) but https://github.com/rust-lang/rust/pull/121877/files has newly added SVGs.
To see what is supported, see the screenshot in the docs: https://docs.rs/anstyle-svg/latest/anstyle_svg/
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Upgrading Hundreds of Kubernetes Clusters
We strongly believe in Rust as a powerful language for building production-grade software, especially for systems like ours that run alongside Kubernetes.
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What Are Const Generics and How Are They Used in Rust?
The above Assert<{N % 2 == 1}> requires #![feature(generic_const_exprs)] and the nightly toolchain. See https://github.com/rust-lang/rust/issues/76560 for more info.
What are some alternatives?
Diffractor.jl - Next-generation AD
carbon-lang - Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
Tullio.jl - ⅀
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
Odin - Odin Programming Language
Infiltrator.jl - No-overhead breakpoints in Julia
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266
Rustup - The Rust toolchain installer