CxxWrap.jl
julia
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| CxxWrap.jl | julia | |
|---|---|---|
| 4 | 329 | |
| 365 | 42,525 | |
| 0.8% | 0.9% | |
| 4.8 | 9.9 | |
| about 1 month ago | 3 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
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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.
CxxWrap.jl
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jluna: a new Julia <-> C++ Wrapper
Kudos for your work. AFAIK, the recommended C++ interop for Julia is CxxWrap https://github.com/JuliaInterop/CxxWrap.jl. Could you provide the main advantages of using jluna vs. CxxWrap?
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A new C++ <-> Julia Wrapper: jluna
If you are interested in C++ interop you can also have a look at Clang.jl and CxxWrap.jl (the usual Julia package chaos applies, where the package mentioned in old talks and docs that you find on google is superseded by some others...)
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Anyone using Julia for physics?
I like about Julia that it is so easy to call packages from other languages. CxxWrap.jl for easy access of C++ libraries. I often use python libraries inside my Julia code.
julia
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
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How does Elixir stack up to Julia in the future of writing machine-learning software?
Lots of this is in progress and doesn't require a 2.0. Better error messages landed last week, see https://twitter.com/ChrisRackauckas/status/1661014235466563591 which was https://github.com/JuliaLang/julia/pull/49795. That makes them a ton more readable, and there's a few more of these kinds of things in process. The parser change is set to make it into v1.10, https://github.com/JuliaLang/julia/pull/46372. v1.10 is supposed to branch in just a few weeks and will possibly be LTS. With that all in mind, there's a time to take stock of what has been a large amount of pretty huge changes and seeing what's next to help Julia improve static compilation.
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Openlibm and Julia
Many functions from openlibm has already been transitioned to pure Julia, and openlibm is on the path of being removed from Julia, https://github.com/JuliaLang/julia/pull/42299.
Some things are written in Julia. see this folder for trig functions, log, hyperbolic trig, and cube root.
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Julia 1.9 Highlights
My favorite change (even though it's not listed in the changelog), is that just-in-time compiled code now has frame pointers[1], making Julia code much more debuggable. Profilers, debuggers, etc. all can now work out of the box.
Extra excited that the project I happen to work (the Parca open source project[2] on influenced this change [3][4]. Shout out to Valentin Churavy for driving this on the Julia front!
[1] https://github.com/JuliaLang/julia/commit/06d4cf072db24ca6df...
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GPU vendor-agnostic fluid dynamics solver in Julia
The release was just cut 9 hours ago, as shown on the releases part of the Github page (https://github.com/JuliaLang/julia/releases/tag/v1.9.0). That then starts the jobs for the creation and deployment of the final binaries, and when that's done the Julialang.org website gets updated to state it's the release, and when that's done the blog post for the new release goes out. You can even follow the last step of the process here (https://github.com/JuliaLang/www.julialang.org/pull/1875), since it all occurs on the open source organization.
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Which one is best for numerical simulations for fluid mechanics [mostly linear terms].
If you want C performance in a high level language I'd recommend Julia: https://julialang.org which is build with numerical computing as a main focus. The Julia syntax is very similar to Matlab, but it's a proper programming language (unlike Matlab).
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Mojo – a new programming language for all AI developers
Julia is already being used in a number of systems with varying levels of real-time-ness. It's early days for sure, but there's a bunch of progress. It turns out lots of "real time" applications actually just need fast code. JIT compile also can be overcome since in real time systems, you generally know all the code you are going to call so you can just make a .so with all of those bits compiled and know that nothing will need to be compiled at runtime.
Also on a related topic, the PR for parallel GC in Julia just merged 4 days ago (https://github.com/JuliaLang/julia/pull/48600), so GC is in the process of getting a bunch faster.
Julia is finally going to start storing generated native code on disk in Julia 1.9:
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With v1.9 about to land, what do you hope v1.10 has?
It’s possible in 1.9 via the foreign threads support, but I don’t believe there’s an API for it https://github.com/JuliaLang/julia/pull/46609
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
NetworkX - Network Analysis in 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.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Numba - NumPy aware dynamic Python compiler using LLVM
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp
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).
LUA - A programming language based upon the lua programming language
femtolisp - a lightweight, robust, scheme-like lisp implementation
JLD2.jl - HDF5-compatible file format in pure Julia
PackageCompiler.jl - Compile your Julia Package