sol2
julia
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sol2 | julia | |
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20 | 350 | |
3,860 | 44,317 | |
- | 0.7% | |
3.9 | 10.0 | |
6 days ago | about 17 hours ago | |
C++ | Julia | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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sol2
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Any tips for how to make moddable games?
As someone said, make the game data-driven is a good first step but I will say, also have some sort of way to add additional game logic. For C++ games, lua is really easy to embed the interpreter in your C++ binary, read in the files from a directory (like /mods) with the C++ filesystem api new in C++17, and it's very easy to use SoL to write an API for lua specific to your game. Many games use lua in this way and it's probably the most common mod path setup.
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Script Interoperability
I've only ever done this from C++, but it's using the same lua C library, so should be durable from C as well. You can look up how sol2 or any other wrapper libraries do it.
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CBN Changelog: December 3, 2022. Improved LUA support in progress!
This version relies on a Lua C++ wrapper called sol2 to hide Lua stack management from the developer, so creating new bindings can be done by adding a few lines of human-readable C++. It still has to be done manually, but at least sol2 is able to automatically figure out types of objects being bound, so it's not much different from our de-/serialization code.
- RTS programming game where you write real C++ code to control your player.
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Tools for rolling your own engine
Here is link number 2 - Previous text "Sol"
Sol for fast lua embedding
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jluna: a new Julia <-> C++ Wrapper
It is half of a pun as I was inspired by [sol3](https://github.com/ThePhD/sol2) which is a lua <-> c++ wrapper. Sol means sun and the julia c-api prefixes all it's functions with jl, luna means moon so it is pronounced "jay luna"
So far, it has been cumbersome to embed it into C-language projects, because it's C-interface is hard to use and poorly documented. Because of this, many choose to just use python or lua instead.
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A new C++ <-> Julia Wrapper: jluna
If you want to be portable I'd recommend C++ and Lua, I used those for years and it runs on everything and there's this most amazing wrapper API which was a huge inspiration
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Why the C Language Will Never Stop You from Making Mistakes
Off topic, but this is the author of my favourite Lua C++ binding library (https://github.com/ThePhD/sol2). Great guy!
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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Best Programming languages for Data Analysis📊
🌟 Visit Github
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
The one you need is this which is already merged but was after the 1.10 feature freeze so it has to wait till 1.11, though you can test it with nightly builds which is available on julialang site: https://github.com/JuliaLang/julia/pull/51435
Unfortunately, the core devs are not too chatty about standalone binaries, because of how Julia's internals are set there are going to be a lot of unforeseen challenges, so they are not trying to promise how things will be rather let's wait and see how things will turnout. Since packagecompiler.jl already has C ABI and one goal discussed about binaries being easily callable from other languages and vice versa, I would bet that it will have shared libraries.
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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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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?
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
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp
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.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
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).
pybind11 - Seamless operability between C++11 and Python
SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
ChaiScript - Embedded Scripting Language Designed for C++