c2nim VS PackageCompiler.jl

Also on the game development front, I maintain a raylib wrapper https://github.com/planetis-m/naylib As long utilities like c2nim https://github.com/nim-lang/c2nim exist, it's trivial to create bindings of C/C++ libraries. One thing I want to experiment more is making it more automatic by writing a callback exposed by c2nim that transform the generated code using Nim's AST. But regardless in that project I was able to write safe language abstractions on top of the bindings that provide a more native experience. It has scope-based memory management, generics and ... function overloading.

Rust also has a library called safer_ffi that makes FFI easier, and I tried to use that, but the library seems to be immature, and I could not get arguments in Rust functions. If this library can be used properly, it will be possible to output C header files from Rust functions and automatically generate Nim interface functions from C header files using c2nim. We look forward to further development of this feature.

Well I'm 99.5% certain at least. Even now I'm uncertain of the C syntax. And I've not been bold enough to test 3rd order C function pointers. I figure that's probably C code you don't wanna touch if possible.

https://github.com/nim-lang/c2nim/blob/11f2c5363dfe7e8c7c8ce...

The other annoying one is that "signed" and "unsigned" are basically adjectives, but "long" can be both a type and a modifier. So it's difficult to parse unless you're the target C compiler. Technically you can, but you have to use backtracking.

c2nim is a tool to translate ANSI C code to Nim. The output is human-readable Nim code that is meant to be tweaked by hand after the translation process. If you are tired of wrapping C library, you can try futhark which supports "simply import C header files directly into Nim". Similar to futhark, cinterop allows one to interop with C/C++ code without having to create wrappers. nimLUA is a glue code generator to bind Nim and Lua together using Nim's powerful macro. nimpy and nimporter is a bridge between Nim and Python. rnim is a bridge between R and Nim. nimjl is a bridge between Nim and Julia! Last but not least, genny generates a shared library and bindings for many languages such as Python, Node.js, C.

Now if only a full binding generator for C and C++ headers to Nim was done, the language would really be cooking!

"What do you mean, Nim has two of these already?"

Yeah, I know, and -- not to hurt anyone's feelings -- but: they kind of suck. And there's no way I see them able to be extended to do the job fully, based on the way they're currently built.

Those are some bold claims to make!

So before I get stoned to death (no offense to the authors, I am grateful that they exist and have used them both) let me attempt an explanation and back up these statements.

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To start off, the two tools available are "c2nim" and "nimterop". c2nim is a Nim official library, while nimterop is a community library.

  https://github.com/nim-lang/c2nim

Yes. Nim compiles to C and has a lot of features for interacting with C code. You can easily call C routines from Nim and Nim functions from C. There's a bit of a ceremony you need to go through, but most of it can be automated with c2nim tool: https://github.com/nim-lang/c2nim

Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.

With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.

Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)

[1] https://docs.julialang.org/en/v1/manual/embedding/

[2] https://pypi.org/project/juliacall/

[3] https://www.rdocumentation.org/packages/JuliaCall/

[4] https://github.com/JuliaLang/PackageCompiler.jl

[5] https://github.com/tshort/StaticCompiler.jl

One of Julia's Achilles heels is standalone, ahead-of-time compilation. Technically this is already possible [1], [2], but there are quite a few limitations when doing this (e.g. "Hello world" is 150 MB [7]) and it's not an easy or natural process.

The immature AoT capabilities are a huge pain to deal with when writing large code packages or even when trying to make command line applications. Things have to be recompiled each time the Julia runtime is shut down. The current strategy in the community to get around this seems to be "keep the REPL alive as long as possible" [3][4][5][6], but this isn't a viable option for all use cases.

Until Julia has better AoT compilation support, it's going to be very difficult to develop large scale programs with it. Version 1.9 has better support for caching compiled code, but I really wish there were better options for AoT compiling small, static, standalone executables and libraries.

[1]: https://julialang.github.io/PackageCompiler.jl/dev/

Doesn’t work on Windows, but https://github.com/JuliaLang/PackageCompiler.jl does.

Also, you can precompile a whole package and just ship the binary. We do this all of the time.

https://github.com/JuliaLang/PackageCompiler.jl

And getting things precompiled: https://sciml.ai/news/2022/09/21/compile_time/

You can have a look at PackageCompiler.jl

I think by PackageManager here you mean package compiler, and yes these improvements do not need a 2.0. v1.8 included a few things to in the near future allow for building binaries without big dependencies like LLVM, and finishing this work is indeed slated for the v1.x releases. Saying "we are not doing a 2.0" is precisely saying that this is more important than things which change the user-facing language semantics.

And TTFP does need to be addressed. It's a current shortcoming of the compiler that native and LLVM code is not cached during the precompilation stages. If such code is able to precompile into binaries, then startup time would be dramatically decreased because then a lot of package code would no longer have to JIT compile. Tim Holy and Valentin Churavy gave a nice talk at JuliaCon 2022 about the current progress of making this work: https://www.youtube.com/watch?v=GnsONc9DYg0 .

This is all tied up with startup time and are all in some sense the same issue. Currently, the only way to get LLVM code cached, and thus startup time essentially eliminated, is to build it into what's called the "system image". That system image is the binary that package compiler builds (https://github.com/JuliaLang/PackageCompiler.jl). Julia then ships with a default system image that includes the standard library in order to remove the major chunk of code that "most" libraries share, which is why all of Julia Base works without JIT lag. However, that means everyone wants to have their thing, be it sparse matrices to statistics, in the standard library so that it gets the JIT-lag free build by default. This means the system image is huge, which is why PackageCompiler, which is simply a system for building binaries by appending package code to the system image, builds big binaries. What needs to happen is for packages to be able to precompile in a way that then caches LLVM and native code. Then there's no major compile time advantage to being in the system image, which will allow things to be pulled out of the system image to have a leaner Julia Base build without major drawbacks, which would then help make the system compile. That will then make it so that an LLVM and BLAS build does not have to be in every binary (which is what takes up most of the space and RAM), which would then allow Julia to much more comfortably move beyond the niche of scientific computing.

You're probably dancing around the edges of what [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl) is capable of targeting. There are a few new capabilities coming online, namely [separating codegen from runtime](https://github.com/JuliaLang/julia/pull/41936) and [compiling small static binaries](https://github.com/tshort/StaticCompiler.jl), but you're likely to hit some snags on the bleeding edge.