GPUCompiler.jl VS PackageCompiler.jl

I don't think it's frowned upon to compile, many people want this capability as well. If you had a program that could be proven to use no dynamic dispatch it would probably be feasible to compile it as a static binary. But as long as you have a tiny bit of dynamic behavior, you need the Julia runtime so currently a binary will be very large, with lots of theoretically unnecessary libraries bundled into it. There are already efforts like GPUCompiler[1] that do fixed-type compilation, there will be more in this space in the future.

[1] https://github.com/JuliaGPU/GPUCompiler.jl

Julia's compiler is made to be extendable. GPUCompiler.jl which adds the .ptx compilation output for example is a package (https://github.com/JuliaGPU/GPUCompiler.jl). The package manager of Julia itself... is an external package (https://github.com/JuliaLang/Pkg.jl). The built in SuiteSparse usage? That's a package too (https://github.com/JuliaLang/SuiteSparse.jl). It's fairly arbitrary what is "external" and "internal" in a language that allows that kind of extendability. Literally the only thing that makes these packages a standard library is that they are built into and shipped with the standard system image. Do you want to make your own distribution of Julia that changes what the "internal" packages are? Here's a tutorial that shows how to add plotting to the system image (https://julialang.github.io/PackageCompiler.jl/dev/examples/...). You could setup a binary server for that and now the first time to plot is 0.4 seconds.

Julia's arrays system is built so that most arrays that are used are not the simple Base.Array. Instead Julia has an AbstractArray interface definition (https://docs.julialang.org/en/v1/manual/interfaces/#man-inte...) which the Base.Array conforms to, and many effectively standard library packages like StaticArrays.jl, OffsetArrays.jl, etc. conform to, and thus they can be used in any other Julia package, like the differential equation solvers, solving nonlinear systems, optimization libraries, etc. There is a higher chance that packages depend on these packages then that they do not. They are only not part of the Julia distribution because the core idea is to move everything possible out to packages. There's not only a plan to make SuiteSparse and sparse matrix support be a package in 2.0, but also ideas about making the rest of linear algebra and arrays themselves into packages where Julia just defines memory buffer intrinsic (with likely the Arrays.jl package still shipped with the default image). At that point, are arrays not built into the language? I can understand using such a narrow definition for systems like Fortran or C where the standard library is essentially a fixed concept, but that just does not make sense with Julia. It's inherently fuzzy.

A fun fact is that the GPUCompiler, which compiles the code to run in GPU's, is the current way to generate binaries without hiding the whole ~200mb of julia runtime in the binary.

https://github.com/JuliaGPU/GPUCompiler.jl/ https://github.com/tshort/StaticCompiler.jl/

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.