GPUCompiler.jl
StaticCompiler.jl
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GPUCompiler.jl | StaticCompiler.jl | |
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5 | 16 | |
146 | 472 | |
3.4% | - | |
8.5 | 7.1 | |
8 days ago | 25 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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GPUCompiler.jl
- Julia and GPU processing, how does it work?
- GenieFramework โ Web Development with Julia
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We Use Julia, 10 Years Later
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
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Why Fortran is easy to learn
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.
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Cuda.jl v3.3: union types, debug info, graph APIs
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/
StaticCompiler.jl
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Potential of the Julia programming language for high energy physics computing
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
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Julia App Deployment
PackageCompiler, but it' s a fat runtime and not cross compile. A thin runtime is currently not possible without sacrifices for feature as https://github.com/tshort/StaticCompiler.jl.
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JuLox: What I Learned Building a Lox Interpreter in Julia
https://github.com/tshort/StaticCompiler.jl/issues/59 Would working on this feasible?
- Making Python 100x faster with less than 100 lines of Rust
- What's Julia's biggest weakness?
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Size of a "hello world" application
I just read the project's documentation at https://github.com/tshort/StaticCompiler.jl. It does produce a "hello world" application that is only 8.4k in size ๐. I do like that it can work on Mac OS. Hopefully Windows support will come soon.
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Why Julia 2.0 isnโt coming anytime soon (and why that is a good thing)
See https://github.com/tshort/StaticCompiler.jl
- My Experiences with Julia
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Julia for health physics/radiation detection
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.
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We Use Julia, 10 Years Later
using StaticCompiler # `] add https://github.com/tshort/StaticCompiler.jl` to get latest master
What are some alternatives?
KernelAbstractions.jl - Heterogeneous programming in Julia
julia - The Julia Programming Language
CUDA.jl - CUDA programming in Julia.
PackageCompiler.jl - Compile your Julia Package
Vulkan.jl - Using Vulkan from Julia
acados - Fast and embedded solvers for nonlinear optimal control
oneAPI.jl - Julia support for the oneAPI programming toolkit.
LoopVectorization.jl - Macro(s) for vectorizing loops.
AMDGPU.jl - AMD GPU (ROCm) programming in Julia
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication