LispSyntax.jl
oneAPI.jl
LispSyntax.jl | oneAPI.jl | |
---|---|---|
7 | 4 | |
222 | 174 | |
- | 1.7% | |
0.0 | 8.7 | |
2 months ago | 6 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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LispSyntax.jl
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GPU vendor-agnostic fluid dynamics solver in Julia
It turns out that Julia is ~a lisp, just with a weird syntax. If you look at the metaprogramming facilities, all expressions are first turned into s-exprs while parsing. There is no problem having a LISP syntax for Julia, and in fact this has been implemented! (https://github.com/swadey/LispSyntax.jl)
https://docs.julialang.org/en/v1/manual/metaprogramming/
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From Common Lisp to Julia
The REPL had a patch, https://github.com/swadey/LispSyntax.jl/issues/36, but for some reason it didn't get released 3 years ago when the patch was actually created.
- Lispsyntax.jl: A Clojure-like Lisp syntax for julia
oneAPI.jl
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GPU vendor-agnostic fluid dynamics solver in Julia
https://github.com/JuliaGPU/oneAPI.jl
As for syntax, Julia syntax scales from a scripting language to a fully typed language. You can write valid and performant code without specifying any types, but you can also specialize methods for specific types. The type notation uses `::`. The types also have parameters in the curly brackets. The other aspect that makes this specific example complicated is the use of Lisp-like macros which starts with `@`. These allow for code transformation as I described earlier. The last aspect is that the author is making extensive use of Unicode. This is purely optional as you can write Julia with just ASCII. Some authors like to use `ε` instead of `in`.
- Writing GPU shaders in Julia?
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Cuda.jl v3.3: union types, debug info, graph APIs
https://github.com/JuliaGPU/AMDGPU.jl
https://github.com/JuliaGPU/oneAPI.jl
These are both less mature than CUDA.jl, but are in active development.
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Unified programming model for all devices – will it catch on?
OpenCL and various other solutions basically require that one writes kernels in C/C++. This is an unfortunate limitation, and can make it hard for less experienced users (researchers especially) to write correct and performant GPU code, since neither language lends itself to writing many mathematical and scientific models in a clean, maintainable manner (in my opinion).
What oneAPI (the runtime), and also AMD's ROCm (specifically the ROCR runtime), do that is new is that they enable packages like oneAPI.jl [1] and AMDGPU.jl [2] to exist (both Julia packages), without having to go through OpenCL or C++ transpilation (which we've tried out before, and it's quite painful). This is a great thing, because now users of an entirely different language can still utilize their GPUs effectively and with near-optimal performance (optimal w.r.t what the device can reasonably attain).
[1] https://github.com/JuliaGPU/oneAPI.jl
What are some alternatives?
femtolisp - a lightweight, robust, scheme-like lisp implementation
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
opendylan - Open Dylan compiler and IDE
Vulkan.jl - Using Vulkan from Julia
awesome-lisp-companies - Awesome Lisp Companies
Makie.jl - Interactive data visualizations and plotting in Julia
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
julia - The Julia Programming Language
AMDGPU.jl - AMD GPU (ROCm) programming in Julia
doc - Flexible documentation generator for Common Lisp projects.
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.