CUDA.jl VS racket

It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.

https://juliagpu.org/

I've written CUDA kernels and I knew nothing about it going in.

See https://juliagpu.org/

If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.

This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.

If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).

If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.

GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.

Should have (2017) in the title.

Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/

I'm excited to eventually see something like JuliaGPU with support for multiple backends.

That's really cool! Have you looked at CUDA.jl for the Julia language? Maybe you could take some ideas from there. I am pretty sure it does the same thing you do here, and they support any arbitrary code with the limitations that you cannot allocate memory, I/O is disallowed, and badly-typed code(dynamic) will not compile.

Racket—the Language-Oriented Programming Language—version 8.12 is now available from https://racket-lang.org

See https://racket.discourse.group/t/racket-v8-12-is-now-availab... for the release announcement and highlights.

Thank you to the many people who contributed to this release!

Feedback Welcome

Racket version 8.11.1 is now available from https://racket-lang.org/

Racket (https://racket-lang.org) has an IDE (DrRacket) which isn't EMACS. ARC (which powers hacker news) is (was?) written in Racket.

Nice! Repeating a comment I just made on HN: I signed up for RacketCon, will be joining remotely. I am looking forward to it a lot. Usually I use the Racket language perhaps for 10% of my personal projects, but I am currently writing a Racket AI book, so all things Racket are of current interest. Past RacketCons have been a lot of fun. I usually use Common Lisp, but Racket is batteries included Scheme, and more, and is a very pleasant language and ecosystem. Just in case you don’t have Racket installed: https://racket-lang.org/

I signed up for RacketCon, joining remotely. I am looking forward to it a lot. Usually I use the Racket language perhaps for 10% of my personal projects, but I am currently writing a Racket AI book, so all things Racket are of current interest.

Past RacketCons have been a lot of fun.

I usually use Common Lisp, but Racket is batteries included Scheme, and more, and is a very pleasant language and ecosystem. Just in case you don’t have Racket installed: https://racket-lang.org/

I'd suggest Racket (https://racket-lang.org) which is a batteries-included language environment that includes scheme and has a lot of high-quality documentation.

Guile (https://www.gnu.org/software/guile/) isn't quite as learner-focused but is another great choice.

How did I get to the bottom of the page and not ONE person has recommended racket?

The Racket fork of CS supports Apple Silicon natively, and can be installed independently: https://github.com/racket/racket/blob/master/racket/src/ChezScheme/BUILDING Chez adds a few features (threads, ffi, ...) to R6RS; there is a useful combined index to TSPL4 and the CS User Guide at http://cisco.github.io/ChezScheme/csug9.5/csug_1.html

If you're using SICP in Scheme (or are you doing the JS version?) then you may want to look at How to Design Programs. It uses Racket which is a Scheme descendent so much of the language you've learned in SICP will work in it without issue. It also has a pretty good set of GUI and drawing capabilities you can find through the Racket docs page and will use some of with HTDP.