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Ask HN: Resources for general purpose GPU development on Apple's M* chips?

by thinking_banana on 12/25/24, 4:58 PM with 82 comments

• by aleinin on 12/25/24, 7:25 PM

  If you're looking for a high level introduction to GPU development on Apple silicon I would recommend learning Metal. It's Apple's GPU acceleration language similar to CUDA for Nvidia hardware. I ported a set of puzzles for CUDA called GPU-Puzzles (a collection of exercises designed to teach GPU programming fundamentals)[1] to Metal [2]. I think it's a very accessible introduction to Metal and writing GPU kernels.

  [1] https://github.com/srush/GPU-Puzzles

  [2] https://github.com/abeleinin/Metal-Puzzles

• by morphle on 12/25/24, 5:26 PM

  You can help with the reverse engineering of Apple Silicon done by a dozen people worldwide, that is how we find out the GPU and NPU instructions[1-4]. There is over 43 trillion float operations per second to unlock at 8 terabit per second 'unified' memory bandwidth and 270 gigabits per second networking (less on the smaller chips)....

  [1] https://github.com/AsahiLinux/gpu

  [2] https://github.com/dougallj/applegpu

  [3] https://github.com/antgroup-skyward/ANETools/tree/main/ANEDi...

  [4] https://github.com/hollance/neural-engine

  You can use a high level APIs like MLX, Metal or CoreML to compute other things on the GPU and NPU.

  Shadama [5] is an example programming language that translates (with Ometa) matrix calculations into WebGPU or WebGL APIs (I forget which). You can do exactly the same with the MLX, Metal or CoreML APIs and only pay around 3% overhead going through the translation stages.

  [5] https://github.com/yoshikiohshima/Shadama

  I estimate it will cost around $22K at my hourly rate to completely reverse engineer the latest A16 and M4 CPU (ARMV9), GPU and NPU instruction sets. I think I am halfway on the reverse engineering, the debugging part is the hardest problem. You would however not be able to sell software with it on the APP Store as Apple forbids undocumented API's or bare metal instructions.

• by barkingcat on 12/25/24, 5:08 PM

  There is no general purpose GPU development on Apple M series.

  There is Metal development. You want to learn Apple M-series gpu and gpgpu development? Learn Metal!

  https://developer.apple.com/metal/

• by rgovostes on 12/25/24, 5:44 PM

  It's hard to answer not knowing exactly what your aim is, or your experience level with CUDA and how easily the concepts you know will map to Metal, and what you find "restricted and convoluted" about the documentation.

  <Insert your favorite LLM> helped me write some simple Metal-accelerated code by scaffolding the compute pipeline, which took most of the nuisance out of learning the API and let me focus on writing the kernel code.

  Here's the code if it's helpful at all. https://github.com/rgov/thps-crack

• by billti on 12/25/24, 10:10 PM

  If you know CUDA, then I assume you know a bit already about GPUs and the major concepts. There’s just minor differences and different terminology for things like “warps” etc.

  With that base, I’ve found their docs decent enough, especially coupled with the Metal Shader Language pdf they provide (https://developer.apple.com/metal/Metal-Shading-Language-Spe...), and quite a few code samples you can download from the docs site (e.g. https://developer.apple.com/documentation/metal/performing_c...).

  I’d note a lot of their stuff was still written in Objective-C, which I’m not that familiar with. But most of that is boilerplate and the rest is largely C/C++ based (including the Metal shader language).

  I just ported some CPU/SIMD number crunching (complex matrices) to Metal, and the speed up has been staggering. What used to take days now takes minutes. It is the hottest my M3 MacBook has ever been though! (See https://x.com/billticehurst/status/1871375773413876089 :-)

• by mkagenius on 12/25/24, 5:29 PM

  Check out MLX[1]. Its a bit like pytorch/tensorflow with added benefit of Apple Silicon.

  1. https://ml-explore.github.io/mlx/build/html/index.html

• by thetwentyone on 12/25/24, 6:11 PM

  I’ve had a good time dabbling with Metal.jl: https://github.com/JuliaGPU/Metal.jl

• by dylanowen on 12/25/24, 5:21 PM

  People have already mentioned Metal, but if you want cross platform, https://github.com/gfx-rs/wgpu has a vulkan-like API and cross compiles to all the various GPU frameworks. I believe it uses https://github.com/KhronosGroup/MoltenVK to run on Macs. You can also see the metal shader transpilation results for debugging.

• by feznyng on 12/25/24, 5:14 PM

  Besides the official docs you can check out llama.cpp as an example that uses metal for accelerated inference on Apple silicon.

• by desideratum on 12/25/24, 7:48 PM

  I'd reccomend checking out the CUDA mode Discord server! They also have a channel for Metal https://discord.gg/ZqckTYcv

• by rowanG077 on 12/25/24, 5:12 PM

  If you are open to run Linux you can use standard opencl and vulkan.

• by TriangleEdge on 12/25/24, 7:56 PM

  Why not OpenCL or OpenGL? You'll not be constrained by the flavor of GPU.

• by amelius on 12/25/24, 6:21 PM

  Apple is known to actively discourage general purpose computing. Better try a different vendor.