applegpu

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 ar

The most complete documentation is in the applegpu repo[1] by dougallj showing a great deal of recent activity (including by alyssarosenzweig). Last I checked, the documentation of barrier instructions wasn't complete enough to tell whether these device-scoped barriers are possible. (Note: on RDNA2, they're accomplished by DLC and GLC flags on memory accesses, combined with cache flush instructions such as S_GL1_INV). There's also a lot of great material, accessibly written, on Alyssa's blog[2], see in particular the posts titled "Dissecting the Apple M1 GPU, part ${I}". [1]: https://github.com/dougallj/applegpu [2]: https://rosenzweig.io/

repo for the GPU: https://github.com/dougallj/applegpu