[P] Built a portable GPU ISA after reading too many architecture manuals [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
I’ve been reading GPU architecture docs in my free time. NVIDIA PTX, AMD ISA reference guides, Intel Xe, reverse-engineered Apple GPU stuff. Over 5,000 pages across 16 microarchitectures.
After a while you notice all four vendors are doing the same 11 things with different names. So I wrote a spec that covers all of them and built a toolchain around it. It’s called WAVE. You write a kernel once, it compiles to a portable binary, then thin backends translate it to Metal, PTX, HIP, or SYCL.
Same binary verified on Apple M4 Pro, NVIDIA T4, and AMD MI300X. My co-author Onyinye built PyTorch integration and got identical training results across all backends.
Please star on GitHub: https://github.com/Oabraham1/wave
Preprint: https://arxiv.org/abs/2603.28793
Read full docs and how I built everything: https://wave.ojima.me
pip install wave-gpu
[link] [comments]
More from r/MachineLearning
-
Improving machine-translated novels via style transfer — looking for advice on the faithfulness/fluency tradeoff [P]
Jul 2
-
How papers are selected for Best Paper, Oral, or Highlight presentation at major ML/CV conferences such as CVPR, ICCV, ECCV, NeurIPS, and ICLR? [D]
Jul 2
-
BMVC 2026 Review Discussion Thread [D]
Jul 2
-
Has anyone tried this approach with Fast Byte Latent Transformers ? [R]
Jul 2
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.