The author describes their approach to a GPU Mode auto-research contest where they used Codex to optimize a batched square compact-Householder QR factorization kernel, achieving a 232x speedup over the baseline and placing 12th out of 183 participants. The post covers their methodology, including introducing idea diversity to escape local maxima, mathematical foundations of QR decomposition, and practical implementation strategies.
Background
GPU Mode hosted an auto-research competition as part of their Linear Algebra Kernels in the Age of Research series, challenging participants to optimize QR decomposition kernels using AI assistance like Codex.
- Source
- hackernews
- Published
- Aug 15, 2026 at 07:00 PM
- Score
- 7.0 / 10