● Covered by 1 source · 1 reportLow impact1 neutral
Participant achieves 232x speedup in GPU Mode's QR factorization contest using Codex
Key points
- Achieved 232x speedup over baseline in QR factorization contest.
- Used Codex to assist in the auto-research process.
- Contest involved implementing batched square compact-Householder QR factorization.
- Placed 12th out of 183 participants.
Contest Overview
GPU Mode, in collaboration with Core Automation, hosted an auto-research themed contest focused on implementing batched square compact-Householder QR factorization. Participants were tasked with optimizing the performance of this linear algebra kernel.
Achieved Speedup
One participant achieved a 232x speedup compared to the provided baseline solution. This performance gain secured the 12th position out of 183 participants in the contest. The problem required returning a compact Householder QR representation similar to torch.geqrf(A).
Methodology and Tools
The approach involved what the participant described as "auto-research" or "loop engineering," utilizing Codex to facilitate the process. Key aspects included learning to ask better questions and introducing idea diversity to overcome local optimization maxima. The contest evaluated runtime using a geometric mean across various matrix shapes and conditioning cases, with important sizes including 512x512, 1024, 2048, and 4096.
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Primary sources
GitHub karpathy/autoresearchReporting from
A participant in GPU Mode's auto-research contest achieved a 232x speedup over the baseline solution for batched square compact-Householder QR factorization. This result was obtained using an approach that involved asking better questions and exploring diverse ideas with the help of Codex, a code generation tool.