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Auto-research with codex: How I achieved a 232x Faster Kernel

sankalp.bearblog.dev

August 15, 2026

26 min read

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Summary

Codex was utilized to achieve a 232x faster kernel for the qr_v2 problem in GPU mode. The implementation involved optimizing the blocked Householder algorithm and introducing idea diversity to enhance performance.

Key Takeaways

The author achieved a 232x speedup over the baseline solution in a GPU Mode contest focused on batched square compact-Householder QR factorization.
The contest allowed participants to make unlimited submissions, facilitating rapid iteration and learning through feedback on performance.
The problem involved implementing QR decomposition for batched square FP32 CUDA matrices, requiring the return of a compact Householder QR representation.
The author made over 1500 submissions during the 14-day contest, emphasizing the importance of tight feedback loops in optimizing GPU kernels.

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Coverage & discussions

Hacker News · sankalp.bearblog.dev

Auto-research with codex: How I achieved a 232x Faster Kernel ↗

Community discussions

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Analysis ready

Community Sentiment

Mixed

Positives

The ability to leverage LLMs for kernel optimization is a game-changer, with users reporting up to 232x speed improvements in their projects.
DeepSeek v4 is proving itself as a reliable partner for optimizing complex tasks, making previously daunting benchmarks feel approachable and achievable.
Several commenters are excited about the potential of AI agents to tackle well-defined optimization problems, indicating a significant leap in developer productivity.

Concerns

There's a clear concern that many AI-generated solutions are overfitting to specific inputs, leading to brittle code that fails under varied conditions.
Commenters worry about the lack of generality in the AI-generated code, emphasizing that while speed is prioritized, maintainability and robustness are often sacrificed.
One user noted that while LLMs excel in specific optimizations, they struggle to create general solutions, highlighting limitations in current AI approaches.