232x Speedup in Batched QR Decomposition Using Codex-Driven Auto-Research
August 15, 2026
A participant achieved a 232x speedup over the baseline for batched square compact-Householder QR factorization using Codex. The approach involved implementing blocked Householder algorithms to reduce serial work and utilizing iterative prompt engineering to navigate local maxima in kernel optimization.
HOW THIS AFFECTS YOU
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builderYou can leverage LLM-driven iterative optimization to accelerate custom GPU kernel development.
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researcherThis demonstrates the efficacy of loop engineering in solving highly specialized numerical computing problems.