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

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Auto-research with codex: How I achieved a 232x Faster Kernel over baseline with Codex in GPU Mode's qr_v2 problem

08 Jul, 2026

Table of Contents

Intro

Contest in short

GPU Mode, in collab with Core Automation, recently hosted an auto-research themed contest. The problem statement was to implement batched square compact-Householder QR factorization aka QR decomposition . I placed 12th out of 183 participants, ending up with a 232x speedup over the baseline solution. This post is about how I got there. I will go through my approach, learnings, and bottlenecks I ran into during the contest. It was my first serious attempt at auto-research. Some people will call this "loop engineering", and honestly that is fine too.

Note that you don't need to go through the mathematics or the problem itself in detail to follow most of this blog post. I have focused on my approach while keeping the math and the problem itself secondary as most people who will read this won't have participated in the contest.

You can check out the full contest page here: Problem Link and Leaderboard

This contest was part of GPU Mode's Linear Algebra Kernels in the Age of Research series.

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