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Achieving 232x Speedup on GPU QR Factorization: A Contest Retrospective

A detailed technical blog post about the author's experience competing in an auto-research themed contest hosted by GPU Mode and Core Automation. The challenge was to implement a batched square compact-Householder QR factorization (QR decomposition) on GPU kernels. The author achieved a 232x speedup over the baseline solution, placing 12th out of 183 participants. The post covers the author's approach, key learnings, bottlenecks encountered, and the iterative process of optimizing GPU kernels through auto-research techniques.

2h ago23 min readenInsight
Read on sankalp.bearblog.dev

Key quotes

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 s

From the article

Table of Contents Intro Contest in short Problem intro Why this problem is auto-research-able Learning Enough to Ask Better Questions (Optional) Math f...
Continue reading on sankalp.bearblog.dev

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