Stop writing complex GPU thread mapping code; a one-time LLM prompt can derive better mathematical equations than expert engineers.
LLMs can automatically generate exact O(1) or O(log N) mapping equations for 2D/3D domains, outperforming symbolic regression. This replaces tedious manual optimization with permanent, hardware-level energy and speed gains derived in seconds.
Leveraging Mathematical Reasoning of LLMs for Efficient GPU Thread Mapping
arXiv · 2604.10387
Mapping parallel threads onto non-box-shaped domains is a known challenge in GPU computing that, if done efficiently, can prevent severe performance penalties from allocating unnecessary computational resources. Currently, achieving this optimal efficiency requires significant analytical human time and effort to manually derive bespoke mapping functions for each specific geometry. This work introduces a novel approach leveraging the symbolic reasoning capabilities of Large Language Models (LLMs)