Data-Driven Dynamic Algorithm Dispatch with Large Language Models
Rushil Shah, Emmanuel Lujan, Rabab Alomairy, Alan Edelman
Abstract
We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By combining prompt engineering with LLaMA 3 and a curated performance database, the model learns to synthesize selection heuristics that exploit structural patterns to identify fast algorithmic choices. A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies. This work, developed as part of the DARPA-MIT SmartSolve project, highlights the promise of LLMs for algorithmic discovery and the development of more adaptive, fast linear algebra software.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.