Data-Driven Dynamic Algorithm Dispatch with Large Language Models

Rushil Shah; Emmanuel Lujan; Rabab Alomairy; Alan Edelman

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arXiv · 2608.21584

Data-Driven Dynamic Algorithm Dispatch with Large Language Models

Rushil Shah·Emmanuel Lujan·Rabab Alomairy·Alan Edelman

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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.

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BibTeX RIS

Rushil Shah, Emmanuel Lujan, Rabab Alomairy, Alan Edelman. 2026-08-21. Data-Driven Dynamic Algorithm Dispatch with Large Language Models. https://arxiv.org/abs/2608.21584

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