Data-Driven Dynamic Algorithm Dispatch with Large Language Models

By Rushil Shah, Emmanuel Lujan, Rabab Alomairy, Alan EdelmanSource:

arXiv cs.AIarXiv:2608.21584v1 Announce Type: new Abstract: We introduce a

large language model(LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By combiningprompt engineeringwithLLaMA3 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.Get AI news in your inbox

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