Data-Driven Dynamic Algorithm Dispatch with Large Language Models

• URL: http://arxiv.org/abs/2608.21584v1
• Date: Fri, 21 Aug 2026 19:37:46 GMT
• Title: Data-Driven Dynamic Algorithm Dispatch with Large Language Models
• Abstract summary: We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatchs in high-performance linear algebra.<n>A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies.
• Score: 1.0382948833881696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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