Data-Driven Dynamic Algorithm Dispatch with Large Language Models

2026-08-21

AI Summary

研究通过结合提示工程与LLaMA 3及性能数据库，利用大型语言模型生成高性能线性代数中的动态算法调度策略，以提高算法选择效率。

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.

Problem

• large language model
• dynamic algorithmic dispatch
• linear algebra
• heuristics

Innovation

• large language model
• dynamic algorithmic dispatch
• prompt engineering
• performance database
• linear algebra