Title: LLM Program Optimization via Retrieval Augmented Search
Primary area: foundation or frontier models, including LLMs
Keywords: program optimization, large language models, contextual retrieval

Abstract: With the advent of large language models (LLMs), there has been great interest in applying them to solve difficult programming tasks. Recent work has demonstrated their potential at program optimization, a key challenge in programming languages research.  We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find that performing contextual retrieval based on an LLM-generated natural language description significantly outperforms retrieval based on the source code. In addition, we propose a method called AEGIS for improving interpretability by decomposing training examples into ''atomic edits'' that are significantly more incremental in nature. We show that RAS performs up to 2.04$\times$ better than prior state-of-the-art blackbox adaptation strategies on optimizing C++ programs, and that AEGIS performs 1.37$\times$ better while performing significantly smaller edits. We also show that using RAS improves the mean runtime percentile of Python programs by 10.27 as compared to other strategies.

TL;DR: We propose two blackbox adaptation techniques for LLM-guided program optimization using contextual retrieval and beam search.