In-Context Learning (ICL) has been shown to be a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. This work proposes \ourtool, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate \ourtool \xspace{} on 12 applications from two well-known benchmark suites of parallel codes: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that \ourtool \xspace{} improves the state-of-the-art LLMs (e.g., GPT-4) by 19.9\% in NAS and 6.48\% in Rodinia benchmark in terms of CodeBERTScore for the task of parallel code generation. Moreover, \ourtool \xspace{} improves the ability of the most powerful LLM to date, GPT-4, by achieving ≈17\% (on NAS benchmark) and ≈16\% (on Rodinia benchmark) better speedup. In addition, we propose \ourscore \xspace{} for evaluating the quality of the parallel code and show its effectiveness in evaluating parallel codes. \ourtool \xspace is available at https://github.com/quazirafi/AutoParLLM.git.
@article{arxiv.2310.04047,
title = {AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs},
author = {Quazi Ishtiaque Mahmud and Ali TehraniJamsaz and Hung Phan and Le Chen and Mihai Capotă and Theodore Willke and Nesreen K. Ahmed and Ali Jannesari},
journal= {arXiv preprint arXiv:2310.04047},
year = {2025}
}