AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs

AbstractIn-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 AutoParLLM, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate AutoParLLM on 12 applications from two well-known benchmark suites of parallel codes: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that AutoParLLM 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, AutoParLLM 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 OMPScore for evaluating the quality of the parallel code and show its effectiveness in evaluating parallel codes.