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

• URL: http://arxiv.org/abs/2310.04047v3
• Date: Wed, 19 Feb 2025 04:30:19 GMT
• Title: AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs
• Authors: Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung Phan, Le Chen, Mihai Capotă, Theodore Willke, Nesreen K. Ahmed, Ali Jannesari,
• Abstract summary: Our work proposes ourtool, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes.<n>ourtool xspace improves the state-of-the-art LLMs by 19.9% in NAS and 6.48% in Rodinia benchmark in terms of CodeBERTScore for the task of parallel code generation.
• Score: 10.648304988994369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\approx$17\% (on NAS benchmark) and $\approx$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.

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