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Data Race Detection Using Large Language Models

Session7th International Workshop on Software Correctness for HPC Applications (Correctness '23)

DescriptionLarge language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques. We create a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with fine-grain labels showing the presence of data race pairs and their associated variables, line numbers, and read/write information. DRB-ML is then used to evaluate representative LLMs and fine-tune open-source ones. Our experiment shows that LLMs can be a viable approach to data race detection. However, they still cannot compete with traditional data race detection tools when we need detailed information about variable pairs causing data races.

Author/Presenters

Le Chen

Iowa State University

Lawrence Livermore National Laboratory

Xianzhong Ding

University of California, Merced

Pei-Hung Lin

Lawrence Livermore National Laboratory