Data Race Detection Using Large Language Models
This is an incremental improvement for developers and researchers in high-performance computing, offering an alternative to manual tool creation.
The paper tackles data race detection in high-performance computing programs by using large language models (LLMs) with prompting and fine-tuning, showing they are a viable approach but cannot yet compete with traditional tools for detailed variable pair information.
Large 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.