The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: 7th International Workshop on Software Correctness for HPC Applications (Correctness '23)
Authors: Le Chen (Iowa State University, Lawrence Livermore National Laboratory); Xianzhong Ding (University of California, Merced); Pei-Hung Lin and Chunhua Liao (Lawrence Livermore National Laboratory); Murali Emani (Argonne National Laboratory (ANL)); and Tristan Vanderbruggen (Lawrence Livermore National Laboratory)
Abstract: 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.