Data race detection via few-shot parameter-efficient fine-tuning

The OpenMP programming model is playing an increasing role in parallelization on shared-memory systems owing to its simplicity of operation and portability. OpenMP provides the semantic equivalent of a parallel program for the original sequential program. Though it is easier to write parallel programs using OpenMP, writing them correctly is a challenge. Data race conditions errors can easily occur during the writing process, particularly by inexperienced programmers. Some data race checkers have been developed to help programmers check for data race in parallel programs. However, several of them have constraints on the input and thread configuration, time overhead, and scope of program analysis. In this study, we target data race detection in OpenMP parallel programs to address the issues of constraints from checkers. We propose a few-shot parameter-efficient fine-tuning method using adapter module to address data race detection issue. The proposed method does not require a large labeled dataset, and it makes data efficient. A generic dataset is constructed with a limited number of labeled data, containing diverse OpenMP patterns for data race detection. A neural architecture search approach is employed to improve the performance of detection. The experimental results on the generated and open-source datasets demonstrate that our method is effective and improves race detection compared with traditional methods.