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Data races pose a significant threat in multi-threaded parallel applicationsdue to their negative impact on program correctness. DataRaceBench, anopen-source benchmark suite, is specifically crafted to assess these data racedetection tools in a systematic and measurable manner. Machine learningtechniques have recently demonstrated considerable potential inhigh-performance computing (HPC) program analysis and optimization. However,these techniques require specialized data formats for training and refinement.This paper presents the latest update to DataRaceBench, incorporating new datarace contributions from Wu et al. \cite{wu2023model}, and introduces a deriveddataset named DataRaceBench-ML (DRB-ML) \cite{drbml}. DRB-ML aligns with theemerging trend of machine learning and large language models. Originating fromDataRaceBench, this dataset includes detailed labels that denote the presenceof a data race and provides comprehensive details of associated variables, suchas variable names, line numbers, and the operation (read/write). Unique toDRB-ML, we have also integrated a series of tailored prompt-response pairsspecifically designed for LLM fine-tuning.

DataRaceBench V1.4.1 and DataRaceBench-ML V0.1: Benchmark Suites for Data Race Detection