Data Race

Data races, occurring when multiple threads access shared data concurrently without proper synchronization, are a significant challenge in parallel computing, hindering program correctness and reliability. Current research focuses on developing efficient detection methods, exploring the use of large language models and concurrent error detection schemes like those based on simpler classifiers running in parallel with the main system to identify data races and other errors in complex systems such as large language models. These efforts aim to improve the robustness and performance of high-performance computing applications and large-scale machine learning systems, ultimately enhancing the reliability of software and hardware across various domains.