HAccRG: Hardware-Accelerated Data Race Detection in GPUs

Anup Holey, Vineeth Mekkat, Antonia Zhai
Department of Computer Science & Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
International Conference on Parallel Processing (ICPP), 2013

   title={Automatic Dynamic Task Distribution between CPU and GPU for Real-Time Systems},

   author={Zamith, Marcelo and Joselli, Mark and Clua, Esteban and Montenegro, Anselmo and Conci, Aura and Leal-Toledo, Regina and Valente, Luis and Feij{‘o}, Bruno and d’Ornellas, Marcos and Pozzer, Cesar and others},



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Modern Graphics Processing Units (GPUs) are capable of supporting thousands of concurrent threads. However, they provide relatively little guarantee with respect to the coherence and consistency of the memory system. Thus, GPUs are prone to multitude of concurrency bugs related to inconsistent memory states. Many such bugs manifest as some form of data races at runtime, and being able to identify these data races can help programmers improve software reliability. Mechanisms that enable efficient and effective data race detection at runtime can form the basis of powerful tools for enhancing GPU software correctness. Most prior works in data race detection for GPU focus on the software-based approaches that incur significant performance overhead. Furthermore, they often focus on the smaller shared memory, while neglecting the larger global memory. We believe that adequate hardware support can enable efficient data race detection in all levels of the memory system for GPUs. In this paper, we propose a hardware-accelerated data race detection mechanism, HAccRG, for efficient data race detection in GPUs. HAccRG provides hardware support for tracking data dependencies across a large number of threads and detects various forms of data races. We incorporate HAccRG on both the shared and global memory spaces in GPU. Our evaluation shows that, with moderate hardware support, HAccRG can detect data races in GPU kernels with a small overhead: 1% for the shared memory and 27% for combined shared and global memory data race detection.
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