HAccRG: Hardware-Accelerated data race detection in GPUs

Anup Holey, Vineeth Mekkat, Antonia B Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings
Subtitle of host publicationInternational Conference on Parallel Processing - The 42nd Annual Conference, ICPP 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-69
Number of pages10
ISBN (Print)9780769551173
DOIs
StatePublished - 2013
Event42nd Annual International Conference on Parallel Processing, ICPP 2013 - Lyon, France
Duration: Oct 1 2013Oct 4 2013

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918

Other

Other42nd Annual International Conference on Parallel Processing, ICPP 2013
CountryFrance
CityLyon
Period10/1/1310/4/13

Keywords

  • Concurrency bugs
  • Data race detection
  • GPUS

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