Data Race Analysis

Data Race Analysis is a systematic method in parallel debugging that identifies and characterizes unintended concurrent memory accesses by multiple threads to shared data locations without proper synchronization, thereby distinguishing potential race conditions from benign or intentional parallel memory interactions.

383

Publications

27.3K

Citations

727

Authors

228

Institutions

Publications per year

2017–2026

63

Authors

727

  1. JH
    Jeff Huang

    Texas A&M University, United States

    13 publications · 467 citations

    11

    h-index

  2. VS
    Vivek Sarkar

    Rice University, United States

    11 publications · 731 citations

    11

    h-index

  3. MV
    Martin Vechev

    ETH Zurich, Switzerland

    10 publications · 479 citations

    10

    h-index

  4. SN
    Satish Narayanasamy

    University of Michigan, United States

    10 publications · 968 citations

    10

    h-index

  5. JT
    Josep Torrellas

    University of Illinois Urbana-Champaign, United States

    9 publications · 331 citations

    9

    h-index

1–5 of 727

Venues

  1. ACM SIGPLAN Notices

    47 publications · 2.7K citations

    27

    h-index

  2. Proceedings of the ACM on Programming Languages

    PACMPL · 8 publications · 284 citations

    7

    h-index

  3. 6

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 4 publications · 103 citations

    4

    h-index

  5. ACM SIGOPS Operating Systems Review

    4 publications · 1K citations

    4

    h-index

1–5