Data Parallel Execution

Data Parallel Execution is a computational paradigm in GPU architecture where a single instruction is simultaneously applied to multiple data elements across parallel processing units, enabling high-throughput execution by exploiting fine-grained data-level concurrency.

619

Publications

31.2K

Citations

2.2K

Authors

638

Institutions

Publications per year

2017–2026

137

Authors

2.2K

  1. MG
    Michael Garland

    University of Illinois Urbana-Champaign, United States

    6 publications · 983 citations

    6

    h-index

  2. DK
    Dhabaleswar K. Panda

    The Ohio State University, United States

    5 publications · 269 citations

    5

    h-index

  3. AM
    Andreas Moshovos

    University of Toronto, Canada

    5 publications · 415 citations

    5

    h-index

  4. NK
    Naga K. Govindaraju

    University of North Carolina at Chapel Hill, United States

    5 publications · 507 citations

    5

    h-index

  5. BH
    Bingsheng He

    National University of Singapore, Singapore

    5 publications · 496 citations

    5

    h-index

1–5 of 2.2K

Venues

  1. Journal of Parallel and Distributed Computing

    16 publications · 514 citations

    12

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 9 publications · 427 citations

    9

    h-index

  3. The Journal of Supercomputing

    9 publications · 193 citations

    9

    h-index

  4. ACM SIGPLAN Notices

    8 publications · 177 citations

    8

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 231 citations

    7

    h-index

1–5