Gpu Performance Modeling

GPU Performance Modeling is a computational framework that predicts the execution behavior and efficiency of graphics processing units under varying workloads by abstracting hardware characteristics and workload dynamics through analytical or statistical methods.

294

Publications

16.1K

Citations

872

Authors

276

Institutions

Publications per year

2017–2026

95

Authors

872

  1. HK
    Hyesoon Kim

    Georgia Institute of Technology, United States

    7 publications · 1.5K citations

    7

    h-index

  2. JM
    Jiayuan Meng

    University of Virginia, United States

    6 publications · 805 citations

    6

    h-index

  3. PG
    Ping Guo

    Beijing Normal University, China

    5 publications · 162 citations

    5

    h-index

  4. SH
    Sunpyo Hong

    Georgia Institute of Technology, United States

    5 publications · 1.4K citations

    5

    h-index

  5. KK
    Kalyan Kumaran

    Argonne National Laboratory, United States

    5 publications · 195 citations

    5

    h-index

1–5 of 872

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 15 publications · 984 citations

    14

    h-index

  2. 7

    h-index

  3. Computers & Fluids

    6 publications · 287 citations

    6

    h-index

  4. Journal of Parallel and Distributed Computing

    6 publications · 753 citations

    6

    h-index

  5. IEEE Computer Architecture Letters

    5 publications · 485 citations

    5

    h-index

1–5