Heterogeneous Load Distribution

Heterogeneous Load Distribution is a computational resource allocation strategy that directs workloads across diverse processing units—such as CPUs, GPUs, and specialized accelerators—according to their architectural strengths, thereby optimizing performance and efficiency in kernel offloading environments.

48

Publications

4.5K

Citations

127

Authors

58

Institutions

Publications per year

2017–2026

10

Authors

127

  1. KL
    Keqin Li

    SUNY New Paltz, United States

    4 publications · 103 citations

    4

    h-index

  2. BV
    Bharadwaj Veeravalli

    National University of Singapore, Singapore

    2 publications · 61 citations

    2

    h-index

  3. AE
    Adilson E. Motter

    Northwestern University, United States

    2 publications · 2.7K citations

    2

    h-index

  4. WH
    Wong Han Min

    National University of Singapore, Singapore

    2 publications · 61 citations

    2

    h-index

  5. JK
    Jaewook Kim

    Korea Advanced Institute of Science and Technology, South Korea

    1 publications · 11 citations

    1

    h-index

1–5 of 127

Venues

  1. Journal of Parallel and Distributed Computing

    4 publications · 174 citations

    4

    h-index

  2. Physical Review Letters

    PRL · 2 publications · 864 citations

    2

    h-index

  3. IEEE Transactions on Communications

    1 publications · 36 citations

    1

    h-index

  4. Physical Review E

    PRE · 1 publications · 50 citations

    1

    h-index

  5. Journal of Grid Computing

    1 publications · 34 citations

    1

    h-index

1–5