Model Parallelism

Model Parallelism is a distributed computing strategy in scalable machine learning wherein the parameters and computations of a single machine learning model are partitioned across multiple devices or nodes, enabling concurrent execution of different model segments during training or inference.

231

Publications

17K

Citations

1K

Authors

216

Institutions

Publications per year

2017–2026

159

Authors

1K

  1. OR
    Olatunji Ruwase

    Carnegie Mellon University, United States

    6 publications · 1.2K citations

    6

    h-index

  2. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    6 publications · 703 citations

    6

    h-index

  3. DL
    Dongsheng Li

    National University of Defense Technology, China

    6 publications · 192 citations

    6

    h-index

  4. YY
    Yang You

    National University of Singapore, Singapore

    6 publications · 251 citations

    6

    h-index

  5. YH
    Yuxiong He

    Microsoft (United States), United States

    6 publications · 1.2K citations

    6

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 62 publications · 4.5K citations

    31

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 6 publications · 245 citations

    6

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 6 publications · 360 citations

    6

    h-index

  4. 3

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 136 citations

    2

    h-index

1–5