High-performance Computing Optimization

High-Performance Computing Optimization is a methodological framework that belongs to the class of computational enhancement strategies, specifically distinguished by its focus on maximizing the efficiency, scalability, and resource utilization of finite difference time domain simulations through parallelization, memory management, and algorithmic refinement in high-performance computing environments.

523

Publications

29.8K

Citations

2.2K

Authors

633

Institutions

Publications per year

2017–2026

204

Authors

2.2K

  1. MS
    Mikhail Smelyanskiy

    Intel (United States), United States

    10 publications · 407 citations

    10

    h-index

  2. JS
    John Shalf

    Lawrence Berkeley National Laboratory, United States

    9 publications · 3K citations

    9

    h-index

  3. HF
    Haohuan Fu

    Tsinghua University, China

    8 publications · 612 citations

    8

    h-index

  4. SW
    Samuel Williams

    Lawrence Berkeley National Laboratory, United States

    8 publications · 2.9K citations

    8

    h-index

  5. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    6 publications · 619 citations

    6

    h-index

1–5 of 2.2K

Venues

  1. 12

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 9 publications · 452 citations

    9

    h-index

  3. arXiv (Cornell University)

    Repository · 9 publications · 176 citations

    8

    h-index

  4. The Journal of Supercomputing

    7 publications · 189 citations

    7

    h-index

  5. Procedia Computer Science

    6 publications · 117 citations

    6

    h-index

1–5