Scalable Parallel Algorithms

Scalable Parallel Algorithms is a computational method designed to solve problems efficiently on increasing numbers of processing elements with proportional growth in problem size, distinguished by maintaining performance efficiency and resource utilization across varying scales of parallelism.

775

Publications

40.8K

Citations

2.2K

Authors

659

Institutions

Publications per year

2017–2026

116

Authors

2.2K

  1. VK
    Vipin Kumar

    University of Minnesota, United States

    17 publications · 1.6K citations

    15

    h-index

  2. GK
    George Karypis

    University of Minnesota, United States

    14 publications · 1.2K citations

    12

    h-index

  3. VK
    Viktor K. Prasanna

    University of Southern California, United States

    7 publications · 166 citations

    7

    h-index

  4. AG
    Anshul Gupta

    University of Minnesota, United States

    6 publications · 506 citations

    6

    h-index

  5. WF
    Wenfei Fan

    University of Edinburgh, United Kingdom

    6 publications · 411 citations

    6

    h-index

1–5 of 2.2K

Venues

  1. SIAM Journal on Scientific Computing

    25 publications · 2.8K citations

    21

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 20 publications · 1.2K citations

    16

    h-index

  3. Journal of Parallel and Distributed Computing

    20 publications · 683 citations

    14

    h-index

  4. Parallel Computing

    20 publications · 748 citations

    14

    h-index

  5. arXiv (Cornell University)

    Repository · 12 publications · 328 citations

    11

    h-index

1–5