Parallel Algorithm Scalability

Parallel Algorithm Scalability is a theoretical construct within computational complexity analysis that characterizes the efficiency with which a parallel algorithm maintains or improves performance gains relative to increasing numbers of processors and problem sizes, under fixed or scalable resource constraints.

640

Publications

34.7K

Citations

1.9K

Authors

668

Institutions

Publications per year

2017–2026

126

Authors

1.9K

  1. SA
    Srinivas Aluru

    Iowa State University, United States

    9 publications · 228 citations

    9

    h-index

  2. AK
    Axel Klawonn

    University of Duisburg-Essen, Germany

    7 publications · 385 citations

    7

    h-index

  3. OR
    Oliver Rheinbach

    University of Duisburg-Essen, Germany

    6 publications · 365 citations

    6

    h-index

  4. BS
    Bertil Schmidt

    Johannes Gutenberg University Mainz, Germany

    5 publications · 548 citations

    5

    h-index

  5. RČ
    Raimondas Čiegis

    Vilnius Gediminas Technical University, Lithuania

    5 publications · 95 citations

    5

    h-index

1–5 of 1.9K

Venues

  1. SIAM Journal on Scientific Computing

    22 publications · 1.8K citations

    20

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 18 publications · 893 citations

    17

    h-index

  3. arXiv (Cornell University)

    Repository · 12 publications · 805 citations

    11

    h-index

  4. 10

    h-index

  5. The Journal of Chemical Physics

    10 publications · 581 citations

    10

    h-index

1–5