Parallel Select Algorithms

Parallel Select Algorithms is a computational method that belongs to the class of parallel search algorithms and is specifically distinguished by its ability to efficiently determine the k-th smallest element in an unordered dataset through concurrent processing across multiple processors or threads.

18

Publications

738

Citations

30

Authors

25

Institutions

Publications per year

2017–2026

0

Authors

30

  1. MA
    Miklós Ajtai

    IBM Research - Almaden, United States

    2 publications · 63 citations

    2

    h-index

  2. DA
    David A. Bader

    Georgia Institute of Technology, United States

    2 publications · 56 citations

    2

    h-index

  3. EL
    E. L. G. Saukas

    Universidade de São Paulo, Brazil

    2 publications · 26 citations

    2

    h-index

  4. SG
    Shane G. Henderson

    Cornell University, United States

    2 publications · 26 citations

    2

    h-index

  5. CG
    C. Gregory Plaxton

    The University of Texas at Austin, United States

    2 publications · 82 citations

    2

    h-index

1–5 of 30

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 1 publications · 20 citations

    1

    h-index

  2. Information Processing Letters

    1 publications · 41 citations

    1

    h-index

  3. Conference on High Performance Computing (Supercomputing)

    Conference · 1 publications · 11 citations

    1

    h-index

  4. Proceedings of the Winter Simulation Conference, 2005.

    Conference · 1 publications · 12 citations

    1

    h-index

  5. Journal of Computer and System Sciences

    1 publications · 35 citations

    1

    h-index

1–5