Parallel Search Algorithms

Parallel Search Algorithms is a class of parallel algorithms designed to systematically explore large solution spaces concurrently by leveraging multiple processing units, distinguished from other parallel algorithms by their focus on accelerating search operations through divide-and-conquer, pruning, or heuristic-guided exploration in scalable and fault-tolerant computational environments.

690

Publications

47.2K

Citations

1.6K

Authors

659

Institutions

Publications per year

2017–2026

119

Authors

1.6K

  1. EA
    Enrique Alba

    Universidad de Málaga, Spain

    8 publications · 1.2K citations

    8

    h-index

  2. AK
    Akihiro Kishimoto

    University of Alberta, Canada

    8 publications · 283 citations

    8

    h-index

  3. VN
    V. Nageshwara Rao

    The University of Texas at Austin, United States

    7 publications · 641 citations

    7

    h-index

  4. PC
    Philippe Codognet

    Sorbonne Université, France

    7 publications · 117 citations

    7

    h-index

  5. TG
    Tamara G. Kolda

    Sandia National Laboratories California, United States

    6 publications · 489 citations

    6

    h-index

1–5 of 1.6K

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 12 publications · 408 citations

    12

    h-index

  2. Journal of Parallel and Distributed Computing

    14 publications · 496 citations

    12

    h-index

  3. Parallel Computing

    10 publications · 391 citations

    10

    h-index

  4. arXiv (Cornell University)

    Repository · 8 publications · 404 citations

    8

    h-index

  5. Artificial Intelligence

    7 publications · 256 citations

    7

    h-index

1–5