Data-parallel Algorithms

Data-Parallel Algorithms is a class of parallel algorithms in which the same operation is applied simultaneously to elements of a data set, distinguished by their reliance on data decomposition and synchronized execution across multiple processing units to achieve scalability in algorithm design.

396

Publications

21.7K

Citations

1.1K

Authors

381

Institutions

Publications per year

2017–2026

53

Authors

1.1K

  1. RS
    Robert Schreiber

    Hewlett-Packard (United States), United States

    7 publications · 437 citations

    7

    h-index

  2. GF
    Geoffrey Fox

    Indiana University Bloomington, United States

    7 publications · 629 citations

    7

    h-index

  3. SC
    Siddhartha Chatterjee

    Carnegie Mellon University, United States

    7 publications · 646 citations

    7

    h-index

  4. SL
    S. Lennart Johnsson

    Yale University, United States

    6 publications · 163 citations

    6

    h-index

  5. GE
    Guy E. Blelloch

    Carnegie Mellon University, United States

    5 publications · 645 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Journal of Parallel and Distributed Computing

    18 publications · 895 citations

    13

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 10 publications · 811 citations

    10

    h-index

  3. arXiv (Cornell University)

    Repository · 9 publications · 249 citations

    9

    h-index

  4. The Journal of Supercomputing

    7 publications · 222 citations

    7

    h-index

  5. ACM SIGPLAN Notices

    5 publications · 102 citations

    5

    h-index

1–5