Data Distribution Strategies

Data Distribution Strategies is a methodological framework in distributed memory programming that systematically determines how data is partitioned, mapped, and allocated across processing nodes to optimize data locality and minimize communication overhead.

430

Publications

21.2K

Citations

1.4K

Authors

519

Institutions

Publications per year

2017–2026

142

Authors

1.4K

  1. RA
    Rassul Ayani

    KTH Royal Institute of Technology, Sweden

    5 publications · 212 citations

    5

    h-index

  2. AB
    Azzedine Boukerche

    University of Ottawa, Canada

    5 publications · 127 citations

    5

    h-index

  3. AC
    Alok Choudhary

    Northwestern University, United States

    4 publications · 163 citations

    4

    h-index

  4. KK
    Ken Kennedy

    Rice University, United States

    4 publications · 170 citations

    4

    h-index

  5. GT
    Gary Tan

    National University of Singapore, Singapore

    4 publications · 185 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 15 publications · 872 citations

    15

    h-index

  2. arXiv (Cornell University)

    Repository · 17 publications · 525 citations

    12

    h-index

  3. IEEE Access

    9 publications · 258 citations

    9

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 6 publications · 234 citations

    6

    h-index

  5. Scientific Programming

    5 publications · 328 citations

    5

    h-index

1–5