Parallel Performance Optimization

Parallel Performance Optimization is a methodological framework within parallel computing that systematically enhances the efficiency and scalability of parallel algorithms by minimizing execution time, resource contention, and communication overhead through targeted modifications to task decomposition, load balancing, and data distribution strategies.

883

Publications

36.2K

Citations

2.6K

Authors

689

Institutions

Publications per year

2017–2026

122

Authors

2.6K

  1. AC
    Alok Choudhary

    Northwestern University, United States

    12 publications · 334 citations

    12

    h-index

  2. PS
    P. Sadayappan

    The Ohio State University, United States

    11 publications · 955 citations

    11

    h-index

  3. AL
    Alexey Lastovetsky

    University College Dublin, Ireland

    9 publications · 305 citations

    9

    h-index

  4. LV
    Laxmikant V. Kalé

    University of Illinois Urbana-Champaign, United States

    8 publications · 422 citations

    8

    h-index

  5. IF
    Ian Foster

    Argonne National Laboratory, United States

    7 publications · 186 citations

    7

    h-index

1–5 of 2.6K

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 43 publications · 1.6K citations

    25

    h-index

  2. ACM SIGPLAN Notices

    16 publications · 714 citations

    14

    h-index

  3. 13

    h-index

  4. Journal of Parallel and Distributed Computing

    13 publications · 1.8K citations

    12

    h-index

  5. Parallel Computing

    11 publications · 488 citations

    11

    h-index

1–5