Computational Granularity

Computational Granularity is the degree of detail or size of individual computational tasks in a parallel or distributed algorithm, distinguishing it by characterizing the balance between computation and communication overhead in scalable algorithm design.

252

Publications

13.6K

Citations

453

Authors

237

Institutions

Publications per year

2017–2026

77

Authors

453

  1. WP
    Witold Pedrycz

    University of Alberta, Canada

    39 publications · 2.6K citations

    24

    h-index

  2. YY
    Yiyu Yao

    University of Regina, Canada

    20 publications · 2.1K citations

    17

    h-index

  3. GW
    Guoyin Wang

    Chongqing University of Posts and Telecommunications, China

    9 publications · 650 citations

    9

    h-index

  4. AS
    Andrzej Skowron

    Institute of Mathematics, Poland

    9 publications · 615 citations

    9

    h-index

  5. TY
    Tsau Young Lin

    San Jose State University, United States

    6 publications · 217 citations

    6

    h-index

1–5 of 453

Venues

  1. Information Sciences

    14 publications · 1.1K citations

    13

    h-index

  2. Applied Soft Computing

    10 publications · 624 citations

    10

    h-index

  3. Granular Computing

    9 publications · 716 citations

    9

    h-index

  4. Fundamenta Informaticae

    8 publications · 196 citations

    8

    h-index

  5. Knowledge-Based Systems

    7 publications · 270 citations

    7

    h-index

1–5