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
Parents
252
ZeRO: Memory optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase et al. · 2020 · 741 citations
Three-way decision and granular computing
Yiyu Yao · International Journal of Approximate Reasoning · 2018 · 593 citations
Granular computing: an introduction
Witold Pedrycz · 2002 · 382 citations
1–5 of 252
453
24
h-index
17
h-index
Chongqing University of Posts and Telecommunications, China
9 publications · 650 citations
9
h-index
9
h-index
6
h-index
1–5 of 453
237

University of Alberta
Edmonton, Canada
24 h-index
49 publications
3K citations

Polish Academy of Sciences
Warsaw, Poland
22 h-index
32 publications
2.4K citations
Systems Research Institute
Warsaw, Poland
21 h-index
28 publications
2.2K citations

University of Regina
Regina, Canada
19 h-index
24 publications
2.2K citations

King Abdulaziz University
Jeddah, Saudi Arabia
16 h-index
28 publications
1.4K citations
Institute of Mathematics
Warsaw, Poland
9 h-index
12 publications
846 citations
Chongqing University of Posts and Telecommunications
Chongqing, China
8 h-index
34 publications
2.9K citations
San Jose State University
San Jose, United States
7 h-index
7 publications
234 citations
1–8 of 237
14 publications · 1.1K citations
13
h-index
10 publications · 624 citations
10
h-index
9 publications · 716 citations
9
h-index
8 publications · 196 citations
8
h-index
7 publications · 270 citations
7
h-index
1–5