Parallel Algorithm Scalability
Parallel Algorithm Scalability is a theoretical construct within computational complexity analysis that characterizes the efficiency with which a parallel algorithm maintains or improves performance gains relative to increasing numbers of processors and problem sizes, under fixed or scalable resource constraints.
640
Publications
34.7K
Citations
1.9K
Authors
668
Institutions
Publications per year
2017–2026
126
Parents
640
Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters
K. J. Bowers, David E. Chow, Huafeng Xu et al. · 2006 · 1.7K citations
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668
Georgia Institute of Technology
Atlanta, United States
15 h-index
41 publications
1.3K citations
University of California, Berkeley
Berkeley, United States
11 h-index
25 publications
1.5K citations

University of Minnesota
Minneapolis, United States
11 h-index
26 publications
2.5K citations
Virginia Tech
Blacksburg, United States
11 h-index
25 publications
1.8K citations
Stanford University
Stanford, United States
11 h-index
20 publications
1.6K citations

Chinese Academy of Sciences
Beijing, China
11 h-index
27 publications
1K citations
Tsinghua University
Beijing, China
10 h-index
27 publications
585 citations
Iowa State University
Ames, United States
10 h-index
17 publications
385 citations
1–8 of 668
22 publications · 1.8K citations
20
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TPDS · 18 publications · 893 citations
17
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Repository · 12 publications · 805 citations
11
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10 publications · 756 citations
10
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10 publications · 581 citations
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