Scalable Parallel Algorithms
Scalable Parallel Algorithms is a computational method designed to solve problems efficiently on increasing numbers of processing elements with proportional growth in problem size, distinguished by maintaining performance efficiency and resource utilization across varying scales of parallelism.
775
Publications
40.8K
Citations
2.2K
Authors
659
Institutions
Publications per year
2017–2026
116
Parents
775
Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters
K. J. Bowers, David E. Chow, Huafeng Xu et al. · 2006 · 1.7K citations
SPRINT: A Scalable Parallel Classifier for Data Mining
John Shafer, Rakesh Agrawal, Manish Mehta · 1996 · 782 citations
Hung‐Chih Yang, Ali Dasdan, Ruey-Lung Hsiao et al. · 2007 · 717 citations
1–5 of 775
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659

University of Minnesota
Minneapolis, United States
23 h-index
73 publications
5.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
20 h-index
58 publications
3.7K citations
The University of Texas at Austin
Austin, United States
15 h-index
37 publications
1.7K citations
Argonne National Laboratory
Lemont, United States
14 h-index
40 publications
1.8K citations

Centre National de la Recherche Scientifique
Paris, France
14 h-index
36 publications
1.2K citations

Lawrence Berkeley National Laboratory
Berkeley, United States
13 h-index
47 publications
1.6K citations
University of Southern California
Los Angeles, United States
12 h-index
38 publications
1.1K citations
IBM (United States)
Armonk, United States
11 h-index
19 publications
846 citations
1–8 of 659
25 publications · 2.8K citations
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TPDS · 20 publications · 1.2K citations
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20 publications · 683 citations
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20 publications · 748 citations
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Repository · 12 publications · 328 citations
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