Data-parallel Algorithms
Data-Parallel Algorithms is a class of parallel algorithms in which the same operation is applied simultaneously to elements of a data set, distinguished by their reliance on data decomposition and synchronized execution across multiple processing units to achieve scalability in algorithm design.
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2017–2026
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Parallel Data, Tools and Interfaces in OPUS
Jörg Tiedemann · Language Resources and Evaluation · 2012 · 1.3K citations
W. Daniel Hillis, Guy L. Steele · Communications of the ACM · 1986 · 891 citations · Full text
Hung‐Chih Yang, Ali Dasdan, Ruey-Lung Hsiao et al. · 2007 · 717 citations
Reynold Xin, Joseph E. Gonzalez, Michael J. Franklin et al. · 2013 · 583 citations
Parallel data processing with MapReduce
Kyong-Ha Lee, Yoon-Joon Lee, Hyun‐Sik Choi et al. · ACM SIGMOD Record · 2012 · 555 citations
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Syracuse University
Syracuse, United States
8 h-index
20 publications
626 citations

Ames Research Center
Mountain View, United States
8 h-index
26 publications
2.4K citations
Microsoft (United States)
Redmond, United States
8 h-index
13 publications
729 citations
University of California, Berkeley
Berkeley, United States
7 h-index
17 publications
3.7K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
29 publications
2.7K citations

Rice University
Houston, United States
7 h-index
28 publications
1.2K citations

Chinese Academy of Sciences
Beijing, China
7 h-index
20 publications
724 citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
9 publications
231 citations
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18 publications · 895 citations
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TPDS · 10 publications · 811 citations
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Repository · 9 publications · 249 citations
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7 publications · 222 citations
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5 publications · 102 citations
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