Data Parallel Execution
Data Parallel Execution is a computational paradigm in GPU architecture where a single instruction is simultaneously applied to multiple data elements across parallel processing units, enabling high-throughput execution by exploiting fine-grained data-level concurrency.
619
Publications
31.2K
Citations
2.2K
Authors
638
Institutions
Publications per year
2017–2026
137
619
Michael Isard, Mihai Budiu, Yuan Yu et al. · 2007 · 2.5K citations
Parallel Data, Tools and Interfaces in OPUS
Jörg Tiedemann · Language Resources and Evaluation · 2012 · 1.3K citations
Reynold Xin, Joseph E. Gonzalez, Michael J. Franklin et al. · 2013 · 583 citations
1–5 of 619
2.2K
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University of North Carolina at Chapel Hill, United States
5 publications · 507 citations
5
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5
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1–5 of 2.2K
638
University of California San Diego
San Diego, United States
14 h-index
42 publications
2.3K citations
The Ohio State University
Columbus, United States
12 h-index
47 publications
2.1K citations
Nvidia (United States)
Santa Clara, United States
12 h-index
27 publications
2.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
32 publications
1.6K citations
The University of Texas at Austin
Austin, United States
11 h-index
16 publications
787 citations

Lawrence Berkeley National Laboratory
Berkeley, United States
11 h-index
36 publications
1.4K citations
Argonne National Laboratory
Lemont, United States
11 h-index
33 publications
619 citations
Microsoft (United States)
Redmond, United States
10 h-index
23 publications
2.4K citations
1–8 of 638
16 publications · 514 citations
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TPDS · 9 publications · 427 citations
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9 publications · 193 citations
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8 publications · 177 citations
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PVLDB · 7 publications · 231 citations
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