Data Parallelism is a form of parallel computing paradigm in which the same operation is simultaneously applied to multiple data elements across different processing units, distinguished by the uniformity of operations over distributed or partitioned data sets.
19.1K
Publications
1.2M
Citations
39.1K
Authors
4.6K
Institutions
Publications per year
2017–2026
3.8K
Publications
19.1K
1–5 of 19.1K
Authors
39.1K
- PS
41
h-index
- JD
34
h-index
- GE
32
h-index
- KP
31
h-index
- UV
30
h-index
1–5 of 39.1K
Institutions
4.6K
Stanford University
Stanford, United States
84 h-index
720 publications
70.8K citations
Carnegie Mellon University
Pittsburgh, United States
84 h-index
746 publications
88.1K citations
University of California, Berkeley
Berkeley, United States
82 h-index
731 publications
192.3K citations
University of Illinois Urbana-Champaign
Urbana, United States
75 h-index
971 publications
93.9K citations
IBM (United States)
Armonk, United States
69 h-index
673 publications
50.4K citations
Massachusetts Institute of Technology
Cambridge, United States
67 h-index
482 publications
51K citations
The Ohio State University
Columbus, United States
66 h-index
820 publications
43.2K citations
The University of Texas at Austin
Austin, United States
63 h-index
525 publications
34.6K citations
1–8 of 4.6K
Venues
- arXiv (Cornell University)
Repository · 460 publications · 37.6K citations
76
h-index
- IEEE Transactions on Computers
224 publications · 19.3K citations
67
h-index
- Parallel Computing
473 publications · 21.1K citations
66
h-index
- Proceedings of the VLDB Endowment
PVLDB · 176 publications · 15.2K citations
64
h-index
- Journal of Parallel and Distributed Computing
350 publications · 17K citations
64
h-index
1–5