Model Parallelism
Model Parallelism is a distributed computing strategy in scalable machine learning wherein the parameters and computations of a single machine learning model are partitioned across multiple devices or nodes, enabling concurrent execution of different model segments during training or inference.
231
Publications
17K
Citations
1K
Authors
216
Institutions
Publications per year
2017–2026
159
231
Building high-level features using large scale unsupervised learning
Quoc V. Le · 2013 · 1.7K citations
ZeRO: Memory optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase et al. · 2020 · 741 citations
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216
Google (United States)
Mountain View, United States
9 h-index
48 publications
10.2K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
36 publications
5.7K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
8 h-index
53 publications
14.3K citations

National University of Defense Technology
Changsha, China
6 h-index
35 publications
1.1K citations
Microsoft (United States)
Redmond, United States
4 h-index
7 publications
592 citations
Tsinghua University
Beijing, China
4 h-index
17 publications
350 citations
Tencent (China)
Shenzhen, China
4 h-index
15 publications
761 citations

Rice University
Houston, United States
4 h-index
16 publications
362 citations
1–8 of 216
Repository · 62 publications · 4.5K citations
31
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TPDS · 6 publications · 245 citations
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PVLDB · 6 publications · 360 citations
6
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TPAMI · 3 publications · 480 citations
3
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Conference · 2 publications · 136 citations
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