Pipeline Throughput Optimization
Pipeline Throughput Optimization is a computational framework within distributed deep learning that specifically enhances the rate of model training iterations by minimizing inter-stage idle times and balancing computational load across pipeline stages in partitioned neural network architectures.
31
Publications
1K
Citations
135
Authors
59
Institutions
Publications per year
2017–2026
27
Parents
31
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics
Evan Sparks, Shivaram Venkataraman, Tomer Kaftan et al. · 2017 · 129 citations
Image Classification at Supercomputer Scale
Chris Ying, Sameer Kumar, Dehao Chen et al. · arXiv (Cornell University) · 2018 · 95 citations · Full text
Juheon Yi, Sunghyun Choi, Youngki Lee · 2020 · 66 citations
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Cornell University
Ithaca, United States
3 h-index
10 publications
183 citations
Meta (United States)
Menlo Park, United States
2 h-index
2 publications
37 citations

University of Toronto
Toronto, Canada
2 h-index
5 publications
90 citations

DEVCOM Army Research Laboratory
Adelphi, United States
2 h-index
2 publications
44 citations

Peking University
Beijing, China
2 h-index
2 publications
44 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
2 h-index
7 publications
149 citations

Pennsylvania State University
State College, United States
2 h-index
4 publications
74 citations
Menlo School
Atherton, United States
2 h-index
2 publications
37 citations
1–8 of 59
Repository · 2 publications · 110 citations
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TMCCI · 2 publications · 44 citations
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TPDS · 1 publications · 22 citations
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1 publications · 15 citations
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Book series · 1 publications · 11 citations
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