When In-Network Computing Meets Distributed Machine Learning
IEEE Network, ISSN: 1558-156X, Vol: 38, Issue: 5, Page: 238-246
2024
- 4Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Emerging In-Network Computing (INC) technique provides a new opportunity to improve application's performance by using network programmability, computational capability, and storage capacity enabled by programmable switches. One typical application is Distributed Machine Learning (DML), which accelerates machine learning training by employing multiple works to train model parallelly. This paper introduces INC-based DML systems, analyzes performance improvement from using INC, and overviews current studies of INC-based DML systems. We also propose potential research directions for applying INC to DML systems.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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