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A Free Stale Synchr...
A Free Stale Synchronous Parallel Strategy for Distributed Machine Learning
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- Shi, Hang (författare)
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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- Zhao, Yue (författare)
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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- Zhang, Bofeng (författare)
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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- Yoshigoe, Kenji (författare)
- Faculty of Information Networking for Innovation and Design (INIAD), Toyo University, Tokyo, Japan
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- Vasilakos, Athanasios (författare)
- Luleå tekniska universitet,Datavetenskap
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(creator_code:org_t)
- 2019-06-11
- 2019
- Engelska.
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Ingår i: BDE 2019. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 23-29
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- With the machine learning applications processing larger and more complex data, people tend to use multiple computing nodes to execute the machine learning tasks in distributed way. However, in real world, people always encounter a problem that a few nodes in system exhibit poor performance and drag down the efficiency of the whole system. In existing parallel strategies such as bulk synchronous parallel and stale synchronous parallel, these nodes with poor performance may not be monitored and found out in time. To address this problem, we proposed a free stale synchronous parallel (FSSP) strategy to free the system from the negative impact of those nodes. Our experimental results on some classical machine leaning algorithms and datasets demonstrated that FSSP strategy outperformed other existing parallel computing strategy.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)