Falcon: Addressing Stragglers in Heterogeneous Parameter Server Via Multiple Parallelism

The parameter server architecture has shown promising performance advantages when handling deep learning (DL) applications. One crucial issue in this regard is the presence of stragglers, which significantly retards DL training progress. Previous solutions for solving stragglers may not fully exploi...

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Vydané v:	IEEE transactions on computers Ročník 70; číslo 1; s. 139 - 155
Hlavní autori:	Zhou, Qihua, Guo, Song, Lu, Haodong, Li, Li, Guo, Minyi, Sun, Yanfei, Wang, Kun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Computation Computational modeling Computer architecture Convergence Design parameters Distributed deep learning Guidelines Heterogeneity heterogeneous environment Inspection Machine learning Parallel processing parameter server Servers straggler Synchronism Task analysis Training
ISSN:	0018-9340, 1557-9956
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