Fast Distributed Deep Learning via Worker-adaptive Batch Sizing-Reference-Cited by-同舟云学术

Fast Distributed Deep Learning via Worker-adaptive Batch Sizing

Published:2018-10-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Symposium on Cloud Computing
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Author:

Chen Chen¹,Weng Qizhen¹,Wang Wei¹,Li Baochun²,Li Bo¹

Affiliation:

1. Hong Kong University of Science and Technology

2. University of Toronto

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3267809.3275463

Reference4 articles.

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2. Aaron Harlap Henggang Cui Wei Dai Jinliang Wei Gregory R Ganger Phillip B Gibbons Garth A Gibson and Eric P Xing. 2016. Addressing the straggler problem for iterative convergent parallel ML. In ACM SoCC. 10.1145/2987550.2987554 Aaron Harlap Henggang Cui Wei Dai Jinliang Wei Gregory R Ganger Phillip B Gibbons Garth A Gibson and Eric P Xing. 2016. Addressing the straggler problem for iterative convergent parallel ML. In ACM SoCC. 10.1145/2987550.2987554

3. Jiawei Jiang Bin Cui Ce Zhang and Lele Yu. 2017. Heterogeneity-aware distributed parameter servers. In ACM SIGMOD. 10.1145/3035918.3035933 Jiawei Jiang Bin Cui Ce Zhang and Lele Yu. 2017. Heterogeneity-aware distributed parameter servers. In ACM SIGMOD. 10.1145/3035918.3035933

4. Mu Li David G Andersen Jun Woo Park Alexander J Smola Amr Ahmed Vanja Josifovski James Long Eugene J Shekita and Bor-Yiing Su. 2014. Scaling Distributed Machine Learning with the Parameter Server.. In USENIX OSDI. Mu Li David G Andersen Jun Woo Park Alexander J Smola Amr Ahmed Vanja Josifovski James Long Eugene J Shekita and Bor-Yiing Su. 2014. Scaling Distributed Machine Learning with the Parameter Server.. In USENIX OSDI.

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