A Proactive Data-Parallel Framework for Machine Learning

Author:

Zhao Guoyi1,Zhou Tian2,Gao Lixin2

Affiliation:

1. University of Massachusetts Amherst, USA

2. University of Massachusetts, Amherst, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference34 articles.

1. Martín Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , 2016 . Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265–283. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265–283.

2. Revisiting Asynchronous Linear Solvers

3. Lukas Balles Javier Romero and Philipp Hennig. 2016. Coupling adaptive batch sizes with learning rates. arXiv preprint arXiv:1612.05086(2016). Lukas Balles Javier Romero and Philipp Hennig. 2016. Coupling adaptive batch sizes with learning rates. arXiv preprint arXiv:1612.05086(2016).

4. Optimization Methods for Large-Scale Machine Learning

5. Thorsten Brants , Ashok  C Popat , Peng Xu , Franz  J Och , and Jeffrey Dean . 2007 . Large language models in machine translation . In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). 858–867 . Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007. Large language models in machine translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). 858–867.

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1. FSP: Towards Flexible Synchronous Parallel Frameworks for Distributed Machine Learning;IEEE Transactions on Parallel and Distributed Systems;2023-02-01

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