The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Scalable distributed DNN training using commodity GPU cloud computing
Abstract
We introduce a new method for scaling up distributed Stochastic Gradient Descent (SGD) training of Deep Neural Networks (DNN). The method solves the well-known communication bottleneck problem that arises for data-parallel SGD because compute nodes frequently need to synchronize a replica of the model. We solve it by purposefully controlling the rate of weight-update per individual weight, which is in contrast to the uniform update-rate customarily imposed by the size of a mini-batch. It is shown empirically that the method can reduce the amount of communication by three orders of magnitude while training a typical DNN for acoustic modelling. This reduction in communication bandwidth enables efficient scaling to more parallel GPU nodes than any other method that we are aware of, and it can be achieved with neither loss in convergence rate nor accuracy in the resulting DNN. Furthermore, the training can be performed on commodity cloud infrastructure and networking.
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Cites
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Deep Neural Networks for Acoustic Modeling in Speech Recognition
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Sequence-discriminative training of deep neural networks
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Cited by
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
“first is transmitting only important gradients by taking advantage of sparse training gradients [193].”
BackgroundDeep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2017) · arXiv · doi:10.48550/arxiv.1712.01887 · Cited by 1,134
Federated Learning Over Wireless Fading Channels
Amiri, M. M., & Gunduz, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2974748 · Cited by 625
Metadata
- Authors
- Strom, N.
- Venue
- Interspeech 2015
- Publisher
- ISCA
- Published
- 2015-09-06
- Pages
- 1488-1492
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2405578611
- Cited by
- 521
- References
- 19
- FWCI
- 11.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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