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Developing a loss prediction-based asynchronous stochastic gradient descent algorithm for distributed training of deep neural networks
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Li, Junyu, He, Ligang, Ren, Shenyuan and Mao, Rui (2020) Developing a loss prediction-based asynchronous stochastic gradient descent algorithm for distributed training of deep neural networks. In: 49th International Conference on Parallel Processing (ICPP2020), Virtual conference, 17-20 Aug 2020. Published in: ICPP '20: 49th International Conference on Parallel Processing - ICPP ISBN 9781450388160. doi:10.1145/3404397.3404432
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WRAP-Developing-loss-prediction-based-asynchronous-stochastic-Li-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1257Kb) | Preview |
Official URL: https://doi.org/10.1145/3404397.3404432
Abstract
Training Deep Neural Network is a computation-intensive and time-consuming task. Asynchronous Stochastic Gradient Descent (ASGD) is an effective solution to accelerate the training process since it enables the network to be trained in a distributed fashion, but with a main issue of the delayed gradient update. A recent notable work called DC-ASGD improves the performance of ASGD by compensating the delay using a cheap approximation of the Hessian matrix. DC-ASGD works well with a short delay; however, the performance drops considerably with an increasing delay between the workers and the server. In real-life large-scale distributed training, such gradient delay experienced by the worker is usually high and volatile. In this paper, we propose a novel algorithm called LC-ASGD to compensate for the delay, basing on Loss Prediction. It effectively extends the tolerable delay duration for the compensation mechanism. Specifically, LC-ASGD utilizes additional models that reside in the parameter server and predict the loss to compensate for the delay, basing on historical losses collected from each worker. The algorithm is evaluated on the popular networks and benchmark datasets. The experimental results show that our LC-ASGD significantly improves over existing methods, especially when the networks are trained with a large number of workers.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Machine learning, Computer-assisted instruction | |||||||||
| Journal or Publication Title: | ICPP '20: 49th International Conference on Parallel Processing - ICPP | |||||||||
| Publisher: | ACM | |||||||||
| ISBN: | 9781450388160 | |||||||||
| Official Date: | 17 August 2020 | |||||||||
| Dates: |
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| Article Number: | 47 | |||||||||
| DOI: | 10.1145/3404397.3404432 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | "© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn" | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 7 August 2020 | |||||||||
| Date of first compliant Open Access: | 7 August 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | 49th International Conference on Parallel Processing (ICPP2020) | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Virtual conference | |||||||||
| Date(s) of Event: | 17-20 Aug 2020 | |||||||||
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