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Mobile Information Systems
Volume 2017, Article ID 3824765, 12 pages
Research Article

A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

1School of Computer and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, China
3College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China
4School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China
5Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis, Hangzhou, Zhejiang, China
6College of Computer Science and Technology, Zhejiang University, Hangzhou 310018, China

Correspondence should be addressed to Jian Wan; nc.ude.udh@naijnaw

Received 25 January 2017; Accepted 23 February 2017; Published 21 March 2017

Academic Editor: Jaegeol Yim

Copyright © 2017 Jilin Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.