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

Author:

Zhang Jilin1234,Xiao Junfeng12,Wan Jian1245ORCID,Yang Jianhua6,Ren Yongjian12,Si Huayou12,Zhou Li12,Tu Hangdi12

Affiliation:

1. School of Computer and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

2. Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, China

3. College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China

4. School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China

5. Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis, Hangzhou, Zhejiang, China

6. College of Computer Science and Technology, Zhejiang University, Hangzhou 310018, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

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