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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
2957-2966

Parallel deep convolution neural network optimization algorithm based on Spark and AMPSO

Liu Weiming1
Luo Quancheng1
Mao Yimin1,2
Peng Zhe3
1. College of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. College of Information Engineering, Shaoguan University, Shaoguan Guangdong 512026, China
3. Dachan Customs District, P. R. China, Shenzhen Guangdong 518000, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0083

Abstract

This paper proposed a parallel deep convolutional neural network optimization algorithm based on Spark and AMPSO(PDCNN-SAMPSO), aiming to address several issues encountered by parallel DCNN algorithms in big data environments, such as excessive redundant parameters, slow convergence speed, easy to fall into local optimal, and low parallel efficiency. Firstly, the algorithm designed a kernel pruning strategy based on importance and similarity(KP-IS) to address the problem of excessive redundant parameters by pruning the redundant convolution kernels in the model. Secondly, it proposed a model pa-rallel training strategy based on adaptive mutation particle swarm optimization algorithm(MPT-AMPSO) to solve the slow convergence speed and easy to fall into local optimal issues of parallel DCNN algorithms by initializing the model parameters using adaptive mutation particle swarm optimization algorithm(AMPSO). Finally, the algorithm proposed a dynamic load balancing strategy based on node performance(DLBNP) to balance the load of each node in the cluster and improve the parallel efficiency. Experiments show that, when using 8 computing nodes to process the CompCars dataset, the runtime of PDCNN-SAMPSO is 22%, 30%, 37% and 27% lower than that of Dis-CNN, DS-DCNN, CLR-Distributed-CNN and RS-DCNN, respectively, the speedup ratio is higher by 1.707, 1.424, 1.859, and 0.922, respectively, and the top-1 accuracy is higher by 4.01%, 4.89%, 2.42%, 5.94%, indicating that PDCNN-AMPSO has good classification performance in the big data environment and is suitable for parallel training of DCNN models in the big data environment.

Foundation Support

科技创新2030-“新一代人工智能”重大项目(2020AAA0109605)
广东省重点提升项目(2022ZDJS048)
韶关市科技计划资助项目(220607154531533)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0083
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Algorithm Research & Explore
Pages: 2957-2966
Serial Number: 1001-3695(2023)10-012-2957-10

Publish History

[2023-05-09] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

刘卫明, 罗全成, 毛伊敏, 等. 基于Spark和AMPSO的并行深度卷积神经网络优化算法 [J]. 计算机应用研究, 2023, 40 (10): 2957-2966. (Liu Weiming, Luo Quancheng, Mao Yimin, et al. Parallel deep convolution neural network optimization algorithm based on Spark and AMPSO [J]. Application Research of Computers, 2023, 40 (10): 2957-2966. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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