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Efficient Graph Mining on Heterogeneous Platforms in the Cloud

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Cloud Computing, Security, Privacy in New Computing Environments (CloudComp 2016, SPNCE 2016)

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Abstract

In this Big Data era, many large-scale and complex graphs have been produced with the rapid growth of novel Internet applications and the new experiment data collecting methods in biological and chemistry areas. As the scale and complexity of the graph data increase explosively, it becomes urgent and challenging to develop more efficient graph processing frameworks which are capable of executing general graph algorithms efficiently. In this paper, we propose to leverage GPUs to accelerate large-scale graph mining in the cloud. To achieve good performance and scalability, we propose the graph summary method and runtime system optimization techniques for load balancing and message handling. Experiment results manifest that the prototype framework outperforms two state-of-the-art distributed frameworks GPS and GraphLab in terms of performance and scalability.

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Acknowledgment

This research is supported by Young Teachers Program of Shanghai Colleges and Universities under grant No. ZZSD15072, Natural Science Foundation of Shanghai under grant No. 16ZR1411200, and Shanghai Innovation Action Plan Project under grant No. 16511101200.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China

    Tao Zhang, Weiqin Tong, Wenfeng Shen, Junjie Peng & Zhihua Niu

Authors
  1. Tao Zhang
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  2. Weiqin Tong
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  3. Wenfeng Shen
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  4. Junjie Peng
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  5. Zhihua Niu
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Corresponding author

Correspondence to Tao Zhang .

Editor information

Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Jiafu Wan

  2. Dalian University of Technology, Dalian, China

    Kai Lin

  3. South China University of Technology, Guangzhou, China

    Delu Zeng

  4. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  5. Deakin University, Burwood, Australia

    Yang Xiang

  6. Southwest University, Chongqing, China

    Xiaofeng Liao

  7. Shenzhen University, Nankai, China

    Jiwu Huang

  8. Nankai University, Nankai, China

    Zheli Liu

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, T., Tong, W., Shen, W., Peng, J., Niu, Z. (2018). Efficient Graph Mining on Heterogeneous Platforms in the Cloud. In: Wan, J., et al. Cloud Computing, Security, Privacy in New Computing Environments. CloudComp SPNCE 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-69605-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-69605-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69604-1

  • Online ISBN: 978-3-319-69605-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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