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Volume 36 Issue 4
July  2021
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Songjie Niu, Shimin Chen. TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance[J]. Journal of Computer Science and Technology, 2021, 36(4): 778-791. DOI: 10.1007/s11390-021-1356-2
Citation: Songjie Niu, Shimin Chen. TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance[J]. Journal of Computer Science and Technology, 2021, 36(4): 778-791. DOI: 10.1007/s11390-021-1356-2
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TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

  • State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China

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  • Author Bio:

    Songjie Niu received her B.E. degree from Beijing Institute of Technology, Beijing, in 2014. She is a Ph.D. candidate at Institute of Computing Technology, Chinese Academy of Sciences, Beijing. Her research interests include graph computation, database systems, and big data processing. She is a student member of CCF.

  • Corresponding author:

    Shimin Chen E-mail: chensm@ict.ac.cn

  • Received Date: February 01, 2021
  • Revised Date: July 09, 2021
  • Published Date: July 04, 2021
    Abstract
  • It is challenging to model the performance of distributed graph computation. Explicit formulation cannot easily capture the diversified factors and complex interactions in the system. Statistical learning methods require a large number of training samples to generate an accurate prediction model. However, it is time-consuming to run the required graph computation tests to obtain the training samples. In this paper, we propose TransGPerf, a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation scenario. Experimental results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and GraphX. It outperforms transfer learning methods proposed for other applications in the literature.
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    • References (33)
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