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A communication-reduced and computation-balanced framework for fast graph computation

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Abstract

The bulk synchronous parallel (BSP) model is very user friendly for coding and debugging parallel graph algorithms. However, existing BSP-based distributed graph-processing frameworks, such as Pregel, GPS and Giraph, routinely suffer from high communication costs. These high communication costs mainly stem from the fine-grained message-passing communication model. In order to address this problem, we propose a new computation model with low communication costs, called LCC-BSP. We use this model to design and implement a high-performance distributed graph-processing framework called LCC-Graph. This framework eliminates high communication costs in existing distributed graph-processing frameworks. Moreover, LCC-Graph also balances the computation workloads among all compute nodes by optimizing graph partitioning, significantly reducing the computation time for each superstep. Evaluation of LCC-Graph on a 32-node cluster, driven by real-world graph datasets, shows that it significantly outperforms existing distributed graph-processing frameworks in terms of runtime, particularly when the system is supported by a high-bandwidth network. For example, LCC-Graph achieves an order of magnitude performance improvement over GPS and GraphLab.

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Acknowledgements

This work is supported by NSFC 61772216, Shenzhen science and technology plan project (JCYJ 20170307172248636), Wuhan application basic research project (2017010201010103). This work is also supported by Key Laboratory of Information Storage System, Ministry of Education and State Key Laboratory of Computer Architecture (CARCH201505).

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Authors and Affiliations

  1. College of Mathematics and Computer Science, FuZhou University, Fuzhou, 350116, China

    Yongli Cheng

  2. Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Fang Wang, Yu Hua, Dan Feng, Lingling Zhang & Jun Zhou

  3. Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518300, China

    Fang Wang, Yu Hua, Dan Feng, Lingling Zhang & Jun Zhou

  4. Department of Computer Science & Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Hong Jiang

Authors
  1. Yongli Cheng
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  2. Fang Wang
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  3. Hong Jiang
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  4. Yu Hua
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  5. Dan Feng
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  6. Lingling Zhang
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  7. Jun Zhou
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Corresponding author

Correspondence to Fang Wang.

Additional information

Yongli Cheng received the BE degree from the Chang’an University, China in 1998, and the MS degree from the FuZhou University, China in 2010. He is currently a PhD student majoring in computer architecture in Huazhong University of Science and Technology, China. His current research interests include computer architecture and graph computing. He has several publications in international conferences, including HPDC and IWQoS.

Fang Wang received her BE degree and Master degree in computer science in 1994, 1997, and PhD degree in computer architecture in 2001 from Huazhong University of Science and Technology (HUST), China. She is a professor of computer science and engineering at HUST. Her interests include distribute file systems, parallel I/O storage systems and graph processing systems. She has more than 50 publications in major journals and international conferences, including FGCS, ACM TACO, SCIENCE CHINA Information Sciences, Chinese Journal of Computers HiPC, ICDCS, HPDC and ICPP.

Hong Jiang received the BE degree from the Huazhong University of Science and Technology, China in 1982, the MASc degree from the University of Toronto, Canada in 1987, and the PhD degree from the Texas A&M University, College Station, in 1991. He is Wendell H. Nedderman Endowed Professor & Chair of Department of Computer Science and Engineering, University of Texas at Arlington. His research interests include computer architecture, computer storage systems and parallel/distributed computing. He serves as an Associate Editor of the IEEE Transactions on Parallel and Distributed Systems. He has over 200 publications in major journals and international Conferences in these areas, including IEEE-TPDS, IEEE-TC, ACM-TOS, ACM TACO, JPDC, ISCA, MICRO, FAST, USENIX ATC, USENIX LISA, SIGMETRICS, MIDDLEWARE, ICDCS, IPDPS, OOPLAS, ECOOP, SC, ICS, HPDC, ICPP, etc., and his research has been supported by NSF, DOD and the State of Nebraska. Dr. Jiang is a Fellow of IEEE.

Yu Hua received the BE and PhD degrees in computer science from the Wuhan University, China in 2001 and 2005, respectively. He is currently a professor at the Huazhong University of Science and Technology, China. His research interests include computer architecture, cloud computing and network storage. He has more than 80 papers to his credit in major journals and international conferences including IEEE Transactions on Computers (TC), IEEE Transactions on Parallel and Distributed Systems (TPDS), USENIX ATC, USENIX FAST, INFOCOM, SC, ICDCS, ICPP and MASCOTS. He has been on the organizing and program committees of multiple international conferences, including INFOCOM, ICDCS, ICPP, RTSS and IWQoS. He is a senior member of the IEEE and CCF, and a member of ACM and USENIX.

Dan Feng received the BE, ME and PhD degrees in Computer Science and Technology in 1991, 1994 and 1997, respectively, from Huazhong University of Science and Technology (HUST), China. She is a professor and vice dean of the School of Computer Science and Technology, HUST. Her research interests include computer architecture, massive storage systems and parallel file systems. She has more than 100 publications in major journals and international conferences, including IEEE-TC, IEEE-TPDS, ACM-TOS, JCST, FAST, USENIX ATC, ICDCS, HPDC, SC, ICS, IPDPS and ICPP. She serves on the program committees of multiple international conferences, including SC 2011, 2013 and MSST 2012. She is a member of IEEE and amember of ACM.

Lingling Zhang is currently a PhD student majoring in computer architecture in Huazhong University of Science and Technology, China. Her current research interests include computer architecture, big data and distributed storage systems.

Jun Zhou received the BE degree in computer science and technology from the Huazhong University of Science and Technology (HUST), China in 2011. He is currently working toward the PhD degree in school of computer science and technology in HUST. His interests include big data and distributed storage systems.

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Cheng, Y., Wang, F., Jiang, H. et al. A communication-reduced and computation-balanced framework for fast graph computation. Front. Comput. Sci. 12, 887–907 (2018). https://doi.org/10.1007/s11704-018-6400-1

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