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A disk I/O optimized system for concurrent graph processing jobs

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Abstract

In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP jobs. In this paper, we propose GraphCP, a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. Moreover, GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future iterations. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 20.5× and 8.9× faster than two out-of-core graph processing systems GridGraph and GraphZ, and 3.5× and 1.7× faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61832020, 61821003 and U1705261), National Defense Preliminary Research Project (No. 31511010202), the Fundamental Research Funds for the Central Universities, the Open Project Program of Wuhan National Laboratory for Optoelectronics (No. 2022WNLOKF017), the Natural Science Foundation of Fujian Province (No. 2020J01493), Zhejiang provincial “Ten Thousand Talents Program” (No. 2021R52007) and Center-initiated Research Project of Zhejiang Lab (No. 2021DA0AM01).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nan**g University of Science and Technology, Nan**g, 210094, China

    **anghao Xu

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

    **anghao Xu, Fang Wang, Dan Feng & Peng Fang

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

    Hong Jiang

  4. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Yongli Cheng

  5. Zhejiang Lab, Hangzhou, 311121, China

    Yongli Cheng

Authors
  1. **anghao Xu
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  2. Fang Wang
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  3. Hong Jiang
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  4. Yongli Cheng
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  5. Dan Feng
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Corresponding author

Correspondence to Yongli Cheng.

Additional information

**anghao Xu received the PhD degree from Huazhong University of Science and Technology, China in 2021. He is currently an assistant professor in School of Computer Science and Engineering, Nan**g University of Science and Technology, China. His current research interests include graph processing, computer architecture and big data analytics. He has several publications in major international conferences and journals, including IEEE-TPDS, JPDC, ICPP, IWQoS and FCS.

Fang Wang received her 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, China. Her interests include distribute file systems, parallel I/O storage systems and graph processing systems. She has more than 80 publications in major journals and conferences, including IEEE-TC, IEEE-TPDS, IEEE-NSM, ACM TACO, SC, MSST, DATE, HiPC, ICDCS, HPDC, ICCD, ICDE, and ICPP.

Hong Jiang received the BE degree from the Huazhong University of Science and Technology, China, and the PhD degree from the Texas A&M University, USA in 1991. He is Wendell H.Nedderman Endowed Professor & Chair of Department of Computer Science and Engineering, University of Texas at Arlington, USA. His research interests include computer architecture, computer storage systems and parallel/distributed computing. He has over 200 publications in major journals and international Conferences in these areas, including IEEE-TPDS, IEEE-TC, ACMTOS, ACM TACO, JPDC, ISCA, MICRO, FAST, USENIX ATC, USENIX LISA, SIGMETRICS, MIDDLEWARE, ICDCS, IPDPS, OOPLAS, ECOOP, SC, ICS, HPDC, ICPP.

Yongli Cheng received the PhD degree from Huazhong University of Science and Technology, China in 2017. He is an associated professor of College of Mathematics and Computer Science at Fuzhou University, China currently. His current research interests include computer architecture and graph computing. He has several publications in major international conferences and journals, including HPDC, IWQoS, INFOCOM, ICPP, FGCS, ToN and FCS.

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 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 is a member of the Association for Computing Machinery and the Chair of the Information Storage Technology Committee, Chinese Computer Academy. She served on the program committees of multiple international conferences, including SC, in 2011 and 2013, and MSST, in 2012 and 2015.

Peng Fang received the BE degree in computer science and technology from Henan Polytechnic University, China in 2014. He is currently a PhD student majoring in computer science and technology in Huazhong University of Science and Technology, China. His current research interests include computer architecture and graph processing.

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Xu, X., Wang, F., Jiang, H. et al. A disk I/O optimized system for concurrent graph processing jobs. Front. Comput. Sci. 18, 183105 (2024). https://doi.org/10.1007/s11704-023-2361-0

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