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LMCC: Lazy Message and Centralized Cache for Asynchronous Graph Computing

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11335))

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Abstract

Graph has been widely used in complex network applications modeling, and the asynchronous graph processing model is superceding the BSP model because of its better convergence speed. However, the asynchronous GAS model proposed by PowerGraph usually results in irregular and unpredictable communication patterns as well as vertex-scale barriers, so it is difficult for programmers to optimize codes. To address these challenges, we propose LMCC, an improved message management approach including lazy pull-message model and vertex-oriented centralized cache, which can reduce communication cost in terms of message quantity, and reduce the number of computation iterations in turn, without compromising the accuracy of application results. Based on the deep investigation of the GAS phases, LMCC is designed to be totally transparent to user applications. Experimental results show that LMCC can deliver speedup for various types of graph computing benchmarks ranging from 129% to 271%.

This work is supported by the National Natural Science Foundation of China (No. 61272528) and the Fundamental Research Funds for the Central Universities (No. ZYGX2016J088).

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References

  1. Abou-Rjeili, A., Karypis, G.: Multilevel algorithms for partitioning power-law graphs. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006, pp. 10-pp. IEEE (2006)

    Google Scholar 

  2. Ahmed, A., Aly, M., Gonzalez, J., Narayanamurthy, S., Smola, A.J.: Scalable inference in latent variable models. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 123–132. ACM (2012)

    Google Scholar 

  3. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM (2006)

    Google Scholar 

  4. Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 73–80. Association for Computational Linguistics (2006)

    Google Scholar 

  5. Chen, H., Li, X., Huang, Z.: Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2005, pp. 141–142. IEEE (2005)

    Google Scholar 

  6. Chen, Q., Bai, S., Li, Z., Gou, Z., Suo, B., Pan, W.: GraphHP: a hybrid platform for iterative graph processing. arXiv preprint arXiv:1706.07221 (2017)

  7. Chen, R., Shi, J., Chen, Y., Chen, H.: PowerLyra: differentiated graph computation and partitioning on skewed graphs. In: Réveillère, L., 0001, T.H., Herlihy, M. (eds.) Proceedings of the Tenth European Conference on Computer Systems, EuroSys 2015, Bordeaux, France, 21–24 April 2015, pp. 1:1–1:15. ACM (2015)

    Google Scholar 

  8. Cisco, Visual Networking Index: The zettabyte era: Trends and analysis (2017). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html. Accessed 07 June 2017

  9. Coffman, T., Greenblatt, S., Marcus, S.: Graph-based technologies for intelligence analysis. Commun. ACM 47(3), 45–47 (2004)

    Article  Google Scholar 

  10. Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Graphlab powergraph v2.2. https://github.com/jegonzal/PowerGraph

  11. Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: distributed graph-parallel computation on natural graphs. In: OSDI, vol. 12, no. 2 (2012)

    Google Scholar 

  12. Gonzalez, J.E., Xin, R.S., Dave, A., Crankshaw, D., Franklin, M.J., Stoica, I.: Graphx: graph processing in a distributed dataflow framework. In: OSDI, vol. 14, pp. 599–613 (2014)

    Google Scholar 

  13. Han, M., Daudjee, K.: Giraph unchained: barrierless asynchronous parallel execution in pregel-like graph processing systems. Proc. VLDB Endow. 8(9), 950–961 (2015)

    Article  Google Scholar 

  14. Han, W.S., et al.: TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 77–85. ACM (2013)

    Google Scholar 

  15. Hoque, I., Gupta, I.: LFGraph: simple and fast distributed graph analytics. In: Proceedings of the First ACM SIGOPS Conference on Timely Results in Operating Systems, p. 9. ACM (2013)

    Google Scholar 

  16. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)

    Google Scholar 

  17. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 116–142 (2004)

    Article  Google Scholar 

  18. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)

    Google Scholar 

  19. Leskovec, J., Krevl, A.: SNAP Datasets: stanford large network dataset collection, June 2014. http://snap.stanford.edu/data

  20. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)

    Article  MathSciNet  Google Scholar 

  21. Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)

    Google Scholar 

  22. Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716–727 (2012)

    Article  Google Scholar 

  23. Malewicz, G., et al.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 135–146. ACM (2010)

    Google Scholar 

  24. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  25. Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39718-2_23

    Chapter  Google Scholar 

  26. Roy, A., Mihailovic, I., Zwaenepoel, W.: X-stream: edge-centric graph processing using streaming partitions. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, pp. 472–488. ACM (2013)

    Google Scholar 

  27. Takac, L., Zabovsky, M.: Data analysis in public social networks. In: International Scientific Conference and International Workshop Present Day Trends of Innovations, vol. 1 (2012)

    Google Scholar 

  28. Tian, Y., Balmin, A., Corsten, S.A., Tatikonda, S., McPherson, J.: From think like a vertex to think like a graph. Proc. VLDB Endow. 7(3), 193–204 (2013)

    Article  Google Scholar 

  29. Vora, K., Koduru, S.C., Gupta, R.: Aspire: exploiting asynchronous parallelism in iterative algorithms using a relaxed consistency based DSM. In: ACM SIGPLAN Notices, vol. 49, pp. 861–878 (2014)

    Article  Google Scholar 

  30. Xie, C., Chen, R., Guan, H., Zang, B., Chen, H.: SYNC or ASYNC: time to fuse for distributed graph-parallel computation. ACM SIGPLAN Not. 50(8), 194–204 (2015)

    Article  Google Scholar 

  31. Yan, D., Cheng, J., Lu, Y., Ng, W.: Blogel: a block-centric framework for distributed computation on real-world graphs. Proc. VLDB Endow. 7(14), 1981–1992 (2014)

    Article  Google Scholar 

  32. Yuan, P., Zhang, W., Xie, C., Jin, H., Liu, L., Lee, K.: Fast iterative graph computation: a path centric approach. In: SC14 International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 401–412. IEEE (2014)

    Google Scholar 

  33. Zhang, M., Wu, Y., Chen, K., Qian, X., Li, X., Zheng, W.: Exploring the hidden dimension in graph processing. In: OSDI, vol. 16, pp. 285–300 (2016)

    Google Scholar 

  34. Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68880-8_32

    Chapter  Google Scholar 

  35. Zhu, X., Chen, W., Zheng, W., Ma, X.: Gemini: a computation-centric distributed graph processing system. In: OSDI, pp. 301–316 (2016)

    Google Scholar 

  36. Zhu, X., Han, W., Chen, W.: GridGraph: large-scale graph processing on a single machine using 2-level hierarchical partitioning. In: USENIX Annual Technical Conference, pp. 375–386 (2015)

    Google Scholar 

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Ruini Xue, Zhibin Dong, Wei Su & Xiaofang Li

Authors
  1. Ruini Xue
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  2. Zhibin Dong
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  3. Wei Su
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  4. Xiaofang Li
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Corresponding author

Correspondence to Zhibin Dong .

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Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Xue, R., Dong, Z., Su, W., Li, X. (2018). LMCC: Lazy Message and Centralized Cache for Asynchronous Graph Computing. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-05054-2_5

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