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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
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
Coffman, T., Greenblatt, S., Marcus, S.: Graph-based technologies for intelligence analysis. Commun. ACM 47(3), 45–47 (2004)
Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Graphlab powergraph v2.2. https://github.com/jegonzal/PowerGraph
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)
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)
Han, M., Daudjee, K.: Giraph unchained: barrierless asynchronous parallel execution in pregel-like graph processing systems. Proc. VLDB Endow. 8(9), 950–961 (2015)
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)
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)
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)
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)
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)
Leskovec, J., Krevl, A.: SNAP Datasets: stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
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)
Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)
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)
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)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)
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
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)
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)
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)
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)
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)
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)
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)
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)
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
Zhu, X., Chen, W., Zheng, W., Ma, X.: Gemini: a computation-centric distributed graph processing system. In: OSDI, pp. 301–316 (2016)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-05054-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05053-5
Online ISBN: 978-3-030-05054-2
eBook Packages: Computer ScienceComputer Science (R0)