ABSTRACT
Determining the shortest-path distance between vertices in the weighted graph is an important problem for a broad range of fields, such as context-aware search and route selection. While many efficient methods for querying shortest-path distance have been proposed, they are poorly suited for parallel architectures, such as multi-core CPUs or computer clusters, due to the strong task dependencies. In this paper, we propose ParaPLL, a new parallelism-friendly framework for fast shortest-path distance query on large-scale weighted graphs. ParaPLL exploits intra-node and inter-node parallelism by using shared memory and message passing paradigms respectively. We also design task assignment and synchronization policies, which allow ParaPLL to reach remarkable speedups compared to state-of-the-art solutions. Moreover, we also prove the correctness of ParaPLL. To the best of our knowledge, ParaPLL is the first parallel framework that utilizing pruned landmark labeling to accelerate shortest-path distance queries on large-scale weighted graphs. Our evaluation results show that ParaPLL is 9.46 times faster than the corresponding serial version on a weighted 0.3M-vertex graph using a 12-core computer. ParaPLL on a 6-node computer cluster can also achieve a speedup of up to 5.6 over the single-node implementation.
- 2018. CAIDA. (2018). http://www.caida.org/dataGoogle Scholar
- 2018. USA Road Network. (2018). http://www.diag.uniroma1.it/challenge9/data/tiger/Google Scholar
- 2018. Xeon X5680 Benchmark. (2018). http://ranker.sisoftware.net/Google Scholar
- Takuya Akiba, Yoichi Iwata, and Yuichi Yoshida. 2013. Fast exact shortest-path distance queries on large networks by pruned landmark labeling. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, 349--360. Google ScholarDigital Library
- Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. 2006. 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. ACM, 44--54. Google ScholarDigital Library
- Randeep Bhatia, Fang Hao, Murali Kodialam, and TV Lakshman. 2015. Optimized network traffic engineering using segment routing. In Computer Communications (INFOCOM), 2015 IEEE Conference on. IEEE, 657--665.Google ScholarCross Ref
- Venkatesan T Chakaravarthy, Fabio Checconi, Prakash Murali, Fabrizio Petrini, and Yogish Sabharwal. 2017. Scalable single source shortest path algorithms for massively parallel systems. IEEE Transactions on Parallel and Distributed Systems 28, 7 (2017), 2031--2045.Google ScholarDigital Library
- Yurong Cheng, Ye Yuan, Lei Chen, Guoren Wang, Christophe Giraud-Carrier, and Yongjiao Sun. 2016. Distr: a distributed method for the reachability query over large uncertain graphs. IEEE Transactions on Parallel and Distributed Systems 27, 11 (2016), 3172--3185. Google ScholarDigital Library
- Edith Cohen, Eran Halperin, Haim Kaplan, and Uri Zwick. 2003. Reachability and distance queries via 2-hop labels. SIAM J. Comput. 32, 5 (2003), 1338--1355. Google ScholarDigital Library
- Thomas H Cormen. 2009. Introduction to algorithms. MIT press. Google Scholar
- Damir Ferizovic.2015. Parallel Pruned Landmark Labeling for Shortest Path Queries on Unit-Weight Networks. Ph.D. Dissertation. National Research Center.Google Scholar
- Ada Wai-Chee Fu, Huanhuan Wu, James Cheng, and Raymond Chi-Wing Wong. 2013. Is-label: an independent-set based labeling scheme for point-to-point distance querying. Proceedings of the VLDB Endowment 6, 6 (2013), 457--468. Google ScholarDigital Library
- Ruoming Jin, Ning Ruan, Yang Xiang, and Victor Lee. 2012. A highway-centric labeling approach for answering distance queries on large sparse graphs. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. ACM, 445--456. Google ScholarDigital Library
- Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web. ACM, 641--650. Google ScholarDigital Library
- Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2007. Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD) 1, 1 (2007), 2. Google ScholarDigital Library
- Grzegorz Malewicz, Matthew H Austern, Aart JC Bik, James C Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 135--146. Google ScholarDigital Library
- Ashwin Paranjape, Austin R Benson, and Jure Leskovec. 2017. Motifs in temporal networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 601--610. Google ScholarDigital Library
- Michalis Potamias, Francesco Bonchi, Carlos Castillo, and Aristides Gionis. 2009. Fast shortest path distance estimation in large networks. In Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 867--876. Google ScholarDigital Library
- Kun Qiu, Siyuan Huang, Qiongwen Xu, Jin Zhao, Xin Wang, and Stefano Secci. 2017. ParaCon: A Parallel Control Plane for Scaling Up Path Computation in SDN. IEEE Transactions on Network and Service Management 14, 4 (2017), 978--990. Google ScholarDigital Library
- Matthew Richardson, Rakesh Agrawal, and Pedro Domingos. 2003. Trust management for the semantic web. In International semantic Web conference. Springer, 351--368. Google ScholarDigital Library
- Antti Ukkonen, Carlos Castillo, Debora Donato, and Aristides Gionis. 2008. Searching the wikipedia with contextual information. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 1351--1352. Google ScholarDigital Library
- Kai Wang, Guoqing (Harry) Xu, Zhendong Su, and Yu David Liu. 2015. GraphQ: Graph Query Processing with Abstraction Refinement-Scalable and Programmable Analytics over Very Large Graphs on a Single PC.. In USENIX Annual Technical Conference. 387--401. Google ScholarDigital Library
- Fang Wei. 2010. TEDI: efficient shortest path query answering on graphs. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 99--110. Google ScholarDigital Library
- Qiongwen Xu, Xu Zhang, Jin Zhao, Xin Wang, and Tilman Wolf. 2016. Fast shortest-path queries on large-scale graphs. In Network Protocols (ICNP), 2016 IEEE 24th International Conference on. IEEE, 1--10.Google Scholar
- Sihem Amer Yahia, Michael Benedikt, Laks VS Lakshmanan, and Julia Stoyanovich. 2008. Efficient network aware search in collaborative tagging sites. Proceedings of the VLDB Endowment 1, 1 (2008), 710--721. Google ScholarDigital Library
- Xiaofei Zhang and Lei Chen. 2017. Distance-aware selective online query processing over large distributed graphs. Data Science and Engineering 2, 1 (2017), 2--21.Google ScholarCross Ref
- Lei Zou, M Tamer Özsu, Lei Chen, Xuchuan Shen, Ruizhe Huang, and Dongyan Zhao. 2014. gStore: a graph-based SPARQL query engine. The VLDB journal 23, 4 (2014), 565--590. Google ScholarDigital Library
Index Terms
- ParaPLL: Fast Parallel Shortest-path Distance Query on Large-scale Weighted Graphs
Recommendations
A Detailed Performance Analysis of the Interpolation Supplemented Lattice Boltzmann Method on the Cray T3E and Cray X1A Detailed Performance Analysis of the Interpolation Supplemented Lattice Boltzmann Method on the Cray T3E and Cray X1
A detailed study of the parallel performance of the interpolation supplemented lattice Boltzmann (ISLB) method using SHMEM and MPI on the Cray T3E-900 and Cray X1 architectures is presented. The noteworthy feature of the ...
Exploiting Distributed-Memory and Shared-Memory Parallelism on Clusters of SMPs with Data Parallel Programs
Clusters of SMPs are hybrid-parallel architectures that combine the main concepts of distributed-memory and shared-memory parallel machines. Although SMP clusters are widely used in the high performance computing community, there exists no single ...
Compiling data-parallel programs for clusters of SMPs: Research Articles
Compilers for Parallel ComputersClusters of shared-memory multiprocessors (SMPs) have become the most promising parallel computing platforms for scientific computing. However, SMP clusters significantly increase the complexity of user application development when using the low-level ...
Comments