Asynchronous large-scale graph processing made easy

CIDR 2013 - 6th Biennial Conference on Innovative Data Systems Research

Volumn , Issue , 2013, Pages

  Wang, Guozhang ^a   Xie, Wenlei ^a   Demers, Alan ^a   Gehrke, Johannes ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; PARALLEL PROGRAMMING; SCALABILITY;

APPLICATION DEVELOPMENT; APPLICATION LOGIC; ASYNCHRONOUS EXECUTIONS; ASYNCHRONOUS MODELS; BULK SYNCHRONOUS PARALLEL MODELS; CONVERGENCE RATES; PARALLEL EXECUTIONS; PROGRAMMING MODELS;

PROGRAM DEBUGGING;

EID: 85084012076     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Hadoop. http://hadoop.apache.org/core/.
2. Hypergraphdb. http://www.hypergraphdb.org/.
3. Infinitegraph: The distributed graph database. http://www.infinitegraph.com/.
4. Neo4j: Nosql for the enterprise. http://neo4j.org/.
5. USC-SIPI image database. sipi.usc.edu/database/.
6. D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation: numerical methods. Prentice-Hall, Inc., 1989.
7. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1-7):107–117, 1998.
8. Y. Bu, B. Howe, M. Balazinska, and M. D. Ernst. Haloop: Efficient iterative data processing on large clusters. PVLDB, 3(1):285–296, 2010.
9. D. Crandall, A. Owens, N. Snavely, and D. P. Huttenlocher. Discrete-Continuous optimization for large-scale structure from motion. In CVPR, pages 3001–3008, 2011.
10. J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI, pages 137–150, 2004.
11. J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae, J. Qiu, and G. Fox. Twister: A runtime for iterative mapreduce. In HPDC, pages 810–818, 2010.
12. G. Elidan, I. McGraw, and D. Koller. Residual belief propagation: Informed scheduling for asynchronous message passing. In UAI, 2006.
13. J. Gonzalez, Y. Low, and C. Guestrin. Residual splash for optimally parallelizing belief propagation. In AISTATS, pages 177–184, 2009.
14. T. H. Haveliwala. Topic-sensitive pagerank. In WWW, pages 517–526, 2002.
15. U. Kang, C. E. Tsourakakis, and C. Faloutsos. Pegasus: A peta-scale graph mining system. In ICDM, pages 229–238, 2009.
16. J. Kleinberg and E. Tardos. Algorithm Design. Addison-Wesley Longman Publishing Co., Inc., 2005.
17. M. Kulkarni, K. Pingali, B. Walter, G. Ramanarayanan, K. Bala, and L. P. Chew. Optimistic parallelism requires abstractions. In PLDI, pages 211–222, 2007.
18. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 6(1), 2009.
19. I. X. Y. Leung, P. Hui, P. Liò, and J. Crowcroft. Towards real-time community detection in large networks. Physical Review E, 79:066107, 2009.
20. F. Lin and W. W. Cohen. Semi-supervised classification of network data using very few labels. In ASONAM, pages 192–199, 2010.
21. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new framework for parallel machine learning. In UAI, pages 340–349, 2010.
22. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Distributed graphlab: A framework for machine learning in the cloud. PVLDB, 5(8):716–727, 2012.
23. G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: A system for large-scale graph processing. In SIGMOD, pages 135–146, 2010.
24. F. McSherry, D. Murray, R. Isaacs, and M. Isard. Differential dataflow. In CIDR, 2013.
25. K. P. Murphy, Y. Weiss, and M. I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In UAI, pages 467–475, 1999.
26. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., 1988.
27. K. Pingali, D. Nguyen, M. Kulkarni, M. Burtscher, M. A. Hassaan, R. Kaleem, T.-H. Lee, A. Lenharth, R. Manevich, M. Méndez-Lojo, D. Prountzos, and X. Sui. The tao of parallelism in algorithms. In PLDI, pages 12–25, 2011.
28. R. Power and J. Li. Piccolo: Building fast, distributed programs with partitioned tables. In OSDI, pages 293–306, 2010.
29. U. N. Raghavan, R. Albert, and S. Kumara. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76:036106.
30. M. Sarwat, S. Elnikety, Y. He, and G. Kliot. Horton: Online query execution engine for large distributed graphs. In ICDE, pages 1289–1292, 2012.
31. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell., 30, 2008.
32. L. G. Valiant. A bridging model for parallel computation. Commun. ACM, 33(8):103–111, 1990.
33. L. G. Valiant. A bridging model for multi-core computing. J. Comput. Syst. Sci., 77(1):154–166, 2011.
34. G. Wang, M. A. V. Salles, B. Sowell, X. Wang, T. Cao, A. J. Demers, J. Gehrke, and W. M. White. Behavioral simulations in mapreduce. PVLDB, 3(1):952–963, 2010.
35. Y. Yu, M. Isard, D. Fetterly, M. Budiu, Úlfar Erlingsson, P. K. Gunda, and J. Currey. DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. In OSDI, pages 1–14, 2008.
36. A. Yzelman and R. H. Bisseling. An object-oriented bulk synchronous parallel library for multicore programming. Concurrency and Computation: Practice and Experience, 24(5):533–553, 2012.
37. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica. Spark: Cluster computing with working sets. In HotCloud, 2010.
38. Y. Zhang, Q. Gao, L. Gao, and C. Wang. Priter: A distributed framework for prioritized iterative computations. In SOCC, 2011.