The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Pregel: a system for large-scale graph processing
Abstract
Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477
Cited by
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
Apache Spark: A unified engine for big data processing
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,757
Apache Hadoop YARN: yet another resource negotiator
Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al. (2013) · Proceedings of the 4th annual Symposium on Cloud Computing · doi:10.1145/2523616.2523633 · Cited by 1,848
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Large-scale cluster management at Google with Borg
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015) · Proceedings of the Tenth European Conference on Computer Systems · doi:10.1145/2741948.2741964 · Cited by 1,468
Metadata
- Authors
- Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G.
- Venue
- Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
- Publisher
- ACM
- Published
- 2010-06-06
- Pages
- 135-146
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2170616854
- Cited by
- 3,515
- References
- 2
- FWCI
- 136.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010). Pregel: a system for large-scale graph processing. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. https://doi.org/10.1145/1807167.1807184
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