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Pregel: A System for Large-Scale Graph Processing

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Manage episode 487366642 series 3670304
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The article is a paper published in 2010 by researchers at Google that introduces Pregel, a large-scale graph processing system. Pregel is designed for processing graphs with billions of vertices and trillions of edges, and it uses a vertex-centric approach where vertices are assigned to individual machines and communicate with each other through message passing. The paper details the model of computation, the C++ API, and the implementation of Pregel, including fault-tolerance mechanisms for dealing with machine failures. It also explores applications of Pregel to various graph algorithms, including PageRank, shortest paths, bipartite matching, and semi-clustering, providing experimental results for shortest paths on graphs ranging in size from 10 million to 1 billion vertices. Finally, the paper discusses related work and future directions for Pregel, highlighting its strengths and potential areas for improvement.

https://15799.courses.cs.cmu.edu/fall2013/static/papers/p135-malewicz.pdf

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43 episodes

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Manage episode 487366642 series 3670304
Content provided by The Binary Breakdown. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Binary Breakdown or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

The article is a paper published in 2010 by researchers at Google that introduces Pregel, a large-scale graph processing system. Pregel is designed for processing graphs with billions of vertices and trillions of edges, and it uses a vertex-centric approach where vertices are assigned to individual machines and communicate with each other through message passing. The paper details the model of computation, the C++ API, and the implementation of Pregel, including fault-tolerance mechanisms for dealing with machine failures. It also explores applications of Pregel to various graph algorithms, including PageRank, shortest paths, bipartite matching, and semi-clustering, providing experimental results for shortest paths on graphs ranging in size from 10 million to 1 billion vertices. Finally, the paper discusses related work and future directions for Pregel, highlighting its strengths and potential areas for improvement.

https://15799.courses.cs.cmu.edu/fall2013/static/papers/p135-malewicz.pdf

  continue reading

43 episodes

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