Abstract
Conventional graph processing algorithms are not designed for those unprecedented large graphs and result in suboptimal performance. To address the problem, Google proposed its Pregel system, which adopts a vertex-centric processing framework for simplifying the development of parallel graph algorithms. In Pregel, the graph computation proceeds iteratively and each iteration is called a superstep. Pregel’s processing engine adopts the Bulk Synchronous Parallel (BSP) model, which simplifies the synchronization mechanism and ensures that the system reaches a global synchronization at the end of each superstep. This strategy however significantly increases the system overhead for algorithms that entail many iterations. In this paper, we propose a new graph processing framework based on Pregel. It extends Pregel by introducing a new data structure, super-vertex, and a new API, internalCompute. Our system is fully compatible with Pregel in that the codes of Pregel can run on it without modification. Moreover, we allow the programmers to optimize their codes with the unique two-phase processing strategy. We evaluated the advantages of our approach by two popular graph algorithms, Shortest Path and PageRank, with real dataset from twitter.
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Zhou, X., Chang, P., Chen, G. (2014). An Efficient Graph Processing System. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_35
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DOI: https://doi.org/10.1007/978-3-319-11116-2_35
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