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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Scalable Data-Driven PageRank: Algorithms, System Issues, and Lessons Learned
verfasst von : Joyce Jiyoung Whang, Andrew Lenharth, Inderjit S. Dhillon, Keshav Pingali
Erschienen in: Euro-Par 2015: Parallel Processing
Verlag: Springer Berlin Heidelberg
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Abstract
Large-scale network and graph analysis has received considerable attention recently. Graph mining techniques often involve an iterative algorithm, which can be implemented in a variety of ways. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve more than 28x the performance of standard PageRank implementations (e.g., those in GraphLab). The design choices affect both single-threaded performance as well as parallel scalability. The implementation lessons not only guide efficient implementations of many graph mining algorithms, but also provide a framework for designing new scalable algorithms.