Abstract
The PageRank algorithm is used by search engines such as Google to order web pages. It uses an iterative numerical method to compute the maximal eigenvector of a transition matrix derived from the web’s hyperlink structure and a user-centred model of web-surfing behaviour. As the web has expanded and as demand for user-tailored web page ordering metrics has grown, scalable parallel computation of PageRank has become a focus of considerable research effort.
In this paper, we seek a scalable problem decomposition for parallel PageRank computation, through the use of state-of-the-art hypergraph-based partitioning schemes. These have not been previously applied in this context. We consider both one and two-dimensional hypergraph decomposition models. Exploiting the recent availability of the Parkway 2.1 parallel hypergraph partitioner, we present empirical results on a gigabit PC cluster for three publicly available web graphs. Our results show that hypergraph-based partitioning substantially reduces communication volume over conventional partitioning schemes (by up to three orders of magnitude), while still maintaining computational load balance. They also show a halving of the per-iteration runtime cost when compared to the most effective alternative approach used to date.
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Bradley, J.T., de Jager, D.V., Knottenbelt, W.J., Trifunović, A. (2005). Hypergraph Partitioning for Faster Parallel PageRank Computation. In: Bravetti, M., Kloul, L., Zavattaro, G. (eds) Formal Techniques for Computer Systems and Business Processes. EPEW WS-FM 2005 2005. Lecture Notes in Computer Science, vol 3670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11549970_12
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DOI: https://doi.org/10.1007/11549970_12
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