Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Fine-Grain Hypergraph Model for 2D Decomposition of Sparse Matrices
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Graph Partitioning and Parallel Solvers: Has the Emperor No Clother? (Extended Abstract)
IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
SIAM Journal on Scientific Computing
A Parallel Algorithm for Multilevel k-Way Hypergraph Partitioning
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
PageRank: Splitting Homogeneous Singular Linear Systems of Index One
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Optimizing web structures using web mining techniques
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A scalable eigensolver for large scale-free graphs using 2D graph partitioning
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Scalable matrix computations on large scale-free graphs using 2D graph partitioning
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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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.