Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Proceedings of the 11th international conference on World Wide Web
Decomposing Irregularly Sparse Matrices for Parallel Matrix-Vector Multiplication
IRREGULAR '96 Proceedings of the Third International Workshop on Parallel Algorithms for Irregularly Structured Problems
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Parallel PageRank Computation on a Gigabit PC Cluster
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
SIAM Journal on Scientific Computing
Permuting Sparse Rectangular Matrices into Block-Diagonal Form
SIAM Journal on Scientific Computing
A Reordering for the PageRank Problem
SIAM Journal on Scientific Computing
Convergence Analysis of a PageRank Updating Algorithm by Langville and Meyer
SIAM Journal on Matrix Analysis and Applications
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
PageRank Computation, with Special Attention to Dangling Nodes
SIAM Journal on Matrix Analysis and Applications
Multi-level direct K-way hypergraph partitioning with multiple constraints and fixed vertices
Journal of Parallel and Distributed Computing
Hypergraph partitioning for faster parallel pagerank computation
EPEW'05/WS-FM'05 Proceedings of the 2005 international conference on European Performance Engineering, and Web Services and Formal Methods, international conference on Formal Techniques for Computer Systems and Business Processes
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
Hi-index | 0.00 |
A power method formulation, which efficiently handles the problem of dangling pages, is investigated for parallelization of PageRank computation. Hypergraph-partitioning-based sparse matrix partitioning methods can be successfully used for efficient parallelization. However, the preprocessing overhead due to hypergraph partitioning, which must be repeated often due to the evolving nature of the Web, is quite significant compared to the duration of the PageRank computation. To alleviate this problem, we utilize the information that sites form a natural clustering on pages to propose a site-based hypergraph-partitioning technique, which does not degrade the quality of the parallelization. We also propose an efficient parallelization scheme for matrix-vector multiplies in order to avoid possible communication due to the pages without in-links. Experimental results on realistic datasets validate the effectiveness of the proposed models.