Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
I/O-efficient techniques for computing pagerank
Proceedings of the eleventh international conference on Information and knowledge management
Distributed Pagerank for P2P Systems
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Parallel PageRank Computation on a Gigabit PC Cluster
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Parallel Adaptive Technique for Computing PageRank
PDP '06 Proceedings of the 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
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The continuing growth of the Internet challenges search engine providers to deliver up-to-date and relevant search results A critical component is the availability of a rapid, scalable technique for PageRank computation of a large web graph In this paper, we propose an efficient parallelized version of the PageRank algorithm based on a mixed MPI and multi-threading model The parallel adaptive PageRank algorithm is implemented and tested on two clusters of SMP hosts In the algorithm, communications between processes on different hosts are managed by a message passing (MPI) model, while those between threads are handled via a inter-thread mechanism We construct a synthesized web graph of approximately 62.6 million nodes and 1.37 billion hyperlinks to test the algorithm on two SMP clusters Preliminary results show that significant speedups are possible; however, large inter-node synchronization operations and issues of shared memory access inhibit efficient CPU utilization We believe that the proposed approach shows promise for large-scale PageRank applications and improvements in the algorithm can achieve more efficient CPU utilization.