Parallel PageRank computation using GPUs
Proceedings of the Third Symposium on Information and Communication Technology
Iterative statistical kernels on contemporary GPUs
International Journal of Computational Science and Engineering
A GPU-based method for computing eigenvector centrality of gene-expression networks
AusPDC '13 Proceedings of the Eleventh Australasian Symposium on Parallel and Distributed Computing - Volume 140
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Google's famous PageRank algorithm is widely used to determine the importance of web pages in search engines. Given the large number of web pages on the World Wide Web, efficient computation of PageRank becomes a challenging problem. We accelerated the power method for computing PageRank on AMD GPUs. The core component of the power method is the Sparse Matrix-Vector Multiplication (SpMV). Its performance is largely determined by the characteristics of the sparse matrix, such as sparseness and distribution of non-zero values. Based on careful analysis on the web linkage matrices, we design a fast and scalable SpMV routine with three passes, using a modified Compressed Sparse Row format. Our PageRank computation achieves 15x speedup on a Radeon 5870 Graphic Card compared with a PhenomII 965 CPU at 3.4GHz. Our method can easily adapt to large scale data sets. We also compare the performance of the same method on the OpenCL platform with our low-level implementation.