A result-driven minimum blocking method for PageRank parallel computing
Wan Tao, Tao Liu, Wei Yu, Gan Huang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Matrix blocking is a common method for improving computational efficiency of PageRank, but the blocking rules are hard to be determined, and the following calculation is complicated. In tackling these problems, we propose a minimum blocking method driven by result needs to accomplish a parallel implementation of PageRank algorithm. The minimum blocking just stores the element which is necessary for the result matrix. In return, the following calculation becomes simple and the consumption of the I/O transmission is cut down. We do experiments on several matrixes of different data size and different sparsity degree. The results show that the proposed method has better computational efficiency than traditional blocking methods.