A result-driven minimum blocking method for PageRank parallel computing

Wan Tao; Tao Liu; Wei Yu; Gan Huang

doi:10.1117/12.2265751

23 January 2017 A result-driven minimum blocking method for PageRank parallel computing

Wan Tao, Tao Liu, Wei Yu, Gan Huang

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 103224E (2017) https://doi.org/10.1117/12.2265751
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

Abstract

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.

Citation Download Citation

Wan Tao, Tao Liu, Wei Yu, and Gan Huang "A result-driven minimum blocking method for PageRank parallel computing", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103224E (23 January 2017); https://doi.org/10.1117/12.2265751

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available