By Topic

A Multi-source Message Passing Model to Improve the Parallelism Efficiency of Graph Mining on MapReduce

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
ZengFeng Zeng ; Beijing Key Lab. of Intell. Telecommun. Software & Multimedia, Beijing Univ. of Posts & Telecommun., Beijing, China ; Bin Wu ; TianTian Zhang

The MapReduce framework has been employed in many papers to process the large-scale graph. In this paper, we propose a multi-source message passing model to achieve multi-source traversal of graph in one iterative progress, which largely improve the parallelism efficiency of graph algorithm involving multi-source traversal which occurs in many complex graph algorithms. As the model can traverse the graph from different sources in one iterative progress, the multi-source traversal will finish in much less iteration than before. In this way, the total runtime of the algorithm involves multi-source traversal will be reduced in a large scale. Besides, the message passing model is flexible enough to express a broad set of algorithms. Hence, we design the interface of message passing to facilitate using our model to develop algorithms. Finally, the experiment shows the efficiency and scalability of the model.

Published in:

Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International

Date of Conference:

21-25 May 2012