DocumentCode
2448322
Title
Parallel Isolation-Aggregation algorithms to solve Markov chains problems with application to page ranking
Author
Touzene, Abderezak
Author_Institution
Comput. Sci. Dept., Sultan Qaboos Univ., Muscat, Oman
fYear
2010
fDate
19-23 April 2010
Firstpage
1
Lastpage
6
Abstract
In this paper, we propose two parallel Aggregation-Isolation iterative methods for solving Markov chains. These parallel methods conserves as much as possible the benefits of aggregation, and Gauss-Seidel effects. Some experiments have been conducted testing models from queuing systems and models from Google Page Ranking. The results of the experiments show super linear speed-up for the parallel Aggregation-Isolation method.
Keywords
Markov processes; iterative methods; parallel algorithms; Gauss-Seidel effect; Google page ranking; Markov chain; parallel aggregation-isolation iterative method; queuing system; Gaussian processes; Iterative algorithms; Iterative methods; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on
Conference_Location
Atlanta, GA
Print_ISBN
978-1-4244-6533-0
Type
conf
DOI
10.1109/IPDPSW.2010.5470779
Filename
5470779
Link To Document