TY - GEN
T1 - Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking
AU - Touzene, Abderezak
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77954079327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954079327&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2010.5470779
DO - 10.1109/IPDPSW.2010.5470779
M3 - Conference contribution
AN - SCOPUS:77954079327
SN - 9781424465347
T3 - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
BT - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
T2 - 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
Y2 - 19 April 2010 through 23 April 2010
ER -