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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 - Atlanta, GA, United States Duration: Apr 19 2010 → Apr 23 2010 |
Other
| Other | 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 |
|---|---|
| Country | United States |
| City | Atlanta, GA |
| Period | 4/19/10 → 4/23/10 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Software
- Theoretical Computer Science
Cite this
Parallel isolation-aggregation algorithms to solve Markov Chains problems with application to page Ranking. / Touzene, Abderezak.
Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010. 2010. 5470779.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
BT - Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010
ER -