Couturier R.,Luniversite Of Franche Comte |
Jezequel F.,CNRS Laboratory for Informatics
Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010 | Year: 2010
In this paper, we show how to solve large sparse linear systems in a grid environment using the Java language and the MPJ library for communication. We describe a parallel version of the GMRES method which takes into account the sparsity of the matrix for message exchanges among processors. Two implementations are compared: one in Java using MPJ and one in C using MPI. The performance of both codes is also compared with that of the PETSc library. Experiments have been carried out using the GRID'5000 platform, on the one hand, on a local cluster, and, on the other hand, on clusters located in distant geographical sites. It is noticeable that the performance of our solver in Java is comparable to the same solver written in C and also to the PETSc library. Our solver in Java allowed us to solve sparse systems of size up to 2 billions with two geographically distant sites. © 2010 IEEE.
Abdou W.,Luniversite Of Franche Comte |
Henriet A.,Luniversite Of Franche Comte |
Bloch C.,Luniversite Of Franche Comte |
Dhoutaut D.,Luniversite Of Franche Comte |
And 2 more authors.
Journal of Network and Computer Applications | Year: 2011
A mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sending safety messages and routing information. To transmit a broadcast message effectively in a wide and high mobility MANET (for instance in vehicular ad hoc network) is a hard task to achieve. An efficient communication algorithm must take into account several aspects like the neighborhood density, the size and shape of the network, the use of the channel. Probabilistic strategies are often used because they do not involve additional latency. Some solutions have been proposed to make their parameters vary dynamically. For instance, the retransmission probability increases when the number of neighbors decreases. But, the authors do not optimize parameters for various environments. This article aims at determining the best communication strategies for each node according to its neighborhood density. It describes a tool combining a network simulator (ns-2) and an evolutionary algorithm (EA). Five types of context are considered. For each of them, we tackle the best behavior for each node to determine the right input parameters. The proposed EA is first compared to three EAs found in the literature: two well-known EAs (NSGA-II and SPEA2) and a more recent one (DECMOSA-SQP). Then, it is applied to the MANET broadcasting problem. © 2010 Elsevier Ltd. All rights reserved.