By Luiz Meyer, Doug Scheftner, Jens Vöckler, Marta Mattoso, Mike Wilde, Ian Foster (auth.), Michel Daydé, José M. L. M. Palma, Álvaro L. G. A. Coutinho, Esther Pacitti, João Correia Lopes (eds.)
This publication constitutes the completely refereed post-proceedings of the seventh overseas convention on excessive functionality Computing for Computational technology, VECPAR 2006, held in Rio de Janeiro, Brazil, in June 2006.
The forty four revised complete papers offered including one invited paper and 12 revised workshop papers have been conscientiously chosen in the course of rounds of reviewing and development for inclusion within the booklet. The papers are geared up in topical sections on Grid computing, cluster computing, numerical equipment, large-scale simulations in Physics, and computing in Biosciences. moreover, the publication comprises a few of the displays of the 2 linked workshops on Computational Grids and Clusters, and on excessive functionality info administration in Grid Environments.
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Additional info for High Performance Computing for Computational Science - VECPAR 2006: 7th International Conference, Rio de Janeiro, Brazil, June 10-13, 2006, Revised Selected and Invited Papers
Employing the wired or solid modes of visualisation. The GUI client interacts with the SDAGS, via its public interface, to analyse the structures. For this, the client sends, via a SOAP (Simple Object Access Protocol) request , an XML ﬁle with the properties of the building to be simulated, together with diﬀerent parameters related to the dynamic analysis. Then, the status of the simulation task is periodically received, and once the structure has been remotely analysed, the output data are retrieved in a SOAP message, and then deleted in the machine that runs the Grid service.
Regarding the parallelisation of the problem, each processor is assigned ﬁrstly a group of N/p consecutive nodes and another one of B/p consecutive structural elements, being N and B the total number of nodes and beams in the building, respectively, and p the number of processors employed. Then, each processor generates and assembles in parallel its local part of the stiﬀness, mass and damping matrices, according to their nodes assigned. In this way, all the matrices of the problem, together with the diﬀerent resulting vectors, will be partitioned among the processors following a row-wise block stripped distribution.
Cameron, R. , Evaluation of an Economic-Based File Replication Strategy for a Data Grid, in Int. Workshop on Agent Based Cluster and Grid Computing at Int. Symposium on Cluster Computing and the Grid (CCGrid2003), Tokyo, Japan, May 2003. 6. , Across Grids Conference 2004, Nicosia, Cyprus 7. , Workflow Management in GriPhyn, The Grid Resource Management, Netherlands 2003. 12 L. Meyer et al. 8. 274-286, 2005. 9. , 1999, Chapter 4 of "The Grid 2: Blueprint for a New Computing Infrastructure", Morgan-Kaufman, 2004.