Abstract
To achieve data intensive computation, the joining of geographically distributed heterogeneous clusters of workstations through the Internet can be an inexpensive approach. To obtain effective collaboration in such a collection of clusters, overcoming processors and networks heterogeneity, a system architecture was defined. This architecture and a model able to predict application performance and to help its design is described. The matrix multiplication algorithm is used as a benchmark and experiments are conducted over two geographically distributed heterogeneous clusters, one in Brazil and the other in Spain. The model obtained over 90% prediction accuracy in the experiments.
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Argollo, E., Rexachs, D., Tinetti, F.G., Luque, E. (2006). Efficient Execution of Scientific Computation on Geographically Distributed Clusters. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958_84
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DOI: https://doi.org/10.1007/11558958_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29067-4
Online ISBN: 978-3-540-33498-9
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