Abstract
The use of scientific computing centers becomes more and more difficult on modern parallel architectures. Users must face a large variety of batch systems (with their own specific syntax) and have to set many parameters to tune their applications (e.g., processors and/or threads mapping, memory resource constraints). Moreover, finding the optimal performance is not the only criteria when a pool of jobs is submitted on the Grid (for numerical parametric analysis for instance) and one must focus on the wall-time completion. In this work we tackle the problem by using the Diet Grid middleware that integrates an adaptable PaStiX service to solve a set of experiments issued from the simulations of the Aster project.
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Caniou, Y., Gay, JS., Ramet, P. (2008). Tunable Parallel Experiments in a GridRPC Framework: Application to Linear Solvers. In: Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2008. VECPAR 2008. Lecture Notes in Computer Science, vol 5336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92859-1_6
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DOI: https://doi.org/10.1007/978-3-540-92859-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92858-4
Online ISBN: 978-3-540-92859-1
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