Abstract
Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for these parameters not only depends on the underlying system but also on the application itself and its input data. This paper introduces a novel approach based on machine learning techniques to estimate the values of MPI runtime parameters that tries to achieve optimal speedup for a target architecture and any unseen input program. The effectiveness of our optimization tool is evaluated against two benchmarks executed on a multi-core SMP machine.
This work is funded by the Tiroler Zukunftsstiftung under contract nr. P7030-015-024.
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Pellegrini, S., Wang, J., Fahringer, T., Moritsch, H. (2009). Optimizing MPI Runtime Parameter Settings by Using Machine Learning. In: Ropo, M., Westerholm, J., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2009. Lecture Notes in Computer Science, vol 5759. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03770-2_26
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DOI: https://doi.org/10.1007/978-3-642-03770-2_26
Publisher Name: Springer, Berlin, Heidelberg
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