Optimizing MPI Runtime Parameter Settings by Using Machine Learning
- Cite this paper as:
- 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
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.
KeywordsMPI optimization runtime parameter tuning multi-core
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