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2009
Springer

Optimizing MPI Runtime Parameter Settings by Using Machine Learning

12 years 2 months ago
Optimizing MPI Runtime Parameter Settings by Using Machine Learning
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. Key words: MPI, optimization, runtime parameter tuning, multi-core
Simone Pellegrini, Jie Wang, Thomas Fahringer, Han
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PVM
Authors Simone Pellegrini, Jie Wang, Thomas Fahringer, Hans Moritsch
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