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

• Type: Conference
• Year: 2009
• Where: PVM
• Authors: Simone Pellegrini, Jie Wang, Thomas Fahringer, Hans Moritsch
• Keywords: Distributed And Parallel Computing, MPI Application Performance, MPI Runtime Parameters, Multi-core Smp Machine, PVM 2009