Optimization of MPI Runtime Parameters Based on Attribute Reduction

Abstract

Abstract: Currently, MPI implementations provide hundreds of tunable runtime parameters, but the default parameters can not achieve ideal application performance. To provide the near-optimal runtime parameters for applications, attribute reduction in rough set theory is adopted to build an optimization model. First, the method generates values of parameters for different benchmarks through attribute reduction, then, predictes parameter values for unknown input application according to their features based on the model built. Experimental evaluations show the method proposed in this paper can predict values of parameters effectively, and achieve approximate 20% performance enhancement.

Key words: rough set theory, optimization model, prediction, Rosetta

ZHU Wei1, WANG Yun-lan2. Optimization of MPI Runtime Parameters Based on Attribute Reduction[J]. Computer and Modernization.

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