MPI Runtime Parameters Tuning Based on Neural Network on Multi-core Clusters

The new features of multi-core add the optimization space for MPI applications,and besides tuning MPI runti-me parameters is a common practice perceived to optimize the MPI application performance.However,the best configuration of the runtime parameters not only depends on the underlying architecture of a specific multi-core cluster but also on the features of MPI application.We constructed and analyzed an effective tuning model bases on artificial neural network to automatically predict the near-optimal configuration of runtime parameters for any unseen input programs under the current multi-core cluster.Experimental results from two different benchmarks were presented to show effectiveness of our approach.We observed that the speedup gained by the predicted runtime parameters can averagely achieve 95% of the speedup gained by the best parameters configuration.