Abstract: The new features of multi core add the optimization space for MPI applications, and besides tuning MPI runtime 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.

Key words: Multi-core clusters, MPI, Runtime parameters tuning, Neural network