COMPARATIVE PERFORMANCE STUDY OF PARALLEL PROGRAMMING MODELS IN A NEURAL NETWORK TRAINING CODE

This paper discusses the performance studies of a coarse grained parallel neural network training code for control of nonlinear dynamical systems, implemented in the shared memory and message passing parallel programming environments OpenMP and MPI, respectively. In addition, these codes are compared to an implementation utilizing SHMEM the native data passing SGI/Cray environment for parallel programming. The multiprocessor platform used in the study is a SGI/Cray Origin 2000 with up to 32 processors, which supports all these programming models efficiently. The dynamical system used in this study is a nonlinear 0D model of a thermonuclear fusion reactor with the EDA-ITER design parameters. The results show that OpenMP outperforms the other two environments when large number of processors are involved, while yielding a similar or a slightly poorer behavior for small number of processors. As expected the native SGI/Cray environment outperforms MPI for the entire range of processors used. Reasons for the observed performance are given. The parallel efficiency of the code is always greater than 60% regardless of the parallel environment for the range of processors used in this study.