Performance Portability of OpenCL with Application to Neural Networks

This whitepaper investigates the parallel performance of a sample application that implements an approximate expectation-maximization method for inferring the network structure and time varying states of a hidden population within the framework of the kinetic Ising model. The size of networks that can yield informative results can be made arbitrarily large, and the long-running computational demand is highly localized, making the application a strong candidate for future exascale platforms.
Previous investigations using OpenMP on the Intel Xeon Phi architecture have suggested that the class of accelerator unit may play a significant part in attainable application performance. An OpenCL parallelization enables experiments with a variety of accelerator units. We examine how this programming model affects the performance of a portable implementation, and use it to compare accelerator technologies in terms of their suitability for future extreme-scale computations.