Semi-Automatic Refactoring for (Heterogeneous) Parallel Programs

Dr Chris Brown, University of St Andrews

Modern multicore systems offer huge computing potential. Exploiting large parallel systems is still a very challenging task, however, especially as many software developers still use overly-sequential programming models. In this talk, I will present a radical and novel approach to introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), that combines algorithmic skeletons, machine-learning, and refactoring tool support. Specifically, I will show how to use skeletons to model the parallelism, machine learning to predict the optimal configuration and mapping and refactoring to introduce the parallelism into the application. Finally, I will demonstrate our tools on a number of applications, showing that we can easily obtain comparable results to hand-tuned optimised versions.