University College Cork
Transition to hybrid CPU/GPU platforms in high performance computing is challenging in the aspect of efficient utilization of the heterogeneous hardware and existing optimized software. During recent years, scientific software has been ported to multicore and GPU architectures and now should be reused on hybrid platforms. In this paper, the authors model the performance of such scientific applications in order to execute them efficiently on hybrid platforms. They consider a hybrid platform as a heterogeneous distributed-memory system and apply the approach of functional performance models, which was originally designed for uniprocessor machines. The Functional Performance Model (FPM) represents the processor speed by a function of problem size and integrates many important features characterizing the performance of the architecture and the application.