Exploring Heterogeneous Scheduling Using the Task-Centric Programming Model

Computer architecture technology is moving towards more heterogeneous solutions, which will contain a number of processing units with different capabilities that may increase the performance of the system as a whole. However, with increased performance comes increased complexity; complexity that is now barely handled in homogeneous multiprocessing systems. The present study tries to solve a small piece of the heterogeneous puzzle; how can we exploit all system resources in a performance-effective and user-friendly way? Our proposed solution includes a run-time system capable of using a variety of different heterogeneous components while providing the user with the already familiar task-centric programming model interface. Furthermore, when dealing with nonuniform workloads, we show that traditional approaches based on centralized or work-stealing queue algorithms do not work well and propose a scheduling algorithm based on trend analysis to distribute work in a performance-effective way across resources.