A deep learning mapper (DLM) for scheduling on heterogeneous systems

Abstract

As heterogeneous systems become more ubiquitous, computer architects will need to develop new CPU scheduling approaches capable of exploiting the diversity of computational resources. Advances in deep learning have unlocked an exceptional opportunity of using these techniques for estimating system performance. However, as of yet no significant leaps have been taken in applying deep learning for scheduling on heterogeneous systems. In this paper we describe a scheduling model that decouples thread selection and mapping routines. We use a conventional scheduler to select threads for execution and propose a deep learning mapper to map the threads onto a heterogeneous hardware. The validation of our preliminary study shows how a simple deep learning based mapper can effectively improve system performance for state-of-the-art schedulers by 8%–30% for CPU and memory intensive applications.