Cross-Layer Exploration of Heterogeneous Multicore Processor Configurations

Abstract

Heterogeneous multicore processors (HMP) present significant advantages over homogenous multiprocessors due to their improved power, performance, and energy efficiency for a given chip/die area. However, due to their diverse and vast design space, selecting a suitable HMP configuration with different core types within a given area-power budget is an extremely challenging task. In this paper, we present a cross-layer approach for exploring and configuring a HMP for a given system goal under system level constraints (such as equal area or power budget) as an optimization problem. Unlike the state-of-the-art approaches, we jointly consider cross-layer features of the application, operating system (task allocation strategies), and hardware architecture while deploying computationally efficient predictive models (of performance and power) in configuring the HMP platform resources (number and types of cores) in an evolutionary optimization framework. Our predictive cross-layer approach enables the designer to comparatively evaluate and select the most promising (e.g., Energy and performance efficient) HMP configuration in over two order of magnitude less simulation time especially during the early design and verification stages when the design space is at its largest.