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Heterogeneous multicore processors (HMPs) are commonly deployed to meet the performance and power requirements of emerging workloads. HMPs demand adaptive and coordinated resource management techniques to control such complex systems. While Multiple-Input-Multiple-Output (MIMO) control theory has been applied to adaptively coordinate resources for single-core processors, the coordinated management of HMPs poses significant additional challenges for achieving robustness and responsiveness, due to the unmanageable complexity of modeling the system dynamics. This paper presents, for the first time, a methodology to design robust MIMO controllers with rapid response and formal guarantees for coordinated management of HMPs. Our approach addresses the challenges of: (1) system decomposition and identification; (2) selection of suitable sensor and actuator granularity; and (3) appropriate system modeling to make the system identifiable as well as controllable. We demonstrate the practical applicability of our approach on an ARM big.LITTLE HMP platform running Linux, and demonstrate the efficiency and robustness of our method by designing MIMO-based resource managers.

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Abstract. The Graphics Processor Unit (GPU) has expanded its role from an accelerator for rendering graphics into an efficient parallel processor for general purpose computing. The GPU, an indispensable component in desktop and server-class computers as well as game consoles, has also become an integrated component in handheld devices, such as smartphones. Since the handheld devices are mostly powered by battery, the mobile GPU is usually designed with an emphasis on low-power rather than on performance. In addition, the memory bus architecture of mobile devices is also quite different from those of desktops, servers, and game consoles. In this paper, we try to provide answers to the following two questions: (1) Can a mobile GPU be used as a powerful accelerator in the mobile platform for general purpose computing, similar to its role in the desktop and server platforms? (2) What is the role of a mobile GPU in energy-optimized real-time mobile applications? We use face recognition as an application driver which is a compute-intensive task and is a core process for several mobile applications. The experiments of our investigation were performed on an Nvidia Tegra development board which consists of a dual-core ARM Cortex A9 CPU and a Nvidia mobile GPU integrated in a SoC. The experiment results show that, utilizing the mobile GPU can achieve a 4.25x speedup in performance and 3.98x reduction in energy consumption, in comparison with a CPU-only implementation on the same platform.