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Multiprocessor systems provide both highperformance and energy-efficient execution of applications on mobile and embedded systems under dynamic workload requirements, and can provide increased lifetime for devices in energy-constrained environments. However, their increasing complexity means that management at runtime has become a non-trivial task, especially in heterogeneous multiprocessor systems. In addition, there is no standardised mechanism to expose and manage the sources of control and monitoring from within applications and hardware resources at runtime.This paper presents an analysis of applications, platforms and runtime management approaches to motivate the need for a standardised framework that enables fully applicationand platform-agnostic runtime management. The exposure of application controls and requirements through the presented framework is demonstrated with a stereo matching algorithm, including runtime management of multi-threading and frequency scaling on the 61-core Xeon Phi platform. In addition, the trading of application parameters, such as throughput and accuracy, is demonstrated within the framework using a runtime controller on the Odroid-XU3 platform. An open-source implementation of this framework has been released.

Heterogeneous multiprocessor systems have increased in complexity to provide both high performance and energy efficiency for a diverse range of applications. This motivates the need for a standard framework that enables the management, at runtime, of software applications executing on these processors. This paper proposes the first fully application- and platform-agnostic framework for runtime management approaches that control and optimise software applications and hardware resources. This is achieved by separating the system into three distinct layers connected by an API and cross-layer constructs called knobs and monitors. The proposed framework also supports the management of applications that are executing concurrently on heterogeneous platforms. The operation of the proposed framework is experimentally validated using a basic runtime controller and two heterogeneous platforms, to show how it is application- and platform-agnostic and easy to use. Furthermore, the management of concurrently executing applications through the framework is demonstrated. Finally, two recently reported runtime management approaches are implemented to demonstrate how the framework enables their operation and comparison. The energy and latency overheads introduced by the framework have been quantified and an open-source implementation has been released.

Due to the operating costs and failure rates of computing platforms, energy efficiency has become a major concern for modern and future many-core systems. In the quest for high performance, the power consumption growth rate must slow down while delivering more performance per unit of power. To improve the energy efficiency of such systems, processors are equipped with low-power techniques such as dynamic voltage and frequency scaling (DVFS) and power capping. These techniques must be controlled carefully as per the workload; otherwise, it may result in significant performance loss and/or power consumption due to system overheads (e.g. DVFS transition latency). Existing approaches [1], [2] are not effective in adapting to workload variations as they do not consider the combined effect of application compute-/memory-intensity, thread synchronization contention, and non-uniform memory accesses (NUMAs) owing to the underlying processor architecture. This poster discusses a workload-aware runtime energy management technique that takes the aforementioned factors into account for efficient V-f control.