Accurate energy modeling for many-core static schedules with streaming applications

Many-core systems provide a great performance potential with the massively parallel hardware structure. Yet, these systems are facing increasing challenges such as high operating temperatures, high electrical bills, unpleasant noise levels due to active cooling and high battery drainage in mobile devices; factors caused directly by poor energy efficiency. Furthermore by pushing the power beyond the limits of the power envelope, parts of the chip cannot be used simultaneously - a phenomenon referred to as "dark silicon". Power management is therefore needed to distribute the resources to the applications on demand. Traditional power management systems have usually been agnostic to the underlying hardware, and voltage and frequency control is mostly driven by the workload. Static schedules, on the other hand, can be a preferable alternative for applications with timing requirements and predictable behavior since the processing resources can be more precisely allocated for the given workload. In order to efficiently implement power management in such systems, an accurate model is important in order to make the appropriate power management decisions at the right time. For making correct decisions, practical issues such as latency for controlling the power saving techniques should be considered when deriving the system model, especially for fine timing granularity. In this paper we present an accurate energy model for many-core systems which includes switching latency of modern power saving techniques. The model is used when calculating an optimal static schedule for many-core task execution on systems with dynamic frequency levels and sleep state mechanisms. We derive the model parameters for an embedded processor with the help of benchmarks, and we validate the model on real hardware with synthetic applications that model streaming applications. We demonstrate that the model accurately forecasts the behavior on an ARM multicore platform, and we also demonstrate that the model is not significantly influenced by variances in common type workloads.