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Heterogeneous multicores offer flexibility in the form of different core types and Dynamic Voltage and Frequency Scaling (DVFS), defining a vast configuration space. The optimal configuration choice is not always straightforward, even for single applications, and becomes a very difficult problem for dynamically changing scenarios of concurrent applications with unpredictable spawn and termination times and individual performance requirements. This article proposes an integrated approach for runtime decision making for energy efficiency on such systems. The approach consists of a model that predicts performance and power for any possible decision and low-complexity heuristics that use this model to evaluate a subset of possible decisions to choose the best. The model predicts performance by projecting standalone application profiling data to the current status of the system and power by using a set of platform-specific parameters that are determined only once for a given system and are independent of the application mix. Our approach is evaluated with a plethora of dynamic, multi-application scenarios. When considering best effort performance to be adequate, our runtime achieves on average 3% higher energy efficiency compared to the powersave governor and 2× better compared to the other linux governors. Moreover, when also considering individual applications’ performance requirements, our runtime is able to satisfy them, giving away 18% of the system’s energy efficiency compared to the powersave, which, however, misses the performance targets by 23%; at the same time, our runtime maintains an efficiency advantage of about 55% compared to the other governors, which also satisfy the performance constraints.

Energy-Efficient Runtime Management of Heterogeneous Multicores using Online Projection