Abstract
This work proposes a novel methodology to predict the optimal performance and energy efficiency trade-off configurations of parallel applications running on a two-cluster Heterogeneous Multi-Processing (HMP) system. we propose an analytic performance and power model that are generated offline using data measurements. These models are then used to estimate the whole configuration space to predict the application’s performance and energy consumption. Then, we use these off-line predictions to choose Pareto-optimal configurations, which is the most efficient among all configurations for the given architecture and multi-threaded application. We validated our methodology on an ODROID XU3 board on several PARSEC and Phoronix Test Suite applications.
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