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This work proposes a methodology to find performance and energy trade-offsfor parallel applications running on Heterogeneous Multi-Processing systemswith a single instruction-set architecture. These offer flexibility in the formof different core types and voltage and frequency pairings, defining a vastdesign space to explore. Therefore, for a given application, choosing aconfiguration that optimizes the performance and energy consumption is notstraightforward. Our method proposes novel analytical models for performanceand power consumption whose parameters can be fitted using only a fewstrategically sampled offline measurements. These models are then used toestimate an application's performance and energy consumption for the wholeconfiguration space. In turn, these offline predictions define the choice ofestimated Pareto-optimal configurations of the model, which are used to informthe selection of the configuration that the application should be executed on.The methodology was validated on an ODROID-XU3 board for eight programs fromthe PARSEC Benchmark, Phoronix Test Suite and Rodinia applications. Thegenerated Pareto-optimal configuration space represented a 99% reduction of theuniverse of all available configurations. Energy savings of up to 59.77%,61.38% and 17.7% were observed when compared to the performance, ondemand andpowersave Linux governors, respectively, with higher or similar performance.

Performance and Energy Trade-Offs for Parallel Applications on Heterogeneous Multi-Processing Systems