Impact of Tuning Parameters of Parallel Pattern Applications for Heterogeneous Embedded Systems on Performance and Energy Efficiency*

Tuning the execution of applications on heterogeneous multicore mobile and embedded systems can increase performance and/or reduce energy consumption. State-of-the-art tuning mechanisms typically focus on tuning the parameters of the hardware platform, such as the number, type and frequencies of the processor cores used. This paper explores applicationlevel parameter tuning of parallel applications. On the example of pipeline applications, we show that it is possible to obtain an improvement of 36 % in performance and 39 % in energy consumption by changing the extra-functional application parameters from their default values, thus preserving the application semantics and avoiding modifying the execution environment. We used regression-based machine learning model to find nearoptimal parameters settings based on workload characteristics. This offers further possibilities for a holistic approach of optimising the performance and energy of mobile systems by tuning both hardware parameters and the applications themselves.