Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC
This work provides an incremental improvement for HPC systems by enabling energy savings in scenarios where existing methods fail, benefiting operators and users concerned with energy consumption.
The paper tackles the problem of energy inefficiency in High-Performance Computing (HPC) systems by developing a self-tuning approach that saves up to 15% energy with minimal runtime overhead, addressing the limitation of traditional methods in load-balanced codes.
System self-tuning is a crucial task to lower the energy consumption of computers. Traditional approaches decrease the processor frequency in idle or synchronisation periods. However, in High-Performance Computing (HPC) this is not sufficient: if the executed code is load balanced, there are neither idle nor synchronisation phases that can be exploited. Therefore, alternative self-tuning approaches are needed, which allow exploiting different compute characteristics of HPC programs. The novel notion of application regions based on function call stacks, introduced in the Horizon 2020 Project READEX, allows us to define such a self-tuning approach. In this paper, we combine these regions with the Q-Learning typical state-action maps, which save information about available states, possible actions to take, and the expected rewards. By exploiting the existing processor power interface, we are able to provide direct feedback to the learning process. This approach allows us to save up to 15% energy, while only adding a minor runtime overhead.