Saving Energy Using the READEX Methodology

With today’s top supercomputers consuming several megawatts of power, optimization of energy consumption has become one of the major challenges on the road to exascale computing. The EU Horizon 2020 project READEX provides a tools-aided auto-tuning methodology to dynamically tune HPC applications for energy-efficiency. READEX is a two-step methodology, consisting of the design-time analysis and runtime tuning stages. At design-time, READEX exploits application dynamism using the $${readex\_intraphase}$$ r e a d e x _ i n t r a p h a s e and the $${readex\_interphase}$$ r e a d e x _ i n t e r p h a s e tuning plugins, which perform tuning steps, and provide tuning advice in the form of a tuning model. During production runs, the runtime tuning stage reads the tuning model and dynamically switches the settings of the tuning parameters for different application regions. Additionally, READEX also includes a tuning model visualizer and support for tuning application level tuning parameters to improve the result beyond the automatic version. This paper describes the state of the art used in READEX for energy-efficiency auto-tuning for HPC. Energy savings achieved for different proxy benchmarks and production level applications on the Haswell and Broadwell processors highlight the effectiveness of this methodology.