Power consumption of computers is becoming an major concern. To optimise their power consumption it is necessary to have precise information on the behavior of applications. With this information, it is possible to choose the right frequency of a processor. The speed of some applications is not really impacted by changes of this frequency, while for some application it has an important effect.

The goal of this internship is to model the fine grained behavior of applications and to link this behavior with the impact (on performance and energy) of frequency changes. As it is difficult even for programmers to know this exact behavior, we will obtain it through fine grained monitoring (hardware performance counters, RAPL, …)

This internship will:

• Acquire behavior of HPC applications at different frequencies (several datasets are already availble)
• Model the performance and energy in function of the frequency and monitored values
• If time allows, propose a scheduling algorithm harnessing the model to select the best frequency

The acquisition will be done on Grid5000 (www.grid5000.fr) using Expetator (https://gitlab.irit.fr/sepia-pub/expetator)

The project is in the context of the ENERGUMEN (https://www.irit.fr/energumen/ funded by ANR) project at IRIT (http://www.irit.fr) in Toulouse in the SEPIA team. For more information contact Georges Da Costa (georges.da-costa@irit.fr) and Tom Guerout (Tom Guerout guerout@laas.fr). The main research topics of the SEPIA team are energy and performance optimisation of datacenter (multi-objective scheduling). Discussions are open on the exact topic as long as it stays coherent with the activity of SEPIA team.

Expected ability of the student

• Machine learning
• Linux
• Knowledge on distributed systems and HPC systems would be a plus