  Power consumption is a major obstacle for High Performance Computing (HPC)
systems in their quest towards the holy grail of ExaFLOP performance.
Significant advances in power efficiency have to be made before this goal can
be attained and accurate modeling is an essential step towards power efficiency
by optimizing system operating parameters to match dynamic energy needs. In
this paper we present a study of power consumption by jobs in Eurora, a hybrid
CPU-GPU-MIC system installed at the largest Italian data center. Using data
from a dedicated monitoring framework, we build a data-driven model of power
consumption for each user in the system and use it to predict the power
requirements of future jobs. We are able to achieve good prediction results for
over 80% of the users in the system. For the remaining users, we identify
possible reasons why prediction performance is not as good. Possible
applications for our predictive modeling results include scheduling
optimization, power-aware billing and system-scale power modeling. All the
scripts used for the study have been made available on GitHub.
