01568nas a2200133   4500008004100000245008400041210006900125260003600194490001400230520111700244100001701361700002001378856003601398       2016                        eng d00aPower Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer0 aPower Consumption Modeling and Prediction in a Hybrid CPUGPUMIC   aGrenoble, FrancebSpringer LNCS0 vLNCS 98333 aPower 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.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://arxiv.org/abs/1601.05961