01787nas a2200121   4500008004100000245006100041210006000102260003800162520137900200100001701579700002001596856004901616       2016                        eng d00aPredicting System-level Power for a Hybrid Supercomputer0 aPredicting Systemlevel Power for a Hybrid Supercomputer  aInnsbruck, AustriabIEEEc07/20163 aFor current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://ieeexplore.ieee.org/document/7568420/