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.
On the road towards ExaFLOP performance, special attention has been given to system-level power consumption by clusters. Recent work at Google @cite_8 describes the use of Artificial Neural Networks to model Power Usage Effectiveness using a mixture of workload and cooling features. System-level prediction of power consumption is also one application of our predictive model . In terms of power-aware scheduling, another possible application of our models, the authors in @cite_1 @cite_9 introduce a method based on Constraint Programming, to achieve power capping on , the same HPC system analyzed here. This could benefit greatly from power prediction offered by our framework.
@cite_9 @cite_1 @cite_8
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Supercomputers machines, HPC systems in general, embed sophisticated and advanced cooling circuits to remove heat and ensuring the required peak performance. Unfortunately removing heat, by means of cold water or air, costs additional power which decreases the overall supercomputer energy efficiency. Free-cooling uses ambient air instead than chiller to cool down warm air or liquid temperature. The amount of heat which can be removed for-free depends on ambient conditions such as temperature and humidity. Power capping can be used to reduce the supercomputer power dissipation to maximize the cooling efficiency. In this paper we present a power capping approach based on Constraint Programming which enables to estimate at every scheduling interval the power consumption of a given job schedule and to select among all possible job schedules the one which maximizes the supercomputer efficiency. Power consumption is a key factor in modern ICT infrastructure, especially in the expanding world of High Performance Computing, Cloud Computing and Big Data. Such consumption is bound to become an even greater issue as supercomputers are envisioned to enter the Exascale by 2020, granted that they obtain an order of magnitude energy efficiency gain. An important component in many strategies devised to decrease energy usage is "power capping", i.e., the possibility to constrain the system power consumption within certain power budget. In this paper we propose two novel approaches for power capped workload dispatching and we demonstrate them on a real-life high-performance machine: the Eurora supercomputer hosted at CINECA computing center in Bologna. Power capping is a feature not included in the commercial Portable Batch System PBS dispatcher currently in use on Eurora. The first method is based on a heuristic technique while the second one relies on a hybrid strategy which combines a CP and a heuristic approach. Both systems are evaluated and compared on simulated job traces.