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Power is becoming an increasingly important concern for large supercomputingcenters. Due to cost concerns, data centers are becoming increasingly limitedin their ability to enhance their power infrastructure to support increasedcompute power on the machine-room floor. At Los Alamos National Laboratory itis projected that future-generation supercomputers will be power-limited ratherthan budget-limited. That is, it will be less costly to acquire a large numberof nodes than it will be to upgrade an existing data-center and machine-roompower infrastructure to run that large number of nodes at full power. In thepower-limited systems of the future, machines will in principle be capable ofdrawing more power than they have available. Thus, power capping at thenode/job level must be used to ensure the total system power draw remains belowthe available level. In this paper, we present a statistically groundedframework with which to predict (with uncertainty) how much power a given jobwill need and use these predictions to provide an optimal node-level powercapping strategy. We model the power drawn by a given job (and subsequently bythe entire machine) using hierarchical Bayesian modeling with hidden Markov andDirichlet process models. We then demonstrate how this model can be used insideof a power-management scheme to minimize the affect of power capping on userjobs.

Modeling and Predicting Power Consumption of High Performance Computing Jobs