Authors: Johannes Gebert (High Performance Computing Center (HLRS), Stuttgart; University of Stuttgart) and Daniel Barry and Jack Dongarra (University of Tennessee, Innovative Computing Laboratory (ICL))
Abstract: Appropriately adjusting the power draw of computational hardware plays a crucial role in its efficient use. While vendors have already implemented hardware-controlled power management, additional energy savings are available, depending on the state of the machine. We propose the online classification of such states based on computationally informed machine learning algorithms to adjust the power cap of the next time step. This research highlights that the overall energy consumption can be reduced significantly, often without a prohibitive penalty in the runtime of the applications.
Best Poster Finalist (BP): no
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