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Reducing HPC Energy Footprint for Large Scale GPU Accelerated Workloads
SessionSustainable Supercomputing
DescriptionAs the energy cost continues to rise, HPC centers need to reduce their energy footprint. We examine a French national machine hosted at CINES in Montpellier, Adastra, based on AMD-MI250X GPUs and #3 in Green500. As a base for the study, we define a set of applications representative of our current HPC/AI production workload. In this parametric study, we characterize our diverse workload by applying a range of frequency or power capping policies at the node level in order to build an efficiency profile of each application. Based on the collected results, we produce guidelines trading between pure energy savings to pure performance for each application and for the production workload as a whole. We hope the results of this study will be of help to accelerators enabled HPC centers seeking to reduce their energy footprint by applying capping policies on their accelerators at the node level.
Event Type
Workshop
TimeSunday, 12 November 202311:25am - 11:40am MST
Location603
Energy Efficiency
Green Computing
Sustainability
W