AI & ML
Green500: Adventure in Qualifying a Cluster
arXiv cs.DC0 views
arXiv:2611.09251v1 Announce Type: new Abstract: Energy effciency has become a key metric for evaluating High Performance Computing (HPC) systems, as reflected by the Green500 ranking. However, energy effciency alone does not capture the full environmental impact of an HPC cluster. Using the Kuma cluster at EPFL, we report on two Green500 measurement campaigns at Level 2 and Level 3, and extend this analysis to a full carbon footprint assessment using the MITSI life-cycle based methodology. GPU power capping experiments show that energy effciency peaks at 450 W, achieving an 11% performance loss offset by a 20% reduction in energy consumption. When embodied emissions are accounted for, the optimal power cap for carbon effciency shifts to 500 W under Switzerland's low-carbon electrical grid, revealing a tension between minimizing usage and embodied contributions. We discuss the challenges of generalizing this approach into a broader carbon-based HPC ranking, and argue that moving beyond energy effciency is both necessary and feasible.
Source：https://arxiv.org/abs/2611.09251PreviousRPEG: Training Reasoning Language Models Scalably and Cost-EffectivelyNextDataHelix: A CPU-GPU Co-Scheduling Data Plane for Multimodal Model Training
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