PM100: A Job Power Consumption Dataset of a Large-scale Production HPC System

The power requirements of modern High-Performance Computing (HPC) systems pose environmental and financial challenges, as they contribute to carbon emissions and strain power grids. Optimizing power consumption together with system performance has thus become a crucial goal for HPC centers. As jobs running on a system contribute to the whole system’s power usage, predicting their power requirements before execution on the system would allow to forecast the overall power consumption and perform techniques like power capping at the workload manager level. Such predictive studies need quality data, which is limited due to the inherent complexity of collecting structured data for job power characterization in a production system. This paper aims to fill the lack of resources for job power prediction and provide the HPC community with (i) a methodology to create a job power consumption dataset from workload manager data and node power metrics logs, and (ii) a novel and large dataset comprising around 230K jobs and their corresponding power consumption values. The dataset is derived from M100, a holistic dataset extracted from a production supercomputer hosted at the HPC centre CINECA in Italy.