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UID:submissions.supercomputing.org_SC23_sess433_ws_ships103@linklings.com
SUMMARY:PM100: A Job Power Consumption Dataset of a Large-Scale Production
  HPC System
DESCRIPTION:Francesco Antici, Mohsen Seyedkazemi Ardebili, Andrea Bartolin
 i, and Zeynep Kiziltan (University of Bologna)\n\nThe power requirements o
 f modern High-Performance Computing (HPC) systems pose environmental and f
 inancial challenges, given their carbon emissions and strain power grids. 
 Optimizing power consumption together with system performance has thus bec
 ome crucial. As jobs running on a system contribute to the whole system's 
 power usage, predicting their power requirements before execution would al
 low forecasting the overall power consumption and perform techniques like 
 power capping. Such predictive studies need quality data, which is limited
  due to the inherent complexity of collecting structured data in a product
 ion system. This paper aims to fill the lack of resources for job power pr
 ediction and provide (i) a methodology to create a job power consumption d
 ataset from workload manager data and node power metrics logs, and (ii) a 
 novel dataset comprising around 230K jobs and their corresponding power co
 nsumption values. The dataset is derived from M100, a holistic dataset ext
 racted from a production supercomputer.\n\nTag: Artificial Intelligence/Ma
 chine Learning, Energy Efficiency, Green Computing, Performance Measuremen
 t, Modeling, and Tools, Sustainability\n\nRegistration Category: Workshop 
 Reg Pass\n\nSession Chairs: Andrea Borghesi (University of Bologna; Depart
 ment of Electrical, Electronic and Information Engineering) and Daniela Lo
 reti (University of Bologna)\n\n
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