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

Authors: Francesco Antici; Mohsen Seyedkazemi Ardebili; Andrea Bartolini; Zeynep Kiziltan;

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

Abstract

The dataset is a collection of jobs extracted from the job_table data of M100 (https://doi.org/10.5281/zenodo.7588815), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. The dataset is stored as a parquet file, where each entry contains the information on a job execution. The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: https://github.com/francescoantici/PM100-data/.

Country
Italy
Keywords

Power Prediction, HPC Jobs Dataset, High-performance computing, M100dataset, job power consumption, machine learning, workload analysis, High Performance Computing, Workload Manager, Power Consumption

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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