@article{antici_online_2025,
  title = {An {{Online Algorithm}} for {{Power Consumption Prediction}} of {{HPC Workload}}},
  author = {Antici, Francesco and Borghesi, Andrea and Kiziltan, Zeynep and Domke, Jens and Bartolini, Andrea},
  date = {2025-08-05},
  journaltitle = {Future Generation Computer Systems},
  pages = {108064},
  issn = {0167-739X},
  doi = {10.1016/j.future.2025.108064},
  url = {https://www.sciencedirect.com/science/article/pii/S0167739X25003590},
  abstract = {As modern High-Performance Computing (HPC) systems push the boundaries of computational capabilities, their power consumption becomes a serious threat to environmental and energy sustainability. In such a context, accurate prediction of the jobs’ power consumption is instrumental to develop efficient power management strategies acting at the system level. To this end, in this paper, we present an online prediction algorithm to predict job power consumption in a production HPC system, prior to job execution. Our solution employs machine learning tools, and it is able to predict the minimum, average and maximum power consumption of a job, aggregated per node throughout its execution. Our approach leverages only information which is available at the time of job submission, and it is validated on two datasets extracted from production supercomputers, namely F-DATA from Supercomputer Fugaku and PM100 from Marconi100. Our experimental results show that our prediction algorithm outperforms state-of-the-art techniques, and it can accurately predict job power consumption, by obtaining an error of less than 12},
  keywords = {green computing,High-performance-computing,HPC workload,Job-level prediction,machine learning,natural language processing}
}