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DTSTART;TZID=America/Denver:20231112T154500
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UID:submissions.supercomputing.org_SC23_sess433_ws_ships105@linklings.com
SUMMARY:Automatic Energy-Efficient Job Scheduling in HPC: A Novel SLURM Pl
 ugin Approach
DESCRIPTION:Anders Aaen Springborg and Michele Albano (Aalborg University,
  Copenhagen) and Samuel Xavier-de-Souza (Federal University of Rio Grande 
 do Norte, Brazil)\n\nWe introduce a novel energy-efficient job scheduling 
 approach for High-Performance Computing (HPC) environments. Its primary ob
 jective is to bridge the gap between research and production in energy-eff
 icient scheduling models for HPC. The proposed architecture and program de
 couple scheduling heuristics to a Python application in the HPC scheduler 
 SLURM, enabling adaptability for production setups. The implementation dem
 onstrates an 11% potential energy saving in the High-Performance Conjugate
  Gradients (HPCG) benchmark, highlighting the practicality of the approach
  in a single-node HPC cluster. This work serves as a foundation for integr
 ating research in the area into production, offering a realistic example o
 f energy-efficient HPC in practice. It also opens possibilities for more a
 dvanced applications, like automatically scheduling jobs during low-cost a
 nd renewable energy periods, as already used by companies employing HPC. T
 his contribution showcases a practical, energy-efficient solution for HPC 
 job scheduling and identifies potential for future enhancements in this ar
 ea.\n\nTag: Artificial Intelligence/Machine Learning, Energy Efficiency, G
 reen Computing, Performance Measurement, Modeling, and Tools, Sustainabili
 ty\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Andrea B
 orghesi (University of Bologna; Department of Electrical, Electronic and I
 nformation Engineering) and Daniela Loreti (University of Bologna)\n\n
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