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Automatic Energy-Efficient Job Scheduling in HPC: A Novel SLURM Plugin Approach

SessionThe 1st International Workshop on the Environmental Sustainability of High-Performance Software

DescriptionWe introduce a novel energy-efficient job scheduling approach for High-Performance Computing (HPC) environments. Its primary objective is to bridge the gap between research and production in energy-efficient scheduling models for HPC. The proposed architecture and program decouple scheduling heuristics to a Python application in the HPC scheduler SLURM, enabling adaptability for production setups. The implementation demonstrates 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 integrating research in the area into production, offering a realistic example of energy-efficient HPC in practice. It also opens possibilities for more advanced applications, like automatically scheduling jobs during low-cost and renewable energy periods, as already used by companies employing HPC. This contribution showcases a practical, energy-efficient solution for HPC job scheduling and identifies potential for future enhancements in this area.

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