Machine Learning-Based Runtime Prediction and Energy Optimization for HPC Job Scheduling Using the NREL Eagle Supercomputer Dataset

Summary

7.3 million jobs from NREL's Eagle supercomputer: users need a median of only 6.7% of the time they request, and machine-learning-adjusted limits save 64.8% of energy, about 7.25 million kWh a year.