In experiments with real DL training jobs and with trace-driven simulations, Pollux reduces average job completion time by 25%-50% relative to state-of-the-art DL schedulers, even when all jobs are submitted with ideal resource and training configurations. Based on the observation that the statistical efficiency of DL training can change over time, we also show that Pollux can reduce the cost of training large models in cloud environments by 25%.
