This work proposes a competitive scheduling approach, designed to scale to large heterogeneous multicore systems. This scheduler overcomes the challenges of (1) the high computation overhead of near-optimal schedulers, and (2) the error introduced by inaccurate performance predictions. This paper presents Agon, a neural network-based classifier that selects from a range of schedulers, from simple to very accurate, and learns which scheduler provides the right balance of accuracy and overhead for each scheduling interval. Agon also employs a de-noising frontend allowing the individual schedulers to be tolerant towards noise in performance predictions, producing better overall schedules. By avoiding expensive scheduling overheads, Agon improves average system performance by 6\% on average, approaching the performance of an oracular scheduler (99.1% of oracle performance).
@article{arxiv.2109.00665,
title = {Agon: A Scalable Competitive Scheduler for Large Heterogeneous Systems},
author = {Andreas Prodromou and Ashish Venkat and Dean M. Tullsen},
journal= {arXiv preprint arXiv:2109.00665},
year = {2021}
}