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).

中文翻译：

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Agon：用于大型异构系统的可扩展竞争调度器

这项工作提出了一种竞争性调度方法，旨在扩展到大型异构多核系统。该调度器克服了以下挑战：(1) 接近最优调度器的高计算开销，以及 (2) 不准确的性能预测引入的错误。本文介绍了 Agon，这是一种基于神经网络的分类器，它从一系列调度程序中进行选择，从简单到非常准确，并了解哪个调度程序为每个调度间隔提供了准确度和开销的正确平衡。Agon 还采用了去噪前端，允许各个调度程序在性能预测中容忍噪声，从而产生更好的整体调度。通过避免昂贵的调度开销，Agon 将平均系统性能平均提高了 6%，接近预言式调度器的性能 (99.