Existing GPU-sharing techniques, including spatial and temporal sharing, aim to improve utilization but face challenges in simultaneously ensuring SLO adherence and maximizing efficiency due to the lack of fine-grained task scheduling on closed-source GPUs. This paper presents Hummingbird, an SLO-oriented GPU scheduling system that overcomes these challenges by enabling microsecond-scale preemption on closed-source GPUs while effectively harvesting idle GPU time slices. Comprehensive evaluations across diverse GPU architectures reveal that Hummingbird improves the SLO attainment of high-priority tasks by 9.7x and 3.5x compared to the state-of-the-art spatial and temporal-sharing approaches. When compared to executing exclusively, the SLO attainment of the high-priority task, collocating with low-priority tasks on Hummingbird, only drops by less than 1%. Meanwhile, the throughput of the low-priority task outperforms the state-of-the-art temporal-sharing approaches by 2.4x. Hummingbird demonstrates significant effectiveness in ensuring the SLO while enhancing GPU utilization.

翻译：现有的GPU共享技术，包括空间共享与时间共享，旨在提升利用率，但由于闭源GPU缺乏细粒度任务调度能力，在同时确保服务等级目标（SLO）遵循与效率最大化方面面临挑战。本文提出蜂鸟（Hummingbird），一种面向SLO的GPU调度系统，它通过在闭源GPU上实现微秒级抢占，并有效收集空闲GPU时间片，从而克服了上述挑战。跨多种GPU架构的综合评估表明，与最先进的空间共享和时间共享方法相比，蜂鸟将高优先级任务的SLO达成率分别提升了9.7倍和3.5倍。与独占执行相比，高优先级任务在蜂鸟上与低优先级任务协同运行时，其SLO达成率下降幅度小于1%。同时，低优先级任务的吞吐量超越了最先进时间共享方法的2.4倍。蜂鸟在确保SLO的同时显著提升了GPU利用率，展现了卓越的有效性。