Balancing Energy Efficiency and Real-Time Performance in GPU Scheduling
Balancing Energy Efficiency and Real-Time Performance in GPU Scheduling
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DOI：
10.1109/rtss52674.2021.00021
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发表时间：
2021-12
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影响因子：
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通讯作者：
Yidi Wang;Mohsen Karimi;Yecheng Xiang;Hyoseung Kim
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文献类型：
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作者：
Yidi Wang;Mohsen Karimi;Yecheng Xiang;Hyoseung Kim
嵌入式平台上提供的通用图形处理单元(GPU)在实时网络物理系统中获得了极大的兴趣。尽管在多任务环境中，GPU在许多计算密集型任务上的表现通常优于CPU，但高功耗仍然是一个具有挑战性的问题。在本文中，我们首先分析了空间多任务调度的GPU内核的功耗和能量消耗，最近的研究发现空间多任务调度有利于可调度性，然而，即使在最新的商用嵌入式GPU如NVIDIA Jetson Xavier AGX中，空间多任务调度也会降低能效。在此基础上，我们提出了一种实时节能的GPU调度框架sBEET，该框架在运行时做出调度决策以优化能耗，同时利用空间多任务来提高实时性能。我们在实际硬件上使用著名的GPU基准测试程序和随机生成的计时参数来评估所提出的sBEET框架的性能。结果表明，在系统过载时，sBEET将截止期错失率降低了13%；在任务集可调度时，sBEET的能耗比已有工作降低了15%~21%。
General-purpose graphics processing units (GPUs) made available on embedded platforms have gained much interest in real-time cyber-physical systems. Despite the fact that GPUs generally outperform CPUs on many compute-intensive tasks in a multitasking environment, high power consumption remains a challenging problem. In this paper, we first analyze the power and energy consumption of GPU kernels scheduled with spatial multitasking, which is found to be advantageous for schedulability in recent studies, and prove that its use, however, degrades energy efficiency even in the latest commercially available embedded GPUs like NVIDIA Jetson Xavier AGX. Then, based on our observations, we propose sBEET, a real-time energy-efficient GPU scheduling framework that makes scheduling decisions at runtime to optimize the energy consumption while utilizing spatial multitasking to improve real-time performance. We evaluate the performance of the proposed sBEET framework using well-known GPU benchmarks and randomly-generated timing parameters on real hardware. The results indicate that sBEET reduces deadline misses up to 13% when the system is overloaded, and also achieves 15% to 21% lower energy consumption when the tasksets are schedulable compared to the existing works.