Airavat: Improving energy efficiency of heterogeneous applications
Airavat: Improving energy efficiency of heterogeneous applications
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Airavat：提高异构应用的能源效率
DOI：
10.23919/date.2018.8342104
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Norman Rubin
中科院分区：
文献类型：
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作者：
Trinayan Baruah;Yifan Sun;Shi Dong;D. Kaeli;Norman Rubin
新兴的应用程序试图在异质系统中同时使用CPU和GPU。当CPU和GPU合作使用时，可以实现这些应用程序的峰值性能。但是，随着绩效的提高，电力和能源管理是一个更大的挑战。在本文中，我们解决了执行以节能方式同时利用CPU和GPU的应用程序的问题。为此，我们提出了一个名为Airavat的电源管理框架，该框架协同调整了CPU，GPU和内存频率，以提高协作CPU-GPU应用程序的能源效率。 Airavat使用基于机器学习的预测模型，结合基于反馈的动态电压和频率缩放，以提高此类应用的能效。我们在NVIDIA JETSON TX1上演示了我们的框架，并观察到能量延迟产品（EDP）的改善，而绩效损失可忽略不计。
An emerging class of applications attempt to make use of both the CPU and GPU in a heterogeneous system. The peak performance for these applications is achieved when both the CPU and GPU are used collaboratively. However, along with this increased gain in performance, power and energy management is a larger challenge. In this paper we address the issue of executing applications that utilize both the CPU and GPU in an energy efficient way. Towards this end, we propose a power management framework named Airavat that tunes the CPU, GPU and memory frequencies, synergestically, in order to improve the energy efficiency of collaborative CPU-GPU applications. Airavat uses machine learning-based prediction models, combined with feedback based Dynamic Voltage and Frequency Scaling to improve the energy efficiency of such applications. We demonstrate our framework on the NVIDIA Jetson TX1 and observe an improvement in terms of Energy Delay Product (EDP) by 24% with negligible performance loss.