4.5 Article

An Adaptive CPU-GPU Governing Framework for Mobile Games on big.LITTLE Architectures

期刊

IEEE TRANSACTIONS ON COMPUTERS
卷 70, 期 9, 页码 1472-1483

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TC.2020.3012987

关键词

Games; Graphics processing units; Central Processing Unit; Smart phones; Computer architecture; Hardware; Rendering (computer graphics); Mobile games; smartphone; big; LITTLE architecture; power management; DVFS

资金

  1. Shenzhen Science and Technology Innovation Committee (SZSTI) [JCYJ20170818090006804]

向作者/读者索取更多资源

Mobile games are increasingly demanding in computational complexity, leading to rapid battery drain of integrated CPUs and GPUs, hindering user experience improvement. By introducing a CPU-GPU governing framework that recognizes performance demand and selects the most energy-efficient hardware configuration for different game scenes, significant power savings can be achieved without compromising user experience.
Games have been one of the most popular applications on smartphones. In order to meet the increasing computational complexity of mobile games, smartphones are now equipped with heterogeneous CPU multi-core architectures like big.LITTLE as well as high-performance GPUs. However, the integrated CPUs and GPUs drain the battery quickly, which has become a bottleneck for improving user experience. In addition to traditional Dynamic Voltage and Frequency Scaling (DVFS) technique for CPUs and GPUs power reduction, heterogeneous multi-core processors, such as the big.LITTLE architecture, have been designed to offer more opportunity for performance-energy tradeoffs. But current processor governors in smartphones can not exploit these power-saving mechanisms wisely, causing considerable energy waste. In this article, we propose a CPU-GPU governing framework that recognizes the performance demand for different game scenes, and select the most energy-efficient hardware configuration for the corresponding scenes. We implement our framework on an ODROID-XU4 mobile platform, and the experiments show that our framework can achieve 26.7, 16.6, and 10.5 percent power saving on average without compromising user experience when compared to the default governor used in our platform and two governors proposed by other researchers, respectively.

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