使用框架结构的交互式3D游戏的电源管理

Yan Gu, S. Chakraborty
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引用次数: 35

摘要

我们提出了一种新的动态电压缩放(DVS)方案,专门针对在电池供电的便携式设备上运行的3D图形密集型交互式游戏应用程序。此分布式交换机方案的关键在于解析每个游戏帧以估计其渲染工作量,然后使用该估计来缩放底层处理器的电压/频率。这个方案的主要新颖之处在于,游戏框架提供了丰富多样的“结构”信息(例如,画笔和别名模型的数量,纹理信息和光线贴图),这些信息可以用来估计它们的处理工作量。尽管分布式交换机已广泛应用于视频解码应用,但压缩视频帧不提供任何信息(除了帧类型- I, B或P),可以以类似的方式用于估计其处理工作量。因此,为视频解码设计的分布式交换机算法主要依赖于控制理论反馈机制,其中帧的工作负载是根据先前呈现帧的工作负载预测的。我们表明,与这些基于历史的预测器相比,我们提出的方案在游戏应用程序中表现得更好。我们基于Windows XP上运行的Quake II游戏引擎的实验结果表明,与基于历史的预测方案相比,在相同的能耗下,我们的方案的质量提高了50%以上(以满足截止日期的帧数衡量)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Management of Interactive 3D Games Using Frame Structures
We propose a novel dynamic voltage scaling (DVS) scheme that is specifically directed towards 3D graphics- intensive interactive game applications running on battery-operated portable devices. The key to this DVS scheme lies in parsing each game frame to estimate its rendering workload and then using such an estimate to scale the voltage/frequency of the underlying processor. The main novelty of this scheme stems from the fact that game frames offer a rich variety of "structural" information (e.g. number of brush and alias models, texture information and light maps) which can be exploited to estimate their processing workload. Although DVS has been extensively applied to video decoding applications, compressed video frames do not offer any information (beyond the frame types - I, B or P) that can be used in a similar manner to estimate their processing workload. As a result, DVS algorithms designed for video decoding mostly rely on control-theoretic feedback mechanisms, where the workload of a frame is predicted from the workloads of the previously-rendered frames. We show that compared to such history-based predictors, our proposed scheme performs significantly better for game applications. Our experimental results, based on the Quake II game engine running on Windows XP, show that for the same energy consumption our scheme results in more than 50% improvement in quality (measured in terms of number of frames meeting their deadlines) compared to history-based prediction schemes.
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