On‐the‐Fly Power‐Aware Rendering

Power saving is a prevailing concern in desktop computers and, especially, in battery‐powered devices such as mobile phones. This is generating a growing demand for power‐aware graphics applications that can extend battery life, while preserving good quality. In this paper, we address this issue by presenting a real‐time power‐efficient rendering framework, able to dynamically select the rendering configuration with the best quality within a given power budget. Different from the current state of the art, our method does not require precomputation of the whole camera‐view space, nor Pareto curves to explore the vast power‐error space; as such, it can also handle dynamic scenes. Our algorithm is based on two key components: our novel power prediction model, and our runtime quality error estimation mechanism. These components allow us to search for the optimal rendering configuration at runtime, being transparent to the user. We demonstrate the performance of our framework on two different platforms: a desktop computer, and a mobile device. In both cases, we produce results close to the maximum quality, while achieving significant power savings.

[1]  Jose-Maria Arnau,et al.  Eliminating redundant fragment shader executions on a mobile GPU via hardware memoization , 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).

[2]  Gordon Wetzstein,et al.  A survey on computational displays: Pushing the boundaries of optics, computation, and perception , 2013, Comput. Graph..

[3]  Chong-Min Kyung,et al.  Energy-Aware System Design: Algorithms and Architectures , 2011 .

[4]  Frédo Durand,et al.  Transform recipes for efficient cloud photo enhancement , 2015, ACM Trans. Graph..

[5]  Timo Koskela,et al.  Power Consumption Model of a Mobile GPU Based on Rendering Complexity , 2013, 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies.

[6]  장훈,et al.  [서평]「Computer Organization and Design, The Hardware/Software Interface」 , 1997 .

[7]  Weifeng Chen,et al.  An energy-saving color scheme for direct volume rendering , 2016, Comput. Graph..

[8]  Hujun Bao,et al.  Real-time rendering on a power budget , 2016, ACM Trans. Graph..

[9]  Lin Zhong,et al.  Power modeling of graphical user interfaces on OLED displays , 2009, 2009 46th ACM/IEEE Design Automation Conference.

[10]  Tomas Akenine-Möller,et al.  Power efficiency for software algorithms running on graphics processors , 2012, EGGH-HPG'12.

[11]  Morgan McGuire,et al.  Filtering approaches for real-time anti-aliasing , 2011, SIGGRAPH '11.

[12]  Donald J. Patterson,et al.  Computer organization and design: the hardware-software interface (appendix a , 1993 .

[13]  Ji Wang,et al.  An image-space energy-saving visualization scheme for OLED displays , 2014, Comput. Graph..

[14]  Lu Luo,et al.  Energy-Adaptive Display System Designs for Future Mobile Environments , 2003, MobiSys '03.

[15]  Hyesoon Kim,et al.  An integrated GPU power and performance model , 2010, ISCA.

[16]  Yiorgos Chrysanthou,et al.  Toward energy-aware balancing of mobile graphics , 2015, Electronic Imaging.

[17]  Anselmo Lastra,et al.  Precision selection for energy-efficient pixel shaders , 2011, HPG '11.

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.