ButterFly: Mobile collaborative rendering over GPU workload migration

The ever increasing of display resolution on mobile devices raises high demand for GPU rendering details. However, the challenge of poor hardware support but fine-grained rendering details often makes user unsatisfied especially in calling for high frame rate scenarios, e.g., game. To resolve such issue, we propose BUTTERFLY, a novel system which collaboratively utilizes mobile GPUs to process high-quality rendering details for on-the-go mobile users. In particular, ButterFly achieves two technical contributions for the collaborative design: (1) a mobile device can migrate GPU workloads in buffer queue to peers, and (2) the collaborative rendering mechanism benefits user high quality details while significant power saving performance. Both techniques are compatible with the OpenGL ES standards. Furthermore, a 40-person survey perceives that ButterFly can provide excellent user experience of both rendering details and frame rate over Wi-Fi network. In addition, our comprehensive trace-driven experiments on Android prototype reveal the benefits of Butterfly have more superior performance over state-of-the-art systems, which achieves more than 28.3% power saving.

[1]  M. Angela Sasse,et al.  Sharp or smooth?: comparing the effects of quantization vs. frame rate for streamed video , 2004, CHI '04.

[2]  Yunxin Liu,et al.  Optimizing Smartphone Power Consumption through Dynamic Resolution Scaling , 2015, MobiCom.

[3]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.

[4]  Alec Wolman,et al.  Demo: Kahawai: high-quality mobile gaming using GPU offload , 2015, MobiSys.

[5]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[6]  Kajal T. Claypool,et al.  Perspectives, frame rates and resolutions: it's all in the game , 2009, FDG.

[7]  Jeffrey S. Vetter,et al.  A Survey of Methods for Analyzing and Improving GPU Energy Efficiency , 2014, ACM Comput. Surv..

[8]  Sokol Kosta,et al.  To offload or not to offload? The bandwidth and energy costs of mobile cloud computing , 2013, 2013 Proceedings IEEE INFOCOM.

[9]  Jörg Widmer,et al.  Survey on Energy Consumption Entities on the Smartphone Platform , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[10]  Yuezhi Zhou,et al.  Transparent Computing: A New Paradigm for Pervasive Computing , 2006, UIC.

[11]  James D. Herbsleb,et al.  Simplifying cyber foraging for mobile devices , 2007, MobiSys '07.

[12]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[13]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[14]  Thomas L. Saaty,et al.  Decision making with the analytic network process , 2013 .

[15]  Xu Chen,et al.  COMET: Code Offload by Migrating Execution Transparently , 2012, OSDI.

[16]  Luis G. Vargas,et al.  Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks , 2013 .