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Improving Energy Efficiency of Mobile Execution Exploiting Similarity of Application Control Flow

Published: 28 November 2016 Publication History

Abstract

In this work, we propose an energy efficient scheme for application ordering and execution on modern smartphone processors. We propose to improve the branch prediction piece present inside pipelined processors, by suitable clustering and scheduling of applications that exhibit similar control flow. We expect that these applications can benefit by sharing predictor table data structures, that can do away with table initializations and predictions every time an application context switch is encountered. Preliminary experiments show promising results, and we believe this proposal will open up several interesting avenues of research.

References

[1]
Battery university. http://batteryuniversity.com/learn/archive/whats_the_best_battery.
[2]
The mobile application market. http://www.berginsight.com/reportpdf/productsheet/bi-app2-ps.pdf.
[3]
Mobile architecture and mobile development. http://www.agile-path.com/services-mobile-architecture-development.php.
[4]
Balasubramanian et al. Energy consumption in mobile phones: a measurement study and implications for network applications. In ACM SIGCOMM, pages 280--293, 2009.
[5]
Banerjee et al. Detecting energy bugs and hotspots in mobile apps. In ACM SIGSOFT, pages 588--598, 2014.
[6]
Banerjee et al. Debugging energy-efficiency related field failures in mobile apps. In MOBILESoft, volume 16, 2016.
[7]
H. Casanova. Simgrid: A toolkit for the simulation of application scheduling. In CCGRID, pages 430--437, 2001.
[8]
S. Dutta, M. Das, and A. Banerjee. Enhancing branch prediction using software evolution. In IEEE NAS, pages 295--304, 2015.
[9]
J. Flinn and M. Satyanarayanan. Powerscope: A tool for profiling the energy usage of mobile applications. In WMCSA'99, pages 2--10, 1999.
[10]
Hicks et al. Towards an energy efficient branch prediction scheme using profiling, adaptive bias measurement and delay region scheduling. In IEEE DTIS, pages 19--24, 2007.
[11]
Huang et al. Moby: A mobile benchmark suite for architectural simulators. In IEEE ISPASS, pages 45--54. IEEE, 2014.
[12]
Kalla et al. Ibm power5 chip: A dual-core multithreaded processor. IEEE micro, 24(2):40--47, 2004.
[13]
R. E. Kessler. The alpha 21264 microprocessor. IEEE micro, 19(2):24--36, 1999.
[14]
Liu et al. Greendroid: Automated diagnosis of energy inefficiency for smartphone applications. IEEE Transactions on Software Engineering, 40(9):911--940, 2014.
[15]
G. Malhotra et al. ParTejas: A parallel simulator for multicore processors. In ISPASS, 2014.
[16]
Marcu et al. Energy consumption model for mobile wireless communication. In ACM international symposium on Mobility management and wireless access, pages 191--194, 2011.
[17]
S. McFarling. Combining branch predictors. Technical report, Technical Report TN-36, Digital Western Research Laboratory, 1993.
[18]
McMillian et al. Dynamic vs. static-stretching warm up: the effect on power and agility performance. The Journal of Strength & Conditioning Research, 20(3):492--499, 2006.
[19]
Molnar et al. Counterflow pipeline processor architecture. 1994.
[20]
Oliner et al. Carat: Collaborative energy diagnosis for mobile devices. In ACM SenSys.
[21]
Pan et al. Improving the accuracy of dynamic branch prediction using branch correlation. In ACM Sigplan Notices, volume 27, pages 76--84, 1992.
[22]
Pathak et al. Fine-grained power modeling for smartphones using system call tracing. In Proceedings of the sixth conference on Computer systems, pages 153--168. ACM, 2011.
[23]
Pathak et al. Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof. In ACM european conference on Computer Systems, pages 29--42, 2012.
[24]
A. Patil. A study of branch prediction in android.
[25]
Pillai et al. Real-time dynamic voltage scaling for low-power embedded operating systems. In ACM SIGOPS Operating Systems Review, volume 35, pages 89--102, 2001.
[26]
Roy et al. Comparison and evaluation of code clone detection techniques and tools: A qualitative approach. Science of Computer Programming, 74(7):470--495, 2009.
[27]
Yeh et al. Two-level adaptive training branch prediction. In MICRO, pages 51--61, 1991.
  1. Improving Energy Efficiency of Mobile Execution Exploiting Similarity of Application Control Flow

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        MoMM '16: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media
        November 2016
        363 pages
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        Published: 28 November 2016

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        Author Tags

        1. Scheduler
        2. branch prediction
        3. multi-tasking

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