Fine-grained energy profiling for power-aware application design

Abstract

Significant opportunities for power optimization exist at application design stage and are not yet fully exploited by system and application designers. We describe the challenges developers face in optimizing software for energy efficiency by exploiting application-level knowledge. To address these challenges, we propose the development of automated tools that profile the energy usage of various resource components used by an application and guide the design choices accordingly. We use a preliminary version of a tool we have developed to demonstrate how automated energy profiling helps a developer choose between alternative designs in the energy-performance trade-off space.

References

1. L. A. Barroso and U. Hölzle. The case for energy-proportional computing. IEEE Computer, 40(12):33--37, 2007.
2. F. Bellosa. The benefits of event driven energy accounting in power-sensitive systems. In 9th ACM SIGOPS European Workshop, pages 37--42, 2000.
3. G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. Energy-aware server provisioning and load dispatching for connection-intensive internet services. In NSDI, San Francisco, CA, April 2008.
4. D. Economou, S. Rivoire, C. Kozyrakis, and P. Ranganathan. Full system power analysis and modeling for server environments. In Workshop on Modeling, Benchmarking, and Simulation, 2006.
5. E.P.A. Report on server and data center energy efficiency. Technical report, US Environmental Protection Agency, Energy Star Program, August 2007.
6. X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In ISCA, pages 13--23, 2007.
7. J. Fenlason and R. Stallman. Gnu gprof. http://www.gnu.org/software/binutils/manual/gprof-2.9.1/html chapter/gprof toc.html.
8. J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. In SOSP '99: Proceedings of the seventeenth ACM symposium on Operating systems principles, pages 48--63, 1999.
9. S. Gurumurthi, A. Sivasubramaniam, M. Irwin, N. Vijaykrishnan, M. Kandemir, T. Li, and L. John. Using complete machine simulation for software power estimation: The softwatt approach. In HPCA, pages 141--150, February 2002.
10. C. Im and S. Ha. Energy optimization for latency- and quality-constrained video applications. IEEE Design and Test of Computers, 21(5):358 -- 366, Sept.-Oct 2004.
11. S. Iyer, L. Luo, R. Mayo, and P. Ranganathan. Energy-adaptive display system designs for future mobile environments. In ACM MobiSys, pages 245--258, 2003.
12. R. Jain, D. Molnar, and Z. Ramzan. Towards a model of energy complexity for algorithms {mobile wireless applications}. In IEEE Wireless Communications and Networking Conference, pages 1884-- 1890, March 2005.
13. R. Jain, D. Molnar, and Z. Ramzan. Towards understanding algorithmic factors affecting energy consumption: switching complexity, randomness, and preliminary experiments. In DIALM-POMC '05: Proceedings of the 2005 joint workshop on Foundations of mobile computing, pages 70--79, New York, NY, USA, 2005. ACM.
14. J. G. Koomey. Estimating total power consumption by servers in the U.S. and the world. Technical report, Lawrence Berkeley National Laboratory, February 2007.
15. R. Kumar, K. I. Farkas, N. P. Jouppi, P. Ranganathan, and D. M. Tullsen. Single-ISA heterogeneous multi-core architectures: The potential for processor power reduction. In ACM/IEEE MICRO, pages 81--92, 2003.
16. X. Liu, P. Shenoy, and M. D. Corner. Chameleon: Application Level Power Management. IEEE Transactions on Mobile Computing, To Appear 2008.
17. D. McIntire, T. Stathopoulos, and W. Kaiser. etop: sensor network application energy profiling on the leap2 platform. In IPSN, pages 576--577, 2007.
18. J. Meier, S. Vasireddy, A. Babbar, and A. Mackman. How to: Use CLR profiler. http://msdn2.microsoft.com/en-us/ library/ms979205.aspx.
19. Microsoft. Event tracing for windows. Microsoft Developer Network, http://www.microsoft.com/whdc/devtools/ tools/EventTracing.mspx.
20. Microsoft. XPerf. http://msdn2.microsoft.com/en-us/library/cc305187.aspx.
21. D. Narayanan, A. Donnelly, and A. Rowstron. Write off-loading: Practical power management for enterprise storage. In FAST, February 2008.
22. S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and D. Wetherall. Reducing network energy consumption via sleeping and rate-adaptation. In NSDI, San Francisco, CA, April 2008.
23. S. Park, W. Jiang, Y. Zhou, and S. Adve. Managing energy-performance tradeoffs for multithreaded applications on multiprocessor architectures. In SIGMETRICS, 2007.
24. R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu. No power struggles: A unified multi-level power management architecture for the data center. In ASPLOS, March 2008.
25. S. Rivoire, M. A. Shah, P. Ranganathan, and C. Kozyrakis. Joulesort: a balanced energy-efficiency benchmark. In SIGMOD, pages 365--376, 2007.
26. J. Shirako, M. Yoshida, N. Oshiyama, Y. Wada, H. Nakano, H. Shikano, K. Kimura, and H. Kasahara. Performance evaluation of compiler controlled power saving scheme. In 20th ACM Int'l Conference on Supercomputing Workshop on Advanced Low Power Systems (ALPS2006), July 2006.
27. V. Shnayder, M. Hempstead, B. rong Chen, G. W. Allen, and M. Welsh. Simulating the power consumption of large-scale sensor network applications. In ACM SenSys, pages 188--200, 2004.
28. S. Singh, M. Woo, and C. S. Raghavendra. Power-aware routing in mobile ad hoc networks. In ACM Mobicom, pages 181--190, 1998.
29. Standard performance evaluation corporation. SPECpower. http://www.spec.org/power ssj2008/.
30. F. Xie, M. Martonosi, and S. Malik. Compile-time dynamic voltage scaling settings: opportunities and limits. SIGPLAN Not., 38(5):49--62, 2003.

Index Terms

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Social and professional topics

  • Professional topics

    • Management of computing and information systems

      • Software management

• Software and its engineering

  • Software creation and management