skip to main content
10.1145/2925426.2926260acmconferencesArticle/Chapter ViewAbstractPublication PagesicsConference Proceedingsconference-collections
research-article

AEQUITAS: Coordinated Energy Management Across Parallel Applications

Published:01 June 2016Publication History

ABSTRACT

A growing number of energy optimization solutions operate at the application runtime level. Despite delivering promising results, these application-scoped optimizations are fundamentally greedy: They assume to have an exclusive access to power management and often perform poorly when multiple power-managing applications co-exist, or different threads of the same application share power management hardware. In this paper, we introduce AEQUITAS, a first step to address this critical yet largely overlooked problem. The insight behind AEQUITAS is that co-existing applications may view power-managing hardware as a shared resource and coordinate power management decisions. As a concrete instance of this philosophy, we evaluated our ideas on top of a state-of-the-art energy-efficient work-stealing runtime. Experiments show that without AEQUITAS, multiple co-existing power-managing application runtimes suffer up to 32% performance loss and negate all power savings. With AEQUITAS, the beneficial energy-performance tradeoff reported in the single-application setting (12.9% energy savings and 2.5% performance loss) can be retained, but in a much more challenging setting where multiple power-managing runtimes co-exist on parallel architectures and multiple CPU cores share the same power domain.

References

  1. Acar, U. A., Chargueraud, A., and Rainey, M. Scheduling parallel programs by work stealing with private deques. In PPoPP '13 (2013), pp. 219--228. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Baek, W., and Chilimbi, T. M. Green: a framework for supporting energy-conscious programming using controlled approximation. In PLDI'10 (2010), pp. 198--209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bartenstein, T., and Liu, Y. D. Green streams for data-intensive software. In ICSE'13 (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Blelloch, G., Fineman, J., Gibbons, P., Kyrola, A., Shun, J., Tangwonsan, K., and Simhadri, H. V. Problem based benchmark suite, 2012.Google ScholarGoogle Scholar
  5. Blumofe, R. D. Executing Multithreaded Programs Efficiently. PhD thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Blumofe, R. D., and Leiserson, C. E. Scheduling multithreaded computations by work stealing. J. ACM 46, 5 (1999), 720--748. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Burd, T. D., and Brodersen, R. W. Design issues for dynamic voltage scaling. In ISLPED'00 (2000), pp. 9--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cohen, M., Zhu, H. S., Emgin, S. E., and Liu, Y. D. Energy types. In OOPSLA'12 (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Cong, G., Kodali, S., Krishnamoorthy, S., Lea, D., Saraswat, V., and Wen, T. Solving large, irregular graph problems using adaptive work-stealing. In ICPP'08 (2008), pp. 536--545. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Corporation, N. I. Ni labview signalexpress, January 2014.Google ScholarGoogle Scholar
  11. Ding, X., Wang, K., Gibbons, P. B., and Zhang, X. Bws: balanced work stealing for time-sharing multicores. In EuroSys'12 (2012), pp. 365--378. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Fei, Y., Zhong, L., and Jha, N. An energy-aware framework for coordinated dynamic software management in mobile computers. In MASCOTS'04 (2004), pp. 306--317. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Flinn, J., and Satyanarayanan, M. Energy-aware adaptation for mobile applications. In SOSP'99 (1999), pp. 48--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Frigo, M., Leiserson, C. E., and Randall, K. H. The implementation of the cilk-5 multithreaded language. In PLDI'98 (1998), pp. 212--223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Horowitz, M., Indermaur, T., and Gonzalez, R. Low-power digital design. In Low Power Electronics, 1994. Digest of Technical Papers., IEEE Symposium (1994), pp. 8--11.Google ScholarGoogle ScholarCross RefCross Ref
  16. Hsu, C.-H., and Kremer, U. The design, implementation, and evaluation of a compiler algorithm for cpu energy reduction. In PLDI'03 (2003), pp. 38--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Intel. Intel cilk plus, 2015.Google ScholarGoogle Scholar
  18. Intel. Intel threading building blocks (intel tbb), 2015.Google ScholarGoogle Scholar
  19. Isci, C., and Martonosi, M. Runtime power monitoring in high-end processors: Methodology and empirical data. In MICRO'03 (2003), p. 93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kandemir, M., Vijaykrishnan, N., Irwin, M. J., and Ye, W. Influence of compiler optimizations on system power. In DAC'00 (2000), pp. 304--307. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kansal, A., Saponas, S., Brush, A. B., McKinley, K. S., Mytkowicz, T., and Ziola, R. The latency, accuracy, and battery (lab) abstraction: Programmer productivity and energy efficiency for continuous mobile context sensing. In OOPSLA'13 (2013), pp. 661--676. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Koufaty, D., Reddy, D., and Hahn, S. Bias scheduling in heterogeneous multi-core architectures. In EuroSys'10 (2010), pp. 125--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Kumar, V., Blackburn, S. M., and Grove, D. Friendly barriers: Efficient work-stealing with return barriers. In VEE '14 (2014), pp. 165--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Kumar, V., Frampton, D., Blackburn, S. M., Grove, D., and Tardieu, O. Work-stealing without the baggage. In OOPSLA'12 (2012), pp. 297--314. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lea, D. A java fork/join framework. In Proceedings of the ACM 2000 conference on Java Grande (2000), JAVA'00, pp. 36--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Leijen, D., Schulte, W., and Burckhardt, S. The design of a task parallel library. In OOPSLA'09 (2009), pp. 227--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Liu, K., Pinto, G., and Liu, Y. D. Data-oriented characterization of application-level energy optimization. In FASE '15.Google ScholarGoogle Scholar
  28. Liu, Y. D. Energy-efficient synchronization through program patterns. In Proceedings of GREENS'12 (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Marlow, S., Peyton Jones, S., and Singh, S. Runtime support for multicore haskell. In ICFP'09 (2009), pp. 65--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Michael, M. M., Vechev, M. T., and Saraswat, V. A. Idempotent work stealing. In PPoPP'09 (2009), pp. 45--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Morrison, A., and Afek, Y. Fence-free work stealing on bounded tso processors. In ASPLOS '14 (2014), pp. 413--426. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Pinheiro, E., Bianchini, R., Carrera, E. V., and Heath, T. Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on compilers and operating systems for low power (2001), vol. 180, Barcelona, Spain, pp. 182--195.Google ScholarGoogle Scholar
  33. Pinto, G., Castor, F., and Liu, Y. D. Understanding energy behaviors of thread management constructs. In OOPSLA'14 (October 2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Ribic, H., and Liu, Y. D. Energy-efficient work-stealing language runtimes. In ASPLOS'14 (2014), pp. 513--528. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Roy, A., Rumble, S. M., Stutsman, R., Levis, P., Mazières, D., and Zeldovich, N. Energy management in mobile devices with the cinder operating system. In EuroSys'11 (2011), pp. 139--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Sampson, A., Dietl, W., Fortuna, E., Gnanapragasam, D., Ceze, L., and Grossman, D. Enerj: Approximate data types for safe and general low-power computation. In PLDI'11 (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Shen, X., Zhong, Y., and Ding, C. Locality phase prediction. In ASPLOS'04 (2004), pp. 165--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Sherwood, T., Perelman, E., and Calder, B. Basic block distribution analysis to find periodic behavior and simulation points in applications. In PACT'01 (2001), pp. 3--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Sherwood, T., Perelman, E., Hamerly, G., and Calder, B. Automatically characterizing large scale program behavior. In ASPLOS'02 (2002), pp. 45--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Sherwood, T., Sair, S., and Calder, B. Phase tracking and prediction. In ISCA'03 (2003), pp. 336--349. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Taliver, Heath, T., Pinheiro, E., Hom, J., Kremer, U., and Bianchini, R. Code transformations for energy-efficient device management. IEEE Transactions on Computers 53 (2004), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Varatkar, G., and Marculescu, R. Communication-aware task scheduling and voltage selection for total systems energy minimization. In ICCAD'03 (2003), pp. 510--517. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Weiser, M., Welch, B., Demers, A., and Shenker, S. Scheduling for reduced cpu energy. In OSDI'94 (1994). Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Xie, F., Martonosi, M., and Malik, S. Compile-time dynamic voltage scaling settings: opportunities and limits. In PLDI'03 (2003), pp. 49--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Yuan, W., and Nahrstedt, K. Energy-efficient soft real-time cpu scheduling for mobile multimedia systems. In SOSP'03 (2003), pp. 149--163. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Zeng, H., Ellis, C. S., Lebeck, A. R., and Vahdat, A. Ecosystem: managing energy as a first class operating system resource. In ASPLOS'02 (2002), pp. 123--132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Zhang, Y., Hu, X., and Chen, D. Task scheduling and voltage selection for energy minimization. In Design Automation Conference, 2002. Proceedings. 39th (2002), pp. 183--188. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Zhu, H. S., Lin, C., and Liu, Y. D. A programming model for sustainable software. In ICSE'15 (2015). Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    ICS '16: Proceedings of the 2016 International Conference on Supercomputing
    June 2016
    547 pages
    ISBN:9781450343619
    DOI:10.1145/2925426

    Copyright © 2016 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 1 June 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate584of2,055submissions,28%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader