skip to main content
10.1145/1188455.1188567acmconferencesArticle/Chapter ViewAbstractPublication PagesscConference Proceedingsconference-collections
Article

Adaptive, transparent frequency and voltage scaling of communication phases in MPI programs

Published:11 November 2006Publication History

ABSTRACT

Although users of high-performance computing are most interested in raw performance, both energy and power consumption have become critical concerns. Some microprocessors allow frequency and voltage scaling, which enables a system to reduce CPU performance and power when the CPU is not on the critical path. When properly directed, such dynamic frequency and voltage scaling can produce significant energy savings with little performance penalty.This paper presents an MPI runtime system that dynamically reduces CPU performance during communication phases in MPI programs. It dynamically identifies such phases and, without profiling or training, selects the CPU frequency in order to minimize energy-delay product. All analysis and subsequent frequency and voltage scaling is within MPI and so is entirely transparent to the application. This means that the large number of existing MPI programs, as well as new ones being developed, can use our system without modification. Results show that the average reduction in energy-delay product over the NAS benchmark suite is 10%---the average energy reduction is 12% while the average execution time increase is only 2.1%.

References

  1. N. D. Adiga et al. An overview of the BlueGene/L supercomputer. In Supercomputing, November 2002.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. ASCI Purple Benchmark Suite. http://www.llnl.gov/asci/platiormstpurple/rfp/benchmarks/.]]Google ScholarGoogle Scholar
  3. D. Bailey, J. Barton, T. Lasinski, and H. Simon. The NAS parallel benchmarks. RNR-91-002, NASA Ames Research Center, August 1991.]]Google ScholarGoogle Scholar
  4. Ali Raza Butt, Chris Gniady, and Y. Charlie Hu. The performance impact of kernel prefetching on buffer cache replacement algorithms. In SIGMETRICS, pages 157--168, 2005.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. W. Cameron, X. Feng, and R. Ge. Performance-constrained, distributed dvs scheduling for scientific applications on power-aware clusters. In Supercomputing, November 2005.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Enrique V. Carrera, Eduardo Pinheiro, and Ricardo Bianchini. Conserving disk energy in network servers. In Intl. Conference on Supercomputing, June 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, Amin Vahdat, and Ronald P. Doyle. Managing energy and server resources in hosting centres. In Symposium on Operating Systems Principles, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Guilin Chen, Konrad Malkowski, Mahmut Kandemir, and Padma Raghavan. Reducing power with performance contraints for parallel sparse applications. In Workshop on High-Performance, Power-Aware Computing, April 2005.]]Google ScholarGoogle Scholar
  9. A. Dhodapkar and J. Smith. Comparing phase detection techniques. In International Symposium on Microarchitecture, pages 217--227, December 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Elmootazbellah Elnozahy, Michael Kistler, and Ramakrishnan Rajamony. Energy conservation policies for web servers. In Usenix Symposium on Internet Technologies and Systems, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. E. N. (Mootaz) Elnozahy, Michael Kistler, and Ramakrishnan Rajamony. Energy-efficient server clusters. In Workshop on Mobile Computing Systems and Applications, Feb 2002.]]Google ScholarGoogle Scholar
  12. Mark E. Femal. Non-uniform power distribution in data centers for safely overprovisioning circuit capacity and booasting throughput. Master's thesis, North Carolina State University, Raleigh, NC, May 2005.]]Google ScholarGoogle Scholar
  13. Vincent W. Freeh, David K. Lowenthal, Feng Pan, and Nandani Kappiah. Using multiple energy gears in MPI programs on a power-scalable cluster. In Principles and Practices of Parallel Programming, June 2005.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Vincent W. Freeh, David K. Lowenthal, Rob Springer, Feng Pan, and Nandani Kappiah. Exploring the energy-time tradeoff in MPI programs on a power-scalable cluster. In International Parallel and Distributed Processing Symposium, April 2005.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Chris Gniady, Ali Raza Butt, and Y. Charlie Hu. Program-counter-based pattern classification in buffer caching. In OSDI, pages 395--408, 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Chris Gniady, Y. Charlie Hu, and Yung-Hsiang Lu. Program counter based techniques for dynamic power management. In HPCA, pages 24--35, 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Richard Goering. Current physical design tools come up short. EE Times, April 14 2000.]]Google ScholarGoogle Scholar
  18. Chung hsing Hsu and Wu chun Feng. A power-aware run-time system for high-performance computing. In Supercomputing, November 2005.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Chung-Hsing Hsu and Wu-chun Feng. Effective dynamic-voltage scaling through CPU-boundedness detection. In Fourth IEEE/ACM Workshop on Power-Aware Computing Systems, December 2004.]]Google ScholarGoogle Scholar
  20. M. Huang, J. Renau, and J. Torellas. Positional adaptation of processors: Application to energy reduction. In International Symposium on Computer Architecture, June 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Nandani Kappiah, Vincent W. Freeh, and David K. Lowenthal. Just in time dynamic voltage scaling: Exploiting inter-node slack to save energy in MPI programs. In Supercomputing, November 2005.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Charles Lefurgy, Karthick Rajamani, Freeman Rawson, Wes Felter, Michael Kistler, and Tom W. Keller. Energy management for commerical servers. IEEE Computer, pages 39--48, December 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Athanasios E. Papathanasiou and Michael L. Scott. Energy efficiency through burstiness. In Workshop on Mobile Computing Systems and Applications, October 2003.]]Google ScholarGoogle Scholar
  24. Eduardo Pinheiro, Ricardo Bianchini, Enrique V. Carrera, and Taliver Heath. Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on Compilers and Operating Systems for Low Power, September 2001.]]Google ScholarGoogle Scholar
  25. Rolf Rabenseifner. Automatic profiling of MPI applications with hardware performance counters. In PVM/MPI, pages 35--42, 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Vivek Sharma, Arun Thomas, Tarek Abdelzaher, and Kevin Skadron. Power-aware QoS management in web servers. In IEEE Real-Time Systems Symposium, Cancun, Mexico, December 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Timothy Sherwood, Erez Perelman, Greg Hamerly, and Brad Calder. Automatically characterizing large scale program behavior. In Architectural Support for Programming Languages and Operating Systems, October 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Robert C. Springer IV, David K. Lowenthal, Barry Rountree, and Vincent W. Freeh. Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster. In ACM Symposium on Principles and Practice of Parallel Programming, March 2006.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M. Warren, E. Weigle, and W. Feng. High-density computing: A 240-node beowulf in one cubic meter. In Supercomputing, November 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Qingbo Zhu, Francis M. David, Christo Devaraj, Zhenmin Li, Yuanyuan Zhou, and Pei Cao. Reducing energy consumption of disk storage using power-aware cache management. In High-Performance Computer Architecture, February 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Adaptive, transparent frequency and voltage scaling of communication phases in MPI programs

              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
                SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing
                November 2006
                746 pages
                ISBN:0769527000
                DOI:10.1145/1188455

                Copyright © 2006 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: 11 November 2006

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • Article

                Acceptance Rates

                SC '06 Paper Acceptance Rate54of239submissions,23%Overall Acceptance Rate1,516of6,373submissions,24%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader

              HTML Format

              View this article in HTML Format .

              View HTML Format