Beyond CPU: Considering Memory Power Consumption of Software

Hayri Acar, Gülfem I. Alptekin, Jean-Patrick Gelas, Parisa Ghodous

Abstract

ICTs (Information and Communication Technologies) are responsible around 2% of worldwide greenhouse gas emissions (Gartner, 2007). And according to the Intergovernmental Panel on Climate Change (IPPC) recent reports, CO2 emissions due to ICTs are increasing widely. For this reason, many works tried to propose various tools to estimate the energy consumption due to software in order to reduce carbon footprint. However, these studies, in the majority of cases, takes into account only the CPU and neglects all others components. Whereas, the trend towards high-density packaging and raised memory involve a great increased of power consumption caused by memory and maybe memory can become the largest power consumer in servers. In this paper, we model and then estimate the power consumed by CPU and memory due to the execution of a software. Thus, we perform several experiments in order to observe the behavior of each component.

References

  1. Gartner, Green IT: The New Industry Shock Wave, Gartner, Presentation at Symposium/ITXPO Conference, 2007.
  2. Power Supply Calculator, February 2014. URL: http://powersupplycalculator.net/.
  3. eXtreme Power Supply Calculator, January 2006. URL: http://outervision.com/power-supply-calculator.
  4. Kern, E., Dick, M., Naumann, S., Guldner, A., Johann, T., 2013. Green software and green software engineeringdefinitions, measurements, and quality aspects. ICT4S 2013: Proceedings of the First International Conference on Information and Communication Technologies for Sustainability.
  5. Joseph, R., Brooks, D., Martonosi, M., 2001. Live, runtime power measurements as a foundation for evaluating power/performance tradeoffs. Workshop on Complexity Effective Design WCED, held in conjunction with ISCA-28.
  6. Kamil, S., Shalf, J., Strohmaier, E., 2008. Power efficiency in high performance computing. IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008.
  7. Kansal, A., Zhao, F., Liu, J., Kothari, N., Bhattacharya, A., 2010. Virtual Machine Power Metering and Provisioning. ACM Symposium on Cloud Computing (SOCC).
  8. Wang, S., Chen, H., Shi, W., 2011. SPAN: A software power analyzer for multicore computer systems. Sustainable Computing: Informatics and Systems, Volume 1, Issue 1.
  9. Noureddine, A., Bourdon, A., Rouvoy, R., Seinturier, L., 2012. A Preliminary Study of the Impact of Software Engineering on GreenIT. First International Workshop on Green and Sustainable Software.
  10. Kim, M., Ju, Y., Chae, J., Park, M., 2014. A Simple Model for Estimating Power Consumption of a Multicore Server System. International Journal of Multimedia and Ubiquitos Engineering.
  11. Zapater, M. et al., 2015. Leakage-Aware Cooling Management for Improving Server Energy Efficiency. IEEE Trans. Parallel Distrib. Syst. 26(10): 2764-2777.
  12. Minas, L., Ellison, B., 2012. The Problem of Power Consumption in Servers. Intel Press.
  13. Kang, U. et al., 2010. 8 Gb 3-D DDR3 DRAM Using Through-Silicon-Via Technology. Journal of SolidState Circuits.
  14. Hur, I. and Lin, C., 2008. A comprehensive approach to DRAM power management. International Symposium on High Performance Computer Architecture.
  15. Emma, P., Reohr, W. and Meterelliyoz, M., 2008. Rethinking Refresh: Increasing Availability and Reducing Power in DRAM for Cache Applications. IEEE Micro.
  16. Zheng, H. et al., 2008. Mini-Rank: Adaptive DRAM Architecture for Improving Memory Power Efficiency. Proceedings of Micro.
  17. Vogelsang, T., 2010. Understanding the energy consumption of dynamic random access memories. Proceedings of the 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.
  18. Rosenfeld, P., Cooper-Balis, E., Jacob, B., 2011. DRAMSim2: A Cycle Accurate Memory System Simulator. CAL.
  19. Thoziyoor, S., Muralimanohar, N., Ahn, J., Jouppi, N., 2008. CACTI 5.1. HP Laboratories Palo Alto.
  20. Micron, 2007. Calculating Memory System Power for DDR3.
  21. Morgan, R. and MacEachern, D. 2010. URL: https://support.hyperic.com/display/SIGAR/Home
  22. Kambadur, M., Kim, M.A., 2014. An experimental survey of energy management across the stack. ACM International Conference on Object Oriented Programming Systems Languages & Applications.
Download


Paper Citation


in Harvard Style

Acar H., Alptekin G., Gelas J. and Ghodous P. (2016). Beyond CPU: Considering Memory Power Consumption of Software . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 417-424. DOI: 10.5220/0005764904170424


in Bibtex Style

@conference{smartgreens16,
author={Hayri Acar and Gülfem I. Alptekin and Jean-Patrick Gelas and Parisa Ghodous},
title={Beyond CPU: Considering Memory Power Consumption of Software},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={417-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005764904170424},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Beyond CPU: Considering Memory Power Consumption of Software
SN - 978-989-758-184-7
AU - Acar H.
AU - Alptekin G.
AU - Gelas J.
AU - Ghodous P.
PY - 2016
SP - 417
EP - 424
DO - 10.5220/0005764904170424