Abstract
Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running HPC workloads, we find power, energy and performance are closely related leading to the possibility to optimize energy without sacrificing (much or at all) performance.
This paper presents the power features of the POWER7 and shows how innovative software can use these features to optimize the power and energy consumptions of large cluster running HPC workloads.
This paper starts by presenting the new features which have been introduced in POWER7 to manage power consumption and the tools available to manage and record the power consumption. We then analyze the power consumption and performance of different HPC workloads at various levels of the POWER7 server (processor, memory, io) for different frequencies. We propose a model to predict both the power and energy consumption of real workloads based on their performance characteristics measured by hardware performance counters (HPM). We show that the power estimation model can achieve less than 5% error versus actual measurements. In conclusion, we present how an innovative scheduler can help to optimize both power and energy consumptions of large HPC clusters.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Kalla R, Sinharoy B (2009) POWER7: IBM Next generation server processor. Hot Chips Conference. HC21.25.826, Stanford University
Floyd MS, Ghiasi S, Keller TW, Rajamani K, Rawson FL, Rubio JC, Ware MS (2007) System power management support in the IBM POWER6 microprocessor. J Res Dev 51(6):733–746
Broyles M, Francois C, Geissler A, Hollinger M, Rosedahl T, Silva G, Van Heuklon J, Veale B. IBM EnergyScale for POWER7 processor based servers. http://www3.ibm.com/systems/power/hardware/whitepapers/energyscale7.html
Allarey J, George V, Jihagirdar S (2008) Power management enhancements in the 45 nm Intel core microarchitecture. Intel Technol J 12(3):169–178
Rajamani K, Hanson H, Rubio JC, Ghiasi S, Rawson FL (14 July 2006) Online power and performance estimation for dynamic power management. IBM Research Technical Report, RC 24007
Lee SJ, Lee HK, Yew PC (2007) Runtime performance projection model for dynamic power management. In: ACSAC 2007, pp 186–197
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Brochard, L., Panda, R. & Vemuganti, S. Optimizing performance and energy of HPC applications on POWER7. Comput Sci Res Dev 25, 135–140 (2010). https://doi.org/10.1007/s00450-010-0123-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-010-0123-3