Copyright by William Lloyd Bircher 2010 - The Laboratory for ...

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[Bl07] Blade Server Technology Overview. http://www.blade.org/techover.cfm (March 2007). [BoEl02] P. Bohrer, E. N. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, C. McDowell, and R. Rajamony. The Case For Power Management in Web Servers. IBM Research, Austin TX, 2002. [BrTiMa00] D. Brooks, V. Tiwari, and M. Martonosi. Wattch: A Framework for Architectural-Level Power Analysis and Optimizations, In Proceedings of the 27 th Annual International Symposium on Computer Architecture, (Vancouver, British Columbia, Canada, June 2000), 83-94. [ChJa09] J. Charles, P. Jassi, N. Ananth, A. Sadat, and A. Fedorova. Evaluation of the Intel® Core i7 Turbo Boost feature. In Proceedings of the 2009 IEEE International Symposium on Workload Characterization (Austin, Texas, October 2009), 188-197. [ChAn01] J. Chase, D. Anderson, P. Thakar, and A. Vahdat. Managing Energy and Server Resources in Hosting Centers. In Proceedings of the 18th ACM Symposium on Operating System Principles (Chateau Lake Louise, Banff, Alberta, Canada, October 2001), 103-116. [ChDa05] Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam, Managing Server Energy and Operational Costs in Hosting Centers. In Proceedings of 160
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Copyright by William Lloyd Bircher
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Predictive Power Management for Mul
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Acknowledgements I would like to th
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Predictive Power Management for Mul
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Table of Contents Chapter 1 Introdu
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5.3.6 Memory ......................
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List of Tables Table 1.1 Windows Vi
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List of Figures Figure 1.1 CPU Core
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Chapter 1 Introduction Computing sy
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increases the overhead of adaptatio
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suboptimal from a power and perform
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Active Core Activity Idle except fo
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4. Design a predictive power manage
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1.7 Organization This dissertation
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Chapter 2 Methodology The developme
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2.1.2 Subsystem-Level Power in a Se
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The main components are subsystem p
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Table 2.4 Laptop System Description
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2.3 Performance Counter Sampling To
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instruction streams that exercise a
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the power trace to the PMC trace co
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The first bar in Figure 3.1 “Fetc
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Table 3.4 Instruction Linear Regres
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long time to complete, more aggress
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power adaptations as c-states. Thes
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Core Power (Watts) 60 50 40 30 20 1
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The difference between C0-Idle and
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case error is 3.3%. Alternatively s
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3. Based on basic domain knowledge,
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3.6 Summary This section describes
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Power (Watts) 30 25 20 15 10 5 0 Co
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workloads become more memory-bound,
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At the other extreme, the productiv
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processor. In both platforms the to
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Table 4.1 Subsystem Power Standard
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the case of I/O, the observed workl
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average distribution. The apparent
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Table 4.4 Workload Phase Classifica
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Frequency 1 10 100 1000 PhaseLength
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power management, it is shown that
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power measurement hardware for mult
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memory. Since the number of main me
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TLB Misses - Loads/stores that miss
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The form of the subsystem power mod
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5.2.2 Memory This section considers
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prefetch traffic does increase afte
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average access time to the distant
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Watts Figure 5.6 Disk Power Model (
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exhibits little variation in power
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The memory model averaged about 9%
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efficiency rather than performance.
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through the application GUI. The nu
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1 data cache access rate dominates
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periods of disconnect, cache snoop
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5.3.4 CPU To test the extensibility
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Despite this, high accuracy of less
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Light activity yields higher precha
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5.3.8 Chipset The Chipset power mod
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applied to the GPU core logic, larg
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Chapter 6 Performance Effects of Dy
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can be considered as predictors whi
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improvement for low idle core frequ
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Page 133 and 134: In order to reduce p-state performa
Page 135 and 136: performance loss and power consumpt
Page 137 and 138: eactive scheme used in Windows Vist
Page 139 and 140: Watts Watts Watts Watts Watts 150 1
Page 141 and 142: 7.2 Commercial DVFS Algorithm Exist
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Page 145 and 146: workload/operating systems adds and
Page 147 and 148: instructions. In APCI[Ac07] termino
Page 149 and 150: confidence level will drop below a
Page 151 and 152: First, prediction accuracy is consi
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Page 155 and 156: which corresponds to the DVFS sched
Page 157 and 158: Table 7.6: SYSmark 2007 Power and P
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Page 161 and 162: 2007 power consumption contains man
Page 163 and 164: Chapter 8 Related Research This sec
Page 165 and 166: 8.2 System-Level Power Characteriza
Page 167 and 168: prediction scheme in this dissertat
Page 169 and 170: Chapter 9 Conclusions and Future Wo
Page 171 and 172: the duration of power and performan
Page 173 and 174: execution, portions of pipelines or
Page 175: [BiJo06-1] W. L. Bircher and L. Joh
Page 179 and 180: [HaKe07] H. Hanson, S.W. Keckler, K
Page 181 and 182: [JoMa01] R. Joseph and M. Martonosi
Page 183 and 184: [LiBr05] Y. Li, D. Brooks, Z. Hu, a
Page 185 and 186: [Os06] Open Source Development Lab,
Page 187 and 188: [WaCh08] X. Wang and M. Chen. Clust
Page 189 and 190: [PiSh01] P. Pillai and K. G. Shin.
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