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

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[Pl08] Report to Congress on Server and Data Center Energy Efficiency: Public law 109-431, Lawrence Berkeley National Laboratory, 2008. [LeSk05] K. Lee and K. Skadron. Using Performance Counters for Runtime Temperature Sensing in High-Performance Processors. In Proceedings of the First Workshop on High-Performance Power-Aware Computing in conjunction with International Parallel and Distributed Processing Symposium (Denver, Colorado, April 2005). [LeWa10] J. Lee, E. Wang, H. Ghasemi, W. L. Bircher, Y. Cao and N. Kim. Workload-Adaptive Process Tuning Strategy for Power-Efficient Multi-Core Processors. In Proceedings of the 2010 International Symposium on Low Power Electronics and Design (Austin, Texas, August 2010), 225-230. [LeWa07] C. Lefurgy, X. Wang, and M. Ware. Server-level power control. In Proceedings of the 4 th IEEE International Conference on Autonomic Computing (Jacksonville, Florida, June 2007), 4. [LiMa06] J. Li and J. Martinez. Dynamic Power-Performance Adaptation of Parallel Computation on Chip Multiprocessors. In Proceedings of the 12th International Symposium on High-Performance Computer Architecture (Austin, Texas, February 2006), 77-87. 166
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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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6.1.5 Direct Performance Effects Si
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of SPEC CPU2000 workloads, almost n
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adjusting a hysteresis timer. The t
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Page 169 and 170: Chapter 9 Conclusions and Future Wo
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Page 175 and 176: [BiJo06-1] W. L. Bircher and L. Joh
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Page 179 and 180: [HaKe07] H. Hanson, S.W. Keckler, K
Page 181: [JoMa01] R. Joseph and M. Martonosi
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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