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

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To this end PPPP is applied to predict the core-level and aggregate power consumption. Results are compared to a last value predictor also at the core and aggregate level. Core- level power is measured using the PMC-based power model. The model allows fine- grain, power management and temperature-aware estimation of core power. Rather than using core activity level to predict core activity level, it is used to cross predict power level. The predicted activity-level in the predictor is replaced by the modeled core power level. The prediction table index remains as sequences of core activity levels. This approach provides better pattern matching as variations in temperature and application of DVFS tends to hide otherwise discernable patterns. Accuracy(%) 100 95 90 85 80 75 70 65 60 55 50 61.4 56.9 Aggregate LastValue Aggregate PPPP Core-Level LastValue Core-Level PPPP 85.6 81.5 77.7 76.6 62.2 68.6 Figure 7.8 Prediction Accuracy of Core Power for Various Predictors Figure 7.8 shows the weighted average percent accuracy of the periodic power phase predictor compared to a last-value predictor. Weighted average is chosen since SYSmark 144 81.2 82.7 91.692.9 71.9 69.0 E-Learning VideoCreation Productivity 3D 86.487.0
2007 power consumption contains many idle, low-power phases. In these phases, a small error in absolute terms yields a large percentage error. Therefore, error values are scaled by the magnitude of measured power sample compared to the maximum observed. For example, a 10% error on a 5W sample has half the impact of a 10% error on a 10W sample. For all subtests, the core-level versions of the predictors outperformed the aggregate versions. The best overall performance is 86% accuracy for the periodic core- level predictor compared to 83% for the core-level version of the last-value predictor. The benefit of core-level prediction of power is less pronounced than for prediction of activity level. This is due to the smaller dynamic range of power consumption compared to activity level. Though activity levels regularly vary from 0% to 100%, power levels remain in a much smaller range of approximately 25% to 75%. 7.7 Summary This section presents the concept of core-level phase prediction and its application to dynamic power management. By observing changes in performance demand and power consumption at the core-level, it is possible to perceive predictable phase behavior. Prediction of phases allows power management to avoid over or under provisioning resources in response to workload changes. Using this concept the PPPP is developed. It is a simple, table-based prediction scheme for directing DVFS selection. It is applied to the SYSmark2007 benchmark suite and attain significant performance and power improvements. Compared to the reactive DVFS algorithm used by Windows Vista, 145
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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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Page 113 and 114: Light activity yields higher precha
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Page 157 and 158: Table 7.6: SYSmark 2007 Power and P
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Page 175 and 176: [BiJo06-1] W. L. Bircher and L. Joh
Page 177 and 178: the 2005 ACM SIGMETRICS Internation
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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