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

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performance is increased by 5.4% and while power consumption is reduced by 3.8%. We show that processor power can be predicted by PPPP with accuracy 4.8% better than a last-value predictor. Predictive schemes such as PPPP are the next step in improving performance and efficiency of systems employing dynamic power management. As it was demonstrated in the pursuit of higher single-threaded processor performance, the highest possible performance is achieved when costly phase changes can be predicted. Prediction allows the use of more aggressive power saving techniques since excessive performance loss can be avoided. 146
Chapter 8 Related Research This section summarizes related research in the areas relating to predictive processor power management. Specifically, performance counter-driven power models, system- level power characterization and predictive power management are covered. 8.1 Performance Counter-Driven Power Models Contemporary research in the area of performance counter-driven power modeling has focused primarily on the single largest consumer, the processor [LiJo03] [Be00] [IsMa03] [CoMa05] [BrTiMa00]. System-level power modeling [HeCe06] is mostly focused on power consumption within a single subsystem [Ja01] [ZeSo03] [KiSu06] [GuSi02]. Consistent throughout processor power modeling is the theme that power consumption is primarily determined by the number of instructions retired per cycle. Li et al [LiJo03] present a simple linear model for power consumption by operating system services. The resultant models are a function of only IPC. Their modeling only considers operating system routines and requires a separate model for each operating system routine. Most importantly, their model is simulation-based and consequently does not correlate well with power consumption of actual processors. In contrast, the models in this dissertation is based on direct, in-system measurement and shows that power depends more on fetched µop/cycle rather than IPC. Bellosa [Be00] uses synthetic workloads to 147
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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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Page 181 and 182: [JoMa01] R. Joseph and M. Martonosi
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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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