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

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power is more difficult. Invariably power is delivered to multiple cores or system components through a shared power plane. Power consumption by individual cores or programs cannot be observed. Consider Figure 1.1. The power consumption of a multi- core, multi-programmed system simultaneously executing four distinct workloads is shown. These four workloads have power and performance characteristics that require distinct adaptations to meet system objectives. Core 1 has the computationally efficient ray-tracing workload, povray that scales performance nearly perfectly with core frequency. This is important for power management since controlling adaptations such as frequency scaling requires accounting for the potential benefit or cost of changing frequency. In contrast cores 0, 2 and 3 are running applications that are sensitive to 58%- 80% of the change in core frequency. This difference in frequency sensitivity leads to varying optimization points for dynamic adaptation. Similarly, the power consumption of each workload is distinct. The highly efficient povray is able to consistently utilize more than 2/3 of the execution pipelines. This high utilization and concentration of floating point instructions, leads to high power consumption. At the other extreme, the gcc compiler application is only able to utilize 1/3 of the execution pipelines using integer instructions exclusively. In addition to differences in steady state power consumption, these workloads have different phase behavior. While povray and gcc have stable power consumption patterns, 3Dsmax and Sketchup (3D rendering) exhibit drastic variations in power consumption over time. Frequent changes in power consumption 2
increases the overhead of adaptation since transition costs cannot be amortized in the short duration phases. CPU Power (Watts) 30 25 20 15 10 5 0 Core 3 Core 2 Core 1 Standard Deviation 2.7W 2.4W Average 7.4W Average 4.9W 0 100 200 300 400 500 Time (Seconds) Figure 1.1 CPU Core-Level Power Accounting The problem of multiple programs sharing power resources is not limited to processors. Attributing power consumption by a program within an entire system presents a similar challenge. Consider Figure 1.2, which illustrates power consumption for of a modern laptop computer system across a range of critical workloads. Similar to CPU cores, the power consumption in memory, chipsets, graphics and hard disks varies drastically across workloads. Effective power management requires that power be attributable to programs across the entire system so that power performance tradeoffs can be made. 3 Standard Deviation 0.5W 0.4W Core 0 Workload - Frequency Sensitivity % 3dsMax - 77% Sketchup - 82% povray - 100% gcc - 58%
Page 1 and 2: Copyright by William Lloyd Bircher
Page 3 and 4: Predictive Power Management for Mul
Page 5 and 6: Acknowledgements I would like to th
Page 7 and 8: Predictive Power Management for Mul
Page 9 and 10: Table of Contents Chapter 1 Introdu
Page 11 and 12: 5.3.6 Memory ......................
Page 13 and 14: List of Tables Table 1.1 Windows Vi
Page 15 and 16: List of Figures Figure 1.1 CPU Core
Page 17: Chapter 1 Introduction Computing sy
Page 21 and 22: suboptimal from a power and perform
Page 23 and 24: Active Core Activity Idle except fo
Page 25 and 26: 4. Design a predictive power manage
Page 27 and 28: 1.7 Organization This dissertation
Page 29 and 30: Chapter 2 Methodology The developme
Page 31 and 32: 2.1.2 Subsystem-Level Power in a Se
Page 33 and 34: The main components are subsystem p
Page 35 and 36: Table 2.4 Laptop System Description
Page 37 and 38: 2.3 Performance Counter Sampling To
Page 39 and 40: instruction streams that exercise a
Page 41 and 42: the power trace to the PMC trace co
Page 43 and 44: The first bar in Figure 3.1 “Fetc
Page 45 and 46: Table 3.4 Instruction Linear Regres
Page 47 and 48: long time to complete, more aggress
Page 49 and 50: power adaptations as c-states. Thes
Page 51 and 52: Core Power (Watts) 60 50 40 30 20 1
Page 53 and 54: The difference between C0-Idle and
Page 55 and 56: case error is 3.3%. Alternatively s
Page 57 and 58: 3. Based on basic domain knowledge,
Page 59 and 60: 3.6 Summary This section describes
Page 61 and 62: Power (Watts) 30 25 20 15 10 5 0 Co
Page 63 and 64: workloads become more memory-bound,
Page 65 and 66: At the other extreme, the productiv
Page 67 and 68: 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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increase/decrease time. Since the i
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In order to reduce p-state performa
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performance loss and power consumpt
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eactive scheme used in Windows Vist
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Watts Watts Watts Watts Watts 150 1
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7.2 Commercial DVFS Algorithm Exist
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of idle-active transitions in the c
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workload/operating systems adds and
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instructions. In APCI[Ac07] termino
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confidence level will drop below a
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First, prediction accuracy is consi
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coverage of 43% and accuracy over 9
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which corresponds to the DVFS sched
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Table 7.6: SYSmark 2007 Power and P
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In this case the predictor achieves
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2007 power consumption contains man
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Chapter 8 Related Research This sec
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8.2 System-Level Power Characteriza
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prediction scheme in this dissertat
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Chapter 9 Conclusions and Future Wo
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the duration of power and performan
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execution, portions of pipelines or
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[BiJo06-1] W. L. Bircher and L. Joh
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the 2005 ACM SIGMETRICS Internation
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[HaKe07] H. Hanson, S.W. Keckler, K
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[JoMa01] R. Joseph and M. Martonosi
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[LiBr05] Y. Li, D. Brooks, Z. Hu, a
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[Os06] Open Source Development Lab,
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[WaCh08] X. Wang and M. Chen. Clust
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[PiSh01] P. Pillai and K. G. Shin.
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