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

Table 4.1 Subsystem Power Standard Deviation (Watts)
Workload CPU Chipset Memory Memory
Controller
GPU Disk Total
idle 0.026 0.010 0.008 0.008 0.002 0.115 0.129
SPEC CPU2006 INT 2.586 0.257 1.518 0.447 0.174 0.361 2.689
SPEC CPU2006 FP 2.334 0.246 1.970 0.500 0.143 0.240 2.263
gt1 0.736 0.093 0.808 0.217 0.904 0.487 1.57
gt2 0.820 0.105 0.905 0.241 1.090 0.488 2.05
cpu1 1.989 0.262 0.706 0.168 0.356 0.469 2.02
cpu2 2.036 0.263 0.709 0.167 0.362 0.421 2.23
hdr1 0.757 0.131 1.043 0.294 1.144 0.527 1.84
hdr2 0.826 0.152 1.134 0.326 1.096 0.497 2.16
EL 0.696 0.158 0.980 0.278 0.051 0.373 1.744
VC 1.774 0.252 0.585 0.114 0.069 0.566 2.540
PR 0.683 0.250 0.811 0.155 0.086 0.438 1.506
3D 1.159 0.170 0.587 0.108 0.029 0.321 1.701
SPECjbb 1.230 0.297 1.096 0.235 0.031 0.232 2.765
4.2 Power Consumption Variation
4.2.1 Server Platform
To quantify the extent of power variation within a workload coefficient of variation
(CoV) metric is used. This metric uses standard deviation to quantify variation in a data
set, and also normalizes the variation to account for differences in average data. Since
the subsystems in this study have average power values that differ by nearly an order of
magnitude, this metric is most appropriate. Table 4.2 provides a summary of the
coefficient of variation for all workloads.
Compared to the variation in average power among workloads on a given subsystem, the
variation within a particular workload is less consistent. Subsystem-workload pairs such
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