tion logs are captured. In this example,
time-domain analysis is not practical
for gathering scalability and utilization.
Instead, a histogram analysis identifies
in which bucket the maximum number
of load points lie for a given frequency
of operation. The peak of the histogram
represents the load value with the highest probability of occurrence for the
given frequency. In the example data in
Figure 5, about 500 samples (or 80% of
total samples) occurred at a 33% load.
Such inferences are obviously hard to
derive from the time chart in Figure 4
even if the frequency is fixed.
The experiment is iterated through
all other frequencies, one at a time, to
get a range of peak histogram values,
as shown in Figure 6.
With the same data from the set of
experiments, a similar histogram ridge
trace for scale-factor can be plotted as
shown in Figure 7.
Scaling of scale factor. A plotting
tool is used to create a map-view of
peak histogram points (in scale factor)
in Figure 7, which is then plotted with
red dots in Figure 8. On the same map-
view, the scaling of scale factor equa-
tion is plotted for different possible
target frequencies based on any one
initial seed value for utilization and
scale factor. The correlation between
the equation and the full data set is
shown in Figure 8.
Scaling of load. The map-view of
peak histogram points of utilization
(Figure 6) is plotted with red dots in
Figure 9. On the same map view, the
scaling of load equation is plotted for
different possible target frequencies
based on one initial seed value for utilization and scale factor.
As summarized by the results shown
in these figures, the independent estimates of the equation fit nearly precisely with the actual experiment’s
statistical results. This methodology
is applied to multiple other workloads
(with different scale factors and load
levels) and verified as noted here.
This article elaborated on the causali-
ties of utilization and scale factor in
relation to possible frequency-selec-
tion choices within a time window.
Based on this, generic workload scal-
ing equations are derived, quantify-
ing utilization impact. The equations
are verified by applying the histogram
ridge trace method at discrete DVFS
block level. Though only the CPU
core example was detailed, similar
equation agreements were observed
on other DVFS blocks (graphics sub
blocks), other workloads and other
operating systems (Windows/Linux).
The equation-estimated curve corre-
lates very accurately with the actual
ridge trace curve. This implies the uti-
lization impact can be “predicted” to
be statistically accurate in every cycle
and any divergence from it can be at-
tributed to workload-inherent utiliza-
tion change in that cycle and treated
as appropriate to the solution using it.
The author is thankful for all the help
from colleagues at Intel, specifically,
valuable input from principal engineers Harinarayanan Seshadri and Rajeev Muralidhar and software engineer
B. M. Shravan K.
Maximizing Power Efficiency with
Asymmetric Multicore Systems
Alexandra Fedorova et al.
Energy Management on Handheld Devices
Marc A. Viredaz et al.
1. Howse, B. Examining Intel’s new Speed Shift tech
on Skylake: more responsive processors. Anand Tech,
2. Intel. Intel 64 and IA- 32 Architectures, Software
Developer’s Manual, Volume 3B: System Programming
Guide, Part 2; https://intel.ly/2sKeSqA
3. Kidd, T. Power management states: P-states, C-states,
and Package C-states. Intel Developer Zone, 2014;
4. Power Shaping and Stress Tool (PSST) for Intel
Noor Mubeen is a software architect at Intel client
R&D group, focusing on power, thermal, and energy
management breakthroughs. He has over 17 years’
experience spanning a breadth of domains including
networking, storage file systems, and embedded devices.
Copyright held by owner/author.
Publication rights licensed to ACM. $15.00
Figure 9. Utilization ridge point contour compared with equation.
Figure 8. Scale-factor ridge points contour compared with equation.
5 10 15
Actual Scaling Factor
Equation Estimate Scale
20 25 30
5 10 15
20 25 30