1. Learning and Leveraging the Relationship between Architecture-Level Measurements and Individual User Satisfaction
Alex Shye, Berkin Ozisikyilmaz, Arindam Mallik, Gokhan Memik, Peter A. Dinda, Robert P. Dick, and Alok N. Choudhary
Northwestern University, EECS
International Symposium on Computer Architecture, June Beijing, China.

2. Overall Summary
Claim: Any optimization ultimately exists to satisfy the end user
Claim: Current architectures largely ignore the individual user
Findings/Contributions
User satisfaction is correlated to CPU performance
User satisfaction is non-linear, application-dependent, and user-dependent
We can use hardware performance counters to learn and leverage user satisfaction to optimize power consumption while maintaining satisfaction
Justin Ratter: What matters? How is information technology used?
End-user meaningful platform performance matters… went on to mention “this is not a typical design point’

3. Why care about the user? 1 2 3 User-centric applications
Architectural trade-offs
exposed to the user 3
Optimization opportunity
User variation = optimization potential

4. Performance vs. User Satisfaction
Your favorite metric
(IPS, throughput, etc.) ? WE

5. Current Architectures
Performance Level ?

6. Our Goal Performance Level Leverage knowledge for optimization
Learn relationship between user
satisfaction and hardware performance
Leverage knowledge
for optimization

7. Measuring Performance
Hardware performance counters are supported on all modern processors
Low overhead
Non-intrusive
WinPAPI interface; 100Hz
For each HPC:
Maximum
Minimum
Standard deviation
Range
Average

8. User Study Setup IBM Thinkpad T43p Two user studies:
Pentium M with Intel Speedstep
Supports 6 Frequencies (2.2Ghz Mhz)
Two user studies:
20 users each
First to learn about user satisfaction
Second to show we can leverage user satisfaction
Three multimedia/interactive applications:
Java game: A first-person-shooter tank game
Shockwave: A 3D shockwave animation
Video: DVD-quality MPEG video

9. First User Study Goal: How:
Learn relationship between HPCs and user satisfaction
How:
Randomly change performance/frequency
Collect HPCs
Ask the user for their satisfaction rating!

10. Correlation to HPCs
Compare each set of HPC values with user satisfaction ratings
Collected 360 satisfaction levels (20 users, 6 frequencies, 3 applications)
45 metrics per satisfaction level
Pearson’s Product Moment Correlation Coefficient (r)
-1: negative linear correlation, 1: positive linear correlation
Strong correlation: 21 of 45 metrics over .7 r value

11. Correlation to the Individual User
HPCs
User ID
User
Satisfaction
Combine all user data
Fit into a neural network
Inputs: HPCs and user ID
Output: User satisfaction
Observe relative importance factor
User more than two times more important than the second-most important factor
User satisfaction is highly user-specific!

12. Performance vs. User Satisfaction
User satisfaction is often non-linear
User satisfaction is application-specific
Most importantly, user satisfaction is user-specific

13. Leveraging User Satisfaction
Observations:
User satisfaction is non-linear
User satisfaction is application dependent
User satisfaction is user dependent
All three represent optimization potential!
Based on observations, we construct Individualized DVFS (iDVFS)
Dynamic voltage and frequency scaling (DVFS) effective for improving power consumption
Common DVFS schemes (i.e., Windows XP DVFS, Linux ondemand governor) are based on CPU-utilization

14. Individualized DVFS (iDVFS)
User-aware performance prediction model
Predictive user-aware Dynamic Frequency Scaling
Building correlation network based on counters stats and user feedback
Learning/Modeling Stage
Runtime Power Management
Hardware counter states
User Satisfaction Feedback

15. iDVFS – Learning/Modeling
HPCs
User Satisfaction
Train per-user and per-application
Small training set!
Two modifications to neural network training
Limit inputs (used two highest correlation HPCs)
BTAC_M-average and TOT_CYC-average
Repeated trainings using most accurate NN
We do per-user, per-application for this work but there can be more (example of Video more app-specific)

16. iDVFS – Control Algorithm
ρ: user satisfaction tradeoff threshold
αf: per frequency threshold
M: maximum user satisfaction
Greedy approach
Make prediction every 500ms
If within user satisfaction within αfρ of M twice in a row, decrease frequency
If not, increase frequency and is αf decreased to prevent ping-ponging between frequency
summarize briefly. Details are in paper.

17. Second User Study Goal: How: Evaluate iDVFS with real users
Users randomly use application with iDVFS and with Windows XP DVFS
Afterwards, users asked to rate each one
Frequency logs maintained through experiments
Replayed through National Instruments DAQ for system power

18. Example Trace- Shockwave
iDVFS can scale frequency effectively based upon user satisfaction
In this case, we slightly decrease power compared to Windows DVFS

19. Example- Video iDVFS significantly improves power consumption
Here, CPU utilization not equal to user satisfaction

20. Results – Video
No change in user satisfaction, significant power savings

21. Results – Java Same user satisfaction, same power savings
Not strange users, non-monotonic.
Few cases where sticking to user loses. In this case, technique combining other techniques (e.g., Vertigo) may be good. Bound by CPU performance.
Still, there are many cases where we maintain user satisfaction and save power
Same user satisfaction, same power savings
Red: Users gave high ratings to lower frequencies
Dashed Black: Neural network bad

22. Results – Shockwave Lowered user satisfaction, improved power
Save power in almost every case. A few are unhappier. Overall, people are still happy and we save power.
Lowered user satisfaction, improved power
Blue: Gave constant ratings during training

23. Energy-Satisfaction Product
Need a metric for tradeoff and this is one possibility; One could argue we improve ESP even for shockwave
Slight increase in ESP
Benefits in energy reduction outweigh loss in user satisfaction with ESP

24. Conclusion
We explore user satisfaction relative to actual hardware performance
Show correlation from HPCs to user satisfaction for interactive applications
Show that user satisfaction is generally non-linear, application-, and user-specific
Demonstrate an example for leveraging user satisfaction to improve power consumption over 25%

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