1. 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 2008. Beijing, China.

2. Findings/Contributions 1. User satisfaction is correlated to CPU performance 2. User satisfaction is non-linear, application-dependent, and user- dependent 1. We can use hardware performance counters to learn and leverage user satisfaction to optimize power consumption while maintaining satisfaction Claim: Any optimization ultimately exists to satisfy the end user Claim: Current architectures largely ignore the individual user

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

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

5. Performance Level ??

6. Leverage knowledge for optimization Leverage knowledge for optimization Learn relationship between user satisfaction and hardware performance Learn relationship between user satisfaction and hardware performance

7.  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.  IBM Thinkpad T43p  Pentium M with Intel Speedstep  Supports 6 Frequencies (2.2Ghz -- 800Mhz)  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.  Goal:  Learn relationship between HPCs and user satisfaction  How:  Randomly change performance/frequency  Collect HPCs  Ask the user for their satisfaction rating!

10.  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.  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 ! HPCs User ID User Satisfaction

12.  User satisfaction is often non-linear  User satisfaction is application-specific  Most importantly, user satisfaction is user-specific

13.  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 ( i DVFS)  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. 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.  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 HPCsUser Satisfaction

16.  ρ: user satisfaction tradeoff threshold  α f : per frequency threshold  M: maximum user satisfaction  Greedy approach  Make prediction every 500ms  If within user satisfaction within α f ρ of M twice in a row, decrease frequency  If not, increase frequency and is α f decreased to prevent ping-ponging between frequency

17.  Goal:  Evaluate i DVFS with real users  How:  Users randomly use application with i DVFS 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.  i DVFS can scale frequency effectively based upon user satisfaction  In this case, we slightly decrease power compared to Windows DVFS

19.  i DVFS significantly improves power consumption  Here, CPU utilization not equal to user satisfaction

20.  No change in user satisfaction, significant power savings

21.  Same user satisfaction, same power savings  Red: Users gave high ratings to lower frequencies  Dashed Black: Neural network bad

22.  Lowered user satisfaction, improved power  Blue: Gave constant ratings during training

23.  Slight increase in ESP  Benefits in energy reduction outweigh loss in user satisfaction with ESP

24.  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%

25.  Questions?  For more information, please visit:  http://www.empathicsystems.org