1. Desktop Workload Characterization for CMP/SMT and Implications for Operating System Design Sven Bachthaler Fernando Belli Alexandra Fedorova Simon Fraser University Canada

2. Objectives  Advanced scheduling algorithms for desktop systems?  Data collection from live systems

3. Motivation  First study for desktop systems (restricted to Windows XP)  Should we address parallelism in periods of activity?

4. Approach  Metric for parallelism  Ready queue length  Characterization of parallelism  Zero parallelism(no threads waiting)  Low parallelism (1-2 threads waiting)  High parallelism(>2 threads waiting)

5. Outline  Methodology and Data Collection  Results  Conclusions  Future Work

6. Methodology  Collect data from three groups  20 university lab computers  10 university staff computers  12 home computers

7. Methodology  Local and remote data collection  Remote data collection  For university computers  Less overhead  No user interaction necessary  Local data collection for home PCs

8. Tools  Performance Monitor  PsList  PsInfo

9. Data Collection  Collected every 15 seconds:  Ready queue length  Number of running processes  Number of running threads  Available main memory  Percentage of time when CPU is busy

10. Results  Presenting the results  Each slide for specific hardware  Several computers grouped according to hardware configuration

11. Results  University lab computers…

12. Results  Three groups of lab computers

13. Results  Three lab computers

14. Results  University staff computers…

15. Results  Single staff computer

16. Results  Six staff computers

17. Results  Home computers…

18. Results  Home computers without CMP/SMT

19. Results  Three home computers with CMP/SMT

20. Results  Special case…

21. Results  Staff computer

22. Conclusion  Low parallelism for a significant number of analyzed workloads  Not too much benefit from performance-optimizing scheduling algorithms

23. Future Work  Expand data collection to gain statistical significance  Investigate better ways for local data collection

24. Acknowledgements  We want to thank the department of Computing Science at SFU  Special thanks to the volunteers for the data collection  Thank you!