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

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

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

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)

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

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

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

Tools  Performance Monitor  PsList  PsInfo

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

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

Results  University lab computers…

Results  Three groups of lab computers

Results  Three lab computers

Results  University staff computers…

Results  Single staff computer

Results  Six staff computers

Results  Home computers…

Results  Home computers without CMP/SMT

Results  Three home computers with CMP/SMT

Results  Special case…

Results  Staff computer

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

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

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