Transcript Desktop Workload Characterization for CMP/SMT and

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
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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:
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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!