1 / 35

Discovering and Exploiting Program Phases

Discovering and Exploiting Program Phases. Timothy Sherwood, Erez Perelman, Greg Hamerly, Suleyman Sair, Brad Calder CSE 231 Presentation by Justin Ma. 400 Million Instructions. Non-Existent Processor. New Processor. New Compiler. Spec2000. Benchmark. Simulator. 400 Million Instructions.

khuyen
Download Presentation

Discovering and Exploiting Program Phases

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Discovering and Exploiting Program Phases Timothy Sherwood, Erez Perelman, Greg Hamerly, Suleyman Sair, Brad Calder CSE 231 Presentation by Justin Ma

  2. 400 Million Instructions Non-Existent Processor New Processor New Compiler Spec2000 Benchmark Simulator

  3. 400 Million Instructions • Suppose you have a time budget… • Less than half second of execution time • What would you simulate? • Beginning? • Middle? • End?

  4. 400 Million Instructions Programs exhibit diverse modes of behavior gzip gcc

  5. 400 Million Instructions • Suppose you have a time budget… • Less than half second of execution time • What would you simulate? • Beginning? • Middle? • End? • Samples of different modes of behavior

  6. Program Phases • Observation: programs exhibit various modes of periodic behavior • These modes are program phases • Challenge: Extract these automatically

  7. Phase Basics • Intervals – slices in times • Phases – intervals with similar behavior IPC Time (Instruction Count)

  8. Phase Basics • Intervals – slices in times • Phases – intervals with similar behavior IPC Time (Instruction Count)

  9. Defining “Similar Behavior” • Metric for comparing intervals? • Cache misses? • IPC? • Branch misprediction rates? • Problem: Performance alone is too architecture dependent

  10. Defining “Similar Behavior” • Code path traversal • Directly affects time-varying behavior • Execute same code, same performance • Architecture independent • Metrics for code path traversal • Frequency of branches • Frequency of function calls • Frequency of basic block calls

  11. Basic Block Vector B1 Time t 0 0 0 0 B1 B2 B3 B4 B2 B3 B4

  12. Basic Block Vector B1 Time t 1 1 0 1 B1 B2 B3 B4 B2 B3 B4

  13. Basic Block Vector B1 Time t 2 1 1 2 B1 B2 B3 B4 B2 B3 B4

  14. Basic Block Vector B1 Time t 2 1 1 2 B1 B2 B3 B4 B2 B3 Time t + 1 0 0 0 0 B1 B2 B3 B4 B4

  15. Basic Block Vector B1 Time t 2 1 1 2 B1 B2 B3 B4 B2 B3 Time t + 1 1 1 0 1 B1 B2 B3 B4 B4

  16. Basic Block Vector B1 Time t 2 1 1 2 B1 B2 B3 B4 B2 B3 Time t + 1 2 2 0 2 B1 B2 B3 B4 B4 Manhattan Distance = |1 – 2| + |1 – 0| = 2 Euclidian Distance = sqrt((1 – 2)2 + (1 – 0)2) = sqrt(2)

  17. Basic Block Similarity Matrix • gzip

  18. Basic Block Similarity Matrix • gcc BBV similarity between intervals reflects performance similarity

  19. Automatic Phase Classification • Classify intervals into phases • We do not know which BBVs correspond to particular phases a priori • k-means clustering • Iterative clustering algorithm • Dimension Reduction • Random Linear Projection • Try different k values • Use BIC to choose best

  20. Automatic Phase Classification

  21. Automatic Phase Classification Clustering accurately distinguishes phases automatically

  22. SimPoint • Simulate large programs on a budget • Perform detailed simulation on representative code snippets • Choose centroid interval from each phase (10 million instructions) • Extrapolate large program performance • Weighted by frequency of phase

  23. SimPoint • Simulate 400 million instructions total Accurate estimate despite instruction budget

  24. Why SimPoint Succeeds • Program behavior varies over time • SimPoint intelligently chooses which intervals to simulate • Regularity within program phases allows accurate extrapolation

  25. Online Classification • Detect phases as program is running • Applications • Thread scheduling • Power management • Predicting future phases • Challenges • One pass of input • Limited storage

  26. Online Classification

  27. Online Classification High variance in metrics across full trace Low variance shows online classification succeeds in finding phases

  28. Conclusions • Phases are a vital abstraction • Performance varies greatly w/in program • Attributable to different modes of behavior • Can discover phases automatically • Offline: k-means clustering • Online • Code path characterization • Strong correlation with actual performance • SimPointexploitsthis with great success

  29. Outline • Introduction (motivate) • Basics (definitions, BBV, BBMatrix) • Offline Phase Classification • SimPoints • Online Phase Classification • Conclusions

  30. Limitations of Clustering

  31. Bayesian Information Criterion • Fit to Gaussians

  32. Self-Modifying Code Self-modifying code 85o Program Phases

  33. Learning Phases

  34. Learning Phases

More Related