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Discovering and Exploiting Program Phases

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alaura
35 diapositives · 503 Views · Aug 15, 2014

This presentation explores methods to discover and exploit various program phases to optimize performance efficiently. Using insights from a simulation of up to 400 million instructions, we delve into the identification of program behavior patterns through automated classification techniques, including k-means clustering. By examining metrics like cache misses and branch misprediction rates, we propose a robust framework for simulating diverse program phases within a limited execution budget. The discussion culminates in the application of SimPoints for extrapolating performance insights, demonstrating significant implications for thread scheduling and power management.

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  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