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Input-sensitivity Develop a greedy algorithm to select representative code variants

Study the correlation among inputs dimensions and develop a greedy algorithm to select representative code variants. Explore the root cause of sensitivity and determine how to mitigate it. Select the best code variants for new inputs using ML-based prediction or analytical models.

gerardwest
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Input-sensitivity Develop a greedy algorithm to select representative code variants

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  1. Input sensitivity Study • Input-sensitivity • Develop a greedy algorithm to select representative code variants

  2. Overall results

  3. Overall results

  4. Overall results

  5. Code variant sensitivity

  6. What can we do? • Study the correlation among inputs dimensions? • D=10 • There are 2^10 combination of dimensions • Which to study? • What is the purpose? • PCA? Feature selection?, etc • Or clustering and slicing?

  7. What can we do? • Study the root cause of sensitivity • Why 1 code variant is much faster on certain inputs but slower on others? • How to get rid of such sensitivity? • Does ICC PGO help with multiple data sets? • Or, instead, we need high-level solution?

  8. What can we do? • Given a new input, select the best code variants • ML-based Prediction • Classification: SVM, Decision tree, Clustering • Analytical model: • Linear regression • Or combined?

  9. Select between 2 code variants

  10. Code variant selection (1) • Greedy • If a code variant’s performance on input I is within 3% (P) to the best performance achieved by all code variants, consider it is the best • Select per-input best code variant which covers most inputs at each step • Perfect (greedy) • P is 0%, always the best performance • Perfect (true) • Pick the per-input best always • 11.7% across 1000 inputs

  11. Code variant selection (2)

  12. Code variant selection (3) • Observations • Different code variants performs best for different subsets of inputs (input sensitivity) • 3 or 4 code variants are sufficient to harvest most performance benefit (code representativeness)

  13. Code variant and critical flags • Observations • Code variants selected in order: 89, 13, 64, 66

  14. Code variant and Inputs

  15. Discussion • What is the goal to do such ad-hoc analysis? • If our goal is prediction with machine learning techniques, we should try decision tree, neural networks, etc • If our goal is prediction with analytical modeling techniques, what could be the target of such models? • Performance modeling: predict duration of a run with input parameters • Or, we can use information derived from analytical models to improve machine learning. • The greedy algorithm could be the target

  16. Plan • Continue study Kripke • More analysis (Analytical approach) • Uni-variate and multi-variate analysis on inputs • Per-code variant analysis • Develop prediction model (ML approach)

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