1 / 22

Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware

Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware. Improving Both the Performance Benefits and Speed of Optimization Phase Sequence Searches- Kulkarni, Jantz and Whalley. Terms used. Phase Ordering Genetic Algorithms Performance measurements

lieu
Download Presentation

Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware

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. Presented by: Sameer Kulkarni Dept of Computer & Information Sciences University of Delaware Improving Both the Performance Benefits and Speed of Optimization Phase Sequence Searches- Kulkarni, Jantz and Whalley

  2. Terms used • Phase Ordering • Genetic Algorithms • Performance measurements • Benchmarks • Search granularity

  3. Introduction • Function vs. program level Granularity • Embedded Systems • Emulation • Cost benefits • Hybrid Search

  4. Ideal Solution? • Oracle  Perfect sequence at the very start • Wise Man Solution  Given the present code predict the best optimization solution

  5. Wise Man • Understand Compilers • Optimizations • Source Code ?

  6. Possible Solutions Pruning the search space Genetic Algorithms Estimating running times Precompiled choices

  7. Genetic Algorithms Fast Searches for Effective Optimization Phase Sequences, Kulkarni et al. PLDI ‘04

  8. Exhaustive vs Heuristic [2]

  9. Related Work • Genetic Algorithms • Other Evolutionary Techniques • HMMs (CGO 06) • Other Statistical methods • Optimization Space Exploration

  10. Present work • Granularity • Function Level • File Level • Program Level • Hybrid

  11. Experimental Setup • VPO (Very Portable Optimizer) • Base Genetic Algorithm • Redundancy elimination

  12. VPO • Single IR • Simplified phase ordering • Configurable/modifiable

  13. Redundancy Elimination • Identical Sequence • Identical Active Sequence • Identical Function instance • Equivalent Function Instance

  14. Improvement…? 120 days > 12.5 days

  15. Granularities Studied… • Function Level • File Level • Program Level • Hybrid …all compared to batch compilation

  16. Graphs, graphs and more graphs… 

  17. Search Requirements http://www.ittc.ku.edu/~kulkarni/research/papers/lctes59f-preprint.pdf

  18. Are they any good? http://www.ittc.ku.edu/~kulkarni/research/papers/lctes59f-preprint.pdf

  19. Performance ?? http://www.ittc.ku.edu/~kulkarni/research/papers/lctes59f-preprint.pdf

  20. Future Work • Other machine learning algorithms • Reduce granularity • Use a cluster to reduce search / learning time

  21. Conclusion • Reduced search overhead

  22. Questions

More Related