1. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Mitigating the Compiler Optimization Phase- Ordering Problem using Machine Learning Sameer Kulkarni John Cavazos

2. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Introduction to Phase Ordering Present State-of-the-Art Proposed Solution Understand code Predict optimization An example Results Questions

3. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department What is Phase Ordering? Change ordering of a given set optimizations Opt. 1Opt. 2Opt. 3Opt. 4Opt. 1Opt. 3Opt. 2Opt. 4

4. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Why is it important? Plethora of optimizations to choose. All interact with each other Register Allocation & Instruction Scheduling Loop Unrolling & CSE Branch Optimization & Static Analysis

5. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Introduction to Phase Ordering Present State-of-the-Art Proposed Solution Understand code Predict optimization An example Results Questions

6. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Present Solutions Developer’s intuition Using a static sequence using search Pseudo Random Search (ML) Hill Climbing, Genetic Algorithm

7. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Generation 1 Generation 2 Using Genetic Algorithms…

8. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Disadvantages of State-of-art One size fits all approach Change in Compiler Change in Architecture Change in Code Expensive Search

9. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department A simple experiment Select a set of optimizations, and set a sequence length Generate 500 random sequences Use each of the optimization sequence to compile and run a set of benchmarks

10. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department

11. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Introduction to Phase Ordering Present State-of-the-Art Proposed Solution Understand code Predict optimization An example Results Questions

12. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Proposed Solution Analyze Code Predict best optimization End optimizations? Apply Optimization

13. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Static Source Code Features Method level information Instruction Mix conditionals memory ops size locals space

14. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Static Source Code Features Method level information Instruction Mix conditionals memory ops size locals space

15. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Proposed Solution Analyze Code Predict best optimization End optimizations? Apply Optimization

16. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Neural Network

17. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Proposed Solution Analyze Code Predict best optimization End optimizations? Apply Optimization

18. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT

19. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Neuro Evolution of Augmented Topologies Evolutionary approach to ANNs Starts with a minimally connected network

20. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Generation 1 Generation n Generation 2 Using NEAT…

21. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Introduction to Phase Ordering Present State-of-the-Art Proposed Solution Understand code Predict optimization An example Results Questions

22. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department scimark.lu bencmark Generated by default O3 Generated by NEAT

23. U NIVERSITY OF D ELAWARE Computer & Information Sciences Department Advantages Source code agnostic Customized optimization sequence Train once, use every-time Elegant stopping criteria Optimization sequence length is not static Saves time by intelligent selection

24. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Introduction to Phase Ordering Present State-of-the-Art Proposed Solution Understand code Predict optimization An example Results Questions

25. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT

26. Results on SpecJVM98 Speedup normalized over O3

27. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Results on SpecJVM2008 Speedup normalized over O3

28. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Results on DaCapo Speedup normalized over O3

29. U NIVERSITY OF D ELAWARE C OMPUTER & I NFORMATION S CIENCES D EPARTMENT Mitigating the Compiler Optimization Phase- Ordering Problem using Machine Learning Thank you!