Inducing heuristics to decide whether to schedule

ACM SIGPLAN Notices

Volumn 39, Issue 6, 2004, Pages 183-194

  Cavazos, John ^a   Eliot, J ^a   Moss, B ^a  

^a The Manning College of Information and Computer Sciences   (United States)

Author keywords

Compiler optimization; Instruction scheduling; Java; Jikes RVM; Machine learning; Supervised learning

Indexed keywords

COMPUTER PROGRAMMING LANGUAGES; LEARNING SYSTEMS; OPTIMIZATION; PROGRAM COMPILERS; SCHEDULING; SEMANTICS;

COMPILER OPTIMIZATION; INSTRUCTION SCHEDULING; JIKES; SUPERVISED LEARNING;

COMPUTER SCIENCE;

EID: 4544268078     PISSN: 03621340     EISSN: None     Source Type: Journal    
DOI: 10.1145/996893.996864     Document Type: Conference Paper

References (19)

1. B. Alpern, C. R. Attanasio, J. J. Barton, M. G. Burke, P.Cheng, J.-D. Choi, A. Cocchi, S. J. Fink, D. Grove, M. Hind, S. F. Hummel, D. Lieber, V. Litvinov, M. F. Mergen, T. Ngo, J. R. Russell, V. Sarkar, M. J. Serrano, J. C. Shepherd, S. E. Smith, V. C. Sreedhar, H. Srinivasan, and J. Whaley. The Jalapeño virtual machine. IBM Systems Journal, 39(1), Feb. 2000.
2. M. Arnold, S. Fink, D. Grove, M. Hind, and P. F. Sweeney. Adaptive optimization in the Jalapeño JVM. In ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA 2000), Minneapolis, MN, Oct. 2000.
3. M. Arnold and B. G. Ryder. A framework for reducing the cost of instrumented code. In SIGPLAN Conference on Programming Language Design and Implementation, pages 168-179, 2001.
4. B. Calder, D. Grunwald, M. Jones, D. Lindsay, J. Martin, M. Mozer, and B. Zoren. Evidence-based static branch prediction using machine learning. ACM Transactions on Programming Languages and Systems, 19(1):188-222, January 1997.
5. C. Chekuri, R. Johnson, R. Motwani, B. Natarajan, B. Rau, and M.Schlansker. Profile-driven instruction level parallel scheduling with application to super blocks. In Proceedings of the 29th International Symposium on Microarchitecture, pages 58-67, 1992.
6. W. W. Cohen. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, CA, Nov. 1995.
7. K. D. Cooper, P. J. Schielke, and D. Subramanian. Optimizing for reduced code space using genetic algorithms. In Workshop on Languages, Compilers, and Tools for Embedded Systems, pages 1-9, 1999.
8. J. R. Ellis. Bulldog: A Compiler for VLIW Architectures. PhD thesis, Yale, Feb. 1985.
9. J. A. Fisher. Trace scheduling: a technique for global microcode compaction. IEEE Transactions on Computers, 30:478-490, July 1981.
10. M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., San Francisco, CA, 1979.
11. P. B. Gibbons and S. S. Muchnick. Efficient instruction scheduling for a pipelined architecture. In Proceedings ACM SIGPLAN '86 Conference on Programming Language Design and Implementation, pages 11-16, 1986.
12. A. McGovern, E. Moss, and A. G. Barto. Building a basic block instruction scheduler with reinforcement learning and rollouts. Machine Learning, 49(2/3):141-160, 2002.
13. A. McGovern and J. E. B. Moss. Scheduling straight-line code using reinforcement learning and rollouts. In S. Solla, editor, Advances in Neural Information Processing Systems 11, Cambridge, MA, 1998. MIT Press.
14. A. Monsifrot and F. Bodin. A machine learning approach to automatic production of compiler heuristics. In Tenth International Conference on Artificial Intelligence: Methodology, Systems, Applications, AIMSA, pages 41-50, September 2002.
15. J. E. B. Moss, P. E. Utgoff, J. Cavazos, D. Precup, D. Stefanović, C. Brodley, and D. Scheeff. Learning to schedule straight-line code. In Proceedings of Neural Information Processing Systems 1997 (NIPS*97), Denver CO, Dec. 1997.
16. S. S. Muchnick. Advanced Compiler Design & Implementation. Morgan Kaufmann Publishers, Inc., San Francisco, CA, 1997.
17. Standard Performance Evaluation Corporation (SPEC), Fairfax, VA. SPEC JVM98 Benchmarks, 1998.
18. M. Stephenson, S. Amarasinghe, M. Martin, and U.-M. O'Reilly. Meta optimization: Improving compiler heuristics with machine learning. In Proceedings of the ACM SIGPLAN '03 Conference on Programming Language Design and Implementation, San Diego, Ca, June 2003. Association of Computing Machinery.
19. C. Young and M. Smith. Better global scheduling using path profiles. In Proceedings of the 28th International Symposium on Microarchitecture, pages 199-206, Nov. 1995.